A power study of bivariate LOD score analysis of a complex trait and fear/discomfort with strangers

PubMed Central

Ji, Fei; Lee, Dayoung; Mendell, Nancy Role

2005-01-01

Complex diseases are often reported along with disease-related traits (DRT). Sometimes investigators consider both disease and DRT phenotypes separately and sometimes they consider individuals as affected if they have either the disease or the DRT, or both. We propose instead to consider the joint distribution of the disease and the DRT and do a linkage analysis assuming a pleiotropic model. We evaluated our results through analysis of the simulated datasets provided by Genetic Analysis Workshop 14. We first conducted univariate linkage analysis of the simulated disease, Kofendrerd Personality Disorder and one of its simulated associated traits, phenotype b (fear/discomfort with strangers). Subsequently, we considered the bivariate phenotype, which combined the information on Kofendrerd Personality Disorder and fear/discomfort with strangers. We developed a program to perform bivariate linkage analysis using an extension to the Elston-Stewart peeling method of likelihood calculation. Using this program we considered the microsatellites within 30 cM of the gene pleiotropic for this simulated disease and DRT. Based on 100 simulations of 300 families we observed excellent power to detect linkage within 10 cM of the disease locus using the DRT and the bivariate trait. PMID:16451570

A power study of bivariate LOD score analysis of a complex trait and fear/discomfort with strangers.

PubMed

Ji, Fei; Lee, Dayoung; Mendell, Nancy Role

2005-12-30

Complex diseases are often reported along with disease-related traits (DRT). Sometimes investigators consider both disease and DRT phenotypes separately and sometimes they consider individuals as affected if they have either the disease or the DRT, or both. We propose instead to consider the joint distribution of the disease and the DRT and do a linkage analysis assuming a pleiotropic model. We evaluated our results through analysis of the simulated datasets provided by Genetic Analysis Workshop 14. We first conducted univariate linkage analysis of the simulated disease, Kofendrerd Personality Disorder and one of its simulated associated traits, phenotype b (fear/discomfort with strangers). Subsequently, we considered the bivariate phenotype, which combined the information on Kofendrerd Personality Disorder and fear/discomfort with strangers. We developed a program to perform bivariate linkage analysis using an extension to the Elston-Stewart peeling method of likelihood calculation. Using this program we considered the microsatellites within 30 cM of the gene pleiotropic for this simulated disease and DRT. Based on 100 simulations of 300 families we observed excellent power to detect linkage within 10 cM of the disease locus using the DRT and the bivariate trait.

Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits.

PubMed

Wu, Yang; Zeng, Jian; Zhang, Futao; Zhu, Zhihong; Qi, Ting; Zheng, Zhili; Lloyd-Jones, Luke R; Marioni, Riccardo E; Martin, Nicholas G; Montgomery, Grant W; Deary, Ian J; Wray, Naomi R; Visscher, Peter M; McRae, Allan F; Yang, Jian

2018-03-02

The identification of genes and regulatory elements underlying the associations discovered by GWAS is essential to understanding the aetiology of complex traits (including diseases). Here, we demonstrate an analytical paradigm of prioritizing genes and regulatory elements at GWAS loci for follow-up functional studies. We perform an integrative analysis that uses summary-level SNP data from multi-omics studies to detect DNA methylation (DNAm) sites associated with gene expression and phenotype through shared genetic effects (i.e., pleiotropy). We identify pleiotropic associations between 7858 DNAm sites and 2733 genes. These DNAm sites are enriched in enhancers and promoters, and >40% of them are mapped to distal genes. Further pleiotropic association analyses, which link both the methylome and transcriptome to 12 complex traits, identify 149 DNAm sites and 66 genes, indicating a plausible mechanism whereby the effect of a genetic variant on phenotype is mediated by genetic regulation of transcription through DNAm.

A Powerful Approach to Estimating Annotation-Stratified Genetic Covariance via GWAS Summary Statistics.

PubMed

Lu, Qiongshi; Li, Boyang; Ou, Derek; Erlendsdottir, Margret; Powles, Ryan L; Jiang, Tony; Hu, Yiming; Chang, David; Jin, Chentian; Dai, Wei; He, Qidu; Liu, Zefeng; Mukherjee, Shubhabrata; Crane, Paul K; Zhao, Hongyu

2017-12-07

Despite the success of large-scale genome-wide association studies (GWASs) on complex traits, our understanding of their genetic architecture is far from complete. Jointly modeling multiple traits' genetic profiles has provided insights into the shared genetic basis of many complex traits. However, large-scale inference sets a high bar for both statistical power and biological interpretability. Here we introduce a principled framework to estimate annotation-stratified genetic covariance between traits using GWAS summary statistics. Through theoretical and numerical analyses, we demonstrate that our method provides accurate covariance estimates, thereby enabling researchers to dissect both the shared and distinct genetic architecture across traits to better understand their etiologies. Among 50 complex traits with publicly accessible GWAS summary statistics (N total ≈ 4.5 million), we identified more than 170 pairs with statistically significant genetic covariance. In particular, we found strong genetic covariance between late-onset Alzheimer disease (LOAD) and amyotrophic lateral sclerosis (ALS), two major neurodegenerative diseases, in single-nucleotide polymorphisms (SNPs) with high minor allele frequencies and in SNPs located in the predicted functional genome. Joint analysis of LOAD, ALS, and other traits highlights LOAD's correlation with cognitive traits and hints at an autoimmune component for ALS. Copyright © 2017 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

The search for Pleiades in trait constellations: functional integration and phenotypic selection in the complex flowers of Morrenia brachystephana (Apocynaceae).

PubMed

Baranzelli, M C; Sérsic, A N; Cocucci, A A

2014-04-01

Pollinator-mediated natural selection on single traits, such as corolla tube or spur length, has been well documented. However, flower phenotypes are usually complex, and selection is expected to act on several traits that functionally interact rather than on a single isolated trait. Despite the fact that selection on complex phenotypes is expectedly widespread, multivariate selection modelling on such phenotypes still remains under-explored in plants. Species of the subfamily Asclepiadoideae (Apocynaceae) provide an opportunity to study such complex flower contrivances integrated by fine-scaled organs from disparate developmental origin. We studied the correlation structure among linear floral traits (i) by testing a priori morphological, functional or developmental hypotheses among traits and (ii) by exploring the organization of flower covariation, considering alternative expectations of modular organization or whole flower integration through conditional dependence analysis (CDA) and integration matrices. The phenotypic selection approach was applied to determine whether floral traits involved in the functioning of the pollination mechanism were affected by natural selection. Floral integration was low, suggesting that flowers are organized in more than just one correlation pleiad; our hypothetical functional correlation matrix was significantly correlated with the empirical matrix, and the CDA revealed three putative modules. Analyses of phenotypic selection showed significant linear and correlational gradients, lending support to expectations of functional interactions between floral traits. Significant correlational selection gradients found involved traits of different floral whorls, providing evidence for the existence of functional integration across developmental domains. © 2014 The Authors. Journal of Evolutionary Biology © 2014 European Society For Evolutionary Biology.

Untargeted Metabolic Quantitative Trait Loci Analyses Reveal a Relationship between Primary Metabolism and Potato Tuber Quality1[W][OA

PubMed Central

Carreno-Quintero, Natalia; Acharjee, Animesh; Maliepaard, Chris; Bachem, Christian W.B.; Mumm, Roland; Bouwmeester, Harro; Visser, Richard G.F.; Keurentjes, Joost J.B.

2012-01-01

Recent advances in -omics technologies such as transcriptomics, metabolomics, and proteomics along with genotypic profiling have permitted dissection of the genetics of complex traits represented by molecular phenotypes in nonmodel species. To identify the genetic factors underlying variation in primary metabolism in potato (Solanum tuberosum), we have profiled primary metabolite content in a diploid potato mapping population, derived from crosses between S. tuberosum and wild relatives, using gas chromatography-time of flight-mass spectrometry. In total, 139 polar metabolites were detected, of which we identified metabolite quantitative trait loci for approximately 72% of the detected compounds. In order to obtain an insight into the relationships between metabolic traits and classical phenotypic traits, we also analyzed statistical associations between them. The combined analysis of genetic information through quantitative trait locus coincidence and the application of statistical learning methods provide information on putative indicators associated with the alterations in metabolic networks that affect complex phenotypic traits. PMID:22223596

Integrating Genomic Analysis with the Genetic Basis of Gene Expression: Preliminary Evidence of the Identification of Causal Genes for Cardiovascular and Metabolic Traits Related to Nutrition in Mexicans123

PubMed Central

Bastarrachea, Raúl A.; Gallegos-Cabriales, Esther C.; Nava-González, Edna J.; Haack, Karin; Voruganti, V. Saroja; Charlesworth, Jac; Laviada-Molina, Hugo A.; Veloz-Garza, Rosa A.; Cardenas-Villarreal, Velia Margarita; Valdovinos-Chavez, Salvador B.; Gomez-Aguilar, Patricia; Meléndez, Guillermo; López-Alvarenga, Juan Carlos; Göring, Harald H. H.; Cole, Shelley A.; Blangero, John; Comuzzie, Anthony G.; Kent, Jack W.

2012-01-01

Whole-transcriptome expression profiling provides novel phenotypes for analysis of complex traits. Gene expression measurements reflect quantitative variation in transcript-specific messenger RNA levels and represent phenotypes lying close to the action of genes. Understanding the genetic basis of gene expression will provide insight into the processes that connect genotype to clinically significant traits representing a central tenet of system biology. Synchronous in vivo expression profiles of lymphocytes, muscle, and subcutaneous fat were obtained from healthy Mexican men. Most genes were expressed at detectable levels in multiple tissues, and RNA levels were correlated between tissue types. A subset of transcripts with high reliability of expression across tissues (estimated by intraclass correlation coefficients) was enriched for cis-regulated genes, suggesting that proximal sequence variants may influence expression similarly in different cellular environments. This integrative global gene expression profiling approach is proving extremely useful for identifying genes and pathways that contribute to complex clinical traits. Clearly, the coincidence of clinical trait quantitative trait loci and expression quantitative trait loci can help in the prioritization of positional candidate genes. Such data will be crucial for the formal integration of positional and transcriptomic information characterized as genetical genomics. PMID:22797999

Estimating genetic effects and quantifying missing heritability explained by identified rare-variant associations.

PubMed

Liu, Dajiang J; Leal, Suzanne M

2012-10-05

Next-generation sequencing has led to many complex-trait rare-variant (RV) association studies. Although single-variant association analysis can be performed, it is grossly underpowered. Therefore, researchers have developed many RV association tests that aggregate multiple variant sites across a genetic region (e.g., gene), and test for the association between the trait and the aggregated genotype. After these aggregate tests detect an association, it is only possible to estimate the average genetic effect for a group of RVs. As a result of the "winner's curse," such an estimate can be biased. Although for common variants one can obtain unbiased estimates of genetic parameters by analyzing a replication sample, for RVs it is desirable to obtain unbiased genetic estimates for the study where the association is identified. This is because there can be substantial heterogeneity of RV sites and frequencies even among closely related populations. In order to obtain an unbiased estimate for aggregated RV analysis, we developed bootstrap-sample-split algorithms to reduce the bias of the winner's curse. The unbiased estimates are greatly important for understanding the population-specific contribution of RVs to the heritability of complex traits. We also demonstrate both theoretically and via simulations that for aggregate RV analysis the genetic variance for a gene or region will always be underestimated, sometimes substantially, because of the presence of noncausal variants or because of the presence of causal variants with effects of different magnitudes or directions. Therefore, even if RVs play a major role in the complex-trait etiologies, a portion of the heritability will remain missing, and the contribution of RVs to the complex-trait etiologies will be underestimated. Copyright © 2012 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

Advances in cereal genomics and applications in crop breeding.

PubMed

Varshney, Rajeev K; Hoisington, David A; Tyagi, Akhilesh K

2006-11-01

Recent advances in cereal genomics have made it possible to analyse the architecture of cereal genomes and their expressed components, leading to an increase in our knowledge of the genes that are linked to key agronomically important traits. These studies have used molecular genetic mapping of quantitative trait loci (QTL) of several complex traits that are important in breeding. The identification and molecular cloning of genes underlying QTLs offers the possibility to examine the naturally occurring allelic variation for respective complex traits. Novel alleles, identified by functional genomics or haplotype analysis, can enrich the genetic basis of cultivated crops to improve productivity. Advances made in cereal genomics research in recent years thus offer the opportunities to enhance the prediction of phenotypes from genotypes for cereal breeding.

Near infrared spectroscopy and chemometrics analysis of complex traits in animal physiology

USDA-ARS?s Scientific Manuscript database

Near infrared reflectance (NIR) applications have been expanding from the traditional framework of small molecule chemical purity and composition (as defined by spectral libraries) to complex system analysis and holistic exploratory approaches to questions in biochemistry, biophysics and environment...

Association analysis of multiple traits by an approach of combining P values.

PubMed

Chen, Lili; Wang, Yong; Zhou, Yajing

2018-03-01

Increasing evidence shows that one variant can affect multiple traits, which is a widespread phenomenon in complex diseases. Joint analysis of multiple traits can increase statistical power of association analysis and uncover the underlying genetic mechanism. Although there are many statistical methods to analyse multiple traits, most of these methods are usually suitable for detecting common variants associated with multiple traits. However, because of low minor allele frequency of rare variant, these methods are not optimal for rare variant association analysis. In this paper, we extend an adaptive combination of P values method (termed ADA) for single trait to test association between multiple traits and rare variants in the given region. For a given region, we use reverse regression model to test each rare variant associated with multiple traits and obtain the P value of single-variant test. Further, we take the weighted combination of these P values as the test statistic. Extensive simulation studies show that our approach is more powerful than several other comparison methods in most cases and is robust to the inclusion of a high proportion of neutral variants and the different directions of effects of causal variants.

Comparing GWAS Results of Complex Traits Using Full Genetic Model and Additive Models for Revealing Genetic Architecture

PubMed Central

Monir, Md. Mamun; Zhu, Jun

2017-01-01

Most of the genome-wide association studies (GWASs) for human complex diseases have ignored dominance, epistasis and ethnic interactions. We conducted comparative GWASs for total cholesterol using full model and additive models, which illustrate the impacts of the ignoring genetic variants on analysis results and demonstrate how genetic effects of multiple loci could differ across different ethnic groups. There were 15 quantitative trait loci with 13 individual loci and 3 pairs of epistasis loci identified by full model, whereas only 14 loci (9 common loci and 5 different loci) identified by multi-loci additive model. Again, 4 full model detected loci were not detected using multi-loci additive model. PLINK-analysis identified two loci and GCTA-analysis detected only one locus with genome-wide significance. Full model identified three previously reported genes as well as several new genes. Bioinformatics analysis showed some new genes are related with cholesterol related chemicals and/or diseases. Analyses of cholesterol data and simulation studies revealed that the full model performs were better than the additive-model performs in terms of detecting power and unbiased estimations of genetic variants of complex traits. PMID:28079101

On the Origin of Complex Adaptive Traits: Progress Since the Darwin Versus Mivart Debate.

PubMed

Suzuki, Takao K

2017-06-01

The evolutionary origin of complex adaptive traits has been a controversial topic in the history of evolutionary biology. Although Darwin argued for the gradual origins of complex adaptive traits within the theory of natural selection, Mivart insisted that natural selection could not account for the incipient stages of complex traits. The debate starting from Darwin and Mivart eventually engendered two opposite views: gradualism and saltationism. Although this has been a long-standing debate, the issue remains unresolved. However, recent studies have interrogated classic examples of complex traits, such as the asymmetrical eyes of flatfishes and leaf mimicry of butterfly wings, whose origins were debated by Darwin and Mivart. Here, I review recent findings as a starting point to provide a modern picture of the evolution of complex adaptive traits. First, I summarize the empirical evidence that unveils the evolutionary steps toward complex traits. I then argue that the evolution of complex traits could be understood within the concept of "reducible complexity." Through these discussions, I propose a conceptual framework for the formation of complex traits, named as reducible-composable multicomponent systems, that satisfy two major characteristics: reducibility into a sum of subcomponents and composability to construct traits from various additional and combinatorial arrangements of the subcomponents. This conceptual framework provides an analytical foundation for exploring evolutionary pathways to build up complex traits. This review provides certain essential avenues for deciphering the origin of complex adaptive traits. © 2017 Wiley Periodicals, Inc.

Improving breeding efficiency in potato using molecular and quantitative genetics.

PubMed

Slater, Anthony T; Cogan, Noel O I; Hayes, Benjamin J; Schultz, Lee; Dale, M Finlay B; Bryan, Glenn J; Forster, John W

2014-11-01

Potatoes are highly heterozygous and the conventional breeding of superior germplasm is challenging, but use of a combination of MAS and EBVs can accelerate genetic gain. Cultivated potatoes are highly heterozygous due to their outbreeding nature, and suffer acute inbreeding depression. Modern potato cultivars also exhibit tetrasomic inheritance. Due to this genetic heterogeneity, the large number of target traits and the specific requirements of commercial cultivars, potato breeding is challenging. A conventional breeding strategy applies phenotypic recurrent selection over a number of generations, a process which can take over 10 years. Recently, major advances in genetics and molecular biology have provided breeders with molecular tools to accelerate gains for some traits. Marker-assisted selection (MAS) can be effectively used for the identification of major genes and quantitative trait loci that exhibit large effects. There are also a number of complex traits of interest, such as yield, that are influenced by a large number of genes of individual small effect where MAS will be difficult to deploy. Progeny testing and the use of pedigree in the analysis can provide effective identification of the superior genetic factors that underpin these complex traits. Recently, it has been shown that estimated breeding values (EBVs) can be developed for complex potato traits. Using a combination of MAS and EBVs for simple and complex traits can lead to a significant reduction in the length of the breeding cycle for the identification of superior germplasm.

An overview of the genetic dissection of complex traits.

PubMed

Rao, D C

2008-01-01

Thanks to the recent revolutionary genomic advances such as the International HapMap consortium, resolution of the genetic architecture of common complex traits is beginning to look hopeful. While demonstrating the feasibility of genome-wide association (GWA) studies, the pathbreaking Wellcome Trust Case Control Consortium (WTCCC) study also serves to underscore the critical importance of very large sample sizes and draws attention to potential problems, which need to be addressed as part of the study design. Even the large WTCCC study had vastly inadequate power for several of the associations reported (and confirmed) and, therefore, most of the regions harboring relevant associations may not be identified anytime soon. This chapter provides an overview of some of the key developments in the methodological approaches to genetic dissection of common complex traits. Constrained Bayesian networks are suggested as especially useful for analysis of pathway-based SNPs. Likewise, composite likelihood is suggested as a promising method for modeling complex systems. It discusses the key steps in a study design, with an emphasis on GWA studies. Potential limitations highlighted by the WTCCC GWA study are discussed, including problems associated with massive genotype imputation, analysis of pooled national samples, shared controls, and the critical role of interactions. GWA studies clearly need massive sample sizes that are only possible through genuine collaborations. After all, for common complex traits, the question is not whether we can find some pieces of the puzzle, but how large and what kind of a sample we need to (nearly) solve the genetic puzzle.

ATG18 and FAB1 are involved in dehydration stress tolerance in Saccharomyces cerevisiae.

PubMed

López-Martínez, Gema; Margalef-Català, Mar; Salinas, Francisco; Liti, Gianni; Cordero-Otero, Ricardo

2015-01-01

Recently, different dehydration-based technologies have been evaluated for the purpose of cell and tissue preservation. Although some early results have been promising, they have not satisfied the requirements for large-scale applications. The long experience of using quantitative trait loci (QTLs) with the yeast Saccharomyces cerevisiae has proven to be a good model organism for studying the link between complex phenotypes and DNA variations. Here, we use QTL analysis as a tool for identifying the specific yeast traits involved in dehydration stress tolerance. Three hybrids obtained from stable haploids and sequenced in the Saccharomyces Genome Resequencing Project showed intermediate dehydration tolerance in most cases. The dehydration resistance trait of 96 segregants from each hybrid was quantified. A smooth, continuous distribution of the anhydrobiosis tolerance trait was found, suggesting that this trait is determined by multiple QTLs. Therefore, we carried out a QTL analysis to identify the determinants of this dehydration tolerance trait at the genomic level. Among the genes identified after reciprocal hemizygosity assays, RSM22, ATG18 and DBR1 had not been referenced in previous studies. We report new phenotypes for these genes using a previously validated test. Finally, our data illustrates the power of this approach in the investigation of the complex cell dehydration phenotype.

Recent advancements to study flowering time in almond and other Prunus species

PubMed Central

Sánchez-Pérez, Raquel; Del Cueto, Jorge; Dicenta, Federico; Martínez-Gómez, Pedro

2014-01-01

Flowering time is an important agronomic trait in almond since it is decisive to avoid the late frosts that affect production in early flowering cultivars. Evaluation of this complex trait is a long process because of the prolonged juvenile period of trees and the influence of environmental conditions affecting gene expression year by year. Consequently, flowering time has to be studied for several years to have statistical significant results. This trait is the result of the interaction between chilling and heat requirements. Flowering time is a polygenic trait with high heritability, although a major gene Late blooming (Lb) was described in “Tardy Nonpareil.” Molecular studies at DNA level confirmed this polygenic nature identifying several genome regions (Quantitative Trait Loci, QTL) involved. Studies about regulation of gene expression are scarcer although several transcription factors have been described as responsible for flowering time. From the metabolomic point of view, the integrated analysis of the mechanisms of accumulation of cyanogenic glucosides and flowering regulation through transcription factors open new possibilities in the analysis of this complex trait in almond and in other Prunus species (apricot, cherry, peach, plum). New opportunities are arising from the integration of recent advancements including phenotypic, genetic, genomic, transcriptomic, and metabolomics studies from the beginning of dormancy until flowering. PMID:25071812

ATG18 and FAB1 Are Involved in Dehydration Stress Tolerance in Saccharomyces cerevisiae

PubMed Central

López-Martínez, Gema; Margalef-Català, Mar; Salinas, Francisco; Liti, Gianni; Cordero-Otero, Ricardo

2015-01-01

Recently, different dehydration-based technologies have been evaluated for the purpose of cell and tissue preservation. Although some early results have been promising, they have not satisfied the requirements for large-scale applications. The long experience of using quantitative trait loci (QTLs) with the yeast Saccharomyces cerevisiae has proven to be a good model organism for studying the link between complex phenotypes and DNA variations. Here, we use QTL analysis as a tool for identifying the specific yeast traits involved in dehydration stress tolerance. Three hybrids obtained from stable haploids and sequenced in the Saccharomyces Genome Resequencing Project showed intermediate dehydration tolerance in most cases. The dehydration resistance trait of 96 segregants from each hybrid was quantified. A smooth, continuous distribution of the anhydrobiosis tolerance trait was found, suggesting that this trait is determined by multiple QTLs. Therefore, we carried out a QTL analysis to identify the determinants of this dehydration tolerance trait at the genomic level. Among the genes identified after reciprocal hemizygosity assays, RSM22, ATG18 and DBR1 had not been referenced in previous studies. We report new phenotypes for these genes using a previously validated test. Finally, our data illustrates the power of this approach in the investigation of the complex cell dehydration phenotype. PMID:25803831

Meta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension.

PubMed

Zhu, Xiaofeng; Feng, Tao; Tayo, Bamidele O; Liang, Jingjing; Young, J Hunter; Franceschini, Nora; Smith, Jennifer A; Yanek, Lisa R; Sun, Yan V; Edwards, Todd L; Chen, Wei; Nalls, Mike; Fox, Ervin; Sale, Michele; Bottinger, Erwin; Rotimi, Charles; Liu, Yongmei; McKnight, Barbara; Liu, Kiang; Arnett, Donna K; Chakravati, Aravinda; Cooper, Richard S; Redline, Susan

2015-01-08

Genome-wide association studies (GWASs) have identified many genetic variants underlying complex traits. Many detected genetic loci harbor variants that associate with multiple-even distinct-traits. Most current analysis approaches focus on single traits, even though the final results from multiple traits are evaluated together. Such approaches miss the opportunity to systemically integrate the phenome-wide data available for genetic association analysis. In this study, we propose a general approach that can integrate association evidence from summary statistics of multiple traits, either correlated, independent, continuous, or binary traits, which might come from the same or different studies. We allow for trait heterogeneity effects. Population structure and cryptic relatedness can also be controlled. Our simulations suggest that the proposed method has improved statistical power over single-trait analysis in most of the cases we studied. We applied our method to the Continental Origins and Genetic Epidemiology Network (COGENT) African ancestry samples for three blood pressure traits and identified four loci (CHIC2, HOXA-EVX1, IGFBP1/IGFBP3, and CDH17; p < 5.0 × 10(-8)) associated with hypertension-related traits that were missed by a single-trait analysis in the original report. Six additional loci with suggestive association evidence (p < 5.0 × 10(-7)) were also observed, including CACNA1D and WNT3. Our study strongly suggests that analyzing multiple phenotypes can improve statistical power and that such analysis can be executed with the summary statistics from GWASs. Our method also provides a way to study a cross phenotype (CP) association by using summary statistics from GWASs of multiple phenotypes. Copyright © 2015 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

Shared activity patterns arising at genetic susceptibility loci reveal underlying genomic and cellular architecture of human disease.

PubMed

Baillie, J Kenneth; Bretherick, Andrew; Haley, Christopher S; Clohisey, Sara; Gray, Alan; Neyton, Lucile P A; Barrett, Jeffrey; Stahl, Eli A; Tenesa, Albert; Andersson, Robin; Brown, J Ben; Faulkner, Geoffrey J; Lizio, Marina; Schaefer, Ulf; Daub, Carsten; Itoh, Masayoshi; Kondo, Naoto; Lassmann, Timo; Kawai, Jun; Mole, Damian; Bajic, Vladimir B; Heutink, Peter; Rehli, Michael; Kawaji, Hideya; Sandelin, Albin; Suzuki, Harukazu; Satsangi, Jack; Wells, Christine A; Hacohen, Nir; Freeman, Thomas C; Hayashizaki, Yoshihide; Carninci, Piero; Forrest, Alistair R R; Hume, David A

2018-03-01

Genetic variants underlying complex traits, including disease susceptibility, are enriched within the transcriptional regulatory elements, promoters and enhancers. There is emerging evidence that regulatory elements associated with particular traits or diseases share similar patterns of transcriptional activity. Accordingly, shared transcriptional activity (coexpression) may help prioritise loci associated with a given trait, and help to identify underlying biological processes. Using cap analysis of gene expression (CAGE) profiles of promoter- and enhancer-derived RNAs across 1824 human samples, we have analysed coexpression of RNAs originating from trait-associated regulatory regions using a novel quantitative method (network density analysis; NDA). For most traits studied, phenotype-associated variants in regulatory regions were linked to tightly-coexpressed networks that are likely to share important functional characteristics. Coexpression provides a new signal, independent of phenotype association, to enable fine mapping of causative variants. The NDA coexpression approach identifies new genetic variants associated with specific traits, including an association between the regulation of the OCT1 cation transporter and genetic variants underlying circulating cholesterol levels. NDA strongly implicates particular cell types and tissues in disease pathogenesis. For example, distinct groupings of disease-associated regulatory regions implicate two distinct biological processes in the pathogenesis of ulcerative colitis; a further two separate processes are implicated in Crohn's disease. Thus, our functional analysis of genetic predisposition to disease defines new distinct disease endotypes. We predict that patients with a preponderance of susceptibility variants in each group are likely to respond differently to pharmacological therapy. Together, these findings enable a deeper biological understanding of the causal basis of complex traits.

Shared activity patterns arising at genetic susceptibility loci reveal underlying genomic and cellular architecture of human disease

PubMed Central

Gray, Alan; Neyton, Lucile P. A.; Barrett, Jeffrey; Stahl, Eli A.; Tenesa, Albert; Andersson, Robin; Brown, J. Ben; Faulkner, Geoffrey J.; Lizio, Marina; Schaefer, Ulf; Daub, Carsten; Kondo, Naoto; Lassmann, Timo; Kawai, Jun; Kawaji, Hideya; Suzuki, Harukazu; Satsangi, Jack; Wells, Christine A.; Hacohen, Nir; Freeman, Thomas C.; Hayashizaki, Yoshihide; Forrest, Alistair R. R.; Hume, David A.

2018-01-01

Genetic variants underlying complex traits, including disease susceptibility, are enriched within the transcriptional regulatory elements, promoters and enhancers. There is emerging evidence that regulatory elements associated with particular traits or diseases share similar patterns of transcriptional activity. Accordingly, shared transcriptional activity (coexpression) may help prioritise loci associated with a given trait, and help to identify underlying biological processes. Using cap analysis of gene expression (CAGE) profiles of promoter- and enhancer-derived RNAs across 1824 human samples, we have analysed coexpression of RNAs originating from trait-associated regulatory regions using a novel quantitative method (network density analysis; NDA). For most traits studied, phenotype-associated variants in regulatory regions were linked to tightly-coexpressed networks that are likely to share important functional characteristics. Coexpression provides a new signal, independent of phenotype association, to enable fine mapping of causative variants. The NDA coexpression approach identifies new genetic variants associated with specific traits, including an association between the regulation of the OCT1 cation transporter and genetic variants underlying circulating cholesterol levels. NDA strongly implicates particular cell types and tissues in disease pathogenesis. For example, distinct groupings of disease-associated regulatory regions implicate two distinct biological processes in the pathogenesis of ulcerative colitis; a further two separate processes are implicated in Crohn’s disease. Thus, our functional analysis of genetic predisposition to disease defines new distinct disease endotypes. We predict that patients with a preponderance of susceptibility variants in each group are likely to respond differently to pharmacological therapy. Together, these findings enable a deeper biological understanding of the causal basis of complex traits. PMID:29494619

Joint Analysis of Strain and Parent-of-Origin Effects for Recombinant Inbred Intercrosses Generated from Multiparent Populations with the Collaborative Cross as an Example.

PubMed

Liu, Yanyan; Xiong, Sican; Sun, Wei; Zou, Fei

2018-02-02

Multiparent populations (MPP) have become popular resources for complex trait mapping because of their wider allelic diversity and larger population size compared with traditional two-way recombinant inbred (RI) strains. In mice, the collaborative cross (CC) is one of the most popular MPP and is derived from eight genetically diverse inbred founder strains. The strategy of generating RI intercrosses (RIX) from MPP in general and from the CC in particular can produce a large number of completely reproducible heterozygote genomes that better represent the (outbred) human population. Since both maternal and paternal haplotypes of each RIX are readily available, RIX is a powerful resource for studying both standing genetic and epigenetic variations of complex traits, in particular, the parent-of-origin (PoO) effects, which are important contributors to many complex traits. Furthermore, most complex traits are affected by >1 genes, where multiple quantitative trait locus mapping could be more advantageous. In this paper, for MPP-RIX data but taking CC-RIX as a working example, we propose a general Bayesian variable selection procedure to simultaneously search for multiple genes with founder allelic effects and PoO effects. The proposed model respects the complex relationship among RIX samples, and the performance of the proposed method is examined by extensive simulations. Copyright © 2018 Liu et al.

The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease.

PubMed

Astle, William J; Elding, Heather; Jiang, Tao; Allen, Dave; Ruklisa, Dace; Mann, Alice L; Mead, Daniel; Bouman, Heleen; Riveros-Mckay, Fernando; Kostadima, Myrto A; Lambourne, John J; Sivapalaratnam, Suthesh; Downes, Kate; Kundu, Kousik; Bomba, Lorenzo; Berentsen, Kim; Bradley, John R; Daugherty, Louise C; Delaneau, Olivier; Freson, Kathleen; Garner, Stephen F; Grassi, Luigi; Guerrero, Jose; Haimel, Matthias; Janssen-Megens, Eva M; Kaan, Anita; Kamat, Mihir; Kim, Bowon; Mandoli, Amit; Marchini, Jonathan; Martens, Joost H A; Meacham, Stuart; Megy, Karyn; O'Connell, Jared; Petersen, Romina; Sharifi, Nilofar; Sheard, Simon M; Staley, James R; Tuna, Salih; van der Ent, Martijn; Walter, Klaudia; Wang, Shuang-Yin; Wheeler, Eleanor; Wilder, Steven P; Iotchkova, Valentina; Moore, Carmel; Sambrook, Jennifer; Stunnenberg, Hendrik G; Di Angelantonio, Emanuele; Kaptoge, Stephen; Kuijpers, Taco W; Carrillo-de-Santa-Pau, Enrique; Juan, David; Rico, Daniel; Valencia, Alfonso; Chen, Lu; Ge, Bing; Vasquez, Louella; Kwan, Tony; Garrido-Martín, Diego; Watt, Stephen; Yang, Ying; Guigo, Roderic; Beck, Stephan; Paul, Dirk S; Pastinen, Tomi; Bujold, David; Bourque, Guillaume; Frontini, Mattia; Danesh, John; Roberts, David J; Ouwehand, Willem H; Butterworth, Adam S; Soranzo, Nicole

2016-11-17

Many common variants have been associated with hematological traits, but identification of causal genes and pathways has proven challenging. We performed a genome-wide association analysis in the UK Biobank and INTERVAL studies, testing 29.5 million genetic variants for association with 36 red cell, white cell, and platelet properties in 173,480 European-ancestry participants. This effort yielded hundreds of low frequency (<5%) and rare (<1%) variants with a strong impact on blood cell phenotypes. Our data highlight general properties of the allelic architecture of complex traits, including the proportion of the heritable component of each blood trait explained by the polygenic signal across different genome regulatory domains. Finally, through Mendelian randomization, we provide evidence of shared genetic pathways linking blood cell indices with complex pathologies, including autoimmune diseases, schizophrenia, and coronary heart disease and evidence suggesting previously reported population associations between blood cell indices and cardiovascular disease may be non-causal. Copyright © 2016 Elsevier Inc. All rights reserved.

Plasticity Regulators Modulate Specific Root Traits in Discrete Nitrogen Environments

PubMed Central

Gifford, Miriam L.; Banta, Joshua A.; Katari, Manpreet S.; Hulsmans, Jo; Chen, Lisa; Ristova, Daniela; Tranchina, Daniel; Purugganan, Michael D.; Coruzzi, Gloria M.; Birnbaum, Kenneth D.

2013-01-01

Plant development is remarkably plastic but how precisely can the plant customize its form to specific environments? When the plant adjusts its development to different environments, related traits can change in a coordinated fashion, such that two traits co-vary across many genotypes. Alternatively, traits can vary independently, such that a change in one trait has little predictive value for the change in a second trait. To characterize such “tunability” in developmental plasticity, we carried out a detailed phenotypic characterization of complex root traits among 96 accessions of the model Arabidopsis thaliana in two nitrogen environments. The results revealed a surprising level of independence in the control of traits to environment – a highly tunable form of plasticity. We mapped genetic architecture of plasticity using genome-wide association studies and further used gene expression analysis to narrow down gene candidates in mapped regions. Mutants in genes implicated by association and expression analysis showed precise defects in the predicted traits in the predicted environment, corroborating the independent control of plasticity traits. The overall results suggest that there is a pool of genetic variability in plants that controls traits in specific environments, with opportunity to tune crop plants to a given environment. PMID:24039603

Genetic Complexity and Quantitative Trait Loci Mapping of Yeast Morphological Traits

PubMed Central

Nogami, Satoru; Ohya, Yoshikazu; Yvert, Gaël

2007-01-01

Functional genomics relies on two essential parameters: the sensitivity of phenotypic measures and the power to detect genomic perturbations that cause phenotypic variations. In model organisms, two types of perturbations are widely used. Artificial mutations can be introduced in virtually any gene and allow the systematic analysis of gene function via mutants fitness. Alternatively, natural genetic variations can be associated to particular phenotypes via genetic mapping. However, the access to genome manipulation and breeding provided by model organisms is sometimes counterbalanced by phenotyping limitations. Here we investigated the natural genetic diversity of Saccharomyces cerevisiae cellular morphology using a very sensitive high-throughput imaging platform. We quantified 501 morphological parameters in over 50,000 yeast cells from a cross between two wild-type divergent backgrounds. Extensive morphological differences were found between these backgrounds. The genetic architecture of the traits was complex, with evidence of both epistasis and transgressive segregation. We mapped quantitative trait loci (QTL) for 67 traits and discovered 364 correlations between traits segregation and inheritance of gene expression levels. We validated one QTL by the replacement of a single base in the genome. This study illustrates the natural diversity and complexity of cellular traits among natural yeast strains and provides an ideal framework for a genetical genomics dissection of multiple traits. Our results did not overlap with results previously obtained from systematic deletion strains, showing that both approaches are necessary for the functional exploration of genomes. PMID:17319748

A Multi-Trait, Meta-analysis for Detecting Pleiotropic Polymorphisms for Stature, Fatness and Reproduction in Beef Cattle

PubMed Central

Bolormaa, Sunduimijid; Pryce, Jennie E.; Reverter, Antonio; Zhang, Yuandan; Barendse, William; Kemper, Kathryn; Tier, Bruce; Savin, Keith; Hayes, Ben J.; Goddard, Michael E.

2014-01-01

Polymorphisms that affect complex traits or quantitative trait loci (QTL) often affect multiple traits. We describe two novel methods (1) for finding single nucleotide polymorphisms (SNPs) significantly associated with one or more traits using a multi-trait, meta-analysis, and (2) for distinguishing between a single pleiotropic QTL and multiple linked QTL. The meta-analysis uses the effect of each SNP on each of n traits, estimated in single trait genome wide association studies (GWAS). These effects are expressed as a vector of signed t-values (t) and the error covariance matrix of these t values is approximated by the correlation matrix of t-values among the traits calculated across the SNP (V). Consequently, t'V−1t is approximately distributed as a chi-squared with n degrees of freedom. An attractive feature of the meta-analysis is that it uses estimated effects of SNPs from single trait GWAS, so it can be applied to published data where individual records are not available. We demonstrate that the multi-trait method can be used to increase the power (numbers of SNPs validated in an independent population) of GWAS in a beef cattle data set including 10,191 animals genotyped for 729,068 SNPs with 32 traits recorded, including growth and reproduction traits. We can distinguish between a single pleiotropic QTL and multiple linked QTL because multiple SNPs tagging the same QTL show the same pattern of effects across traits. We confirm this finding by demonstrating that when one SNP is included in the statistical model the other SNPs have a non-significant effect. In the beef cattle data set, cluster analysis yielded four groups of QTL with similar patterns of effects across traits within a group. A linear index was used to validate SNPs having effects on multiple traits and to identify additional SNPs belonging to these four groups. PMID:24675618

Phenotyping: Using Machine Learning for Improved Pairwise Genotype Classification Based on Root Traits

PubMed Central

Zhao, Jiangsan; Bodner, Gernot; Rewald, Boris

2016-01-01

Phenotyping local crop cultivars is becoming more and more important, as they are an important genetic source for breeding – especially in regard to inherent root system architectures. Machine learning algorithms are promising tools to assist in the analysis of complex data sets; novel approaches are need to apply them on root phenotyping data of mature plants. A greenhouse experiment was conducted in large, sand-filled columns to differentiate 16 European Pisum sativum cultivars based on 36 manually derived root traits. Through combining random forest and support vector machine models, machine learning algorithms were successfully used for unbiased identification of most distinguishing root traits and subsequent pairwise cultivar differentiation. Up to 86% of pea cultivar pairs could be distinguished based on top five important root traits (Timp5) – Timp5 differed widely between cultivar pairs. Selecting top important root traits (Timp) provided a significant improved classification compared to using all available traits or randomly selected trait sets. The most frequent Timp of mature pea cultivars was total surface area of lateral roots originating from tap root segments at 0–5 cm depth. The high classification rate implies that culturing did not lead to a major loss of variability in root system architecture in the studied pea cultivars. Our results illustrate the potential of machine learning approaches for unbiased (root) trait selection and cultivar classification based on rather small, complex phenotypic data sets derived from pot experiments. Powerful statistical approaches are essential to make use of the increasing amount of (root) phenotyping information, integrating the complex trait sets describing crop cultivars. PMID:27999587

Use of biological priors enhances understanding of genetic architecture and genomic prediction of complex traits within and between dairy cattle breeds.

PubMed

Fang, Lingzhao; Sahana, Goutam; Ma, Peipei; Su, Guosheng; Yu, Ying; Zhang, Shengli; Lund, Mogens Sandø; Sørensen, Peter

2017-08-10

A better understanding of the genetic architecture underlying complex traits (e.g., the distribution of causal variants and their effects) may aid in the genomic prediction. Here, we hypothesized that the genomic variants of complex traits might be enriched in a subset of genomic regions defined by genes grouped on the basis of "Gene Ontology" (GO), and that incorporating this independent biological information into genomic prediction models might improve their predictive ability. Four complex traits (i.e., milk, fat and protein yields, and mastitis) together with imputed sequence variants in Holstein (HOL) and Jersey (JER) cattle were analysed. We first carried out a post-GWAS analysis in a HOL training population to assess the degree of enrichment of the association signals in the gene regions defined by each GO term. We then extended the genomic best linear unbiased prediction model (GBLUP) to a genomic feature BLUP (GFBLUP) model, including an additional genomic effect quantifying the joint effect of a group of variants located in a genomic feature. The GBLUP model using a single random effect assumes that all genomic variants contribute to the genomic relationship equally, whereas GFBLUP attributes different weights to the individual genomic relationships in the prediction equation based on the estimated genomic parameters. Our results demonstrate that the immune-relevant GO terms were more associated with mastitis than milk production, and several biologically meaningful GO terms improved the prediction accuracy with GFBLUP for the four traits, as compared with GBLUP. The improvement of the genomic prediction between breeds (the average increase across the four traits was 0.161) was more apparent than that it was within the HOL (the average increase across the four traits was 0.020). Our genomic feature modelling approaches provide a framework to simultaneously explore the genetic architecture and genomic prediction of complex traits by taking advantage of independent biological knowledge.

Power Analysis of Artificial Selection Experiments Using Efficient Whole Genome Simulation of Quantitative Traits

PubMed Central

Kessner, Darren; Novembre, John

2015-01-01

Evolve and resequence studies combine artificial selection experiments with massively parallel sequencing technology to study the genetic basis for complex traits. In these experiments, individuals are selected for extreme values of a trait, causing alleles at quantitative trait loci (QTL) to increase or decrease in frequency in the experimental population. We present a new analysis of the power of artificial selection experiments to detect and localize quantitative trait loci. This analysis uses a simulation framework that explicitly models whole genomes of individuals, quantitative traits, and selection based on individual trait values. We find that explicitly modeling QTL provides qualitatively different insights than considering independent loci with constant selection coefficients. Specifically, we observe how interference between QTL under selection affects the trajectories and lengthens the fixation times of selected alleles. We also show that a substantial portion of the genetic variance of the trait (50–100%) can be explained by detected QTL in as little as 20 generations of selection, depending on the trait architecture and experimental design. Furthermore, we show that power depends crucially on the opportunity for recombination during the experiment. Finally, we show that an increase in power is obtained by leveraging founder haplotype information to obtain allele frequency estimates. PMID:25672748

Genomic selection and complex trait prediction using a fast EM algorithm applied to genome-wide markers

PubMed Central

2010-01-01

Background The information provided by dense genome-wide markers using high throughput technology is of considerable potential in human disease studies and livestock breeding programs. Genome-wide association studies relate individual single nucleotide polymorphisms (SNP) from dense SNP panels to individual measurements of complex traits, with the underlying assumption being that any association is caused by linkage disequilibrium (LD) between SNP and quantitative trait loci (QTL) affecting the trait. Often SNP are in genomic regions of no trait variation. Whole genome Bayesian models are an effective way of incorporating this and other important prior information into modelling. However a full Bayesian analysis is often not feasible due to the large computational time involved. Results This article proposes an expectation-maximization (EM) algorithm called emBayesB which allows only a proportion of SNP to be in LD with QTL and incorporates prior information about the distribution of SNP effects. The posterior probability of being in LD with at least one QTL is calculated for each SNP along with estimates of the hyperparameters for the mixture prior. A simulated example of genomic selection from an international workshop is used to demonstrate the features of the EM algorithm. The accuracy of prediction is comparable to a full Bayesian analysis but the EM algorithm is considerably faster. The EM algorithm was accurate in locating QTL which explained more than 1% of the total genetic variation. A computational algorithm for very large SNP panels is described. Conclusions emBayesB is a fast and accurate EM algorithm for implementing genomic selection and predicting complex traits by mapping QTL in genome-wide dense SNP marker data. Its accuracy is similar to Bayesian methods but it takes only a fraction of the time. PMID:20969788

Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large-scale genetic studies.

PubMed

Atkinson, Jonathan A; Lobet, Guillaume; Noll, Manuel; Meyer, Patrick E; Griffiths, Marcus; Wells, Darren M

2017-10-01

Genetic analyses of plant root systems require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a limited number of architectural traits) or semi-automated ones (that are highly time consuming). We trained a Random Forest algorithm to infer architectural traits from automatically extracted image descriptors. The training was performed on a subset of the dataset, then applied to its entirety. This strategy allowed us to (i) decrease the image analysis time by 73% and (ii) extract meaningful architectural traits based on image descriptors. We also show that these traits are sufficient to identify the quantitative trait loci that had previously been discovered using a semi-automated method. We have shown that combining semi-automated image analysis with machine learning algorithms has the power to increase the throughput of large-scale root studies. We expect that such an approach will enable the quantification of more complex root systems for genetic studies. We also believe that our approach could be extended to other areas of plant phenotyping. © The Authors 2017. Published by Oxford University Press.

Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large-scale genetic studies

PubMed Central

Atkinson, Jonathan A.; Lobet, Guillaume; Noll, Manuel; Meyer, Patrick E.; Griffiths, Marcus

2017-01-01

Abstract Genetic analyses of plant root systems require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a limited number of architectural traits) or semi-automated ones (that are highly time consuming). We trained a Random Forest algorithm to infer architectural traits from automatically extracted image descriptors. The training was performed on a subset of the dataset, then applied to its entirety. This strategy allowed us to (i) decrease the image analysis time by 73% and (ii) extract meaningful architectural traits based on image descriptors. We also show that these traits are sufficient to identify the quantitative trait loci that had previously been discovered using a semi-automated method. We have shown that combining semi-automated image analysis with machine learning algorithms has the power to increase the throughput of large-scale root studies. We expect that such an approach will enable the quantification of more complex root systems for genetic studies. We also believe that our approach could be extended to other areas of plant phenotyping. PMID:29020748

Phenotypic integration among trabecular and cortical bone traits establishes mechanical functionality of inbred mouse vertebrae.

PubMed

Tommasini, Steven M; Hu, Bin; Nadeau, Joseph H; Jepsen, Karl J

2009-04-01

Conventional approaches to identifying quantitative trait loci (QTLs) regulating bone mass and fragility are limited because they examine cortical and trabecular traits independently. Prior work examining long bones from young adult mice and humans indicated that skeletal traits are functionally related and that compensatory interactions among morphological and compositional traits are critical for establishing mechanical function. However, it is not known whether trait covariation (i.e., phenotypic integration) also is important for establishing mechanical function in more complex, corticocancellous structures. Covariation among trabecular, cortical, and compositional bone traits was examined in the context of mechanical functionality for L(4) vertebral bodies across a panel of 16-wk-old female AXB/BXA recombinant inbred (RI) mouse strains. The unique pattern of randomization of the A/J and C57BL/6J (B6) genome among the RI panel provides a powerful tool that can be used to measure the tendency for different traits to covary and to study the biology of complex traits. We tested the hypothesis that genetic variants affecting vertebral size and mass are buffered by changes in the relative amounts of cortical and trabecular bone and overall mineralization. Despite inheriting random sets of A/J and B6 genomes, the RI strains inherited nonrandom sets of cortical and trabecular bone traits. Path analysis, which is a multivariate analysis that shows how multiple traits covary simultaneously when confounding variables like body size are taken into consideration, showed that RI strains that tended to have smaller vertebrae relative to body size achieved mechanical functionality by increasing mineralization and the relative amounts of cortical and trabecular bone. The interdependence among corticocancellous traits in the vertebral body indicated that variation in trabecular bone traits among inbred mouse strains, which is often thought to arise from genetic factors, is also determined in part by the adaptive response to variation in traits describing the cortical shell. The covariation among corticocancellous traits has important implications for genetic analyses and for interpreting the response of bone to genetic and environmental perturbations.

A Strategy for Identifying Quantitative Trait Genes Using Gene Expression Analysis and Causal Analysis.

PubMed

Ishikawa, Akira

2017-11-27

Large numbers of quantitative trait loci (QTL) affecting complex diseases and other quantitative traits have been reported in humans and model animals. However, the genetic architecture of these traits remains elusive due to the difficulty in identifying causal quantitative trait genes (QTGs) for common QTL with relatively small phenotypic effects. A traditional strategy based on techniques such as positional cloning does not always enable identification of a single candidate gene for a QTL of interest because it is difficult to narrow down a target genomic interval of the QTL to a very small interval harboring only one gene. A combination of gene expression analysis and statistical causal analysis can greatly reduce the number of candidate genes. This integrated approach provides causal evidence that one of the candidate genes is a putative QTG for the QTL. Using this approach, I have recently succeeded in identifying a single putative QTG for resistance to obesity in mice. Here, I outline the integration approach and discuss its usefulness using my studies as an example.

Male pregnancy and the evolution of body segmentation in seahorses and pipefishes.

PubMed

Hoffman, Eric A; Mobley, Kenyon B; Jones, Adam G

2006-02-01

The evolution of complex traits, which are specified by the interplay of multiple genetic loci and environmental effects, is a topic of central importance in evolutionary biology. Here, we show that body and tail vertebral numbers in fishes of the pipefish and seahorse family (Syngnathidae) can serve as a model for studies of quantitative trait evolution. A quantitative genetic analysis of body and tail vertebrae from field-collected families of the Gulf pipefish, Syngnathus scovelli, shows that both traits exhibit significantly positive additive genetic variance, with heritabilities of 0.75 +/- 0.13 (mean +/- standard error) and 0.46 +/- 0.18, respectively. We do not find any evidence for either phenotypic or genetic correlations between the two traits. Pipefish are characterized by male pregnancy, and phylogenetic consideration of body proportions suggests that the position of eggs on the pregnant male's body may have contributed to the evolution of vertebral counts. In terms of numbers of vertebrae, tail-brooding males have longer tails for a given trunk size than do trunk-brooding males. Overall, these results suggest that vertebral counts in pipefish are heritable traits, capable of a response to selection, and they may have experienced an interesting history of selection due to the phenomenon of male pregnancy. Given that these traits vary among populations within species as well as among species, they appear to provide an excellent model for further research on complex trait evolution. Body segmentation may thus afford excellent opportunities for comparative study of homologous complex traits among disparate vertebrate taxa.

A high-density genetic map and QTL analysis of agronomic traits in foxtail millet [Setaria italica (L.) P. Beauv.] using RAD-seq.

PubMed

Wang, Jun; Wang, Zhilan; Du, Xiaofen; Yang, Huiqing; Han, Fang; Han, Yuanhuai; Yuan, Feng; Zhang, Linyi; Peng, Shuzhong; Guo, Erhu

2017-01-01

Foxtail millet (Setaria italica), a very important grain crop in China, has become a new model plant for cereal crops and biofuel grasses. Although its reference genome sequence was released recently, quantitative trait loci (QTLs) controlling complex agronomic traits remains limited. The development of massively parallel genotyping methods and next-generation sequencing technologies provides an excellent opportunity for developing single-nucleotide polymorphisms (SNPs) for linkage map construction and QTL analysis of complex quantitative traits. In this study, a high-throughput and cost-effective RAD-seq approach was employed to generate a high-density genetic map for foxtail millet. A total of 2,668,587 SNP loci were detected according to the reference genome sequence; meanwhile, 9,968 SNP markers were used to genotype 124 F2 progenies derived from the cross between Hongmiaozhangu and Changnong35; a high-density genetic map spanning 1648.8 cM, with an average distance of 0.17 cM between adjacent markers was constructed; 11 major QTLs for eight agronomic traits were identified; five co-dominant DNA markers were developed. These findings will be of value for the identification of candidate genes and marker-assisted selection in foxtail millet.

A high-density genetic map and QTL analysis of agronomic traits in foxtail millet [Setaria italica (L.) P. Beauv.] using RAD-seq

PubMed Central

Wang, Zhilan; Du, Xiaofen; Yang, Huiqing; Han, Fang; Han, Yuanhuai; Yuan, Feng; Zhang, Linyi; Peng, Shuzhong; Guo, Erhu

2017-01-01

Foxtail millet (Setaria italica), a very important grain crop in China, has become a new model plant for cereal crops and biofuel grasses. Although its reference genome sequence was released recently, quantitative trait loci (QTLs) controlling complex agronomic traits remains limited. The development of massively parallel genotyping methods and next-generation sequencing technologies provides an excellent opportunity for developing single-nucleotide polymorphisms (SNPs) for linkage map construction and QTL analysis of complex quantitative traits. In this study, a high-throughput and cost-effective RAD-seq approach was employed to generate a high-density genetic map for foxtail millet. A total of 2,668,587 SNP loci were detected according to the reference genome sequence; meanwhile, 9,968 SNP markers were used to genotype 124 F2 progenies derived from the cross between Hongmiaozhangu and Changnong35; a high-density genetic map spanning 1648.8 cM, with an average distance of 0.17 cM between adjacent markers was constructed; 11 major QTLs for eight agronomic traits were identified; five co-dominant DNA markers were developed. These findings will be of value for the identification of candidate genes and marker-assisted selection in foxtail millet. PMID:28644843

Genetic architecture of wood properties based on association analysis and co-expression networks in white spruce.

PubMed

Lamara, Mebarek; Raherison, Elie; Lenz, Patrick; Beaulieu, Jean; Bousquet, Jean; MacKay, John

2016-04-01

Association studies are widely utilized to analyze complex traits but their ability to disclose genetic architectures is often limited by statistical constraints, and functional insights are usually minimal in nonmodel organisms like forest trees. We developed an approach to integrate association mapping results with co-expression networks. We tested single nucleotide polymorphisms (SNPs) in 2652 candidate genes for statistical associations with wood density, stiffness, microfibril angle and ring width in a population of 1694 white spruce trees (Picea glauca). Associations mapping identified 229-292 genes per wood trait using a statistical significance level of P < 0.05 to maximize discovery. Over-representation of genes associated for nearly all traits was found in a xylem preferential co-expression group developed in independent experiments. A xylem co-expression network was reconstructed with 180 wood associated genes and several known MYB and NAC regulators were identified as network hubs. The network revealed a link between the gene PgNAC8, wood stiffness and microfibril angle, as well as considerable within-season variation for both genetic control of wood traits and gene expression. Trait associations were distributed throughout the network suggesting complex interactions and pleiotropic effects. Our findings indicate that integration of association mapping and co-expression networks enhances our understanding of complex wood traits. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

Using a system of differential equations that models cattle growth to uncover the genetic basis of complex traits.

PubMed

Freua, Mateus Castelani; Santana, Miguel Henrique de Almeida; Ventura, Ricardo Vieira; Tedeschi, Luis Orlindo; Ferraz, José Bento Sterman

2017-08-01

The interplay between dynamic models of biological systems and genomics is based on the assumption that genetic variation of the complex trait (i.e., outcome of model behavior) arises from component traits (i.e., model parameters) in lower hierarchical levels. In order to provide a proof of concept of this statement for a cattle growth model, we ask whether model parameters map genomic regions that harbor quantitative trait loci (QTLs) already described for the complex trait. We conducted a genome-wide association study (GWAS) with a Bayesian hierarchical LASSO method in two parameters of the Davis Growth Model, a system of three ordinary differential equations describing DNA accretion, protein synthesis and degradation, and fat synthesis. Phenotypic and genotypic data were available for 893 Nellore (Bos indicus) cattle. Computed values for parameter k 1 (DNA accretion rate) ranged from 0.005 ± 0.003 and for α (constant for energy for maintenance requirement) 0.134 ± 0.024. The expected biological interpretation of the parameters is confirmed by QTLs mapped for k 1 and α. QTLs within genomic regions mapped for k 1 are expected to be correlated with the DNA pool: body size and weight. Single nucleotide polymorphisms (SNPs) which were significant for α mapped QTLs that had already been associated with residual feed intake, feed conversion ratio, average daily gain (ADG), body weight, and also dry matter intake. SNPs identified for k 1 were able to additionally explain 2.2% of the phenotypic variability of the complex ADG, even when SNPs for k 1 did not match the genomic regions associated with ADG. Although improvements are needed, our findings suggest that genomic analysis on component traits may help to uncover the genetic basis of more complex traits, particularly when lower biological hierarchies are mechanistically described by mathematical simulation models.

Rare-Variant Association Analysis: Study Designs and Statistical Tests

PubMed Central

Lee, Seunggeung; Abecasis, Gonçalo R.; Boehnke, Michael; Lin, Xihong

2014-01-01

Despite the extensive discovery of trait- and disease-associated common variants, much of the genetic contribution to complex traits remains unexplained. Rare variants can explain additional disease risk or trait variability. An increasing number of studies are underway to identify trait- and disease-associated rare variants. In this review, we provide an overview of statistical issues in rare-variant association studies with a focus on study designs and statistical tests. We present the design and analysis pipeline of rare-variant studies and review cost-effective sequencing designs and genotyping platforms. We compare various gene- or region-based association tests, including burden tests, variance-component tests, and combined omnibus tests, in terms of their assumptions and performance. Also discussed are the related topics of meta-analysis, population-stratification adjustment, genotype imputation, follow-up studies, and heritability due to rare variants. We provide guidelines for analysis and discuss some of the challenges inherent in these studies and future research directions. PMID:24995866

A computational interactome for prioritizing genes associated with complex agronomic traits in rice (Oryza sativa).

PubMed

Liu, Shiwei; Liu, Yihui; Zhao, Jiawei; Cai, Shitao; Qian, Hongmei; Zuo, Kaijing; Zhao, Lingxia; Zhang, Lida

2017-04-01

Rice (Oryza sativa) is one of the most important staple foods for more than half of the global population. Many rice traits are quantitative, complex and controlled by multiple interacting genes. Thus, a full understanding of genetic relationships will be critical to systematically identify genes controlling agronomic traits. We developed a genome-wide rice protein-protein interaction network (RicePPINet, http://netbio.sjtu.edu.cn/riceppinet) using machine learning with structural relationship and functional information. RicePPINet contained 708 819 predicted interactions for 16 895 non-transposable element related proteins. The power of the network for discovering novel protein interactions was demonstrated through comparison with other publicly available protein-protein interaction (PPI) prediction methods, and by experimentally determined PPI data sets. Furthermore, global analysis of domain-mediated interactions revealed RicePPINet accurately reflects PPIs at the domain level. Our studies showed the efficiency of the RicePPINet-based method in prioritizing candidate genes involved in complex agronomic traits, such as disease resistance and drought tolerance, was approximately 2-11 times better than random prediction. RicePPINet provides an expanded landscape of computational interactome for the genetic dissection of agronomically important traits in rice. © 2017 The Authors The Plant Journal © 2017 John Wiley & Sons Ltd.

Searching new signals for production traits through gene-based association analysis in three Italian cattle breeds.

PubMed

Capomaccio, Stefano; Milanesi, Marco; Bomba, Lorenzo; Cappelli, Katia; Nicolazzi, Ezequiel L; Williams, John L; Ajmone-Marsan, Paolo; Stefanon, Bruno

2015-08-01

Genome-wide association studies (GWAS) have been widely applied to disentangle the genetic basis of complex traits. In cattle breeds, classical GWAS approaches with medium-density marker panels are far from conclusive, especially for complex traits. This is due to the intrinsic limitations of GWAS and the assumptions that are made to step from the association signals to the functional variations. Here, we applied a gene-based strategy to prioritize genotype-phenotype associations found for milk production and quality traits with classical approaches in three Italian dairy cattle breeds with different sample sizes (Italian Brown n = 745; Italian Holstein n = 2058; Italian Simmental n = 477). Although classical regression on single markers revealed only a single genome-wide significant genotype-phenotype association, for Italian Holstein, the gene-based approach identified specific genes in each breed that are associated with milk physiology and mammary gland development. As no standard method has yet been established to step from variation to functional units (i.e., genes), the strategy proposed here may contribute to revealing new genes that play significant roles in complex traits, such as those investigated here, amplifying low association signals using a gene-centric approach. © 2015 Stichting International Foundation for Animal Genetics.

Mapping of epistatic quantitative trait loci in four-way crosses.

PubMed

He, Xiao-Hong; Qin, Hongde; Hu, Zhongli; Zhang, Tianzhen; Zhang, Yuan-Ming

2011-01-01

Four-way crosses (4WC) involving four different inbred lines often appear in plant and animal commercial breeding programs. Direct mapping of quantitative trait loci (QTL) in these commercial populations is both economical and practical. However, the existing statistical methods for mapping QTL in a 4WC population are built on the single-QTL genetic model. This simple genetic model fails to take into account QTL interactions, which play an important role in the genetic architecture of complex traits. In this paper, therefore, we attempted to develop a statistical method to detect epistatic QTL in 4WC population. Conditional probabilities of QTL genotypes, computed by the multi-point single locus method, were used to sample the genotypes of all putative QTL in the entire genome. The sampled genotypes were used to construct the design matrix for QTL effects. All QTL effects, including main and epistatic effects, were simultaneously estimated by the penalized maximum likelihood method. The proposed method was confirmed by a series of Monte Carlo simulation studies and real data analysis of cotton. The new method will provide novel tools for the genetic dissection of complex traits, construction of QTL networks, and analysis of heterosis.

Genome-wide association analysis of seedling root development in maize (Zea mays L.).

PubMed

Pace, Jordon; Gardner, Candice; Romay, Cinta; Ganapathysubramanian, Baskar; Lübberstedt, Thomas

2015-02-05

Plants rely on the root system for anchorage to the ground and the acquisition and absorption of nutrients critical to sustaining productivity. A genome wide association analysis enables one to analyze allelic diversity of complex traits and identify superior alleles. 384 inbred lines from the Ames panel were genotyped with 681,257 single nucleotide polymorphism markers using Genotyping-by-Sequencing technology and 22 seedling root architecture traits were phenotyped. Utilizing both a general linear model and mixed linear model, a GWAS study was conducted identifying 268 marker trait associations (p ≤ 5.3×10(-7)). Analysis of significant SNP markers for multiple traits showed that several were located within gene models with some SNP markers localized within regions of previously identified root quantitative trait loci. Gene model GRMZM2G153722 located on chromosome 4 contained nine significant markers. This predicted gene is expressed in roots and shoots. This study identifies putatively associated SNP markers associated with root traits at the seedling stage. Some SNPs were located within or near (<1 kb) gene models. These gene models identify possible candidate genes involved in root development at the seedling stage. These and respective linked or functional markers could be targets for breeders for marker assisted selection of seedling root traits.

A powerful approach reveals numerous expression quantitative trait haplotypes in multiple tissues.

PubMed

Ying, Dingge; Li, Mulin Jun; Sham, Pak Chung; Li, Miaoxin

2018-04-26

Recently many studies showed single nucleotide polymorphisms (SNPs) affect gene expression and contribute to development of complex traits/diseases in a tissue context-dependent manner. However, little is known about haplotype's influence on gene expression and complex traits, which reflects the interaction effect between SNPs. In the present study, we firstly proposed a regulatory region guided eQTL haplotype association analysis approach, and then systematically investigate the expression quantitative trait loci (eQTL) haplotypes in 20 different tissues by the approach. The approach has a powerful design of reducing computational burden by the utilization of regulatory predictions for candidate SNP selection and multiple testing corrections on non-independent haplotypes. The application results in multiple tissues showed that haplotype-based eQTLs not only increased the number of eQTL genes in a tissue specific manner, but were also enriched in loci that associated with complex traits in a tissue-matched manner. In addition, we found that tag SNPs of eQTL haplotypes from whole blood were selectively enriched in certain combination of regulatory elements (e.g. promoters and enhancers) according to predicted chromatin states. In summary, this eQTL haplotype detection approach, together with the application results, shed insights into synergistic effect of sequence variants on gene expression and their susceptibility to complex diseases. The executable application "eHaplo" is implemented in Java and is publicly available at http://grass.cgs.hku.hk/limx/ehaplo/. jonsonfox@gmail.com, limiaoxin@mail.sysu.edu.cn. Supplementary data are available at Bioinformatics online.

How rare bone diseases have informed our knowledge of complex diseases.

PubMed

Johnson, Mark L

2016-01-01

Rare bone diseases, generally defined as monogenic traits with either autosomal recessive or dominant patterns of inheritance, have provided a rich database of genes and associated pathways over the past 2-3 decades. The molecular genetic dissection of these bone diseases has yielded some major surprises in terms of the causal genes and/or involved pathways. The discovery of genes/pathways involved in diseases such as osteopetrosis, osteosclerosis, osteogenesis imperfecta and many other rare bone diseases have all accelerated our understanding of complex traits. Importantly these discoveries have provided either direct validation for a specific gene embedded in a group of genes within an interval identified through a complex trait genome-wide association study (GWAS) or based upon the pathway associated with a monogenic trait gene, provided a means to prioritize a large number of genes for functional validation studies. In some instances GWAS studies have yielded candidate genes that fall within linkage intervals associated with monogenic traits and resulted in the identification of causal mutations in those rare diseases. Driving all of this discovery is a complement of technologies such as genome sequencing, bioinformatics and advanced statistical analysis methods that have accelerated genetic dissection and greatly reduced the cost. Thus, rare bone disorders in partnership with GWAS have brought us to the brink of a new era of personalized genomic medicine in which the prevention and management of complex diseases will be driven by the molecular understanding of each individuals contributing genetic risks for disease.

Levels and domains in personality: an introduction.

PubMed

Emmons, R A

1995-09-01

This special issue is centered around the problem of levels and domains in personality functioning. What kind of constructs--and at what levels and in what domains--are needed to understand what a person is like? To account for the complexity and scope of human lives, personality psychologists have traditionally put forth lists and taxonomies of factors, features, and variables that must be taken into consideration in formulating an adequate psychological portrait of the whole person. The five-factor model of personality traits has recently been offered as a comprehensive framework; however, critical analyses of the trait concept have revealed the limitations of a trait-based model of personality. Recognizing that the concept of trait is indispensable to a vital psychology of personality, this special issue aims to (a) communicate recent developments and organizational frameworks for understanding the person at multiple levels and in varied domains, and (b) articulate and elaborate units of analysis that, when combined with trait assessments, yield a psychology of personality that is commensurate with the complexity of individual functioning and that offers greater potential for the attainment of the original goals of the discipline.

Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models

PubMed Central

Chiu, Chi-yang; Jung, Jeesun; Chen, Wei; Weeks, Daniel E; Ren, Haobo; Boehnke, Michael; Amos, Christopher I; Liu, Aiyi; Mills, James L; Ting Lee, Mei-ling; Xiong, Momiao; Fan, Ruzong

2017-01-01

To analyze next-generation sequencing data, multivariate functional linear models are developed for a meta-analysis of multiple studies to connect genetic variant data to multiple quantitative traits adjusting for covariates. The goal is to take the advantage of both meta-analysis and pleiotropic analysis in order to improve power and to carry out a unified association analysis of multiple studies and multiple traits of complex disorders. Three types of approximate F -distributions based on Pillai–Bartlett trace, Hotelling–Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants. Simulation analysis is performed to evaluate false-positive rates and power of the proposed tests. The proposed methods are applied to analyze lipid traits in eight European cohorts. It is shown that it is more advantageous to perform multivariate analysis than univariate analysis in general, and it is more advantageous to perform meta-analysis of multiple studies instead of analyzing the individual studies separately. The proposed models require individual observations. The value of the current paper can be seen at least for two reasons: (a) the proposed methods can be applied to studies that have individual genotype data; (b) the proposed methods can be used as a criterion for future work that uses summary statistics to build test statistics to meta-analyze the data. PMID:28000696

Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models.

PubMed

Chiu, Chi-Yang; Jung, Jeesun; Chen, Wei; Weeks, Daniel E; Ren, Haobo; Boehnke, Michael; Amos, Christopher I; Liu, Aiyi; Mills, James L; Ting Lee, Mei-Ling; Xiong, Momiao; Fan, Ruzong

2017-02-01

To analyze next-generation sequencing data, multivariate functional linear models are developed for a meta-analysis of multiple studies to connect genetic variant data to multiple quantitative traits adjusting for covariates. The goal is to take the advantage of both meta-analysis and pleiotropic analysis in order to improve power and to carry out a unified association analysis of multiple studies and multiple traits of complex disorders. Three types of approximate F -distributions based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants. Simulation analysis is performed to evaluate false-positive rates and power of the proposed tests. The proposed methods are applied to analyze lipid traits in eight European cohorts. It is shown that it is more advantageous to perform multivariate analysis than univariate analysis in general, and it is more advantageous to perform meta-analysis of multiple studies instead of analyzing the individual studies separately. The proposed models require individual observations. The value of the current paper can be seen at least for two reasons: (a) the proposed methods can be applied to studies that have individual genotype data; (b) the proposed methods can be used as a criterion for future work that uses summary statistics to build test statistics to meta-analyze the data.

Gene- and pathway-based association tests for multiple traits with GWAS summary statistics.

PubMed

Kwak, Il-Youp; Pan, Wei

2017-01-01

To identify novel genetic variants associated with complex traits and to shed new insights on underlying biology, in addition to the most popular single SNP-single trait association analysis, it would be useful to explore multiple correlated (intermediate) traits at the gene- or pathway-level by mining existing single GWAS or meta-analyzed GWAS data. For this purpose, we present an adaptive gene-based test and a pathway-based test for association analysis of multiple traits with GWAS summary statistics. The proposed tests are adaptive at both the SNP- and trait-levels; that is, they account for possibly varying association patterns (e.g. signal sparsity levels) across SNPs and traits, thus maintaining high power across a wide range of situations. Furthermore, the proposed methods are general: they can be applied to mixed types of traits, and to Z-statistics or P-values as summary statistics obtained from either a single GWAS or a meta-analysis of multiple GWAS. Our numerical studies with simulated and real data demonstrated the promising performance of the proposed methods. The methods are implemented in R package aSPU, freely and publicly available at: https://cran.r-project.org/web/packages/aSPU/ CONTACT: weip@biostat.umn.eduSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Sexual selection and the evolution of genital shape and complexity in water striders.

PubMed

Rowe, Locke; Arnqvist, Göran

2012-01-01

Animal genitalia show two striking but incompletely understood evolutionary trends: a great evolutionary divergence in the shape of genitalic structures, and characteristic structural complexity. Both features are thought to result from sexual selection, but explicit comparative tests are hampered by the fact that it is difficult to quantify both morphological complexity and divergence in shape. We undertake a comparative study of multiple nongenitalic and male genital traits in a clade of 15 water strider species to quantify complexity and shape divergence. We show that genital structures are more complex and their shape more divergent among species than nongenital traits. Further, intromittent genital traits are more complex and have evolved more divergently than nonintromittent genital traits. More importantly, shape and complexity of nonintromittent genital traits show correlated evolution with indices of premating sexual selection and intromittent genital traits with postmating sexual selection, suggesting that the evolution of different components of genital morphology are shaped independently by distinct forms of sexual selection. Our quantitative results provide direct comparative support for the hypothesis that sexual selection is associated with morphological complexity in genitalic traits and highlight the importance of quantifying morphological shape and complexity, rather than size in studies of genital evolution. © 2011 The Author(s). Evolution © 2011 The Society for the Study of Evolution.

Improved resolution in the position of drought-related QTLs in a single mapping population of rice by meta-analysis

PubMed Central

Khowaja, Farkhanda S; Norton, Gareth J; Courtois, Brigitte; Price, Adam H

2009-01-01

Background Meta-analysis of QTLs combines the results of several QTL detection studies and provides narrow confidence intervals for meta-QTLs, permitting easier positional candidate gene identification. It is usually applied to multiple mapping populations, but can be applied to one. Here, a meta-analysis of drought related QTLs in the Bala × Azucena mapping population compiles data from 13 experiments and 25 independent screens providing 1,650 individual QTLs separated into 5 trait categories; drought avoidance, plant height, plant biomass, leaf morphology and root traits. A heat map of the overlapping 1 LOD confidence intervals provides an overview of the distribution of QTLs. The programme BioMercator is then used to conduct a formal meta-analysis at example QTL clusters to illustrate the value of meta-analysis of QTLs in this population. Results The heat map graphically illustrates the genetic complexity of drought related traits in rice. QTLs can be linked to their physical position on the rice genome using Additional file 1 provided. Formal meta-analysis on chromosome 1, where clusters of QTLs for all trait categories appear close, established that the sd1 semi-dwarfing gene coincided with a plant height meta-QTL, that the drought avoidance meta-QTL was not likely to be associated with this gene, and that this meta-QTL was not pleiotropic with close meta-QTLs for leaf morphology and root traits. On chromosome 5, evidence suggests that a drought avoidance meta-QTL was pleiotropic with leaf morphology and plant biomass meta-QTLs, but not with meta-QTLs for root traits and plant height 10 cM lower down. A region of dense root QTL activity graphically visible on chromosome 9 was dissected into three meta-QTLs within a space of 35 cM. The confidence intervals for meta-QTLs obtained ranged from 5.1 to 14.5 cM with an average of 9.4 cM, which is approximately 180 genes in rice. Conclusion The meta-analysis is valuable in providing improved ability to dissect the complex genetic structure of traits, and distinguish between pleiotropy and close linkage. It also provides relatively small target regions for the identification of positional candidate genes. PMID:19545420

Dissection of complex adult traits in a mouse synthetic population.

PubMed

Burke, David T; Kozloff, Kenneth M; Chen, Shu; West, Joshua L; Wilkowski, Jodi M; Goldstein, Steven A; Miller, Richard A; Galecki, Andrzej T

2012-08-01

Finding the causative genetic variations that underlie complex adult traits is a significant experimental challenge. The unbiased search strategy of genome-wide association (GWAS) has been used extensively in recent human population studies. These efforts, however, typically find only a minor fraction of the genetic loci that are predicted to affect variation. As an experimental model for the analysis of adult polygenic traits, we measured a mouse population for multiple phenotypes and conducted a genome-wide search for effector loci. Complex adult phenotypes, related to body size and bone structure, were measured as component phenotypes, and each subphenotype was associated with a genomic spectrum of candidate effector loci. The strategy successfully detected several loci for the phenotypes, at genome-wide significance, using a single, modest-sized population (N = 505). The effector loci each explain 2%-10% of the measured trait variation and, taken together, the loci can account for over 25% of a trait's total population variation. A replicate population (N = 378) was used to confirm initially observed loci for one trait (femur length), and, when the two groups were merged, the combined population demonstrated increased power to detect loci. In contrast to human population studies, our mouse genome-wide searches find loci that individually explain a larger fraction of the observed variation. Also, the additive effects of our detected mouse loci more closely match the predicted genetic component of variation. The genetic loci discovered are logical candidates for components of the genetic networks having evolutionary conservation with human biology.

Plasticity first: molecular signatures of a complex morphological trait in filamentous cyanobacteria.

PubMed

Koch, Robin; Kupczok, Anne; Stucken, Karina; Ilhan, Judith; Hammerschmidt, Katrin; Dagan, Tal

2017-08-31

Filamentous cyanobacteria that differentiate multiple cell types are considered the peak of prokaryotic complexity and their evolution has been studied in the context of multicellularity origins. Species that form true-branching filaments exemplify the most complex cyanobacteria. However, the mechanisms underlying the true-branching morphology remain poorly understood despite of several investigations that focused on the identification of novel genes or pathways. An alternative route for the evolution of novel traits is based on existing phenotypic plasticity. According to that scenario - termed genetic assimilation - the fixation of a novel phenotype precedes the fixation of the genotype. Here we show that the evolution of transcriptional regulatory elements constitutes a major mechanism for the evolution of new traits. We found that supplementation with sucrose reconstitutes the ancestral branchless phenotype of two true-branching Fischerella species and compared the transcription start sites (TSSs) between the two phenotypic states. Our analysis uncovers several orthologous TSSs whose transcription level is correlated with the true-branching phenotype. These TSSs are found in genes that encode components of the septosome and elongasome (e.g., fraC and mreB). The concept of genetic assimilation supplies a tenable explanation for the evolution of novel traits but testing its feasibility is hindered by the inability to recreate and study the evolution of present-day traits. We present a novel approach to examine transcription data for the plasticity first route and provide evidence for its occurrence during the evolution of complex colony morphology in true-branching cyanobacteria. Our results reveal a route for evolution of the true-branching phenotype in cyanobacteria via modification of the transcription level of pre-existing genes. Our study supplies evidence for the 'plasticity-first' hypothesis and highlights the importance of transcriptional regulation in the evolution of novel traits.

Characterization of mature maize (Zea mays L.) root system architecture and complexity in a diverse set of Ex-PVP inbreds and hybrids.

PubMed

Hauck, Andrew L; Novais, Joana; Grift, Tony E; Bohn, Martin O

2015-01-01

The mature root system is a vital plant organ, which is critical to plant performance. Commercial maize (Zea mays L.) breeding has resulted in a steady increase in plant performance over time, along with noticeable changes in above ground vegetative traits, but the corresponding changes in the root system are not presently known. In this study, roughly 2500 core root systems from field trials of a set of 10 diverse elite inbreds formerly protected by Plant Variety Protection plus B73 and Mo17 and the 66 diallel intercrosses among them were evaluated for root traits using high throughput image-based phenotyping. Overall root architecture was modeled by root angle (RA) and stem diameter (SD), while root complexity, the amount of root branching, was quantified using fractal analysis to obtain values for fractal dimension (FD) and fractal abundance (FA). For each trait, per se line effects were highly significant and the most important contributor to trait performance. Mid-parent heterosis and specific combining ability was also highly significant for FD, FA, and RA, while none of the traits showed significant general combining ability. The interaction between the environment and the additive line effect was also significant for all traits. Within the inbred and hybrid generations, FD and FA were highly correlated (rp ≥ 0.74), SD was moderately correlated to FD and FA (0.69 ≥ rp ≥ 0.48), while the correlation between RA and other traits was low (0.13 ≥ rp ≥ -0.40). Inbreds with contrasting effects on complexity and architecture traits were observed, suggesting that root complexity and architecture traits are inherited independently. A more comprehensive understanding of the maize root system and the way it interacts with the environment will be useful for defining adaptation to nutrient acquisition and tolerance to stress from drought and high plant densities, critical factors in the yield gains of modern hybrids.

Assessing Dimensionality of Noncompensatory Multidimensional Item Response Theory with Complex Structures

ERIC Educational Resources Information Center

Svetina, Dubravka

2013-01-01

The purpose of this study was to investigate the effect of complex structure on dimensionality assessment in noncompensatory multidimensional item response models using dimensionality assessment procedures based on DETECT (dimensionality evaluation to enumerate contributing traits) and NOHARM (normal ogive harmonic analysis robust method). Five…

Identification and Validation of Loci Governing Seed Coat Color by Combining Association Mapping and Bulk Segregation Analysis in Soybean

PubMed Central

Ma, Yansong; Tian, Long; Li, Xinxiu; Li, Ying-Hui; Guan, Rongxia; Guo, Yong; Qiu, Li-Juan

2016-01-01

Soybean seed coat exists in a range of colors from yellow, green, brown, black, to bicolor. Classical genetic analysis suggested that soybean seed color was a moderately complex trait controlled by multi-loci. However, only a couple of loci could be detected using a single biparental segregating population. In this study, a combination of association mapping and bulk segregation analysis was employed to identify genes/loci governing this trait in soybean. A total of 14 loci, including nine novel and five previously reported ones, were identified using 176,065 coding SNPs selected from entire SNP dataset among 56 soybean accessions. Four of these loci were confirmed and further mapped using a biparental population developed from the cross between ZP95-5383 (yellow seed color) and NY279 (brown seed color), in which different seed coat colors were further dissected into simple trait pairs (green/yellow, green/black, green/brown, yellow/black, yellow/brown, and black/brown) by continuously developing residual heterozygous lines. By genotyping entire F2 population using flanking markers located in fine-mapping regions, the genetic basis of seed coat color was fully dissected and these four loci could explain all variations of seed colors in this population. These findings will be useful for map-based cloning of genes as well as marker-assisted breeding in soybean. This work also provides an alternative strategy for systematically isolating genes controlling relative complex trait by association analysis followed by biparental mapping. PMID:27404272

Signatures of negative selection in the genetic architecture of human complex traits.

PubMed

Zeng, Jian; de Vlaming, Ronald; Wu, Yang; Robinson, Matthew R; Lloyd-Jones, Luke R; Yengo, Loic; Yap, Chloe X; Xue, Angli; Sidorenko, Julia; McRae, Allan F; Powell, Joseph E; Montgomery, Grant W; Metspalu, Andres; Esko, Tonu; Gibson, Greg; Wray, Naomi R; Visscher, Peter M; Yang, Jian

2018-05-01

We develop a Bayesian mixed linear model that simultaneously estimates single-nucleotide polymorphism (SNP)-based heritability, polygenicity (proportion of SNPs with nonzero effects), and the relationship between SNP effect size and minor allele frequency for complex traits in conventionally unrelated individuals using genome-wide SNP data. We apply the method to 28 complex traits in the UK Biobank data (N = 126,752) and show that on average, 6% of SNPs have nonzero effects, which in total explain 22% of phenotypic variance. We detect significant (P < 0.05/28) signatures of natural selection in the genetic architecture of 23 traits, including reproductive, cardiovascular, and anthropometric traits, as well as educational attainment. The significant estimates of the relationship between effect size and minor allele frequency in complex traits are consistent with a model of negative (or purifying) selection, as confirmed by forward simulation. We conclude that negative selection acts pervasively on the genetic variants associated with human complex traits.

Evaluating the dimensionality of first grade written composition

PubMed Central

Kim, Young-Suk; Al Otaiba, Stephanie; Folsom, Jessica S.; Greulich, Luana; Puranik, Cynthia

2013-01-01

Purpose We examined dimensions of written composition using multiple evaluative approaches such as an adapted 6+1 trait scoring, syntactic complexity measures, and productivity measures. We further examined unique relations of oral language and literacy skills to the identified dimensions of written composition. Method A large sample of first grade students (N = 527) was assessed on their language, reading, spelling, letter writing automaticity, and writing in the spring. Data were analyzed using a latent variable approach including confirmatory factor analysis and structural equation modeling. Results The seven traits in the 6+1 trait system were best described as two constructs: substantive quality, and spelling and writing conventions. When the other evaluation procedures such as productivity and syntactic complexity indicators were included, four dimensions emerged: substantive quality, productivity, syntactic complexity, and spelling and writing conventions. Language and literacy predictors were differentially related to each dimension in written composition. Conclusions These four dimensions may be a useful guideline for evaluating developing beginning writer’s compositions. PMID:24687472

Cross-Lagged Analysis of Interplay Between Differential Traits in Sibling Pairs: Validation and Application to Parenting Behavior and ADHD Symptomatology.

PubMed

Moscati, Arden; Verhulst, Brad; McKee, Kevin; Silberg, Judy; Eaves, Lindon

2018-01-01

Understanding the factors that contribute to behavioral traits is a complex task, and partitioning variance into latent genetic and environmental components is a useful beginning, but it should not also be the end. Many constructs are influenced by their contextual milieu, and accounting for background effects (such as gene-environment correlation) is necessary to avoid bias. This study introduces a method for examining the interplay between traits, in a longitudinal design using differential items in sibling pairs. The model is validated via simulation and power analysis, and we conclude with an application to paternal praise and ADHD symptoms in a twin sample. The model can help identify what type of genetic and environmental interplay may contribute to the dynamic relationship between traits using a cross-lagged panel framework. Overall, it presents a way to estimate and explicate the developmental interplay between a set of traits, free from many common sources of bias.

Emerging applications of genome-editing technology to examine functionality of GWAS-associated variants for complex traits.

PubMed

Smith, Andrew J P; Deloukas, Panos; Munroe, Patricia B

2018-04-13

Over the last decade, genome-wide association studies (GWAS) have propelled the discovery of thousands of loci associated with complex diseases. The focus is now turning towards the function of these association signals, determining the causal variant(s) amongst those in strong linkage disequilibrium, and identifying their underlying mechanisms, such as long-range gene regulation. Genome-editing techniques utilising zinc-finger nucleases (ZFN), transcription activator-like effector nucleases (TALENs) and clustered regularly-interspaced short palindromic repeats with Cas9 nuclease (CRISPR-Cas9), are becoming the tools of choice to establish functionality for these variants, due to the ability to assess effects of single variants in vivo. This review will discuss examples of how these technologies have begun to aid functional analysis of GWAS loci for complex traits such as cardiovascular disease, type 2 diabetes, cancer, obesity and autoimmune disease. We focus on analysis of variants occurring within non-coding genomic regions, as these comprise the majority of GWAS variants, providing the greatest challenges to determining functionality, and compare editing strategies that provide different levels of evidence for variant functionality. The review describes molecular insights into some of these potentially causal variants, and how these may relate to the pathology of the trait, and look towards future directions for these technologies in post-GWAS analysis, such as base-editing.

Linkage Analysis Using Co-Phenotypes in the BRIGHT Study Reveals Novel Potential Susceptibility Loci for Hypertension

PubMed Central

Wallace, Chris; Xue, Ming-Zhan; Newhouse, Stephen J.; Marçano, Ana Carolina B.; Onipinla, Abiodun K.; Burke, Beverley; Gungadoo, Johannie; Dobson, Richard J.; Brown, Morris; Connell, John M.; Dominiczak, Anna; Lathrop, G. Mark; Webster, John; Farrall, Martin; Mein, Charles; Samani, Nilesh J.; Caulfield, Mark J.; Clayton, David G.; Munroe, Patricia B.

2006-01-01

Identification of the genetic influences on human essential hypertension and other complex diseases has proved difficult, partly because of genetic heterogeneity. In many complex-trait resources, additional phenotypic data have been collected, allowing comorbid intermediary phenotypes to be used to characterize more genetically homogeneous subsets. The traditional approach to analyzing covariate-defined subsets has typically depended on researchers’ previous expectations for definition of a comorbid subset and leads to smaller data sets, with a concomitant attrition in power. An alternative is to test for dependence between genetic sharing and covariates across the entire data set. This approach offers the advantage of exploiting the full data set and could be widely applied to complex-trait genome scans. However, existing maximum-likelihood methods can be prohibitively computationally expensive, especially since permutation is often required to determine significance. We developed a less computationally intensive score test and applied it to biometric and biochemical covariate data, from 2,044 sibling pairs with severe hypertension, collected by the British Genetics of Hypertension (BRIGHT) study. We found genomewide-significant evidence for linkage with hypertension and several related covariates. The strongest signals were with leaner-body-mass measures on chromosome 20q (maximum LOD=4.24) and with parameters of renal function on chromosome 5p (maximum LOD=3.71). After correction for the multiple traits and genetic locations studied, our global genomewide P value was .046. This is the first identity-by-descent regression analysis of hypertension to our knowledge, and it demonstrates the value of this approach for the incorporation of additional phenotypic information in genetic studies of complex traits. PMID:16826522

Linkage analysis using co-phenotypes in the BRIGHT study reveals novel potential susceptibility loci for hypertension.

PubMed

Wallace, Chris; Xue, Ming-Zhan; Newhouse, Stephen J; Marcano, Ana Carolina B; Onipinla, Abiodun K; Burke, Beverley; Gungadoo, Johannie; Dobson, Richard J; Brown, Morris; Connell, John M; Dominiczak, Anna; Lathrop, G Mark; Webster, John; Farrall, Martin; Mein, Charles; Samani, Nilesh J; Caulfield, Mark J; Clayton, David G; Munroe, Patricia B

2006-08-01

Identification of the genetic influences on human essential hypertension and other complex diseases has proved difficult, partly because of genetic heterogeneity. In many complex-trait resources, additional phenotypic data have been collected, allowing comorbid intermediary phenotypes to be used to characterize more genetically homogeneous subsets. The traditional approach to analyzing covariate-defined subsets has typically depended on researchers' previous expectations for definition of a comorbid subset and leads to smaller data sets, with a concomitant attrition in power. An alternative is to test for dependence between genetic sharing and covariates across the entire data set. This approach offers the advantage of exploiting the full data set and could be widely applied to complex-trait genome scans. However, existing maximum-likelihood methods can be prohibitively computationally expensive, especially since permutation is often required to determine significance. We developed a less computationally intensive score test and applied it to biometric and biochemical covariate data, from 2,044 sibling pairs with severe hypertension, collected by the British Genetics of Hypertension (BRIGHT) study. We found genomewide-significant evidence for linkage with hypertension and several related covariates. The strongest signals were with leaner-body-mass measures on chromosome 20q (maximum LOD = 4.24) and with parameters of renal function on chromosome 5p (maximum LOD = 3.71). After correction for the multiple traits and genetic locations studied, our global genomewide P value was .046. This is the first identity-by-descent regression analysis of hypertension to our knowledge, and it demonstrates the value of this approach for the incorporation of additional phenotypic information in genetic studies of complex traits.

Analysis of cohort studies with multivariate and partially observed disease classification data.

PubMed

Chatterjee, Nilanjan; Sinha, Samiran; Diver, W Ryan; Feigelson, Heather Spencer

2010-09-01

Complex diseases like cancers can often be classified into subtypes using various pathological and molecular traits of the disease. In this article, we develop methods for analysis of disease incidence in cohort studies incorporating data on multiple disease traits using a two-stage semiparametric Cox proportional hazards regression model that allows one to examine the heterogeneity in the effect of the covariates by the levels of the different disease traits. For inference in the presence of missing disease traits, we propose a generalization of an estimating equation approach for handling missing cause of failure in competing-risk data. We prove asymptotic unbiasedness of the estimating equation method under a general missing-at-random assumption and propose a novel influence-function-based sandwich variance estimator. The methods are illustrated using simulation studies and a real data application involving the Cancer Prevention Study II nutrition cohort.

Genetic and Quantitative Trait Locus Analysis for Bio-Oil Compounds after Fast Pyrolysis in Maize Cobs.

PubMed

Jeffrey, Brandon; Kuzhiyil, Najeeb; de Leon, Natalia; Lübberstedt, Thomas

2016-01-01

Fast pyrolysis has been identified as one of the biorenewable conversion platforms that could be a part of an alternative energy future, but it has not yet received the same attention as cellulosic ethanol in the analysis of genetic inheritance within potential feedstocks such as maize. Ten bio-oil compounds were measured via pyrolysis/gas chromatography-mass spectrometry (Py/GC-MS) in maize cobs. 184 recombinant inbred lines (RILs) of the intermated B73 x Mo17 (IBM) Syn4 population were analyzed in two environments, using 1339 markers, for quantitative trait locus (QTL) mapping. QTL mapping was performed using composite interval mapping with significance thresholds established by 1000 permutations at α = 0.05. 50 QTL were found in total across those ten traits with R2 values ranging from 1.7 to 5.8%, indicating a complex quantitative inheritance of these traits.

Genomic Prediction and Association Mapping of Curd-Related Traits in Gene Bank Accessions of Cauliflower.

PubMed

Thorwarth, Patrick; Yousef, Eltohamy A A; Schmid, Karl J

2018-02-02

Genetic resources are an important source of genetic variation for plant breeding. Genome-wide association studies (GWAS) and genomic prediction greatly facilitate the analysis and utilization of useful genetic diversity for improving complex phenotypic traits in crop plants. We explored the potential of GWAS and genomic prediction for improving curd-related traits in cauliflower ( Brassica oleracea var. botrytis ) by combining 174 randomly selected cauliflower gene bank accessions from two different gene banks. The collection was genotyped with genotyping-by-sequencing (GBS) and phenotyped for six curd-related traits at two locations and three growing seasons. A GWAS analysis based on 120,693 single-nucleotide polymorphisms identified a total of 24 significant associations for curd-related traits. The potential for genomic prediction was assessed with a genomic best linear unbiased prediction model and BayesB. Prediction abilities ranged from 0.10 to 0.66 for different traits and did not differ between prediction methods. Imputation of missing genotypes only slightly improved prediction ability. Our results demonstrate that GWAS and genomic prediction in combination with GBS and phenotyping of highly heritable traits can be used to identify useful quantitative trait loci and genotypes among genetically diverse gene bank material for subsequent utilization as genetic resources in cauliflower breeding. Copyright © 2018 Thorwarth et al.

Impacts of Population Structure and Analytical Models in Genome-Wide Association Studies of Complex Traits in Forest Trees: A Case Study in Eucalyptus globulus

PubMed Central

Garcia, Martín N.; Acuña, Cintia; Borralho, Nuno M. G.; Grattapaglia, Dario; Marcucci Poltri, Susana N.

2013-01-01

The promise of association genetics to identify genes or genomic regions controlling complex traits has generated a flurry of interest. Such phenotype-genotype associations could be useful to accelerate tree breeding cycles, increase precision and selection intensity for late expressing, low heritability traits. However, the prospects of association genetics in highly heterozygous undomesticated forest trees can be severely impacted by the presence of cryptic population and pedigree structure. To investigate how to better account for this, we compared the GLM and five combinations of the Unified Mixed Model (UMM) on data of a low-density genome-wide association study for growth and wood property traits carried out in a Eucalyptus globulus population (n = 303) with 7,680 Diversity Array Technology (DArT) markers. Model comparisons were based on the degree of deviation from the uniform distribution and estimates of the mean square differences between the observed and expected p-values of all significant marker-trait associations detected. Our analysis revealed the presence of population and family structure. There was not a single best model for all traits. Striking differences in detection power and accuracy were observed among the different models especially when population structure was not accounted for. The UMM method was the best and produced superior results when compared to GLM for all traits. Following stringent correction for false discoveries, 18 marker-trait associations were detected, 16 for tree diameter growth and two for lignin monomer composition (S∶G ratio), a key wood property trait. The two DArT markers associated with S∶G ratio on chromosome 10, physically map within 1 Mbp of the ferulate 5-hydroxylase (F5H) gene, providing a putative independent validation of this marker-trait association. This study details the merit of collectively integrate population structure and relatedness in association analyses in undomesticated, highly heterozygous forest trees, and provides additional insights into the nature of complex quantitative traits in Eucalyptus. PMID:24282578

Genome-wide association study reveals putative regulators of bioenergy traits in Populus deltoides

DOE PAGES

Fahrenkrog, Annette M.; Neves, Leandro G.; Resende, Jr., Marcio F. R.; ...

2016-09-06

Genome-wide association studies (GWAS) have been used extensively to dissect the genetic regulation of complex traits in plants. These studies have focused largely on the analysis of common genetic variants despite the abundance of rare polymorphisms in several species, and their potential role in trait variation. Here, we conducted the first GWAS in Populus deltoides, a genetically diverse keystone forest species in North America and an important short rotation woody crop for the bioenergy industry. We searched for associations between eight growth and wood composition traits, and common and low-frequency single-nucleotide polymorphisms detected by targeted resequencing of 18 153 genesmore » in a population of 391 unrelated individuals. To increase power to detect associations with low-frequency variants, multiple-marker association tests were used in combination with single-marker association tests. Significant associations were discovered for all phenotypes and are indicative that low-frequency polymorphisms contribute to phenotypic variance of several bioenergy traits. Our results suggest that both common and low-frequency variants need to be considered for a comprehensive understanding of the genetic regulation of complex traits, particularly in species that carry large numbers of rare polymorphisms. Lastly, these polymorphisms may be critical for the development of specialized plant feedstocks for bioenergy.« less

Statistical power and utility of meta-analysis methods for cross-phenotype genome-wide association studies.

PubMed

Zhu, Zhaozhong; Anttila, Verneri; Smoller, Jordan W; Lee, Phil H

2018-01-01

Advances in recent genome wide association studies (GWAS) suggest that pleiotropic effects on human complex traits are widespread. A number of classic and recent meta-analysis methods have been used to identify genetic loci with pleiotropic effects, but the overall performance of these methods is not well understood. In this work, we use extensive simulations and case studies of GWAS datasets to investigate the power and type-I error rates of ten meta-analysis methods. We specifically focus on three conditions commonly encountered in the studies of multiple traits: (1) extensive heterogeneity of genetic effects; (2) characterization of trait-specific association; and (3) inflated correlation of GWAS due to overlapping samples. Although the statistical power is highly variable under distinct study conditions, we found the superior power of several methods under diverse heterogeneity. In particular, classic fixed-effects model showed surprisingly good performance when a variant is associated with more than a half of study traits. As the number of traits with null effects increases, ASSET performed the best along with competitive specificity and sensitivity. With opposite directional effects, CPASSOC featured the first-rate power. However, caution is advised when using CPASSOC for studying genetically correlated traits with overlapping samples. We conclude with a discussion of unresolved issues and directions for future research.

Regression and Data Mining Methods for Analyses of Multiple Rare Variants in the Genetic Analysis Workshop 17 Mini-Exome Data

PubMed Central

Bailey-Wilson, Joan E.; Brennan, Jennifer S.; Bull, Shelley B; Culverhouse, Robert; Kim, Yoonhee; Jiang, Yuan; Jung, Jeesun; Li, Qing; Lamina, Claudia; Liu, Ying; Mägi, Reedik; Niu, Yue S.; Simpson, Claire L.; Wang, Libo; Yilmaz, Yildiz E.; Zhang, Heping; Zhang, Zhaogong

2012-01-01

Group 14 of Genetic Analysis Workshop 17 examined several issues related to analysis of complex traits using DNA sequence data. These issues included novel methods for analyzing rare genetic variants in an aggregated manner (often termed collapsing rare variants), evaluation of various study designs to increase power to detect effects of rare variants, and the use of machine learning approaches to model highly complex heterogeneous traits. Various published and novel methods for analyzing traits with extreme locus and allelic heterogeneity were applied to the simulated quantitative and disease phenotypes. Overall, we conclude that power is (as expected) dependent on locus-specific heritability or contribution to disease risk, large samples will be required to detect rare causal variants with small effect sizes, extreme phenotype sampling designs may increase power for smaller laboratory costs, methods that allow joint analysis of multiple variants per gene or pathway are more powerful in general than analyses of individual rare variants, population-specific analyses can be optimal when different subpopulations harbor private causal mutations, and machine learning methods may be useful for selecting subsets of predictors for follow-up in the presence of extreme locus heterogeneity and large numbers of potential predictors. PMID:22128066

The genetic architecture of growth and fillet traits in farmed Atlantic salmon (Salmo salar).

PubMed

Tsai, Hsin Yuan; Hamilton, Alastair; Guy, Derrick R; Tinch, Alan E; Bishop, Stephen C; Houston, Ross D

2015-05-19

Performance and quality traits such as harvest weight, fillet weight and flesh color are of economic importance to the Atlantic salmon aquaculture industry. The genetic factors underlying these traits are of scientific and commercial interest. However, such traits are typically polygenic in nature, with the number and size of QTL likely to vary between studies and populations. The aim of this study was to investigate the genetic basis of several growth and fillet traits measured at harvest in a large farmed salmon population by using SNP markers. Due to the marked heterochiasmy in salmonids, an efficient two-stage mapping approach was applied whereby QTL were detected using a sire-based linkage analysis, a sparse SNP marker map and exploiting low rates of recombination, while a subsequent dam-based analysis focused on the significant chromosomes with a denser map to confirm QTL and estimate their position. The harvest traits all showed significant heritability, ranging from 0.05 for fillet yield up to 0.53 for the weight traits. In the sire-based analysis, 1695 offspring with trait records and their 20 sires were successfully genotyped for the SNPs on the sparse map. Chromosomes 13, 18, 19 and 20 were shown to harbor genome-wide significant QTL affecting several growth-related traits. The QTL on chr. 13, 18 and 20 were detected in the dam-based analysis using 512 offspring from 10 dams and explained approximately 6-7 % of the within-family variation in these traits. We have detected several QTL affecting economically important complex traits in a commercial salmon population. Overall, the results suggest that the traits are relatively polygenic and that QTL tend to be pleiotropic (affecting the weight of several components of the harvested fish). Comparison of QTL regions across studies suggests that harvest trait QTL tend to be relatively population-specific. Therefore, the application of marker or genomic selection for improvement in these traits is likely to be most effective when the discovery population is closely related to the selection candidates (e.g. within-family genomic selection).

Association Analysis in Rice: From Application to Utilization

PubMed Central

Zhang, Peng; Zhong, Kaizhen; Shahid, Muhammad Qasim; Tong, Hanhua

2016-01-01

Association analysis based on linkage disequilibrium (LD) is an efficient way to dissect complex traits and to identify gene functions in rice. Although association analysis is an effective way to construct fine maps for quantitative traits, there are a few issues which need to be addressed. In this review, we will first summarize type, structure, and LD level of populations used for association analysis of rice, and then discuss the genotyping methods and statistical approaches used for association analysis in rice. Moreover, we will review current shortcomings and benefits of association analysis as well as specific types of future research to overcome these shortcomings. Furthermore, we will analyze the reasons for the underutilization of the results within association analysis in rice breeding. PMID:27582745

Students' proficiency scores within multitrait item response theory

NASA Astrophysics Data System (ADS)

Scott, Terry F.; Schumayer, Daniel

2015-12-01

In this paper we present a series of item response models of data collected using the Force Concept Inventory. The Force Concept Inventory (FCI) was designed to poll the Newtonian conception of force viewed as a multidimensional concept, that is, as a complex of distinguishable conceptual dimensions. Several previous studies have developed single-trait item response models of FCI data; however, we feel that multidimensional models are also appropriate given the explicitly multidimensional design of the inventory. The models employed in the research reported here vary in both the number of fitting parameters and the number of underlying latent traits assumed. We calculate several model information statistics to ensure adequate model fit and to determine which of the models provides the optimal balance of information and parsimony. Our analysis indicates that all item response models tested, from the single-trait Rasch model through to a model with ten latent traits, satisfy the standard requirements of fit. However, analysis of model information criteria indicates that the five-trait model is optimal. We note that an earlier factor analysis of the same FCI data also led to a five-factor model. Furthermore the factors in our previous study and the traits identified in the current work match each other well. The optimal five-trait model assigns proficiency scores to all respondents for each of the five traits. We construct a correlation matrix between the proficiencies in each of these traits. This correlation matrix shows strong correlations between some proficiencies, and strong anticorrelations between others. We present an interpretation of this correlation matrix.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fahrenkrog, Annette M.; Neves, Leandro G.; Resende, Jr., Marcio F. R.

Genome-wide association studies (GWAS) have been used extensively to dissect the genetic regulation of complex traits in plants. These studies have focused largely on the analysis of common genetic variants despite the abundance of rare polymorphisms in several species, and their potential role in trait variation. Here, we conducted the first GWAS in Populus deltoides, a genetically diverse keystone forest species in North America and an important short rotation woody crop for the bioenergy industry. We searched for associations between eight growth and wood composition traits, and common and low-frequency single-nucleotide polymorphisms detected by targeted resequencing of 18 153 genesmore » in a population of 391 unrelated individuals. To increase power to detect associations with low-frequency variants, multiple-marker association tests were used in combination with single-marker association tests. Significant associations were discovered for all phenotypes and are indicative that low-frequency polymorphisms contribute to phenotypic variance of several bioenergy traits. Our results suggest that both common and low-frequency variants need to be considered for a comprehensive understanding of the genetic regulation of complex traits, particularly in species that carry large numbers of rare polymorphisms. Lastly, these polymorphisms may be critical for the development of specialized plant feedstocks for bioenergy.« less

Estimation and Partitioning of Heritability in Human Populations using Whole Genome Analysis Methods

PubMed Central

Vinkhuyzen, Anna AE; Wray, Naomi R; Yang, Jian; Goddard, Michael E; Visscher, Peter M

2014-01-01

Understanding genetic variation of complex traits in human populations has moved from the quantification of the resemblance between close relatives to the dissection of genetic variation into the contributions of individual genomic loci. But major questions remain unanswered: how much phenotypic variation is genetic, how much of the genetic variation is additive and what is the joint distribution of effect size and allele frequency at causal variants? We review and compare three whole-genome analysis methods that use mixed linear models (MLM) to estimate genetic variation, using the relationship between close or distant relatives based on pedigree or SNPs. We discuss theory, estimation procedures, bias and precision of each method and review recent advances in the dissection of additive genetic variation of complex traits in human populations that are based upon the application of MLM. Using genome wide data, SNPs account for far more of the genetic variation than the highly significant SNPs associated with a trait, but they do not account for all of the genetic variance estimated by pedigree based methods. We explain possible reasons for this ‘missing’ heritability. PMID:23988118

Candidate Loci for Yield-Related Traits in Maize Revealed by a Combination of MetaQTL Analysis and Regional Association Mapping

PubMed Central

Chen, Lin; An, Yixin; Li, Yong-xiang; Li, Chunhui; Shi, Yunsu; Song, Yanchun; Zhang, Dengfeng; Wang, Tianyu; Li, Yu

2017-01-01

Maize grain yield and related traits are complex and are controlled by a large number of genes of small effect or quantitative trait loci (QTL). Over the years, a large number of yield-related QTLs have been identified in maize and deposited in public databases. However, integrating and re-analyzing these data and mining candidate loci for yield-related traits has become a major issue in maize. In this study, we collected information on QTLs conferring maize yield-related traits from 33 published studies. Then, 999 of these QTLs were iteratively projected and subjected to meta-analysis to obtain metaQTLs (MQTLs). A total of 76 MQTLs were found across the maize genome. Based on a comparative genomics strategy, several maize orthologs of rice yield-related genes were identified in these MQTL regions. Furthermore, three potential candidate genes (Gene ID: GRMZM2G359974, GRMZM2G301884, and GRMZM2G083894) associated with kernel size and weight within three MQTL regions were identified using regional association mapping, based on the results of the meta-analysis. This strategy, combining MQTL analysis and regional association mapping, is helpful for functional marker development and rapid identification of candidate genes or loci. PMID:29312420

Evidence of major genes affecting stress response in rainbow trout using Bayesian methods of complex segregation analysis

USDA-ARS?s Scientific Manuscript database

As a first step towards the genetic mapping of quantitative trait loci (QTL) affecting stress response variation in rainbow trout, we performed complex segregation analyses (CSA) fitting mixed inheritance models of plasma cortisol using Bayesian methods in large full-sib families of rainbow trout. ...

A fast boosting-based screening method for large-scale association study in complex traits with genetic heterogeneity.

PubMed

Wang, Lu-Yong; Fasulo, D

2006-01-01

Genome-wide association study for complex diseases will generate massive amount of single nucleotide polymorphisms (SNPs) data. Univariate statistical test (i.e. Fisher exact test) was used to single out non-associated SNPs. However, the disease-susceptible SNPs may have little marginal effects in population and are unlikely to retain after the univariate tests. Also, model-based methods are impractical for large-scale dataset. Moreover, genetic heterogeneity makes the traditional methods harder to identify the genetic causes of diseases. A more recent random forest method provides a more robust method for screening the SNPs in thousands scale. However, for more large-scale data, i.e., Affymetrix Human Mapping 100K GeneChip data, a faster screening method is required to screening SNPs in whole-genome large scale association analysis with genetic heterogeneity. We propose a boosting-based method for rapid screening in large-scale analysis of complex traits in the presence of genetic heterogeneity. It provides a relatively fast and fairly good tool for screening and limiting the candidate SNPs for further more complex computational modeling task.

Putting problem formulation at the forefront of GMO risk analysis.

PubMed

Tepfer, Mark; Racovita, Monica; Craig, Wendy

2013-01-01

When applying risk assessment and the broader process of risk analysis to decisions regarding the dissemination of genetically modified organisms (GMOs), the process has a tendency to become remarkably complex. Further, as greater numbers of countries consider authorising the large-scale dissemination of GMOs, and as GMOs with more complex traits reach late stages of development, there has been increasing concern about the burden posed by the complexity of risk analysis. We present here an improved approach for GMO risk analysis that gives a central role to problem formulation. Further, the risk analysis strategy has been clarified and simplified in order to make rigorously scientific risk assessment and risk analysis more broadly accessible to diverse stakeholder groups.

Network-based Analysis of Genome Wide Association Data Provides Novel Candidate Genes for Lipid and Lipoprotein Traits*

PubMed Central

Sharma, Amitabh; Gulbahce, Natali; Pevzner, Samuel J.; Menche, Jörg; Ladenvall, Claes; Folkersen, Lasse; Eriksson, Per; Orho-Melander, Marju; Barabási, Albert-László

2013-01-01

Genome wide association studies (GWAS) identify susceptibility loci for complex traits, but do not identify particular genes of interest. Integration of functional and network information may help in overcoming this limitation and identifying new susceptibility loci. Using GWAS and comorbidity data, we present a network-based approach to predict candidate genes for lipid and lipoprotein traits. We apply a prediction pipeline incorporating interactome, co-expression, and comorbidity data to Global Lipids Genetics Consortium (GLGC) GWAS for four traits of interest, identifying phenotypically coherent modules. These modules provide insights regarding gene involvement in complex phenotypes with multiple susceptibility alleles and low effect sizes. To experimentally test our predictions, we selected four candidate genes and genotyped representative SNPs in the Malmö Diet and Cancer Cardiovascular Cohort. We found significant associations with LDL-C and total-cholesterol levels for a synonymous SNP (rs234706) in the cystathionine beta-synthase (CBS) gene (p = 1 × 10−5 and adjusted-p = 0.013, respectively). Further, liver samples taken from 206 patients revealed that patients with the minor allele of rs234706 had significant dysregulation of CBS (p = 0.04). Despite the known biological role of CBS in lipid metabolism, SNPs within the locus have not yet been identified in GWAS of lipoprotein traits. Thus, the GWAS-based Comorbidity Module (GCM) approach identifies candidate genes missed by GWAS studies, serving as a broadly applicable tool for the investigation of other complex disease phenotypes. PMID:23882023

Models of Cultural Niche Construction with Selection and Assortative Mating

PubMed Central

Feldman, Marcus W.

2012-01-01

Niche construction is a process through which organisms modify their environment and, as a result, alter the selection pressures on themselves and other species. In cultural niche construction, one or more cultural traits can influence the evolution of other cultural or biological traits by affecting the social environment in which the latter traits may evolve. Cultural niche construction may include either gene-culture or culture-culture interactions. Here we develop a model of this process and suggest some applications of this model. We examine the interactions between cultural transmission, selection, and assorting, paying particular attention to the complexities that arise when selection and assorting are both present, in which case stable polymorphisms of all cultural phenotypes are possible. We compare our model to a recent model for the joint evolution of religion and fertility and discuss other potential applications of cultural niche construction theory, including the evolution and maintenance of large-scale human conflict and the relationship between sex ratio bias and marriage customs. The evolutionary framework we introduce begins to address complexities that arise in the quantitative analysis of multiple interacting cultural traits. PMID:22905167

The Value of Extended Pedigrees for Next-Generation Analysis of Complex Disease in the Rhesus Macaque

PubMed Central

Vinson, Amanda; Prongay, Kamm; Ferguson, Betsy

2013-01-01

Complex diseases (e.g., cardiovascular disease and type 2 diabetes, among many others) pose the biggest threat to human health worldwide and are among the most challenging to investigate. Susceptibility to complex disease may be caused by multiple genetic variants (GVs) and their interaction, by environmental factors, and by interaction between GVs and environment, and large study cohorts with substantial analytical power are typically required to elucidate these individual contributions. Here, we discuss the advantages of both power and feasibility afforded by the use of extended pedigrees of rhesus macaques (Macaca mulatta) for genetic studies of complex human disease based on next-generation sequence data. We present these advantages in the context of previous research conducted in rhesus macaques for several representative complex diseases. We also describe a single, multigeneration pedigree of Indian-origin rhesus macaques and a sample biobank we have developed for genetic analysis of complex disease, including power of this pedigree to detect causal GVs using either genetic linkage or association methods in a variance decomposition approach. Finally, we summarize findings of significant heritability for a number of quantitative traits that demonstrate that genetic contributions to risk factors for complex disease can be detected and measured in this pedigree. We conclude that the development and application of an extended pedigree to analysis of complex disease traits in the rhesus macaque have shown promising early success and that genome-wide genetic and higher order -omics studies in this pedigree are likely to yield useful insights into the architecture of complex human disease. PMID:24174435

Translational genomics for analysis of complex traits in peanut and sorghum

USDA-ARS?s Scientific Manuscript database

The integration of sequencing and genotype data from natural variation studies (by whole genome resequencing [wgs] or genotype by sequencing [gbs]), transcriptome (RNA-seq) and mutant analysis (also by wgs) facilitated the development of DNA markers in the form of single nucleotide polymorphic (SNP)...

Association of candidate genes with drought tolerance traits in diverse perennial ryegrass accessions

Treesearch

Xiaoqing Yu; Guihua Bai; Shuwei Liu; Na Luo; Ying Wang; Douglas S. Richmond; Paula M. Pijut; Scott A. Jackson; Jianming Yu; Yiwei Jiang

2013-01-01

Drought is a major environmental stress limiting growth of perennial grasses in temperate regions. Plant drought tolerance is a complex trait that is controlled by multiple genes. Candidate gene association mapping provides a powerful tool for dissection of complex traits. Candidate gene association mapping of drought tolerance traits was conducted in 192 diverse...

Assessing the complex architecture of polygenic traits in diverged yeast populations.

PubMed

Cubillos, Francisco A; Billi, Eleonora; Zörgö, Enikö; Parts, Leopold; Fargier, Patrick; Omholt, Stig; Blomberg, Anders; Warringer, Jonas; Louis, Edward J; Liti, Gianni

2011-04-01

Phenotypic variation arising from populations adapting to different niches has a complex underlying genetic architecture. A major challenge in modern biology is to identify the causative variants driving phenotypic variation. Recently, the baker's yeast, Saccharomyces cerevisiae has emerged as a powerful model for dissecting complex traits. However, past studies using a laboratory strain were unable to reveal the complete architecture of polygenic traits. Here, we present a linkage study using 576 recombinant strains obtained from crosses of isolates representative of the major lineages. The meiotic recombinational landscape appears largely conserved between populations; however, strain-specific hotspots were also detected. Quantitative measurements of growth in 23 distinct ecologically relevant environments show that our recombinant population recapitulates most of the standing phenotypic variation described in the species. Linkage analysis detected an average of 6.3 distinct QTLs for each condition tested in all crosses, explaining on average 39% of the phenotypic variation. The QTLs detected are not constrained to a small number of loci, and the majority are specific to a single cross-combination and to a specific environment. Moreover, crosses between strains of similar phenotypes generate greater variation in the offspring, suggesting the presence of many antagonistic alleles and epistatic interactions. We found that subtelomeric regions play a key role in defining individual quantitative variation, emphasizing the importance of the adaptive nature of these regions in natural populations. This set of recombinant strains is a powerful tool for investigating the complex architecture of polygenic traits. © 2011 Blackwell Publishing Ltd.

Studying Gene and Gene-Environment Effects of Uncommon and Common Variants on Continuous Traits: A Marker-Set Approach Using Gene-Trait Similarity Regression

PubMed Central

Tzeng, Jung-Ying; Zhang, Daowen; Pongpanich, Monnat; Smith, Chris; McCarthy, Mark I.; Sale, Michèle M.; Worrall, Bradford B.; Hsu, Fang-Chi; Thomas, Duncan C.; Sullivan, Patrick F.

2011-01-01

Genomic association analyses of complex traits demand statistical tools that are capable of detecting small effects of common and rare variants and modeling complex interaction effects and yet are computationally feasible. In this work, we introduce a similarity-based regression method for assessing the main genetic and interaction effects of a group of markers on quantitative traits. The method uses genetic similarity to aggregate information from multiple polymorphic sites and integrates adaptive weights that depend on allele frequencies to accomodate common and uncommon variants. Collapsing information at the similarity level instead of the genotype level avoids canceling signals that have the opposite etiological effects and is applicable to any class of genetic variants without the need for dichotomizing the allele types. To assess gene-trait associations, we regress trait similarities for pairs of unrelated individuals on their genetic similarities and assess association by using a score test whose limiting distribution is derived in this work. The proposed regression framework allows for covariates, has the capacity to model both main and interaction effects, can be applied to a mixture of different polymorphism types, and is computationally efficient. These features make it an ideal tool for evaluating associations between phenotype and marker sets defined by linkage disequilibrium (LD) blocks, genes, or pathways in whole-genome analysis. PMID:21835306

Four Linked Genes Participate in Controlling Sporulation Efficiency in Budding Yeast

PubMed Central

Ben-Ari, Giora; Zenvirth, Drora; Sherman, Amir; David, Lior; Klutstein, Michael; Lavi, Uri; Hillel, Jossi; Simchen, Giora

2006-01-01

Quantitative traits are conditioned by several genetic determinants. Since such genes influence many important complex traits in various organisms, the identification of quantitative trait loci (QTLs) is of major interest, but still encounters serious difficulties. We detected four linked genes within one QTL, which participate in controlling sporulation efficiency in Saccharomyces cerevisiae. Following the identification of single nucleotide polymorphisms by comparing the sequences of 145 genes between the parental strains SK1 and S288c, we analyzed the segregating progeny of the cross between them. Through reciprocal hemizygosity analysis, four genes, RAS2, PMS1, SWS2, and FKH2, located in a region of 60 kilobases on Chromosome 14, were found to be associated with sporulation efficiency. Three of the four “high” sporulation alleles are derived from the “low” sporulating strain. Two of these sporulation-related genes were verified through allele replacements. For RAS2, the causative variation was suggested to be a single nucleotide difference in the upstream region of the gene. This quantitative trait nucleotide accounts for sporulation variability among a set of ten closely related winery yeast strains. Our results provide a detailed view of genetic complexity in one “QTL region” that controls a quantitative trait and reports a single nucleotide polymorphism-trait association in wild strains. Moreover, these findings have implications on QTL identification in higher eukaryotes. PMID:17112318

Integrated and translational genomics for analysis of complex traits in crops

USDA-ARS?s Scientific Manuscript database

We report here on integration of sequencing and genotype data from natural variation (by whole genome resequencing [wgs] or genotype by sequencing [gbs]), transcriptome (RNA-seq) and mutant analysis (also by wgs) with the goal of translating gems from these resources into useable DNA markers in the ...

Comparative analysis of transcriptome in two wheat genotypes with contrasting levels of drought tolerance

USDA-ARS?s Scientific Manuscript database

Drought tolerance is a complex trait that is governed by multiple genes. To identify the potential candidate genes, comparative analysis of drought stress-responsive transcriptome between drought-tolerant (Triticum aestivum Cv. C306) and drought-sensitive (Triticum aestivum Cv. WL711) genotypes was ...

Relationships between structural complexity, coral traits, and reef fish assemblages

NASA Astrophysics Data System (ADS)

Darling, Emily S.; Graham, Nicholas A. J.; Januchowski-Hartley, Fraser A.; Nash, Kirsty L.; Pratchett, Morgan S.; Wilson, Shaun K.

2017-06-01

With the ongoing loss of coral cover and the associated flattening of reef architecture, understanding the links between coral habitat and reef fishes is of critical importance. Here, we investigate whether considering coral traits and functional diversity provides new insights into the relationship between structural complexity and reef fish communities, and whether coral traits and community composition can predict structural complexity. Across 157 sites in Seychelles, Maldives, the Chagos Archipelago, and Australia's Great Barrier Reef, we find that structural complexity and reef zone are the strongest and most consistent predictors of reef fish abundance, biomass, species richness, and trophic structure. However, coral traits, diversity, and life histories provided additional predictive power for models of reef fish assemblages, and were key drivers of structural complexity. Our findings highlight that reef complexity relies on living corals—with different traits and life histories—continuing to build carbonate skeletons, and that these nuanced relationships between coral assemblages and habitat complexity can affect the structure of reef fish assemblages. Seascape-level estimates of structural complexity are rapid and cost effective with important implications for the structure and function of fish assemblages, and should be incorporated into monitoring programs.

A Fast Multiple-Kernel Method With Applications to Detect Gene-Environment Interaction.

PubMed

Marceau, Rachel; Lu, Wenbin; Holloway, Shannon; Sale, Michèle M; Worrall, Bradford B; Williams, Stephen R; Hsu, Fang-Chi; Tzeng, Jung-Ying

2015-09-01

Kernel machine (KM) models are a powerful tool for exploring associations between sets of genetic variants and complex traits. Although most KM methods use a single kernel function to assess the marginal effect of a variable set, KM analyses involving multiple kernels have become increasingly popular. Multikernel analysis allows researchers to study more complex problems, such as assessing gene-gene or gene-environment interactions, incorporating variance-component based methods for population substructure into rare-variant association testing, and assessing the conditional effects of a variable set adjusting for other variable sets. The KM framework is robust, powerful, and provides efficient dimension reduction for multifactor analyses, but requires the estimation of high dimensional nuisance parameters. Traditional estimation techniques, including regularization and the "expectation-maximization (EM)" algorithm, have a large computational cost and are not scalable to large sample sizes needed for rare variant analysis. Therefore, under the context of gene-environment interaction, we propose a computationally efficient and statistically rigorous "fastKM" algorithm for multikernel analysis that is based on a low-rank approximation to the nuisance effect kernel matrices. Our algorithm is applicable to various trait types (e.g., continuous, binary, and survival traits) and can be implemented using any existing single-kernel analysis software. Through extensive simulation studies, we show that our algorithm has similar performance to an EM-based KM approach for quantitative traits while running much faster. We also apply our method to the Vitamin Intervention for Stroke Prevention (VISP) clinical trial, examining gene-by-vitamin effects on recurrent stroke risk and gene-by-age effects on change in homocysteine level. © 2015 WILEY PERIODICALS, INC.

Resolving the Effects of Maternal and Offspring Genotype on Dyadic Outcomes in Genome Wide Complex Trait Analysis (“M-GCTA”)

PubMed Central

Pourcain, Beate St.; Smith, George Davey; York, Timothy P.; Evans, David M.

2014-01-01

Genome wide complex trait analysis (GCTA) is extended to include environmental effects of the maternal genotype on offspring phenotype (“maternal effects”, M-GCTA). The model includes parameters for the direct effects of the offspring genotype, maternal effects and the covariance between direct and maternal effects. Analysis of simulated data, conducted in OpenMx, confirmed that model parameters could be recovered by full information maximum likelihood (FIML) and evaluated the biases that arise in conventional GCTA when indirect genetic effects are ignored. Estimates derived from FIML in OpenMx showed very close agreement to those obtained by restricted maximum likelihood using the published algorithm for GCTA. The method was also applied to illustrative perinatal phenotypes from ∼4,000 mother-offspring pairs from the Avon Longitudinal Study of Parents and Children. The relative merits of extended GCTA in contrast to quantitative genetic approaches based on analyzing the phenotypic covariance structure of kinships are considered. PMID:25060210

Global genetic differentiation of complex traits shaped by natural selection in humans.

PubMed

Guo, Jing; Wu, Yang; Zhu, Zhihong; Zheng, Zhili; Trzaskowski, Maciej; Zeng, Jian; Robinson, Matthew R; Visscher, Peter M; Yang, Jian

2018-05-14

There are mean differences in complex traits among global human populations. We hypothesize that part of the phenotypic differentiation is due to natural selection. To address this hypothesis, we assess the differentiation in allele frequencies of trait-associated SNPs among African, Eastern Asian, and European populations for ten complex traits using data of large sample size (up to ~405,000). We show that SNPs associated with height ([Formula: see text]), waist-to-hip ratio ([Formula: see text]), and schizophrenia ([Formula: see text]) are significantly more differentiated among populations than matched "control" SNPs, suggesting that these trait-associated SNPs have undergone natural selection. We further find that SNPs associated with height ([Formula: see text]) and schizophrenia ([Formula: see text]) show significantly higher variance in linkage disequilibrium (LD) scores across populations than control SNPs. Our results support the hypothesis that natural selection has shaped the genetic differentiation of complex traits, such as height and schizophrenia, among worldwide populations.

Visual analysis of geocoded twin data puts nature and nurture on the map.

PubMed

Davis, O S P; Haworth, C M A; Lewis, C M; Plomin, R

2012-09-01

Twin studies allow us to estimate the relative contributions of nature and nurture to human phenotypes by comparing the resemblance of identical and fraternal twins. Variation in complex traits is a balance of genetic and environmental influences; these influences are typically estimated at a population level. However, what if the balance of nature and nurture varies depending on where we grow up? Here we use statistical and visual analysis of geocoded data from over 6700 families to show that genetic and environmental contributions to 45 childhood cognitive and behavioral phenotypes vary geographically in the United Kingdom. This has implications for detecting environmental exposures that may interact with the genetic influences on complex traits, and for the statistical power of samples recruited for genetic association studies. More broadly, our experience demonstrates the potential for collaborative exploratory visualization to act as a lingua franca for large-scale interdisciplinary research.

Ab initio genotype–phenotype association reveals intrinsic modularity in genetic networks

PubMed Central

Slonim, Noam; Elemento, Olivier; Tavazoie, Saeed

2006-01-01

Microbial species express an astonishing diversity of phenotypic traits, behaviors, and metabolic capacities. However, our molecular understanding of these phenotypes is based almost entirely on studies in a handful of model organisms that together represent only a small fraction of this phenotypic diversity. Furthermore, many microbial species are not amenable to traditional laboratory analysis because of their exotic lifestyles and/or lack of suitable molecular genetic techniques. As an adjunct to experimental analysis, we have developed a computational information-theoretic framework that produces high-confidence gene–phenotype predictions using cross-species distributions of genes and phenotypes across 202 fully sequenced archaea and eubacteria. In addition to identifying the genetic basis of complex traits, our approach reveals the organization of these genes into generic preferentially co-inherited modules, many of which correspond directly to known enzymatic pathways, molecular complexes, signaling pathways, and molecular machines. PMID:16732191

A strategy to apply quantitative epistasis analysis on developmental traits.

PubMed

Labocha, Marta K; Yuan, Wang; Aleman-Meza, Boanerges; Zhong, Weiwei

2017-05-15

Genetic interactions are keys to understand complex traits and evolution. Epistasis analysis is an effective method to map genetic interactions. Large-scale quantitative epistasis analysis has been well established for single cells. However, there is a substantial lack of such studies in multicellular organisms and their complex phenotypes such as development. Here we present a method to extend quantitative epistasis analysis to developmental traits. In the nematode Caenorhabditis elegans, we applied RNA interference on mutants to inactivate two genes, used an imaging system to quantitatively measure phenotypes, and developed a set of statistical methods to extract genetic interactions from phenotypic measurement. Using two different C. elegans developmental phenotypes, body length and sex ratio, as examples, we showed that this method could accommodate various metazoan phenotypes with performances comparable to those methods in single cell growth studies. Comparing with qualitative observations, this method of quantitative epistasis enabled detection of new interactions involving subtle phenotypes. For example, several sex-ratio genes were found to interact with brc-1 and brd-1, the orthologs of the human breast cancer genes BRCA1 and BARD1, respectively. We confirmed the brc-1 interactions with the following genes in DNA damage response: C34F6.1, him-3 (ortholog of HORMAD1, HORMAD2), sdc-1, and set-2 (ortholog of SETD1A, SETD1B, KMT2C, KMT2D), validating the effectiveness of our method in detecting genetic interactions. We developed a reliable, high-throughput method for quantitative epistasis analysis of developmental phenotypes.

Directed evolution and synthetic biology applications to microbial systems.

PubMed

Bassalo, Marcelo C; Liu, Rongming; Gill, Ryan T

2016-06-01

Biotechnology applications require engineering complex multi-genic traits. The lack of knowledge on the genetic basis of complex phenotypes restricts our ability to rationally engineer them. However, complex phenotypes can be engineered at the systems level, utilizing directed evolution strategies that drive whole biological systems toward desired phenotypes without requiring prior knowledge of the genetic basis of the targeted trait. Recent developments in the synthetic biology field accelerates the directed evolution cycle, facilitating engineering of increasingly complex traits in biological systems. In this review, we summarize some of the most recent advances in directed evolution and synthetic biology that allows engineering of complex traits in microbial systems. Then, we discuss applications that can be achieved through engineering at the systems level. Copyright © 2016 Elsevier Ltd. All rights reserved.

SNP by SNP by environment interaction network of alcoholism.

PubMed

Zollanvari, Amin; Alterovitz, Gil

2017-03-14

Alcoholism has a strong genetic component. Twin studies have demonstrated the heritability of a large proportion of phenotypic variance of alcoholism ranging from 50-80%. The search for genetic variants associated with this complex behavior has epitomized sequence-based studies for nearly a decade. The limited success of genome-wide association studies (GWAS), possibly precipitated by the polygenic nature of complex traits and behaviors, however, has demonstrated the need for novel, multivariate models capable of quantitatively capturing interactions between a host of genetic variants and their association with non-genetic factors. In this regard, capturing the network of SNP by SNP or SNP by environment interactions has recently gained much interest. Here, we assessed 3,776 individuals to construct a network capable of detecting and quantifying the interactions within and between plausible genetic and environmental factors of alcoholism. In this regard, we propose the use of first-order dependence tree of maximum weight as a potential statistical learning technique to delineate the pattern of dependencies underpinning such a complex trait. Using a predictive based analysis, we further rank the genes, demographic factors, biological pathways, and the interactions represented by our SNP [Formula: see text]SNP[Formula: see text]E network. The proposed framework is quite general and can be potentially applied to the study of other complex traits.

Retrospective Binary-Trait Association Test Elucidates Genetic Architecture of Crohn Disease

PubMed Central

Jiang, Duo; Zhong, Sheng; McPeek, Mary Sara

2016-01-01

In genetic association testing, failure to properly control for population structure can lead to severely inflated type 1 error and power loss. Meanwhile, adjustment for relevant covariates is often desirable and sometimes necessary to protect against spurious association and to improve power. Many recent methods to account for population structure and covariates are based on linear mixed models (LMMs), which are primarily designed for quantitative traits. For binary traits, however, LMM is a misspecified model and can lead to deteriorated performance. We propose CARAT, a binary-trait association testing approach based on a mixed-effects quasi-likelihood framework, which exploits the dichotomous nature of the trait and achieves computational efficiency through estimating equations. We show in simulation studies that CARAT consistently outperforms existing methods and maintains high power in a wide range of population structure settings and trait models. Furthermore, CARAT is based on a retrospective approach, which is robust to misspecification of the phenotype model. We apply our approach to a genome-wide analysis of Crohn disease, in which we replicate association with 17 previously identified regions. Moreover, our analysis on 5p13.1, an extensively reported region of association, shows evidence for the presence of multiple independent association signals in the region. This example shows how CARAT can leverage known disease risk factors to shed light on the genetic architecture of complex traits. PMID:26833331

A principal component meta-analysis on multiple anthropometric traits identifies novel loci for body shape

PubMed Central

Ried, Janina S.; Jeff M., Janina; Chu, Audrey Y.; Bragg-Gresham, Jennifer L.; van Dongen, Jenny; Huffman, Jennifer E.; Ahluwalia, Tarunveer S.; Cadby, Gemma; Eklund, Niina; Eriksson, Joel; Esko, Tõnu; Feitosa, Mary F.; Goel, Anuj; Gorski, Mathias; Hayward, Caroline; Heard-Costa, Nancy L.; Jackson, Anne U.; Jokinen, Eero; Kanoni, Stavroula; Kristiansson, Kati; Kutalik, Zoltán; Lahti, Jari; Luan, Jian'an; Mägi, Reedik; Mahajan, Anubha; Mangino, Massimo; Medina-Gomez, Carolina; Monda, Keri L.; Nolte, Ilja M.; Pérusse, Louis; Prokopenko, Inga; Qi, Lu; Rose, Lynda M.; Salvi, Erika; Smith, Megan T.; Snieder, Harold; Stančáková, Alena; Ju Sung, Yun; Tachmazidou, Ioanna; Teumer, Alexander; Thorleifsson, Gudmar; van der Harst, Pim; Walker, Ryan W.; Wang, Sophie R.; Wild, Sarah H.; Willems, Sara M.; Wong, Andrew; Zhang, Weihua; Albrecht, Eva; Couto Alves, Alexessander; Bakker, Stephan J. L.; Barlassina, Cristina; Bartz, Traci M.; Beilby, John; Bellis, Claire; Bergman, Richard N.; Bergmann, Sven; Blangero, John; Blüher, Matthias; Boerwinkle, Eric; Bonnycastle, Lori L.; Bornstein, Stefan R.; Bruinenberg, Marcel; Campbell, Harry; Chen, Yii-Der Ida; Chiang, Charleston W. K.; Chines, Peter S.; Collins, Francis S; Cucca, Fracensco; Cupples, L Adrienne; D'Avila, Francesca; de Geus, Eco J .C.; Dedoussis, George; Dimitriou, Maria; Döring, Angela; Eriksson, Johan G.; Farmaki, Aliki-Eleni; Farrall, Martin; Ferreira, Teresa; Fischer, Krista; Forouhi, Nita G.; Friedrich, Nele; Gjesing, Anette Prior; Glorioso, Nicola; Graff, Mariaelisa; Grallert, Harald; Grarup, Niels; Gräßler, Jürgen; Grewal, Jagvir; Hamsten, Anders; Harder, Marie Neergaard; Hartman, Catharina A.; Hassinen, Maija; Hastie, Nicholas; Hattersley, Andrew Tym; Havulinna, Aki S.; Heliövaara, Markku; Hillege, Hans; Hofman, Albert; Holmen, Oddgeir; Homuth, Georg; Hottenga, Jouke-Jan; Hui, Jennie; Husemoen, Lise Lotte; Hysi, Pirro G.; Isaacs, Aaron; Ittermann, Till; Jalilzadeh, Shapour; James, Alan L.; Jørgensen, Torben; Jousilahti, Pekka; Jula, Antti; Marie Justesen, Johanne; Justice, Anne E.; Kähönen, Mika; Karaleftheri, Maria; Tee Khaw, Kay; Keinanen-Kiukaanniemi, Sirkka M.; Kinnunen, Leena; Knekt, Paul B.; Koistinen, Heikki A.; Kolcic, Ivana; Kooner, Ishminder K.; Koskinen, Seppo; Kovacs, Peter; Kyriakou, Theodosios; Laitinen, Tomi; Langenberg, Claudia; Lewin, Alexandra M.; Lichtner, Peter; Lindgren, Cecilia M.; Lindström, Jaana; Linneberg, Allan; Lorbeer, Roberto; Lorentzon, Mattias; Luben, Robert; Lyssenko, Valeriya; Männistö, Satu; Manunta, Paolo; Leach, Irene Mateo; McArdle, Wendy L.; Mcknight, Barbara; Mohlke, Karen L.; Mihailov, Evelin; Milani, Lili; Mills, Rebecca; Montasser, May E.; Morris, Andrew P.; Müller, Gabriele; Musk, Arthur W.; Narisu, Narisu; Ong, Ken K.; Oostra, Ben A.; Osmond, Clive; Palotie, Aarno; Pankow, James S.; Paternoster, Lavinia; Penninx, Brenda W.; Pichler, Irene; Pilia, Maria G.; Polašek, Ozren; Pramstaller, Peter P.; Raitakari, Olli T; Rankinen, Tuomo; Rao, D. C.; Rayner, Nigel W.; Ribel-Madsen, Rasmus; Rice, Treva K.; Richards, Marcus; Ridker, Paul M.; Rivadeneira, Fernando; Ryan, Kathy A.; Sanna, Serena; Sarzynski, Mark A.; Scholtens, Salome; Scott, Robert A.; Sebert, Sylvain; Southam, Lorraine; Sparsø, Thomas Hempel; Steinthorsdottir, Valgerdur; Stirrups, Kathleen; Stolk, Ronald P.; Strauch, Konstantin; Stringham, Heather M.; Swertz, Morris A.; Swift, Amy J.; Tönjes, Anke; Tsafantakis, Emmanouil; van der Most, Peter J.; Van Vliet-Ostaptchouk, Jana V.; Vandenput, Liesbeth; Vartiainen, Erkki; Venturini, Cristina; Verweij, Niek; Viikari, Jorma S.; Vitart, Veronique; Vohl, Marie-Claude; Vonk, Judith M.; Waeber, Gérard; Widén, Elisabeth; Willemsen, Gonneke; Wilsgaard, Tom; Winkler, Thomas W.; Wright, Alan F.; Yerges-Armstrong, Laura M.; Hua Zhao, Jing; Carola Zillikens, M.; Boomsma, Dorret I.; Bouchard, Claude; Chambers, John C.; Chasman, Daniel I.; Cusi, Daniele; Gansevoort, Ron T.; Gieger, Christian; Hansen, Torben; Hicks, Andrew A.; Hu, Frank; Hveem, Kristian; Jarvelin, Marjo-Riitta; Kajantie, Eero; Kooner, Jaspal S.; Kuh, Diana; Kuusisto, Johanna; Laakso, Markku; Lakka, Timo A.; Lehtimäki, Terho; Metspalu, Andres; Njølstad, Inger; Ohlsson, Claes; Oldehinkel, Albertine J.; Palmer, Lyle J.; Pedersen, Oluf; Perola, Markus; Peters, Annette; Psaty, Bruce M.; Puolijoki, Hannu; Rauramaa, Rainer; Rudan, Igor; Salomaa, Veikko; Schwarz, Peter E. H.; Shudiner, Alan R.; Smit, Jan H.; Sørensen, Thorkild I. A.; Spector, Timothy D.; Stefansson, Kari; Stumvoll, Michael; Tremblay, Angelo; Tuomilehto, Jaakko; Uitterlinden, André G.; Uusitupa, Matti; Völker, Uwe; Vollenweider, Peter; Wareham, Nicholas J.; Watkins, Hugh; Wilson, James F.; Zeggini, Eleftheria; Abecasis, Goncalo R.; Boehnke, Michael; Borecki, Ingrid B.; Deloukas, Panos; van Duijn, Cornelia M.; Fox, Caroline; Groop, Leif C.; Heid, Iris M.; Hunter, David J.; Kaplan, Robert C.; McCarthy, Mark I.; North, Kari E.; O'Connell, Jeffrey R.; Schlessinger, David; Thorsteinsdottir, Unnur; Strachan, David P.; Frayling, Timothy; Hirschhorn, Joel N.; Müller-Nurasyid, Martina; Loos, Ruth J. F.

2016-01-01

Large consortia have revealed hundreds of genetic loci associated with anthropometric traits, one trait at a time. We examined whether genetic variants affect body shape as a composite phenotype that is represented by a combination of anthropometric traits. We developed an approach that calculates averaged PCs (AvPCs) representing body shape derived from six anthropometric traits (body mass index, height, weight, waist and hip circumference, waist-to-hip ratio). The first four AvPCs explain >99% of the variability, are heritable, and associate with cardiometabolic outcomes. We performed genome-wide association analyses for each body shape composite phenotype across 65 studies and meta-analysed summary statistics. We identify six novel loci: LEMD2 and CD47 for AvPC1, RPS6KA5/C14orf159 and GANAB for AvPC3, and ARL15 and ANP32 for AvPC4. Our findings highlight the value of using multiple traits to define complex phenotypes for discovery, which are not captured by single-trait analyses, and may shed light onto new pathways. PMID:27876822

Invasion complexity at large spatial scales is an emergent property of interactions among landscape characteristics and invader traits

PubMed Central

Jordan, Nicholas R.; Forester, James D.

2018-01-01

Invasion potential should be part of the evaluation of candidate species for any species introduction. However, estimating invasion risks remains a challenging problem, particularly in complex landscapes. Certain plant traits are generally considered to increase invasive potential and there is an understanding that landscapes influence invasions dynamics, but little research has been done to explore how those drivers of invasions interact. We evaluate the relative roles of, and potential interactions between, plant invasiveness traits and landscape characteristics on invasions with a case study using a model parameterized for the potentially invasive biomass crop, Miscanthus × giganteus. Using that model we simulate invasions on 1000 real landscapes to evaluate how landscape characteristics, including both composition and spatial structure, affect invasion outcomes. We conducted replicate simulations with differing strengths of plant invasiveness traits (dispersal ability, establishment ability, population growth rate, and the ability to utilize dispersal corridors) to evaluate how the importance of landscape characteristics for predicting invasion patterns changes depending on the invader details. Analysis of simulations showed that the presence of highly suitable habitat (e.g., grasslands) is generally the strongest determinant of invasion dynamics but that there are also more subtle interactions between landscapes and invader traits. These effects can also vary between different aspects of invasion dynamics (short vs. long time scales and population size vs. spatial extent). These results illustrate that invasions are complex emergent processes with multiple drivers and effective management needs to reflect the ecology of the species of interest and the particular goals or risks for which efforts need to be optimized. PMID:29771923

Exploiting induced variation to dissect quantitative traits in barley.

PubMed

Druka, Arnis; Franckowiak, Jerome; Lundqvist, Udda; Bonar, Nicola; Alexander, Jill; Guzy-Wrobelska, Justyna; Ramsay, Luke; Druka, Ilze; Grant, Iain; Macaulay, Malcolm; Vendramin, Vera; Shahinnia, Fahimeh; Radovic, Slobodanka; Houston, Kelly; Harrap, David; Cardle, Linda; Marshall, David; Morgante, Michele; Stein, Nils; Waugh, Robbie

2010-04-01

The identification of genes underlying complex quantitative traits such as grain yield by means of conventional genetic analysis (positional cloning) requires the development of several large mapping populations. However, it is possible that phenotypically related, but more extreme, allelic variants generated by mutational studies could provide a means for more efficient cloning of QTLs (quantitative trait loci). In barley (Hordeum vulgare), with the development of high-throughput genome analysis tools, efficient genome-wide identification of genetic loci harbouring mutant alleles has recently become possible. Genotypic data from NILs (near-isogenic lines) that carry induced or natural variants of genes that control aspects of plant development can be compared with the location of QTLs to potentially identify candidate genes for development--related traits such as grain yield. As yield itself can be divided into a number of allometric component traits such as tillers per plant, kernels per spike and kernel size, mutant alleles that both affect these traits and are located within the confidence intervals for major yield QTLs may represent extreme variants of the underlying genes. In addition, the development of detailed comparative genomic models based on the alignment of a high-density barley gene map with the rice and sorghum physical maps, has enabled an informed prioritization of 'known function' genes as candidates for both QTLs and induced mutant genes.

KMgene: a unified R package for gene-based association analysis for complex traits.

PubMed

Yan, Qi; Fang, Zhou; Chen, Wei; Stegle, Oliver

2018-02-09

In this report, we introduce an R package KMgene for performing gene-based association tests for familial, multivariate or longitudinal traits using kernel machine (KM) regression under a generalized linear mixed model (GLMM) framework. Extensive simulations were performed to evaluate the validity of the approaches implemented in KMgene. http://cran.r-project.org/web/packages/KMgene. qi.yan@chp.edu or wei.chen@chp.edu. Supplementary data are available at Bioinformatics online. © The Author(s) 2018. Published by Oxford University Press.

Dissecting the genetics of complex traits using summary association statistics.

PubMed

Pasaniuc, Bogdan; Price, Alkes L

2017-02-01

During the past decade, genome-wide association studies (GWAS) have been used to successfully identify tens of thousands of genetic variants associated with complex traits and diseases. These studies have produced extensive repositories of genetic variation and trait measurements across large numbers of individuals, providing tremendous opportunities for further analyses. However, privacy concerns and other logistical considerations often limit access to individual-level genetic data, motivating the development of methods that analyse summary association statistics. Here, we review recent progress on statistical methods that leverage summary association data to gain insights into the genetic basis of complex traits and diseases.

Dissecting the genetics of complex traits using summary association statistics

PubMed Central

Pasaniuc, Bogdan; Price, Alkes L.

2017-01-01

During the past decade, genome-wide association studies (GWAS) have successfully identified tens of thousands of genetic variants associated with complex traits and diseases. These studies have produced extensive repositories of genetic variation and trait measurements across large numbers of individuals, providing tremendous opportunities for further analyses. However, privacy concerns and other logistical considerations often limit access to individual-level genetic data, motivating the development of methods that analyze summary association statistics. Here we review recent progress on statistical methods that leverage summary association data to gain insights into the genetic basis of complex traits and diseases. PMID:27840428

SNP-Based QTL Mapping of 15 Complex Traits in Barley under Rain-Fed and Well-Watered Conditions by a Mixed Modeling Approach.

PubMed

Mora, Freddy; Quitral, Yerko A; Matus, Ivan; Russell, Joanne; Waugh, Robbie; Del Pozo, Alejandro

2016-01-01

This study identified single nucleotide polymorphism (SNP) markers associated with 15 complex traits in a breeding population of barley (Hordeum vulgare L.) consisting of 137 recombinant chromosome substitution lines (RCSL), evaluated under contrasting water availability conditions in the Mediterranean climatic region of central Chile. Given that markers showed a very strong segregation distortion, a quantitative trait locus/loci (QTL) mapping mixed model was used to account for the heterogeneity in genetic relatedness between genotypes. Fifty-seven QTL were detected under rain-fed conditions, which accounted for 5-22% of the phenotypic variation. In full irrigation conditions, 84 SNPs were significantly associated with the traits studied, explaining 5-35% of phenotypic variation. Most of the QTL were co-localized on chromosomes 2H and 3H. Environment-specific genomic regions were detected for 12 of the 15 traits scored. Although most QTL-trait associations were environment and trait specific, some important and stable associations were also detected. In full irrigation conditions, a relatively major genomic region was found underlying hectoliter weight (HW), on chromosome 1H, which explained between 27% (SNP 2711-234) and 35% (SNP 1923-265) of the phenotypic variation. Interestingly, the locus 1923-265 was also detected for grain yield at both environmental conditions, accounting for 9 and 18%, in the rain-fed and irrigation conditions, respectively. Analysis of QTL in this breeding population identified significant genomic regions that can be used for marker-assisted selection (MAS) of barley in areas where drought is a significant constraint.

SNP-Based QTL Mapping of 15 Complex Traits in Barley under Rain-Fed and Well-Watered Conditions by a Mixed Modeling Approach

PubMed Central

Mora, Freddy; Quitral, Yerko A.; Matus, Ivan; Russell, Joanne; Waugh, Robbie; del Pozo, Alejandro

2016-01-01

This study identified single nucleotide polymorphism (SNP) markers associated with 15 complex traits in a breeding population of barley (Hordeum vulgare L.) consisting of 137 recombinant chromosome substitution lines (RCSL), evaluated under contrasting water availability conditions in the Mediterranean climatic region of central Chile. Given that markers showed a very strong segregation distortion, a quantitative trait locus/loci (QTL) mapping mixed model was used to account for the heterogeneity in genetic relatedness between genotypes. Fifty-seven QTL were detected under rain-fed conditions, which accounted for 5–22% of the phenotypic variation. In full irrigation conditions, 84 SNPs were significantly associated with the traits studied, explaining 5–35% of phenotypic variation. Most of the QTL were co-localized on chromosomes 2H and 3H. Environment-specific genomic regions were detected for 12 of the 15 traits scored. Although most QTL-trait associations were environment and trait specific, some important and stable associations were also detected. In full irrigation conditions, a relatively major genomic region was found underlying hectoliter weight (HW), on chromosome 1H, which explained between 27% (SNP 2711-234) and 35% (SNP 1923-265) of the phenotypic variation. Interestingly, the locus 1923-265 was also detected for grain yield at both environmental conditions, accounting for 9 and 18%, in the rain-fed and irrigation conditions, respectively. Analysis of QTL in this breeding population identified significant genomic regions that can be used for marker-assisted selection (MAS) of barley in areas where drought is a significant constraint. PMID:27446139

Convergence of Personality and Interests: Meta-Analysis of the Multidimensional Personality Questionnaire and the Strong Interest Inventory

ERIC Educational Resources Information Center

Staggs, Gena D.; Larson, Lisa M.; Borgen, Fred H.

2007-01-01

Using meta-analysis, we revised Ackerman and Heggestad's (1997) identification of four trait complexes that propose personality and interest (P-I) linkages. Studies that had reported correlations between general and specific measures of vocational interests (Strong Interest Inventory [Strong; Hansen & Campbell, 1985; Harmon, Hansen, Borgen,…

Pathway-Based Genome-Wide Association Studies for Two Meat Production Traits in Simmental Cattle.

PubMed

Fan, Huizhong; Wu, Yang; Zhou, Xiaojing; Xia, Jiangwei; Zhang, Wengang; Song, Yuxin; Liu, Fei; Chen, Yan; Zhang, Lupei; Gao, Xue; Gao, Huijiang; Li, Junya

2015-12-17

Most single nucleotide polymorphisms (SNPs) detected by genome-wide association studies (GWAS), explain only a small fraction of phenotypic variation. Pathway-based GWAS were proposed to improve the proportion of genes for some human complex traits that could be explained by enriching a mass of SNPs within genetic groups. However, few attempts have been made to describe the quantitative traits in domestic animals. In this study, we used a dataset with approximately 7,700,000 SNPs from 807 Simmental cattle and analyzed live weight and longissimus muscle area using a modified pathway-based GWAS method to orthogonalise the highly linked SNPs within each gene using principal component analysis (PCA). As a result, of the 262 biological pathways of cattle collected from the KEGG database, the gamma aminobutyric acid (GABA)ergic synapse pathway and the non-alcoholic fatty liver disease (NAFLD) pathway were significantly associated with the two traits analyzed. The GABAergic synapse pathway was biologically applicable to the traits analyzed because of its roles in feed intake and weight gain. The proposed method had high statistical power and a low false discovery rate, compared to those of the smallest P-value and SNP set enrichment analysis methods.

Dissecting the Phenotypic Components of Crop Plant Growth and Drought Responses Based on High-Throughput Image Analysis[W][OPEN

PubMed Central

Chen, Dijun; Neumann, Kerstin; Friedel, Swetlana; Kilian, Benjamin; Chen, Ming; Altmann, Thomas; Klukas, Christian

2014-01-01

Significantly improved crop varieties are urgently needed to feed the rapidly growing human population under changing climates. While genome sequence information and excellent genomic tools are in place for major crop species, the systematic quantification of phenotypic traits or components thereof in a high-throughput fashion remains an enormous challenge. In order to help bridge the genotype to phenotype gap, we developed a comprehensive framework for high-throughput phenotype data analysis in plants, which enables the extraction of an extensive list of phenotypic traits from nondestructive plant imaging over time. As a proof of concept, we investigated the phenotypic components of the drought responses of 18 different barley (Hordeum vulgare) cultivars during vegetative growth. We analyzed dynamic properties of trait expression over growth time based on 54 representative phenotypic features. The data are highly valuable to understand plant development and to further quantify growth and crop performance features. We tested various growth models to predict plant biomass accumulation and identified several relevant parameters that support biological interpretation of plant growth and stress tolerance. These image-based traits and model-derived parameters are promising for subsequent genetic mapping to uncover the genetic basis of complex agronomic traits. Taken together, we anticipate that the analytical framework and analysis results presented here will be useful to advance our views of phenotypic trait components underlying plant development and their responses to environmental cues. PMID:25501589

Revisiting the Holy Grail: using plant functional traits to understand ecological processes.

PubMed

Funk, Jennifer L; Larson, Julie E; Ames, Gregory M; Butterfield, Bradley J; Cavender-Bares, Jeannine; Firn, Jennifer; Laughlin, Daniel C; Sutton-Grier, Ariana E; Williams, Laura; Wright, Justin

2017-05-01

One of ecology's grand challenges is developing general rules to explain and predict highly complex systems. Understanding and predicting ecological processes from species' traits has been considered a 'Holy Grail' in ecology. Plant functional traits are increasingly being used to develop mechanistic models that can predict how ecological communities will respond to abiotic and biotic perturbations and how species will affect ecosystem function and services in a rapidly changing world; however, significant challenges remain. In this review, we highlight recent work and outstanding questions in three areas: (i) selecting relevant traits; (ii) describing intraspecific trait variation and incorporating this variation into models; and (iii) scaling trait data to community- and ecosystem-level processes. Over the past decade, there have been significant advances in the characterization of plant strategies based on traits and trait relationships, and the integration of traits into multivariate indices and models of community and ecosystem function. However, the utility of trait-based approaches in ecology will benefit from efforts that demonstrate how these traits and indices influence organismal, community, and ecosystem processes across vegetation types, which may be achieved through meta-analysis and enhancement of trait databases. Additionally, intraspecific trait variation and species interactions need to be incorporated into predictive models using tools such as Bayesian hierarchical modelling. Finally, existing models linking traits to community and ecosystem processes need to be empirically tested for their applicability to be realized. © 2016 Cambridge Philosophical Society.

Mapping complex traits as a dynamic system

PubMed Central

Sun, Lidan; Wu, Rongling

2017-01-01

Despite increasing emphasis on the genetic study of quantitative traits, we are still far from being able to chart a clear picture of their genetic architecture, given an inherent complexity involved in trait formation. A competing theory for studying such complex traits has emerged by viewing their phenotypic formation as a “system” in which a high-dimensional group of interconnected components act and interact across different levels of biological organization from molecules through cells to whole organisms. This system is initiated by a machinery of DNA sequences that regulate a cascade of biochemical pathways to synthesize endophenotypes and further assemble these endophenotypes toward the end-point phenotype in virtue of various developmental changes. This review focuses on a conceptual framework for genetic mapping of complex traits by which to delineate the underlying components, interactions and mechanisms that govern the system according to biological principles and understand how these components function synergistically under the control of quantitative trait loci (QTLs) to comprise a unified whole. This framework is built by a system of differential equations that quantifies how alterations of different components lead to the global change of trait development and function, and provides a quantitative and testable platform for assessing the multiscale interplay between QTLs and development. The method will enable geneticists to shed light on the genetic complexity of any biological system and predict, alter or engineer its physiological and pathological states. PMID:25772476

Selection for sow longevity.

PubMed

Serenius, T; Stalder, K J

2006-04-01

Sow longevity plays an important role in economically efficient piglet production because sow longevity is related to the number of piglets produced during its productive lifetime; however, selection for sow longevity is not commonly practiced in any pig breeding program. There is relatively little scientific literature concerning the genetic parameters (genetic variation and genetic correlations) or methods available for breeding value estimation for effective selection for sow longevity. This paper summarizes the current knowledge about the genetics of sow longevity and discusses the available breeding value estimation methods for sow longevity traits. The studies in the literature clearly indicate that sow longevity is a complex trait, and even the definition of sow longevity is variable depending on the researcher and research objective. In general, the measures and analyses of sow longevity can be divided into 1) continuous traits (e.g., productive lifetime) analyzed with proportional hazard models; and 2) more simple binary traits such as stayability until some predetermined fixed parity. Most studies have concluded that sufficient genetic variation exists for effective selection on sow longevity, and heritability estimates have ranged between 0.02 and 0.25. Moreover, sow longevity has shown to be genetically associated with prolificacy and leg conformation traits. Variable results from previous research have led to a lack of consensus among swine breeders concerning the valid methodology of estimating breeding values for longevity traits. One can not deny the superiority of survival analysis in the modeling approach of longevity data; however, multiple-trait analyses are not possible using currently available survival analysis software. Less sophisticated approaches have the advantage of evaluating multiple traits simultaneously, and thus, can use the genetic associations between sow longevity and other traits. Additional research is needed to identify the most efficient selection methods for sow longevity. Future research needs to concentrate on multiple trait analysis of sow longevity traits. Moreover, because longevity is a fitness trait, the nonadditive genetic effects (e.g., dominance) may play important role in the inheritance of sow longevity. Currently, not a single estimate for dominance variance of sow longevity could be identified from the scientific literature.

GWAMA: software for genome-wide association meta-analysis.

PubMed

Mägi, Reedik; Morris, Andrew P

2010-05-28

Despite the recent success of genome-wide association studies in identifying novel loci contributing effects to complex human traits, such as type 2 diabetes and obesity, much of the genetic component of variation in these phenotypes remains unexplained. One way to improving power to detect further novel loci is through meta-analysis of studies from the same population, increasing the sample size over any individual study. Although statistical software analysis packages incorporate routines for meta-analysis, they are ill equipped to meet the challenges of the scale and complexity of data generated in genome-wide association studies. We have developed flexible, open-source software for the meta-analysis of genome-wide association studies. The software incorporates a variety of error trapping facilities, and provides a range of meta-analysis summary statistics. The software is distributed with scripts that allow simple formatting of files containing the results of each association study and generate graphical summaries of genome-wide meta-analysis results. The GWAMA (Genome-Wide Association Meta-Analysis) software has been developed to perform meta-analysis of summary statistics generated from genome-wide association studies of dichotomous phenotypes or quantitative traits. Software with source files, documentation and example data files are freely available online at http://www.well.ox.ac.uk/GWAMA.

SCOPA and META-SCOPA: software for the analysis and aggregation of genome-wide association studies of multiple correlated phenotypes.

PubMed

Mägi, Reedik; Suleimanov, Yury V; Clarke, Geraldine M; Kaakinen, Marika; Fischer, Krista; Prokopenko, Inga; Morris, Andrew P

2017-01-11

Genome-wide association studies (GWAS) of single nucleotide polymorphisms (SNPs) have been successful in identifying loci contributing genetic effects to a wide range of complex human diseases and quantitative traits. The traditional approach to GWAS analysis is to consider each phenotype separately, despite the fact that many diseases and quantitative traits are correlated with each other, and often measured in the same sample of individuals. Multivariate analyses of correlated phenotypes have been demonstrated, by simulation, to increase power to detect association with SNPs, and thus may enable improved detection of novel loci contributing to diseases and quantitative traits. We have developed the SCOPA software to enable GWAS analysis of multiple correlated phenotypes. The software implements "reverse regression" methodology, which treats the genotype of an individual at a SNP as the outcome and the phenotypes as predictors in a general linear model. SCOPA can be applied to quantitative traits and categorical phenotypes, and can accommodate imputed genotypes under a dosage model. The accompanying META-SCOPA software enables meta-analysis of association summary statistics from SCOPA across GWAS. Application of SCOPA to two GWAS of high-and low-density lipoprotein cholesterol, triglycerides and body mass index, and subsequent meta-analysis with META-SCOPA, highlighted stronger association signals than univariate phenotype analysis at established lipid and obesity loci. The META-SCOPA meta-analysis also revealed a novel signal of association at genome-wide significance for triglycerides mapping to GPC5 (lead SNP rs71427535, p = 1.1x10 -8 ), which has not been reported in previous large-scale GWAS of lipid traits. The SCOPA and META-SCOPA software enable discovery and dissection of multiple phenotype association signals through implementation of a powerful reverse regression approach.

A simple model clarifies the complicated relationships of complex networks

PubMed Central

Zheng, Bojin; Wu, Hongrun; Kuang, Li; Qin, Jun; Du, Wenhua; Wang, Jianmin; Li, Deyi

2014-01-01

Real-world networks such as the Internet and WWW have many common traits. Until now, hundreds of models were proposed to characterize these traits for understanding the networks. Because different models used very different mechanisms, it is widely believed that these traits origin from different causes. However, we find that a simple model based on optimisation can produce many traits, including scale-free, small-world, ultra small-world, Delta-distribution, compact, fractal, regular and random networks. Moreover, by revising the proposed model, the community-structure networks are generated. By this model and the revised versions, the complicated relationships of complex networks are illustrated. The model brings a new universal perspective to the understanding of complex networks and provide a universal method to model complex networks from the viewpoint of optimisation. PMID:25160506

Berry and phenology-related traits in grapevine (Vitis vinifera L.): From Quantitative Trait Loci to underlying genes

PubMed Central

Costantini, Laura; Battilana, Juri; Lamaj, Flutura; Fanizza, Girolamo; Grando, Maria Stella

2008-01-01

Background The timing of grape ripening initiation, length of maturation period, berry size and seed content are target traits in viticulture. The availability of early and late ripening varieties is desirable for staggering harvest along growing season, expanding production towards periods when the fruit gets a higher value in the market and ensuring an optimal plant adaptation to climatic and geographic conditions. Berry size determines grape productivity; seedlessness is especially demanded in the table grape market and is negatively correlated to fruit size. These traits result from complex developmental processes modified by genetic, physiological and environmental factors. In order to elucidate their genetic determinism we carried out a quantitative analysis in a 163 individuals-F1 segregating progeny obtained by crossing two table grape cultivars. Results Molecular linkage maps covering most of the genome (2n = 38 for Vitis vinifera) were generated for each parent. Eighteen pairs of homologous groups were integrated into a consensus map spanning over 1426 cM with 341 markers (mainly microsatellite, AFLP and EST-derived markers) and an average map distance between loci of 4.2 cM. Segregating traits were evaluated in three growing seasons by recording flowering, veraison and ripening dates and by measuring berry size, seed number and weight. QTL (Quantitative Trait Loci) analysis was carried out based on single marker and interval mapping methods. QTLs were identified for all but one of the studied traits, a number of them steadily over more than one year. Clusters of QTLs for different characters were detected, suggesting linkage or pleiotropic effects of loci, as well as regions affecting specific traits. The most interesting QTLs were investigated at the gene level through a bioinformatic analysis of the underlying Pinot noir genomic sequence. Conclusion Our results revealed novel insights into the genetic control of relevant grapevine features. They provide a basis for performing marker-assisted selection and testing the role of specific genes in trait variation. PMID:18419811

Urbanization and Fertility: An Event-History Analysis of Coastal Ghana

PubMed Central

WHITE, MICHAEL J.; MUHIDIN, SALUT; ANDRZEJEWSKI, CATHERINE; TAGOE, EVA; KNIGHT, RODNEY; REED, HOLLY

2008-01-01

In this article, we undertake an event-history analysis of fertility in Ghana. We exploit detailed life history calendar data to conduct a more refined and definitive analysis of the relationship among personal traits, urban residence, and fertility. Although urbanization is generally associated with lower fertility in developing countries, inferences in most studies have been hampered by a lack of information about the timing of residence in relationship to childbearing. We find that the effect of urbanization itself is strong, evident, and complex, and persists after we control for the effects of age, cohort, union status, and education. Our discrete-time event-history analysis shows that urban women exhibit fertility rates that are, on average, 11% lower than those of rural women, but the effects vary by parity. Differences in urban population traits would augment the effects of urban adaptation itself. Extensions of the analysis point to the operation of a selection effect in rural-to-urban mobility but provide limited evidence for disruption effects. The possibility of further selection of urbanward migrants on unmeasured traits remains. The analysis also demonstrates the utility of an annual life history calendar for collecting such data in the field. PMID:19110898

Integrating Gene Expression with Summary Association Statistics to Identify Genes Associated with 30 Complex Traits.

PubMed

Mancuso, Nicholas; Shi, Huwenbo; Goddard, Pagé; Kichaev, Gleb; Gusev, Alexander; Pasaniuc, Bogdan

2017-03-02

Although genome-wide association studies (GWASs) have identified thousands of risk loci for many complex traits and diseases, the causal variants and genes at these loci remain largely unknown. Here, we introduce a method for estimating the local genetic correlation between gene expression and a complex trait and utilize it to estimate the genetic correlation due to predicted expression between pairs of traits. We integrated gene expression measurements from 45 expression panels with summary GWAS data to perform 30 multi-tissue transcriptome-wide association studies (TWASs). We identified 1,196 genes whose expression is associated with these traits; of these, 168 reside more than 0.5 Mb away from any previously reported GWAS significant variant. We then used our approach to find 43 pairs of traits with significant genetic correlation at the level of predicted expression; of these, eight were not found through genetic correlation at the SNP level. Finally, we used bi-directional regression to find evidence that BMI causally influences triglyceride levels and that triglyceride levels causally influence low-density lipoprotein. Together, our results provide insight into the role of gene expression in the susceptibility of complex traits and diseases. Copyright © 2017 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

TATES: Efficient Multivariate Genotype-Phenotype Analysis for Genome-Wide Association Studies

PubMed Central

van der Sluis, Sophie; Posthuma, Danielle; Dolan, Conor V.

2013-01-01

To date, the genome-wide association study (GWAS) is the primary tool to identify genetic variants that cause phenotypic variation. As GWAS analyses are generally univariate in nature, multivariate phenotypic information is usually reduced to a single composite score. This practice often results in loss of statistical power to detect causal variants. Multivariate genotype–phenotype methods do exist but attain maximal power only in special circumstances. Here, we present a new multivariate method that we refer to as TATES (Trait-based Association Test that uses Extended Simes procedure), inspired by the GATES procedure proposed by Li et al (2011). For each component of a multivariate trait, TATES combines p-values obtained in standard univariate GWAS to acquire one trait-based p-value, while correcting for correlations between components. Extensive simulations, probing a wide variety of genotype–phenotype models, show that TATES's false positive rate is correct, and that TATES's statistical power to detect causal variants explaining 0.5% of the variance can be 2.5–9 times higher than the power of univariate tests based on composite scores and 1.5–2 times higher than the power of the standard MANOVA. Unlike other multivariate methods, TATES detects both genetic variants that are common to multiple phenotypes and genetic variants that are specific to a single phenotype, i.e. TATES provides a more complete view of the genetic architecture of complex traits. As the actual causal genotype–phenotype model is usually unknown and probably phenotypically and genetically complex, TATES, available as an open source program, constitutes a powerful new multivariate strategy that allows researchers to identify novel causal variants, while the complexity of traits is no longer a limiting factor. PMID:23359524

Genetic studies of plasma analytes identify novel potential biomarkers for several complex traits

PubMed Central

Deming, Yuetiva; Xia, Jian; Cai, Yefei; Lord, Jenny; Del-Aguila, Jorge L.; Fernandez, Maria Victoria; Carrell, David; Black, Kathleen; Budde, John; Ma, ShengMei; Saef, Benjamin; Howells, Bill; Bertelsen, Sarah; Bailey, Matthew; Ridge, Perry G.; Hefti, Franz; Fillit, Howard; Zimmerman, Earl A.; Celmins, Dzintra; Brown, Alice D.; Carrillo, Maria; Fleisher, Adam; Reeder, Stephanie; Trncic, Nadira; Burke, Anna; Tariot, Pierre; Reiman, Eric M.; Chen, Kewei; Sabbagh, Marwan N.; Beiden, Christine M.; Jacobson, Sandra A.; Sirrel, Sherye A.; Doody, Rachelle S.; Villanueva-Meyer, Javier; Chowdhury, Munir; Rountree, Susan; Dang, Mimi; Kowall, Neil; Killiany, Ronald; Budson, Andrew E.; Norbash, Alexander; Johnson, Patricia Lynn; Green, Robert C.; Marshall, Gad; Johnson, Keith A.; Sperling, Reisa A.; Snyder, Peter; Salloway, Stephen; Malloy, Paul; Correia, Stephen; Bernick, Charles; Munic, Donna; Stern, Yaakov; Honig, Lawrence S.; Bell, Karen L.; Relkin, Norman; Chaing, Gloria; Ravdin, Lisa; Paul, Steven; Flashman, Laura A.; Seltzer, Marc; Hynes, Mary L.; Santulli, Robert B.; Bates, Vernice; Capote, Horacio; Rainka, Michelle; Friedl, Karl; Murali Doraiswamy, P.; Petrella, Jeffrey R.; Borges-Neto, Salvador; James, Olga; Wong, Terence; Coleman, Edward; Schwartz, Adam; Cellar, Janet S.; Levey, Allan L.; Lah, James J.; Behan, Kelly; Scott Turner, Raymond; Johnson, Kathleen; Reynolds, Brigid; Pearlson, Godfrey D.; Blank, Karen; Anderson, Karen; Obisesan, Thomas O.; Wolday, Saba; Allard, Joanne; Lerner, Alan; Ogrocki, Paula; Tatsuoka, Curtis; Fatica, Parianne; Farlow, Martin R.; Saykin, Andrew J.; Foroud, Tatiana M.; Shen, Li; Faber, Kelly; Kim, Sungeun; Nho, Kwangsik; Marie Hake, Ann; Matthews, Brandy R.; Brosch, Jared R.; Herring, Scott; Hunt, Cynthia; Albert, Marilyn; Onyike, Chiadi; D’Agostino, Daniel; Kielb, Stephanie; Graff-Radford, Neill R; Parfitt, Francine; Kendall, Tracy; Johnson, Heather; Petersen, Ronald; Jack, Clifford R.; Bernstein, Matthew; Borowski, Bret; Gunter, Jeff; Senjem, Matt; Vemuri, Prashanthi; Jones, David; Kantarci, Kejal; Ward, Chad; Mason, Sara S.; Albers, Colleen S.; Knopman, David; Johnson, Kris; Chertkow, Howard; Hosein, Chris; Mintzer, Jacob; Spicer, Kenneth; Bachman, David; Grossman, Hillel; Mitsis, Effie; Pomara, Nunzio; Hernando, Raymundo; Sarrael, Antero; Potter, William; Buckholtz, Neil; Hsiao, John; Kittur, Smita; Galvin, James E.; Cerbone, Brittany; Michel, Christina A.; Pogorelec, Dana M.; Rusinek, Henry; de Leon, Mony J; Glodzik, Lidia; De Santi, Susan; Johnson, Nancy; Chuang-Kuo; Kerwin, Diana; Bonakdarpour, Borna; Weintraub, Sandra; Grafman, Jordan; Lipowski, Kristine; Mesulam, Marek-Marsel; Scharre, Douglas W.; Kataki, Maria; Adeli, Anahita; Kaye, Jeffrey; Quinn, Joseph; Silbert, Lisa; Lind, Betty; Carter, Raina; Dolen, Sara; Borrie, Michael; Lee, T-Y; Bartha, Rob; Martinez, Walter; Villena, Teresa; Sadowsky, Carl; Khachaturian, Zaven; Ott, Brian R.; Querfurth, Henry; Tremont, Geoffrey; Frank, Richard; Fleischman, Debra; Arfanakis, Konstantinos; Shah, Raj C.; deToledo-Morrell, Leyla; Sorensen, Greg; Finger, Elizabeth; Pasternack, Stephen; Rachinsky, Irina; Drost, Dick; Rogers, John; Kertesz, Andrew; Furst, Ansgar J.; Chad, Stevan; Yesavage, Jerome; Taylor, Joy L.; Lane, Barton; Rosen, Allyson; Tinklenberg, Jared; Black, Sandra; Stefanovic, Bojana; Caldwell, Curtis; Robin Hsiung, Ging-Yuek; Mudge, Benita; Assaly, Michele; Fox, Nick; Schultz, Susan K.; Boles Ponto, Laura L.; Shim, Hyungsub; Ekstam Smith, Karen; Burns, Jeffrey M.; Swerdlow, Russell H.; Brooks, William M.; Marson, Daniel; Griffith, Randall; Clark, David; Geldmacher, David; Brockington, John; Roberson, Erik; Natelson Love, Marissa; DeCarli, Charles; Carmichael, Owen; Olichney, John; Maillard, Pauline; Fletcher, Evan; Nguyen, Dana; Preda, Andrian; Potkin, Steven; Mulnard, Ruth A.; Thai, Gaby; McAdams-Ortiz, Catherine; Landau, Susan; Jagust, William; Apostolova, Liana; Tingus, Kathleen; Woo, Ellen; Silverman, Daniel H.S.; Lu, Po H.; Bartzokis, George; Thompson, Paul; Donohue, Michael; Thomas, Ronald G.; Walter, Sarah; Gessert, Devon; Brewer, James; Vanderswag, Helen; Sather, Tamie; Jiminez, Gus; Balasubramanian, Archana B.; Mason, Jennifer; Sim, Iris; Aisen, Paul; Davis, Melissa; Morrison, Rosemary; Harvey, Danielle; Thal, Lean; Beckett, Laurel; Neylan, Thomas; Finley, Shannon; Weiner, Michael W.; Hayes, Jacqueline; Rosen, Howard J.; Miller, Bruce L.; Perry, David; Massoglia, Dino; Brawman-Mentzer, Olga; Schuff, Norbert; Smith, Charles D.; Hardy, Peter; Sinha, Partha; Oates, Elizabeth; Conrad, Gary; Koeppe, Robert A.; Lord, Joanne L.; Heidebrink, Judith L.; Arnold, Steven E.; Karlawish, Jason H.; Wolk, David; Clark, Christopher M.; Trojanowki, John Q.; Shaw, Leslie M.; Lee, Virginia; Korecka, Magdalena; Figurski, Michal; Toga, Arthur W.; Crawford, Karen; Neu, Scott; Schneider, Lon S.; Pawluczyk, Sonia; Beccera, Mauricio; Teodoro, Liberty; Spann, Bryan M.; Womack, Kyle; Mathews, Dana; Quiceno, Mary; Foster, Norm; Montine, Tom; Fruehling, J. Jay; Harding, Sandra; Johnson, Sterling; Asthana, Sanjay; Carlsson, Cynthia M.; Petrie, Eric C.; Peskind, Elaine; Li, Gail; Porsteinsson, Anton P.; Goldstein, Bonnie S.; Martin, Kim; Makino, Kelly M.; Ismail, M. Saleem; Brand, Connie; Smith, Amanda; Ashok Raj, Balebail; Fargher, Kristin; Kuller, Lew; Mathis, Chet; Ann Oakley, Mary; Lopez, Oscar L.; Simpson, Donna M.; Sink, Kaycee M.; Gordineer, Leslie; Williamson, Jeff D.; Garg, Pradeep; Watkins, Franklin; Cairns, Nigel J.; Raichle, Marc; Morris, John C.; Householder, Erin; Taylor-Reinwald, Lisa; Holtzman, David; Ances, Beau; Carroll, Maria; Creech, Mary L.; Franklin, Erin; Mintun, Mark A.; Schneider, Stacy; Oliver, Angela; Duara, Ranjan; Varon, Daniel; Greig, Maria T.; Roberts, Peggy; Varma, Pradeep; MacAvoy, Martha G.; Carson, Richard E.; van Dyck, Christopher H.; Davies, Peter; Holtzman, David; Morris, John C.; Bales, Kelly; Pickering, Eve H.; Lee, Jin-Moo; Heitsch, Laura; Kauwe, John; Goate, Alison; Piccio, Laura; Cruchaga, Carlos

2016-01-01

Genome-wide association studies of 146 plasma protein levels in 818 individuals revealed 56 genome-wide significant associations (28 novel) with 47 analytes. Loci associated with plasma levels of 39 proteins tested have been previously associated with various complex traits such as heart disease, inflammatory bowel disease, Type 2 diabetes, and multiple sclerosis. These data suggest that these plasma protein levels may constitute informative endophenotypes for these complex traits. We found three potential pleiotropic genes: ABO for plasma SELE and ACE levels, FUT2 for CA19-9 and CEA plasma levels, and APOE for ApoE and CRP levels. We also found multiple independent signals in loci associated with plasma levels of ApoH, CA19-9, FetuinA, IL6r, and LPa. Our study highlights the power of biological traits for genetic studies to identify genetic variants influencing clinically relevant traits, potential pleiotropic effects, and complex disease associations in the same locus.

Comparative quantitative trait locus mapping of maize flowering-related traits in an F2:3 and recombinant inbred line population.

PubMed

Liu, Y H; Yi, Q; Hou, X B; Zhang, X G; Zhang, J J; Liu, H M; Hu, Y F; Huang, Y B

2016-06-30

Flowering-related traits in maize are affected by complex factors and are important for the improvement of cropping systems in the maize zone. Quantitative trait loci (QTLs) detected using different materials and methods usually vary. In the present study, 266 maize (Zea mays) F2:3 families and 301 recombinant inbred lines (RIL) derived from a cross between 08-641 (founding parent from southeast China) and Ye478 (founding parent from China) were evaluated for four flowering-related traits, including days to tasseling (DTT), days to pollen shedding (DPS), days to silking (DTS), and anthesis-silking interval. Sixty-six QTLs controlling the target traits were detected in the F2:3 and RIL populations via single environment analysis and joint analysis across all environments (JAAE). The QTLs explained 0.8-13.47% of the phenotypic variation, with 12 QTLs explaining more than 10%. The results of meta-QTL (MQTL) analysis indicated that 41 QTLs could be integrated into 14 MQTLs. One MQTL included 2.9 QTLs, ranging from two to ten QTLs for one to three traits. QTLs, including MQTL1-1 and MQTL9-1, were detected across the F2:3 and RIL populations via SAE and JAAE. Among the MQTLs, nine QTLs were integrated into MQTL9-1 and affected DTT, DPS, and DTS, with the favored allele being derived from 08-641. MQTL3-2 showed high phenotypic variation and was suitable for fine mapping to determine the genetic mechanisms of flowering. MQTL3-2 could be applied to improve inbred lines using marker-assisted selection.

Decoupled evolution of floral traits and climatic preferences in a clade of Neotropical Gesneriaceae.

PubMed

Serrano-Serrano, Martha Liliana; Perret, Mathieu; Guignard, Maïté; Chautems, Alain; Silvestro, Daniele; Salamin, Nicolas

2015-11-10

Major factors influencing the phenotypic diversity of a lineage can be recognized by characterizing the extent and mode of trait evolution between related species. Here, we compared the evolutionary dynamics of traits associated with floral morphology and climatic preferences in a clade composed of the genera Codonanthopsis, Codonanthe and Nematanthus (Gesneriaceae). To test the mode and specific components that lead to phenotypic diversity in this group, we performed a Bayesian phylogenetic analysis of combined nuclear and plastid DNA sequences and modeled the evolution of quantitative traits related to flower shape and size and to climatic preferences. We propose an alternative approach to display graphically the complex dynamics of trait evolution along a phylogenetic tree using a wide range of evolutionary scenarios. Our results demonstrated heterogeneous trait evolution. Floral shapes displaced into separate regimes selected by the different pollinator types (hummingbirds versus insects), while floral size underwent a clade-specific evolution. Rates of evolution were higher for the clade that is hummingbird pollinated and experienced flower resupination, compared with species pollinated by bees, suggesting a relevant role of plant-pollinator interactions in lowland rainforest. The evolution of temperature preferences is best explained by a model with distinct selective regimes between the Brazilian Atlantic Forest and the other biomes, whereas differentiation along the precipitation axis was characterized by higher rates, compared with temperature, and no regime or clade-specific patterns. Our study shows different selective regimes and clade-specific patterns in the evolution of morphological and climatic components during the diversification of Neotropical species. Our new graphical visualization tool allows the representation of trait trajectories under parameter-rich models, thus contributing to a better understanding of complex evolutionary dynamics.

Will Big Data Close the Missing Heritability Gap?

PubMed

Kim, Hwasoon; Grueneberg, Alexander; Vazquez, Ana I; Hsu, Stephen; de Los Campos, Gustavo

2017-11-01

Despite the important discoveries reported by genome-wide association (GWA) studies, for most traits and diseases the prediction R-squared (R-sq.) achieved with genetic scores remains considerably lower than the trait heritability. Modern biobanks will soon deliver unprecedentedly large biomedical data sets: Will the advent of big data close the gap between the trait heritability and the proportion of variance that can be explained by a genomic predictor? We addressed this question using Bayesian methods and a data analysis approach that produces a surface response relating prediction R-sq. with sample size and model complexity ( e.g. , number of SNPs). We applied the methodology to data from the interim release of the UK Biobank. Focusing on human height as a model trait and using 80,000 records for model training, we achieved a prediction R-sq. in testing ( n = 22,221) of 0.24 (95% C.I.: 0.23-0.25). Our estimates show that prediction R-sq. increases with sample size, reaching an estimated plateau at values that ranged from 0.1 to 0.37 for models using 500 and 50,000 (GWA-selected) SNPs, respectively. Soon much larger data sets will become available. Using the estimated surface response, we forecast that larger sample sizes will lead to further improvements in prediction R-sq. We conclude that big data will lead to a substantial reduction of the gap between trait heritability and the proportion of interindividual differences that can be explained with a genomic predictor. However, even with the power of big data, for complex traits we anticipate that the gap between prediction R-sq. and trait heritability will not be fully closed. Copyright © 2017 by the Genetics Society of America.

Will Big Data Close the Missing Heritability Gap?

PubMed Central

Kim, Hwasoon; Grueneberg, Alexander; Vazquez, Ana I.; Hsu, Stephen; de los Campos, Gustavo

2017-01-01

Despite the important discoveries reported by genome-wide association (GWA) studies, for most traits and diseases the prediction R-squared (R-sq.) achieved with genetic scores remains considerably lower than the trait heritability. Modern biobanks will soon deliver unprecedentedly large biomedical data sets: Will the advent of big data close the gap between the trait heritability and the proportion of variance that can be explained by a genomic predictor? We addressed this question using Bayesian methods and a data analysis approach that produces a surface response relating prediction R-sq. with sample size and model complexity (e.g., number of SNPs). We applied the methodology to data from the interim release of the UK Biobank. Focusing on human height as a model trait and using 80,000 records for model training, we achieved a prediction R-sq. in testing (n = 22,221) of 0.24 (95% C.I.: 0.23–0.25). Our estimates show that prediction R-sq. increases with sample size, reaching an estimated plateau at values that ranged from 0.1 to 0.37 for models using 500 and 50,000 (GWA-selected) SNPs, respectively. Soon much larger data sets will become available. Using the estimated surface response, we forecast that larger sample sizes will lead to further improvements in prediction R-sq. We conclude that big data will lead to a substantial reduction of the gap between trait heritability and the proportion of interindividual differences that can be explained with a genomic predictor. However, even with the power of big data, for complex traits we anticipate that the gap between prediction R-sq. and trait heritability will not be fully closed. PMID:28893854

CNV-association meta-analysis in 191,161 European adults reveals new loci associated with anthropometric traits.

PubMed

Macé, Aurélien; Tuke, Marcus A; Deelen, Patrick; Kristiansson, Kati; Mattsson, Hannele; Nõukas, Margit; Sapkota, Yadav; Schick, Ursula; Porcu, Eleonora; Rüeger, Sina; McDaid, Aaron F; Porteous, David; Winkler, Thomas W; Salvi, Erika; Shrine, Nick; Liu, Xueping; Ang, Wei Q; Zhang, Weihua; Feitosa, Mary F; Venturini, Cristina; van der Most, Peter J; Rosengren, Anders; Wood, Andrew R; Beaumont, Robin N; Jones, Samuel E; Ruth, Katherine S; Yaghootkar, Hanieh; Tyrrell, Jessica; Havulinna, Aki S; Boers, Harmen; Mägi, Reedik; Kriebel, Jennifer; Müller-Nurasyid, Martina; Perola, Markus; Nieminen, Markku; Lokki, Marja-Liisa; Kähönen, Mika; Viikari, Jorma S; Geller, Frank; Lahti, Jari; Palotie, Aarno; Koponen, Päivikki; Lundqvist, Annamari; Rissanen, Harri; Bottinger, Erwin P; Afaq, Saima; Wojczynski, Mary K; Lenzini, Petra; Nolte, Ilja M; Sparsø, Thomas; Schupf, Nicole; Christensen, Kaare; Perls, Thomas T; Newman, Anne B; Werge, Thomas; Snieder, Harold; Spector, Timothy D; Chambers, John C; Koskinen, Seppo; Melbye, Mads; Raitakari, Olli T; Lehtimäki, Terho; Tobin, Martin D; Wain, Louise V; Sinisalo, Juha; Peters, Annette; Meitinger, Thomas; Martin, Nicholas G; Wray, Naomi R; Montgomery, Grant W; Medland, Sarah E; Swertz, Morris A; Vartiainen, Erkki; Borodulin, Katja; Männistö, Satu; Murray, Anna; Bochud, Murielle; Jacquemont, Sébastien; Rivadeneira, Fernando; Hansen, Thomas F; Oldehinkel, Albertine J; Mangino, Massimo; Province, Michael A; Deloukas, Panos; Kooner, Jaspal S; Freathy, Rachel M; Pennell, Craig; Feenstra, Bjarke; Strachan, David P; Lettre, Guillaume; Hirschhorn, Joel; Cusi, Daniele; Heid, Iris M; Hayward, Caroline; Männik, Katrin; Beckmann, Jacques S; Loos, Ruth J F; Nyholt, Dale R; Metspalu, Andres; Eriksson, Johan G; Weedon, Michael N; Salomaa, Veikko; Franke, Lude; Reymond, Alexandre; Frayling, Timothy M; Kutalik, Zoltán

2017-09-29

There are few examples of robust associations between rare copy number variants (CNVs) and complex continuous human traits. Here we present a large-scale CNV association meta-analysis on anthropometric traits in up to 191,161 adult samples from 26 cohorts. The study reveals five CNV associations at 1q21.1, 3q29, 7q11.23, 11p14.2, and 18q21.32 and confirms two known loci at 16p11.2 and 22q11.21, implicating at least one anthropometric trait. The discovered CNVs are recurrent and rare (0.01-0.2%), with large effects on height (>2.4 cm), weight (>5 kg), and body mass index (BMI) (>3.5 kg/m 2 ). Burden analysis shows a 0.41 cm decrease in height, a 0.003 increase in waist-to-hip ratio and increase in BMI by 0.14 kg/m 2 for each Mb of total deletion burden (P = 2.5 × 10 -10 , 6.0 × 10 -5 , and 2.9 × 10 -3 ). Our study provides evidence that the same genes (e.g., MC4R, FIBIN, and FMO5) harbor both common and rare variants affecting body size and that anthropometric traits share genetic loci with developmental and psychiatric disorders.Individual SNPs have small effects on anthropometric traits, yet the impact of CNVs has remained largely unknown. Here, Kutalik and co-workers perform a large-scale genome-wide meta-analysis of structural variation and find rare CNVs associated with height, weight and BMI with large effect sizes.

Survey of the Heritability and Sparse Architecture of Gene Expression Traits across Human Tissues.

PubMed

Wheeler, Heather E; Shah, Kaanan P; Brenner, Jonathon; Garcia, Tzintzuni; Aquino-Michaels, Keston; Cox, Nancy J; Nicolae, Dan L; Im, Hae Kyung

2016-11-01

Understanding the genetic architecture of gene expression traits is key to elucidating the underlying mechanisms of complex traits. Here, for the first time, we perform a systematic survey of the heritability and the distribution of effect sizes across all representative tissues in the human body. We find that local h2 can be relatively well characterized with 59% of expressed genes showing significant h2 (FDR < 0.1) in the DGN whole blood cohort. However, current sample sizes (n ≤ 922) do not allow us to compute distal h2. Bayesian Sparse Linear Mixed Model (BSLMM) analysis provides strong evidence that the genetic contribution to local expression traits is dominated by a handful of genetic variants rather than by the collective contribution of a large number of variants each of modest size. In other words, the local architecture of gene expression traits is sparse rather than polygenic across all 40 tissues (from DGN and GTEx) examined. This result is confirmed by the sparsity of optimal performing gene expression predictors via elastic net modeling. To further explore the tissue context specificity, we decompose the expression traits into cross-tissue and tissue-specific components using a novel Orthogonal Tissue Decomposition (OTD) approach. Through a series of simulations we show that the cross-tissue and tissue-specific components are identifiable via OTD. Heritability and sparsity estimates of these derived expression phenotypes show similar characteristics to the original traits. Consistent properties relative to prior GTEx multi-tissue analysis results suggest that these traits reflect the expected biology. Finally, we apply this knowledge to develop prediction models of gene expression traits for all tissues. The prediction models, heritability, and prediction performance R2 for original and decomposed expression phenotypes are made publicly available (https://github.com/hakyimlab/PrediXcan).

Quantitative trait locus mapping and analysis of heritable variation in affiliative social behavior and co-occurring traits.

PubMed

Knoll, A T; Jiang, K; Levitt, P

2018-06-01

Humans exhibit broad heterogeneity in affiliative social behavior. Twin and family studies show that individual differences in core dimensions of social behavior are heritable, yet there are knowledge gaps in understanding the underlying genetic and neurobiological mechanisms. Animal genetic reference panels (GRPs) provide a tractable strategy for examining the behavioral and genetic architecture of complex traits. Here, using males from 50 mouse strains from the BXD GRP, 4 domains of affiliative social behavior-social approach, social recognition, direct social interaction (DSI) (partner sniffing) and vocal communication-were examined in 2 widely used behavioral tasks-the 3-chamber and DSI tasks. There was continuous and broad variation in social and nonsocial traits, with moderate to high heritability of social approach sniff preference (0.31), ultrasonic vocalization (USV) count (0.39), partner sniffing (0.51), locomotor activity (0.54-0.66) and anxiety-like behavior (0.36). Principal component analysis shows that variation in social and nonsocial traits are attributable to 5 independent factors. Genome-wide mapping identified significant quantitative trait loci for USV count on chromosome (Chr) 18 and locomotor activity on Chr X, with suggestive loci and candidate quantitative trait genes identified for all traits with one notable exception-partner sniffing in the DSI task. The results show heritable variation in sociability, which is independent of variation in activity and anxiety-like traits. In addition, a highly heritable and ethological domain of affiliative sociability-partner sniffing-appears highly polygenic. These findings establish a basis for identifying functional natural variants, leading to a new understanding typical and atypical sociability. © 2017 The Authors. Genes, Brain and Behavior published by International Behavioural and Neural Genetics Society and John Wiley & Sons Ltd.

Systems genetics approaches to understand complex traits

PubMed Central

Civelek, Mete; Lusis, Aldons J.

2014-01-01

Systems genetics is an approach to understand the flow of biological information that underlies complex traits. It uses a range of experimental and statistical methods to quantitate and integrate intermediate phenotypes, such as transcript, protein or metabolite levels, in populations that vary for traits of interest. Systems genetics studies have provided the first global view of the molecular architecture of complex traits and are useful for the identification of genes, pathways and networks that underlie common human diseases. Given the urgent need to understand how the thousands of loci that have been identified in genome-wide association studies contribute to disease susceptibility, systems genetics is likely to become an increasingly important approach to understanding both biology and disease. PMID:24296534

Complex Genetics of Behavior: BXDs in the Automated Home-Cage.

PubMed

Loos, Maarten; Verhage, Matthijs; Spijker, Sabine; Smit, August B

2017-01-01

This chapter describes a use case for the genetic dissection and automated analysis of complex behavioral traits using the genetically diverse panel of BXD mouse recombinant inbred strains. Strains of the BXD resource differ widely in terms of gene and protein expression in the brain, as well as in their behavioral repertoire. A large mouse resource opens the possibility for gene finding studies underlying distinct behavioral phenotypes, however, such a resource poses a challenge in behavioral phenotyping. To address the specifics of large-scale screening we describe how to investigate: (1) how to assess mouse behavior systematically in addressing a large genetic cohort, (2) how to dissect automation-derived longitudinal mouse behavior into quantitative parameters, and (3) how to map these quantitative traits to the genome, deriving loci underlying aspects of behavior.

Genetic constraints on wing pattern variation in Lycaeides butterflies: A case study on mapping complex, multifaceted traits in structured populations.

PubMed

Lucas, Lauren K; Nice, Chris C; Gompert, Zachariah

2018-03-13

Patterns of phenotypic variation within and among species can be shaped and constrained by trait genetic architecture. This is particularly true for complex traits, such as butterfly wing patterns, that consist of multiple elements. Understanding the genetics of complex trait variation across species boundaries is difficult, as it necessitates mapping in structured populations and can involve many loci with small or variable phenotypic effects. Here, we investigate the genetic architecture of complex wing pattern variation in Lycaeides butterflies as a case study of mapping multivariate traits in wild populations that include multiple nominal species or groups. We identify conserved modules of integrated wing pattern elements within populations and species. We show that trait covariances within modules have a genetic basis and thus represent genetic constraints that can channel evolution. Consistent with this, we find evidence that evolutionary changes in wing patterns among populations and species occur in the directions of genetic covariances within these groups. Thus, we show that genetic constraints affect patterns of biological diversity (wing pattern) in Lycaeides, and we provide an analytical template for similar work in other systems. © 2018 John Wiley & Sons Ltd.

Assessing the value of phenotypic information from non-genotyped animals for QTL mapping of complex traits in real and simulated populations.

PubMed

Melo, Thaise P; Takada, Luciana; Baldi, Fernando; Oliveira, Henrique N; Dias, Marina M; Neves, Haroldo H R; Schenkel, Flavio S; Albuquerque, Lucia G; Carvalheiro, Roberto

2016-06-21

QTL mapping through genome-wide association studies (GWAS) is challenging, especially in the case of low heritability complex traits and when few animals possess genotypic and phenotypic information. When most of the phenotypic information is from non-genotyped animals, GWAS can be performed using the weighted single-step GBLUP (WssGBLUP) method, which permits to combine all available information, even that of non-genotyped animals. However, it is not clear to what extent phenotypic information from non-genotyped animals increases the power of QTL detection, and whether factors such as the extent of linkage disequilibrium (LD) in the population and weighting SNPs in WssGBLUP affect the importance of using information from non-genotyped animals in GWAS. These questions were investigated in this study using real and simulated data. Analysis of real data showed that the use of phenotypes of non-genotyped animals affected SNP effect estimates and, consequently, QTL mapping. Despite some coincidence, the most important genomic regions identified by the analyses, either using or ignoring phenotypes of non-genotyped animals, were not the same. The simulation results indicated that the inclusion of all available phenotypic information, even that of non-genotyped animals, tends to improve QTL detection for low heritability complex traits. For populations with low levels of LD, this trend of improvement was less pronounced. Stronger shrinkage on SNPs explaining lower variance was not necessarily associated with better QTL mapping. The use of phenotypic information from non-genotyped animals in GWAS may improve the ability to detect QTL for low heritability complex traits, especially in populations in which the level of LD is high.

[Fine mapping of complex disease susceptibility loci].

PubMed

Song, Qingfeng; Zhang, Hongxing; Ma, Yilong; Zhou, Gangqiao

2014-01-01

Genome-wide association studies (GWAS) using single nucleotide polymorphism (SNP) markers have identified more than 3800 susceptibility loci for more than 660 diseases or traits. However, the most significantly associated variants or causative variants in these loci and their biological functions have remained to be clarified. These causative variants can help to elucidate the pathogenesis and discover new biomarkers of complex diseases. One of the main goals in the post-GWAS era is to identify the causative variants and susceptibility genes, and clarify their functional aspects by fine mapping. For common variants, imputation or re-sequencing based strategies were implemented to increase the number of analyzed variants and help to identify the most significantly associated variants. In addition, functional element, expression quantitative trait locus (eQTL) and haplotype analyses were performed to identify functional common variants and susceptibility genes. For rare variants, fine mapping was carried out by re-sequencing, rare haplotype analysis, family-based analysis, burden test, etc.This review summarizes the strategies and problems for fine mapping.

Genome sequencing reveals loci under artificial selection that underlie disease phenotypes in the laboratory rat.

PubMed

Atanur, Santosh S; Diaz, Ana Garcia; Maratou, Klio; Sarkis, Allison; Rotival, Maxime; Game, Laurence; Tschannen, Michael R; Kaisaki, Pamela J; Otto, Georg W; Ma, Man Chun John; Keane, Thomas M; Hummel, Oliver; Saar, Kathrin; Chen, Wei; Guryev, Victor; Gopalakrishnan, Kathirvel; Garrett, Michael R; Joe, Bina; Citterio, Lorena; Bianchi, Giuseppe; McBride, Martin; Dominiczak, Anna; Adams, David J; Serikawa, Tadao; Flicek, Paul; Cuppen, Edwin; Hubner, Norbert; Petretto, Enrico; Gauguier, Dominique; Kwitek, Anne; Jacob, Howard; Aitman, Timothy J

2013-08-01

Large numbers of inbred laboratory rat strains have been developed for a range of complex disease phenotypes. To gain insights into the evolutionary pressures underlying selection for these phenotypes, we sequenced the genomes of 27 rat strains, including 11 models of hypertension, diabetes, and insulin resistance, along with their respective control strains. Altogether, we identified more than 13 million single-nucleotide variants, indels, and structural variants across these rat strains. Analysis of strain-specific selective sweeps and gene clusters implicated genes and pathways involved in cation transport, angiotensin production, and regulators of oxidative stress in the development of cardiovascular disease phenotypes in rats. Many of the rat loci that we identified overlap with previously mapped loci for related traits in humans, indicating the presence of shared pathways underlying these phenotypes in rats and humans. These data represent a step change in resources available for evolutionary analysis of complex traits in disease models. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

Genome Sequencing Reveals Loci under Artificial Selection that Underlie Disease Phenotypes in the Laboratory Rat

PubMed Central

Atanur, Santosh S.; Diaz, Ana Garcia; Maratou, Klio; Sarkis, Allison; Rotival, Maxime; Game, Laurence; Tschannen, Michael R.; Kaisaki, Pamela J.; Otto, Georg W.; Ma, Man Chun John; Keane, Thomas M.; Hummel, Oliver; Saar, Kathrin; Chen, Wei; Guryev, Victor; Gopalakrishnan, Kathirvel; Garrett, Michael R.; Joe, Bina; Citterio, Lorena; Bianchi, Giuseppe; McBride, Martin; Dominiczak, Anna; Adams, David J.; Serikawa, Tadao; Flicek, Paul; Cuppen, Edwin; Hubner, Norbert; Petretto, Enrico; Gauguier, Dominique; Kwitek, Anne; Jacob, Howard; Aitman, Timothy J.

2013-01-01

Summary Large numbers of inbred laboratory rat strains have been developed for a range of complex disease phenotypes. To gain insights into the evolutionary pressures underlying selection for these phenotypes, we sequenced the genomes of 27 rat strains, including 11 models of hypertension, diabetes, and insulin resistance, along with their respective control strains. Altogether, we identified more than 13 million single-nucleotide variants, indels, and structural variants across these rat strains. Analysis of strain-specific selective sweeps and gene clusters implicated genes and pathways involved in cation transport, angiotensin production, and regulators of oxidative stress in the development of cardiovascular disease phenotypes in rats. Many of the rat loci that we identified overlap with previously mapped loci for related traits in humans, indicating the presence of shared pathways underlying these phenotypes in rats and humans. These data represent a step change in resources available for evolutionary analysis of complex traits in disease models. PaperClip PMID:23890820

Genetic Analysis of Kernel Traits in Maize-Teosinte Introgression Populations.

PubMed

Liu, Zhengbin; Garcia, Arturo; McMullen, Michael D; Flint-Garcia, Sherry A

2016-08-09

Seed traits have been targeted by human selection during the domestication of crop species as a way to increase the caloric and nutritional content of food during the transition from hunter-gather to early farming societies. The primary seed trait under selection was likely seed size/weight as it is most directly related to overall grain yield. Additional seed traits involved in seed shape may have also contributed to larger grain. Maize (Zea mays ssp. mays) kernel weight has increased more than 10-fold in the 9000 years since domestication from its wild ancestor, teosinte (Z. mays ssp. parviglumis). In order to study how size and shape affect kernel weight, we analyzed kernel morphometric traits in a set of 10 maize-teosinte introgression populations using digital imaging software. We identified quantitative trait loci (QTL) for kernel area and length with moderate allelic effects that colocalize with kernel weight QTL. Several genomic regions with strong effects during maize domestication were detected, and a genetic framework for kernel traits was characterized by complex pleiotropic interactions. Our results both confirm prior reports of kernel domestication loci and identify previously uncharacterized QTL with a range of allelic effects, enabling future research into the genetic basis of these traits. Copyright © 2016 Liu et al.

Genetic Analysis of Kernel Traits in Maize-Teosinte Introgression Populations

PubMed Central

Liu, Zhengbin; Garcia, Arturo; McMullen, Michael D.; Flint-Garcia, Sherry A.

2016-01-01

Seed traits have been targeted by human selection during the domestication of crop species as a way to increase the caloric and nutritional content of food during the transition from hunter-gather to early farming societies. The primary seed trait under selection was likely seed size/weight as it is most directly related to overall grain yield. Additional seed traits involved in seed shape may have also contributed to larger grain. Maize (Zea mays ssp. mays) kernel weight has increased more than 10-fold in the 9000 years since domestication from its wild ancestor, teosinte (Z. mays ssp. parviglumis). In order to study how size and shape affect kernel weight, we analyzed kernel morphometric traits in a set of 10 maize-teosinte introgression populations using digital imaging software. We identified quantitative trait loci (QTL) for kernel area and length with moderate allelic effects that colocalize with kernel weight QTL. Several genomic regions with strong effects during maize domestication were detected, and a genetic framework for kernel traits was characterized by complex pleiotropic interactions. Our results both confirm prior reports of kernel domestication loci and identify previously uncharacterized QTL with a range of allelic effects, enabling future research into the genetic basis of these traits. PMID:27317774

Progress of genome wide association study in domestic animals

PubMed Central

2012-01-01

Domestic animals are invaluable resources for study of the molecular architecture of complex traits. Although the mapping of quantitative trait loci (QTL) responsible for economically important traits in domestic animals has achieved remarkable results in recent decades, not all of the genetic variation in the complex traits has been captured because of the low density of markers used in QTL mapping studies. The genome wide association study (GWAS), which utilizes high-density single-nucleotide polymorphism (SNP), provides a new way to tackle this issue. Encouraging achievements in dissection of the genetic mechanisms of complex diseases in humans have resulted from the use of GWAS. At present, GWAS has been applied to the field of domestic animal breeding and genetics, and some advances have been made. Many genes or markers that affect economic traits of interest in domestic animals have been identified. In this review, advances in the use of GWAS in domestic animals are described. PMID:22958308

GWAS4D: multidimensional analysis of context-specific regulatory variant for human complex diseases and traits.

PubMed

Huang, Dandan; Yi, Xianfu; Zhang, Shijie; Zheng, Zhanye; Wang, Panwen; Xuan, Chenghao; Sham, Pak Chung; Wang, Junwen; Li, Mulin Jun

2018-05-16

Genome-wide association studies have generated over thousands of susceptibility loci for many human complex traits, and yet for most of these associations the true causal variants remain unknown. Tissue/cell type-specific prediction and prioritization of non-coding regulatory variants will facilitate the identification of causal variants and underlying pathogenic mechanisms for particular complex diseases and traits. By leveraging recent large-scale functional genomics/epigenomics data, we develop an intuitive web server, GWAS4D (http://mulinlab.tmu.edu.cn/gwas4d or http://mulinlab.org/gwas4d), that systematically evaluates GWAS signals and identifies context-specific regulatory variants. The updated web server includes six major features: (i) updates the regulatory variant prioritization method with our new algorithm; (ii) incorporates 127 tissue/cell type-specific epigenomes data; (iii) integrates motifs of 1480 transcriptional regulators from 13 public resources; (iv) uniformly processes Hi-C data and generates significant interactions at 5 kb resolution across 60 tissues/cell types; (v) adds comprehensive non-coding variant functional annotations; (vi) equips a highly interactive visualization function for SNP-target interaction. Using a GWAS fine-mapped set for 161 coronary artery disease risk loci, we demonstrate that GWAS4D is able to efficiently prioritize disease-causal regulatory variants.

Phenotypic landscape inference reveals multiple evolutionary paths to C4 photosynthesis

PubMed Central

Williams, Ben P; Johnston, Iain G; Covshoff, Sarah; Hibberd, Julian M

2013-01-01

C4 photosynthesis has independently evolved from the ancestral C3 pathway in at least 60 plant lineages, but, as with other complex traits, how it evolved is unclear. Here we show that the polyphyletic appearance of C4 photosynthesis is associated with diverse and flexible evolutionary paths that group into four major trajectories. We conducted a meta-analysis of 18 lineages containing species that use C3, C4, or intermediate C3–C4 forms of photosynthesis to parameterise a 16-dimensional phenotypic landscape. We then developed and experimentally verified a novel Bayesian approach based on a hidden Markov model that predicts how the C4 phenotype evolved. The alternative evolutionary histories underlying the appearance of C4 photosynthesis were determined by ancestral lineage and initial phenotypic alterations unrelated to photosynthesis. We conclude that the order of C4 trait acquisition is flexible and driven by non-photosynthetic drivers. This flexibility will have facilitated the convergent evolution of this complex trait. DOI: http://dx.doi.org/10.7554/eLife.00961.001 PMID:24082995

Genetic architecture of hybrid male sterility in Drosophila: analysis of intraspecies variation for interspecies isolation.

PubMed

Reed, Laura K; LaFlamme, Brooke A; Markow, Therese A

2008-08-27

The genetic basis of postzygotic isolation is a central puzzle in evolutionary biology. Evolutionary forces causing hybrid sterility or inviability act on the responsible genes while they still are polymorphic, thus we have to study these traits as they arise, before isolation is complete. Isofemale strains of D. mojavensis vary significantly in their production of sterile F(1) sons when females are crossed to D. arizonae males. We took advantage of the intraspecific polymorphism, in a novel design, to perform quantitative trait locus (QTL) mapping analyses directly on F(1) hybrid male sterility itself. We found that the genetic architecture of the polymorphism for hybrid male sterility (HMS) in the F(1) is complex, involving multiple QTL, epistasis, and cytoplasmic effects. The role of extensive intraspecific polymorphism, multiple QTL, and epistatic interactions in HMS in this young species pair shows that HMS is arising as a complex trait in this system. Directional selection alone would be unlikely to maintain polymorphism at multiple loci, thus we hypothesize that directional selection is unlikely to be the only evolutionary force influencing postzygotic isolation.

Quantitative analysis of bristle number in Drosophila mutants identifies genes involved in neural development

NASA Technical Reports Server (NTRS)

Norga, Koenraad K.; Gurganus, Marjorie C.; Dilda, Christy L.; Yamamoto, Akihiko; Lyman, Richard F.; Patel, Prajal H.; Rubin, Gerald M.; Hoskins, Roger A.; Mackay, Trudy F.; Bellen, Hugo J.

2003-01-01

BACKGROUND: The identification of the function of all genes that contribute to specific biological processes and complex traits is one of the major challenges in the postgenomic era. One approach is to employ forward genetic screens in genetically tractable model organisms. In Drosophila melanogaster, P element-mediated insertional mutagenesis is a versatile tool for the dissection of molecular pathways, and there is an ongoing effort to tag every gene with a P element insertion. However, the vast majority of P element insertion lines are viable and fertile as homozygotes and do not exhibit obvious phenotypic defects, perhaps because of the tendency for P elements to insert 5' of transcription units. Quantitative genetic analysis of subtle effects of P element mutations that have been induced in an isogenic background may be a highly efficient method for functional genome annotation. RESULTS: Here, we have tested the efficacy of this strategy by assessing the extent to which screening for quantitative effects of P elements on sensory bristle number can identify genes affecting neural development. We find that such quantitative screens uncover an unusually large number of genes that are known to function in neural development, as well as genes with yet uncharacterized effects on neural development, and novel loci. CONCLUSIONS: Our findings establish the use of quantitative trait analysis for functional genome annotation through forward genetics. Similar analyses of quantitative effects of P element insertions will facilitate our understanding of the genes affecting many other complex traits in Drosophila.

E-Index for Differentiating Complex Dynamic Traits

PubMed Central

Qi, Jiandong; Sun, Jianfeng; Wang, Jianxin

2016-01-01

While it is a daunting challenge in current biology to understand how the underlying network of genes regulates complex dynamic traits, functional mapping, a tool for mapping quantitative trait loci (QTLs) and single nucleotide polymorphisms (SNPs), has been applied in a variety of cases to tackle this challenge. Though useful and powerful, functional mapping performs well only when one or more model parameters are clearly responsible for the developmental trajectory, typically being a logistic curve. Moreover, it does not work when the curves are more complex than that, especially when they are not monotonic. To overcome this inadaptability, we therefore propose a mathematical-biological concept and measurement, E-index (earliness-index), which cumulatively measures the earliness degree to which a variable (or a dynamic trait) increases or decreases its value. Theoretical proofs and simulation studies show that E-index is more general than functional mapping and can be applied to any complex dynamic traits, including those with logistic curves and those with nonmonotonic curves. Meanwhile, E-index vector is proposed as well to capture more subtle differences of developmental patterns. PMID:27064292

Association mapping analysis of fiber yield and quality traits in Upland cotton (Gossypium hirsutum L.).

PubMed

Ademe, Mulugeta Seyoum; He, Shoupu; Pan, Zhaoe; Sun, Junling; Wang, Qinglian; Qin, Hongde; Liu, Jinhai; Liu, Hui; Yang, Jun; Xu, Dongyong; Yang, Jinlong; Ma, Zhiying; Zhang, Jinbiao; Li, Zhikun; Cai, Zhongmin; Zhang, Xuelin; Zhang, Xin; Huang, Aifen; Yi, Xianda; Zhou, Guanyin; Li, Lin; Zhu, Haiyong; Pang, Baoyin; Wang, Liru; Jia, Yinhua; Du, Xiongming

2017-12-01

Fiber yield and quality are the most important traits for Upland cotton (Gossypium hirsutum L.). Identifying high yield and good fiber quality genes are the prime concern of researchers in cotton breeding. Association mapping offers an alternative and powerful method for detecting those complex agronomic traits. In this study, 198 simple sequence repeats (SSRs) were used to screen markers associated with fiber yield and quality traits with 302 elite Upland cotton accessions that were evaluated in 12 locations representing the Yellow River and Yangtze River cotton growing regions of China. Three subpopulations were found after the estimation of population structure. The pair-wise kinship values varied from 0 to 0.867. Only 1.59% of the total marker locus pairs showed significant linkage disequilibrium (LD, p < 0.001). The genome-wide LD decayed within the genetic distance of ~30 to 32 cM at r 2  = 0.1, and decreased to ~1 to 2 cM at r 2  = 0.2, indicating the potential for association mapping. Analysis based on a mixed linear model detected 57 significant (p < 0.01) marker-trait associations, including seven associations for fiber length, ten for fiber micronaire, nine for fiber strength, eight for fiber elongation, five for fiber uniformity index, five for fiber uniformity ratio, six for boll weight and seven for lint percent, for a total of 35 SSR markers, of which 11 markers were associated with more than one trait. Among marker-trait associations, 24 associations coincided with the previously reported quantitative trait loci (QTLs), the remainder were newly identified QTLs/genes. The QTLs identified in this study will potentially facilitate improvement of fiber yield and quality in the future cotton molecular breeding programs.

Relationship between QTL for grain shape, grain weight, test weight, milling yield, and plant height in the spring wheat cross RL4452/'AC Domain'.

PubMed

Cabral, Adrian L; Jordan, Mark C; Larson, Gary; Somers, Daryl J; Humphreys, D Gavin; McCartney, Curt A

2018-01-01

Kernel morphology characteristics of wheat are complex and quantitatively inherited. A doubled haploid (DH) population of the cross RL4452/'AC Domain' was used to study the genetic basis of seed shape. Quantitative trait loci (QTL) analyses were conducted on a total of 18 traits: 14 grain shape traits, flour yield (Fyd), and three agronomic traits (Plant height [Plht], 1000 Grain weight [Gwt], Test weight [Twt]), using data from trial locations at Glenlea, Brandon, and Morden in Manitoba, Canada, between 1999 and 2004. Kernel shape was studied through digital image analysis with an Acurum® grain analyzer. Plht, Gwt, Twt, Fyd, and grain shape QTL were correlated with each other and QTL analysis revealed that QTL for these traits often mapped to the same genetic locations. The most significant QTL for the grain shape traits were located on chromosomes 4B and 4D, each accounting for up to 24.4% and 53.3% of the total phenotypic variation, respectively. In addition, the most significant QTL for Plht, Gwt, and Twt were all detected on chromosome 4D at the Rht-D1 locus. Rht-D1b decreased Plht, Gwt, Twt, and kernel width relative to the Rht-D1a allele. A narrow genetic interval on chromosome 4B contained significant QTL for grain shape, Gwt, and Plht. The 'AC Domain' allele reduced Plht, Gwt, kernel length and width traits, but had no detectable effect on Twt. The data indicated that this variation was inconsistent with segregation at Rht-B1. Numerous QTL were identified that control these traits in this population.

Relationship between QTL for grain shape, grain weight, test weight, milling yield, and plant height in the spring wheat cross RL4452/‘AC Domain’

PubMed Central

Cabral, Adrian L.; Jordan, Mark C.; Larson, Gary; Somers, Daryl J.; Humphreys, D. Gavin

2018-01-01

Kernel morphology characteristics of wheat are complex and quantitatively inherited. A doubled haploid (DH) population of the cross RL4452/‘AC Domain’ was used to study the genetic basis of seed shape. Quantitative trait loci (QTL) analyses were conducted on a total of 18 traits: 14 grain shape traits, flour yield (Fyd), and three agronomic traits (Plant height [Plht], 1000 Grain weight [Gwt], Test weight [Twt]), using data from trial locations at Glenlea, Brandon, and Morden in Manitoba, Canada, between 1999 and 2004. Kernel shape was studied through digital image analysis with an Acurum® grain analyzer. Plht, Gwt, Twt, Fyd, and grain shape QTL were correlated with each other and QTL analysis revealed that QTL for these traits often mapped to the same genetic locations. The most significant QTL for the grain shape traits were located on chromosomes 4B and 4D, each accounting for up to 24.4% and 53.3% of the total phenotypic variation, respectively. In addition, the most significant QTL for Plht, Gwt, and Twt were all detected on chromosome 4D at the Rht-D1 locus. Rht-D1b decreased Plht, Gwt, Twt, and kernel width relative to the Rht-D1a allele. A narrow genetic interval on chromosome 4B contained significant QTL for grain shape, Gwt, and Plht. The ‘AC Domain’ allele reduced Plht, Gwt, kernel length and width traits, but had no detectable effect on Twt. The data indicated that this variation was inconsistent with segregation at Rht-B1. Numerous QTL were identified that control these traits in this population. PMID:29357369

Use of genetic data to infer population-specific ecological and phenotypic traits from mixed aggregations

USGS Publications Warehouse

Moran, Paul; Bromaghin, Jeffrey F.; Masuda, Michele

2014-01-01

Many applications in ecological genetics involve sampling individuals from a mixture of multiple biological populations and subsequently associating those individuals with the populations from which they arose. Analytical methods that assign individuals to their putative population of origin have utility in both basic and applied research, providing information about population-specific life history and habitat use, ecotoxins, pathogen and parasite loads, and many other non-genetic ecological, or phenotypic traits. Although the question is initially directed at the origin of individuals, in most cases the ultimate desire is to investigate the distribution of some trait among populations. Current practice is to assign individuals to a population of origin and study properties of the trait among individuals within population strata as if they constituted independent samples. It seemed that approach might bias population-specific trait inference. In this study we made trait inferences directly through modeling, bypassing individual assignment. We extended a Bayesian model for population mixture analysis to incorporate parameters for the phenotypic trait and compared its performance to that of individual assignment with a minimum probability threshold for assignment. The Bayesian mixture model outperformed individual assignment under some trait inference conditions. However, by discarding individuals whose origins are most uncertain, the individual assignment method provided a less complex analytical technique whose performance may be adequate for some common trait inference problems. Our results provide specific guidance for method selection under various genetic relationships among populations with different trait distributions.

Use of Genetic Data to Infer Population-Specific Ecological and Phenotypic Traits from Mixed Aggregations

PubMed Central

Moran, Paul; Bromaghin, Jeffrey F.; Masuda, Michele

2014-01-01

Many applications in ecological genetics involve sampling individuals from a mixture of multiple biological populations and subsequently associating those individuals with the populations from which they arose. Analytical methods that assign individuals to their putative population of origin have utility in both basic and applied research, providing information about population-specific life history and habitat use, ecotoxins, pathogen and parasite loads, and many other non-genetic ecological, or phenotypic traits. Although the question is initially directed at the origin of individuals, in most cases the ultimate desire is to investigate the distribution of some trait among populations. Current practice is to assign individuals to a population of origin and study properties of the trait among individuals within population strata as if they constituted independent samples. It seemed that approach might bias population-specific trait inference. In this study we made trait inferences directly through modeling, bypassing individual assignment. We extended a Bayesian model for population mixture analysis to incorporate parameters for the phenotypic trait and compared its performance to that of individual assignment with a minimum probability threshold for assignment. The Bayesian mixture model outperformed individual assignment under some trait inference conditions. However, by discarding individuals whose origins are most uncertain, the individual assignment method provided a less complex analytical technique whose performance may be adequate for some common trait inference problems. Our results provide specific guidance for method selection under various genetic relationships among populations with different trait distributions. PMID:24905464

The transformative potential of an integrative approach to pregnancy.

PubMed

Eidem, Haley R; McGary, Kriston L; Capra, John A; Abbot, Patrick; Rokas, Antonis

2017-09-01

Complex traits typically involve diverse biological pathways and are shaped by numerous genetic and environmental factors. Pregnancy-associated traits and pathologies are further complicated by extensive communication across multiple tissues in two individuals, interactions between two genomes-maternal and fetal-that obscure causal variants and lead to genetic conflict, and rapid evolution of pregnancy-associated traits across mammals and in the human lineage. Given the multi-faceted complexity of human pregnancy, integrative approaches that synthesize diverse data types and analyses harbor tremendous promise to identify the genetic architecture and environmental influences underlying pregnancy-associated traits and pathologies. We review current research that addresses the extreme complexities of traits and pathologies associated with human pregnancy. We find that successful efforts to address the many complexities of pregnancy-associated traits and pathologies often harness the power of many and diverse types of data, including genome-wide association studies, evolutionary analyses, multi-tissue transcriptomic profiles, and environmental conditions. We propose that understanding of pregnancy and its pathologies will be accelerated by computational platforms that provide easy access to integrated data and analyses. By simplifying the integration of diverse data, such platforms will provide a comprehensive synthesis that transcends many of the inherent challenges present in studies of pregnancy. Copyright © 2017 Elsevier Ltd. All rights reserved.

Joint QTL linkage mapping for multiple-cross mating design sharing one common parent

USDA-ARS?s Scientific Manuscript database

Nested association mapping (NAM) is a novel genetic mating design that combines the advantages of linkage analysis and association mapping. This design provides opportunities to study the inheritance of complex traits, but also requires more advanced statistical methods. In this paper, we present th...

DNA mismatch repair gene MSH6 implicated in determining age at natural menopause

PubMed Central

Perry, John R.B.; Hsu, Yi-Hsiang; Chasman, Daniel I.; Johnson, Andrew D.; Elks, Cathy; Albrecht, Eva; Andrulis, Irene L.; Beesley, Jonathan; Berenson, Gerald S.; Bergmann, Sven; Bojesen, Stig E.; Bolla, Manjeet K.; Brown, Judith; Buring, Julie E.; Campbell, Harry; Chang-Claude, Jenny; Chenevix-Trench, Georgia; Corre, Tanguy; Couch, Fergus J.; Cox, Angela; Czene, Kamila; D'adamo, Adamo Pio; Davies, Gail; Deary, Ian J.; Dennis, Joe; Easton, Douglas F.; Engelhardt, Ellen G.; Eriksson, Johan G.; Esko, Tõnu; Fasching, Peter A.; Figueroa, Jonine D.; Flyger, Henrik; Fraser, Abigail; Garcia-Closas, Montse; Gasparini, Paolo; Gieger, Christian; Giles, Graham; Guenel, Pascal; Hägg, Sara; Hall, Per; Hayward, Caroline; Hopper, John; Ingelsson, Erik; Kardia, Sharon L.R.; Kasiman, Katherine; Knight, Julia A.; Lahti, Jari; Lawlor, Debbie A.; Magnusson, Patrik K.E.; Margolin, Sara; Marsh, Julie A.; Metspalu, Andres; Olson, Janet E.; Pennell, Craig E.; Polasek, Ozren; Rahman, Iffat; Ridker, Paul M.; Robino, Antonietta; Rudan, Igor; Rudolph, Anja; Salumets, Andres; Schmidt, Marjanka K.; Schoemaker, Minouk J.; Smith, Erin N.; Smith, Jennifer A.; Southey, Melissa; Stöckl, Doris; Swerdlow, Anthony J.; Thompson, Deborah J.; Truong, Therese; Ulivi, Sheila; Waldenberger, Melanie; Wang, Qin; Wild, Sarah; Wilson, James F; Wright, Alan F.; Zgaga, Lina; Ong, Ken K.; Murabito, Joanne M.; Karasik, David; Murray, Anna

2014-01-01

The length of female reproductive lifespan is associated with multiple adverse outcomes, including breast cancer, cardiovascular disease and infertility. The biological processes that govern the timing of the beginning and end of reproductive life are not well understood. Genetic variants are known to contribute to ∼50% of the variation in both age at menarche and menopause, but to date the known genes explain <15% of the genetic component. We have used genome-wide association in a bivariate meta-analysis of both traits to identify genes involved in determining reproductive lifespan. We observed significant genetic correlation between the two traits using genome-wide complex trait analysis. However, we found no robust statistical evidence for individual variants with an effect on both traits. A novel association with age at menopause was detected for a variant rs1800932 in the mismatch repair gene MSH6 (P = 1.9 × 10−9), which was also associated with altered expression levels of MSH6 mRNA in multiple tissues. This study contributes to the growing evidence that DNA repair processes play a key role in ovarian ageing and could be an important therapeutic target for infertility. PMID:24357391

Genome-wide association analysis of metabolic traits in a birth cohort from a founder population.

PubMed

Sabatti, Chiara; Service, Susan K; Hartikainen, Anna-Liisa; Pouta, Anneli; Ripatti, Samuli; Brodsky, Jae; Jones, Chris G; Zaitlen, Noah A; Varilo, Teppo; Kaakinen, Marika; Sovio, Ulla; Ruokonen, Aimo; Laitinen, Jaana; Jakkula, Eveliina; Coin, Lachlan; Hoggart, Clive; Collins, Andrew; Turunen, Hannu; Gabriel, Stacey; Elliot, Paul; McCarthy, Mark I; Daly, Mark J; Järvelin, Marjo-Riitta; Freimer, Nelson B; Peltonen, Leena

2009-01-01

Genome-wide association studies (GWAS) of longitudinal birth cohorts enable joint investigation of environmental and genetic influences on complex traits. We report GWAS results for nine quantitative metabolic traits (triglycerides, high-density lipoprotein, low-density lipoprotein, glucose, insulin, C-reactive protein, body mass index, and systolic and diastolic blood pressure) in the Northern Finland Birth Cohort 1966 (NFBC1966), drawn from the most genetically isolated Finnish regions. We replicate most previously reported associations for these traits and identify nine new associations, several of which highlight genes with metabolic functions: high-density lipoprotein with NR1H3 (LXRA), low-density lipoprotein with AR and FADS1-FADS2, glucose with MTNR1B, and insulin with PANK1. Two of these new associations emerged after adjustment of results for body mass index. Gene-environment interaction analyses suggested additional associations, which will require validation in larger samples. The currently identified loci, together with quantified environmental exposures, explain little of the trait variation in NFBC1966. The association observed between low-density lipoprotein and an infrequent variant in AR suggests the potential of such a cohort for identifying associations with both common, low-impact and rarer, high-impact quantitative trait loci.

Assessment of the Potential Impacts of Wheat Plant Traits across Environments by Combining Crop Modeling and Global Sensitivity Analysis

PubMed Central

Casadebaig, Pierre; Zheng, Bangyou; Chapman, Scott; Huth, Neil; Faivre, Robert; Chenu, Karine

2016-01-01

A crop can be viewed as a complex system with outputs (e.g. yield) that are affected by inputs of genetic, physiology, pedo-climatic and management information. Application of numerical methods for model exploration assist in evaluating the major most influential inputs, providing the simulation model is a credible description of the biological system. A sensitivity analysis was used to assess the simulated impact on yield of a suite of traits involved in major processes of crop growth and development, and to evaluate how the simulated value of such traits varies across environments and in relation to other traits (which can be interpreted as a virtual change in genetic background). The study focused on wheat in Australia, with an emphasis on adaptation to low rainfall conditions. A large set of traits (90) was evaluated in a wide target population of environments (4 sites × 125 years), management practices (3 sowing dates × 3 nitrogen fertilization levels) and CO2 (2 levels). The Morris sensitivity analysis method was used to sample the parameter space and reduce computational requirements, while maintaining a realistic representation of the targeted trait × environment × management landscape (∼ 82 million individual simulations in total). The patterns of parameter × environment × management interactions were investigated for the most influential parameters, considering a potential genetic range of +/- 20% compared to a reference cultivar. Main (i.e. linear) and interaction (i.e. non-linear and interaction) sensitivity indices calculated for most of APSIM-Wheat parameters allowed the identification of 42 parameters substantially impacting yield in most target environments. Among these, a subset of parameters related to phenology, resource acquisition, resource use efficiency and biomass allocation were identified as potential candidates for crop (and model) improvement. PMID:26799483

Assessment of the Potential Impacts of Wheat Plant Traits across Environments by Combining Crop Modeling and Global Sensitivity Analysis.

PubMed

Casadebaig, Pierre; Zheng, Bangyou; Chapman, Scott; Huth, Neil; Faivre, Robert; Chenu, Karine

2016-01-01

A crop can be viewed as a complex system with outputs (e.g. yield) that are affected by inputs of genetic, physiology, pedo-climatic and management information. Application of numerical methods for model exploration assist in evaluating the major most influential inputs, providing the simulation model is a credible description of the biological system. A sensitivity analysis was used to assess the simulated impact on yield of a suite of traits involved in major processes of crop growth and development, and to evaluate how the simulated value of such traits varies across environments and in relation to other traits (which can be interpreted as a virtual change in genetic background). The study focused on wheat in Australia, with an emphasis on adaptation to low rainfall conditions. A large set of traits (90) was evaluated in a wide target population of environments (4 sites × 125 years), management practices (3 sowing dates × 3 nitrogen fertilization levels) and CO2 (2 levels). The Morris sensitivity analysis method was used to sample the parameter space and reduce computational requirements, while maintaining a realistic representation of the targeted trait × environment × management landscape (∼ 82 million individual simulations in total). The patterns of parameter × environment × management interactions were investigated for the most influential parameters, considering a potential genetic range of +/- 20% compared to a reference cultivar. Main (i.e. linear) and interaction (i.e. non-linear and interaction) sensitivity indices calculated for most of APSIM-Wheat parameters allowed the identification of 42 parameters substantially impacting yield in most target environments. Among these, a subset of parameters related to phenology, resource acquisition, resource use efficiency and biomass allocation were identified as potential candidates for crop (and model) improvement.

Variants in TTC25 affect autistic trait in patients with autism spectrum disorder and general population.

PubMed

Vojinovic, Dina; Brison, Nathalie; Ahmad, Shahzad; Noens, Ilse; Pappa, Irene; Karssen, Lennart C; Tiemeier, Henning; van Duijn, Cornelia M; Peeters, Hilde; Amin, Najaf

2017-08-01

Autism spectrum disorder (ASD) is a highly heritable neurodevelopmental disorder with a complex genetic architecture. To identify genetic variants underlying ASD, we performed single-variant and gene-based genome-wide association studies using a dense genotyping array containing over 2.3 million single-nucleotide variants in a discovery sample of 160 families with at least one child affected with non-syndromic ASD using a binary (ASD yes/no) phenotype and a quantitative autistic trait. Replication of the top findings was performed in Psychiatric Genomics Consortium and Erasmus Rucphen Family (ERF) cohort study. Significant association of quantitative autistic trait was observed with the TTC25 gene at 17q21.2 (effect size=10.2, P-value=3.4 × 10 -7 ) in the gene-based analysis. The gene also showed nominally significant association in the cohort-based ERF study (effect=1.75, P-value=0.05). Meta-analysis of discovery and replication improved the association signal (P-value meta =1.5 × 10 -8 ). No genome-wide significant signal was observed in the single-variant analysis of either the binary ASD phenotype or the quantitative autistic trait. Our study has identified a novel gene TTC25 to be associated with quantitative autistic trait in patients with ASD. The replication of association in a cohort-based study and the effect estimate suggest that variants in TTC25 may also be relevant for broader ASD phenotype in the general population. TTC25 is overexpressed in frontal cortex and testis and is known to be involved in cilium movement and thus an interesting candidate gene for autistic trait.

Integrated genomics and molecular breeding approaches for dissecting the complex quantitative traits in crop plants.

PubMed

Kujur, Alice; Saxena, Maneesha S; Bajaj, Deepak; Laxmi; Parida, Swarup K

2013-12-01

The enormous population growth, climate change and global warming are now considered major threats to agriculture and world's food security. To improve the productivity and sustainability of agriculture, the development of highyielding and durable abiotic and biotic stress-tolerant cultivars and/climate resilient crops is essential. Henceforth, understanding the molecular mechanism and dissection of complex quantitative yield and stress tolerance traits is the prime objective in current agricultural biotechnology research. In recent years, tremendous progress has been made in plant genomics and molecular breeding research pertaining to conventional and next-generation whole genome, transcriptome and epigenome sequencing efforts, generation of huge genomic, transcriptomic and epigenomic resources and development of modern genomics-assisted breeding approaches in diverse crop genotypes with contrasting yield and abiotic stress tolerance traits. Unfortunately, the detailed molecular mechanism and gene regulatory networks controlling such complex quantitative traits is not yet well understood in crop plants. Therefore, we propose an integrated strategies involving available enormous and diverse traditional and modern -omics (structural, functional, comparative and epigenomics) approaches/resources and genomics-assisted breeding methods which agricultural biotechnologist can adopt/utilize to dissect and decode the molecular and gene regulatory networks involved in the complex quantitative yield and stress tolerance traits in crop plants. This would provide clues and much needed inputs for rapid selection of novel functionally relevant molecular tags regulating such complex traits to expedite traditional and modern marker-assisted genetic enhancement studies in target crop species for developing high-yielding stress-tolerant varieties.

Poly-Omic Prediction of Complex Traits: OmicKriging

PubMed Central

Wheeler, Heather E.; Aquino-Michaels, Keston; Gamazon, Eric R.; Trubetskoy, Vassily V.; Dolan, M. Eileen; Huang, R. Stephanie; Cox, Nancy J.; Im, Hae Kyung

2014-01-01

High-confidence prediction of complex traits such as disease risk or drug response is an ultimate goal of personalized medicine. Although genome-wide association studies have discovered thousands of well-replicated polymorphisms associated with a broad spectrum of complex traits, the combined predictive power of these associations for any given trait is generally too low to be of clinical relevance. We propose a novel systems approach to complex trait prediction, which leverages and integrates similarity in genetic, transcriptomic, or other omics-level data. We translate the omic similarity into phenotypic similarity using a method called Kriging, commonly used in geostatistics and machine learning. Our method called OmicKriging emphasizes the use of a wide variety of systems-level data, such as those increasingly made available by comprehensive surveys of the genome, transcriptome, and epigenome, for complex trait prediction. Furthermore, our OmicKriging framework allows easy integration of prior information on the function of subsets of omics-level data from heterogeneous sources without the sometimes heavy computational burden of Bayesian approaches. Using seven disease datasets from the Wellcome Trust Case Control Consortium (WTCCC), we show that OmicKriging allows simple integration of sparse and highly polygenic components yielding comparable performance at a fraction of the computing time of a recently published Bayesian sparse linear mixed model method. Using a cellular growth phenotype, we show that integrating mRNA and microRNA expression data substantially increases performance over either dataset alone. Using clinical statin response, we show improved prediction over existing methods. PMID:24799323

How powerful are summary-based methods for identifying expression-trait associations under different genetic architectures?

PubMed

Veturi, Yogasudha; Ritchie, Marylyn D

2018-01-01

Transcriptome-wide association studies (TWAS) have recently been employed as an approach that can draw upon the advantages of genome-wide association studies (GWAS) and gene expression studies to identify genes associated with complex traits. Unlike standard GWAS, summary level data suffices for TWAS and offers improved statistical power. Two popular TWAS methods include either (a) imputing the cis genetic component of gene expression from smaller sized studies (using multi-SNP prediction or MP) into much larger effective sample sizes afforded by GWAS - TWAS-MP or (b) using summary-based Mendelian randomization - TWAS-SMR. Although these methods have been effective at detecting functional variants, it remains unclear how extensive variability in the genetic architecture of complex traits and diseases impacts TWAS results. Our goal was to investigate the different scenarios under which these methods yielded enough power to detect significant expression-trait associations. In this study, we conducted extensive simulations based on 6000 randomly chosen, unrelated Caucasian males from Geisinger's MyCode population to compare the power to detect cis expression-trait associations (within 500 kb of a gene) using the above-described approaches. To test TWAS across varying genetic backgrounds we simulated gene expression and phenotype using different quantitative trait loci per gene and cis-expression /trait heritability under genetic models that differentiate the effect of causality from that of pleiotropy. For each gene, on a training set ranging from 100 to 1000 individuals, we either (a) estimated regression coefficients with gene expression as the response using five different methods: LASSO, elastic net, Bayesian LASSO, Bayesian spike-slab, and Bayesian ridge regression or (b) performed eQTL analysis. We then sampled with replacement 50,000, 150,000, and 300,000 individuals respectively from the testing set of the remaining 5000 individuals and conducted GWAS on each set. Subsequently, we integrated the GWAS summary statistics derived from the testing set with the weights (or eQTLs) derived from the training set to identify expression-trait associations using (a) TWAS-MP (b) TWAS-SMR (c) eQTL-based GWAS, or (d) standalone GWAS. Finally, we examined the power to detect functionally relevant genes using the different approaches under the considered simulation scenarios. In general, we observed great similarities among TWAS-MP methods although the Bayesian methods resulted in improved power in comparison to LASSO and elastic net as the trait architecture grew more complex while training sample sizes and expression heritability remained small. Finally, we observed high power under causality but very low to moderate power under pleiotropy.

MicroRNA-guided prioritization of genome-wide association signals reveals the importance of microRNA-target gene networks for complex traits in cattle.

PubMed

Fang, Lingzhao; Sørensen, Peter; Sahana, Goutam; Panitz, Frank; Su, Guosheng; Zhang, Shengli; Yu, Ying; Li, Bingjie; Ma, Li; Liu, George; Lund, Mogens Sandø; Thomsen, Bo

2018-06-19

MicroRNAs (miRNA) are key modulators of gene expression and so act as putative fine-tuners of complex phenotypes. Here, we hypothesized that causal variants of complex traits are enriched in miRNAs and miRNA-target networks. First, we conducted a genome-wide association study (GWAS) for seven functional and milk production traits using imputed sequence variants (13~15 million) and >10,000 animals from three dairy cattle breeds, i.e., Holstein (HOL), Nordic red cattle (RDC) and Jersey (JER). Second, we analyzed for enrichments of association signals in miRNAs and their miRNA-target networks. Our results demonstrated that genomic regions harboring miRNA genes were significantly (P < 0.05) enriched with GWAS signals for milk production traits and mastitis, and that enrichments within miRNA-target gene networks were significantly higher than in random gene-sets for the majority of traits. Furthermore, most between-trait and across-breed correlations of enrichments with miRNA-target networks were significantly greater than with random gene-sets, suggesting pleiotropic effects of miRNAs. Intriguingly, genes that were differentially expressed in response to mammary gland infections were significantly enriched in the miRNA-target networks associated with mastitis. All these findings were consistent across three breeds. Collectively, our observations demonstrate the importance of miRNAs and their targets for the expression of complex traits.

Coordination and plasticity in leaf anatomical traits of invasive and native vine species.

PubMed

Osunkoya, Olusegun O; Boyne, Richard; Scharaschkin, Tanya

2014-09-01

• Plant invasiveness can be promoted by higher values of adaptive traits (e.g., photosynthetic capacity, biomass accumulation), greater plasticity and coordination of these traits, and by higher and positive relative influence of these functionalities on fitness, such as increasing reproductive output. However, the data set for this premise rarely includes linkages between epidermal-stomatal traits, leaf internal anatomy, and physiological performance.• Three ecological pairs of invasive vs. noninvasive (native) woody vine species of South-East Queensland, Australia were investigated for trait differences in leaf morphology and anatomy under varying light intensity. The linkages of these traits with physiological performance (e.g., water-use efficiency, photosynthesis, and leaf construction cost) and plant adaptive traits of specific leaf area, biomass, and relative growth rates were also explored.• Except for stomatal size, mean leaf anatomical traits differed significantly between the two groups. Plasticity of traits and, to a very limited extent, their phenotypic integration were higher in the invasive relative to the native species. ANOVA, ordination, and analysis of similarity suggest that for leaf morphology and anatomy, the three functional strategies contribute to the differences between the two groups in the order phenotypic plasticity > trait means > phenotypic integration.• The linkages demonstrated in the study between stomatal complex/gross anatomy and physiology are scarce in the ecological literature of plant invasiveness, but the findings suggest that leaf anatomical traits need to be considered routinely as part of weed species assessment and in the worldwide leaf economic spectrum. © 2014 Botanical Society of America, Inc.

Meta-analysis of Polyploid Cotton QTL Shows Unequal Contributions of Subgenomes to a Complex Network of Genes and Gene Clusters Implicated in Lint Fiber Development

PubMed Central

Rong, Junkang; Feltus, F. Alex; Waghmare, Vijay N.; Pierce, Gary J.; Chee, Peng W.; Draye, Xavier; Saranga, Yehoshua; Wright, Robert J.; Wilkins, Thea A.; May, O. Lloyd; Smith, C. Wayne; Gannaway, John R.; Wendel, Jonathan F.; Paterson, Andrew H.

2007-01-01

QTL mapping experiments yield heterogeneous results due to the use of different genotypes, environments, and sampling variation. Compilation of QTL mapping results yields a more complete picture of the genetic control of a trait and reveals patterns in organization of trait variation. A total of 432 QTL mapped in one diploid and 10 tetraploid interspecific cotton populations were aligned using a reference map and depicted in a CMap resource. Early demonstrations that genes from the non-fiber-producing diploid ancestor contribute to tetraploid lint fiber genetics gain further support from multiple populations and environments and advanced-generation studies detecting QTL of small phenotypic effect. Both tetraploid subgenomes contribute QTL at largely non-homeologous locations, suggesting divergent selection acting on many corresponding genes before and/or after polyploid formation. QTL correspondence across studies was only modest, suggesting that additional QTL for the target traits remain to be discovered. Crosses between closely-related genotypes differing by single-gene mutants yield profoundly different QTL landscapes, suggesting that fiber variation involves a complex network of interacting genes. Members of the lint fiber development network appear clustered, with cluster members showing heterogeneous phenotypic effects. Meta-analysis linked to synteny-based and expression-based information provides clues about specific genes and families involved in QTL networks. PMID:17565937

Meta-analysis of polyploid cotton QTL shows unequal contributions of subgenomes to a complex network of genes and gene clusters implicated in lint fiber development.

PubMed

Rong, Junkang; Feltus, F Alex; Waghmare, Vijay N; Pierce, Gary J; Chee, Peng W; Draye, Xavier; Saranga, Yehoshua; Wright, Robert J; Wilkins, Thea A; May, O Lloyd; Smith, C Wayne; Gannaway, John R; Wendel, Jonathan F; Paterson, Andrew H

2007-08-01

QTL mapping experiments yield heterogeneous results due to the use of different genotypes, environments, and sampling variation. Compilation of QTL mapping results yields a more complete picture of the genetic control of a trait and reveals patterns in organization of trait variation. A total of 432 QTL mapped in one diploid and 10 tetraploid interspecific cotton populations were aligned using a reference map and depicted in a CMap resource. Early demonstrations that genes from the non-fiber-producing diploid ancestor contribute to tetraploid lint fiber genetics gain further support from multiple populations and environments and advanced-generation studies detecting QTL of small phenotypic effect. Both tetraploid subgenomes contribute QTL at largely non-homeologous locations, suggesting divergent selection acting on many corresponding genes before and/or after polyploid formation. QTL correspondence across studies was only modest, suggesting that additional QTL for the target traits remain to be discovered. Crosses between closely-related genotypes differing by single-gene mutants yield profoundly different QTL landscapes, suggesting that fiber variation involves a complex network of interacting genes. Members of the lint fiber development network appear clustered, with cluster members showing heterogeneous phenotypic effects. Meta-analysis linked to synteny-based and expression-based information provides clues about specific genes and families involved in QTL networks.

Integrative approaches for large-scale transcriptome-wide association studies

PubMed Central

Gusev, Alexander; Ko, Arthur; Shi, Huwenbo; Bhatia, Gaurav; Chung, Wonil; Penninx, Brenda W J H; Jansen, Rick; de Geus, Eco JC; Boomsma, Dorret I; Wright, Fred A; Sullivan, Patrick F; Nikkola, Elina; Alvarez, Marcus; Civelek, Mete; Lusis, Aldons J.; Lehtimäki, Terho; Raitoharju, Emma; Kähönen, Mika; Seppälä, Ilkka; Raitakari, Olli T.; Kuusisto, Johanna; Laakso, Markku; Price, Alkes L.; Pajukanta, Päivi; Pasaniuc, Bogdan

2016-01-01

Many genetic variants influence complex traits by modulating gene expression, thus altering the abundance levels of one or multiple proteins. Here, we introduce a powerful strategy that integrates gene expression measurements with summary association statistics from large-scale genome-wide association studies (GWAS) to identify genes whose cis-regulated expression is associated to complex traits. We leverage expression imputation to perform a transcriptome wide association scan (TWAS) to identify significant expression-trait associations. We applied our approaches to expression data from blood and adipose tissue measured in ~3,000 individuals overall. We imputed gene expression into GWAS data from over 900,000 phenotype measurements to identify 69 novel genes significantly associated to obesity-related traits (BMI, lipids, and height). Many of the novel genes are associated with relevant phenotypes in the Hybrid Mouse Diversity Panel. Our results showcase the power of integrating genotype, gene expression and phenotype to gain insights into the genetic basis of complex traits. PMID:26854917

Cross-Population Joint Analysis of eQTLs: Fine Mapping and Functional Annotation

PubMed Central

Wen, Xiaoquan; Luca, Francesca; Pique-Regi, Roger

2015-01-01

Mapping expression quantitative trait loci (eQTLs) has been shown as a powerful tool to uncover the genetic underpinnings of many complex traits at molecular level. In this paper, we present an integrative analysis approach that leverages eQTL data collected from multiple population groups. In particular, our approach effectively identifies multiple independent cis-eQTL signals that are consistent across populations, accounting for population heterogeneity in allele frequencies and linkage disequilibrium patterns. Furthermore, by integrating genomic annotations, our analysis framework enables high-resolution functional analysis of eQTLs. We applied our statistical approach to analyze the GEUVADIS data consisting of samples from five population groups. From this analysis, we concluded that i) jointly analysis across population groups greatly improves the power of eQTL discovery and the resolution of fine mapping of causal eQTL ii) many genes harbor multiple independent eQTLs in their cis regions iii) genetic variants that disrupt transcription factor binding are significantly enriched in eQTLs (p-value = 4.93 × 10-22). PMID:25906321

Neural Basis of Interpersonal Traits in Neurodegenerative Diseases

PubMed Central

Sollberger, Marc; Stanley, Christine M.; Wilson, Stephen M.; Gyurak, Anett; Beckman, Victoria; Growdon, Matthew; Jang, Jung; Weiner, Michael W.; Miller, Bruce L.; Rankin, Katherine P.

2009-01-01

Several functional and structural imaging studies have investigated the neural basis of personality in healthy adults, but human lesions studies are scarce. Personality changes are a common symptom in patients with neurodegenerative diseases like frontotemporal dementia (FTD) and semantic dementia (SD), allowing a unique window into the neural basis of personality. In this study, we used the Interpersonal Adjective Scales to investigate the structural basis of eight interpersonal traits (dominance, arrogance, coldness, introversion, submissiveness, ingenuousness, warmth, and extraversion) in 257 subjects: 214 patients with neurodegenerative diseases such as FTD, SD, progressive non-fluent aphasia, Alzheimer’s disease, amnestic mild cognitive impairment, corticobasal degeneration, and progressive supranuclear palsy and 43 healthy elderly people. Measures of interpersonal traits were correlated with regional atrophy pattern using voxel-based morphometry (VBM) analysis of structural MR images. Interpersonal traits mapped onto distinct brain regions depending on the degree to which they involved agency and affiliation. Interpersonal traits high in agency related to left dorsolateral prefrontal and left lateral frontopolar regions, whereas interpersonal traits high in affiliation related to right ventromedial prefrontal and right anteromedial temporal regions. Consistent with the existing literature on neural networks underlying social cognition, these results indicate that brain regions related to externally-focused, executive control-related processes underlie agentic interpersonal traits such as dominance, whereas brain regions related to internally-focused, emotion- and reward-related processes underlie affiliative interpersonal traits such as warmth. In addition, these findings indicate that interpersonal traits are subserved by complex neural networks rather than discrete anatomic areas. PMID:19540253

Association analysis for udder index and milking speed with imputed whole-genome sequence variants in Nordic Holstein cattle.

PubMed

Jardim, Júlia Gazzoni; Guldbrandtsen, Bernt; Lund, Mogens Sandø; Sahana, Goutam

2018-03-01

Genome-wide association testing facilitates the identification of genetic variants associated with complex traits. Mapping genes that promote genetic resistance to mastitis could reduce the cost of antibiotic use and enhance animal welfare and milk production by improving outcomes of breeding for udder health. Using imputed whole-genome sequence variants, we carried out association studies for 2 traits related to udder health, udder index, and milking speed in Nordic Holstein cattle. A total of 4,921 bulls genotyped with the BovineSNP50 BeadChip array were imputed to high-density genotypes (Illumina BovineHD BeadChip, Illumina, San Diego, CA) and, subsequently, to whole-genome sequence variants. An association analysis was carried out using a linear mixed model. Phenotypes used in the association analyses were deregressed breeding values. Multitrait meta-analysis was carried out for these 2 traits. We identified 10 and 8 chromosomes harboring markers that were significantly associated with udder index and milking speed, respectively. Strongest association signals were observed on chromosome 20 for udder index and chromosome 19 for milking speed. Multitrait meta-analysis identified 13 chromosomes harboring associated markers for the combination of udder index and milking speed. The associated region on chromosome 20 overlapped with earlier reported quantitative trait loci for similar traits in other cattle populations. Moreover, this region was located close to the FYB gene, which is involved in platelet activation and controls IL-2 expression; FYB is a strong candidate gene for udder health and worthy of further investigation. Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

The genetic architecture of maize (Zea mays L.) kernel weight determination.

PubMed

Alvarez Prado, Santiago; López, César G; Senior, M Lynn; Borrás, Lucas

2014-09-18

Individual kernel weight is an important trait for maize yield determination. We have identified genomic regions controlling this trait by using the B73xMo17 population; however, the effect of genetic background on control of this complex trait and its physiological components is not yet known. The objective of this study was to understand how genetic background affected our previous results. Two nested stable recombinant inbred line populations (N209xMo17 and R18xMo17) were designed for this purpose. A total of 408 recombinant inbred lines were genotyped and phenotyped at two environments for kernel weight and five other traits related to kernel growth and development. All traits showed very high and significant (P < 0.001) phenotypic variability and medium-to-high heritability (0.60-0.90). When N209xMo17 and R18xMo17 were analyzed separately, a total of 23 environmentally stable quantitative trait loci (QTL) and five epistatic interactions were detected for N209xMo17. For R18xMo17, 59 environmentally stable QTL and 17 epistatic interactions were detected. A joint analysis detected 14 stable QTL regardless of the genetic background. Between 57 and 83% of detected QTL were population specific, denoting medium-to-high genetic background effects. This percentage was dependent on the trait. A meta-analysis including our previous B73xMo17 results identified five relevant genomic regions deserving further characterization. In summary, our grain filling traits were dominated by small additive QTL with several epistatic and few environmental interactions and medium-to-high genetic background effects. This study demonstrates that the number of detected QTL and additive effects for different physiologically related grain filling traits need to be understood relative to the specific germplasm. Copyright © 2014 Alvarez Prado et al.

Rethinking plant functional types in Earth System Models: pan-tropical analysis of tree survival across environmental gradients

NASA Astrophysics Data System (ADS)

Johnson, D. J.; Needham, J.; Xu, C.; Davies, S. J.; Bunyavejchewin, S.; Giardina, C. P.; Condit, R.; Cordell, S.; Litton, C. M.; Hubbell, S.; Kassim, A. R. B.; Shawn, L. K. Y.; Nasardin, M. B.; Ong, P.; Ostertag, R.; Sack, L.; Tan, S. K. S.; Yap, S.; McDowell, N. G.; McMahon, S.

2016-12-01

Terrestrial carbon cycling is a function of the growth and survival of trees. Current model representations of tree growth and survival at a global scale rely on coarse plant functional traits that are parameterized very generally. In view of the large biodiversity in the tropical forests, it is important that we account for the functional diversity in order to better predict tropical forest responses to future climate changes. Several next generation Earth System Models are moving towards a size-structured, trait-based approach to modelling vegetation globally, but the challenge of which and how many traits are necessary to capture forest complexity remains. Additionally, the challenge of collecting sufficient trait data to describe the vast species richness of tropical forests is enormous. We propose a more fundamental approach to these problems by characterizing forests by their patterns of survival. We expect our approach to distill real-world tree survival into a reasonable number of functional types. Using 10 large-area tropical forest plots that span geographic, edaphic and climatic gradients, we model tree survival as a function of tree size for hundreds of species. We found surprisingly few categories of size-survival functions emerge. This indicates some fundamental strategies at play across diverse forests to constrain the range of possible size-survival functions. Initial cluster analysis indicates that four to eight functional forms are necessary to describe variation in size-survival relations. Temporal variation in size-survival functions can be related to local environmental variation, allowing us to parameterize how demographically similar groups of species respond to perturbations in the ecosystem. We believe this methodology will yield a synthetic approach to classifying forest systems that will greatly reduce uncertainty and complexity in global vegetation models.

Adrenal cortex expression quantitative trait loci in a German Holstein × Charolais cross.

PubMed

Brand, Bodo; Scheinhardt, Markus O; Friedrich, Juliane; Zimmer, Daisy; Reinsch, Norbert; Ponsuksili, Siriluck; Schwerin, Manfred; Ziegler, Andreas

2016-10-06

The importance of the adrenal gland in regard to lactation and reproduction in cattle has been recognized early. Caused by interest in animal welfare and the impact of stress on economically important traits in farm animals the adrenal gland and its function within the stress response is of increasing interest. However, the molecular mechanisms and pathways involved in stress-related effects on economically important traits in farm animals are not fully understood. Gene expression is an important mechanism underlying complex traits, and genetic variants affecting the transcript abundance are thought to influence the manifestation of an expressed phenotype. We therefore investigated the genetic background of adrenocortical gene expression by applying an adaptive linear rank test to identify genome-wide expression quantitative trait loci (eQTL) for adrenal cortex transcripts in cattle. A total of 10,986 adrenal cortex transcripts and 37,204 single nucleotide polymorphisms (SNPs) were analysed in 145 F2 cows of a Charolais × German Holstein cross. We identified 505 SNPs that were associated with the abundance of 129 transcripts, comprising 482 cis effects and 17 trans effects. These SNPs were located on all chromosomes but X, 16, 24 and 28. Associated genes are mainly involved in molecular and cellular functions comprising free radical scavenging, cellular compromise, cell morphology and lipid metabolism, including genes such as CYP27A1 and LHCGR that have been shown to affect economically important traits in cattle. In this study we showed that adrenocortical eQTL affect the expression of genes known to contribute to the phenotypic manifestation in cattle. Furthermore, some of the identified genes and related molecular pathways were previously shown to contribute to the phenotypic variation of behaviour, temperament and growth at the onset of puberty in the same population investigated here. We conclude that eQTL analysis appears to be a useful approach providing insight into the molecular and genetic background of complex traits in cattle and will help to understand molecular networks involved.

Constitutional mechanisms of vulnerability and resilience to nicotine dependence

PubMed Central

Hiroi, N; Scott, D

2017-01-01

The core nature of nicotine dependence is evident in wide variations in how individuals become and remain smokers. Individuals with pre-existing behavioral traits are more likely to develop nicotine dependence and experience difficulty when attempting to quit. Many molecular factors likely contribute to individual variations in the development of nicotine dependence and behavioral traits in complex manners. However, the identification of such molecules has been hampered by the phenotypic complexity of nicotine dependence and the complex ways molecules affect elements of nicotine dependence. We hypothesize that nicotine dependence is, in part, a result of interactions between nicotine and pre-existing behavioral traits. This perspective suggests that the identification of the molecular bases of such pre-existing behavioral traits will contribute to the development of effective methods for reducing smoking dependence and for helping smokers to quit. PMID:19238150

Identification, Replication, and Functional Fine-Mapping of Expression Quantitative Trait Loci in Primary Human Liver Tissue

PubMed Central

Stanaway, Ian B.; Gamazon, Eric R.; Smith, Joshua D.; Mirkov, Snezana; Ramirez, Jacqueline; Liu, Wanqing; Lin, Yvonne S.; Moloney, Cliona; Aldred, Shelly Force; Trinklein, Nathan D.; Schuetz, Erin; Nickerson, Deborah A.; Thummel, Ken E.; Rieder, Mark J.; Rettie, Allan E.; Ratain, Mark J.; Cox, Nancy J.; Brown, Christopher D.

2011-01-01

The discovery of expression quantitative trait loci (“eQTLs”) can help to unravel genetic contributions to complex traits. We identified genetic determinants of human liver gene expression variation using two independent collections of primary tissue profiled with Agilent (n = 206) and Illumina (n = 60) expression arrays and Illumina SNP genotyping (550K), and we also incorporated data from a published study (n = 266). We found that ∼30% of SNP-expression correlations in one study failed to replicate in either of the others, even at thresholds yielding high reproducibility in simulations, and we quantified numerous factors affecting reproducibility. Our data suggest that drug exposure, clinical descriptors, and unknown factors associated with tissue ascertainment and analysis have substantial effects on gene expression and that controlling for hidden confounding variables significantly increases replication rate. Furthermore, we found that reproducible eQTL SNPs were heavily enriched near gene starts and ends, and subsequently resequenced the promoters and 3′UTRs for 14 genes and tested the identified haplotypes using luciferase assays. For three genes, significant haplotype-specific in vitro functional differences correlated directly with expression levels, suggesting that many bona fide eQTLs result from functional variants that can be mechanistically isolated in a high-throughput fashion. Finally, given our study design, we were able to discover and validate hundreds of liver eQTLs. Many of these relate directly to complex traits for which liver-specific analyses are likely to be relevant, and we identified dozens of potential connections with disease-associated loci. These included previously characterized eQTL contributors to diabetes, drug response, and lipid levels, and they suggest novel candidates such as a role for NOD2 expression in leprosy risk and C2orf43 in prostate cancer. In general, the work presented here will be valuable for future efforts to precisely identify and functionally characterize genetic contributions to a variety of complex traits. PMID:21637794

Exploring DNA variant segregation types in pooled genome sequencing enables effective mapping of weeping trait in Malus

USDA-ARS?s Scientific Manuscript database

In recent years, next generation sequencing (NGS) based bulked segregant analysis (BSA) has become a powerful approach for allele discovery in non-model plant species. However, challenges remain, particular for out-crossing species with complex genomes. Here, the genetic control of a weeping bran...

Methods for meta-analysis of multiple traits using GWAS summary statistics.

PubMed

Ray, Debashree; Boehnke, Michael

2018-03-01

Genome-wide association studies (GWAS) for complex diseases have focused primarily on single-trait analyses for disease status and disease-related quantitative traits. For example, GWAS on risk factors for coronary artery disease analyze genetic associations of plasma lipids such as total cholesterol, LDL-cholesterol, HDL-cholesterol, and triglycerides (TGs) separately. However, traits are often correlated and a joint analysis may yield increased statistical power for association over multiple univariate analyses. Recently several multivariate methods have been proposed that require individual-level data. Here, we develop metaUSAT (where USAT is unified score-based association test), a novel unified association test of a single genetic variant with multiple traits that uses only summary statistics from existing GWAS. Although the existing methods either perform well when most correlated traits are affected by the genetic variant in the same direction or are powerful when only a few of the correlated traits are associated, metaUSAT is designed to be robust to the association structure of correlated traits. metaUSAT does not require individual-level data and can test genetic associations of categorical and/or continuous traits. One can also use metaUSAT to analyze a single trait over multiple studies, appropriately accounting for overlapping samples, if any. metaUSAT provides an approximate asymptotic P-value for association and is computationally efficient for implementation at a genome-wide level. Simulation experiments show that metaUSAT maintains proper type-I error at low error levels. It has similar and sometimes greater power to detect association across a wide array of scenarios compared to existing methods, which are usually powerful for some specific association scenarios only. When applied to plasma lipids summary data from the METSIM and the T2D-GENES studies, metaUSAT detected genome-wide significant loci beyond the ones identified by univariate analyses. Evidence from larger studies suggest that the variants additionally detected by our test are, indeed, associated with lipid levels in humans. In summary, metaUSAT can provide novel insights into the genetic architecture of a common disease or traits. © 2017 WILEY PERIODICALS, INC.

A new method of linkage analysis using LOD scores for quantitative traits supports linkage of monoamine oxidase activity to D17S250 in the Collaborative Study on the Genetics of Alcoholism pedigrees.

PubMed

Curtis, David; Knight, Jo; Sham, Pak C

2005-09-01

Although LOD score methods have been applied to diseases with complex modes of inheritance, linkage analysis of quantitative traits has tended to rely on non-parametric methods based on regression or variance components analysis. Here, we describe a new method for LOD score analysis of quantitative traits which does not require specification of a mode of inheritance. The technique is derived from the MFLINK method for dichotomous traits. A range of plausible transmission models is constructed, constrained to yield the correct population mean and variance for the trait but differing with respect to the contribution to the variance due to the locus under consideration. Maximized LOD scores under homogeneity and admixture are calculated, as is a model-free LOD score which compares the maximized likelihoods under admixture assuming linkage and no linkage. These LOD scores have known asymptotic distributions and hence can be used to provide a statistical test for linkage. The method has been implemented in a program called QMFLINK. It was applied to data sets simulated using a variety of transmission models and to a measure of monoamine oxidase activity in 105 pedigrees from the Collaborative Study on the Genetics of Alcoholism. With the simulated data, the results showed that the new method could detect linkage well if the true allele frequency for the trait was close to that specified. However, it performed poorly on models in which the true allele frequency was much rarer. For the Collaborative Study on the Genetics of Alcoholism data set only a modest overlap was observed between the results obtained from the new method and those obtained when the same data were analysed previously using regression and variance components analysis. Of interest is that D17S250 produced a maximized LOD score under homogeneity and admixture of 2.6 but did not indicate linkage using the previous methods. However, this region did produce evidence for linkage in a separate data set, suggesting that QMFLINK may have been able to detect a true linkage which was not picked up by the other methods. The application of model-free LOD score analysis to quantitative traits is novel and deserves further evaluation of its merits and disadvantages relative to other methods.

Small- and Large-Effect Quantitative Trait Locus Interactions Underlie Variation in Yeast Sporulation Efficiency

PubMed Central

Lorenz, Kim; Cohen, Barak A.

2012-01-01

Quantitative trait loci (QTL) with small effects on phenotypic variation can be difficult to detect and analyze. Because of this a large fraction of the genetic architecture of many complex traits is not well understood. Here we use sporulation efficiency in Saccharomyces cerevisiae as a model complex trait to identify and study small-effect QTL. In crosses where the large-effect quantitative trait nucleotides (QTN) have been genetically fixed we identify small-effect QTL that explain approximately half of the remaining variation not explained by the major effects. We find that small-effect QTL are often physically linked to large-effect QTL and that there are extensive genetic interactions between small- and large-effect QTL. A more complete understanding of quantitative traits will require a better understanding of the numbers, effect sizes, and genetic interactions of small-effect QTL. PMID:22942125

New insights from monogenic diabetes for “common” type 2 diabetes

PubMed Central

Tallapragada, Divya Sri Priyanka; Bhaskar, Seema; Chandak, Giriraj R.

2015-01-01

Boundaries between monogenic and complex genetic diseases are becoming increasingly blurred, as a result of better understanding of phenotypes and their genetic determinants. This had a large impact on the way complex disease genetics is now being investigated. Starting with conventional approaches like familial linkage, positional cloning and candidate genes strategies, the scope of complex disease genetics has grown exponentially with scientific and technological advances in recent times. Despite identification of multiple loci harboring common and rare variants associated with complex diseases, interpreting and evaluating their functional role has proven to be difficult. Information from monogenic diseases, especially related to the intermediate traits associated with complex diseases comes handy. The significant overlap between traits and phenotypes of monogenic diseases with related complex diseases provides a platform to understand the disease biology better. In this review, we would discuss about one such complex disease, type 2 diabetes, which shares marked similarity of intermediate traits with different forms of monogenic diabetes. PMID:26300908

Mapping of quantitative trait loci controlling adaptive traits in coastal Douglas-fir. III

Treesearch

Kathleen D. Jermstad; Daniel L. Bassoni; Keith S. Jech; Gary A. Ritchie; Nicholas C. Wheeler; David B. Neale

2003-01-01

Quantitative trait loci (QTL) were mapped in the woody perennial Douglas fir (Pseudotsuga menziesii var. menziesii [Mirb.] Franco) for complex traits controlling the timing of growth initiation and growth cessation. QTL were estimated under controlled environmental conditions to identify QTL interactions with photoperiod, moisture stress, winter chilling, and spring...

The Impact of Population Demography and Selection on the Genetic Architecture of Complex Traits

PubMed Central

Lohmueller, Kirk E.

2014-01-01

Population genetic studies have found evidence for dramatic population growth in recent human history. It is unclear how this recent population growth, combined with the effects of negative natural selection, has affected patterns of deleterious variation, as well as the number, frequency, and effect sizes of mutations that contribute risk to complex traits. Because researchers are performing exome sequencing studies aimed at uncovering the role of low-frequency variants in the risk of complex traits, this topic is of critical importance. Here I use simulations under population genetic models where a proportion of the heritability of the trait is accounted for by mutations in a subset of the exome. I show that recent population growth increases the proportion of nonsynonymous variants segregating in the population, but does not affect the genetic load relative to a population that did not expand. Under a model where a mutation's effect on a trait is correlated with its effect on fitness, rare variants explain a greater portion of the additive genetic variance of the trait in a population that has recently expanded than in a population that did not recently expand. Further, when using a single-marker test, for a given false-positive rate and sample size, recent population growth decreases the expected number of significant associations with the trait relative to the number detected in a population that did not expand. However, in a model where there is no correlation between a mutation's effect on fitness and the effect on the trait, common variants account for much of the additive genetic variance, regardless of demography. Moreover, here demography does not affect the number of significant associations detected. These findings suggest recent population history may be an important factor influencing the power of association tests and in accounting for the missing heritability of certain complex traits. PMID:24875776

Quantitative trait loci (QTLs) for water use and crop production traits co-locate with major QTL for tolerance to water deficit in a fine-mapping population of pearl millet (Pennisetum glaucum L. R.Br.).

PubMed

Tharanya, Murugesan; Kholova, Jana; Sivasakthi, Kaliamoorthy; Seghal, Deepmala; Hash, Charles Tom; Raj, Basker; Srivastava, Rakesh Kumar; Baddam, Rekha; Thirunalasundari, Thiyagarajan; Yadav, Rattan; Vadez, Vincent

2018-04-21

Four genetic regions associated with water use traits, measured at different levels of plant organization, and with agronomic traits were identified within a previously reported region for terminal water deficit adaptation on linkage group 2. Close linkages between these traits showed the value of phenotyping both for agronomic and secondary traits to better understand plant productive processes. Water saving traits are critical for water stress adaptation of pearl millet, whereas maximizing water use is key to the absence of stress. This research aimed at demonstrating the close relationship between traits measured at different levels of plant organization, some putatively involved in water stress adaptation, and those responsible for agronomic performance. A fine-mapping population of pearl millet, segregating for a previously identified quantitative trait locus (QTL) for adaptation to terminal drought stress on LG02, was phenotyped for traits at different levels of plant organization in different experimental environments (pot culture, high-throughput phenotyping platform, lysimeters, and field). The linkages among traits across the experimental systems were analysed using principal component analysis and QTL co-localization approach. Four regions within the LG02-QTL were found and revealed substantial co-mapping of water use and agronomic traits. These regions, identified across experimental systems, provided genetic evidence of the tight linkages between traits phenotyped at a lower level of plant organization and agronomic traits assessed in the field, therefore deepening our understanding of complex traits and then benefiting both geneticists and breeders. In short: (1) under no/mild stress conditions, increasing biomass and tiller production increased water use and eventually yield; (2) under severe stress conditions, water savings at vegetative stage, from lower plant vigour and fewer tillers in that population, led to more water available during grain filling, expression of stay-green phenotypes, and higher yield.

Quantitative trait loci for energy balance traits in an advanced intercross line derived from mice divergently selected for heat loss

PubMed Central

Nielsen, Merlyn K.; Thorn, Stephanie R.; Valdar, William; Pomp, Daniel

2014-01-01

Obesity in human populations, currently a serious health concern, is considered to be the consequence of an energy imbalance in which more energy in calories is consumed than is expended. We used interval mapping techniques to investigate the genetic basis of a number of energy balance traits in an F11 advanced intercross population of mice created from an original intercross of lines selected for increased and decreased heat loss. We uncovered a total of 137 quantitative trait loci (QTLs) for these traits at 41 unique sites on 18 of the 20 chromosomes in the mouse genome, with X-linked QTLs being most prevalent. Two QTLs were found for the selection target of heat loss, one on distal chromosome 1 and another on proximal chromosome 2. The number of QTLs affecting the various traits generally was consistent with previous estimates of heritabilities in the same population, with the most found for two bone mineral traits and the least for feed intake and several body composition traits. QTLs were generally additive in their effects, and some, especially those affecting the body weight traits, were sex-specific. Pleiotropy was extensive within trait groups (body weights, adiposity and organ weight traits, bone traits) and especially between body composition traits adjusted and not adjusted for body weight at sacrifice. Nine QTLs were found for one or more of the adiposity traits, five of which appeared to be unique. The confidence intervals among all QTLs averaged 13.3 Mb, much smaller than usually observed in an F2 cross, and in some cases this allowed us to make reasonable inferences about candidate genes underlying these QTLs. This study combined QTL mapping with genetic parameter analysis in a large segregating population, and has advanced our understanding of the genetic architecture of complex traits related to obesity. PMID:24918027

Genetic Architecture of Hybrid Male Sterility in Drosophila: Analysis of Intraspecies Variation for Interspecies Isolation

PubMed Central

Reed, Laura K.; LaFlamme, Brooke A.; Markow, Therese A.

2008-01-01

Background The genetic basis of postzygotic isolation is a central puzzle in evolutionary biology. Evolutionary forces causing hybrid sterility or inviability act on the responsible genes while they still are polymorphic, thus we have to study these traits as they arise, before isolation is complete. Methodology/Principal Findings Isofemale strains of D. mojavensis vary significantly in their production of sterile F1 sons when females are crossed to D. arizonae males. We took advantage of the intraspecific polymorphism, in a novel design, to perform quantitative trait locus (QTL) mapping analyses directly on F1 hybrid male sterility itself. We found that the genetic architecture of the polymorphism for hybrid male sterility (HMS) in the F1 is complex, involving multiple QTL, epistasis, and cytoplasmic effects. Conclusions/Significance The role of extensive intraspecific polymorphism, multiple QTL, and epistatic interactions in HMS in this young species pair shows that HMS is arising as a complex trait in this system. Directional selection alone would be unlikely to maintain polymorphism at multiple loci, thus we hypothesize that directional selection is unlikely to be the only evolutionary force influencing postzygotic isolation. PMID:18728782

Informatics and machine learning to define the phenotype.

PubMed

Basile, Anna Okula; Ritchie, Marylyn DeRiggi

2018-03-01

For the past decade, the focus of complex disease research has been the genotype. From technological advancements to the development of analysis methods, great progress has been made. However, advances in our definition of the phenotype have remained stagnant. Phenotype characterization has recently emerged as an exciting area of informatics and machine learning. The copious amounts of diverse biomedical data that have been collected may be leveraged with data-driven approaches to elucidate trait-related features and patterns. Areas covered: In this review, the authors discuss the phenotype in traditional genetic associations and the challenges this has imposed.Approaches for phenotype refinement that can aid in more accurate characterization of traits are also discussed. Further, the authors highlight promising machine learning approaches for establishing a phenotype and the challenges of electronic health record (EHR)-derived data. Expert commentary: The authors hypothesize that through unsupervised machine learning, data-driven approaches can be used to define phenotypes rather than relying on expert clinician knowledge. Through the use of machine learning and an unbiased set of features extracted from clinical repositories, researchers will have the potential to further understand complex traits and identify patient subgroups. This knowledge may lead to more preventative and precise clinical care.

Modelling the Interplay between Lifestyle Factors and Genetic Predisposition on Markers of Type 2 Diabetes Mellitus Risk.

PubMed

Walker, Celia G; Solis-Trapala, Ivonne; Holzapfel, Christina; Ambrosini, Gina L; Fuller, Nicholas R; Loos, Ruth J F; Hauner, Hans; Caterson, Ian D; Jebb, Susan A

2015-01-01

The risk of developing type 2 diabetes mellitus (T2DM) is determined by a complex interplay involving lifestyle factors and genetic predisposition. Despite this, many studies do not consider the relative contributions of this complex array of factors to identify relationships which are important in progression or prevention of complex diseases. We aimed to describe the integrated effect of a number of lifestyle changes (weight, diet and physical activity) in the context of genetic susceptibility, on changes in glycaemic traits in overweight or obese participants following 12-months of a weight management programme. A sample of 353 participants from a behavioural weight management intervention were included in this study. A graphical Markov model was used to describe the impact of the intervention, by dividing the effects into various pathways comprising changes in proportion of dietary saturated fat, physical activity and weight loss, and a genetic predisposition score (T2DM-GPS), on changes in insulin sensitivity (HOMA-IR), insulin secretion (HOMA-B) and short and long term glycaemia (glucose and HbA1c). We demonstrated the use of graphical Markov modelling to identify the importance and interrelationships of a number of possible variables changed as a result of a lifestyle intervention, whilst considering fixed factors such as genetic predisposition, on changes in traits. Paths which led to weight loss and change in dietary saturated fat were important factors in the change of all glycaemic traits, whereas the T2DM-GPS only made a significant direct contribution to changes in HOMA-IR and plasma glucose after considering the effects of lifestyle factors. This analysis shows that modifiable factors relating to body weight, diet, and physical activity are more likely to impact on glycaemic traits than genetic predisposition during a behavioural intervention.

Genetic factors controlling wool shedding in a composite Easycare sheep flock.

PubMed

Matika, O; Bishop, S C; Pong-Wong, R; Riggio, V; Headon, D J

2013-12-01

Historically, sheep have been selectively bred for desirable traits including wool characteristics. However, recent moves towards extensive farming and reduced farm labour have seen a renewed interest in Easycare breeds. The aim of this study was to quantify the underlying genetic architecture of wool shedding in an Easycare flock. Wool shedding scores were collected from 565 pedigreed commercial Easycare sheep from 2002 to 2010. The wool scoring system was based on a 10-point (0-9) scale, with score 0 for animals retaining full fleece and 9 for those completely shedding. DNA was sampled from 200 animals of which 48 with extreme phenotypes were genotyped using a 50-k SNP chip. Three genetic analyses were performed: heritability analysis, complex segregation analysis to test for a major gene hypothesis and a genome-wide association study to map regions in the genome affecting the trait. Phenotypes were treated as a continuous or binary variable and categories. High estimates of heritability (0.80 when treated as a continuous, 0.65-0.75 as binary and 0.75 as categories) for shedding were obtained from linear mixed model analyses. Complex segregation analysis gave similar estimates (0.80 ± 0.06) to those above with additional evidence for a major gene with dominance effects. Mixed model association analyses identified four significant (P < 0.05) SNPs. Further analyses of these four SNPs in all 200 animals revealed that one of the SNPs displayed dominance effects similar to those obtained from the complex segregation analyses. In summary, we found strong genetic control for wool shedding, demonstrated the possibility of a single putative dominant gene controlling this trait and identified four SNPs that may be in partial linkage disequilibrium with gene(s) controlling shedding. © 2013 University of Edinburgh, Animal Genetics © 2013 Stichting International Foundation for Animal Genetics.

Genetic correlations among and between wool, growth and reproduction traits in Merino sheep.

PubMed

Safari, E; Fogarty, N M; Gilmour, A R; Atkins, K D; Mortimer, S I; Swan, A A; Brien, F D; Greeff, J C; van der Werf, J H J

2007-04-01

Data from seven research resource flocks across Australia were combined to provide accurate estimates of genetic correlations among production traits in Merino sheep. The flocks represented contemporary Australian Merino fine, medium and broad wool strains over the past 30 years. Over 110,000 records were available for analysis for each of the major wool traits, and 50,000 records for reproduction and growth traits with over 2700 sires and 25,000 dams. Individual models developed from the single trait analyses were extended to the various combinations of two-trait models to obtain genetic correlations among six wool traits [clean fleece weight (CFW), greasy fleece weight, fibre diameter (FD), yield, coefficient of variation of fibre diameter and standard deviation of fibre diameter], four growth traits [birth weight, weaning weight, yearling weight (YWT), and hogget weight] and four reproduction traits [fertility, litter size, lambs born per ewe joined, lambs weaned per ewe joined (LW/EJ)]. This study has provided for the first time a comprehensive matrix of genetic correlations among these 14 wool, growth and reproduction traits. The large size of the data set has also provided estimates with very low standard errors. A moderate positive genetic correlation was observed between CFW and FD (0.29 +/- 0.02). YWT was positively correlated with CFW (0.23 +/- 0.04), FD (0.17 +/- 0.04) and LWEJ (0.58 +/- 0.06), while LW/EJ was negatively correlated with CFW (-0.26 +/- 0.05) and positively correlated with FD (0.06 +/- 0.04) and LS (0.68 +/- 0.04). These genetic correlations, together with the estimates of heritability and other parameters provide the basis for more accurate prediction of outcomes in complex sheep-breeding programmes designed to improve several traits.

From genotype to phenotype: unraveling the complexities of cold adaptation in forest trees

Treesearch

Glenn T. Howe; Sally N. Aitken; David B. Neale; Kathleen D. Jermstad; Nicholas C. Wheeler; Tony H.H Chen

2003-01-01

Adaptation to winter cold in temperate and boreal trees involves complex genetic, physiological, and developmental processes. Genecological studies demonstrate the existence of steep genetic clines for cold adaptation traits in relation to environmental (mostly temperature related) gradients. Population differentiation is generally stronger for cold adaptation traits...

Genome size evolution in Ontario ferns (Polypodiidae): evolutionary correlations with cell size, spore size, and habitat type and an absence of genome downsizing.

PubMed

Henry, Thomas A; Bainard, Jillian D; Newmaster, Steven G

2014-10-01

Genome size is known to correlate with a number of traits in angiosperms, but less is known about the phenotypic correlates of genome size in ferns. We explored genome size variation in relation to a suite of morphological and ecological traits in ferns. Thirty-six fern taxa were collected from wild populations in Ontario, Canada. 2C DNA content was measured using flow cytometry. We tested for genome downsizing following polyploidy using a phylogenetic comparative analysis to explore the correlation between 1Cx DNA content and ploidy. There was no compelling evidence for the occurrence of widespread genome downsizing during the evolution of Ontario ferns. The relationship between genome size and 11 morphological and ecological traits was explored using a phylogenetic principal component regression analysis. Genome size was found to be significantly associated with cell size, spore size, spore type, and habitat type. These results are timely as past and recent studies have found conflicting support for the association between ploidy/genome size and spore size in fern polyploid complexes; this study represents the first comparative analysis of the trend across a broad taxonomic group of ferns.

Strategies for mapping heterogeneous recessive traits by allele-sharing methods.

PubMed Central

Feingold, E; Siegmund, D O

1997-01-01

We investigate strategies for detecting linkage of recessive and partially recessive traits, using sibling pairs and inbred individuals. We assume that a genomewide search is being conducted and that locus heterogeneity of the trait is likely. For sibling pairs, we evaluate the efficiency of different statistics under the assumption that one does not know the true degree of recessiveness of the trait. We recommend a sibling-pair statistic that is a linear compromise between two previously suggested statistics. We also compare the power of sibling pairs to that of more distant relatives, such as cousins. For inbred individuals, we evaluate the power of offspring of different types of matings and compare them to sibling pairs. Over a broad range of trait etiologies, sibling pairs are more powerful than inbred individuals, but for traits caused by very rare alleles, particularly in the case of heterogeneity, inbred individuals can be much more powerful. The models we develop can also be used to examine specific situations other than those we look at. We present this analysis in the idealized context of a dense set of highly polymorphic markers. In general, incorporation of real-world complexities makes inbred individuals, particularly offspring of distant relatives, look slightly less useful than our results imply. PMID:9106544

Missing heritability in the tails of quantitative traits? A simulation study on the impact of slightly altered true genetic models.

PubMed

Pütter, Carolin; Pechlivanis, Sonali; Nöthen, Markus M; Jöckel, Karl-Heinz; Wichmann, Heinz-Erich; Scherag, André

2011-01-01

Genome-wide association studies have identified robust associations between single nucleotide polymorphisms and complex traits. As the proportion of phenotypic variance explained is still limited for most of the traits, larger and larger meta-analyses are being conducted to detect additional associations. Here we investigate the impact of the study design and the underlying assumption about the true genetic effect in a bimodal mixture situation on the power to detect associations. We performed simulations of quantitative phenotypes analysed by standard linear regression and dichotomized case-control data sets from the extremes of the quantitative trait analysed by standard logistic regression. Using linear regression, markers with an effect in the extremes of the traits were almost undetectable, whereas analysing extremes by case-control design had superior power even for much smaller sample sizes. Two real data examples are provided to support our theoretical findings and to explore our mixture and parameter assumption. Our findings support the idea to re-analyse the available meta-analysis data sets to detect new loci in the extremes. Moreover, our investigation offers an explanation for discrepant findings when analysing quantitative traits in the general population and in the extremes. Copyright © 2011 S. Karger AG, Basel.

Fear of dental pain in Italian children: child personality traits and parental dental fear.

PubMed

D'Alessandro, Giovanni; Alkhamis, Nadia; Mattarozzi, Katia; Mazzetti, Michela; Piana, Gabriela

2016-06-01

Dental anxiety could impede dental treatment in children. Evidence shows that parents' fear of dentists contributes to children's anxiety towards dentists. The aim of the present study was to determine whether and to what extent: a) parents' anxiety and depression personality traits, b) parent's dental fear, and c) child personality traits can predict children's dental anxiety in an Italian population. One hundred and four children (5-14 years old) and one of their parents participated in the study. Well-known and validated questionnaires were administered to children (MCDASf, CFSS-DS, TAD) and parents (FDPQ, STAI Y1, Y2, and BDI-II). Dental anxiety is significantly associated with the anxiety personality trait and depression of the child and with parental fear of dental pain. A hierarchical regression analysis showed that, regardless of age and gender, the best predictor of child dental anxiety is parent's fear of dental pain, rather than relatively stable temperaments of the child. In line with the literature concerning adults, these findings highlight the children dental anxiety as a complex phenomena consisting of different components, including the child's personality traits (anxiety trait and depression) and parents' dental fear. Clinical implications of this evidence are discussed. © 2015 American Association of Public Health Dentistry.

Genetic Map Construction and Quantitative Trait Locus (QTL) Detection of Growth-Related Traits in Litopenaeus vannamei for Selective Breeding Applications

PubMed Central

Andriantahina, Farafidy; Liu, Xiaolin; Huang, Hao

2013-01-01

Growth is a priority trait from the point of view of genetic improvement. Molecular markers linked to quantitative trait loci (QTL) have been regarded as useful for marker-assisted selection (MAS) in complex traits as growth. Using an intermediate F2 cross of slow and fast growth parents, a genetic linkage map of Pacific whiteleg shrimp, Litopenaeusvannamei , based on amplified fragment length polymorphisms (AFLP) and simple sequence repeats (SSR) markers was constructed. Meanwhile, QTL analysis was performed for growth-related traits. The linkage map consisted of 451 marker loci (429 AFLPs and 22 SSRs) which formed 49 linkage groups with an average marker space of 7.6 cM; they spanned a total length of 3627.6 cM, covering 79.50% of estimated genome size. 14 QTLs were identified for growth-related traits, including three QTLs for body weight (BW), total length (TL) and partial carapace length (PCL), two QTLs for body length (BL), one QTL for first abdominal segment depth (FASD), third abdominal segment depth (TASD) and first abdominal segment width (FASW), which explained 2.62 to 61.42% of phenotypic variation. Moreover, comparison of linkage maps between L . vannamei and Penaeus japonicus was applied, providing a new insight into the genetic base of QTL affecting the growth-related traits. The new results will be useful for conducting MAS breeding schemes in L . vannamei . PMID:24086466

Image-Based High-Throughput Field Phenotyping of Crop Roots1[W][OPEN

PubMed Central

Bucksch, Alexander; Burridge, James; York, Larry M.; Das, Abhiram; Nord, Eric; Weitz, Joshua S.; Lynch, Jonathan P.

2014-01-01

Current plant phenotyping technologies to characterize agriculturally relevant traits have been primarily developed for use in laboratory and/or greenhouse conditions. In the case of root architectural traits, this limits phenotyping efforts, largely, to young plants grown in specialized containers and growth media. Hence, novel approaches are required to characterize mature root systems of older plants grown under actual soil conditions in the field. Imaging methods able to address the challenges associated with characterizing mature root systems are rare due, in part, to the greater complexity of mature root systems, including the larger size, overlap, and diversity of root components. Our imaging solution combines a field-imaging protocol and algorithmic approach to analyze mature root systems grown in the field. Via two case studies, we demonstrate how image analysis can be utilized to estimate localized root traits that reliably capture heritable architectural diversity as well as environmentally induced architectural variation of both monocot and dicot plants. In the first study, we show that our algorithms and traits (including 13 novel traits inaccessible to manual estimation) can differentiate nine maize (Zea mays) genotypes 8 weeks after planting. The second study focuses on a diversity panel of 188 cowpea (Vigna unguiculata) genotypes to identify which traits are sufficient to differentiate genotypes even when comparing plants whose harvesting date differs up to 14 d. Overall, we find that automatically derived traits can increase both the speed and reproducibility of the trait estimation pipeline under field conditions. PMID:25187526

Identification of genotyping-by-sequencing sequence tags associated with milling performance and end-use quality traits in hard red spring wheat (Triticum aestivum L.)

USDA-ARS?s Scientific Manuscript database

Wheat quality is defined by culinary end-uses and processing characteristics. Wheat breeders are interested to identify quantitative trait loci for grain, milling, and end-use quality traits because it is imperative to understand the genetic complexity underlying quantitatively inherited traits to ...

Relationship between Anxiety and Burnout among Chinese Physicians: A Moderated Mediation Model

PubMed Central

Zhou, Jiawei; Yang, Yanjie; Qiu, Xiaohui; Yang, Xiuxian; Pan, Hui; Ban, Bo; Qiao, Zhengxue; Wang, Lin; Wang, Wenbo

2016-01-01

Objective The main goal of this research was to investigate the complex relationships among coping styles, personality, burnout, and anxiety using a moderated mediation analysis. Methods A random cluster sampling procedure was used to select a total of 1274 physicians from two tertiary grade A hospitals in Heilongjiang Province, which is located in northeast China. The Zung Self-Rating Anxiety Scale (SAS), Chinese Maslach Burnout Inventory (CMBI), Chinese version of the EPQ-revised Short Scale, and the Trait Coping Style Questionnaire (TCSQ) were used to gather data. Moderated mediation analysis was used in this study; it was executed using the PROCESS macro so that the mediators and moderator could function together in the same model. Results The prevalence of anxiety symptoms among the physicians was 31%, and there were no differences between the sexes. The results showed that positive and negative coping styles partially mediated the association between burnout and anxiety symptoms in physicians. The mediated effect of positive coping styles was moderated by Eysenck’s Psychoticism traits. Conclusions Personality traits moderate the strength of the relationships between burnout and anxiety mediated by positive coping styles; however, personality traits do not moderate the strength of the relationships between burnout and anxiety mediated by negative coping styles. PMID:27479002

Linkage Analysis of a Model Quantitative Trait in Humans: Finger Ridge Count Shows Significant Multivariate Linkage to 5q14.1

PubMed Central

Medland, Sarah E; Loesch, Danuta Z; Mdzewski, Bogdan; Zhu, Gu; Montgomery, Grant W; Martin, Nicholas G

2007-01-01

The finger ridge count (a measure of pattern size) is one of the most heritable complex traits studied in humans and has been considered a model human polygenic trait in quantitative genetic analysis. Here, we report the results of the first genome-wide linkage scan for finger ridge count in a sample of 2,114 offspring from 922 nuclear families. Both univariate linkage to the absolute ridge count (a sum of all the ridge counts on all ten fingers), and multivariate linkage analyses of the counts on individual fingers, were conducted. The multivariate analyses yielded significant linkage to 5q14.1 (Logarithm of odds [LOD] = 3.34, pointwise-empirical p-value = 0.00025) that was predominantly driven by linkage to the ring, index, and middle fingers. The strongest univariate linkage was to 1q42.2 (LOD = 2.04, point-wise p-value = 0.002, genome-wide p-value = 0.29). In summary, the combination of univariate and multivariate results was more informative than simple univariate analyses alone. Patterns of quantitative trait loci factor loadings consistent with developmental fields were observed, and the simple pleiotropic model underlying the absolute ridge count was not sufficient to characterize the interrelationships between the ridge counts of individual fingers. PMID:17907812

Does structural complexity determine the morphology of assemblages? An experimental test on three continents.

PubMed

Gibb, Heloise; Parr, Catherine L

2013-01-01

Understanding how species will respond to global change depends on our ability to distinguish generalities from idiosyncrasies. For diverse, but poorly known taxa, such as insects, species traits may provide a short-cut to predicting species turnover. We tested whether ant traits respond consistently to habitat complexity across geographically independent ant assemblages, using an experimental approach and baits. We repeated our study in six paired simple and complex habitats on three continents with distinct ant faunas. We also compared traits amongst ants with different foraging strategies. We hypothesised that ants would be larger, broader, have longer legs and more dorsally positioned eyes in simpler habitats. In agreement with predictions, ants had longer femurs and dorsally positioned eyes in simple habitats. This pattern was most pronounced for ants that discovered resources. Body size and pronotum width responded as predicted for experimental treatments, but were inconsistent across continents. Monopolising ants were smaller, with shorter femurs than those that occupied or discovered resources. Consistent responses for several traits suggest that many, but not all, aspects of morphology respond predictably to habitat complexity, and that foraging strategy is linked with morphology. Some traits thus have the potential to be used to predict the direction of species turnover, changes in foraging strategy and, potentially, evolution in response to changes in habitat structure.

Biodiversity in a complex world: consolidation and progress in functional biodiversity research.

PubMed

Hillebrand, Helmut; Matthiessen, Birte

2009-12-01

The global decline of biodiversity caused by human domination of ecosystems worldwide is supposed to alter important process rates and state variables in these ecosystems. However, there is considerable debate on the prevalence and importance of biodiversity effects on ecosystem function (BDEF). Here, we argue that much of the debate stems from two major shortcomings. First, most studies do not directly link the traits leading to increased or decreased function to the traits needed for species coexistence and dominance. We argue that implementing a trait-based approach and broadening the perception of diversity to include trait dissimilarity or trait divergence will result in more realistic predictions on the consequences of altered biodiversity. Second, the empirical and theoretical studies do not reflect the complexity of natural ecosystems, which makes it difficult to transfer the results to natural situations of species loss. We review how different aspects of complexity (trophic structure, multifunctionality, spatial or temporal heterogeneity, and spatial population dynamics) alter our perception of BDEF. We propose future research avenues concisely testing whether acknowledging this complexity will strengthen the observed biodiversity effects. Finally, we propose that a major future task is to disentangle biodiversity effects on ecosystem function from direct changes in function due to human alterations of abiotic constraints.

High Density Linkage Map Construction and QTL Detection for Three Silique-Related Traits in Orychophragmus violaceus Derived Brassica napus Population.

PubMed

Yang, Yi; Shen, Yusen; Li, Shunda; Ge, Xianhong; Li, Zaiyun

2017-01-01

Seeds per silique (SS), seed weight (SW), and silique length (SL) are important determinant traits of seed yield potential in rapeseed ( Brassica napus L.), and are controlled by naturally occurring quantitative trait loci (QTLs). Mapping QTLs to narrow chromosomal regions provides an effective means of characterizing the genetic basis of these complex traits. Orychophragmus violaceus is a crucifer with long siliques, many SS, and heavy seeds. A novel B. napus introgression line with many SS was previously selected from multiple crosses ( B. rapa ssp. chinesis × O. violaceus ) × B. napus . In present study, a doubled haploid (DH) population with 167 lines was established from a cross between the introgression line and a line with far fewer SS, in order to detect QTLs for silique-related traits. By screening with a Brassica 60K single nucleotide polymorphism (SNP) array, a high-density linkage map consisting of 1,153 bins and spanning a cumulative length of 2,209.1 cM was constructed, using 12,602 high-quality polymorphic SNPs in the DH population. The average recombination bin densities of the A and C subgenomes were 1.7 and 2.4 cM, respectively. 45 QTLs were identified for the three traits in all, which explained 4.0-34.4% of the total phenotypic variation; 20 of them were integrated into three unique QTLs by meta-analysis. These unique QTLs revealed a significant positive correlation between SS and SL and a significant negative correlation between SW and SS, and were mapped onto the linkage groups A05, C08, and C09. A trait-by-trait meta-analysis revealed eight, four, and seven consensus QTLs for SS, SW, and SL, respectively, and five major QTLs ( cqSS.A09b, cqSS.C09, cqSW.A05, cqSW.C09 , and cqSL.C09 ) were identified. Five, three, and four QTLs for SS, SW, and SL, respectively, might be novel QTLs because of the existence of alien genetic loci for these traits in the alien introgression. Thirty-eight candidate genes underlying nine QTLs for silique-related traits were identified.

Sunflower Hybrid Breeding: From Markers to Genomic Selection

PubMed Central

Dimitrijevic, Aleksandra; Horn, Renate

2018-01-01

In sunflower, molecular markers for simple traits as, e.g., fertility restoration, high oleic acid content, herbicide tolerance or resistances to Plasmopara halstedii, Puccinia helianthi, or Orobanche cumana have been successfully used in marker-assisted breeding programs for years. However, agronomically important complex quantitative traits like yield, heterosis, drought tolerance, oil content or selection for disease resistance, e.g., against Sclerotinia sclerotiorum have been challenging and will require genome-wide approaches. Plant genetic resources for sunflower are being collected and conserved worldwide that represent valuable resources to study complex traits. Sunflower association panels provide the basis for genome-wide association studies, overcoming disadvantages of biparental populations. Advances in technologies and the availability of the sunflower genome sequence made novel approaches on the whole genome level possible. Genotype-by-sequencing, and whole genome sequencing based on next generation sequencing technologies facilitated the production of large amounts of SNP markers for high density maps as well as SNP arrays and allowed genome-wide association studies and genomic selection in sunflower. Genome wide or candidate gene based association studies have been performed for traits like branching, flowering time, resistance to Sclerotinia head and stalk rot. First steps in genomic selection with regard to hybrid performance and hybrid oil content have shown that genomic selection can successfully address complex quantitative traits in sunflower and will help to speed up sunflower breeding programs in the future. To make sunflower more competitive toward other oil crops higher levels of resistance against pathogens and better yield performance are required. In addition, optimizing plant architecture toward a more complex growth type for higher plant densities has the potential to considerably increase yields per hectare. Integrative approaches combining omic technologies (genomics, transcriptomics, proteomics, metabolomics and phenomics) using bioinformatic tools will facilitate the identification of target genes and markers for complex traits and will give a better insight into the mechanisms behind the traits. PMID:29387071

Comparison of statistical tests for association between rare variants and binary traits.

PubMed

Bacanu, Silviu-Alin; Nelson, Matthew R; Whittaker, John C

2012-01-01

Genome-wide association studies have found thousands of common genetic variants associated with a wide variety of diseases and other complex traits. However, a large portion of the predicted genetic contribution to many traits remains unknown. One plausible explanation is that some of the missing variation is due to the effects of rare variants. Nonetheless, the statistical analysis of rare variants is challenging. A commonly used method is to contrast, within the same region (gene), the frequency of minor alleles at rare variants between cases and controls. However, this strategy is most useful under the assumption that the tested variants have similar effects. We previously proposed a method that can accommodate heterogeneous effects in the analysis of quantitative traits. Here we extend this method to include binary traits that can accommodate covariates. We use simulations for a variety of causal and covariate impact scenarios to compare the performance of the proposed method to standard logistic regression, C-alpha, SKAT, and EREC. We found that i) logistic regression methods perform well when the heterogeneity of the effects is not extreme and ii) SKAT and EREC have good performance under all tested scenarios but they can be computationally intensive. Consequently, it would be more computationally desirable to use a two-step strategy by (i) selecting promising genes by faster methods and ii) analyzing selected genes using SKAT/EREC. To select promising genes one can use (1) regression methods when effect heterogeneity is assumed to be low and the covariates explain a non-negligible part of trait variability, (2) C-alpha when heterogeneity is assumed to be large and covariates explain a small fraction of trait's variability and (3) the proposed trend and heterogeneity test when the heterogeneity is assumed to be non-trivial and the covariates explain a large fraction of trait variability.

Genomic study and Medical Subject Headings enrichment analysis of early pregnancy rate and antral follicle numbers in Nelore heifers

USDA-ARS?s Scientific Manuscript database

Zebu animals (Bos indicus) are known to take longer to reach puberty when compared to taurine animals (Bos taurus), limiting the supply of animals for harvest or breeding and impacting profitability. Genomic information can be a helpful tool to better understand complex traits, and improve genetic g...

A consensus framework map of durum wheat (Triticum durum Desf.) suitable for linkage disequilibrium analysis and genome-wide association mapping

USDA-ARS?s Scientific Manuscript database

Genomics applications in durum (Triticum durum Desf.) wheat have the potential to boost exploitation of genetic resources and to advance understanding of the genetics of important complex traits (e.g. resilience to environmental and biotic stresses). A dense and accurate consensus map specific for ...

Ensemble learning of QTL models improves prediction of complex traits

USDA-ARS?s Scientific Manuscript database

Quantitative trait locus (QTL) models can provide useful insights into trait genetic architecture because of their straightforward interpretability, but are less useful for genetic prediction due to difficulty in including the effects of numerous small effect loci without overfitting. Tight linkage ...

The genetic architecture of a complex ecological trait: host plant use in the specialist moth, HELIOTHIS SUBFLEXA

USDA-ARS?s Scientific Manuscript database

The study of the genetic basis of ecological adaptation remains in its infancy, and most studies have focused on phenotypically simple traits. Host plant use by herbivorous insects is phenotypically complex. While research has illuminated the evolutionary determinants of host use, knowledge of its...

The Genetic Architecture of Complex Traits in Teosinte (Zea mays ssp. parviglumis): New Evidence from Association Mapping

USDA-ARS?s Scientific Manuscript database

Our previous association analyses showed that variation at major regulatory genes contributes to standing variation for complex traits in Balsas teosinte, the progenitor of maize. This study expands our previous association mapping effort in teosinte by testing 123 markers in 52 candidate genes for ...

Genetic analysis of safflower domestication

PubMed Central

2014-01-01

Background Safflower (Carthamus tinctorius L.) is an oilseed crop in the Compositae (a.k.a. Asteraceae) that is valued for its oils rich in unsaturated fatty acids. Here, we present an analysis of the genetic architecture of safflower domestication and compare our findings to those from sunflower (Helianthus annuus L.), an independently domesticated oilseed crop within the same family. We mapped quantitative trait loci (QTL) underlying 24 domestication-related traits in progeny from a cross between safflower and its wild progenitor, Carthamus palaestinus Eig. Also, we compared QTL positions in safflower against those that have been previously identified in cultivated x wild sunflower crosses to identify instances of colocalization. Results We mapped 61 QTL, the vast majority of which (59) exhibited minor or moderate phenotypic effects. The two large-effect QTL corresponded to one each for flower color and leaf spininess. A total of 14 safflower QTL colocalized with previously reported sunflower QTL for the same traits. Of these, QTL for three traits (days to flower, achene length, and number of selfed seed) had cultivar alleles that conferred effects in the same direction in both species. Conclusions As has been observed in sunflower, and unlike many other crops, our results suggest that the genetics of safflower domestication is quite complex. Moreover, our comparative mapping results indicate that safflower and sunflower exhibit numerous instances of QTL colocalization, suggesting that parallel trait transitions during domestication may have been driven, at least in part, by parallel genotypic evolution at some of the same underlying genes. PMID:24502326

Differentially expressed genes during the imbibition of dormant and after-ripened seeds - a reverse genetics approach.

PubMed

Yazdanpanah, Farzaneh; Hanson, Johannes; Hilhorst, Henk W M; Bentsink, Leónie

2017-09-11

Seed dormancy, defined as the incapability of a viable seed to germinate under favourable conditions, is an important trait in nature and agriculture. Despite extensive research on dormancy and germination, many questions about the molecular mechanisms controlling these traits remain unanswered, likely due to its genetic complexity and the large environmental effects which are characteristic of these quantitative traits. To boost research towards revealing mechanisms in the control of seed dormancy and germination we depend on the identification of genes controlling those traits. We used transcriptome analysis combined with a reverse genetics approach to identify genes that are prominent for dormancy maintenance and germination in imbibed seeds of Arabidopsis thaliana. Comparative transcriptomics analysis was employed on freshly harvested (dormant) and after-ripened (AR; non-dormant) 24-h imbibed seeds of four different DELAY OF GERMINATION near isogenic lines (DOGNILs) and the Landsberg erecta (Ler) wild type with varying levels of primary dormancy. T-DNA knock-out lines of the identified genes were phenotypically investigated for their effect on dormancy and AR. We identified conserved sets of 46 and 25 genes which displayed higher expression in seeds of all dormant and all after-ripened DOGNILs and Ler, respectively. Knock-out mutants in these genes showed dormancy and germination related phenotypes. Most of the identified genes had not been implicated in seed dormancy or germination. This research will be useful to further decipher the molecular mechanisms by which these important ecological and commercial traits are regulated.

Handling Complexity in Animal and Plant Science Research-From Single to Functional Traits: Are We There Yet?

PubMed

Roberts, Jessica; Power, Aoife; Chandra, Shaneel; Chapman, James; Cozzolino, Daniel

2018-05-28

The current knowledge of the main factors governing livestock, crop and plant quality as well as yield in different species is incomplete. For example, this can be evidenced by the persistence of benchmark crop varieties for many decades in spite of the gains achieved over the same period. In recent years, it has been demonstrated that molecular breeding based on DNA markers has led to advances in breeding (animal and crops). However, these advances are not in the way that it was anticipated initially by the researcher in the field. According to several scientists, one of the main reasons for this was related to the evidence that complex target traits such as grain yield, composition or nutritional quality depend on multiple factors in addition to genetics. Therefore, some questions need to be asked: are the current approaches in molecular genetics the most appropriate to deal with complex traits such as yield or quality? Are the current tools for phenotyping complex traits enough to differentiate among genotypes? Do we need to change the way that data is collected and analysed?

Genetic differentiation in life history traits and thermal stress performance across a heterogeneous dune landscape in Arabidopsis lyrata.

PubMed

Wos, Guillaume; Willi, Yvonne

2018-05-26

Over very short spatial scales, the habitat of a species can differ in multiple abiotic and biotic factors. These factors may impose natural selection on several traits and can cause genetic differentiation within a population. We studied multivariate genetic differentiation in a plant species of a sand dune landscape by linking environmental variation with differences in genotypic trait values and gene expression levels to find traits and candidate genes of microgeographical adaptation. Maternal seed families of Arabidopsis lyrata were collected in Saugatuck Dunes State Park, Michigan, USA, and environmental parameters were recorded at each collection site. Offspring plants were raised in climate chambers and exposed to one of three temperature treatments: regular occurrence of frost, heat, or constant control conditions. Several traits were assessed: plant growth, time to flowering, and frost and heat resistance. The strongest trait-environment association was between a fast switch to sexual reproduction and weaker growth under frost, and growing in the open, away from trees. The second strongest association was between the trait combination of small plant size and early flowering under control conditions combined with large size under frost, and the combination of environmental conditions of growing close to trees, at low vegetation cover, on dune bottoms. Gene expression analysis by RNA-seq revealed candidate genes involved in multivariate trait differentiation. The results support the hypothesis that in natural populations, many environmental factors impose selection, and that they affect multiple traits, with the relative direction of trait change being complex. The results highlight that heterogeneity in the selection environment over small spatial scales is a main driver of the maintenance of adaptive genetic variation within populations.

Social traits, social networks and evolutionary biology.

PubMed

Fisher, D N; McAdam, A G

2017-12-01

The social environment is both an important agent of selection for most organisms, and an emergent property of their interactions. As an aggregation of interactions among members of a population, the social environment is a product of many sets of relationships and so can be represented as a network or matrix. Social network analysis in animals has focused on why these networks possess the structure they do, and whether individuals' network traits, representing some aspect of their social phenotype, relate to their fitness. Meanwhile, quantitative geneticists have demonstrated that traits expressed in a social context can depend on the phenotypes and genotypes of interacting partners, leading to influences of the social environment on the traits and fitness of individuals and the evolutionary trajectories of populations. Therefore, both fields are investigating similar topics, yet have arrived at these points relatively independently. We review how these approaches are diverged, and yet how they retain clear parallelism and so strong potential for complementarity. This demonstrates that, despite separate bodies of theory, advances in one might inform the other. Techniques in network analysis for quantifying social phenotypes, and for identifying community structure, should be useful for those studying the relationship between individual behaviour and group-level phenotypes. Entering social association matrices into quantitative genetic models may also reduce bias in heritability estimates, and allow the estimation of the influence of social connectedness on trait expression. Current methods for measuring natural selection in a social context explicitly account for the fact that a trait is not necessarily the property of a single individual, something the network approaches have not yet considered when relating network metrics to individual fitness. Harnessing evolutionary models that consider traits affected by genes in other individuals (i.e. indirect genetic effects) provides the potential to understand how entire networks of social interactions in populations influence phenotypes and predict how these traits may evolve. By theoretical integration of social network analysis and quantitative genetics, we hope to identify areas of compatibility and incompatibility and to direct research efforts towards the most promising areas. Continuing this synthesis could provide important insights into the evolution of traits expressed in a social context and the evolutionary consequences of complex and nuanced social phenotypes. © 2017 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2017 European Society For Evolutionary Biology.

Geostatistics for spatial genetic structures: study of wild populations of perennial ryegrass.

PubMed

Monestiez, P; Goulard, M; Charmet, G

1994-04-01

Methods based on geostatistics were applied to quantitative traits of agricultural interest measured on a collection of 547 wild populations of perennial ryegrass in France. The mathematical background of these methods, which resembles spatial autocorrelation analysis, is briefly described. When a single variable is studied, the spatial structure analysis is similar to spatial autocorrelation analysis, and a spatial prediction method, called "kriging", gives a filtered map of the spatial pattern over all the sampled area. When complex interactions of agronomic traits with different evaluation sites define a multivariate structure for the spatial analysis, geostatistical methods allow the spatial variations to be broken down into two main spatial structures with ranges of 120 km and 300 km, respectively. The predicted maps that corresponded to each range were interpreted as a result of the isolation-by-distance model and as a consequence of selection by environmental factors. Practical collecting methodology for breeders may be derived from such spatial structures.

Local Genetic Correlation Gives Insights into the Shared Genetic Architecture of Complex Traits.

PubMed

Shi, Huwenbo; Mancuso, Nicholas; Spendlove, Sarah; Pasaniuc, Bogdan

2017-11-02

Although genetic correlations between complex traits provide valuable insights into epidemiological and etiological studies, a precise quantification of which genomic regions disproportionately contribute to the genome-wide correlation is currently lacking. Here, we introduce ρ-HESS, a technique to quantify the correlation between pairs of traits due to genetic variation at a small region in the genome. Our approach requires GWAS summary data only and makes no distributional assumption on the causal variant effect sizes while accounting for linkage disequilibrium (LD) and overlapping GWAS samples. We analyzed large-scale GWAS summary data across 36 quantitative traits, and identified 25 genomic regions that contribute significantly to the genetic correlation among these traits. Notably, we find 6 genomic regions that contribute to the genetic correlation of 10 pairs of traits that show negligible genome-wide correlation, further showcasing the power of local genetic correlation analyses. Finally, we report the distribution of local genetic correlations across the genome for 55 pairs of traits that show putative causal relationships. Copyright © 2017 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

Genomic and Genetic Diversity within the Pseudomonas fluorescens Complex

PubMed Central

Garrido-Sanz, Daniel; Meier-Kolthoff, Jan P.; Göker, Markus; Martín, Marta; Rivilla, Rafael; Redondo-Nieto, Miguel

2016-01-01

The Pseudomonas fluorescens complex includes Pseudomonas strains that have been taxonomically assigned to more than fifty different species, many of which have been described as plant growth-promoting rhizobacteria (PGPR) with potential applications in biocontrol and biofertilization. So far the phylogeny of this complex has been analyzed according to phenotypic traits, 16S rDNA, MLSA and inferred by whole-genome analysis. However, since most of the type strains have not been fully sequenced and new species are frequently described, correlation between taxonomy and phylogenomic analysis is missing. In recent years, the genomes of a large number of strains have been sequenced, showing important genomic heterogeneity and providing information suitable for genomic studies that are important to understand the genomic and genetic diversity shown by strains of this complex. Based on MLSA and several whole-genome sequence-based analyses of 93 sequenced strains, we have divided the P. fluorescens complex into eight phylogenomic groups that agree with previous works based on type strains. Digital DDH (dDDH) identified 69 species and 75 subspecies within the 93 genomes. The eight groups corresponded to clustering with a threshold of 31.8% dDDH, in full agreement with our MLSA. The Average Nucleotide Identity (ANI) approach showed inconsistencies regarding the assignment to species and to the eight groups. The small core genome of 1,334 CDSs and the large pan-genome of 30,848 CDSs, show the large diversity and genetic heterogeneity of the P. fluorescens complex. However, a low number of strains were enough to explain most of the CDSs diversity at core and strain-specific genomic fractions. Finally, the identification and analysis of group-specific genome and the screening for distinctive characters revealed a phylogenomic distribution of traits among the groups that provided insights into biocontrol and bioremediation applications as well as their role as PGPR. PMID:26915094

Social support and anxiety in pregnant and postpartum women: a secondary analysis.

PubMed

Aktan, Nadine M

2012-05-01

The purpose of study was to examine relationships between social support and anxiety in pregnant and postpartum women. Hypotheses were that there will be an inverse relationship between social support and anxiety in pregnancy and the postpartum. The sample consisted of 177 women. The Personal Resource Questionnaire (PRQ) 85-Part 2 and the State Trait Anxiety Inventory (STAI) were used. The STAI demonstrated coefficient alphas from 0.90 to 0.93; 0.87 and 0.93 for the PRQ. All five hypotheses were supported--social support and state anxiety in pregnancy (r = -.308, p = .000) and trait anxiety (r = -.420, p = .000) and postpartum (r = -.497, p = .000) and social support and state anxiety in the postpartum (r = -.375, p = .000) and trait anxiety (r = -.276, p = .013). Findings are relevant to advance nursing theory, science, and practice. Nurses must understand these complex processes to effectively intervene.

Model-Based Linkage Analysis of a Quantitative Trait.

PubMed

Song, Yeunjoo E; Song, Sunah; Schnell, Audrey H

2017-01-01

Linkage Analysis is a family-based method of analysis to examine whether any typed genetic markers cosegregate with a given trait, in this case a quantitative trait. If linkage exists, this is taken as evidence in support of a genetic basis for the trait. Historically, linkage analysis was performed using a binary disease trait, but has been extended to include quantitative disease measures. Quantitative traits are desirable as they provide more information than binary traits. Linkage analysis can be performed using single-marker methods (one marker at a time) or multipoint (using multiple markers simultaneously). In model-based linkage analysis the genetic model for the trait of interest is specified. There are many software options for performing linkage analysis. Here, we use the program package Statistical Analysis for Genetic Epidemiology (S.A.G.E.). S.A.G.E. was chosen because it also includes programs to perform data cleaning procedures and to generate and test genetic models for a quantitative trait, in addition to performing linkage analysis. We demonstrate in detail the process of running the program LODLINK to perform single-marker analysis, and MLOD to perform multipoint analysis using output from SEGREG, where SEGREG was used to determine the best fitting statistical model for the trait.

Open reading frames associated with cancer in the dark matter of the human genome.

PubMed

Delgado, Ana Paula; Brandao, Pamela; Chapado, Maria Julia; Hamid, Sheilin; Narayanan, Ramaswamy

2014-01-01

The uncharacterized proteins (open reading frames, ORFs) in the human genome offer an opportunity to discover novel targets for cancer. A systematic analysis of the dark matter of the human proteome for druggability and biomarker discovery is crucial to mining the genome. Numerous data mining tools are available to mine these ORFs to develop a comprehensive knowledge base for future target discovery and validation. Using the Genetic Association Database, the ORFs of the human dark matter proteome were screened for evidence of association with neoplasms. The Phenome-Genome Integrator tool was used to establish phenotypic association with disease traits including cancer. Batch analysis of the tools for protein expression analysis, gene ontology and motifs and domains was used to characterize the ORFs. Sixty-two ORFs were identified for neoplasm association. The expression Quantitative Trait Loci (eQTL) analysis identified thirteen ORFs related to cancer traits. Protein expression, motifs and domain analysis and genome-wide association studies verified the relevance of these OncoORFs in diverse tumors. The OncoORFs are also associated with a wide variety of human diseases and disorders. Our results link the OncoORFs to diverse diseases and disorders. This suggests a complex landscape of the uncharacterized proteome in human diseases. These results open the dark matter of the proteome to novel cancer target research. Copyright© 2014, International Institute of Anticancer Research (Dr. John G. Delinasios), All rights reserved.

Neuronal Correlates of Individual Differences in the Big Five Personality Traits: Evidences from Cortical Morphology and Functional Homogeneity.

PubMed

Li, Ting; Yan, Xu; Li, Yuan; Wang, Junjie; Li, Qiang; Li, Hong; Li, Junfeng

2017-01-01

There have been many neuroimaging studies of human personality traits, and it have already provided glimpse into the neurobiology of complex traits. And most of previous studies adopt voxel-based morphology (VBM) analysis to explore the brain-personality mechanism from two levels (vertex and regional based), the findings are mixed with great inconsistencies and the brain-personality relations are far from a full understanding. Here, we used a new method of surface-based morphology (SBM) analysis, which provides better alignment of cortical landmarks to generate about the associations between cortical morphology and the personality traits across 120 healthy individuals at both vertex and regional levels. While to further reveal local functional correlates of the morphology-personality relationships, we related surface-based functional homogeneity measures to the regions identified in the regional-based SBM correlation. Vertex-wise analysis revealed that people with high agreeableness exhibited larger areas in the left superior temporal gyrus. Based on regional parcellation we found that extroversion was negatively related with the volume of the left lateral occipito-temporal gyrus and agreeableness was negatively associated with the sulcus depth of the left superior parietal lobule. Moreover, increased regional homogeneity in the left lateral occipito-temporal gyrus is related to the scores of extroversion, and increased regional homogeneity in the left superior parietal lobule is related to the scores of agreeableness. These findings provide supporting evidence of a link between personality and brain structural mysteries with a method of SBM, and further suggest that local functional homogeneity of personality traits has neurobiological relevance that is likely based on anatomical substrates.

A systems-genetics approach and data mining tool to assist in the discovery of genes underlying complex traits in Oryza sativa.

PubMed

Ficklin, Stephen P; Feltus, Frank Alex

2013-01-01

Many traits of biological and agronomic significance in plants are controlled in a complex manner where multiple genes and environmental signals affect the expression of the phenotype. In Oryza sativa (rice), thousands of quantitative genetic signals have been mapped to the rice genome. In parallel, thousands of gene expression profiles have been generated across many experimental conditions. Through the discovery of networks with real gene co-expression relationships, it is possible to identify co-localized genetic and gene expression signals that implicate complex genotype-phenotype relationships. In this work, we used a knowledge-independent, systems genetics approach, to discover a high-quality set of co-expression networks, termed Gene Interaction Layers (GILs). Twenty-two GILs were constructed from 1,306 Affymetrix microarray rice expression profiles that were pre-clustered to allow for improved capture of gene co-expression relationships. Functional genomic and genetic data, including over 8,000 QTLs and 766 phenotype-tagged SNPs (p-value < = 0.001) from genome-wide association studies, both covering over 230 different rice traits were integrated with the GILs. An online systems genetics data-mining resource, the GeneNet Engine, was constructed to enable dynamic discovery of gene sets (i.e. network modules) that overlap with genetic traits. GeneNet Engine does not provide the exact set of genes underlying a given complex trait, but through the evidence of gene-marker correspondence, co-expression, and functional enrichment, site visitors can identify genes with potential shared causality for a trait which could then be used for experimental validation. A set of 2 million SNPs was incorporated into the database and serve as a potential set of testable biomarkers for genes in modules that overlap with genetic traits. Herein, we describe two modules found using GeneNet Engine, one with significant overlap with the trait amylose content and another with significant overlap with blast disease resistance.

A Systems-Genetics Approach and Data Mining Tool to Assist in the Discovery of Genes Underlying Complex Traits in Oryza sativa

PubMed Central

Ficklin, Stephen P.; Feltus, Frank Alex

2013-01-01

Many traits of biological and agronomic significance in plants are controlled in a complex manner where multiple genes and environmental signals affect the expression of the phenotype. In Oryza sativa (rice), thousands of quantitative genetic signals have been mapped to the rice genome. In parallel, thousands of gene expression profiles have been generated across many experimental conditions. Through the discovery of networks with real gene co-expression relationships, it is possible to identify co-localized genetic and gene expression signals that implicate complex genotype-phenotype relationships. In this work, we used a knowledge-independent, systems genetics approach, to discover a high-quality set of co-expression networks, termed Gene Interaction Layers (GILs). Twenty-two GILs were constructed from 1,306 Affymetrix microarray rice expression profiles that were pre-clustered to allow for improved capture of gene co-expression relationships. Functional genomic and genetic data, including over 8,000 QTLs and 766 phenotype-tagged SNPs (p-value < = 0.001) from genome-wide association studies, both covering over 230 different rice traits were integrated with the GILs. An online systems genetics data-mining resource, the GeneNet Engine, was constructed to enable dynamic discovery of gene sets (i.e. network modules) that overlap with genetic traits. GeneNet Engine does not provide the exact set of genes underlying a given complex trait, but through the evidence of gene-marker correspondence, co-expression, and functional enrichment, site visitors can identify genes with potential shared causality for a trait which could then be used for experimental validation. A set of 2 million SNPs was incorporated into the database and serve as a potential set of testable biomarkers for genes in modules that overlap with genetic traits. Herein, we describe two modules found using GeneNet Engine, one with significant overlap with the trait amylose content and another with significant overlap with blast disease resistance. PMID:23874666

Interspecies synteny mapping identifies a quantitative trait locus for bone mineral density on human chromosome Xp22.

PubMed

Parsons, Claire A; Mroczkowski, H Joel; McGuigan, Fiona E A; Albagha, Omar M E; Manolagas, Stavros; Reid, David M; Ralston, Stuart H; Shmookler Reis, Robert J

2005-11-01

Bone mineral density (BMD) is a complex trait with a strong genetic component and an important predictor of osteoporotic fracture risk. Here we report the use of a cross-species strategy to identify genes that regulate BMD, proceeding from quantitative trait mapping in mice to association mapping of the syntenic region in the human genome. We identified a quantitative trait locus (QTL) on the mouse X-chromosome for post-maturity change in spine BMD in a cross of SAMP6 and AKR/J mice and conducted association mapping of the syntenic region on human chromosome Xp22. We studied 76 single nucleotide polymorphisms (SNP) from the human region in two sets of DNA pools prepared from individuals with lumbar spine-BMD (LS-BMD) values falling into the top and bottom 13th percentiles of a population-based study of 3100 post-menopausal women. This procedure identified a region of significant association for two adjacent SNP (rs234494 and rs234495) within the Xp22 locus (P<0.001). Individual genotyping for rs234494 in the BMD pools confirmed the presence of an association for alleles (P=0.018) and genotypes (P=0.008). Analysis of rs234494 and rs234495 in 1053 women derived from the same population who were not selected for BMD values showed an association with LS-BMD for rs234495 (P=0.01) and for haplotypes defined by both SNP (P=0.002). Our study illustrates that interspecies synteny can be used to identify and refine QTL for complex traits and represents the first example where a human QTL for BMD regulation has been mapped using this approach.

Genome-enabled Modeling of Microbial Biogeochemistry using a Trait-based Approach. Does Increasing Metabolic Complexity Increase Predictive Capabilities?

NASA Astrophysics Data System (ADS)

King, E.; Karaoz, U.; Molins, S.; Bouskill, N.; Anantharaman, K.; Beller, H. R.; Banfield, J. F.; Steefel, C. I.; Brodie, E.

2015-12-01

The biogeochemical functioning of ecosystems is shaped in part by genomic information stored in the subsurface microbiome. Cultivation-independent approaches allow us to extract this information through reconstruction of thousands of genomes from a microbial community. Analysis of these genomes, in turn, gives an indication of the organisms present and their functional roles. However, metagenomic analyses can currently deliver thousands of different genomes that range in abundance/importance, requiring the identification and assimilation of key physiologies and metabolisms to be represented as traits for successful simulation of subsurface processes. Here we focus on incorporating -omics information into BioCrunch, a genome-informed trait-based model that represents the diversity of microbial functional processes within a reactive transport framework. This approach models the rate of nutrient uptake and the thermodynamics of coupled electron donors and acceptors for a range of microbial metabolisms including heterotrophs and chemolithotrophs. Metabolism of exogenous substrates fuels catabolic and anabolic processes, with the proportion of energy used for cellular maintenance, respiration, biomass development, and enzyme production based upon dynamic intracellular and environmental conditions. This internal resource partitioning represents a trade-off against biomass formation and results in microbial community emergence across a fitness landscape. Biocrunch was used here in simulations that included organisms and metabolic pathways derived from a dataset of ~1200 non-redundant genomes reflecting a microbial community in a floodplain aquifer. Metagenomic data was directly used to parameterize trait values related to growth and to identify trait linkages associated with respiration, fermentation, and key enzymatic functions such as plant polymer degradation. Simulations spanned a range of metabolic complexities and highlight benefits originating from simulations including a larger number of organisms that more appropriately reflect the in situ microbial community.

ATHENA: A knowledge-based hybrid backpropagation-grammatical evolution neural network algorithm for discovering epistasis among quantitative trait Loci

PubMed Central

2010-01-01

Background Growing interest and burgeoning technology for discovering genetic mechanisms that influence disease processes have ushered in a flood of genetic association studies over the last decade, yet little heritability in highly studied complex traits has been explained by genetic variation. Non-additive gene-gene interactions, which are not often explored, are thought to be one source of this "missing" heritability. Methods Stochastic methods employing evolutionary algorithms have demonstrated promise in being able to detect and model gene-gene and gene-environment interactions that influence human traits. Here we demonstrate modifications to a neural network algorithm in ATHENA (the Analysis Tool for Heritable and Environmental Network Associations) resulting in clear performance improvements for discovering gene-gene interactions that influence human traits. We employed an alternative tree-based crossover, backpropagation for locally fitting neural network weights, and incorporation of domain knowledge obtainable from publicly accessible biological databases for initializing the search for gene-gene interactions. We tested these modifications in silico using simulated datasets. Results We show that the alternative tree-based crossover modification resulted in a modest increase in the sensitivity of the ATHENA algorithm for discovering gene-gene interactions. The performance increase was highly statistically significant when backpropagation was used to locally fit NN weights. We also demonstrate that using domain knowledge to initialize the search for gene-gene interactions results in a large performance increase, especially when the search space is larger than the search coverage. Conclusions We show that a hybrid optimization procedure, alternative crossover strategies, and incorporation of domain knowledge from publicly available biological databases can result in marked increases in sensitivity and performance of the ATHENA algorithm for detecting and modelling gene-gene interactions that influence a complex human trait. PMID:20875103

Autistic traits and social anxiety predict differential performance on social cognitive tasks in typically developing young adults

PubMed Central

Burk, Joshua A.; Fleckenstein, Katarina; Kozikowski, C. Teal

2018-01-01

The current work examined the unique contribution that autistic traits and social anxiety have on tasks examining attention and emotion processing. In Study 1, 119 typically-developing college students completed a flanker task assessing the control of attention to target faces and away from distracting faces during emotion identification. In Study 2, 208 typically-developing college students performed a visual search task which required identification of whether a series of 8 or 16 emotional faces depicted the same or different emotions. Participants with more self-reported autistic traits performed more slowly on the flanker task in Study 1 than those with fewer autistic traits when stimuli depicted complex emotions. In Study 2, participants higher in social anxiety performed less accurately on trials showing all complex faces; participants with autistic traits showed no differences. These studies suggest that traits related to autism and to social anxiety differentially impact social cognitive processing. PMID:29596523

Fitness consequences of larval traits persist across the metamorphic boundary.

PubMed

Crean, Angela J; Monro, Keyne; Marshall, Dustin J

2011-11-01

Metamorphosis is thought to provide an adaptive decoupling between traits specialized for each life-history stage in species with complex life cycles. However, an increasing number of studies are finding that larval traits can carry-over to influence postmetamorphic performance, suggesting that these life-history stages may not be free to evolve independently of each other. We used a phenotypic selection framework to compare the relative and interactive effects of larval size, time to hatching, and time to settlement on postmetamorphic survival and growth in a marine invertebrate, Styela plicata. Time to hatching was the only larval trait found to be under directional selection, individuals that took more time to hatch into larvae survived better after metamorphosis but grew more slowly. Nonlinear selection was found to act on multivariate trait combinations, once again acting in opposite directions for selection acting via survival and growth. Individuals with above average values of larval traits were most likely to survive, but surviving individuals with intermediate larval traits grew to the largest size. These results demonstrate that larval traits can have multiple, complex fitness consequences that persist across the metamorphic boundary; and thus postmetamorphic selection pressures may constrain the evolution of larval traits. © 2011 The Author(s). Evolution© 2011 The Society for the Study of Evolution.

An experimental validation of genomic selection in octoploid strawberry

PubMed Central

Gezan, Salvador A; Osorio, Luis F; Verma, Sujeet; Whitaker, Vance M

2017-01-01

The primary goal of genomic selection is to increase genetic gains for complex traits by predicting performance of individuals for which phenotypic data are not available. The objective of this study was to experimentally evaluate the potential of genomic selection in strawberry breeding and to define a strategy for its implementation. Four clonally replicated field trials, two in each of 2 years comprised of a total of 1628 individuals, were established in 2013–2014 and 2014–2015. Five complex yield and fruit quality traits with moderate to low heritability were assessed in each trial. High-density genotyping was performed with the Affymetrix Axiom IStraw90 single-nucleotide polymorphism array, and 17 479 polymorphic markers were chosen for analysis. Several methods were compared, including Genomic BLUP, Bayes B, Bayes C, Bayesian LASSO Regression, Bayesian Ridge Regression and Reproducing Kernel Hilbert Spaces. Cross-validation within training populations resulted in higher values than for true validations across trials. For true validations, Bayes B gave the highest predictive abilities on average and also the highest selection efficiencies, particularly for yield traits that were the lowest heritability traits. Selection efficiencies using Bayes B for parent selection ranged from 74% for average fruit weight to 34% for early marketable yield. A breeding strategy is proposed in which advanced selection trials are utilized as training populations and in which genomic selection can reduce the breeding cycle from 3 to 2 years for a subset of untested parents based on their predicted genomic breeding values. PMID:28090334

Influence of cognitive function on quality of life in anorexia nervosa patients.

PubMed

Hamatani, Sayo; Tomotake, Masahito; Takeda, Tomoya; Kameoka, Naomi; Kawabata, Masashi; Kubo, Hiroko; Tada, Yukio; Tomioka, Yukiko; Watanabe, Shinya; Inoshita, Masatoshi; Kinoshita, Makoto; Ohta, Masashi; Ohmori, Tetsuro

2017-05-01

The purpose of this study was to elucidate determinants of quality of life (QOL) in anorexia nervosa (AN) patients. Twenty-one female patients with AN participated in the study. QOL was assessed with the 36-Item Short Form Health Survey (SF-36), and cognitive function was evaluated using the Wisconsin Card Sorting Test Keio version, the Rey Complex Figure Test, and the Social Cognition Screening Questionnaire. Clinical symptoms were evaluated with the Beck Depression Inventory-II, the State-Trait Anxiety Inventory-Form JYZ (STAI-JYZ), and the Maudsley Obsessive Compulsive Inventory. The Difficulty Maintaining Set score of the Wisconsin Card Sorting Test Keio version was negatively correlated to the SF-36 Physical Component Summary. Scores of the Beck Depression Inventory-II and the STAI-JYZ State and Trait were negatively correlated to the SF-36 Mental Component Summary (MCS), and the Central Coherence Index 30-min Delayed Recall score of the Rey Complex Figure Test was positively correlated with the MCS. Stepwise regression analysis showed that the Difficulty Maintaining Set score was an independent predictor of the Physical Component Summary and scores for Central Coherence Index 30-min Delayed Recall and the STAI-JYZ Trait-predicted MCS. These results suggest that not only trait anxiety but also poor central coherence and impaired ability to maintain new rule worsen AN patients' QOL. © 2016 The Authors. Psychiatry and Clinical Neurosciences © 2016 Japanese Society of Psychiatry and Neurology.

Association of candidate genes with drought tolerance traits in diverse perennial ryegrass accessions

PubMed Central

Jiang, Yiwei

2013-01-01

Drought is a major environmental stress limiting growth of perennial grasses in temperate regions. Plant drought tolerance is a complex trait that is controlled by multiple genes. Candidate gene association mapping provides a powerful tool for dissection of complex traits. Candidate gene association mapping of drought tolerance traits was conducted in 192 diverse perennial ryegrass (Lolium perenne L.) accessions from 43 countries. The panel showed significant variations in leaf wilting, leaf water content, canopy and air temperature difference, and chlorophyll fluorescence under well-watered and drought conditions across six environments. Analysis of 109 simple sequence repeat markers revealed five population structures in the mapping panel. A total of 2520 expression-based sequence readings were obtained for a set of candidate genes involved in antioxidant metabolism, dehydration, water movement across membranes, and signal transduction, from which 346 single nucleotide polymorphisms were identified. Significant associations were identified between a putative LpLEA3 encoding late embryogenesis abundant group 3 protein and a putative LpFeSOD encoding iron superoxide dismutase and leaf water content, as well as between a putative LpCyt Cu-ZnSOD encoding cytosolic copper-zinc superoxide dismutase and chlorophyll fluorescence under drought conditions. Four of these identified significantly associated single nucleotide polymorphisms from these three genes were also translated to amino acid substitutions in different genotypes. These results indicate that allelic variation in these genes may affect whole-plant response to drought stress in perennial ryegrass. PMID:23386684

Different Statistical Approaches to Investigate Porcine Muscle Metabolome Profiles to Highlight New Biomarkers for Pork Quality Assessment

PubMed Central

Welzenbach, Julia; Neuhoff, Christiane; Looft, Christian; Schellander, Karl; Tholen, Ernst; Große-Brinkhaus, Christine

2016-01-01

The aim of this study was to elucidate the underlying biochemical processes to identify potential key molecules of meat quality traits drip loss, pH of meat 1 h post-mortem (pH1), pH in meat 24 h post-mortem (pH24) and meat color. An untargeted metabolomics approach detected the profiles of 393 annotated and 1,600 unknown metabolites in 97 Duroc × Pietrain pigs. Despite obvious differences regarding the statistical approaches, the four applied methods, namely correlation analysis, principal component analysis, weighted network analysis (WNA) and random forest regression (RFR), revealed mainly concordant results. Our findings lead to the conclusion that meat quality traits pH1, pH24 and color are strongly influenced by processes of post-mortem energy metabolism like glycolysis and pentose phosphate pathway, whereas drip loss is significantly associated with metabolites of lipid metabolism. In case of drip loss, RFR was the most suitable method to identify reliable biomarkers and to predict the phenotype based on metabolites. On the other hand, WNA provides the best parameters to investigate the metabolite interactions and to clarify the complex molecular background of meat quality traits. In summary, it was possible to attain findings on the interaction of meat quality traits and their underlying biochemical processes. The detected key metabolites might be better indicators of meat quality especially of drip loss than the measured phenotype itself and potentially might be used as bio indicators. PMID:26919205

An integrated approach of comparative genomics and heritability analysis of pig and human on obesity trait: evidence for candidate genes on human chromosome 2.

PubMed

Kim, Jaemin; Lee, Taeheon; Kim, Tae-Hun; Lee, Kyung-Tai; Kim, Heebal

2012-12-19

Traditional candidate gene approach has been widely used for the study of complex diseases including obesity. However, this approach is largely limited by its dependence on existing knowledge of presumed biology of the phenotype under investigation. Our combined strategy of comparative genomics and chromosomal heritability estimate analysis of obesity traits, subscapular skinfold thickness and back-fat thickness in Korean cohorts and pig (Sus scrofa), may overcome the limitations of candidate gene analysis and allow us to better understand genetic predisposition to human obesity. We found common genes including FTO, the fat mass and obesity associated gene, identified from significant SNPs by association studies of each trait. These common genes were related to blood pressure and arterial stiffness (P = 1.65E-05) and type 2 diabetes (P = 0.00578). Through the estimation of variance of genetic component (heritability) for each chromosome by SNPs, we observed a significant positive correlation (r = 0.479) between genetic contributions of human and pig to obesity traits. Furthermore, we noted that human chromosome 2 (syntenic to pig chromosomes 3 and 15) was most important in explaining the phenotypic variance for obesity. Obesity genetics still awaits further discovery. Navigating syntenic regions suggests obesity candidate genes on chromosome 2 that are previously known to be associated with obesity-related diseases: MRPL33, PARD3B, ERBB4, STK39, and ZNF385B.

LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis.

PubMed

Zheng, Jie; Erzurumluoglu, A Mesut; Elsworth, Benjamin L; Kemp, John P; Howe, Laurence; Haycock, Philip C; Hemani, Gibran; Tansey, Katherine; Laurin, Charles; Pourcain, Beate St; Warrington, Nicole M; Finucane, Hilary K; Price, Alkes L; Bulik-Sullivan, Brendan K; Anttila, Verneri; Paternoster, Lavinia; Gaunt, Tom R; Evans, David M; Neale, Benjamin M

2017-01-15

LD score regression is a reliable and efficient method of using genome-wide association study (GWAS) summary-level results data to estimate the SNP heritability of complex traits and diseases, partition this heritability into functional categories, and estimate the genetic correlation between different phenotypes. Because the method relies on summary level results data, LD score regression is computationally tractable even for very large sample sizes. However, publicly available GWAS summary-level data are typically stored in different databases and have different formats, making it difficult to apply LD score regression to estimate genetic correlations across many different traits simultaneously. In this manuscript, we describe LD Hub - a centralized database of summary-level GWAS results for 173 diseases/traits from different publicly available resources/consortia and a web interface that automates the LD score regression analysis pipeline. To demonstrate functionality and validate our software, we replicated previously reported LD score regression analyses of 49 traits/diseases using LD Hub; and estimated SNP heritability and the genetic correlation across the different phenotypes. We also present new results obtained by uploading a recent atopic dermatitis GWAS meta-analysis to examine the genetic correlation between the condition and other potentially related traits. In response to the growing availability of publicly accessible GWAS summary-level results data, our database and the accompanying web interface will ensure maximal uptake of the LD score regression methodology, provide a useful database for the public dissemination of GWAS results, and provide a method for easily screening hundreds of traits for overlapping genetic aetiologies. The web interface and instructions for using LD Hub are available at http://ldsc.broadinstitute.org/ CONTACT: jie.zheng@bristol.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Genetic Analysis of Strawberry Fruit Aroma and Identification of O-Methyltransferase FaOMT as the Locus Controlling Natural Variation in Mesifurane Content1[C][W][OA

PubMed Central

Zorrilla-Fontanesi, Yasmín; Rambla, José-Luis; Cabeza, Amalia; Medina, Juan J.; Sánchez-Sevilla, José F.; Valpuesta, Victoriano; Botella, Miguel A.; Granell, Antonio; Amaya, Iraida

2012-01-01

Improvement of strawberry (Fragaria × ananassa) fruit flavor is an important goal in breeding programs. To investigate genetic factors controlling this complex trait, a strawberry mapping population derived from genotype ‘1392’, selected for its superior flavor, and ‘232’ was profiled for volatile compounds over 4 years by headspace solid phase microextraction coupled to gas chromatography and mass spectrometry. More than 300 volatile compounds were detected, of which 87 were identified by comparison of mass spectrum and retention time to those of pure standards. Parental line ‘1392’ displayed higher volatile levels than ‘232’, and these and many other compounds with similar levels in both parents segregated in the progeny. Cluster analysis grouped the volatiles into distinct chemically related families and revealed a complex metabolic network underlying volatile production in strawberry fruit. Quantitative trait loci (QTL) detection was carried out over 3 years based on a double pseudo-testcross strategy. Seventy QTLs covering 48 different volatiles were detected, with several of them being stable over time and mapped as major QTLs. Loci controlling γ-decalactone and mesifurane content were mapped as qualitative traits. Using a candidate gene approach we have assigned genes that are likely responsible for several of the QTLs. As a proof of concept we show that one homoeolog of the O-methyltransferase gene (FaOMT) is the locus responsible for the natural variation of mesifurane content. Sequence analysis identified 30 bp in the promoter of this FaOMT homoeolog containing putative binding sites for basic/helix-loop-helix, MYB, and BZIP transcription factors. This polymorphism fully cosegregates with both the presence of mesifurane and the high expression of FaOMT during ripening. PMID:22474217

Direct power comparisons between simple LOD scores and NPL scores for linkage analysis in complex diseases.

PubMed

Abreu, P C; Greenberg, D A; Hodge, S E

1999-09-01

Several methods have been proposed for linkage analysis of complex traits with unknown mode of inheritance. These methods include the LOD score maximized over disease models (MMLS) and the "nonparametric" linkage (NPL) statistic. In previous work, we evaluated the increase of type I error when maximizing over two or more genetic models, and we compared the power of MMLS to detect linkage, in a number of complex modes of inheritance, with analysis assuming the true model. In the present study, we compare MMLS and NPL directly. We simulated 100 data sets with 20 families each, using 26 generating models: (1) 4 intermediate models (penetrance of heterozygote between that of the two homozygotes); (2) 6 two-locus additive models; and (3) 16 two-locus heterogeneity models (admixture alpha = 1.0,.7,.5, and.3; alpha = 1.0 replicates simple Mendelian models). For LOD scores, we assumed dominant and recessive inheritance with 50% penetrance. We took the higher of the two maximum LOD scores and subtracted 0.3 to correct for multiple tests (MMLS-C). We compared expected maximum LOD scores and power, using MMLS-C and NPL as well as the true model. Since NPL uses only the affected family members, we also performed an affecteds-only analysis using MMLS-C. The MMLS-C was both uniformly more powerful than NPL for most cases we examined, except when linkage information was low, and close to the results for the true model under locus heterogeneity. We still found better power for the MMLS-C compared with NPL in affecteds-only analysis. The results show that use of two simple modes of inheritance at a fixed penetrance can have more power than NPL when the trait mode of inheritance is complex and when there is heterogeneity in the data set.

Quantitative trait loci mapping and gene network analysis implicate protocadherin-15 as a determinant of brain serotonin transporter expression.

PubMed

Ye, R; Carneiro, A M D; Han, Q; Airey, D; Sanders-Bush, E; Zhang, B; Lu, L; Williams, R; Blakely, R D

2014-03-01

Presynaptic serotonin (5-hydroxytryptamine, 5-HT) transporters (SERT) regulate 5-HT signaling via antidepressant-sensitive clearance of released neurotransmitter. Polymorphisms in the human SERT gene (SLC6A4) have been linked to risk for multiple neuropsychiatric disorders, including depression, obsessive-compulsive disorder and autism. Using BXD recombinant inbred mice, a genetic reference population that can support the discovery of novel determinants of complex traits, merging collective trait assessments with bioinformatics approaches, we examine phenotypic and molecular networks associated with SERT gene and protein expression. Correlational analyses revealed a network of genes that significantly associated with SERT mRNA levels. We quantified SERT protein expression levels and identified region- and gender-specific quantitative trait loci (QTLs), one of which associated with male midbrain SERT protein expression, centered on the protocadherin-15 gene (Pcdh15), overlapped with a QTL for midbrain 5-HT levels. Pcdh15 was also the only QTL-associated gene whose midbrain mRNA expression significantly associated with both SERT protein and 5-HT traits, suggesting an unrecognized role of the cell adhesion protein in the development or function of 5-HT neurons. To test this hypothesis, we assessed SERT protein and 5-HT traits in the Pcdh15 functional null line (Pcdh15(av-) (3J) ), studies that revealed a strong, negative influence of Pcdh15 on these phenotypes. Together, our findings illustrate the power of multidimensional profiling of recombinant inbred lines in the analysis of molecular networks that support synaptic signaling, and that, as in the case of Pcdh15, can reveal novel relationships that may underlie risk for mental illness. © 2014 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society.

Identifying Specific Genes Controlling Complex Traits Through A Genome-Wide Screen For cis-Acting Regulatory Elements - An Example Using Marek's Disease

USDA-ARS?s Scientific Manuscript database

The identification of specific genes underlying phenotypic variation of complex traits remains one of the greatest challenges in biology despite having genome sequences and more powerful tools. Most genome-wide screens lack sufficient resolving power as they typically depend on linkage. One altern...

Comprehensive Identification Of Specific Genes Controlling Complex Traits Through A Genome-Wide Screen for Cis-Acting Regulatory Elements - An Example Using Marek's Disease

USDA-ARS?s Scientific Manuscript database

The comprehensive identification of genes underlying phenotypic variation of complex traits remains a major challenge. Most genome-wide screens lack sufficient resolving power as they typically depend on linkage. An alternate method is to screen for allele-specific expression (ASE), a simple yet pow...

Mapping, fine mapping, and molecular dissection of quantitative trait Loci in domestic animals.

PubMed

Georges, Michel

2007-01-01

Artificial selection has created myriad breeds of domestic animals, each characterized by unique phenotypes pertaining to behavior, morphology, physiology, and disease. Most domestic animal populations share features with isolated founder populations, making them well suited for positional cloning. Genome sequences are now available for most domestic species, and with them a panoply of tools including high-density single-nucleotide polymorphism panels. As a result, domestic animal populations are becoming invaluable resources for studying the molecular architecture of complex traits and of adaptation. Here we review recent progress and issues in the positional identification of genes underlying complex traits in domestic animals. As many phenotypes studied in animals are quantitative, we focus on mapping, fine mapping, and cloning of quantitative trait loci.

Deciphering the Interdependence between Ecological and Evolutionary Networks.

PubMed

Melián, Carlos J; Matthews, Blake; de Andreazzi, Cecilia S; Rodríguez, Jorge P; Harmon, Luke J; Fortuna, Miguel A

2018-05-24

Biological systems consist of elements that interact within and across hierarchical levels. For example, interactions among genes determine traits of individuals, competitive and cooperative interactions among individuals influence population dynamics, and interactions among species affect the dynamics of communities and ecosystem processes. Such systems can be represented as hierarchical networks, but can have complex dynamics when interdependencies among levels of the hierarchy occur. We propose integrating ecological and evolutionary processes in hierarchical networks to explore interdependencies in biological systems. We connect gene networks underlying predator-prey trait distributions to food webs. Our approach addresses longstanding questions about how complex traits and intraspecific trait variation affect the interdependencies among biological levels and the stability of meta-ecosystems. Copyright © 2018 Elsevier Ltd. All rights reserved.

Tilting at Quixotic Trait Loci (QTL): An Evolutionary Perspective on Genetic Causation

PubMed Central

Weiss, Kenneth M.

2008-01-01

Recent years have seen great advances in generating and analyzing data to identify the genetic architecture of biological traits. Human disease has understandably received intense research focus, and the genes responsible for most Mendelian diseases have successfully been identified. However, the same advances have shown a consistent if less satisfying pattern, in which complex traits are affected by variation in large numbers of genes, most of which have individually minor or statistically elusive effects, leaving the bulk of genetic etiology unaccounted for. This pattern applies to diverse and unrelated traits, not just disease, in basically all species, and is consistent with evolutionary expectations, raising challenging questions about the best way to approach and understand biological complexity. PMID:18711218

Integrating modelling and phenotyping approaches to identify and screen complex traits - Illustration for transpiration efficiency in cereals.

PubMed

Chenu, K; van Oosterom, E J; McLean, G; Deifel, K S; Fletcher, A; Geetika, G; Tirfessa, A; Mace, E S; Jordan, D R; Sulman, R; Hammer, G L

2018-02-21

Following advances in genetics, genomics, and phenotyping, trait selection in breeding is limited by our ability to understand interactions within the plants and with their environments, and to target traits of most relevance for the target population of environments. We propose an integrated approach that combines insights from crop modelling, physiology, genetics, and breeding to identify traits valuable for yield gain in the target population of environments, develop relevant high-throughput phenotyping platforms, and identify genetic controls and their values in production environments. This paper uses transpiration efficiency (biomass produced per unit of water used) as an example of a complex trait of interest to illustrate how the approach can guide modelling, phenotyping, and selection in a breeding program. We believe that this approach, by integrating insights from diverse disciplines, can increase the resource use efficiency of breeding programs for improving yield gains in target populations of environments.

Genotypic Complexity of Fisher’s Geometric Model

PubMed Central

Hwang, Sungmin; Park, Su-Chan; Krug, Joachim

2017-01-01

Fisher’s geometric model was originally introduced to argue that complex adaptations must occur in small steps because of pleiotropic constraints. When supplemented with the assumption of additivity of mutational effects on phenotypic traits, it provides a simple mechanism for the emergence of genotypic epistasis from the nonlinear mapping of phenotypes to fitness. Of particular interest is the occurrence of reciprocal sign epistasis, which is a necessary condition for multipeaked genotypic fitness landscapes. Here we compute the probability that a pair of randomly chosen mutations interacts sign epistatically, which is found to decrease with increasing phenotypic dimension n, and varies nonmonotonically with the distance from the phenotypic optimum. We then derive expressions for the mean number of fitness maxima in genotypic landscapes comprised of all combinations of L random mutations. This number increases exponentially with L, and the corresponding growth rate is used as a measure of the complexity of the landscape. The dependence of the complexity on the model parameters is found to be surprisingly rich, and three distinct phases characterized by different landscape structures are identified. Our analysis shows that the phenotypic dimension, which is often referred to as phenotypic complexity, does not generally correlate with the complexity of fitness landscapes and that even organisms with a single phenotypic trait can have complex landscapes. Our results further inform the interpretation of experiments where the parameters of Fisher’s model have been inferred from data, and help to elucidate which features of empirical fitness landscapes can be described by this model. PMID:28450460

The Evolution of Biological Complexity in Digital Organisms

NASA Astrophysics Data System (ADS)

Ofria, Charles

2013-03-01

When Darwin first proposed his theory of evolution by natural selection, he realized that it had a problem explaining the origins of traits of ``extreme perfection and complication'' such as the vertebrate eye. Critics of Darwin's theory have latched onto this perceived flaw as a proof that Darwinian evolution is impossible. In anticipation of this issue, Darwin described the perfect data needed to understand this process, but lamented that such data are ``scarcely ever possible'' to obtain. In this talk, I will discuss research where we use populations of digital organisms (self-replicating and evolving computer programs) to elucidate the genetic and evolutionary processes by which new, highly-complex traits arise, drawing inspiration directly from Darwin's wistful thinking and hypotheses. During the process of evolution in these fully-transparent computational environments we can measure the incorporation of new information into the genome, a process akin to a natural Maxwell's Demon, and identify the original source of any such information. We show that, as Darwin predicted, much of the information used to encode a complex trait was already in the genome as part of simpler evolved traits, and that many routes must be possible for a new complex trait to have a high probability of successfully evolving. In even more extreme examples of the evolution of complexity, we are now using these same principles to examine the evolutionary dynamics the drive major transitions in evolution; that is transitions to higher-levels of organization, which are some of the most complex evolutionary events to occur in nature. Finally, I will explore some of the implications of this research to other aspects of evolutionary biology and as well as ways that these evolutionary principles can be applied toward solving computational and engineering problems.

Quantitative genetic analysis of agronomic and morphological traits in sorghum, Sorghum bicolor

PubMed Central

Mohammed, Riyazaddin; Are, Ashok K.; Bhavanasi, Ramaiah; Munghate, Rajendra S.; Kavi Kishor, Polavarapu B.; Sharma, Hari C.

2015-01-01

The productivity in sorghum is low, owing to various biotic and abiotic constraints. Combining insect resistance with desirable agronomic and morphological traits is important to increase sorghum productivity. Therefore, it is important to understand the variability for various agronomic traits, their heritabilities and nature of gene action to develop appropriate strategies for crop improvement. Therefore, a full diallel set of 10 parents and their 90 crosses including reciprocals were evaluated in replicated trials during the 2013–14 rainy and postrainy seasons. The crosses between the parents with early- and late-flowering flowered early, indicating dominance of earliness for anthesis in the test material used. Association between the shoot fly resistance, morphological, and agronomic traits suggested complex interactions between shoot fly resistance and morphological traits. Significance of the mean sum of squares for GCA (general combining ability) and SCA (specific combining ability) of all the studied traits suggested the importance of both additive and non-additive components in inheritance of these traits. The GCA/SCA, and the predictability ratios indicated predominance of additive gene effects for majority of the traits studied. High broad-sense and narrow-sense heritability estimates were observed for most of the morphological and agronomic traits. The significance of reciprocal combining ability effects for days to 50% flowering, plant height and 100 seed weight, suggested maternal effects for inheritance of these traits. Plant height and grain yield across seasons, days to 50% flowering, inflorescence exsertion, and panicle shape in the postrainy season showed greater specific combining ability variance, indicating the predominance of non-additive type of gene action/epistatic interactions in controlling the expression of these traits. Additive gene action in the rainy season, and dominance in the postrainy season for days to 50% flowering and plant height suggested G X E interactions for these traits. PMID:26579183

Does Structural Complexity Determine the Morphology of Assemblages? An Experimental Test on Three Continents

PubMed Central

Gibb, Heloise; Parr, Catherine L.

2013-01-01

Understanding how species will respond to global change depends on our ability to distinguish generalities from idiosyncrasies. For diverse, but poorly known taxa, such as insects, species traits may provide a short-cut to predicting species turnover. We tested whether ant traits respond consistently to habitat complexity across geographically independent ant assemblages, using an experimental approach and baits. We repeated our study in six paired simple and complex habitats on three continents with distinct ant faunas. We also compared traits amongst ants with different foraging strategies. We hypothesised that ants would be larger, broader, have longer legs and more dorsally positioned eyes in simpler habitats. In agreement with predictions, ants had longer femurs and dorsally positioned eyes in simple habitats. This pattern was most pronounced for ants that discovered resources. Body size and pronotum width responded as predicted for experimental treatments, but were inconsistent across continents. Monopolising ants were smaller, with shorter femurs than those that occupied or discovered resources. Consistent responses for several traits suggest that many, but not all, aspects of morphology respond predictably to habitat complexity, and that foraging strategy is linked with morphology. Some traits thus have the potential to be used to predict the direction of species turnover, changes in foraging strategy and, potentially, evolution in response to changes in habitat structure. PMID:23691137

CONAN: copy number variation analysis software for genome-wide association studies

PubMed Central

2010-01-01

Background Genome-wide association studies (GWAS) based on single nucleotide polymorphisms (SNPs) revolutionized our perception of the genetic regulation of complex traits and diseases. Copy number variations (CNVs) promise to shed additional light on the genetic basis of monogenic as well as complex diseases and phenotypes. Indeed, the number of detected associations between CNVs and certain phenotypes are constantly increasing. However, while several software packages support the determination of CNVs from SNP chip data, the downstream statistical inference of CNV-phenotype associations is still subject to complicated and inefficient in-house solutions, thus strongly limiting the performance of GWAS based on CNVs. Results CONAN is a freely available client-server software solution which provides an intuitive graphical user interface for categorizing, analyzing and associating CNVs with phenotypes. Moreover, CONAN assists the evaluation process by visualizing detected associations via Manhattan plots in order to enable a rapid identification of genome-wide significant CNV regions. Various file formats including the information on CNVs in population samples are supported as input data. Conclusions CONAN facilitates the performance of GWAS based on CNVs and the visual analysis of calculated results. CONAN provides a rapid, valid and straightforward software solution to identify genetic variation underlying the 'missing' heritability for complex traits that remains unexplained by recent GWAS. The freely available software can be downloaded at http://genepi-conan.i-med.ac.at. PMID:20546565

Building phenotype networks to improve QTL detection: a comparative analysis of fatty acid and fat traits in pigs.

PubMed

Yang, B; Navarro, N; Noguera, J L; Muñoz, M; Guo, T F; Yang, K X; Ma, J W; Folch, J M; Huang, L S; Pérez-Enciso, M

2011-10-01

Models in QTL mapping can be improved by considering all potential variables, i.e. we can use remaining traits other than the trait under study as potential predictors. QTL mapping is often conducted by correcting for a few fixed effects or covariates (e.g. sex, age), although many traits with potential causal relationships between them are recorded. In this work, we evaluate by simulation several procedures to identify optimum models in QTL scans: forward selection, undirected dependency graph and QTL-directed dependency graph (QDG). The latter, QDG, performed better in terms of power and false discovery rate and was applied to fatty acid (FA) composition and fat deposition traits in two pig F2 crosses from China and Spain. Compared with the typical QTL mapping, QDG approach revealed several new QTL. To the contrary, several FA QTL on chromosome 4 (e.g. Palmitic, C16:0; Stearic, C18:0) detected by typical mapping vanished after adjusting for phenotypic covariates in QDG mapping. This suggests that the QTL detected in typical mapping could be indirect. When a QTL is supported by both approaches, there is an increased confidence that the QTL have a primary effect on the corresponding trait. An example is a QTL for C16:1 on chromosome 8. In conclusion, mapping QTL based on causal phenotypic networks can increase power and help to make more biologically sound hypothesis on the genetic architecture of complex traits. © 2011 Blackwell Verlag GmbH.

Comparative analysis of genetic architectures for nine developmental traits of rye.

PubMed

Masojć, Piotr; Milczarski, P; Kruszona, P

2017-08-01

Genetic architectures of plant height, stem thickness, spike length, awn length, heading date, thousand-kernel weight, kernel length, leaf area and chlorophyll content were aligned on the DArT-based high-density map of the 541 × Ot1-3 RILs population of rye using the genes interaction assorting by divergent selection (GIABDS) method. Complex sets of QTL for particular traits contained 1-5 loci of the epistatic D class and 10-28 loci of the hypostatic, mostly R and E classes controlling traits variation through D-E or D-R types of two-loci interactions. QTL were distributed on each of the seven rye chromosomes in unique positions or as a coinciding loci for 2-8 traits. Detection of considerable numbers of the reversed (D', E' and R') classes of QTL might be attributed to the transgression effects observed for most of the studied traits. First examples of E* and F QTL classes, defined in the model, are reported for awn length, leaf area, thousand-kernel weight and kernel length. The results of this study extend experimental data to 11 quantitative traits (together with pre-harvest sprouting and alpha-amylase activity) for which genetic architectures fit the model of mechanism underlying alleles distribution within tails of bi-parental populations. They are also a valuable starting point for map-based search of genes underlying detected QTL and for planning advanced marker-assisted multi-trait breeding strategies.

Investigation of four porcine candidate genes (H-FABP, MYOD1, UCP3 and MASTR) for meat quality traits in Large White pigs.

PubMed

Han, Xuelei; Jiang, Tengfei; Yang, Huawei; Zhang, Qingde; Wang, Weimin; Fan, Bin; Liu, Bang

2012-06-01

Meat quality traits are economically important traits of swine, and are controlled by multiple genes as complex quantitative traits. In the present study four genes, H-FABP (heart fatty acid-binding protein), MASTR (MEF2 activating motif and SAP domain containing transcriptional regulator), UCP3 (uncoupling protein 3) and MYOD1 (myogenic differentiation 1) were researched in Large White pigs. The polymorphisms H-FABP T/C of 5'UTR, MYOD1 g.257 A>C, UCP3 g.1406 G>A in exon 3 and MASTR c.187 C>T have been reported to be associated with meat quality traits in pigs. The aim of this study was to analyze the effect of single and multiple markers for single traits in Large White pigs. The single marker association analysis showed that the H-FABP and MASTR genes were associated with IMF (intramuscular fat content) (P < 0.05), and that the g.257 A>C of MYOD1 gene was most significantly related to muscle pH value (P < 0.01). The multiple markers for IMF were analyzed by combining the markers and quantitative trait modes into the linear regression. The results revealed that H-FABP and MASTR integrate gene networks for IMF. Thus, our study results suggested that H-FABP and MASTR polymorphisms could be used as genetic markers in the marker-assisted selection towards the improvement of IMF in Large White pigs.

Environmental variability and acoustic signals: a multi-level approach in songbirds.

PubMed

Medina, Iliana; Francis, Clinton D

2012-12-23

Among songbirds, growing evidence suggests that acoustic adaptation of song traits occurs in response to habitat features. Despite extensive study, most research supporting acoustic adaptation has only considered acoustic traits averaged for species or populations, overlooking intraindividual variation of song traits, which may facilitate effective communication in heterogeneous and variable environments. Fewer studies have explicitly incorporated sexual selection, which, if strong, may favour variation across environments. Here, we evaluate the prevalence of acoustic adaptation among 44 species of songbirds by determining how environmental variability and sexual selection intensity are associated with song variability (intraindividual and intraspecific) and short-term song complexity. We show that variability in precipitation can explain short-term song complexity among taxonomically diverse songbirds, and that precipitation seasonality and the intensity of sexual selection are related to intraindividual song variation. Our results link song complexity to environmental variability, something previously found for mockingbirds (Family Mimidae). Perhaps more importantly, our results illustrate that individual variation in song traits may be shaped by both environmental variability and strength of sexual selection.

Sensory trait variation in an echolocating bat suggests roles for both selection and plasticity

PubMed Central

2014-01-01

Background Across heterogeneous environments selection and gene flow interact to influence the rate and extent of adaptive trait evolution. This complex relationship is further influenced by the rarely considered role of phenotypic plasticity in the evolution of adaptive population variation. Plasticity can be adaptive if it promotes colonization and survival in novel environments and in doing so may increase the potential for future population differentiation via selection. Gene flow between selectively divergent environments may favour the evolution of phenotypic plasticity or conversely, plasticity itself may promote gene flow, leading to a pattern of trait differentiation in the presence of gene flow. Variation in sensory traits is particularly informative in testing the role of environment in trait and population differentiation. Here we test the hypothesis of ‘adaptive differentiation with minimal gene flow’ in resting echolocation frequencies (RF) of Cape horseshoe bats (Rhinolophus capensis) across a gradient of increasingly cluttered habitats. Results Our analysis reveals a geographically structured pattern of increasing RF from open to highly cluttered habitats in R. capensis; however genetic drift appears to be a minor player in the processes influencing this pattern. Although Bayesian analysis of population structure uncovered a number of spatially defined mitochondrial groups and coalescent methods revealed regional-scale gene flow, phylogenetic analysis of mitochondrial sequences did not correlate with RF differentiation. Instead, habitat discontinuities between biomes, and not genetic and geographic distances, best explained echolocation variation in this species. We argue that both selection for increased detection distance in relatively less cluttered habitats and adaptive phenotypic plasticity may have influenced the evolution of matched echolocation frequencies and habitats across different populations. Conclusions Our study reveals significant sensory trait differentiation in the presence of historical gene flow and suggests roles for both selection and plasticity in the evolution of echolocation variation in R. capensis. These results highlight the importance of population level analyses to i) illuminate the subtle interplay between selection, plasticity and gene flow in the evolution of adaptive traits and ii) demonstrate that evolutionary processes may act simultaneously and that their relative influence may vary across different environments. PMID:24674227

Sensory trait variation in an echolocating bat suggests roles for both selection and plasticity.

PubMed

Odendaal, Lizelle J; Jacobs, David S; Bishop, Jacqueline M

2014-03-27

Across heterogeneous environments selection and gene flow interact to influence the rate and extent of adaptive trait evolution. This complex relationship is further influenced by the rarely considered role of phenotypic plasticity in the evolution of adaptive population variation. Plasticity can be adaptive if it promotes colonization and survival in novel environments and in doing so may increase the potential for future population differentiation via selection. Gene flow between selectively divergent environments may favour the evolution of phenotypic plasticity or conversely, plasticity itself may promote gene flow, leading to a pattern of trait differentiation in the presence of gene flow. Variation in sensory traits is particularly informative in testing the role of environment in trait and population differentiation. Here we test the hypothesis of 'adaptive differentiation with minimal gene flow' in resting echolocation frequencies (RF) of Cape horseshoe bats (Rhinolophus capensis) across a gradient of increasingly cluttered habitats. Our analysis reveals a geographically structured pattern of increasing RF from open to highly cluttered habitats in R. capensis; however genetic drift appears to be a minor player in the processes influencing this pattern. Although Bayesian analysis of population structure uncovered a number of spatially defined mitochondrial groups and coalescent methods revealed regional-scale gene flow, phylogenetic analysis of mitochondrial sequences did not correlate with RF differentiation. Instead, habitat discontinuities between biomes, and not genetic and geographic distances, best explained echolocation variation in this species. We argue that both selection for increased detection distance in relatively less cluttered habitats and adaptive phenotypic plasticity may have influenced the evolution of matched echolocation frequencies and habitats across different populations. Our study reveals significant sensory trait differentiation in the presence of historical gene flow and suggests roles for both selection and plasticity in the evolution of echolocation variation in R. capensis. These results highlight the importance of population level analyses to i) illuminate the subtle interplay between selection, plasticity and gene flow in the evolution of adaptive traits and ii) demonstrate that evolutionary processes may act simultaneously and that their relative influence may vary across different environments.

Enhancing knowledge and technology adoption in a misunderstood discipline: The weight trait project

USDA-ARS?s Scientific Manuscript database

Currently several commercial DNA marker panels are available for complex traits. In the fall of 2009, the American Angus Association integrated the results of an Angus-specific marker panel into their national cattle evaluation for carcass traits. Despite this advancement, there still exists treme...

Systems genetics: a paradigm to improve discovery of candidate genes and mechanisms underlying complex traits.

PubMed

Feltus, F Alex

2014-06-01

Understanding the control of any trait optimally requires the detection of causal genes, gene interaction, and mechanism of action to discover and model the biochemical pathways underlying the expressed phenotype. Functional genomics techniques, including RNA expression profiling via microarray and high-throughput DNA sequencing, allow for the precise genome localization of biological information. Powerful genetic approaches, including quantitative trait locus (QTL) and genome-wide association study mapping, link phenotype with genome positions, yet genetics is less precise in localizing the relevant mechanistic information encoded in DNA. The coupling of salient functional genomic signals with genetically mapped positions is an appealing approach to discover meaningful gene-phenotype relationships. Techniques used to define this genetic-genomic convergence comprise the field of systems genetics. This short review will address an application of systems genetics where RNA profiles are associated with genetically mapped genome positions of individual genes (eQTL mapping) or as gene sets (co-expression network modules). Both approaches can be applied for knowledge independent selection of candidate genes (and possible control mechanisms) underlying complex traits where multiple, likely unlinked, genomic regions might control specific complex traits. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Callous-unemotional traits are associated with deficits in recognizing complex emotions in preadolescent children.

PubMed

Sharp, Carla; Vanwoerden, Salome; Van Baardewijk, Y; Tackett, J L; Stegge, H

2015-06-01

The aims of the current study were to show that the affective component of psychopathy (callous-unemotional traits) is related to deficits in recognizing emotions over and above other psychopathy dimensions and to show that this relationship is driven by a specific deficit in recognizing complex emotions more so than basic emotions. The authors administered the Child Eyes Test to assess emotion recognition in a community sample of preadolescent children between the ages of 10 and 12 (N = 417; 53.6% boys). The task required children to identify a broad array of emotions from photographic stimuli depicting the eye region of the face. Stimuli were then divided into complex or basic emotions. Results demonstrated a unique association between callous-unemotional traits and complex emotions, with weaker associations with basic emotion recognition, over and above other dimensions of psychopathy.

Shade avoidance components and pathways in adult plants revealed by phenotypic profiling.

PubMed

Nozue, Kazunari; Tat, An V; Kumar Devisetty, Upendra; Robinson, Matthew; Mumbach, Maxwell R; Ichihashi, Yasunori; Lekkala, Saradadevi; Maloof, Julin N

2015-04-01

Shade from neighboring plants limits light for photosynthesis; as a consequence, plants have a variety of strategies to avoid canopy shade and compete with their neighbors for light. Collectively the response to foliar shade is called the shade avoidance syndrome (SAS). The SAS includes elongation of a variety of organs, acceleration of flowering time, and additional physiological responses, which are seen throughout the plant life cycle. However, current mechanistic knowledge is mainly limited to shade-induced elongation of seedlings. Here we use phenotypic profiling of seedling, leaf, and flowering time traits to untangle complex SAS networks. We used over-representation analysis (ORA) of shade-responsive genes, combined with previous annotation, to logically select 59 known and candidate novel mutants for phenotyping. Our analysis reveals shared and separate pathways for each shade avoidance response. In particular, auxin pathway components were required for shade avoidance responses in hypocotyl, petiole, and flowering time, whereas jasmonic acid pathway components were only required for petiole and flowering time responses. Our phenotypic profiling allowed discovery of seventeen novel shade avoidance mutants. Our results demonstrate that logical selection of mutants increased success of phenotypic profiling to dissect complex traits and discover novel components.

Population and allelic variation of A-to-I RNA editing in human transcriptomes.

PubMed

Park, Eddie; Guo, Jiguang; Shen, Shihao; Demirdjian, Levon; Wu, Ying Nian; Lin, Lan; Xing, Yi

2017-07-28

A-to-I RNA editing is an important step in RNA processing in which specific adenosines in some RNA molecules are post-transcriptionally modified to inosines. RNA editing has emerged as a widespread mechanism for generating transcriptome diversity. However, there remain significant knowledge gaps about the variation and function of RNA editing. In order to determine the influence of genetic variation on A-to-I RNA editing, we integrate genomic and transcriptomic data from 445 human lymphoblastoid cell lines by combining an RNA editing QTL (edQTL) analysis with an allele-specific RNA editing (ASED) analysis. We identify 1054 RNA editing events associated with cis genetic polymorphisms. Additionally, we find that a subset of these polymorphisms is linked to genome-wide association study signals of complex traits or diseases. Finally, compared to random cis polymorphisms, polymorphisms associated with RNA editing variation are located closer spatially to their respective editing sites and have a more pronounced impact on RNA secondary structure. Our study reveals widespread cis variation in RNA editing among genetically distinct individuals and sheds light on possible phenotypic consequences of such variation on complex traits and diseases.

Clinical Applications of Molecular Genetic Discoveries

PubMed Central

Marian, A.J.

2015-01-01

Genome-wide association studies (GWAS) of complex traits have mapped more than 15,000 common single nucleotide variants (SNVs). Likewise, applications of massively parallel nucleic acid sequencing technologies often referred to as Next Generation Sequencing, to molecular genetic studies of complex traits have catalogued a large number of rare variants (population frequency of <0.01) in cases with complex traits. Moreover, high throughput nucleic acid sequencing, variant burden analysis, and linkage studies are illuminating the presence of large number of SNVs in cases and families with single gene disorders. The plethora of the genetic variants has exposed the formidable challenge of identifying the causal and pathogenic variants from the enormous number of innocuous common and rare variants that exist in the population as well as in an individual genome. The arduous task of identifying the causal and pathogenic variants is further compounded by the pleiotropic effects of the variants, complexity of cis and trans interactions in the genome, variability in phenotypic expression of the disease, as well as phenotypic plasticity, and the multifarious determinants of the phenotype. Population genetic studies offer the initial roadmaps and have the potential to elucidate novel pathways involved in the pathogenesis of the disease. However, the genome of an individual is unique, rendering unambiguous identification of the causal or pathogenic variant in a single individual exceedingly challenging. Yet, the focus of the practice of medicine is on the individual, as Sir William Osler elegantly expressed in his insightful quotation: “The good physician treats the disease; the great physician treats the patient who has the disease.” The daunting task facing physicians, patients, and researchers alike is to apply the modern genetic discoveries to care of the individual with or at risk of the disease. PMID:26548329

Geographical Variation in Community Divergence: Insights from Tropical Forest Monodominance by Ectomycorrhizal Trees.

PubMed

Fukami, Tadashi; Nakajima, Mifuyu; Fortunel, Claire; Fine, Paul V A; Baraloto, Christopher; Russo, Sabrina E; Peay, Kabir G

2017-08-01

Convergence occurs in both species traits and community structure, but how convergence at the two scales influences each other remains unclear. To address this question, we focus on tropical forest monodominance, in which a single, often ectomycorrhizal (EM) tree species occasionally dominates forest stands within a landscape otherwise characterized by diverse communities of arbuscular mycorrhizal (AM) trees. Such monodominance is a striking potential example of community divergence resulting in alternative stable states. However, it is observed only in some tropical regions. A diverse suite of AM and EM trees locally codominate forest stands elsewhere. We develop a hypothesis to explain this geographical difference using a simulation model of plant community assembly. Simulation results suggest that in a region with a few EM species (e.g., South America), EM trees experience strong selection for convergent traits that match the abiotic conditions of the environment. Consequently, EM species successfully compete against other species to form monodominant stands via positive plant-soil feedbacks. By contrast, in a region with many EM species (e.g., Southeast Asia), species maintain divergent traits because of complex plant-soil feedbacks, with no species having traits that enable monodominance. An analysis of plant trait data from Borneo and Peruvian Amazon was inconclusive. Overall, this work highlights the utility of geographical comparison in understanding the relationship between trait convergence and community convergence.

Identification of expression quantitative trait loci by the interaction analysis using genetic algorithm.

PubMed

Namkung, Junghyun; Nam, Jin-Wu; Park, Taesung

2007-01-01

Many genes with major effects on quantitative traits have been reported to interact with other genes. However, finding a group of interacting genes from thousands of SNPs is challenging. Hence, an efficient and robust algorithm is needed. The genetic algorithm (GA) is useful in searching for the optimal solution from a very large searchable space. In this study, we show that genome-wide interaction analysis using GA and a statistical interaction model can provide a practical method to detect biologically interacting loci. We focus our search on transcriptional regulators by analyzing gene x gene interactions for cancer-related genes. The expression values of three cancer-related genes were selected from the expression data of the Genetic Analysis Workshop 15 Problem 1 data set. We implemented a GA to identify the expression quantitative trait loci that are significantly associated with expression levels of the cancer-related genes. The time complexity of the GA was compared with that of an exhaustive search algorithm. As a result, our GA, which included heuristic methods, such as archive, elitism, and local search, has greatly reduced computational time in a genome-wide search for gene x gene interactions. In general, the GA took one-fifth the computation time of an exhaustive search for the most significant pair of single-nucleotide polymorphisms.

Identification of expression quantitative trait loci by the interaction analysis using genetic algorithm

PubMed Central

Namkung, Junghyun; Nam, Jin-Wu; Park, Taesung

2007-01-01

Many genes with major effects on quantitative traits have been reported to interact with other genes. However, finding a group of interacting genes from thousands of SNPs is challenging. Hence, an efficient and robust algorithm is needed. The genetic algorithm (GA) is useful in searching for the optimal solution from a very large searchable space. In this study, we show that genome-wide interaction analysis using GA and a statistical interaction model can provide a practical method to detect biologically interacting loci. We focus our search on transcriptional regulators by analyzing gene × gene interactions for cancer-related genes. The expression values of three cancer-related genes were selected from the expression data of the Genetic Analysis Workshop 15 Problem 1 data set. We implemented a GA to identify the expression quantitative trait loci that are significantly associated with expression levels of the cancer-related genes. The time complexity of the GA was compared with that of an exhaustive search algorithm. As a result, our GA, which included heuristic methods, such as archive, elitism, and local search, has greatly reduced computational time in a genome-wide search for gene × gene interactions. In general, the GA took one-fifth the computation time of an exhaustive search for the most significant pair of single-nucleotide polymorphisms. PMID:18466570

Data Sources for Trait Databases: Comparing the Phenomic Content of Monographs and Evolutionary Matrices.

PubMed

Dececchi, T Alex; Mabee, Paula M; Blackburn, David C

2016-01-01

Databases of organismal traits that aggregate information from one or multiple sources can be leveraged for large-scale analyses in biology. Yet the differences among these data streams and how well they capture trait diversity have never been explored. We present the first analysis of the differences between phenotypes captured in free text of descriptive publications ('monographs') and those used in phylogenetic analyses ('matrices'). We focus our analysis on osteological phenotypes of the limbs of four extinct vertebrate taxa critical to our understanding of the fin-to-limb transition. We find that there is low overlap between the anatomical entities used in these two sources of phenotype data, indicating that phenotypes represented in matrices are not simply a subset of those found in monographic descriptions. Perhaps as expected, compared to characters found in matrices, phenotypes in monographs tend to emphasize descriptive and positional morphology, be somewhat more complex, and relate to fewer additional taxa. While based on a small set of focal taxa, these qualitative and quantitative data suggest that either source of phenotypes alone will result in incomplete knowledge of variation for a given taxon. As a broader community develops to use and expand databases characterizing organismal trait diversity, it is important to recognize the limitations of the data sources and develop strategies to more fully characterize variation both within species and across the tree of life.

Data Sources for Trait Databases: Comparing the Phenomic Content of Monographs and Evolutionary Matrices

PubMed Central

Dececchi, T. Alex; Mabee, Paula M.; Blackburn, David C.

2016-01-01

Databases of organismal traits that aggregate information from one or multiple sources can be leveraged for large-scale analyses in biology. Yet the differences among these data streams and how well they capture trait diversity have never been explored. We present the first analysis of the differences between phenotypes captured in free text of descriptive publications (‘monographs’) and those used in phylogenetic analyses (‘matrices’). We focus our analysis on osteological phenotypes of the limbs of four extinct vertebrate taxa critical to our understanding of the fin-to-limb transition. We find that there is low overlap between the anatomical entities used in these two sources of phenotype data, indicating that phenotypes represented in matrices are not simply a subset of those found in monographic descriptions. Perhaps as expected, compared to characters found in matrices, phenotypes in monographs tend to emphasize descriptive and positional morphology, be somewhat more complex, and relate to fewer additional taxa. While based on a small set of focal taxa, these qualitative and quantitative data suggest that either source of phenotypes alone will result in incomplete knowledge of variation for a given taxon. As a broader community develops to use and expand databases characterizing organismal trait diversity, it is important to recognize the limitations of the data sources and develop strategies to more fully characterize variation both within species and across the tree of life. PMID:27191170

Domestication impacts on plant–herbivore interactions: a meta-analysis

PubMed Central

Poveda, Katja

2017-01-01

For millennia, humans have imposed strong selection on domesticated crops, resulting in drastically altered crop phenotypes compared with wild ancestors. Crop yields have increased, but a long-held hypothesis is that domestication has also unintentionally decreased plant defences against herbivores. To test this hypothesis, we conducted a phylogenetically controlled meta-analysis comparing insect herbivore resistance and putative plant defence traits between crops and their wild relatives. Our database included 2098 comparisons made across 73 crops in 89 studies. We found that domestication consistently reduced plant resistance to herbivores, although the magnitude of the effects varied across plant organs and depended on how resistance was measured. However, domestication had no consistent effects on the specific plant defence traits underlying resistance, including secondary metabolites and physical feeding barriers. The values of these traits sometimes increased and sometimes decreased during domestication. Consistent negative effects of domestication were observed only when defence traits were measured in reproductive organs or in the plant organ that was harvested. These results highlight the complexity of evolution under domestication and the need for an improved theoretical understanding of the mechanisms through which agronomic selection can influence the species interactions that impact both the yield and sustainability of our food systems. This article is part of the themed issue ‘Human influences on evolution, and the ecological and societal consequences’. PMID:27920379

In Vivo fitness associated with high virulence in a vertebrate virus is a complex trait regulated by host entry, replication, and shedding

USGS Publications Warehouse

Wargo, Andrew R.; Kurath, Gael

2011-01-01

The relationship between pathogen fitness and virulence is typically examined by quantifying only one or two pathogen fitness traits. More specifically, it is regularly assumed that within-host replication, as a precursor to transmission, is the driving force behind virulence. In reality, many traits contribute to pathogen fitness, and each trait could drive the evolution of virulence in different ways. Here, we independently quantified four viral infection cycle traits, namely, host entry, within-host replication, within-host coinfection fitness, and shedding, in vivo, in the vertebrate virus Infectious hematopoietic necrosis virus (IHNV). We examined how each of these stages of the viral infection cycle contributes to the fitness of IHNV genotypes that differ in virulence in rainbow trout. This enabled us to determine how infection cycle fitness traits are independently associated with virulence. We found that viral fitness was independently regulated by each of the traits examined, with the largest impact on fitness being provided by within-host replication. Furthermore, the more virulent of the two genotypes of IHNV we used had advantages in all of the traits quantified. Our results are thus congruent with the assumption that virulence and within-host replication are correlated but suggest that infection cycle fitness is complex and that replication is not the only trait associated with virulence.

Domesticated, Genetically Engineered, and Wild Plant Relatives Exhibit Unintended Phenotypic Differences: A Comparative Meta-Analysis Profiling Rice, Canola, Maize, Sunflower, and Pumpkin

PubMed Central

Hernández-Terán, Alejandra; Wegier, Ana; Benítez, Mariana; Lira, Rafael; Escalante, Ana E.

2017-01-01

Agronomic management of plants is a powerful evolutionary force acting on their populations. The management of cultivated plants is carried out by the traditional process of human selection or plant breeding and, more recently, by the technologies used in genetic engineering (GE). Even though crop modification through GE is aimed at specific traits, it is possible that other non-target traits can be affected by genetic modification due to the complex regulatory processes of plant metabolism and development. In this study, we conducted a meta-analysis profiling the phenotypic consequences of plant breeding and GE, and compared modified cultivars with wild relatives in five crops of global economic and cultural importance: rice, maize, canola, sunflower, and pumpkin. For these five species, we analyzed the literature with documentation of phenotypic traits that are potentially related to fitness for the same species in comparable conditions. The information was analyzed to evaluate whether the different processes of modification had influenced the phenotype in such a way as to cause statistical differences in the state of specific phenotypic traits or grouping of the organisms depending on their genetic origin [wild, domesticated with genetic engineering (domGE), and domesticated without genetic engineering (domNGE)]. In addition, we tested the hypothesis that, given that transgenic plants are a construct designed to impact, in many cases, a single trait of the plant (e.g., lepidopteran resistance), the phenotypic differences between domGE and domNGE would be either less (or inexistent) than between the wild and domesticated relatives (either domGE or domNGE). We conclude that (1) genetic modification (either by selective breeding or GE) can be traced phenotypically when comparing wild relatives with their domesticated relatives (domGE and domNGE) and (2) the existence and the magnitude of the phenotypic differences between domGE and domNGE of the same crop suggest consequences of genetic modification beyond the target trait(s). PMID:29259610

Expression quantitative trait loci and genetic regulatory network analysis reveals that Gabra2 is involved in stress responses in the mouse.

PubMed

Dai, Jiajuan; Wang, Xusheng; Chen, Ying; Wang, Xiaodong; Zhu, Jun; Lu, Lu

2009-11-01

Previous studies have revealed that the subunit alpha 2 (Gabra2) of the gamma-aminobutyric acid receptor plays a critical role in the stress response. However, little is known about the gentetic regulatory network for Gabra2 and the stress response. We combined gene expression microarray analysis and quantitative trait loci (QTL) mapping to characterize the genetic regulatory network for Gabra2 expression in the hippocampus of BXD recombinant inbred (RI) mice. Our analysis found that the expression level of Gabra2 exhibited much variation in the hippocampus across the BXD RI strains and between the parental strains, C57BL/6J, and DBA/2J. Expression QTL (eQTL) mapping showed three microarray probe sets of Gabra2 to have highly significant linkage likelihood ratio statistic (LRS) scores. Gene co-regulatory network analysis showed that 10 genes, including Gria3, Chka, Drd3, Homer1, Grik2, Odz4, Prkag2, Grm5, Gabrb1, and Nlgn1 are directly or indirectly associated with stress responses. Eleven genes were implicated as Gabra2 downstream genes through mapping joint modulation. The genetical genomics approach demonstrates the importance and the potential power of the eQTL studies in identifying genetic regulatory networks that contribute to complex traits, such as stress responses.

Divergence times and the evolution of morphological complexity in an early land plant lineage (Marchantiopsida) with a slow molecular rate.

PubMed

Villarreal A, Juan Carlos; Crandall-Stotler, Barbara J; Hart, Michelle L; Long, David G; Forrest, Laura L

2016-03-01

We present a complete generic-level phylogeny of the complex thalloid liverworts, a lineage that includes the model system Marchantia polymorpha. The complex thalloids are remarkable for their slow rate of molecular evolution and for being the only extant plant lineage to differentiate gas exchange tissues in the gametophyte generation. We estimated the divergence times and analyzed the evolutionary trends of morphological traits, including air chambers, rhizoids and specialized reproductive structures. A multilocus dataset was analyzed using maximum likelihood and Bayesian approaches. Relative rates were estimated using local clocks. Our phylogeny cements the early branching in complex thalloids. Marchantia is supported in one of the earliest divergent lineages. The rate of evolution in organellar loci is slower than for other liverwort lineages, except for two annual lineages. Most genera diverged in the Cretaceous. Marchantia polymorpha diversified in the Late Miocene, giving a minimum age estimate for the evolution of its sex chromosomes. The complex thalloid ancestor, excluding Blasiales, is reconstructed as a plant with a carpocephalum, with filament-less air chambers opening via compound pores, and without pegged rhizoids. Our comprehensive study of the group provides a temporal framework for the analysis of the evolution of critical traits essential for plants during land colonization. © 2015 Royal Botanic Garden Edinburgh. New Phytologist © 2015 New Phytologist Trust.

Identification of multiple genetic loci in the mouse controlling immobility time in the tail suspension and forced swimming tests.

PubMed

Abou-Elnaga, Ahmed F; Torigoe, Daisuke; Fouda, Mohamed M; Darwish, Ragab A; Abou-Ismail, Usama A; Morimatsu, Masami; Agui, Takashi

2015-05-01

Depression is one of the most famous psychiatric disorders in humans in all over the countries and considered a complex neurobehavioral trait and difficult to identify causal genes. Tail suspension test (TST) and forced swimming test (FST) are widely used for assessing depression-like behavior and antidepressant activity in mice. A variety of antidepressant agents are known to reduce immobility time in both TST and FST. To identify genetic determinants of immobility duration in both tests, we analyzed 101 F2 mice from an intercross between C57BL/6 and DBA/2 strains. Quantitative trait locus (QTL) mapping using 106 microsatellite markers revealed three loci (two significant and one suggestive) and five suggestive loci controlling immobility time in the TST and FST, respectively. Results of QTL analysis suggest a broad description of the genetic architecture underlying depression, providing underpinnings for identifying novel molecular targets for antidepressants to clear the complex genetic mechanisms of depressive disorders.

Partitioning heritability by functional annotation using genome-wide association summary statistics.

PubMed

Finucane, Hilary K; Bulik-Sullivan, Brendan; Gusev, Alexander; Trynka, Gosia; Reshef, Yakir; Loh, Po-Ru; Anttila, Verneri; Xu, Han; Zang, Chongzhi; Farh, Kyle; Ripke, Stephan; Day, Felix R; Purcell, Shaun; Stahl, Eli; Lindstrom, Sara; Perry, John R B; Okada, Yukinori; Raychaudhuri, Soumya; Daly, Mark J; Patterson, Nick; Neale, Benjamin M; Price, Alkes L

2015-11-01

Recent work has demonstrated that some functional categories of the genome contribute disproportionately to the heritability of complex diseases. Here we analyze a broad set of functional elements, including cell type-specific elements, to estimate their polygenic contributions to heritability in genome-wide association studies (GWAS) of 17 complex diseases and traits with an average sample size of 73,599. To enable this analysis, we introduce a new method, stratified LD score regression, for partitioning heritability from GWAS summary statistics while accounting for linked markers. This new method is computationally tractable at very large sample sizes and leverages genome-wide information. Our findings include a large enrichment of heritability in conserved regions across many traits, a very large immunological disease-specific enrichment of heritability in FANTOM5 enhancers and many cell type-specific enrichments, including significant enrichment of central nervous system cell types in the heritability of body mass index, age at menarche, educational attainment and smoking behavior.

In Search of the Perfect Phenotype: An Analysis of Linkage and Association Studies of Reading and Reading-Related Processes

PubMed Central

Skiba, Thomas; Landi, Nicole; Wagner, Richard

2011-01-01

Reading ability and specific reading disability (SRD) are complex traits involving several cognitive processes and are shaped by a complex interplay of genetic and environmental forces. Linkage studies of these traits have identified several susceptibility loci. Association studies have gone further in detecting candidate genes that might underlie these signals. These results have been obtained in samples of mainly European ancestry, which vary in their languages, inclusion criteria, and phenotype assessments. Such phenotypic heterogeneity across samples makes understanding the relationship between reading (dis)ability and reading-related processes and the genetic factors difficult; in addition, it may negatively influence attempts at replication. In moving forward, the identification of preferable phenotypes for future sample collection may improve the replicability of findings. This review of all published linkage and association results from the past 15 years was conducted to determine if certain phenotypes produce more replicable and consistent results than others. PMID:21243420

The Internet As a Large-Scale Complex System

NASA Astrophysics Data System (ADS)

Park, Kihong; Willinger, Walter

2005-06-01

The Internet may be viewed as a "complex system" with diverse features and many components that can give rise to unexpected emergent phenomena, revealing much about its own engineering. This book brings together chapter contributions from a workshop held at the Santa Fe Institute in March 2001. This volume captures a snapshot of some features of the Internet that may be fruitfully approached using a complex systems perspective, meaning using interdisciplinary tools and methods to tackle the subject area. The Internet penetrates the socioeconomic fabric of everyday life; a broader and deeper grasp of the Internet may be needed to meet the challenges facing the future. The resulting empirical data have already proven to be invaluable for gaining novel insights into the network's spatio-temporal dynamics, and can be expected to become even more important when tryin to explain the Internet's complex and emergent behavior in terms of elementary networking-based mechanisms. The discoveries of fractal or self-similar network traffic traces, power-law behavior in network topology and World Wide Web connectivity are instances of unsuspected, emergent system traits. Another important factor at the heart of fair, efficient, and stable sharing of network resources is user behavior. Network systems, when habited by selfish or greedy users, take on the traits of a noncooperative multi-party game, and their stability and efficiency are integral to understanding the overall system and its dynamics. Lastly, fault-tolerance and robustness of large-scale network systems can exhibit spatial and temporal correlations whose effective analysis and management may benefit from rescaling techniques applied in certain physical and biological systems. The present book will bring together several of the leading workers involved in the analysis of complex systems with the future development of the Internet.

Genetic architecture and temporal patterns of biomass accumulation in spring barley revealed by image analysis.

PubMed

Neumann, Kerstin; Zhao, Yusheng; Chu, Jianting; Keilwagen, Jens; Reif, Jochen C; Kilian, Benjamin; Graner, Andreas

2017-08-10

Genetic mapping of phenotypic traits generally focuses on a single time point, but biomass accumulates continuously during plant development. Resolution of the temporal dynamics that affect biomass recently became feasible using non-destructive imaging. With the aim to identify key genetic factors for vegetative biomass formation from the seedling stage to flowering, we explored growth over time in a diverse collection of two-rowed spring barley accessions. High heritabilities facilitated the temporal analysis of trait relationships and identification of quantitative trait loci (QTL). Biomass QTL tended to persist only a short period during early growth. More persistent QTL were detected around the booting stage. We identified seven major biomass QTL, which together explain 55% of the genetic variance at the seedling stage, and 43% at the booting stage. Three biomass QTL co-located with genes or QTL involved in phenology. The most important locus for biomass was independent from phenology and is located on chromosome 7HL at 141 cM. This locus explained ~20% of the genetic variance, was significant over a long period of time and co-located with HvDIM, a gene involved in brassinosteroid synthesis. Biomass is a dynamic trait and is therefore orchestrated by different QTL during early and late growth stages. Marker-assisted selection for high biomass at booting stage is most effective by also including favorable alleles from seedling biomass QTL. Selection for dynamic QTL may enhance genetic gain for complex traits such as biomass or, in the future, even grain yield.

Applications of machine learning and data mining methods to detect associations of rare and common variants with complex traits.

PubMed

Lu, Ake Tzu-Hui; Austin, Erin; Bonner, Ashley; Huang, Hsin-Hsiung; Cantor, Rita M

2014-09-01

Machine learning methods (MLMs), designed to develop models using high-dimensional predictors, have been used to analyze genome-wide genetic and genomic data to predict risks for complex traits. We summarize the results from six contributions to our Genetic Analysis Workshop 18 working group; these investigators applied MLMs and data mining to analyses of rare and common genetic variants measured in pedigrees. To develop risk profiles, group members analyzed blood pressure traits along with single-nucleotide polymorphisms and rare variant genotypes derived from sequence and imputation analyses in large Mexican American pedigrees. Supervised MLMs included penalized regression with varying penalties, support vector machines, and permanental classification. Unsupervised MLMs included sparse principal components analysis and sparse graphical models. Entropy-based components analyses were also used to mine these data. None of the investigators fully capitalized on the genetic information provided by the complete pedigrees. Their approaches either corrected for the nonindependence of the individuals within the pedigrees or analyzed only those who were independent. Some methods allowed for covariate adjustment, whereas others did not. We evaluated these methods using a variety of metrics. Four contributors conducted primary analyses on the real data, and the other two research groups used the simulated data with and without knowledge of the underlying simulation model. One group used the answers to the simulated data to assess power and type I errors. Although the MLMs applied were substantially different, each research group concluded that MLMs have advantages over standard statistical approaches with these high-dimensional data. © 2014 WILEY PERIODICALS, INC.

An efficient genome-wide association test for multivariate phenotypes based on the Fisher combination function.

PubMed

Yang, James J; Li, Jia; Williams, L Keoki; Buu, Anne

2016-01-05

In genome-wide association studies (GWAS) for complex diseases, the association between a SNP and each phenotype is usually weak. Combining multiple related phenotypic traits can increase the power of gene search and thus is a practically important area that requires methodology work. This study provides a comprehensive review of existing methods for conducting GWAS on complex diseases with multiple phenotypes including the multivariate analysis of variance (MANOVA), the principal component analysis (PCA), the generalizing estimating equations (GEE), the trait-based association test involving the extended Simes procedure (TATES), and the classical Fisher combination test. We propose a new method that relaxes the unrealistic independence assumption of the classical Fisher combination test and is computationally efficient. To demonstrate applications of the proposed method, we also present the results of statistical analysis on the Study of Addiction: Genetics and Environment (SAGE) data. Our simulation study shows that the proposed method has higher power than existing methods while controlling for the type I error rate. The GEE and the classical Fisher combination test, on the other hand, do not control the type I error rate and thus are not recommended. In general, the power of the competing methods decreases as the correlation between phenotypes increases. All the methods tend to have lower power when the multivariate phenotypes come from long tailed distributions. The real data analysis also demonstrates that the proposed method allows us to compare the marginal results with the multivariate results and specify which SNPs are specific to a particular phenotype or contribute to the common construct. The proposed method outperforms existing methods in most settings and also has great applications in GWAS on complex diseases with multiple phenotypes such as the substance abuse disorders.

A new tool called DISSECT for analysing large genomic data sets using a Big Data approach

PubMed Central

Canela-Xandri, Oriol; Law, Andy; Gray, Alan; Woolliams, John A.; Tenesa, Albert

2015-01-01

Large-scale genetic and genomic data are increasingly available and the major bottleneck in their analysis is a lack of sufficiently scalable computational tools. To address this problem in the context of complex traits analysis, we present DISSECT. DISSECT is a new and freely available software that is able to exploit the distributed-memory parallel computational architectures of compute clusters, to perform a wide range of genomic and epidemiologic analyses, which currently can only be carried out on reduced sample sizes or under restricted conditions. We demonstrate the usefulness of our new tool by addressing the challenge of predicting phenotypes from genotype data in human populations using mixed-linear model analysis. We analyse simulated traits from 470,000 individuals genotyped for 590,004 SNPs in ∼4 h using the combined computational power of 8,400 processor cores. We find that prediction accuracies in excess of 80% of the theoretical maximum could be achieved with large sample sizes. PMID:26657010

Independent evolution of complex life history adaptations in two families of fishes, live-bearing halfbeaks (zenarchopteridae, beloniformes) and poeciliidae (cyprinodontiformes).

PubMed

Reznick, David; Meredith, Robert; Collette, Bruce B

2007-11-01

We have previously documented multiple, independent origins of placentas in the fish family Poeciliidae. Here we summarize similar analyses of fishes in the family Zenarchopteridae. This family includes three live-bearing genera. Earlier studies documented the presence of superfetation, or the ability to carry multiple litters of young in different stages of development in the same ovary, in some species in all three genera. There is also one earlier report of matrotrophy, or extensive postfertilization maternal provisioning, in two of these genera. We present detailed life-history data for approximately half of the species in all three genera and combine them with the best available phylogeny to make inferences about the pattern of life-history evolution within this family. Three species of Hemirhamphodon have superfetation but lack matrotrophy. Most species in Nomorhamphus and Dermogenys either lack superfetation and matrotrophy or have both superfetation and matrotrophy. Our phylogenetic analysis shows that matrotrophy may have evolved independently in each genus. In Dermogenys, matrotrophic species produce fewer, larger offspring than nonmatrotrophic species. In Nomorhamphus; matrotrophic species instead produce more and smaller offspring than lecithotrophic species. However, the matrotrophic species in both genera have significantly smaller masses of reproductive tissue relative to their body sizes. All aspects of these results are duplicated in the fish family Poeciliidae. We discuss the possible adaptive significance of matrotrophy in the light of these new results. The two families together present a remarkable opportunity to study the evolution of a complex trait because they contain multiple, independent origins of the trait that often include close relatives that vary in either the presence or absence of the matrotrophy or in the degree to which matrotrophy is developed. These are the raw materials that are required for either an analysis of the adaptive significance of the trait or for studies of the genetic mechanisms that underlie the evolution of the trait.

From cultural traditions to cumulative culture: parameterizing the differences between human and nonhuman culture.

PubMed

Kempe, Marius; Lycett, Stephen J; Mesoudi, Alex

2014-10-21

Diverse species exhibit cultural traditions, i.e. population-specific profiles of socially learned traits, from songbird dialects to primate tool-use behaviours. However, only humans appear to possess cumulative culture, in which cultural traits increase in complexity over successive generations. Theoretically, it is currently unclear what factors give rise to these phenomena, and consequently why cultural traditions are found in several species but cumulative culture in only one. Here, we address this by constructing and analysing cultural evolutionary models of both phenomena that replicate empirically attestable levels of cultural variation and complexity in chimpanzees and humans. In our model of cultural traditions (Model 1), we find that realistic cultural variation between populations can be maintained even when individuals in different populations invent the same traits and migration between populations is frequent, and under a range of levels of social learning accuracy. This lends support to claims that putative cultural traditions are indeed cultural (rather than genetic) in origin, and suggests that cultural traditions should be widespread in species capable of social learning. Our model of cumulative culture (Model 2) indicates that both the accuracy of social learning and the number of cultural demonstrators interact to determine the complexity of a trait that can be maintained in a population. Combining these models (Model 3) creates two qualitatively distinct regimes in which there are either a few, simple traits, or many, complex traits. We suggest that these regimes correspond to nonhuman and human cultures, respectively. The rarity of cumulative culture in nature may result from this interaction between social learning accuracy and number of demonstrators. Copyright © 2014 Elsevier Ltd. All rights reserved.

Does plant architectural complexity increase with increasing habitat complexity? A test with a pioneer shrub in the Brazilian Cerrado.

PubMed

Silveira, F A O; Oliveira, E G

2013-05-01

Understanding variation in plant traits in heterogeneous habitats is important to predict responses to changing environments, but trait-environment associations are poorly known along ecological gradients. We tested the hypothesis that plant architectural complexity increases with habitat complexity along a soil fertility gradient in a Cerrado (Neotropical savanna) area in southeastern Brazil. Plant architecture and productivity (estimated as the total number of healthy infructescences) of Miconia albicans (SW.) Triana were examined in three types of vegetation which together form a natural gradient of increasing soil fertility, tree density and canopy cover: grasslands (campo sujo, CS), shrublands (cerrado sensu strico, CE) and woodlands (cerradão, CD). As expected, plants growing at the CS were shorter and had a lower branching pattern, whereas plants at the CD were the tallest. Unexpectedly, however, CD plants did not show higher architectural complexity compared to CE plants. Higher architectural similarity between CE and CD plants compared to similarity between CS and CE plants suggests reduced expression of functional architectural traits under shade. Plants growing at the CE produced more quaternary shoots, leading to a larger number of infructescences. This higher plant productivity in CE indicates that trait variation in ecological gradients is more complex than previously thought. Nematode-induced galls accounted for fruit destruction in 76.5% infructescences across physiognomies, but percentage of attack was poorly related to architectural variables. Our data suggest shade-induced limitation in M. albicans architecture, and point to complex phenotypic variation in heterogeneous habitats in Neotropical savannas.

Urban habitat complexity affects species richness but not environmental filtering of morphologically-diverse ants

PubMed Central

Nash, Michael A.; Christie, Fiona J.; Hahs, Amy K.; Livesley, Stephen J.

2015-01-01

Habitat complexity is a major determinant of structure and diversity of ant assemblages. Following the size-grain hypothesis, smaller ant species are likely to be advantaged in more complex habitats compared to larger species. Habitat complexity can act as an environmental filter based on species size and morphological traits, therefore affecting the overall structure and diversity of ant assemblages. In natural and semi-natural ecosystems, habitat complexity is principally regulated by ecological successions or disturbance such as fire and grazing. Urban ecosystems provide an opportunity to test relationships between habitat, ant assemblage structure and ant traits using novel combinations of habitat complexity generated and sustained by human management. We sampled ant assemblages in low-complexity and high-complexity parks, and high-complexity woodland remnants, hypothesizing that (i) ant abundance and species richness would be higher in high-complexity urban habitats, (ii) ant assemblages would differ between low- and high-complexity habitats and (iii) ants living in high-complexity habitats would be smaller than those living in low-complexity habitats. Contrary to our hypothesis, ant species richness was higher in low-complexity habitats compared to high-complexity habitats. Overall, ant assemblages were significantly different among the habitat complexity types investigated, although ant size and morphology remained the same. Habitat complexity appears to affect the structure of ant assemblages in urban ecosystems as previously observed in natural and semi-natural ecosystems. However, the habitat complexity filter does not seem to be linked to ant morphological traits related to body size. PMID:26528416

Comprehensive genotyping of the USA national maize inbred seed bank

PubMed Central

2013-01-01

Background Genotyping by sequencing, a new low-cost, high-throughput sequencing technology was used to genotype 2,815 maize inbred accessions, preserved mostly at the National Plant Germplasm System in the USA. The collection includes inbred lines from breeding programs all over the world. Results The method produced 681,257 single-nucleotide polymorphism (SNP) markers distributed across the entire genome, with the ability to detect rare alleles at high confidence levels. More than half of the SNPs in the collection are rare. Although most rare alleles have been incorporated into public temperate breeding programs, only a modest amount of the available diversity is present in the commercial germplasm. Analysis of genetic distances shows population stratification, including a small number of large clusters centered on key lines. Nevertheless, an average fixation index of 0.06 indicates moderate differentiation between the three major maize subpopulations. Linkage disequilibrium (LD) decays very rapidly, but the extent of LD is highly dependent on the particular group of germplasm and region of the genome. The utility of these data for performing genome-wide association studies was tested with two simply inherited traits and one complex trait. We identified trait associations at SNPs very close to known candidate genes for kernel color, sweet corn, and flowering time; however, results suggest that more SNPs are needed to better explore the genetic architecture of complex traits. Conclusions The genotypic information described here allows this publicly available panel to be exploited by researchers facing the challenges of sustainable agriculture through better knowledge of the nature of genetic diversity. PMID:23759205

Comprehensive genotyping of the USA national maize inbred seed bank.

PubMed

Romay, Maria C; Millard, Mark J; Glaubitz, Jeffrey C; Peiffer, Jason A; Swarts, Kelly L; Casstevens, Terry M; Elshire, Robert J; Acharya, Charlotte B; Mitchell, Sharon E; Flint-Garcia, Sherry A; McMullen, Michael D; Holland, James B; Buckler, Edward S; Gardner, Candice A

2013-06-11

Genotyping by sequencing, a new low-cost, high-throughput sequencing technology was used to genotype 2,815 maize inbred accessions, preserved mostly at the National Plant Germplasm System in the USA. The collection includes inbred lines from breeding programs all over the world. The method produced 681,257 single-nucleotide polymorphism (SNP) markers distributed across the entire genome, with the ability to detect rare alleles at high confidence levels. More than half of the SNPs in the collection are rare. Although most rare alleles have been incorporated into public temperate breeding programs, only a modest amount of the available diversity is present in the commercial germplasm. Analysis of genetic distances shows population stratification, including a small number of large clusters centered on key lines. Nevertheless, an average fixation index of 0.06 indicates moderate differentiation between the three major maize subpopulations. Linkage disequilibrium (LD) decays very rapidly, but the extent of LD is highly dependent on the particular group of germplasm and region of the genome. The utility of these data for performing genome-wide association studies was tested with two simply inherited traits and one complex trait. We identified trait associations at SNPs very close to known candidate genes for kernel color, sweet corn, and flowering time; however, results suggest that more SNPs are needed to better explore the genetic architecture of complex traits. The genotypic information described here allows this publicly available panel to be exploited by researchers facing the challenges of sustainable agriculture through better knowledge of the nature of genetic diversity.

Dissecting Situational Strength: Theoretical Analysis and Empirical Tests

DTIC Science & Technology

2012-09-01

behavior , and to the complexity of personality and its multiple and interacting determinants ” (Mischel, 1999; p. 456; see also Mischel, 1968). Mischel...outcome relationships. Specifically, he posited that traits have less of a determining impact on behaviors in “strong” situations, which provide: (1...which situations interact with personality in determining voluntary work behavior (Project One) and the extent to which people respond adversely to

Association analysis of the monoamine oxidase A gene in bipolar affective disorder by using family-based internal controls

DOE Office of Scientific and Technical Information (OSTI.GOV)

Noethen, M.M.; Eggermann, K.; Propping, P.

1995-10-01

It is well accepted that association studies are a major tool in investigating the contribution of single genes to the development of diseases that do not follow simple Mendelian inheritance pattern (so-called complex traits). Such major psychiatric diseases as bipolar affective disorder and schizophrenia clearly fall into this category of diseases. 7 refs., 1 tab.

Multiple-trait multiple-country genetic evaluation of Holstein bulls for female fertility and milk production traits.

PubMed

Nilforooshan, M A; Jakobsen, J H; Fikse, W F; Berglund, B; Jorjani, H

2014-06-01

The aim of this study was to investigate the effect of including milk yield data in the international genetic evaluation of female fertility traits to reduce or eliminate a possible bias because of across-country selection for milk yield. Data included two female fertility traits from Great Britain, Italy and the Netherlands, together with milk yield data from the same countries and from the United States, because the genetic trends in other countries may be influenced by selection decisions on bulls in the United States. Potentially, female fertility data had been corrected nationally for within-country selection and management biases for milk yield. Using a multiple-trait multiple across-country evaluation (MT-MACE) for the analysis of female fertility traits with milk yield, across-country selection patterns both for female fertility and milk yield can be considered simultaneously. Four analyses were performed; one single-trait multiple across-country evaluation analysis including only milk yield data, one MT-MACE analysis including only female fertility traits, and one MT-MACE analysis including both female fertility and milk yield traits. An additional MT-MACE analysis was performed including both female fertility and milk yield traits, but excluding the United States. By including milk yield traits to the analysis, female fertility reliabilities increased, but not for all bulls in all the countries by trait combinations. The presence of milk yield traits in the analysis did not considerably change the genetic correlations, genetic trends or bull rankings of female fertility traits. Even though the predicted genetic merits of female fertility traits hardly changed by including milk yield traits to the analysis, the change was not equally distributed to the whole data. The number of bulls in common between the two sets of Top 100 bulls for each trait in the two analyses of female fertility traits, with and without the four milk yield traits and their rank correlations were low, not necessarily because of the absence of the US milk yield data. The joint international genetic evaluation of female fertility traits with milk yield is recommended to make use of information on several female fertility traits from different countries simultaneously, to consider selection decisions for milk yield in the genetic evaluation of female fertility traits for obtaining more accurate estimating breeding values (EBV) and to acquire female fertility EBV for bulls evaluated for milk yield, but not for female fertility.

Inference of the genetic architecture underlying BMI and height with the use of 20,240 sibling pairs.

PubMed

Hemani, Gibran; Yang, Jian; Vinkhuyzen, Anna; Powell, Joseph E; Willemsen, Gonneke; Hottenga, Jouke-Jan; Abdellaoui, Abdel; Mangino, Massimo; Valdes, Ana M; Medland, Sarah E; Madden, Pamela A; Heath, Andrew C; Henders, Anjali K; Nyholt, Dale R; de Geus, Eco J C; Magnusson, Patrik K E; Ingelsson, Erik; Montgomery, Grant W; Spector, Timothy D; Boomsma, Dorret I; Pedersen, Nancy L; Martin, Nicholas G; Visscher, Peter M

2013-11-07

Evidence that complex traits are highly polygenic has been presented by population-based genome-wide association studies (GWASs) through the identification of many significant variants, as well as by family-based de novo sequencing studies indicating that several traits have a large mutational target size. Here, using a third study design, we show results consistent with extreme polygenicity for body mass index (BMI) and height. On a sample of 20,240 siblings (from 9,570 nuclear families), we used a within-family method to obtain narrow-sense heritability estimates of 0.42 (SE = 0.17, p = 0.01) and 0.69 (SE = 0.14, p = 6 × 10(-)(7)) for BMI and height, respectively, after adjusting for covariates. The genomic inflation factors from locus-specific linkage analysis were 1.69 (SE = 0.21, p = 0.04) for BMI and 2.18 (SE = 0.21, p = 2 × 10(-10)) for height. This inflation is free of confounding and congruent with polygenicity, consistent with observations of ever-increasing genomic-inflation factors from GWASs with large sample sizes, implying that those signals are due to true genetic signals across the genome rather than population stratification. We also demonstrate that the distribution of the observed test statistics is consistent with both rare and common variants underlying a polygenic architecture and that previous reports of linkage signals in complex traits are probably a consequence of polygenic architecture rather than the segregation of variants with large effects. The convergent empirical evidence from GWASs, de novo studies, and within-family segregation implies that family-based sequencing studies for complex traits require very large sample sizes because the effects of causal variants are small on average. Copyright © 2013 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

Statistical epistasis between candidate gene alleles for complex tuber traits in an association mapping population of tetraploid potato

PubMed Central

Li, Li; Paulo, Maria-João; van Eeuwijk, Fred

2010-01-01

Association mapping using DNA-based markers is a novel tool in plant genetics for the analysis of complex traits. Potato tuber yield, starch content, starch yield and chip color are complex traits of agronomic relevance, for which carbohydrate metabolism plays an important role. At the functional level, the genes and biochemical pathways involved in carbohydrate metabolism are among the best studied in plants. Quantitative traits such as tuber starch and sugar content are therefore models for association genetics in potato based on candidate genes. In an association mapping experiment conducted with a population of 243 tetraploid potato varieties and breeding clones, we previously identified associations between individual candidate gene alleles and tuber starch content, starch yield and chip quality. In the present paper, we tested 190 DNA markers at 36 loci scored in the same association mapping population for pairwise statistical epistatic interactions. Fifty marker pairs were associated mainly with tuber starch content and/or starch yield, at a cut-off value of q ≤ 0.20 for the experiment-wide false discovery rate (FDR). Thirteen marker pairs had an FDR of q ≤ 0.10. Alleles at loci encoding ribulose-bisphosphate carboxylase/oxygenase activase (Rca), sucrose phosphate synthase (Sps) and vacuolar invertase (Pain1) were most frequently involved in statistical epistatic interactions. The largest effect on tuber starch content and starch yield was observed for the paired alleles Pain1-8c and Rca-1a, explaining 9 and 10% of the total variance, respectively. The combination of these two alleles increased the means of tuber starch content and starch yield. Biological models to explain the observed statistical epistatic interactions are discussed. Electronic supplementary material The online version of this article (doi:10.1007/s00122-010-1389-3) contains supplementary material, which is available to authorized users. PMID:20603706

Host Genome Influence on Gut Microbial Composition and Microbial Prediction of Complex Traits in Pigs.

PubMed

Camarinha-Silva, Amelia; Maushammer, Maria; Wellmann, Robin; Vital, Marius; Preuss, Siegfried; Bennewitz, Jörn

2017-07-01

The aim of the present study was to analyze the interplay between gastrointestinal tract (GIT) microbiota, host genetics, and complex traits in pigs using extended quantitative-genetic methods. The study design consisted of 207 pigs that were housed and slaughtered under standardized conditions, and phenotyped for daily gain, feed intake, and feed conversion rate. The pigs were genotyped with a standard 60 K SNP chip. The GIT microbiota composition was analyzed by 16S rRNA gene amplicon sequencing technology. Eight from 49 investigated bacteria genera showed a significant narrow sense host heritability, ranging from 0.32 to 0.57. Microbial mixed linear models were applied to estimate the microbiota variance for each complex trait. The fraction of phenotypic variance explained by the microbial variance was 0.28, 0.21, and 0.16 for daily gain, feed conversion, and feed intake, respectively. The SNP data and the microbiota composition were used to predict the complex traits using genomic best linear unbiased prediction (G-BLUP) and microbial best linear unbiased prediction (M-BLUP) methods, respectively. The prediction accuracies of G-BLUP were 0.35, 0.23, and 0.20 for daily gain, feed conversion, and feed intake, respectively. The corresponding prediction accuracies of M-BLUP were 0.41, 0.33, and 0.33. Thus, in addition to SNP data, microbiota abundances are an informative source of complex trait predictions. Since the pig is a well-suited animal for modeling the human digestive tract, M-BLUP, in addition to G-BLUP, might be beneficial for predicting human predispositions to some diseases, and, consequently, for preventative and personalized medicine. Copyright © 2017 by the Genetics Society of America.

Multidimensional assessment of severe asthma: A systematic review and meta-analysis.

PubMed

Clark, Vanessa L; Gibson, Peter G; Genn, Grayson; Hiles, Sarah A; Pavord, Ian D; McDonald, Vanessa M

2017-10-01

The management of severe asthma is complex. Multidimensional assessment (MDA) of specific traits has been proposed as an effective strategy to manage severe asthma, although it is supported by few prospective studies. We aimed to systematically review the literature published on MDA in severe asthma, to identify the traits included in MDA and to determine the effect of MDA on asthma-related outcomes. We identified 26 studies and classified these based on study type (cohort/cross-sectional studies; experimental/outcome studies; and severe asthma disease registries). Study type determined the comprehensiveness of the assessment. Assessed traits were classified into three domains (airways, co-morbidities and risk factors). The airway domain had the largest number of traits assessed (mean ± SD = 4.2 ± 1.7) compared with co-morbidities (3.6 ± 2.2) and risk factors (3.9 ± 2.1). Bronchodilator reversibility and airflow limitation were assessed in 92% of studies, whereas airway inflammation was only assessed in 50%. Commonly assessed co-morbidities were psychological dysfunction, sinusitis (both 73%) and gastro-oesophageal reflux disease (GORD; 69%). Atopic and smoking statuses were the most commonly assessed risk factors (85% and 86%, respectively). There were six outcome studies, of which five concluded that MDA is effective at improving asthma-related outcomes. Among these studies, significantly more traits were assessed than treated. MDA studies have assessed a variety of different traits and have shown evidence of improved outcomes. This promising model of care requires more research to inform which traits should be assessed, which traits should be treated and what effect MDA has on patient outcomes. © 2017 Asian Pacific Society of Respirology.

Linkage and Association Mapping for Two Major Traits Used in the Maritime Pine Breeding Program: Height Growth and Stem Straightness

PubMed Central

Bink, Marco CAM; van Heerwaarden, Joost; Chancerel, Emilie; Boury, Christophe; Lesur, Isabelle; Isik, Fikret; Bouffier, Laurent; Plomion, Christophe

2016-01-01

Background Increasing our understanding of the genetic architecture of complex traits, through analyses of genotype-phenotype associations and of the genes/polymorphisms accounting for trait variation, is crucial, to improve the integration of molecular markers into forest tree breeding. In this study, two full-sib families and one breeding population of maritime pine were used to identify quantitative trait loci (QTLs) for height growth and stem straightness, through linkage analysis (LA) and linkage disequilibrium (LD) mapping approaches. Results The populations used for LA consisted of two unrelated three-generation full-sib families (n = 197 and n = 477). These populations were assessed for height growth or stem straightness and genotyped for 248 and 217 markers, respectively. The population used for LD mapping consisted of 661 founders of the first and second generations of the breeding program. This population was phenotyped for the same traits and genotyped for 2,498 single-nucleotide polymorphism (SNP) markers corresponding to 1,652 gene loci. The gene-based reference genetic map of maritime pine was used to localize and compare the QTLs detected by the two approaches, for both traits. LA identified three QTLs for stem straightness and two QTLs for height growth. The LD study yielded seven significant associations (P ≤ 0.001): four for stem straightness and three for height growth. No colocalisation was found between QTLs identified by LA and SNPs detected by LD mapping for the same trait. Conclusions This study provides the first comparison of LA and LD mapping approaches in maritime pine, highlighting the complementary nature of these two approaches for deciphering the genetic architecture of two mandatory traits of the breeding program. PMID:27806077

Linkage and Association Mapping for Two Major Traits Used in the Maritime Pine Breeding Program: Height Growth and Stem Straightness.

PubMed

Bartholomé, Jérôme; Bink, Marco Cam; van Heerwaarden, Joost; Chancerel, Emilie; Boury, Christophe; Lesur, Isabelle; Isik, Fikret; Bouffier, Laurent; Plomion, Christophe

2016-01-01

Increasing our understanding of the genetic architecture of complex traits, through analyses of genotype-phenotype associations and of the genes/polymorphisms accounting for trait variation, is crucial, to improve the integration of molecular markers into forest tree breeding. In this study, two full-sib families and one breeding population of maritime pine were used to identify quantitative trait loci (QTLs) for height growth and stem straightness, through linkage analysis (LA) and linkage disequilibrium (LD) mapping approaches. The populations used for LA consisted of two unrelated three-generation full-sib families (n = 197 and n = 477). These populations were assessed for height growth or stem straightness and genotyped for 248 and 217 markers, respectively. The population used for LD mapping consisted of 661 founders of the first and second generations of the breeding program. This population was phenotyped for the same traits and genotyped for 2,498 single-nucleotide polymorphism (SNP) markers corresponding to 1,652 gene loci. The gene-based reference genetic map of maritime pine was used to localize and compare the QTLs detected by the two approaches, for both traits. LA identified three QTLs for stem straightness and two QTLs for height growth. The LD study yielded seven significant associations (P ≤ 0.001): four for stem straightness and three for height growth. No colocalisation was found between QTLs identified by LA and SNPs detected by LD mapping for the same trait. This study provides the first comparison of LA and LD mapping approaches in maritime pine, highlighting the complementary nature of these two approaches for deciphering the genetic architecture of two mandatory traits of the breeding program.

Accuracy of whole-genome prediction using a genetic architecture-enhanced variance-covariance matrix.

PubMed

Zhang, Zhe; Erbe, Malena; He, Jinlong; Ober, Ulrike; Gao, Ning; Zhang, Hao; Simianer, Henner; Li, Jiaqi

2015-02-09

Obtaining accurate predictions of unobserved genetic or phenotypic values for complex traits in animal, plant, and human populations is possible through whole-genome prediction (WGP), a combined analysis of genotypic and phenotypic data. Because the underlying genetic architecture of the trait of interest is an important factor affecting model selection, we propose a new strategy, termed BLUP|GA (BLUP-given genetic architecture), which can use genetic architecture information within the dataset at hand rather than from public sources. This is achieved by using a trait-specific covariance matrix ( T: ), which is a weighted sum of a genetic architecture part ( S: matrix) and the realized relationship matrix ( G: ). The algorithm of BLUP|GA (BLUP-given genetic architecture) is provided and illustrated with real and simulated datasets. Predictive ability of BLUP|GA was validated with three model traits in a dairy cattle dataset and 11 traits in three public datasets with a variety of genetic architectures and compared with GBLUP and other approaches. Results show that BLUP|GA outperformed GBLUP in 20 of 21 scenarios in the dairy cattle dataset and outperformed GBLUP, BayesA, and BayesB in 12 of 13 traits in the analyzed public datasets. Further analyses showed that the difference of accuracies for BLUP|GA and GBLUP significantly correlate with the distance between the T: and G: matrices. The new strategy applied in BLUP|GA is a favorable and flexible alternative to the standard GBLUP model, allowing to account for the genetic architecture of the quantitative trait under consideration when necessary. This feature is mainly due to the increased similarity between the trait-specific relationship matrix ( T: matrix) and the genetic relationship matrix at unobserved causal loci. Applying BLUP|GA in WGP would ease the burden of model selection. Copyright © 2015 Zhang et al.

Genomic architecture of habitat-related divergence and signature of directional selection in the body shapes of Gnathopogon fishes.

PubMed

Kakioka, Ryo; Kokita, Tomoyuki; Kumada, Hiroki; Watanabe, Katsutoshi; Okuda, Noboru

2015-08-01

Evolution of ecomorphologically relevant traits such as body shapes is important to colonize and persist in a novel environment. Habitat-related adaptive divergence of these traits is therefore common among animals. We studied the genomic architecture of habitat-related divergence in the body shape of Gnathopogon fishes, a novel example of lake-stream ecomorphological divergence, and tested for the action of directional selection on body shape differentiation. Compared to stream-dwelling Gnathopogon elongatus, the sister species Gnathopogon caerulescens, exclusively inhabiting a large ancient lake, had an elongated body, increased proportion of the caudal region and small head, which would be advantageous in the limnetic environment. Using an F2 interspecific cross between the two Gnathopogon species (195 individuals), quantitative trait locus (QTL) analysis with geometric morphometric quantification of body shape and restriction-site associated DNA sequencing-derived markers (1622 loci) identified 26 significant QTLs associated with the interspecific differences of body shape-related traits. These QTLs had small to moderate effects, supporting polygenic inheritance of the body shape-related traits. Each QTL was mostly located on different genomic regions, while colocalized QTLs were detected for some ecomorphologically relevant traits that are proxy of body and caudal peduncle depths, suggesting different degree of modularity among traits. The directions of the body shape QTLs were mostly consistent with the interspecific difference, and QTL sign test suggested a genetic signature of directional selection in the body shape divergence. Thus, we successfully elucidated the genomic architecture underlying the adaptive changes of the quantitative and complex morphological trait in a novel system. © 2015 John Wiley & Sons Ltd.

Shared genetic influences between attention-deficit/hyperactivity disorder (ADHD) traits in children and clinical ADHD.

PubMed

Stergiakouli, Evie; Martin, Joanna; Hamshere, Marian L; Langley, Kate; Evans, David M; St Pourcain, Beate; Timpson, Nicholas J; Owen, Michael J; O'Donovan, Michael; Thapar, Anita; Davey Smith, George

2015-04-01

Twin studies and genome-wide complex trait analysis (GCTA) are not in agreement regarding heritability estimates for behavioral traits in children from the general population. This has sparked a debate on the possible difference in genetic architecture between behavioral traits and psychiatric disorders. In this study, we test whether polygenic risk scores associated with variation in attention-deficit/hyperactivity disorder (ADHD) trait levels in children from the general population predict ADHD diagnostic status and severity in an independent clinical sample. Single nucleotide polymorphisms (SNPs) with p < .5 from a genome-wide association study of ADHD traits in 4,546 children (mean age, 7 years 7 months) from the Avon Longitudinal Study of Parents and Children (ALSPAC; general population sample) were selected to calculate polygenic risk scores in 508 children with an ADHD diagnosis (independent clinical sample) and 5,081 control participants. Polygenic scores were tested for association with case-control status and severity of disorder in the clinical sample. Increased polygenic score for ADHD traits predicted ADHD case-control status (odds ratio = 1.17 [95% CI = 1.08-1.28], p = .0003), higher ADHD symptom severity (β = 0.29 [95% CI = 0.04-0.54], p = 0.02), and symptom domain severity in the clinical sample. This study highlights the relevance of additive genetic variance in ADHD, and provides evidence that shared genetic factors contribute to both behavioral traits in the general population and psychiatric disorders at least in the case of ADHD. Copyright © 2015 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.

Unique aspects of impulsive traits in substance use and overeating: specific contributions of common assessments of impulsivity.

PubMed

Beaton, Derek; Abdi, Hervé; Filbey, Francesca M

2014-11-01

Abstract Background: Impulsivity is a complex trait often studied in substance abuse and overeating disorders, but the exact nature of impulsivity traits and their contribution to these disorders are still debated. Thus, understanding how to measure impulsivity is essential for comprehending addictive behaviors. Identify unique impulsivity traits specific to substance use and overeating. Impulsive Sensation Seeking (ImpSS) and Barratt's Impulsivity scales (BIS) Scales were analyzed with a non-parametric factor analytic technique (discriminant correspondence analysis) to identify group-specific traits on 297 individuals from five groups: Marijuana (n = 88), Nicotine (n = 82), Overeaters (n = 27), Marijuauna + Nicotine (n = 63), and CONTROLs (n = 37). A significant overall factor structure revealed three components of impulsivity that explained respectively 50.19% (pperm < 0.0005), 24.18% (pperm < 0.0005), and 15.98% (pperm < 0.0005) of the variance. All groups were significantly different from one another. When analyzed together, the BIS and ImpSS produce a multi-factorial structure that identified the impulsivity traits specific to these groups. The group specific traits are (1) CONTROL: low impulse, avoids thrill-seeking behaviors; (2) Marijuana: seeks mild sensation, is focused and attentive; (3) Marijuana + Nicotine: pursues thrill-seeking, lacks focus and attention; (4) Nicotine: lacks focus and planning; (5) Overeating: lacks focus, but plans (short and long term). Our results reveal impulsivity traits specific to each group. This may provide better criteria to define spectrums and trajectories - instead of categories - of symptoms for substance use and eating disorders. Defining symptomatic spectrums could be an important step forward in diagnostic strategies.

Unique aspects of impulsive traits in substance use and overeating: specific contributions of common assessments of impulsivity

PubMed Central

Beaton, Derek; Abdi, Hervé; Filbey, Francesca M.

2015-01-01

Background Impulsivity is a complex trait often studied in substance abuse and overeating disorders, but the exact nature of impulsivity traits and their contribution to these disorders are still debated. Thus, understanding how to measure impulsivity is essential for comprehending addictive behaviors. Objectives Identify unique impulsivity traits specific to substance use and overeating. Methods Impulsive Sensation Seeking (ImpSS) and Barratt’s Impulsivity scales (BIS) Scales were analyzed with a non-parametric factor analytic technique (discriminant correspondence analysis) to identify group-specific traits on 297 individuals from five groups: Marijuana (n = 88), Nicotine (n = 82), Overeaters (n = 27), Marijuauna + Nicotine (n = 63), and Controls (n = 37). Results A significant overall factor structure revealed three components of impulsivity that explained respectively 50.19% (pperm<0.0005), 24.18% (pperm<0.0005), and 15.98% (pperm<0.0005) of the variance. All groups were significantly different from one another. When analyzed together, the BIS and ImpSS produce a multi-factorial structure that identified the impulsivity traits specific to these groups. The group specific traits are (1) Control: low impulse, avoids thrill-seeking behaviors; (2) Marijuana: seeks mild sensation, is focused and attentive; (3) Marijuana + Nicotine: pursues thrill-seeking, lacks focus and attention; (4) Nicotine: lacks focus and planning; (5) Overeating: lacks focus, but plans (short and long term). Conclusions Our results reveal impulsivity traits specific to each group. This may provide better criteria to define spectrums and trajectories – instead of categories – of symptoms for substance use and eating disorders. Defining symptomatic spectrums could be an important step forward in diagnostic strategies. PMID:25115831

Comparative genome analysis of a thermotolerant Escherichia coli obtained by Genome Replication Engineering Assisted Continuous Evolution (GREACE) and its parent strain provides new understanding of microbial heat tolerance.

PubMed

Luan, Guodong; Bao, Guanhui; Lin, Zhao; Li, Yang; Chen, Zugen; Li, Yin; Cai, Zhen

2015-12-25

Heat tolerance of microbes is of great importance for efficient biorefinery and bioconversion. However, engineering and understanding of microbial heat tolerance are difficult and insufficient because it is a complex physiological trait which probably correlates with all gene functions, genetic regulations, and cellular metabolisms and activities. In this work, a novel strain engineering approach named Genome Replication Engineering Assisted Continuous Evolution (GREACE) was employed to improve the heat tolerance of Escherichia coli. When the E. coli strain carrying a mutator was cultivated under gradually increasing temperature, genome-wide mutations were continuously generated during genome replication and the mutated strains with improved thermotolerance were autonomously selected. A thermotolerant strain HR50 capable of growing at 50°C on LB agar plate was obtained within two months, demonstrating the efficiency of GREACE in improving such a complex physiological trait. To understand the improved heat tolerance, genomes of HR50 and its wildtype strain DH5α were sequenced. Evenly distributed 361 mutations covering all mutation types were found in HR50. Closed material transportations, loose genome conformation, and possibly altered cell wall structure and transcription pattern were the main differences of HR50 compared with DH5α, which were speculated to be responsible for the improved heat tolerance. This work not only expanding our understanding of microbial heat tolerance, but also emphasizing that the in vivo continuous genome mutagenesis method, GREACE, is efficient in improving microbial complex physiological trait. Copyright © 2015 Elsevier B.V. All rights reserved.

A Simple Test of Class-Level Genetic Association Can Reveal Novel Cardiometabolic Trait Loci.

PubMed

Qian, Jing; Nunez, Sara; Reed, Eric; Reilly, Muredach P; Foulkes, Andrea S

2016-01-01

Characterizing the genetic determinants of complex diseases can be further augmented by incorporating knowledge of underlying structure or classifications of the genome, such as newly developed mappings of protein-coding genes, epigenetic marks, enhancer elements and non-coding RNAs. We apply a simple class-level testing framework, termed Genetic Class Association Testing (GenCAT), to identify protein-coding gene association with 14 cardiometabolic (CMD) related traits across 6 publicly available genome wide association (GWA) meta-analysis data resources. GenCAT uses SNP-level meta-analysis test statistics across all SNPs within a class of elements, as well as the size of the class and its unique correlation structure, to determine if the class is statistically meaningful. The novelty of findings is evaluated through investigation of regional signals. A subset of findings are validated using recently updated, larger meta-analysis resources. A simulation study is presented to characterize overall performance with respect to power, control of family-wise error and computational efficiency. All analysis is performed using the GenCAT package, R version 3.2.1. We demonstrate that class-level testing complements the common first stage minP approach that involves individual SNP-level testing followed by post-hoc ascribing of statistically significant SNPs to genes and loci. GenCAT suggests 54 protein-coding genes at 41 distinct loci for the 13 CMD traits investigated in the discovery analysis, that are beyond the discoveries of minP alone. An additional application to biological pathways demonstrates flexibility in defining genetic classes. We conclude that it would be prudent to include class-level testing as standard practice in GWA analysis. GenCAT, for example, can be used as a simple, complementary and efficient strategy for class-level testing that leverages existing data resources, requires only summary level data in the form of test statistics, and adds significant value with respect to its potential for identifying multiple novel and clinically relevant trait associations.

Expanding Omics Resources for Improvement of Soybean Seed Composition Traits

PubMed Central

Chaudhary, Juhi; Patil, Gunvant B.; Sonah, Humira; Deshmukh, Rupesh K.; Vuong, Tri D.; Valliyodan, Babu; Nguyen, Henry T.

2015-01-01

Food resources of the modern world are strained due to the increasing population. There is an urgent need for innovative methods and approaches to augment food production. Legume seeds are major resources of human food and animal feed with their unique nutrient compositions including oil, protein, carbohydrates, and other beneficial nutrients. Recent advances in next-generation sequencing (NGS) together with “omics” technologies have considerably strengthened soybean research. The availability of well annotated soybean genome sequence along with hundreds of identified quantitative trait loci (QTL) associated with different seed traits can be used for gene discovery and molecular marker development for breeding applications. Despite the remarkable progress in these technologies, the analysis and mining of existing seed genomics data are still challenging due to the complexity of genetic inheritance, metabolic partitioning, and developmental regulations. Integration of “omics tools” is an effective strategy to discover key regulators of various seed traits. In this review, recent advances in “omics” approaches and their use in soybean seed trait investigations are presented along with the available databases and technological platforms and their applicability in the improvement of soybean. This article also highlights the use of modern breeding approaches, such as genome-wide association studies (GWAS), genomic selection (GS), and marker-assisted recurrent selection (MARS) for developing superior cultivars. A catalog of available important resources for major seed composition traits, such as seed oil, protein, carbohydrates, and yield traits are provided to improve the knowledge base and future utilization of this information in the soybean crop improvement programs. PMID:26635846

Intelligence, personality, and interests: evidence for overlapping traits.

PubMed

Ackerman, P L; Heggestad, E D

1997-03-01

The authors review the development of the modern paradigm for intelligence assessment and application and consider the differentiation between intelligence-as-maximal performance and intelligence-as-typical performance. They review theories of intelligence, personality, and interest as a means to establish potential overlap. Consideration of intelligence-as-typical performance provides a basis for evaluation of intelligence-personality and intelligence-interest relations. Evaluation of relations among personality constructs, vocational interests, and intellectual abilities provides evidence for communality across the domains of personality of J. L. Holland's (1959) model of vocational interests. The authors provide an extensive meta-analysis of personality-intellectual ability correlations, and a review of interest-intellectual ability associations. They identify 4 trait complexes: social, clerical/conventional, science/math, and intellectual/cultural.

Pathways to Identity: Aiding Law Enforcement in Identification Tasks With Visual Analytics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bruce, Joseph R.; Scholtz, Jean; Hodges, Duncan

The nature of identity has changed dramatically in recent years, and has grown in complexity. Identities are defined in multiple domains: biological and psychological elements strongly contribute, but also biographical and cyber elements are necessary to complete the picture. Law enforcement is beginning to adjust to these changes, recognizing its importance in criminal justice. The SuperIdentity project seeks to aid law enforcement officials in their identification tasks through research of techniques for discovering identity traits, generation of statistical models of identity and analysis of identity traits through visualization. We present use cases compiled through user interviews in multiple fields, includingmore » law enforcement, as well as the modeling and visualization tools design to aid in those use cases.« less

Pathways to Identity. Using Visualization to Aid Law Enforcement in Identification Tasks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bruce, Joseph R.; Scholtz, Jean; Hodges, Duncan

The nature of identity has changed dramatically in recent years and has grown in complexity. Identities are defined in multiple domains: biological and psychological elements strongly contribute, but biographical and cyber elements also are necessary to complete the picture. Law enforcement is beginning to adjust to these changes, recognizing identity’s importance in criminal justice. The SuperIdentity project seeks to aid law enforcement officials in their identification tasks through research of techniques for discovering identity traits, generation of statistical models of identity and analysis of identity traits through visualization. We present use cases compiled through user interviews in multiple fields, includingmore » law enforcement, and describe the modeling and visualization tools design to aid in those use cases.« less

Disentangling the Correlated Evolution of Monogamy and Cooperation.

PubMed

Dillard, Jacqueline R; Westneat, David F

2016-07-01

Lifetime genetic monogamy, by increasing sibling relatedness, has been proposed as an important causal factor in the evolution of altruism. Monogamy, however, could influence the subsequent evolution of cooperation in other ways. We present several alternative, non-mutually exclusive, evolutionary processes that could explain the correlated evolution of monogamy and cooperation. Our analysis of these possibilities reveals that many ecological or social factors can affect all three variables of Hamilton's Rule simultaneously, thus calling for a more holistic, systems-level approach to studying the evolution of social traits. This perspective reveals novel dimensions to coevolutionary relationships and provides solutions for assigning causality in complex cases of correlated social trait evolution, such as the sequential evolution of monogamy and cooperation. Copyright © 2016 Elsevier Ltd. All rights reserved.

Limited plasticity in the phenotypic variance-covariance matrix for male advertisement calls in the black field cricket, Teleogryllus commodus

PubMed Central

Pitchers, W. R.; Brooks, R.; Jennions, M. D.; Tregenza, T.; Dworkin, I.; Hunt, J.

2013-01-01

Phenotypic integration and plasticity are central to our understanding of how complex phenotypic traits evolve. Evolutionary change in complex quantitative traits can be predicted using the multivariate breeders’ equation, but such predictions are only accurate if the matrices involved are stable over evolutionary time. Recent work, however, suggests that these matrices are temporally plastic, spatially variable and themselves evolvable. The data available on phenotypic variance-covariance matrix (P) stability is sparse, and largely focused on morphological traits. Here we compared P for the structure of the complex sexual advertisement call of six divergent allopatric populations of the Australian black field cricket, Teleogryllus commodus. We measured a subset of calls from wild-caught crickets from each of the populations and then a second subset after rearing crickets under common-garden conditions for three generations. In a second experiment, crickets from each population were reared in the laboratory on high- and low-nutrient diets and their calls recorded. In both experiments, we estimated P for call traits and used multiple methods to compare them statistically (Flury hierarchy, geometric subspace comparisons and random skewers). Despite considerable variation in means and variances of individual call traits, the structure of P was largely conserved among populations, across generations and between our rearing diets. Our finding that P remains largely stable, among populations and between environmental conditions, suggests that selection has preserved the structure of call traits in order that they can function as an integrated unit. PMID:23530814

Joint analysis of longitudinal feed intake and single recorded production traits in pigs using a novel Horizontal model.

PubMed

Shirali, M; Strathe, A B; Mark, T; Nielsen, B; Jensen, J

2017-03-01

A novel Horizontal model is presented for multitrait analysis of longitudinal traits through random regression analysis combined with single recorded traits. Weekly ADFI on test for Danish Duroc, Landrace, and Yorkshire boars were available from the national test station and were collected from 30 to 100 kg BW. Single recorded production traits of ADG from birth to 30 kg BW (ADG30), ADG from 30 to 100 kg BW (ADG100), and lean meat percentage (LMP) were available from breeding herds or the national test station. The Horizontal model combined random regression analysis of feed intake (FI) with single recorded traits of ADG100, LMP, and ADG30. In the Horizontal model, the FI data were horizontally structured with FI on each week as a "trait." The additive genetic and litter effects were modeled to be common across different FI records by reducing the rank of the covariance matrices using second- and first-order Legendre polynomials of age on test, respectively. The fixed effect and random residual variance were estimated for each weekly FI trait. Residual feed intake (RFI) was derived from the conditional distribution of FI given the breeding values of ADG100 and LMP. The heritability of FI varied by week on test in Duroc (0.12 to 0.19), Landrace (0.13 to 0.22), and Yorkshire (0.21 to 0.23). The heritability of RFI was lowest and highest in wk 6 (0.03) and 10 (0.10), respectively, in Duroc and wk 7 (0.04 and 0.02) and 1 (0.09 and 0.20), respectively, in Landrace and Yorkshire. The proportion of FI genetic variance explained by RFI ranged from 20 to 75% in Duroc, from 19 to 75% in Landrace, and from 11 to 91% in Yorkshire. Average daily gain from 30 to 100 kg BW and ADG30 heritabilities were moderate in Duroc (0.24 and 0.22, respectively), Landrace (0.34 and 0.25, respectively), and Yorkshire (0.34 and 0.22, respectively). Lean meat percentage heritability was moderate in Duroc (0.37) and large in Landrace (0.62) and Yorkshire (0.60). The genetic correlation of FI with ADG100 increased by week on test followed by a 32% decrease from wk 7 in Duroc and a 7% decrease in dam line breeds. Defining RFI as genetically independent of production traits leads to consistent and easy interpretable breeding values. The genetic parameters of traits in the feed efficiency complex and their dynamics over the test period showed breed differences that could be related to the fatness and growth potential of the breeds. The Horizontal model can be used to simultaneously analyze repeated and single recorded traits through proper modeling of the environmental variances and covariances.

Quantifying male attractiveness.

PubMed Central

McNamara, John M; Houston, Alasdair I; Marques Dos Santos, Miguel; Kokko, Hanna; Brooks, Rob

2003-01-01

Genetic models of sexual selection are concerned with a dynamic process in which female preference and male trait values coevolve. We present a rigorous method for characterizing evolutionary endpoints of this process in phenotypic terms. In our phenotypic characterization the mate-choice strategy of female population members determines how attractive females should find each male, and a population is evolutionarily stable if population members are actually behaving in this way. This provides a justification of phenotypic explanations of sexual selection and the insights into sexual selection that they provide. Furthermore, the phenotypic approach also has enormous advantages over a genetic approach when computing evolutionarily stable mate-choice strategies, especially when strategies are allowed to be complex time-dependent preference rules. For simplicity and clarity our analysis deals with haploid mate-choice genetics and a male trait that is inherited phenotypically, for example by vertical cultural transmission. The method is, however, easily extendible to other cases. An example illustrates that the sexy son phenomenon can occur when there is phenotypic inheritance of the male trait. PMID:14561306

Accelerated Evolution in Distinctive Species Reveals Candidate Elements for Clinically Relevant Traits, Including Mutation and Cancer Resistance.

PubMed

Ferris, Elliott; Abegglen, Lisa M; Schiffman, Joshua D; Gregg, Christopher

2018-03-06

The identity of most functional elements in the mammalian genome and the phenotypes they impact are unclear. Here, we perform a genome-wide comparative analysis of patterns of accelerated evolution in species with highly distinctive traits to discover candidate functional elements for clinically important phenotypes. We identify accelerated regions (ARs) in the elephant, hibernating bat, orca, dolphin, naked mole rat, and thirteen-lined ground squirrel lineages in mammalian conserved regions, uncovering ∼33,000 elements that bind hundreds of different regulatory proteins in humans and mice. ARs in the elephant, the largest land mammal, are uniquely enriched near elephant DNA damage response genes. The genomic hotspot for elephant ARs is the E3 ligase subunit of the Fanconi anemia complex, a master regulator of DNA repair. Additionally, ARs in the six species are associated with specific human clinical phenotypes that have apparent concordance with overt traits in each species. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

Novel throughput phenotyping platforms in plant genetic studies.

PubMed

Montes, Juan M; Melchinger, Albrecht E; Reif, Jochen C

2007-10-01

Unraveling the genetic basis of complex traits in plants is limited by the lack of appropriate phenotyping platforms that enable high-throughput screening of many genotypes in multilocation field trials. Near-infrared spectroscopy on agricultural harvesters and spectral reflectance of plant canopies have recently been reported as promising components of novel phenotyping platforms. Understanding the genetic basis of complex traits is now within reach with the use of these new techniques.

Receiver discriminability drives the evolution of complex sexual signals by sexual selection.

PubMed

Cui, Jianguo; Song, Xiaowei; Zhu, Bicheng; Fang, Guangzhan; Tang, Yezhong; Ryan, Michael J

2016-04-01

A hallmark of sexual selection by mate choice is the evolution of exaggerated traits, such as longer tails in birds and more acoustic components in the calls of birds and frogs. Trait elaboration can be opposed by costs such as increased metabolism and greater predation risk, but cognitive processes of the receiver can also put a brake on trait elaboration. For example, according to Weber's Law traits of a fixed absolute difference will be more difficult to discriminate as the absolute magnitude increases. Here, we show that in the Emei music frog (Babina daunchina) increases in the fundamental frequency between successive notes in the male advertisement call, which increases the spectral complexity of the call, facilitates the female's ability to compare the number of notes between calls. These results suggest that female's discriminability provides the impetus to switch from enhancement of signaling magnitude (i.e., adding more notes into calls) to employing a new signal feature (i.e., increasing frequency among notes) to increase complexity. We suggest that increasing the spectral complexity of notes ameliorates some of the effects of Weber's Law, and highlights how perceptual and cognitive biases of choosers can have important influences on the evolution of courtship signals. © 2016 The Author(s). Evolution © 2016 The Society for the Study of Evolution.

Epistatic effects between pairs of the growth hormone secretagogue receptor 1a, growth hormone, growth hormone receptor, non-SMC condensin I complex, subunit G and stearoyl-CoA desaturase genes on carcass, price-related and fatty acid composition traits in Japanese Black cattle.

PubMed

Komatsu, Masanori; Nishino, Kagetomo; Fujimori, Yuki; Haga, Yasutoshi; Iwama, Nagako; Arakawa, Aisaku; Aihara, Yoshito; Takeda, Hisato; Takahashi, Hideaki

2018-02-01

Growth hormone secretagogue receptor 1a (GHSR1a), growth hormone (GH), growth hormone receptor (GHR), non-SMC condensin I complex, subunit G (NCAPG) and stearoyl-CoA desaturase (SCD), are known to play important roles in growth and lipid metabolisms. Single and epistatic effects of the five genes on carcass, price-related and fatty acid (FA) composition traits were analyzed in a commercial Japanese Black cattle population of Ibaraki Prefecture. A total of 650 steers and 116 heifers for carcass and price-related traits, and 158 steers for FA composition traits were used in this study. Epistatic effects between pairs of the five genes were found in several traits. Alleles showing strain-specific differences in the five genes had significant single and epistatic effects in some traits. The data suggest that a TG-repeat polymorphism of the GHSR1a.5'UTR-(TG) n locus plays a central role in gene-gene epistatic interaction of FA composition traits in the adipose tissue of Japanese Black cattle. © 2017 Japanese Society of Animal Science.

Cognitive ability is heritable and predicts the success of an alternative mating tactic.

PubMed

Smith, Carl; Philips, André; Reichard, Martin

2015-06-22

The ability to attract mates, acquire resources for reproduction, and successfully outcompete rivals for fertilizations may make demands on cognitive traits--the mechanisms by which an animal acquires, processes, stores and acts upon information from its environment. Consequently, cognitive traits potentially undergo sexual selection in some mating systems. We investigated the role of cognitive traits on the reproductive performance of male rose bitterling (Rhodeus ocellatus), a freshwater fish with a complex mating system and alternative mating tactics. We quantified the learning accuracy of males and females in a spatial learning task and scored them for learning accuracy. Males were subsequently allowed to play the roles of a guarder and a sneaker in competitive mating trials, with reproductive success measured using paternity analysis. We detected a significant interaction between male mating role and learning accuracy on reproductive success, with the best-performing males in maze trials showing greater reproductive success in a sneaker role than as a guarder. Using a cross-classified breeding design, learning accuracy was demonstrated to be heritable, with significant additive maternal and paternal effects. Our results imply that male cognitive traits may undergo intra-sexual selection. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

The complexity underlying invasiveness precludes the identification of invasive traits: A comparative study of invasive and non-invasive heterocarpic Atriplex congeners

PubMed Central

Doudová, Jana; Douda, Jan; Mandák, Bohumil

2017-01-01

Heterocarpy enables species to effectively spread under unfavourable conditions by producing two or more types of fruit differing in ecological characteristics. Although it is frequent in annuals occupying disturbed habitats that are vulnerable to invasion, there is still a lack of congeneric studies addressing the importance of heterocarpy for species invasion success. We compared two pairs of heterocarpic Atriplex species, each of them comprising one invasive and one non-invasive non-native congener. In two common garden experiments, we (i) simulated the influence of different levels of nutrients and population density on plants grown from different types of fruits and examined several traits that are generally positively associated with invasion success, and (ii) grew plants in a replacement series experiment to evaluate resource partitioning between them and to compare their competitive ability. We found that specific functional traits or competitiveness of species cannot explain the invasiveness of Atriplex species, indicating that species invasiveness involves more complex interactions of traits that are important only in certain ecological contexts, i.e. in specific environmental conditions and only some habitats. Interestingly, species trait differences related to invasion success were found between plants growing from the ecologically most contrasting fruit types. We suggest that fruit types differing in ecological behaviour may be essential in the process of invasion or in the general spreading of heterocarpic species, as they either the maximize population growth (type C fruit) or enhance the chance of survival of new populations (type A fruit). Congeners offer the best available methodical framework for comparing traits among phylogenetically closely related invasive and non-invasive species. However, as indicated by our results, this approach is unlikely to reveal invasive traits because of the complexity underlying invasiveness. PMID:28445514

PRECOG: a tool for automated extraction and visualization of fitness components in microbial growth phenomics.

PubMed

Fernandez-Ricaud, Luciano; Kourtchenko, Olga; Zackrisson, Martin; Warringer, Jonas; Blomberg, Anders

2016-06-23

Phenomics is a field in functional genomics that records variation in organismal phenotypes in the genetic, epigenetic or environmental context at a massive scale. For microbes, the key phenotype is the growth in population size because it contains information that is directly linked to fitness. Due to technical innovations and extensive automation our capacity to record complex and dynamic microbial growth data is rapidly outpacing our capacity to dissect and visualize this data and extract the fitness components it contains, hampering progress in all fields of microbiology. To automate visualization, analysis and exploration of complex and highly resolved microbial growth data as well as standardized extraction of the fitness components it contains, we developed the software PRECOG (PREsentation and Characterization Of Growth-data). PRECOG allows the user to quality control, interact with and evaluate microbial growth data with ease, speed and accuracy, also in cases of non-standard growth dynamics. Quality indices filter high- from low-quality growth experiments, reducing false positives. The pre-processing filters in PRECOG are computationally inexpensive and yet functionally comparable to more complex neural network procedures. We provide examples where data calibration, project design and feature extraction methodologies have a clear impact on the estimated growth traits, emphasising the need for proper standardization in data analysis. PRECOG is a tool that streamlines growth data pre-processing, phenotypic trait extraction, visualization, distribution and the creation of vast and informative phenomics databases.

graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architecture.

PubMed

Chung, Dongjun; Kim, Hang J; Zhao, Hongyu

2017-02-01

Genome-wide association studies (GWAS) have identified tens of thousands of genetic variants associated with hundreds of phenotypes and diseases, which have provided clinical and medical benefits to patients with novel biomarkers and therapeutic targets. However, identification of risk variants associated with complex diseases remains challenging as they are often affected by many genetic variants with small or moderate effects. There has been accumulating evidence suggesting that different complex traits share common risk basis, namely pleiotropy. Recently, several statistical methods have been developed to improve statistical power to identify risk variants for complex traits through a joint analysis of multiple GWAS datasets by leveraging pleiotropy. While these methods were shown to improve statistical power for association mapping compared to separate analyses, they are still limited in the number of phenotypes that can be integrated. In order to address this challenge, in this paper, we propose a novel statistical framework, graph-GPA, to integrate a large number of GWAS datasets for multiple phenotypes using a hidden Markov random field approach. Application of graph-GPA to a joint analysis of GWAS datasets for 12 phenotypes shows that graph-GPA improves statistical power to identify risk variants compared to statistical methods based on smaller number of GWAS datasets. In addition, graph-GPA also promotes better understanding of genetic mechanisms shared among phenotypes, which can potentially be useful for the development of improved diagnosis and therapeutics. The R implementation of graph-GPA is currently available at https://dongjunchung.github.io/GGPA/.

Meta-analysis of cotton fiber quality QTLs across diverse environments in a Gossypium hirsutum x G. barbadense RIL population.

PubMed

Lacape, Jean-Marc; Llewellyn, Danny; Jacobs, John; Arioli, Tony; Becker, David; Calhoun, Steve; Al-Ghazi, Yves; Liu, Shiming; Palaï, Oumarou; Georges, Sophie; Giband, Marc; de Assunção, Henrique; Barroso, Paulo Augusto Vianna; Claverie, Michel; Gawryziak, Gérard; Jean, Janine; Vialle, Michèle; Viot, Christopher

2010-06-28

Cotton fibers (produced by Gossypium species) are the premier natural fibers for textile production. The two tetraploid species, G. barbadense (Gb) and G. hirsutum (Gh), differ significantly in their fiber properties, the former having much longer, finer and stronger fibers that are highly prized. A better understanding of the genetics and underlying biological causes of these differences will aid further improvement of cotton quality through breeding and biotechnology. We evaluated an inter-specific Gh x Gb recombinant inbred line (RIL) population for fiber characteristics in 11 independent experiments under field and glasshouse conditions. Sites were located on 4 continents and 5 countries and some locations were analyzed over multiple years. The RIL population displayed a large variability for all major fiber traits. QTL analyses were performed on a per-site basis by composite interval mapping. Among the 651 putative QTLs (LOD > 2), 167 had a LOD exceeding permutation based thresholds. Coincidence in QTL location across data sets was assessed for the fiber trait categories strength, elongation, length, length uniformity, fineness/maturity, and color. A meta-analysis of more than a thousand putative QTLs was conducted with MetaQTL software to integrate QTL data from the RIL and 3 backcross populations (from the same parents) and to compare them with the literature. Although the global level of congruence across experiments and populations was generally moderate, the QTL clustering was possible for 30 trait x chromosome combinations (5 traits in 19 different chromosomes) where an effective co-localization of unidirectional (similar sign of additivity) QTLs from at least 5 different data sets was observed. Most consistent meta-clusters were identified for fiber color on chromosomes c6, c8 and c25, fineness on c15, and fiber length on c3. Meta-analysis provided a reliable means of integrating phenotypic and genetic mapping data across multiple populations and environments for complex fiber traits. The consistent chromosomal regions contributing to fiber quality traits constitute good candidates for the further dissection of the genetic and genomic factors underlying important fiber characteristics, and for marker-assisted selection.

Assessing the genetic overlap between BMI and cognitive function

PubMed Central

Marioni, R E; Yang, J; Dykiert, D; Mõttus, R; Campbell, A; Ibrahim-Verbaas, Carla A; Bressler, Jan; Debette, Stephanie; Schuur, Maaike; Smith, Albert V; Davies, Gail; Bennett, David A; Deary, Ian J; Ikram, M Arfan; Launer, Lenore J; Fitzpatrick, Annette L; Seshadri, Sudha; van Duijn, Cornelia M; Mosely Jr, Thomas H; Davies, G; Hayward, C; Porteous, D J; Visscher, P M; Deary, I J

2016-01-01

Obesity and low cognitive function are associated with multiple adverse health outcomes across the life course. They have a small phenotypic correlation (r=−0.11; high body mass index (BMI)−low cognitive function), but whether they have a shared genetic aetiology is unknown. We investigated the phenotypic and genetic correlations between the traits using data from 6815 unrelated, genotyped members of Generation Scotland, an ethnically homogeneous cohort from five sites across Scotland. Genetic correlations were estimated using the following: same-sample bivariate genome-wide complex trait analysis (GCTA)–GREML; independent samples bivariate GCTA–GREML using Generation Scotland for cognitive data and four other samples (n=20 806) for BMI; and bivariate LDSC analysis using the largest genome-wide association study (GWAS) summary data on cognitive function (n=48 462) and BMI (n=339 224) to date. The GWAS summary data were also used to create polygenic scores for the two traits, with within- and cross-trait prediction taking place in the independent Generation Scotland cohort. A large genetic correlation of −0.51 (s.e. 0.15) was observed using the same-sample GCTA–GREML approach compared with −0.10 (s.e. 0.08) from the independent-samples GCTA–GREML approach and −0.22 (s.e. 0.03) from the bivariate LDSC analysis. A genetic profile score using cognition-specific genetic variants accounts for 0.08% (P=0.020) of the variance in BMI and a genetic profile score using BMI-specific variants accounts for 0.42% (P=1.9 × 10−7) of the variance in cognitive function. Seven common genetic variants are significantly associated with both traits at P<5 × 10−5, which is significantly more than expected by chance (P=0.007). All these results suggest there are shared genetic contributions to BMI and cognitive function. PMID:26857597

Pre and Post-copulatory Selection Favor Similar Genital Phenotypes in the Male Broad Horned Beetle

PubMed Central

House, Clarissa M.; Sharma, M. D.; Okada, Kensuke; Hosken, David J.

2016-01-01

Sexual selection can operate before and after copulation and the same or different trait(s) can be targeted during these episodes of selection. The direction and form of sexual selection imposed on characters prior to mating has been relatively well described, but the same is not true after copulation. In general, when male–male competition and female choice favor the same traits then there is the expectation of reinforcing selection on male sexual traits that improve competitiveness before and after copulation. However, when male–male competition overrides pre-copulatory choice then the opposite could be true. With respect to studies of selection on genitalia there is good evidence that male genital morphology influences mating and fertilization success. However, whether genital morphology affects reproductive success in more than one context (i.e., mating versus fertilization success) is largely unknown. Here we use multivariate analysis to estimate linear and nonlinear selection on male body size and genital morphology in the flour beetle Gnatocerus cornutus, simulated in a non-competitive (i.e., monogamous) setting. This analysis estimates the form of selection on multiple traits and typically, linear (directional) selection is easiest to detect, while nonlinear selection is more complex and can be stabilizing, disruptive, or correlational. We find that mating generates stabilizing selection on male body size and genitalia, and fertilization causes a blend of directional and stabilizing selection. Differences in the form of selection across these bouts of selection result from a significant alteration of nonlinear selection on body size and a marginally significant difference in nonlinear selection on a component of genital shape. This suggests that both bouts of selection favor similar genital phenotypes, whereas the strong stabilizing selection imposed on male body size during mate acquisition is weak during fertilization. PMID:27371390

Localization of canine brachycephaly using an across breed mapping approach.

PubMed

Bannasch, Danika; Young, Amy; Myers, Jeffrey; Truvé, Katarina; Dickinson, Peter; Gregg, Jeffrey; Davis, Ryan; Bongcam-Rudloff, Eric; Webster, Matthew T; Lindblad-Toh, Kerstin; Pedersen, Niels

2010-03-10

The domestic dog, Canis familiaris, exhibits profound phenotypic diversity and is an ideal model organism for the genetic dissection of simple and complex traits. However, some of the most interesting phenotypes are fixed in particular breeds and are therefore less tractable to genetic analysis using classical segregation-based mapping approaches. We implemented an across breed mapping approach using a moderately dense SNP array, a low number of animals and breeds carefully selected for the phenotypes of interest to identify genetic variants responsible for breed-defining characteristics. Using a modest number of affected (10-30) and control (20-60) samples from multiple breeds, the correct chromosomal assignment was identified in a proof of concept experiment using three previously defined loci; hyperuricosuria, white spotting and chondrodysplasia. Genome-wide association was performed in a similar manner for one of the most striking morphological traits in dogs: brachycephalic head type. Although candidate gene approaches based on comparable phenotypes in mice and humans have been utilized for this trait, the causative gene has remained elusive using this method. Samples from nine affected breeds and thirteen control breeds identified strong genome-wide associations for brachycephalic head type on Cfa 1. Two independent datasets identified the same genomic region. Levels of relative heterozygosity in the associated region indicate that it has been subjected to a selective sweep, consistent with it being a breed defining morphological characteristic. Genotyping additional dogs in the region confirmed the association. To date, the genetic structure of dog breeds has primarily been exploited for genome wide association for segregating traits. These results demonstrate that non-segregating traits under strong selection are equally tractable to genetic analysis using small sample numbers.

Maternal factors predicting cognitive and behavioral characteristics of children with fetal alcohol spectrum disorders.

PubMed

May, Philip A; Tabachnick, Barbara G; Gossage, J Phillip; Kalberg, Wendy O; Marais, Anna-Susan; Robinson, Luther K; Manning, Melanie A; Blankenship, Jason; Buckley, David; Hoyme, H Eugene; Adnams, Colleen M

2013-06-01

To provide an analysis of multiple predictors of cognitive and behavioral traits for children with fetal alcohol spectrum disorders (FASDs). Multivariate correlation techniques were used with maternal and child data from epidemiologic studies in a community in South Africa. Data on 561 first-grade children with fetal alcohol syndrome (FAS), partial FAS (PFAS), and not FASD and their mothers were analyzed by grouping 19 maternal variables into categories (physical, demographic, childbearing, and drinking) and used in structural equation models (SEMs) to assess correlates of child intelligence (verbal and nonverbal) and behavior. A first SEM using only 7 maternal alcohol use variables to predict cognitive/behavioral traits was statistically significant (B = 3.10, p < .05) but explained only 17.3% of the variance. The second model incorporated multiple maternal variables and was statistically significant explaining 55.3% of the variance. Significantly correlated with low intelligence and problem behavior were demographic (B = 3.83, p < .05) (low maternal education, low socioeconomic status [SES], and rural residence) and maternal physical characteristics (B = 2.70, p < .05) (short stature, small head circumference, and low weight). Childbearing history and alcohol use composites were not statistically significant in the final complex model and were overpowered by SES and maternal physical traits. Although other analytic techniques have amply demonstrated the negative effects of maternal drinking on intelligence and behavior, this highly controlled analysis of multiple maternal influences reveals that maternal demographics and physical traits make a significant enabling or disabling contribution to child functioning in FASD.

Physical Analysis of the Complex Rye (Secale cereale L.) Alt4 Aluminium (Aluminum) Tolerance Locus Using a Whole-Genome BAC Library of Rye cv. Blanco

USDA-ARS?s Scientific Manuscript database

Rye is a diploid crop species with many outstanding qualities, and is also important as a source of new traits for wheat and triticale improvement. Here we describe a BAC library of rye cv. Blanco, representing a valuable resource for rye molecular genetic studies. The library provides a 6 × genome ...

Word Reading Fluency: Role of Genome-Wide Single-Nucleotide Polymorphisms in Developmental Stability and Correlations with Print Exposure

ERIC Educational Resources Information Center

Harlaar, Nicole; Trzaskowski, Maciej; Dale, Philip S.; Plomin, Robert

2014-01-01

The genetic effects on individual differences in reading development were examined using genome-wide complex trait analysis (GCTA) in a twin sample. In unrelated individuals (one twin per pair, n = 2,942), the GCTA-based heritability of reading fluency was ~20%-29% at ages 7 and 12. GCTA bivariate results showed that the phenotypic stability of…

Divide and conquer: intermediate levels of population fragmentation maximize cultural accumulation.

PubMed

Derex, Maxime; Perreault, Charles; Boyd, Robert

2018-04-05

Identifying the determinants of cumulative cultural evolution is a key issue in the interdisciplinary field of cultural evolution. A widely held view is that large and well-connected social networks facilitate cumulative cultural evolution because they promote the spread of useful cultural traits and prevent the loss of cultural knowledge through factors such as drift. This view stems from models that focus on the transmission of cultural information, without considering how new cultural traits actually arise. In this paper, we review the literature from various fields that suggest that, under some circumstances, increased connectedness can decrease cultural diversity and reduce innovation rates. Incorporating this idea into an agent-based model, we explore the effect of population fragmentation on cumulative culture and show that, for a given population size, there exists an intermediate level of population fragmentation that maximizes the rate of cumulative cultural evolution. This result is explained by the fact that fully connected, non-fragmented populations are able to maintain complex cultural traits but produce insufficient variation and so lack the cultural diversity required to produce highly complex cultural traits. Conversely, highly fragmented populations produce a variety of cultural traits but cannot maintain complex ones. In populations with intermediate levels of fragmentation, cultural loss and cultural diversity are balanced in a way that maximizes cultural complexity. Our results suggest that population structure needs to be taken into account when investigating the relationship between demography and cumulative culture.This article is part of the theme issue 'Bridging cultural gaps: interdisciplinary studies in human cultural evolution'. © 2018 The Author(s).

Associating quantitative behavioral traits with gene expression in the brain: searching for diamonds in the hay.

PubMed

Reiner-Benaim, Anat; Yekutieli, Daniel; Letwin, Noah E; Elmer, Gregory I; Lee, Norman H; Kafkafi, Neri; Benjamini, Yoav

2007-09-01

Gene expression and phenotypic functionality can best be associated when they are measured quantitatively within the same experiment. The analysis of such a complex experiment is presented, searching for associations between measures of exploratory behavior in mice and gene expression in brain regions. The analysis of such experiments raises several methodological problems. First and foremost, the size of the pool of potential discoveries being screened is enormous yet only few biologically relevant findings are expected, making the problem of multiple testing especially severe. We present solutions based on screening by testing related hypotheses, then testing the hypotheses of interest. In one variant the subset is selected directly, in the other one a tree of hypotheses is tested hierarchical; both variants control the False Discovery Rate (FDR). Other problems in such experiments are in the fact that the level of data aggregation may be different for the quantitative traits (one per animal) and gene expression measurements (pooled across animals); in that the association may not be linear; and in the resolution of interest only few replications exist. We offer solutions to these problems as well. The hierarchical FDR testing strategies presented here can serve beyond the structure of our motivating example study to any complex microarray study. Supplementary data are available at Bioinformatics online.

RAPD of controlled crosses and clones from the field suggests that hybrids are rare in the Salix alba-Salix fragilis complex.

PubMed

Triest, L; De Greef, B; De Bondt, R; Van Slycken, J

2000-05-01

The polyploid Salix alba-Salix fragilis hybrid complex is rather difficult to study when using only morphological characters. Most of the features have a low diagnostic value for unambiguously identifying the hybrids, introgression patterns and population structures, though morphological traits have proved to be useful in making a hybrid index. Morphology and molecular variation from RAPDs were investigated in several case studies on willows from Belgium. A thorough screening of full-sib progenies of interspecific controlled crosses was made to select homologous amplification products. The selected amplified products proved to be useful in a principal coordinate analysis for the estimation of variability of hybrid progenies. On the basis of genetic similarities and ordination analysis, a method for the identification of clones in the field was established using presumed pure species and presumed introgressants. The chosen reference clones were checked against additional European samples of putative pure species to ensure the reliability of the method beyond a regional scale. The RAPDs suggested that both species have kept their gene pools well separated and that hybridization actually does not seem to be a dominating process. The observation that molecular markers do not always follow the morphological traits or allozyme data is discussed.

When Homoplasy Is Not Homoplasy: Dissecting Trait Evolution by Contrasting Composite and Reductive Coding.

PubMed

Torres-Montúfar, Alejandro; Borsch, Thomas; Ochoterena, Helga

2018-05-01

The conceptualization and coding of characters is a difficult issue in phylogenetic systematics, no matter which inference method is used when reconstructing phylogenetic trees or if the characters are just mapped onto a specific tree. Complex characters are groups of features that can be divided into simpler hierarchical characters (reductive coding), although the implied hierarchical relational information may change depending on the type of coding (composite vs. reductive). Up to now, there is no common agreement to either code characters as complex or simple. Phylogeneticists have discussed which coding method is best but have not incorporated the heuristic process of reciprocal illumination to evaluate the coding. Composite coding allows to test whether 1) several characters were linked resulting in a structure described as a complex character or trait or 2) independently evolving characters resulted in the configuration incorrectly interpreted as a complex character. We propose that complex characters or character states should be decomposed iteratively into simpler characters when the original homology hypothesis is not corroborated by a phylogenetic analysis, and the character or character state is retrieved as homoplastic. We tested this approach using the case of fruit types within subfamily Cinchonoideae (Rubiaceae). The iterative reductive coding of characters associated with drupes allowed us to unthread fruit evolution within Cinchonoideae. Our results show that drupes and berries are not homologous. As a consequence, a more precise ontology for the Cinchonoideae drupes is required.

Emotional Intelligence and Nurse Recruitment: Rasch and confirmatory factor analysis of the trait emotional intelligence questionnaire short form.

PubMed

Snowden, Austyn; Watson, Roger; Stenhouse, Rosie; Hale, Claire

2015-12-01

To examine the construct validity of the Trait Emotional Intelligence Questionnaire Short form. Emotional intelligence involves the identification and regulation of our own emotions and the emotions of others. It is therefore a potentially useful construct in the investigation of recruitment and retention in nursing and many questionnaires have been constructed to measure it. Secondary analysis of existing dataset of responses to Trait Emotional Intelligence Questionnaire Short form using concurrent application of Rasch analysis and confirmatory factor analysis. First year undergraduate nursing and computing students completed Trait Emotional Intelligence Questionnaire-Short Form in September 2013. Responses were analysed by synthesising results of Rasch analysis and confirmatory factor analysis. Participants (N = 938) completed Trait Emotional Intelligence Questionnaire Short form. Rasch analysis showed the majority of the Trait Emotional Intelligence Questionnaire-Short Form items made a unique contribution to the latent trait of emotional intelligence. Five items did not fit the model and differential item functioning (gender) accounted for this misfit. Confirmatory factor analysis revealed a four-factor structure consisting of: self-confidence, empathy, uncertainty and social connection. All five misfitting items from the Rasch analysis belonged to the 'social connection' factor. The concurrent use of Rasch and factor analysis allowed for novel interpretation of Trait Emotional Intelligence Questionnaire Short form. Much of the response variation in Trait Emotional Intelligence Questionnaire Short form can be accounted for by the social connection factor. Implications for practice are discussed. © 2015 John Wiley & Sons Ltd.

Lessons learned from the dog genome.

PubMed

Wayne, Robert K; Ostrander, Elaine A

2007-11-01

Extensive genetic resources and a high-quality genome sequence position the dog as an important model species for understanding genome evolution, population genetics and genes underlying complex phenotypic traits. Newly developed genomic resources have expanded our understanding of canine evolutionary history and dog origins. Domestication involved genetic contributions from multiple populations of gray wolves probably through backcrossing. More recently, the advent of controlled breeding practices has segregated genetic variability into distinct dog breeds that possess specific phenotypic traits. Consequently, genome-wide association and selective sweep scans now allow the discovery of genes underlying breed-specific characteristics. The dog is finally emerging as a novel resource for studying the genetic basis of complex traits, including behavior.

Neuroanatomical profiles of personality change in frontotemporal lobar degeneration.

PubMed

Mahoney, Colin J; Rohrer, Jonathan D; Omar, Rohani; Rossor, Martin N; Warren, Jason D

2011-05-01

The neurobiological basis of personality is poorly understood. Frontotemporal lobar degeneration (FTLD) frequently presents with complex behavioural changes, and therefore potentially provides a disease model in which to investigate brain substrates of personality. To assess neuroanatomical correlates of personality change in a cohort of individuals with FTLD using voxel-based morphometry (VBM). Thirty consecutive individuals fulfilling consensus criteria for FTLD were assessed. Each participant's carer completed a Big Five Inventory (BFI) questionnaire on five key personality traits; for each trait, a change score was derived based on current compared with estimated premorbid characteristics. All participants underwent volumetric brain magnetic resonance imaging. A VBM analysis was implemented regressing change score for each trait against regional grey matter volume across the FTLD group. The FTLD group showed a significant decline in extraversion, agreeableness, conscientiousness and openness and an increase in neuroticism. Change in particular personality traits was associated with overlapping profiles of grey matter loss in more anterior cortical areas and relative preservation of grey matter in more posterior areas; the most robust neuroanatomical correlate was identified for reduced conscientiousness in the region of the posterior superior temporal gyrus. Quantitative measures of personality change in FTLD can be correlated with changes in regional grey matter. The neuroanatomical profiles for particular personality traits overlap brain circuits previously implicated in aspects of social cognition and suggest that dysfunction at the level of distributed cortical networks underpins personality change in FTLD.

Chronic subjective dizziness: Analysis of underlying personality factors.

PubMed

Chiarella, G; Petrolo, C; Riccelli, R; Giofrè, L; Olivadese, G; Gioacchini, F M; Scarpa, A; Cassandro, E; Passamonti, L

2016-11-03

Chronic subjective dizziness (CSD) is characterized by persistent dizziness, unsteadiness, and hypersensitivity to one's own motion or exposure to complex visual stimuli. CSD may be triggered, in predisposed individuals with specific personality traits, by acute vestibular diseases. CSD is also thought to arise from failure to re-establish normal balance strategies after resolution of acute vestibular events which may be modulated by diathesis to develop anxiety and depression. To confirm the role of personality traits linked to anxiety and depression (i.e., neuroticism, introversion, low openness) as predisposing factors for CSD and to evaluate how individual differences in these personality traits are associated with CSD severity. We compared 19 CSD patients with 24 individuals who had suffered from periferal vestibular disorders (PVD) (i.e., Benign Paroxysmal Postural Vertigo or Vestibular Neuritis) but had not developed CSD as well as with 25 healthy controls (HC) in terms of personality traits, assessed via the NEO-PI-R questionnaire. CSD patients, relative to PVD patients and HCs, scored higher on the anxiety facet of neuroticism. Total neuroticism scores were also significantly associated with dizziness severity in CSD patients but not PVD patients. Pre-existing anxiety-related personality traits may promote and sustain the initial etiophatogenetic mechanisms linked with the development of CSD. Targeting anxiety-related mechanisms in CSD may be therefore a promising way to reduce the disability associated with CSD.

Characterization, expression profiles, intracellular distribution and association analysis of porcine PNAS-4 gene with production traits.

PubMed

Mo, Delin; Zhu, Zhengmao; te Pas, Marinus F W; Li, Xinyun; Yang, Shulin; Wang, Heng; Wang, Huanling; Li, Kui

2008-06-30

In a previous screen to identify differentially expressed genes associated with embryonic development, the porcine PNAS-4 gene had been found. Considering differentially expressed genes in early stages of muscle development are potential candidate genes to improve meat quality and production efficiency, we determined how porcine PNAS-4 gene regulates meat production. Therefore, this gene has been sequenced, expression analyzed and associated with meat production traits. We cloned the full-length cDNA of porcine PNAS-4 gene encoding a protein of 194 amino acids which was expressed in the Golgi complex. This gene was mapped to chromosome 10, q11-16, in a region of conserved synteny with human chromosome 1 where the human homologous gene was localized. Real-time PCR revealed that PNAS-4 mRNA was widely expressed with highest expression levels in skeletal muscle followed by lymph, liver and other tissues, and showed a down-regulated expression pattern during prenatal development while a up-regulated expression pattern after weaning. Association analysis revealed that allele C of SNP A1813C was prevalent in Chinese indigenous breeds whereas A was dominant allele in Landrace and Large White, and the pigs with homozygous CC had a higher fat content than those of the pigs with other genotypes (P < 0.05). Porcine PNAS-4 protein tagged with green fluorescent protein accumulated in the Golgi complex, and its mRNA showed a widespread expression across many tissues and organs in pigs. It may be an important factor affecting the meat production efficiency, because its down-regulated expression pattern during early embryogenesis suggests involvement in increase of muscle fiber number. In addition, the SNP A1813C associated with fat traits might be a genetic marker for molecular-assisted selection in animal breeding.

Birth Cohort, Age, and Sex Strongly Modulate Effects of Lipid Risk Alleles Identified in Genome-Wide Association Studies

PubMed Central

Kulminski, Alexander M.; Culminskaya, Irina; Arbeev, Konstantin G.; Arbeeva, Liubov; Ukraintseva, Svetlana V.; Stallard, Eric; Wu, Deqing; Yashin, Anatoliy I.

2015-01-01

Insights into genetic origin of diseases and related traits could substantially impact strategies for improving human health. The results of genome-wide association studies (GWAS) are often positioned as discoveries of unconditional risk alleles of complex health traits. We re-analyzed the associations of single nucleotide polymorphisms (SNPs) associated with total cholesterol (TC) in a large-scale GWAS meta-analysis. We focused on three generations of genotyped participants of the Framingham Heart Study (FHS). We show that the effects of all ten directly-genotyped SNPs were clustered in different FHS generations and/or birth cohorts in a sex-specific or sex-unspecific manner. The sample size and procedure-therapeutic issues play, at most, a minor role in this clustering. An important result was clustering of significant associations with the strongest effects in the youngest, or 3rd Generation, cohort. These results imply that an assumption of unconditional connections of these SNPs with TC is generally implausible and that a demographic perspective can substantially improve GWAS efficiency. The analyses of genetic effects in age-matched samples suggest a role of environmental and age-related mechanisms in the associations of different SNPs with TC. Analysis of the literature supports systemic roles for genes for these SNPs beyond those related to lipid metabolism. Our analyses reveal strong antagonistic effects of rs2479409 (the PCSK9 gene) that cautions strategies aimed at targeting this gene in the next generation of lipid drugs. Our results suggest that standard GWAS strategies need to be advanced in order to appropriately address the problem of genetic susceptibility to complex traits that is imperative for translation to health care. PMID:26295473

Leaf traits in parental and hybrid species of Sorbus (Rosaceae).

PubMed

Durkovic, Jaroslav; Kardosová, Monika; Canová, Ingrid; Lagana, Rastislav; Priwitzer, Tibor; Chorvát, Dusan; Cicák, Alojz; Pichler, Viliam

2012-09-01

Knowledge of functional leaf traits can provide important insights into the processes structuring plant communities. In the genus Sorbus, the generation of taxonomic novelty through reticulate evolution that gives rise to new microspecies is believed to be driven primarily by a series of interspecific hybridizations among closely related taxa. We tested hypotheses for dispersion of intermediacy across the leaf traits in Sorbus hybrids and for trait linkages with leaf area and specific leaf area. Here, we measured and compared the whole complex of growth, vascular, and ecophysiological leaf traits among parental (Sorbus aria, Sorbus aucuparia, Sorbus chamaemespilus) and natural hybrid (Sorbus montisalpae, Sorbus zuzanae) species growing under field conditions. A recently developed atomic force microscopy technique, PeakForce quantitative nanomechanical mapping, was used to characterize the topography of cell wall surfaces of tracheary elements and to map the reduced Young's modulus of elasticity. Intermediacy was associated predominantly with leaf growth traits, whereas vascular and ecophysiological traits were mainly parental-like and transgressive phenotypes. Larger-leaf species tended to have lower modulus of elasticity values for midrib tracheary element cell walls. Leaves with a biomass investment related to a higher specific leaf area had a lower density. Leaf area- and length-normalized theoretical hydraulic conductivity was related to leaf thickness. For the whole complex of examined leaf traits, hybrid microspecies were mosaics of parental-like, intermediate, and transgressive phenotypes. The high proportion of transgressive character expressions found in Sorbus hybrids implies that generation of extreme traits through transgressive segregation played a key role in the speciation process.

Identification of loci governing eight agronomic traits using a GBS-GWAS approach and validation by QTL mapping in soya bean.

PubMed

Sonah, Humira; O'Donoughue, Louise; Cober, Elroy; Rajcan, Istvan; Belzile, François

2015-02-01

Soya bean is a major source of edible oil and protein for human consumption as well as animal feed. Understanding the genetic basis of different traits in soya bean will provide important insights for improving breeding strategies for this crop. A genome-wide association study (GWAS) was conducted to accelerate molecular breeding for the improvement of agronomic traits in soya bean. A genotyping-by-sequencing (GBS) approach was used to provide dense genome-wide marker coverage (>47,000 SNPs) for a panel of 304 short-season soya bean lines. A subset of 139 lines, representative of the diversity among these, was characterized phenotypically for eight traits under six environments (3 sites × 2 years). Marker coverage proved sufficient to ensure highly significant associations between the genes known to control simple traits (flower, hilum and pubescence colour) and flanking SNPs. Between one and eight genomic loci associated with more complex traits (maturity, plant height, seed weight, seed oil and protein) were also identified. Importantly, most of these GWAS loci were located within genomic regions identified by previously reported quantitative trait locus (QTL) for these traits. In some cases, the reported QTLs were also successfully validated by additional QTL mapping in a biparental population. This study demonstrates that integrating GBS and GWAS can be used as a powerful complementary approach to classical biparental mapping for dissecting complex traits in soya bean. © 2014 Society for Experimental Biology, Association of Applied Biologists and John Wiley & Sons Ltd.

QTLomics in Soybean: A Way Forward for Translational Genomics and Breeding

PubMed Central

Kumawat, Giriraj; Gupta, Sanjay; Ratnaparkhe, Milind B.; Maranna, Shivakumar; Satpute, Gyanesh K.

2016-01-01

Food legumes play an important role in attaining both food and nutritional security along with sustainable agricultural production for the well-being of humans globally. The various traits of economic importance in legume crops are complex and quantitative in nature, which are governed by quantitative trait loci (QTLs). Mapping of quantitative traits is a tedious and costly process, however, a large number of QTLs has been mapped in soybean for various traits albeit their utilization in breeding programmes is poorly reported. For their effective use in breeding programme it is imperative to narrow down the confidence interval of QTLs, to identify the underlying genes, and most importantly allelic characterization of these genes for identifying superior variants. In the field of functional genomics, especially in the identification and characterization of gene responsible for quantitative traits, soybean is far ahead from other legume crops. The availability of genic information about quantitative traits is more significant because it is easy and effective to identify homologs than identifying shared syntenic regions in other crop species. In soybean, genes underlying QTLs have been identified and functionally characterized for phosphorous efficiency, flowering and maturity, pod dehiscence, hard-seededness, α-Tocopherol content, soybean cyst nematode, sudden death syndrome, and salt tolerance. Candidate genes have also been identified for many other quantitative traits for which functional validation is required. Using the sequence information of identified genes from soybean, comparative genomic analysis of homologs in other legume crops could discover novel structural variants and useful alleles for functional marker development. The functional markers may be very useful for molecular breeding in soybean and harnessing benefit of translational research from soybean to other leguminous crops. Thus, soybean crop can act as a model crop for translational genomics and breeding of quantitative traits in legume crops. In this review, we summarize current status of identification and characterization of genes underlying QTLs for various quantitative traits in soybean and their significance in translational genomics and breeding of other legume crops. PMID:28066449

Sperm competition, sperm numbers and sperm quality in muroid rodents.

PubMed

Gómez Montoto, Laura; Magaña, Concepción; Tourmente, Maximiliano; Martín-Coello, Juan; Crespo, Cristina; Luque-Larena, Juan José; Gomendio, Montserrat; Roldan, Eduardo R S

2011-03-25

Sperm competition favors increases in relative testes mass and production efficiency, and changes in sperm phenotype that result in faster swimming speeds. However, little is known about its effects on traits that contribute to determine the quality of a whole ejaculate (i.e., proportion of motile, viable, morphologically normal and acrosome intact sperm) and that are key determinants of fertilization success. Two competing hypotheses lead to alternative predictions: (a) sperm quantity and quality traits co-evolve under sperm competition because they play complementary roles in determining ejaculate's competitive ability, or (b) energetic constraints force trade-offs between traits depending on their relevance in providing a competitive advantage. We examined relationships between sperm competition levels, sperm quantity, and traits that determine ejaculate quality, in a comparative study of 18 rodent species using phylogenetically controlled analyses. Total sperm numbers were positively correlated to proportions of normal sperm, acrosome integrity and motile sperm; the latter three were also significantly related among themselves, suggesting no trade-offs between traits. In addition, testes mass corrected for body mass (i.e., relative testes mass), showed a strong association with sperm numbers, and positive significant associations with all sperm traits that determine ejaculate quality with the exception of live sperm. An "overall sperm quality" parameter obtained by principal component analysis (which explained 85% of the variance) was more strongly associated with relative testes mass than any individual quality trait. Overall sperm quality was as strongly associated with relative testes mass as sperm numbers. Thus, sperm quality traits improve under sperm competition in an integrated manner suggesting that a combination of all traits is what makes ejaculates more competitive. In evolutionary terms this implies that a complex network of genetic and developmental pathways underlying processes of sperm formation, maturation, transport in the female reproductive tract, and preparation for fertilization must all evolve in concert.

Genetic Parameters and the Impact of Off-Types for Theobroma cacao L. in a Breeding Program in Brazil

PubMed Central

DuVal, Ashley; Gezan, Salvador A.; Mustiga, Guiliana; Stack, Conrad; Marelli, Jean-Philippe; Chaparro, José; Livingstone, Donald; Royaert, Stefan; Motamayor, Juan C.

2017-01-01

Breeding programs of cacao (Theobroma cacao L.) trees share the many challenges of breeding long-living perennial crops, and genetic progress is further constrained by both the limited understanding of the inheritance of complex traits and the prevalence of technical issues, such as mislabeled individuals (off-types). To better understand the genetic architecture of cacao, in this study, 13 years of phenotypic data collected from four progeny trials in Bahia, Brazil were analyzed jointly in a multisite analysis. Three separate analyses (multisite, single site with and without off-types) were performed to estimate genetic parameters from statistical models fitted on nine important agronomic traits (yield, seed index, pod index, % healthy pods, % pods infected with witches broom, % of pods other loss, vegetative brooms, diameter, and tree height). Genetic parameters were estimated along with variance components and heritabilities from the multisite analysis, and a trial was fingerprinted with low-density SNP markers to determine the impact of off-types on estimations. Heritabilities ranged from 0.37 to 0.64 for yield and its components and from 0.03 to 0.16 for disease resistance traits. A weighted index was used to make selections for clonal evaluation, and breeding values estimated for the parental selection and estimation of genetic gain. The impact of off-types to breeding progress in cacao was assessed for the first time. Even when present at <5% of the total population, off-types altered selections by 48%, and impacted heritability estimations for all nine of the traits analyzed, including a 41% difference in estimated heritability for yield. These results show that in a mixed model analysis, even a low level of pedigree error can significantly alter estimations of genetic parameters and selections in a breeding program. PMID:29250097

Dissecting repulsion linkage in the dwarfing gene Dw3 region for sorghum plant height provides insights into heterosis.

PubMed

Li, Xin; Li, Xianran; Fridman, Eyal; Tesso, Tesfaye T; Yu, Jianming

2015-09-22

Heterosis is a main contributor to yield increase in many crop species. Different mechanisms have been proposed for heterosis: dominance, overdominance, epistasis, epigenetics, and protein metabolite changes. However, only limited examples of molecular dissection and validation of these mechanisms are available. Here, we present an example of discovery and validation of heterosis generated by a combination of repulsion linkage and dominance. Using a recombinant inbred line population, a separate quantitative trait locus (QTL) for plant height (qHT7.1) was identified near the genomic region harboring the known auxin transporter Dw3 gene. With two loci having repulsion linkage between two inbreds, heterosis in the hybrid can appear as a single locus with an overdominance mode of inheritance (i.e., pseudo-overdominance). Individually, alleles conferring taller plant height exhibited complete dominance over alleles conferring shorter height. Detailed analyses of different height components demonstrated that qHT7.1 affects both the upper and lower parts of the plant, whereas Dw3 affects only the part below the flag leaf. Computer simulations show that repulsion linkage could influence QTL detection and estimation of effect in segregating populations. Guided by findings in linkage mapping, a genome-wide association study of plant height with a sorghum diversity panel pinpointed genomic regions underlying the trait variation, including Dw1, Dw2, Dw3, Dw4, and qHT7.1. Multilocus mixed model analysis confirmed the advantage of complex trait dissection using an integrated approach. Besides identifying a specific genetic example of heterosis, our research indicated that integrated molecular dissection of complex traits in different population types can enable plant breeders to fine tune the breeding process for crop production.

Ecophysiology Tracks Phylogeny and Meets Ecological Models in an Iberian Gecko.

PubMed

Rato, C; Carretero, M A

2015-01-01

Because fitness of ectotherms, including reptiles, is highly dependent on temperature and water availability, the study of ecophysiological traits, such as preferred temperature (T p) and water loss rates (WLRs), may provide mechanistic evidence on the restricting factors to the species ranges. The Moorish gecko, Tarentola mauritanica, is a species complex with a circum-Mediterranean distribution. In the Iberian Peninsula, two sister parapatric forms of the complex, known as the Iberian and the European clades, are found. Ecological models previously performed using presence records and bioclimatic variables suggest niche divergence between both lineages correlated with precipitation rather than with temperature. In this study, we test this correlative hypothesis using ecophysiological evidence. In the laboratory, we analyzed the T p and WLRs for 84 adult males from seven distinct populations ascribed to one of the two lineages present in Iberia. Specifically, we evaluated the existence of trait conservatism versus adaptation among populations, lineages, or both. In addition, we tested for a trade-off between water and thermal traits and assessed whether climate regime of sampling localities had any influence on the ecophysiological patterns found. We found that T p is quite conserved at both the population and lineage levels and independent from body size. In contrast, water loss experiments revealed some variation among populations, but the regression analysis failed to detect correlation between T p and WLR at any level. Overall, the European lineage displayed a trend for higher water loss and was more diverse among populations when compared with the Iberian lineage. The lack of correspondence between ecophysiological traits and local climatic conditions favors phylogenetic signal versus adaptation. This suggests divergent evolutionary responses to the environment, mainly acting on water ecology, in both lineages, which may account for the differences in their range expansion.

Genome-wide identification and analysis of the B3 superfamily of transcription factors in Brassicaceae and major crop plants.

PubMed

Peng, Fred Y; Weselake, Randall J

2013-05-01

The plant-specific B3 superfamily of transcription factors has diverse functions in plant growth and development. Using a genome-wide domain analysis, we identified 92, 187, 58, 90, 81, 55, and 77 B3 transcription factor genes in the sequenced genome of Arabidopsis, Brassica rapa, castor bean (Ricinus communis), cocoa (Theobroma cacao), soybean (Glycine max), maize (Zea mays), and rice (Oryza sativa), respectively. The B3 superfamily has substantially expanded during the evolution in eudicots particularly in Brassicaceae, as compared to monocots in the analysis. We observed domain duplication in some of these B3 proteins, forming more complex domain architectures than currently understood. We found that the length of B3 domains exhibits a large variation, which may affect their exact number of α-helices and β-sheets in the core structure of B3 domains, and possibly have functional implications. Analysis of the public microarray data indicated that most of the B3 gene pairs encoding Arabidopsis-rice orthologs are preferentially expressed in different tissues, suggesting their different roles in these two species. Using ESTs in crops, we identified many B3 genes preferentially expressed in reproductive tissues. In a sequence-based quantitative trait loci analysis in rice and maize, we have found many B3 genes associated with traits such as grain yield, seed weight and number, and protein content. Our results provide a framework for future studies into the function of B3 genes in different phases of plant development, especially the ones related to traits in major crops.

Genetic, Genomic, and Breeding Approaches to Further Explore Kernel Composition Traits and Grain Yield in Maize

ERIC Educational Resources Information Center

Da Silva, Helena Sofia Pereira

2009-01-01

Maize ("Zea mays L.") is a model species well suited for the dissection of complex traits which are often of commercial value. The purpose of this research was to gain a deeper understanding of the genetic control of maize kernel composition traits starch, protein, and oil concentration, and also kernel weight and grain yield. Germplasm with…

Multivariate Phylogenetic Comparative Methods: Evaluations, Comparisons, and Recommendations.

PubMed

Adams, Dean C; Collyer, Michael L

2018-01-01

Recent years have seen increased interest in phylogenetic comparative analyses of multivariate data sets, but to date the varied proposed approaches have not been extensively examined. Here we review the mathematical properties required of any multivariate method, and specifically evaluate existing multivariate phylogenetic comparative methods in this context. Phylogenetic comparative methods based on the full multivariate likelihood are robust to levels of covariation among trait dimensions and are insensitive to the orientation of the data set, but display increasing model misspecification as the number of trait dimensions increases. This is because the expected evolutionary covariance matrix (V) used in the likelihood calculations becomes more ill-conditioned as trait dimensionality increases, and as evolutionary models become more complex. Thus, these approaches are only appropriate for data sets with few traits and many species. Methods that summarize patterns across trait dimensions treated separately (e.g., SURFACE) incorrectly assume independence among trait dimensions, resulting in nearly a 100% model misspecification rate. Methods using pairwise composite likelihood are highly sensitive to levels of trait covariation, the orientation of the data set, and the number of trait dimensions. The consequences of these debilitating deficiencies are that a user can arrive at differing statistical conclusions, and therefore biological inferences, simply from a dataspace rotation, like principal component analysis. By contrast, algebraic generalizations of the standard phylogenetic comparative toolkit that use the trace of covariance matrices are insensitive to levels of trait covariation, the number of trait dimensions, and the orientation of the data set. Further, when appropriate permutation tests are used, these approaches display acceptable Type I error and statistical power. We conclude that methods summarizing information across trait dimensions, as well as pairwise composite likelihood methods should be avoided, whereas algebraic generalizations of the phylogenetic comparative toolkit provide a useful means of assessing macroevolutionary patterns in multivariate data. Finally, we discuss areas in which multivariate phylogenetic comparative methods are still in need of future development; namely highly multivariate Ornstein-Uhlenbeck models and approaches for multivariate evolutionary model comparisons. © The Author(s) 2017. Published by Oxford University Press on behalf of the Systematic Biology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

On the role of mid-infrared predicted phenotypes in fertility and health dairy breeding programs.

PubMed

Bastin, C; Théron, L; Lainé, A; Gengler, N

2016-05-01

Fertility and health traits are of prime importance in dairy breeding programs. However, these traits are generally complex, difficult to record, and lowly heritable (<0.10), thereby hampering genetic improvement in disease resistance and fertility. Hence, indicators are useful in the prediction of genetic merit for fertility and health traits as long as they are easier to measure than direct fitness traits, heritable, and genetically correlated. Considering that changes in (fine) milk composition over a lactation reflect the physiological status of the cow, mid-infrared (MIR) analysis of milk opens the door to a wide range of potential indicator traits of fertility and health. Previous studies investigated the phenotypic and genetic relationships between fertility and MIR-predicted phenotypes, most being related to negative postpartum energy balance and body fat mobilization (e.g., fat:protein ratio, urea, fatty acids profile). Results showed that a combination of various fatty acid traits (e.g., C18:1 cis-9 and C10:0) could be used to improve fertility. Furthermore, occurrence of (sub)clinical ketosis has been related to milk-based phenotypes such as fat:protein ratio, fatty acids, and ketone bodies. Hence, MIR-predicted acetone and β-hydroxybutyrate contents in milk could be useful for breeding cows less susceptible to ketosis. Although studies investigating the genetic association among mastitis and MIR-predicted phenotypes are scarce, a wide range of traits, potentially predicted by MIR spectrometry, are worthy of consideration. These include traits related to the disease response of the cow (e.g., lactoferrin), reduced secretory activity (e.g., casein), and the alteration of the blood-milk barrier (e.g., minerals). Moreover, direct MIR prediction of fertility and health traits should be further considered. To conclude, MIR-predicted phenotypes have a role to play in the improvement of dairy cow fertility and health. However, further studies are warranted to (1) grasp underlying associations among MIR-predicted indicator and fitness traits, (2) estimate the genetic parameters, and (3) include these traits in broader breeding strategies. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Occam's shadow: levels of analysis in evolutionary ecology - where to next?

USGS Publications Warehouse

Cooch, E.G.; Cam, E.; Link, W.A.

2002-01-01

Evolutionary ecology is the study of evolutionary processes, and the ecological conditions that influence them. A fundamental paradigm underlying the study of evolution is natural selection. Although there are a variety of operational definitions for natural selection in the literature, perhaps the most general one is that which characterizes selection as the process whereby heritable variation in fitness associated with variation in one or more phenotypic traits leads to intergenerational change in the frequency distribution of those traits. The past 20 years have witnessed a marked increase in the precision and reliability of our ability to estimate one or more components of fitness and characterize natural selection in wild populations, owing particularly to significant advances in methods for analysis of data from marked individuals. In this paper, we focus on several issues that we believe are important considerations for the application and development of these methods in the context of addressing questions in evolutionary ecology. First, our traditional approach to estimation often rests upon analysis of aggregates of individuals, which in the wild may reflect increasingly non-random (selected) samples with respect to the trait(s) of interest. In some cases, analysis at the aggregate level, rather than the individual level, may obscure important patterns. While there are a growing number of analytical tools available to estimate parameters at the individual level, and which can cope (to varying degrees) with progressive selection of the sample, the advent of new methods does not reduce the need to consider carefully the appropriate level of analysis in the first place. Estimation should be motivated a priori by strong theoretical analysis. Doing so provides clear guidance, in terms of both (i) assisting in the identification of realistic and meaningful models to include in the candidate model set, and (ii) providing the appropriate context under which the results are interpreted. Second, while it is true that selection (as defined) operates at the level of the individual, the selection gradient is often (if not generally) conditional on the abundance of the population. As such, it may be important to consider estimating transition rates conditional on both the parameter values of the other individuals in the population (or at least their distribution), and population abundance. This will undoubtedly pose a considerable challenge, for both single- and multi-strata applications. It will also require renewed consideration of the estimation of abundance, especially for open populations. Thirdly, selection typically operates on dynamic, individually varying traits. Such estimation may require characterizing fitness in terms of individual plasticity in one or more state variables, constituting analysis of the norms of reaction of individuals to variable environments. This can be quite complex, especially for traits that are under facultative control. Recent work has indicated that the pattern of selection on such traits is conditional on the relative rates of movement among and frequency of spatially heterogeneous habitats, suggesting analyses of evolution of life histories in open populations can be misleading in some cases.

Improving the baking quality of bread wheat by genomic selection in early generations.

PubMed

Michel, Sebastian; Kummer, Christian; Gallee, Martin; Hellinger, Jakob; Ametz, Christian; Akgöl, Batuhan; Epure, Doru; Güngör, Huseyin; Löschenberger, Franziska; Buerstmayr, Hermann

2018-02-01

Genomic selection shows great promise for pre-selecting lines with superior bread baking quality in early generations, 3 years ahead of labour-intensive, time-consuming, and costly quality analysis. The genetic improvement of baking quality is one of the grand challenges in wheat breeding as the assessment of the associated traits often involves time-consuming, labour-intensive, and costly testing forcing breeders to postpone sophisticated quality tests to the very last phases of variety development. The prospect of genomic selection for complex traits like grain yield has been shown in numerous studies, and might thus be also an interesting method to select for baking quality traits. Hence, we focused in this study on the accuracy of genomic selection for laborious and expensive to phenotype quality traits as well as its selection response in comparison with phenotypic selection. More than 400 genotyped wheat lines were, therefore, phenotyped for protein content, dough viscoelastic and mixing properties related to baking quality in multi-environment trials 2009-2016. The average prediction accuracy across three independent validation populations was r = 0.39 and could be increased to r = 0.47 by modelling major QTL as fixed effects as well as employing multi-trait prediction models, which resulted in an acceptable prediction accuracy for all dough rheological traits (r = 0.38-0.63). Genomic selection can furthermore be applied 2-3 years earlier than direct phenotypic selection, and the estimated selection response was nearly twice as high in comparison with indirect selection by protein content for baking quality related traits. This considerable advantage of genomic selection could accordingly support breeders in their selection decisions and aid in efficiently combining superior baking quality with grain yield in newly developed wheat varieties.

Redefining the endophenotype concept to accommodate transdiagnostic vulnerabilities and etiological complexity.

PubMed

Beauchaine, Theodore P; Constantino, John N

2017-09-11

In psychopathology research, endophenotypes are a subset of biomarkers that indicate genetic vulnerability independent of clinical state. To date, an explicit expectation is that endophenotypes be specific to single disorders. We evaluate this expectation considering recent advances in psychiatric genetics, recognition that transdiagnostic vulnerability traits are often more useful than clinical diagnoses in psychiatric genetics, and appreciation for etiological complexity across genetic, neural, hormonal and environmental levels of analysis. We suggest that the disorder-specificity requirement of endophenotypes be relaxed, that neural functions are preferable to behaviors as starting points in searches for endophenotypes, and that future research should focus on interactive effects of multiple endophenotypes on complex psychiatric disorders, some of which are 'phenocopies' with distinct etiologies.

P-TRAP: a Panicle TRAit Phenotyping tool.

PubMed

A L-Tam, Faroq; Adam, Helene; Anjos, António dos; Lorieux, Mathias; Larmande, Pierre; Ghesquière, Alain; Jouannic, Stefan; Shahbazkia, Hamid Reza

2013-08-29

In crops, inflorescence complexity and the shape and size of the seed are among the most important characters that influence yield. For example, rice panicles vary considerably in the number and order of branches, elongation of the axis, and the shape and size of the seed. Manual low-throughput phenotyping methods are time consuming, and the results are unreliable. However, high-throughput image analysis of the qualitative and quantitative traits of rice panicles is essential for understanding the diversity of the panicle as well as for breeding programs. This paper presents P-TRAP software (Panicle TRAit Phenotyping), a free open source application for high-throughput measurements of panicle architecture and seed-related traits. The software is written in Java and can be used with different platforms (the user-friendly Graphical User Interface (GUI) uses Netbeans Platform 7.3). The application offers three main tools: a tool for the analysis of panicle structure, a spikelet/grain counting tool, and a tool for the analysis of seed shape. The three tools can be used independently or simultaneously for analysis of the same image. Results are then reported in the Extensible Markup Language (XML) and Comma Separated Values (CSV) file formats. Images of rice panicles were used to evaluate the efficiency and robustness of the software. Compared to data obtained by manual processing, P-TRAP produced reliable results in a much shorter time. In addition, manual processing is not repeatable because dry panicles are vulnerable to damage. The software is very useful, practical and collects much more data than human operators. P-TRAP is a new open source software that automatically recognizes the structure of a panicle and the seeds on the panicle in numeric images. The software processes and quantifies several traits related to panicle structure, detects and counts the grains, and measures their shape parameters. In short, P-TRAP offers both efficient results and a user-friendly environment for experiments. The experimental results showed very good accuracy compared to field operator, expert verification and well-known academic methods.

P-TRAP: a Panicle Trait Phenotyping tool

PubMed Central

2013-01-01

Background In crops, inflorescence complexity and the shape and size of the seed are among the most important characters that influence yield. For example, rice panicles vary considerably in the number and order of branches, elongation of the axis, and the shape and size of the seed. Manual low-throughput phenotyping methods are time consuming, and the results are unreliable. However, high-throughput image analysis of the qualitative and quantitative traits of rice panicles is essential for understanding the diversity of the panicle as well as for breeding programs. Results This paper presents P-TRAP software (Panicle TRAit Phenotyping), a free open source application for high-throughput measurements of panicle architecture and seed-related traits. The software is written in Java and can be used with different platforms (the user-friendly Graphical User Interface (GUI) uses Netbeans Platform 7.3). The application offers three main tools: a tool for the analysis of panicle structure, a spikelet/grain counting tool, and a tool for the analysis of seed shape. The three tools can be used independently or simultaneously for analysis of the same image. Results are then reported in the Extensible Markup Language (XML) and Comma Separated Values (CSV) file formats. Images of rice panicles were used to evaluate the efficiency and robustness of the software. Compared to data obtained by manual processing, P-TRAP produced reliable results in a much shorter time. In addition, manual processing is not repeatable because dry panicles are vulnerable to damage. The software is very useful, practical and collects much more data than human operators. Conclusions P-TRAP is a new open source software that automatically recognizes the structure of a panicle and the seeds on the panicle in numeric images. The software processes and quantifies several traits related to panicle structure, detects and counts the grains, and measures their shape parameters. In short, P-TRAP offers both efficient results and a user-friendly environment for experiments. The experimental results showed very good accuracy compared to field operator, expert verification and well-known academic methods. PMID:23987653

Network-Assisted Investigation of Combined Causal Signals from Genome-Wide Association Studies in Schizophrenia

PubMed Central

Jia, Peilin; Wang, Lily; Fanous, Ayman H.; Pato, Carlos N.; Edwards, Todd L.; Zhao, Zhongming

2012-01-01

With the recent success of genome-wide association studies (GWAS), a wealth of association data has been accomplished for more than 200 complex diseases/traits, proposing a strong demand for data integration and interpretation. A combinatory analysis of multiple GWAS datasets, or an integrative analysis of GWAS data and other high-throughput data, has been particularly promising. In this study, we proposed an integrative analysis framework of multiple GWAS datasets by overlaying association signals onto the protein-protein interaction network, and demonstrated it using schizophrenia datasets. Building on a dense module search algorithm, we first searched for significantly enriched subnetworks for schizophrenia in each single GWAS dataset and then implemented a discovery-evaluation strategy to identify module genes with consistent association signals. We validated the module genes in an independent dataset, and also examined them through meta-analysis of the related SNPs using multiple GWAS datasets. As a result, we identified 205 module genes with a joint effect significantly associated with schizophrenia; these module genes included a number of well-studied candidate genes such as DISC1, GNA12, GNA13, GNAI1, GPR17, and GRIN2B. Further functional analysis suggested these genes are involved in neuronal related processes. Additionally, meta-analysis found that 18 SNPs in 9 module genes had P meta<1×10−4, including the gene HLA-DQA1 located in the MHC region on chromosome 6, which was reported in previous studies using the largest cohort of schizophrenia patients to date. These results demonstrated our bi-directional network-based strategy is efficient for identifying disease-associated genes with modest signals in GWAS datasets. This approach can be applied to any other complex diseases/traits where multiple GWAS datasets are available. PMID:22792057

Mapping in an apple (Malus x domestica) F1 segregating population based on physical clustering of differentially expressed genes.

PubMed

Jensen, Philip J; Fazio, Gennaro; Altman, Naomi; Praul, Craig; McNellis, Timothy W

2014-04-04

Apple tree breeding is slow and difficult due to long generation times, self-incompatibility, and complex genetics. The identification of molecular markers linked to traits of interest is a way to expedite the breeding process. In the present study, we aimed to identify genes whose steady-state transcript abundance was associated with inheritance of specific traits segregating in an apple (Malus × domestica) rootstock F1 breeding population, including resistance to powdery mildew (Podosphaera leucotricha) disease and woolly apple aphid (Eriosoma lanigerum). Transcription profiling was performed for 48 individual F1 apple trees from a cross of two highly heterozygous parents, using RNA isolated from healthy, actively-growing shoot tips and a custom apple DNA oligonucleotide microarray representing 26,000 unique transcripts. Genome-wide expression profiles were not clear indicators of powdery mildew or woolly apple aphid resistance phenotype. However, standard differential gene expression analysis between phenotypic groups of trees revealed relatively small sets of genes with trait-associated expression levels. For example, thirty genes were identified that were differentially expressed between trees resistant and susceptible to powdery mildew. Interestingly, the genes encoding twenty-four of these transcripts were physically clustered on chromosome 12. Similarly, seven genes were identified that were differentially expressed between trees resistant and susceptible to woolly apple aphid, and the genes encoding five of these transcripts were also clustered, this time on chromosome 17. In each case, the gene clusters were in the vicinity of previously identified major quantitative trait loci for the corresponding trait. Similar results were obtained for a series of molecular traits. Several of the differentially expressed genes were used to develop DNA polymorphism markers linked to powdery mildew disease and woolly apple aphid resistance. Gene expression profiling and trait-associated transcript analysis using an apple F1 population readily identified genes physically linked to powdery mildew disease resistance and woolly apple aphid resistance loci. This result was especially useful in apple, where extreme levels of heterozygosity make the development of reliable DNA markers quite difficult. The results suggest that this approach could prove effective in crops with complicated genetics, or for which few genomic information resources are available.

A Three‐Domain Personality Analysis of a Mentally Tough Athlete

PubMed Central

Mallett, Clifford J.; Singer, Jefferson A.

2017-01-01

Abstract The current study adopted McAdams' multilayer framework as the basis to develop a psychological portrait of an elite athlete who was identified as being particularly ‘mentally tough’. The aim was to use this single case as an exemplar to demonstrate the utility of McAdams' framework for understanding the complexity of sport performers across three domains of personality: dispositional traits, characteristic adaptations, and narrative identity. We operationalised these domains through the development of specific research questions and, subsequently, the collection and integration of the participant's Big Five traits, personal strivings, coping strategies, and response to a life story interview. The results offered a comprehensive insight into the nature of one athlete's personality that, in turn, informed conceptual perspectives of mental toughness in sport psychology literature and qualitatively supported emerging evidence of the validity of a three‐layer framework in personality psychology. Specifically, the study's design showed how a holistic approach to personality analysis can lead to a more complete psychological representation of competitors in sport, and people generally. It demonstrated how motivational, sociocultural, and meaning‐making aspects of personality can complement a trait profile to achieving a satisfying assessment of the whole person. Copyright © 2017 European Association of Personality Psychology PMID:29540960

Cutaneous malignant melanoma and familial dysplastic nevi: evidence for autosomal dominance and pleiotropy.

PubMed Central

Bale, S J; Chakravarti, A; Greene, M H

1986-01-01

Segregation of familial cutaneous melanoma has been shown to be compatible with autosomal dominant transmission with incomplete penetrance. However, the combined phenotype of melanoma and a known melanoma-precursor lesion, the dysplastic nevus (DN), has not previously been found to fit a Mendelian model of inheritance using complex segregation analysis. Employing a life-table and disease-free survival analysis approach, we estimated the lifetime incidence of melanoma in the sibs and offspring of DN-affected individuals to be 46%, consistent with a highly penetrant, autosomal dominant mode of inheritance. To further elucidate the relationship between the two traits, we conducted a linkage analysis between the melanoma locus and a hypothetical DN locus, and obtained a maximum lod score of 3.857 at theta = .08. Furthermore, all families giving evidence for linkage were in the coupling phase and the maximum likelihood estimate of theta was not significantly different from 0 (P = .1). This provides evidence that the DN and melanoma traits may represent pleiotropic effects of a single, highly penetrant gene behaving in an autosomal dominant manner. PMID:3456198

Pleiotropy Analysis of Quantitative Traits at Gene Level by Multivariate Functional Linear Models

PubMed Central

Wang, Yifan; Liu, Aiyi; Mills, James L.; Boehnke, Michael; Wilson, Alexander F.; Bailey-Wilson, Joan E.; Xiong, Momiao; Wu, Colin O.; Fan, Ruzong

2015-01-01

In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. A common approach is to analyze the phenotypic traits separately using univariate analyses and combine the test results through multiple comparisons. This approach may lead to low power. Multivariate functional linear models are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates for a unified analysis. Three types of approximate F-distribution tests based on Pillai–Bartlett trace, Hotelling–Lawley trace, and Wilks’s Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants in one genetic region. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and optimal sequence kernel association test (SKAT-O). Extensive simulations were performed to evaluate the false positive rates and power performance of the proposed models and tests. We show that the approximate F-distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid traits in eight European cohorts, and (2) three biochemical traits in the Trinity Students Study. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and SKAT-O for the three biochemical traits. The approximate F-distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT-O in the univariate case. The analysis of the four lipid traits and the three biochemical traits detects more association than SKAT-O in the univariate case. PMID:25809955

Pleiotropy analysis of quantitative traits at gene level by multivariate functional linear models.

PubMed

Wang, Yifan; Liu, Aiyi; Mills, James L; Boehnke, Michael; Wilson, Alexander F; Bailey-Wilson, Joan E; Xiong, Momiao; Wu, Colin O; Fan, Ruzong

2015-05-01

In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. A common approach is to analyze the phenotypic traits separately using univariate analyses and combine the test results through multiple comparisons. This approach may lead to low power. Multivariate functional linear models are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates for a unified analysis. Three types of approximate F-distribution tests based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants in one genetic region. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and optimal sequence kernel association test (SKAT-O). Extensive simulations were performed to evaluate the false positive rates and power performance of the proposed models and tests. We show that the approximate F-distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid traits in eight European cohorts, and (2) three biochemical traits in the Trinity Students Study. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and SKAT-O for the three biochemical traits. The approximate F-distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT-O in the univariate case. The analysis of the four lipid traits and the three biochemical traits detects more association than SKAT-O in the univariate case. © 2015 WILEY PERIODICALS, INC.

[Progress in genetic research of human height].

PubMed

Chen, Kaixu; Wang, Weilan; Zhang, Fuchun; Zheng, Xiufen

2015-08-01

It is well known that both environmental and genetic factors contribute to adult height variation in general population. However, heritability studies have shown that the variation in height is more affected by genetic factors. Height is a typical polygenic trait which has been studied by traditional linkage analysis and association analysis to identify common DNA sequence variation associated with height, but progress has been slow. More recently, with the development of genotyping and DNA sequencing technologies, tremendous achievements have been made in genetic research of human height. Hundreds of single nucleotide polymorphisms (SNPs) associated with human height have been identified and validated with the application of genome-wide association studies (GWAS) methodology, which deepens our understanding of the genetics of human growth and development and also provides theoretic basis and reference for studying other complex human traits. In this review, we summarize recent progress in genetic research of human height and discuss problems and prospects in this research area which may provide some insights into future genetic studies of human height.

Sequential recruitment of study participants may inflate genetic heritability estimates.

PubMed

Noce, Damia; Gögele, Martin; Schwienbacher, Christine; Caprioli, Giulia; De Grandi, Alessandro; Foco, Luisa; Platzgummer, Stefan; Pramstaller, Peter P; Pattaro, Cristian

2017-06-01

After the success of genome-wide association studies to uncover complex trait loci, attempts to explain the remaining genetic heritability (h 2 ) are mainly focused on unraveling rare variant associations and gene-gene or gene-environment interactions. Little attention is paid to the possibility that h 2 estimates are inflated as a consequence of the epidemiological study design. We studied the time series of 54 biochemical traits in 4373 individuals from the Cooperative Health Research In South Tyrol (CHRIS) study, a pedigree-based study enrolling ten participants/day over several years, with close relatives preferentially invited within the same day. We observed distributional changes of measured traits over time. We hypothesized that the combination of such changes with the pedigree structure might generate a shared-environment component with consequent h 2 inflation. We performed variance components (VC) h 2 estimation for all traits after accounting for the enrollment period in a linear mixed model (two-stage approach). Accounting for the enrollment period caused a median h 2 reduction of 4%. For 9 traits, the reduction was of >20%. Results were confirmed by a Bayesian Markov chain Monte Carlo analysis with all VCs included at the same time (one-stage approach). The electrolytes were the traits most affected by the enrollment period. The h 2 inflation was independent of the h 2 magnitude, laboratory protocol changes, and length of the enrollment period. The enrollment process may induce shared-environment effects even under very stringent and standardized operating procedures, causing h 2 inflation. Including the day of participation as a random effect is a sensitive way to avoid overestimation.

Chromosomes 3B and 4D are associated with several milling and baking quality traits in a soft white spring wheat (Triticum aestivum L.) population.

PubMed

Carter, A H; Garland-Campbell, K; Morris, C F; Kidwell, K K

2012-04-01

Wheat is marketed based on end-use quality characteristics and better knowledge of the underlying genetics of specific quality parameters is essential to enhance the breeding process. A set of 188 recombinant inbred lines from a 'Louise' by 'Penawawa' mapping population was grown in two crop years at two locations in the Pacific Northwest region of the United States and data were collected on 17 end-use quality traits using established quality analysis protocols. Using an established genetic linkage map, composite interval mapping was used to identify QTL associated with 16 of the 17 quality traits. QTL were found on 13 of the 21 wheat chromosomes. A large number of QTL were located on chromosomes 3B and 4D and coincided with traits for milling quality and starch functionality. Chromosome 3B contained 10 QTL, which were localized to a 26.2 cM region. Chromosome 4D contained 7 QTL, all of which were located on an 18.8 cM region of this chromosome. The majority of the alleles for superior end-use quality were associated with the cultivar Louise. The identified QTL detected remained highly significant independent of grain yield and protein quantity. The identification of these QTL for end-use quality gives key insight into the relationship and complexity of end-use quality traits. It also improves our understanding of these relationships, thereby allowing plant breeders to make valuable gains from selection for these important traits.

Morphology, sociality, and ecology: can morphology predict pairing behavior in coral reef fishes?

NASA Astrophysics Data System (ADS)

Brandl, S. J.; Bellwood, D. R.

2013-09-01

Morphology can contain valuable information about the ecological performance of reef fishes, but it has rarely been used in combination with social traits. Social behavior is known to influence the ecological role of fishes; however, the ecological basis for pairing in reef fishes is not well understood. Field observations of 2,753 individuals, in 47 species in six families of biting reef fishes (Acanthuridae, Chaetodontidae, Kyphosidae, Labridae, Pomacanthidae, Siganidae), were used in combination with six morphological measurements, to examine the morphology of fishes in different social systems. A principal components analysis of morphological traits segregated species with high proportions of pairing individuals from non-pairing species along principal component 1, explaining 40.8 % of the variation. Pairing species were characterized by large eyes, concave foreheads, pointed snouts, deep bodies, and small maximum sizes. There was a significant positive relationship between these morphological traits (i.e., scores on PC1) and the prevalence of pairing within the Chaetodontidae ( r 2 = 0.59; P = 0.026), Siganidae ( r 2 = 0.72; P = 0.004), and Acanthuridae ( r 2 = 0.82; P < 0.001). This was consistent when traits were corrected for phylogenetic effects. No pattern was evident in the scarine Labridae ( r 2 = 0.15; P = 0.17). The morphological characteristics found among pairing species suggest that pairing species share common ecological traits, including foraging for small prey items in micro-topographically complex environments such as reef crevices. These ecological traits may have played a role in the evolution of pairing behavior and subsequently led to the development of reproductive patterns based on monogamy.

Refining genome-wide linkage intervals using a meta-analysis of genome-wide association studies identifies loci influencing personality dimensions

PubMed Central

Amin, Najaf; Hottenga, Jouke-Jan; Hansell, Narelle K; Janssens, A Cecile JW; de Moor, Marleen HM; Madden, Pamela AF; Zorkoltseva, Irina V; Penninx, Brenda W; Terracciano, Antonio; Uda, Manuela; Tanaka, Toshiko; Esko, Tonu; Realo, Anu; Ferrucci, Luigi; Luciano, Michelle; Davies, Gail; Metspalu, Andres; Abecasis, Goncalo R; Deary, Ian J; Raikkonen, Katri; Bierut, Laura J; Costa, Paul T; Saviouk, Viatcheslav; Zhu, Gu; Kirichenko, Anatoly V; Isaacs, Aaron; Aulchenko, Yurii S; Willemsen, Gonneke; Heath, Andrew C; Pergadia, Michele L; Medland, Sarah E; Axenovich, Tatiana I; de Geus, Eco; Montgomery, Grant W; Wright, Margaret J; Oostra, Ben A; Martin, Nicholas G; Boomsma, Dorret I; van Duijn, Cornelia M

2013-01-01

Personality traits are complex phenotypes related to psychosomatic health. Individually, various gene finding methods have not achieved much success in finding genetic variants associated with personality traits. We performed a meta-analysis of four genome-wide linkage scans (N=6149 subjects) of five basic personality traits assessed with the NEO Five-Factor Inventory. We compared the significant regions from the meta-analysis of linkage scans with the results of a meta-analysis of genome-wide association studies (GWAS) (N∼17 000). We found significant evidence of linkage of neuroticism to chromosome 3p14 (rs1490265, LOD=4.67) and to chromosome 19q13 (rs628604, LOD=3.55); of extraversion to 14q32 (ATGG002, LOD=3.3); and of agreeableness to 3p25 (rs709160, LOD=3.67) and to two adjacent regions on chromosome 15, including 15q13 (rs970408, LOD=4.07) and 15q14 (rs1055356, LOD=3.52) in the individual scans. In the meta-analysis, we found strong evidence of linkage of extraversion to 4q34, 9q34, 10q24 and 11q22, openness to 2p25, 3q26, 9p21, 11q24, 15q26 and 19q13 and agreeableness to 4q34 and 19p13. Significant evidence of association in the GWAS was detected between openness and rs677035 at 11q24 (P-value=2.6 × 10−06, KCNJ1). The findings of our linkage meta-analysis and those of the GWAS suggest that 11q24 is a susceptible locus for openness, with KCNJ1 as the possible candidate gene. PMID:23211697

Selection of Yeasts as Starter Cultures for Table Olives: A Step-by-Step Procedure

PubMed Central

Bevilacqua, Antonio; Corbo, Maria Rosaria; Sinigaglia, Milena

2012-01-01

The selection of yeasts intended as starters for table olives is a complex process, including a characterization step at laboratory level and a validation at lab level and factory-scale. The characterization at lab level deals with the assessment of some technological traits (growth under different temperatures and at alkaline pHs, effect of salt, and for probiotic strains the resistance to preservatives), enzymatic activities, and some new functional properties (probiotic traits, production of vitamin B-complex, biological debittering). The paper reports on these traits, focusing both on their theoretical implications and lab protocols; moreover, there are some details on predictive microbiology for yeasts of table olives and on the use of multivariate approaches to select suitable starters. PMID:22666220

Association genetics of growth and adaptive traits in loblolly pine (Pinus taeda L.) using whole-exome-discovered polymorphisms

Treesearch

Mengmeng Lu; Konstantin V. Krutovsky; C. Dana Nelson; Jason B. West; Nathalie A. Reilly; Carol A. Loopstra

2017-01-01

In the USA, forest genetics research began over 100 years ago and loblolly pine breeding programs were established in the 1950s. However, the genetics underlying complex traits of loblolly pine remains to be discovered. To address this, adaptive and growth traits were measured and analyzed in a clonally tested loblolly pine (Pinus taeda L.) population. Over 2.8 million...

Topological analysis of metabolic networks integrating co-segregating transcriptomes and metabolomes in type 2 diabetic rat congenic series.

PubMed

Dumas, Marc-Emmanuel; Domange, Céline; Calderari, Sophie; Martínez, Andrea Rodríguez; Ayala, Rafael; Wilder, Steven P; Suárez-Zamorano, Nicolas; Collins, Stephan C; Wallis, Robert H; Gu, Quan; Wang, Yulan; Hue, Christophe; Otto, Georg W; Argoud, Karène; Navratil, Vincent; Mitchell, Steve C; Lindon, John C; Holmes, Elaine; Cazier, Jean-Baptiste; Nicholson, Jeremy K; Gauguier, Dominique

2016-09-30

The genetic regulation of metabolic phenotypes (i.e., metabotypes) in type 2 diabetes mellitus occurs through complex organ-specific cellular mechanisms and networks contributing to impaired insulin secretion and insulin resistance. Genome-wide gene expression profiling systems can dissect the genetic contributions to metabolome and transcriptome regulations. The integrative analysis of multiple gene expression traits and metabolic phenotypes (i.e., metabotypes) together with their underlying genetic regulation remains a challenge. Here, we introduce a systems genetics approach based on the topological analysis of a combined molecular network made of genes and metabolites identified through expression and metabotype quantitative trait locus mapping (i.e., eQTL and mQTL) to prioritise biological characterisation of candidate genes and traits. We used systematic metabotyping by 1 H NMR spectroscopy and genome-wide gene expression in white adipose tissue to map molecular phenotypes to genomic blocks associated with obesity and insulin secretion in a series of rat congenic strains derived from spontaneously diabetic Goto-Kakizaki (GK) and normoglycemic Brown-Norway (BN) rats. We implemented a network biology strategy approach to visualize the shortest paths between metabolites and genes significantly associated with each genomic block. Despite strong genomic similarities (95-99 %) among congenics, each strain exhibited specific patterns of gene expression and metabotypes, reflecting the metabolic consequences of series of linked genetic polymorphisms in the congenic intervals. We subsequently used the congenic panel to map quantitative trait loci underlying specific mQTLs and genome-wide eQTLs. Variation in key metabolites like glucose, succinate, lactate, or 3-hydroxybutyrate and second messenger precursors like inositol was associated with several independent genomic intervals, indicating functional redundancy in these regions. To navigate through the complexity of these association networks we mapped candidate genes and metabolites onto metabolic pathways and implemented a shortest path strategy to highlight potential mechanistic links between metabolites and transcripts at colocalized mQTLs and eQTLs. Minimizing the shortest path length drove prioritization of biological validations by gene silencing. These results underline the importance of network-based integration of multilevel systems genetics datasets to improve understanding of the genetic architecture of metabotype and transcriptomic regulation and to characterize novel functional roles for genes determining tissue-specific metabolism.

A complete mass spectrometric map for the analysis of the yeast proteome and its application to quantitative trait analysis

PubMed Central

Picotti, Paola; Clement-Ziza, Mathieu; Lam, Henry; Campbell, David S.; Schmidt, Alexander; Deutsch, Eric W.; Röst, Hannes; Sun, Zhi; Rinner, Oliver; Reiter, Lukas; Shen, Qin; Michaelson, Jacob J.; Frei, Andreas; Alberti, Simon; Kusebauch, Ulrike; Wollscheid, Bernd; Moritz, Robert; Beyer, Andreas; Aebersold, Ruedi

2013-01-01

Complete reference maps or datasets, like the genomic map of an organism, are highly beneficial tools for biological and biomedical research. Attempts to generate such reference datasets for a proteome so far failed to reach complete proteome coverage, with saturation apparent at approximately two thirds of the proteomes tested, even for the most thoroughly characterized proteomes. Here, we used a strategy based on high-throughput peptide synthesis and mass spectrometry to generate a close to complete reference map (97% of the genome-predicted proteins) of the S. cerevisiae proteome. We generated two versions of this mass spectrometric map one supporting discovery- (shotgun) and the other hypothesis-driven (targeted) proteomic measurements. The two versions of the map, therefore, constitute a complete set of proteomic assays to support most studies performed with contemporary proteomic technologies. The reference libraries can be browsed via a web-based repository and associated navigation tools. To demonstrate the utility of the reference libraries we applied them to a protein quantitative trait locus (pQTL) analysis, which requires measurement of the same peptides over a large number of samples with high precision. Protein measurements over a set of 78 S. cerevisiae strains revealed a complex relationship between independent genetic loci, impacting on the levels of related proteins. Our results suggest that selective pressure favors the acquisition of sets of polymorphisms that maintain the stoichiometry of protein complexes and pathways. PMID:23334424

Genome-wide linkage and association analysis of cardiometabolic phenotypes in Hispanic Americans.

PubMed

Hellwege, Jacklyn N; Palmer, Nicholette D; Dimitrov, Latchezar; Keaton, Jacob M; Tabb, Keri L; Sajuthi, Satria; Taylor, Kent D; Ng, Maggie C Y; Speliotes, Elizabeth K; Hawkins, Gregory A; Long, Jirong; Ida Chen, Yii-Der; Lorenzo, Carlos; Norris, Jill M; Rotter, Jerome I; Langefeld, Carl D; Wagenknecht, Lynne E; Bowden, Donald W

2017-02-01

Linkage studies of complex genetic diseases have been largely replaced by genome-wide association studies, due in part to limited success in complex trait discovery. However, recent interest in rare and low-frequency variants motivates re-examination of family-based methods. In this study, we investigated the performance of two-point linkage analysis for over 1.6 million single-nucleotide polymorphisms (SNPs) combined with single variant association analysis to identify high impact variants, which are both strongly linked and associated with cardiometabolic traits in up to 1414 Hispanics from the Insulin Resistance Atherosclerosis Family Study (IRASFS). Evaluation of all 50 phenotypes yielded 83 557 000 LOD (logarithm of the odds) scores, with 9214 LOD scores ⩾3.0, 845 ⩾4.0 and 89 ⩾5.0, with a maximal LOD score of 6.49 (rs12956744 in the LAMA1 gene for tumor necrosis factor-α (TNFα) receptor 2). Twenty-seven variants were associated with P<0.005 as well as having an LOD score >4, including variants in the NFIB gene under a linkage peak with TNFα receptor 2 levels on chromosome 9. Linkage regions of interest included a broad peak (31 Mb) on chromosome 1q with acute insulin response (max LOD=5.37). This region was previously documented with type 2 diabetes in family-based studies, providing support for the validity of these results. Overall, we have demonstrated the utility of two-point linkage and association in comprehensive genome-wide array-based SNP genotypes.

An integration of genome-wide association study and gene expression profiling to prioritize the discovery of novel susceptibility Loci for osteoporosis-related traits.

PubMed

Hsu, Yi-Hsiang; Zillikens, M Carola; Wilson, Scott G; Farber, Charles R; Demissie, Serkalem; Soranzo, Nicole; Bianchi, Estelle N; Grundberg, Elin; Liang, Liming; Richards, J Brent; Estrada, Karol; Zhou, Yanhua; van Nas, Atila; Moffatt, Miriam F; Zhai, Guangju; Hofman, Albert; van Meurs, Joyce B; Pols, Huibert A P; Price, Roger I; Nilsson, Olle; Pastinen, Tomi; Cupples, L Adrienne; Lusis, Aldons J; Schadt, Eric E; Ferrari, Serge; Uitterlinden, André G; Rivadeneira, Fernando; Spector, Timothy D; Karasik, David; Kiel, Douglas P

2010-06-10

Osteoporosis is a complex disorder and commonly leads to fractures in elderly persons. Genome-wide association studies (GWAS) have become an unbiased approach to identify variations in the genome that potentially affect health. However, the genetic variants identified so far only explain a small proportion of the heritability for complex traits. Due to the modest genetic effect size and inadequate power, true association signals may not be revealed based on a stringent genome-wide significance threshold. Here, we take advantage of SNP and transcript arrays and integrate GWAS and expression signature profiling relevant to the skeletal system in cellular and animal models to prioritize the discovery of novel candidate genes for osteoporosis-related traits, including bone mineral density (BMD) at the lumbar spine (LS) and femoral neck (FN), as well as geometric indices of the hip (femoral neck-shaft angle, NSA; femoral neck length, NL; and narrow-neck width, NW). A two-stage meta-analysis of GWAS from 7,633 Caucasian women and 3,657 men, revealed three novel loci associated with osteoporosis-related traits, including chromosome 1p13.2 (RAP1A, p = 3.6x10(-8)), 2q11.2 (TBC1D8), and 18q11.2 (OSBPL1A), and confirmed a previously reported region near TNFRSF11B/OPG gene. We also prioritized 16 suggestive genome-wide significant candidate genes based on their potential involvement in skeletal metabolism. Among them, 3 candidate genes were associated with BMD in women. Notably, 2 out of these 3 genes (GPR177, p = 2.6x10(-13); SOX6, p = 6.4x10(-10)) associated with BMD in women have been successfully replicated in a large-scale meta-analysis of BMD, but none of the non-prioritized candidates (associated with BMD) did. Our results support the concept of our prioritization strategy. In the absence of direct biological support for identified genes, we highlighted the efficiency of subsequent functional characterization using publicly available expression profiling relevant to the skeletal system in cellular or whole animal models to prioritize candidate genes for further functional validation.

Mapping by sequencing in cotton (Gossypium hirsutum) line MD52ne identified candidate genes for fiber strength and its related quality attributes.

PubMed

Islam, Md S; Zeng, Linghe; Thyssen, Gregory N; Delhom, Christopher D; Kim, Hee Jin; Li, Ping; Fang, David D

2016-06-01

Three QTL regions controlling three fiber quality traits were validated and further fine-mapped with 27 new single nucleotide polymorphism (SNP) markers. Transcriptome analysis suggests that receptor-like kinases found within the validated QTLs are potential candidate genes responsible for superior fiber strength in cotton line MD52ne. Fiber strength, length, maturity and fineness determine the market value of cotton fibers and the quality of spun yarn. Cotton fiber strength has been recognized as a critical quality attribute in the modern textile industry. Fine mapping along with quantitative trait loci (QTL) validation and candidate gene prediction can uncover the genetic and molecular basis of fiber quality traits. Four previously-identified QTLs (qFBS-c3, qSFI-c14, qUHML-c14 and qUHML-c24) related to fiber bundle strength, short fiber index and fiber length, respectively, were validated using an F3 population that originated from a cross of MD90ne × MD52ne. A group of 27 new SNP markers generated from mapping-by-sequencing (MBS) were placed in QTL regions to improve and validate earlier maps. Our refined QTL regions spanned 4.4, 1.8 and 3.7 Mb of physical distance in the Gossypium raimondii reference genome. We performed RNA sequencing (RNA-seq) of 15 and 20 days post-anthesis fiber cells from MD52ne and MD90ne and aligned reads to the G. raimondii genome. The QTL regions contained 21 significantly differentially expressed genes (DEGs) between the two near-isogenic parental lines. SNPs that result in non-synonymous substitutions to amino acid sequences of annotated genes were identified within these DEGs, and mapped. Taken together, transcriptome and amino acid mutation analysis indicate that receptor-like kinase pathway genes are likely candidates for superior fiber strength and length in MD52ne. MBS along with RNA-seq demonstrated a powerful strategy to elucidate candidate genes for the QTLs that control complex traits in a complex genome like tetraploid upland cotton.

Using network analysis to study behavioural phenotypes: an example using domestic dogs.

PubMed

Goold, Conor; Vas, Judit; Olsen, Christine; Newberry, Ruth C

2016-10-01

Phenotypic integration describes the complex interrelationships between organismal traits, traditionally focusing on morphology. Recently, research has sought to represent behavioural phenotypes as composed of quasi-independent latent traits. Concurrently, psychologists have opposed latent variable interpretations of human behaviour, proposing instead a network perspective envisaging interrelationships between behaviours as emerging from causal dependencies. Network analysis could also be applied to understand integrated behavioural phenotypes in animals. Here, we assimilate this cross-disciplinary progression of ideas by demonstrating the use of network analysis on survey data collected on behavioural and motivational characteristics of police patrol and detection dogs ( Canis lupus familiaris ). Networks of conditional independence relationships illustrated a number of functional connections between descriptors, which varied between dog types. The most central descriptors denoted desirable characteristics in both patrol and detection dog networks, with 'Playful' being widely correlated and possessing mediating relationships between descriptors. Bootstrap analyses revealed the stability of network results. We discuss the results in relation to previous research on dog personality, and benefits of using network analysis to study behavioural phenotypes. We conclude that a network perspective offers widespread opportunities for advancing the understanding of phenotypic integration in animal behaviour.

Sherlock: Detecting Gene-Disease Associations by Matching Patterns of Expression QTL and GWAS

PubMed Central

He, Xin; Fuller, Chris K.; Song, Yi; Meng, Qingying; Zhang, Bin; Yang, Xia; Li, Hao

2013-01-01

Genetic mapping of complex diseases to date depends on variations inside or close to the genes that perturb their activities. A strong body of evidence suggests that changes in gene expression play a key role in complex diseases and that numerous loci perturb gene expression in trans. The information in trans variants, however, has largely been ignored in the current analysis paradigm. Here we present a statistical framework for genetic mapping by utilizing collective information in both cis and trans variants. We reason that for a disease-associated gene, any genetic variation that perturbs its expression is also likely to influence the disease risk. Thus, the expression quantitative trait loci (eQTL) of the gene, which constitute a unique “genetic signature,” should overlap significantly with the set of loci associated with the disease. We translate this idea into a computational algorithm (named Sherlock) to search for gene-disease associations from GWASs, taking advantage of independent eQTL data. Application of this strategy to Crohn disease and type 2 diabetes predicts a number of genes with possible disease roles, including several predictions supported by solid experimental evidence. Importantly, predicted genes are often implicated by multiple trans eQTL with moderate associations. These genes are far from any GWAS association signals and thus cannot be identified from the GWAS alone. Our approach allows analysis of association data from a new perspective and is applicable to any complex phenotype. It is readily generalizable to molecular traits other than gene expression, such as metabolites, noncoding RNAs, and epigenetic modifications. PMID:23643380

Network-Based Identification of Adaptive Pathways in Evolved Ethanol-Tolerant Bacterial Populations

PubMed Central

Swings, Toon; Weytjens, Bram; Schalck, Thomas; Bonte, Camille; Verstraeten, Natalie; Michiels, Jan

2017-01-01

Abstract Efficient production of ethanol for use as a renewable fuel requires organisms with a high level of ethanol tolerance. However, this trait is complex and increased tolerance therefore requires mutations in multiple genes and pathways. Here, we use experimental evolution for a system-level analysis of adaptation of Escherichia coli to high ethanol stress. As adaptation to extreme stress often results in complex mutational data sets consisting of both causal and noncausal passenger mutations, identifying the true adaptive mutations in these settings is not trivial. Therefore, we developed a novel method named IAMBEE (Identification of Adaptive Mutations in Bacterial Evolution Experiments). IAMBEE exploits the temporal profile of the acquisition of mutations during evolution in combination with the functional implications of each mutation at the protein level. These data are mapped to a genome-wide interaction network to search for adaptive mutations at the level of pathways. The 16 evolved populations in our data set together harbored 2,286 mutated genes with 4,470 unique mutations. Analysis by IAMBEE significantly reduced this number and resulted in identification of 90 mutated genes and 345 unique mutations that are most likely to be adaptive. Moreover, IAMBEE not only enabled the identification of previously known pathways involved in ethanol tolerance, but also identified novel systems such as the AcrAB-TolC efflux pump and fatty acids biosynthesis and even allowed to gain insight into the temporal profile of adaptation to ethanol stress. Furthermore, this method offers a solid framework for identifying the molecular underpinnings of other complex traits as well. PMID:28961727

Anxiety promotes memory for mood-congruent faces but does not alter loss aversion

PubMed Central

Charpentier, Caroline J.; Hindocha, Chandni; Roiser, Jonathan P.; Robinson, Oliver J.

2016-01-01

Pathological anxiety is associated with disrupted cognitive processing, including working memory and decision-making. In healthy individuals, experimentally-induced state anxiety or high trait anxiety often results in the deployment of adaptive harm-avoidant behaviours. However, how these processes affect cognition is largely unknown. To investigate this question, we implemented a translational within-subjects anxiety induction, threat of shock, in healthy participants reporting a wide range of trait anxiety scores. Participants completed a gambling task, embedded within an emotional working memory task, with some blocks under unpredictable threat and others safe from shock. Relative to the safe condition, threat of shock improved recall of threat-congruent (fearful) face location, especially in highly trait anxious participants. This suggests that threat boosts working memory for mood-congruent stimuli in vulnerable individuals, mirroring memory biases in clinical anxiety. By contrast, Bayesian analysis indicated that gambling decisions were better explained by models that did not include threat or treat anxiety, suggesting that: (i) higher-level executive functions are robust to these anxiety manipulations; and (ii) decreased risk-taking may be specific to pathological anxiety. These findings provide insight into the complex interactions between trait anxiety, acute state anxiety and cognition, and may help understand the cognitive mechanisms underlying adaptive anxiety. PMID:27098489

Anxiety promotes memory for mood-congruent faces but does not alter loss aversion.

PubMed

Charpentier, Caroline J; Hindocha, Chandni; Roiser, Jonathan P; Robinson, Oliver J

2016-04-21

Pathological anxiety is associated with disrupted cognitive processing, including working memory and decision-making. In healthy individuals, experimentally-induced state anxiety or high trait anxiety often results in the deployment of adaptive harm-avoidant behaviours. However, how these processes affect cognition is largely unknown. To investigate this question, we implemented a translational within-subjects anxiety induction, threat of shock, in healthy participants reporting a wide range of trait anxiety scores. Participants completed a gambling task, embedded within an emotional working memory task, with some blocks under unpredictable threat and others safe from shock. Relative to the safe condition, threat of shock improved recall of threat-congruent (fearful) face location, especially in highly trait anxious participants. This suggests that threat boosts working memory for mood-congruent stimuli in vulnerable individuals, mirroring memory biases in clinical anxiety. By contrast, Bayesian analysis indicated that gambling decisions were better explained by models that did not include threat or treat anxiety, suggesting that: (i) higher-level executive functions are robust to these anxiety manipulations; and (ii) decreased risk-taking may be specific to pathological anxiety. These findings provide insight into the complex interactions between trait anxiety, acute state anxiety and cognition, and may help understand the cognitive mechanisms underlying adaptive anxiety.

Arms race between selfishness and policing: two-trait quantitative genetic model for caste fate conflict in eusocial Hymenoptera.

PubMed

Dobata, Shigeto

2012-12-01

Policing against selfishness is now regarded as the main force maintaining cooperation, by reducing costly conflict in complex social systems. Although policing has been studied extensively in social insect colonies, its coevolution against selfishness has not been fully captured by previous theories. In this study, I developed a two-trait quantitative genetic model of the conflict between selfish immature females (usually larvae) and policing workers in eusocial Hymenoptera over the immatures' propensity to develop into new queens. This model allows for the analysis of coevolution between genomes expressed in immatures and workers that collectively determine the immatures' queen caste fate. The main prediction of the model is that a higher level of polyandry leads to a smaller fraction of queens produced among new females through caste fate policing. The other main prediction of the present model is that, as a result of arms race, caste fate policing by workers coevolves with exaggerated selfishness of the immatures achieving maximum potential to develop into queens. Moreover, the model can incorporate genetic correlation between traits, which has been largely unexplored in social evolution theory. This study highlights the importance of understanding social traits as influenced by the coevolution of conflicting genomes. © 2012 The Author. Evolution© 2012 The Society for the Study of Evolution.

Sky island bird populations isolated by ancient genetic barriers are characterized by different song traits than those isolated by recent deforestation.

PubMed

Purushotham, Chetana B; Robin, V V

2016-10-01

Various mechanisms of isolation can structure populations and result in cultural and genetic differentiation. Similar to genetic markers, for songbirds, culturally transmitted sexual signals such as breeding song can be used as a measure of differentiation as songs can also be impacted by geographic isolation resulting in population-level differences in song structure. Several studies have found differences in song structure either across ancient geographic barriers or across contemporary habitat barriers owing to deforestation. However, very few studies have examined the effect of both ancient barriers and recent deforestation in the same system. In this study, we examined the geographic variation in song structure across six populations of the White-bellied Shortwing, a threatened and endemic songbird species complex found on isolated mountaintops or "sky islands" of the Western Ghats. While some sky islands in the system are isolated by ancient valleys, others are separated by deforestation. We examined 14 frequency and temporal spectral traits and two syntax traits from 835 songs of 38 individuals across the six populations. We identified three major song clusters based on a discriminant model of spectral traits, degree of similarity of syntax features, as well as responses of birds to opportunistic playback. However, some traits like complex vocal mechanisms (CVM), relating to the use of syrinxes, clearly differentiated both ancient and recently fragmented populations. We suggest that CVMs may have a cultural basis and can be used to identify culturally isolated populations that cannot be differentiated using genetic markers or commonly used frequency-based song traits. Our results demonstrate the use of bird songs to reconstruct phylogenetic groups and impacts of habitat fragmentation even in complex scenarios of historic and contemporary isolation.

Beyond The Blueprint: Development Of Genome-Informed Trait-Based Models For Prediction Of Microbial Dynamics And Biogeochemical Rates

NASA Astrophysics Data System (ADS)

Brodie, E.; King, E.; Molins, S.; Karaoz, U.; Johnson, J. N.; Bouskill, N.; Hug, L. A.; Thomas, B. C.; Castelle, C. J.; Beller, H. R.; Banfield, J. F.; Steefel, C. I.

2014-12-01

In soils and sediments microorganisms perform essential ecosystem services through their roles in regulating the stability of carbon and the flux of nutrients, and the purification of water. But these are complex systems with the physical, chemical and biological components all intimately connected. Components of this complexity are gradually being uncovered and our understanding of the extent of microbial functional diversity in particular has been enhanced greatly with the development of cultivation independent approaches. However we have not moved far beyond a descriptive and correlative use of this powerful resource. As the ability to reconstruct thousands of genomes from microbial populations using metagenomic techniques gains momentum, the challenge will be to develop an understanding of how these metabolic blueprints serve to influence the fitness of organisms within these complex systems and how populations emerge and impact the physical and chemical properties of their environment. In the presentation we will discuss the development of a trait-based model of microbial activity that simulates coupled guilds of microorganisms that are parameterized including traits extracted from large-scale metagenomic data. Using a reactive transport framework we simulate the thermodynamics of coupled electron donor and acceptor reactions to predict the energy available for respiration, biomass development and exo-enzyme production. Each group within a functional guild is parameterized with a unique combination of traits governing organism fitness under dynamic environmental conditions. This presentation will address our latest developments in the estimation of trait values related to growth rate and the identification and linkage of key fitness traits associated with respiratory and fermentative pathways, macromolecule depolymerization enzymes and nitrogen fixation from metagenomic data. We are testing model sensitivity to initial microbial composition and intra-guild trait variability amongst other parameters and are using this model to explore abiotic controls on community emergence and impact on rates of reactions that contribute to the cycling of carbon across biogeochemical gradients from the soil to the subsurface.

Maternal Factors Predicting Cognitive and Behavioral Characteristics of Children with Fetal Alcohol Spectrum Disorders

PubMed Central

May, Philip A.; Tabachnick, Barbara G.; Gossage, J. Phillip; Kalberg, Wendy O.; Marais, Anna-Susan; Robinson, Luther K.; Manning, Melanie A.; Blankenship, Jason; Buckley, David; Hoyme, H. Eugene; Adnams, Colleen M.

2013-01-01

Objective To provide an analysis of multiple predictors of cognitive and behavioral traits for children with fetal alcohol spectrum disorders (FASD). Method Multivariate correlation techniques were employed with maternal and child data from epidemiologic studies in a community in South Africa. Data on 561 first grade children with fetal alcohol syndrome (FAS), partial FAS (PFAS), and not FASD and their mothers were analyzed by grouping 19 maternal variables into categories (physical, demographic, childbearing, and drinking) and employed in structural equation models (SEM) to assess correlates of child intelligence (verbal and non-verbal) and behavior. Results A first SEM utilizing only seven maternal alcohol use variables to predict cognitive/behavioral traits was statistically significant (B = 3.10, p < .05), but explained only 17.3% of the variance. The second model incorporated multiple maternal variables and was statistically significant explaining 55.3% of the variance. Significantly correlated with low intelligence and problem behavior were demographic (B = 3.83, p < .05) (low maternal education, low socioeconomic status (SES), and rural residence) and maternal physical characteristics (B = 2.70, p < .05) (short stature, small head circumference, and low weight). Childbearing history and alcohol use composites were not statistically significant in the final complex model, and were overpowered by SES and maternal physical traits. Conclusions While other analytic techniques have amply demonstrated the negative effects of maternal drinking on intelligence and behavior, this highly-controlled analysis of multiple maternal influences reveals that maternal demographics and physical traits make a significant enabling or disabling contribution to child functioning in FASD. PMID:23751886

Coping mediates the influence of personality on life satisfaction in patients with rheumatic diseases.

PubMed

Vollmann, Manja; Pukrop, Jörg; Salewski, Christel

2016-04-01

A rheumatic disease can severely impair a person's quality of life. The degree of impairment, however, is not closely related to objective indicators of disease severity. This study investigated the influence and the interplay of core psychological factors, i.e., personality and coping, on life satisfaction in patients with rheumatic diseases. Particularly, it was tested whether coping mediates the effects of personality on life satisfaction. In a cross-sectional design, 158 patients diagnosed with a rheumatic disease completed questionnaires assessing the Big 5 personality traits (BFI-10), several disease-related coping strategies (EFK) and life satisfaction (HSWBS). Data were analyzed using a complex multiple mediation analysis with the Big 5 personality traits as predictors, coping strategies as mediators and life satisfaction as outcome. All personality traits and seven of the nine coping strategies were associated with life satisfaction (rs > |0.16|, ps ≤ 0.05). The mediation analysis revealed that personality traits had no direct, but rather indirect effects on life satisfaction through coping. Neuroticism had a negative indirect effect on life satisfaction through less active problem solving and more depressive coping (indirect effects > -0.03, ps < 0.05). Extraversion, agreeableness, and conscientiousness had positive indirect effects on life satisfaction through more active problem solving, less depressive coping and/or a more active search for social support (indirect effects > 0.06, ps < 0.05). Personality and coping play a role in adjustment to rheumatic diseases. The interplay of these variables should be considered in psychological interventions for patients with rheumatic diseases.

Combining field performance with controlled environment plant imaging to identify the genetic control of growth and transpiration underlying yield response to water-deficit stress in wheat

PubMed Central

Parent, Boris; Shahinnia, Fahimeh; Maphosa, Lance; Berger, Bettina; Rabie, Huwaida; Chalmers, Ken; Kovalchuk, Alex; Langridge, Peter; Fleury, Delphine

2015-01-01

Crop yield in low-rainfall environments is a complex trait under multigenic control that shows significant genotype×environment (G×E) interaction. One way to understand and track this trait is to link physiological studies to genetics by using imaging platforms to phenotype large segregating populations. A wheat population developed from parental lines contrasting in their mechanisms of yield maintenance under water deficit was studied in both an imaging platform and in the field. We combined phenotyping methods in a common analysis pipeline to estimate biomass and leaf area from images and then inferred growth and relative growth rate, transpiration, and water-use efficiency, and applied these to genetic analysis. From the 20 quantitative trait loci (QTLs) found for several traits in the platform, some showed strong effects, accounting for between 26 and 43% of the variation on chromosomes 1A and 1B, indicating that the G×E interaction could be reduced in a controlled environment and by using dynamic variables. Co-location of QTLs identified in the platform and in the field showed a possible common genetic basis at some loci. Co-located QTLs were found for average growth rate, leaf expansion rate, transpiration rate, and water-use efficiency from the platform with yield, spike number, grain weight, grain number, and harvest index in the field. These results demonstrated that imaging platforms are a suitable alternative to field-based screening and may be used to phenotype recombinant lines for positional cloning. PMID:26179580

Locus-specific view of flax domestication history

PubMed Central

Fu, Yong-Bi; Diederichsen, Axel; Allaby, Robin G

2012-01-01

Crop domestication has been inferred genetically from neutral markers and increasingly from specific domestication-associated loci. However, some crops are utilized for multiple purposes that may or may not be reflected in a single domestication-associated locus. One such example is cultivated flax (Linum usitatissimum L.), the earliest oil and fiber crop, for which domestication history remains poorly understood. Oil composition of cultivated flax and pale flax (L. bienne Mill.) indicates that the sad2 locus is a candidate domestication locus associated with increased unsaturated fatty acid production in cultivated flax. A phylogenetic analysis of the sad2 locus in 43 pale and 70 cultivated flax accessions established a complex domestication history for flax that has not been observed previously. The analysis supports an early, independent domestication of a primitive flax lineage, in which the loss of seed dispersal through capsular indehiscence was not established, but increased oil content was likely occurred. A subsequent flax domestication process occurred that probably involved multiple domestications and includes lineages that contain oil, fiber, and winter varieties. In agreement with previous studies, oil rather than fiber varieties occupy basal phylogenetic positions. The data support multiple paths of flax domestication for oil-associated traits before selection of the other domestication-associated traits of seed dispersal loss and fiber production. The sad2 locus is less revealing about the origin of winter tolerance. In this case, a single domestication-associated locus is informative about the history of domesticated forms with the associated trait while partially informative on forms less associated with the trait. PMID:22408732

Root morphology and seed and leaf ionomic traits in a Brassica napus L. diversity panel show wide phenotypic variation and are characteristic of crop habit.

PubMed

Thomas, C L; Alcock, T D; Graham, N S; Hayden, R; Matterson, S; Wilson, L; Young, S D; Dupuy, L X; White, P J; Hammond, J P; Danku, J M C; Salt, D E; Sweeney, A; Bancroft, I; Broadley, M R

2016-10-04

Mineral nutrient uptake and utilisation by plants are controlled by many traits relating to root morphology, ion transport, sequestration and translocation. The aims of this study were to determine the phenotypic diversity in root morphology and leaf and seed mineral composition of a polyploid crop species, Brassica napus L., and how these traits relate to crop habit. Traits were quantified in a diversity panel of up to 387 genotypes: 163 winter, 127 spring, and seven semiwinter oilseed rape (OSR) habits, 35 swede, 15 winter fodder, and 40 exotic/unspecified habits. Root traits of 14 d old seedlings were measured in a 'pouch and wick' system (n = ~24 replicates per genotype). The mineral composition of 3-6 rosette-stage leaves, and mature seeds, was determined on compost-grown plants from a designed experiment (n = 5) by inductively coupled plasma-mass spectrometry (ICP-MS). Seed size explained a large proportion of the variation in root length. Winter OSR and fodder habits had longer primary and lateral roots than spring OSR habits, with generally lower mineral concentrations. A comparison of the ratios of elements in leaf and seed parts revealed differences in translocation processes between crop habits, including those likely to be associated with crop-selection for OSR seeds with lower sulphur-containing glucosinolates. Combining root, leaf and seed traits in a discriminant analysis provided the most accurate characterisation of crop habit, illustrating the interdependence of plant tissues. High-throughput morphological and composition phenotyping reveals complex interrelationships between mineral acquisition and accumulation linked to genetic control within and between crop types (habits) in B. napus. Despite its recent genetic ancestry (<10 ky), root morphology, and leaf and seed composition traits could potentially be used in crop improvement, if suitable markers can be identified and if these correspond with suitable agronomy and quality traits.

Traits and trade-offs in whole-tree hydraulic architecture along the vertical axis of Eucalyptus grandis.

PubMed

Pfautsch, Sebastian; Aspinwall, Michael J; Drake, John E; Chacon-Doria, Larissa; Langelaan, Rob J A; Tissue, David T; Tjoelker, Mark G; Lens, Frederic

2018-01-25

Sapwood traits like vessel diameter and intervessel pit characteristics play key roles in maintaining hydraulic integrity of trees. Surprisingly little is known about how sapwood traits covary with tree height and how such trait-based variation could affect the efficiency of water transport in tall trees. This study presents a detailed analysis of structural and functional traits along the vertical axes of tall Eucalyptus grandis trees. To assess a wide range of anatomical and physiological traits, light and electron microscopy was used, as well as field measurements of tree architecture, water use, stem water potential and leaf area distribution. Strong apical dominance of water transport resulted in increased volumetric water supply per unit leaf area with tree height. This was realized by continued narrowing (from 250 to 20 µm) and an exponential increase in frequency (from 600 to 13 000 cm-2) of vessels towards the apex. The widest vessels were detected at least 4 m above the stem base, where they were associated with the thickest intervessel pit membranes. In addition, this study established the lower limit of pit membrane thickness in tall E. grandis at ~375 nm. This minimum thickness was maintained over a large distance in the upper stem, where vessel diameters continued to narrow. The analyses of xylem ultrastructure revealed complex, synchronized trait covariation and trade-offs with increasing height in E. grandis. Anatomical traits related to xylem vessels and those related to architecture of pit membranes were found to increase efficiency and apical dominance of water transport. This study underlines the importance of studying tree hydraulic functioning at organismal scale. Results presented here will improve understanding height-dependent structure-function patterns in tall trees. © The Author(s) 2018. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Genetic Regulation of Hypothalamic Cocaine and Amphetamine-Regulated Transcript (CART) in BxD Inbred Mice

PubMed Central

Hawks, Brian W.; Li, Wei; Garlow, Steven J.

2009-01-01

Cocaine-Amphetamine Regulated Transcript (CART) peptides are implicated in a wide range of behaviors including in the reinforcing properties of psychostimulants, feeding and energy balance and stress and anxiety responses. We conducted a complex trait analysis to examine natural variation in the regulation of CART transcript abundance (CARTta) in the hypothalamus. CART transcript abundance was measured in total hypothalamic RNA from 26 BxD recombinant inbred (RI) mouse strains and in the C57BL/6 (B6) and DBA/2J (D2) progenitor strains. The strain distribution pattern for CARTta was continuous across the RI panel, which is consistent with this being a quantitative trait. Marker regression and interval mapping revealed significant quantitative trait loci (QTL) on mouse chromosome 4 (around 58.2cM) and chromosome 11 (between 20–36cM) that influence CARTta and account for 31% of the between strain variance in this phenotype. There are numerous candidate genes and QTL in these chromosomal regions that may indicate shared genetic regulation between CART expression and other neurobiological processes referable to known actions of this neuropeptide. PMID:18199428

A fully traits-based approach to modeling global vegetation distribution.

PubMed

van Bodegom, Peter M; Douma, Jacob C; Verheijen, Lieneke M

2014-09-23

Dynamic Global Vegetation Models (DGVMs) are indispensable for our understanding of climate change impacts. The application of traits in DGVMs is increasingly refined. However, a comprehensive analysis of the direct impacts of trait variation on global vegetation distribution does not yet exist. Here, we present such analysis as proof of principle. We run regressions of trait observations for leaf mass per area, stem-specific density, and seed mass from a global database against multiple environmental drivers, making use of findings of global trait convergence. This analysis explained up to 52% of the global variation of traits. Global trait maps, generated by coupling the regression equations to gridded soil and climate maps, showed up to orders of magnitude variation in trait values. Subsequently, nine vegetation types were characterized by the trait combinations that they possess using Gaussian mixture density functions. The trait maps were input to these functions to determine global occurrence probabilities for each vegetation type. We prepared vegetation maps, assuming that the most probable (and thus, most suited) vegetation type at each location will be realized. This fully traits-based vegetation map predicted 42% of the observed vegetation distribution correctly. Our results indicate that a major proportion of the predictive ability of DGVMs with respect to vegetation distribution can be attained by three traits alone if traits like stem-specific density and seed mass are included. We envision that our traits-based approach, our observation-driven trait maps, and our vegetation maps may inspire a new generation of powerful traits-based DGVMs.

Good genes, complementary genes and human mate preferences.

PubMed

Roberts, S Craig; Little, Anthony C

2008-03-01

The past decade has witnessed a rapidly growing interest in the biological basis of human mate choice. Here we review recent studies that demonstrate preferences for traits which might reveal genetic quality to prospective mates, with potential but still largely unknown influence on offspring fitness. These include studies assessing visual, olfactory and auditory preferences for potential good-gene indicator traits, such as dominance or bilateral symmetry. Individual differences in these robust preferences mainly arise through within and between individual variation in condition and reproductive status. Another set of studies have revealed preferences for traits indicating complementary genes, focussing on discrimination of dissimilarity at genes in the major histocompatibility complex (MHC). As in animal studies, we are only just beginning to understand how preferences for specific traits vary and inter-relate, how consideration of good and compatible genes can lead to substantial variability in individual mate choice decisions and how preferences expressed in one sensory modality may reflect those in another. Humans may be an ideal model species in which to explore these interesting complexities.

Good genes, complementary genes and human mate preferences.

PubMed

Roberts, S Craig; Little, Anthony C

2008-09-01

The past decade has witnessed a rapidly growing interest in the biological basis of human mate choice. Here we review recent studies that demonstrate preferences for traits which might reveal genetic quality to prospective mates, with potential but still largely unknown influence on offspring fitness. These include studies assessing visual, olfactory and auditory preferences for potential good-gene indicator traits, such as dominance or bilateral symmetry. Individual differences in these robust preferences mainly arise through within and between individual variation in condition and reproductive status. Another set of studies have revealed preferences for traits indicating complementary genes, focussing on discrimination of dissimilarity at genes in the major histocompatibility complex (MHC). As in animal studies, we are only just beginning to understand how preferences for specific traits vary and inter-relate, how consideration of good and compatible genes can lead to substantial variability in individual mate choice decisions and how preferences expressed in one sensory modality may reflect those in another. Humans may be an ideal model species in which to explore these interesting complexities.

A Tricky Trait: Applying the Fruits of the "Function Debate" in the Philosophy of Biology to the "Venom Debate" in the Science of Toxinology.

PubMed

Jackson, Timothy N W; Fry, Bryan G

2016-09-07

The "function debate" in the philosophy of biology and the "venom debate" in the science of toxinology are conceptually related. Venom systems are complex multifunctional traits that have evolved independently numerous times throughout the animal kingdom. No single concept of function, amongst those popularly defended, appears adequate to describe these systems in all their evolutionary contexts and extant variations. As such, a pluralistic view of function, previously defended by some philosophers of biology, is most appropriate. Venom systems, like many other functional traits, exist in nature as points on a continuum and the boundaries between "venomous" and "non-venomous" species may not always be clearly defined. This paper includes a brief overview of the concept of function, followed by in-depth discussion of its application to venom systems. A sound understanding of function may aid in moving the venom debate forward. Similarly, consideration of a complex functional trait such as venom may be of interest to philosophers of biology.

Beyond the climate envelope: using trait filtering models to predict biome boundaries from plant physiology.

NASA Astrophysics Data System (ADS)

Fisher, R.; Hoffmann, W. A.; Muszala, S.

2014-12-01

The introduction of second-generation dynamic vegetation models - which simulate the distribution of light resources between plant types along the vertical canopy profile, and therefore facilitate the representation of plant competition explicitly - is a large increase in the complexity and fidelity with which the terrestrial biosphere is abstracted into Earth System Models. In this new class of model, biome boundaries are predicted as the emergent properties of plant physiology, and are therefore sensitive to the high-dimensional parameterizations of plant functional traits. These new approaches offer the facility to quantitatively test ecophysiological hypotheses of plant distribution at large scales, a field which remains surprisingly under-developed. Here we describe experiments conducted with the Community Land Model Ecosystem Demography component, CLM(ED), in which we reduce the complexity of the problem by testing how individual plant functional trait changes to control the location of biome boundaries between functional types. Specifically, we investigate which physiological trade-offs determine the boundary between frequently burned savanna and forest biomes, and attempt to distinguish how each strategic life-history trade-off (carbon storage, bark investment, re-sprouting strategy) contributes towards the maintenance of sharp geographical gradients between fire adapted and typically inflammable closed canopy ecosystems. This study forms part of the planning for a model-inspired fire manipulation experiment at the cerrado-forest boundary in South-Eastern Brazil, and the results will be used to guide future data-collection and analysis strategies.

Trans-ethnic Meta-analysis and Functional Annotation Illuminates the Genetic Architecture of Fasting Glucose and Insulin.

PubMed

Liu, Ching-Ti; Raghavan, Sridharan; Maruthur, Nisa; Kabagambe, Edmond Kato; Hong, Jaeyoung; Ng, Maggie C Y; Hivert, Marie-France; Lu, Yingchang; An, Ping; Bentley, Amy R; Drolet, Anne M; Gaulton, Kyle J; Guo, Xiuqing; Armstrong, Loren L; Irvin, Marguerite R; Li, Man; Lipovich, Leonard; Rybin, Denis V; Taylor, Kent D; Agyemang, Charles; Palmer, Nicholette D; Cade, Brian E; Chen, Wei-Min; Dauriz, Marco; Delaney, Joseph A C; Edwards, Todd L; Evans, Daniel S; Evans, Michele K; Lange, Leslie A; Leong, Aaron; Liu, Jingmin; Liu, Yongmei; Nayak, Uma; Patel, Sanjay R; Porneala, Bianca C; Rasmussen-Torvik, Laura J; Snijder, Marieke B; Stallings, Sarah C; Tanaka, Toshiko; Yanek, Lisa R; Zhao, Wei; Becker, Diane M; Bielak, Lawrence F; Biggs, Mary L; Bottinger, Erwin P; Bowden, Donald W; Chen, Guanjie; Correa, Adolfo; Couper, David J; Crawford, Dana C; Cushman, Mary; Eicher, John D; Fornage, Myriam; Franceschini, Nora; Fu, Yi-Ping; Goodarzi, Mark O; Gottesman, Omri; Hara, Kazuo; Harris, Tamara B; Jensen, Richard A; Johnson, Andrew D; Jhun, Min A; Karter, Andrew J; Keller, Margaux F; Kho, Abel N; Kizer, Jorge R; Krauss, Ronald M; Langefeld, Carl D; Li, Xiaohui; Liang, Jingling; Liu, Simin; Lowe, William L; Mosley, Thomas H; North, Kari E; Pacheco, Jennifer A; Peyser, Patricia A; Patrick, Alan L; Rice, Kenneth M; Selvin, Elizabeth; Sims, Mario; Smith, Jennifer A; Tajuddin, Salman M; Vaidya, Dhananjay; Wren, Mary P; Yao, Jie; Zhu, Xiaofeng; Ziegler, Julie T; Zmuda, Joseph M; Zonderman, Alan B; Zwinderman, Aeilko H; Adeyemo, Adebowale; Boerwinkle, Eric; Ferrucci, Luigi; Hayes, M Geoffrey; Kardia, Sharon L R; Miljkovic, Iva; Pankow, James S; Rotimi, Charles N; Sale, Michele M; Wagenknecht, Lynne E; Arnett, Donna K; Chen, Yii-Der Ida; Nalls, Michael A; Province, Michael A; Kao, W H Linda; Siscovick, David S; Psaty, Bruce M; Wilson, James G; Loos, Ruth J F; Dupuis, Josée; Rich, Stephen S; Florez, Jose C; Rotter, Jerome I; Morris, Andrew P; Meigs, James B

2016-07-07

Knowledge of the genetic basis of the type 2 diabetes (T2D)-related quantitative traits fasting glucose (FG) and insulin (FI) in African ancestry (AA) individuals has been limited. In non-diabetic subjects of AA (n = 20,209) and European ancestry (EA; n = 57,292), we performed trans-ethnic (AA+EA) fine-mapping of 54 established EA FG or FI loci with detailed functional annotation, assessed their relevance in AA individuals, and sought previously undescribed loci through trans-ethnic (AA+EA) meta-analysis. We narrowed credible sets of variants driving association signals for 22/54 EA-associated loci; 18/22 credible sets overlapped with active islet-specific enhancers or transcription factor (TF) binding sites, and 21/22 contained at least one TF motif. Of the 54 EA-associated loci, 23 were shared between EA and AA. Replication with an additional 10,096 AA individuals identified two previously undescribed FI loci, chrX FAM133A (rs213676) and chr5 PELO (rs6450057). Trans-ethnic analyses with regulatory annotation illuminate the genetic architecture of glycemic traits and suggest gene regulation as a target to advance precision medicine for T2D. Our approach to utilize state-of-the-art functional annotation and implement trans-ethnic association analysis for discovery and fine-mapping offers a framework for further follow-up and characterization of GWAS signals of complex trait loci. Copyright © 2016 American Society of Human Genetics. All rights reserved.

MaGelLAn 1.0: a software to facilitate quantitative and population genetic analysis of maternal inheritance by combination of molecular and pedigree information.

PubMed

Ristov, Strahil; Brajkovic, Vladimir; Cubric-Curik, Vlatka; Michieli, Ivan; Curik, Ino

2016-09-10

Identification of genes or even nucleotides that are responsible for quantitative and adaptive trait variation is a difficult task due to the complex interdependence between a large number of genetic and environmental factors. The polymorphism of the mitogenome is one of the factors that can contribute to quantitative trait variation. However, the effects of the mitogenome have not been comprehensively studied, since large numbers of mitogenome sequences and recorded phenotypes are required to reach the adequate power of analysis. Current research in our group focuses on acquiring the necessary mitochondria sequence information and analysing its influence on the phenotype of a quantitative trait. To facilitate these tasks we have produced software for processing pedigrees that is optimised for maternal lineage analysis. We present MaGelLAn 1.0 (maternal genealogy lineage analyser), a suite of four Python scripts (modules) that is designed to facilitate the analysis of the impact of mitogenome polymorphism on quantitative trait variation by combining molecular and pedigree information. MaGelLAn 1.0 is primarily used to: (1) optimise the sampling strategy for molecular analyses; (2) identify and correct pedigree inconsistencies; and (3) identify maternal lineages and assign the corresponding mitogenome sequences to all individuals in the pedigree, this information being used as input to any of the standard software for quantitative genetic (association) analysis. In addition, MaGelLAn 1.0 allows computing the mitogenome (maternal) effective population sizes and probability of mitogenome (maternal) identity that are useful for conservation management of small populations. MaGelLAn is the first tool for pedigree analysis that focuses on quantitative genetic analyses of mitogenome data. It is conceived with the purpose to significantly reduce the effort in handling and preparing large pedigrees for processing the information linked to maternal lines. The software source code, along with the manual and the example files can be downloaded at http://lissp.irb.hr/software/magellan-1-0/ and https://github.com/sristov/magellan .

"Touching Triton": Building Student Understanding of Complex Disease Risk.

PubMed

Loftin, Madelene; East, Kelly; Hott, Adam; Lamb, Neil

2016-01-01

Life science classrooms often emphasize the exception to the rule when it comes to teaching genetics, focusing heavily on rare single-gene and Mendelian traits. By contrast, the vast majority of human traits and diseases are caused by more complicated interactions between genetic and environmental factors. Research indicates that students have a deterministic view of genetics, generalize Mendelian inheritance patterns to all traits, and have unrealistic expectations of genetic technologies. The challenge lies in how to help students analyze complex disease risk with a lack of curriculum materials. Providing open access to both content resources and an engaging storyline can be achieved using a "serious game" model. "Touching Triton" was developed as a serious game in which students are asked to analyze data from a medical record, family history, and genomic report in order to develop an overall lifetime risk estimate of six common, complex diseases. Evaluation of student performance shows significant learning gains in key content areas along with a high level of engagement.

Selection and explosive growth alter genetic architecture and hamper the detection of causal rare variants

PubMed Central

Zaitlen, Noah A.; Ye, Chun Jimmie; Witte, John S.

2016-01-01

The role of rare alleles in complex phenotypes has been hotly debated, but most rare variant association tests (RVATs) do not account for the evolutionary forces that affect genetic architecture. Here, we use simulation and numerical algorithms to show that explosive population growth, as experienced by human populations, can dramatically increase the impact of very rare alleles on trait variance. We then assess the ability of RVATs to detect causal loci using simulations and human RNA-seq data. Surprisingly, we find that statistical performance is worst for phenotypes in which genetic variance is due mainly to rare alleles, and explosive population growth decreases power. Although many studies have attempted to identify causal rare variants, few have reported novel associations. This has sometimes been interpreted to mean that rare variants make negligible contributions to complex trait heritability. Our work shows that RVATs are not robust to realistic human evolutionary forces, so general conclusions about the impact of rare variants on complex traits may be premature. PMID:27197206

Quantitative genetic methods depending on the nature of the phenotypic trait.

PubMed

de Villemereuil, Pierre

2018-01-24

A consequence of the assumptions of the infinitesimal model, one of the most important theoretical foundations of quantitative genetics, is that phenotypic traits are predicted to be most often normally distributed (so-called Gaussian traits). But phenotypic traits, especially those interesting for evolutionary biology, might be shaped according to very diverse distributions. Here, I show how quantitative genetics tools have been extended to account for a wider diversity of phenotypic traits using first the threshold model and then more recently using generalized linear mixed models. I explore the assumptions behind these models and how they can be used to study the genetics of non-Gaussian complex traits. I also comment on three recent methodological advances in quantitative genetics that widen our ability to study new kinds of traits: the use of "modular" hierarchical modeling (e.g., to study survival in the context of capture-recapture approaches for wild populations); the use of aster models to study a set of traits with conditional relationships (e.g., life-history traits); and, finally, the study of high-dimensional traits, such as gene expression. © 2018 New York Academy of Sciences.

Variation in canopy duration in the perennial biofuel crop Miscanthus reveals complex associations with yield.

PubMed

Robson, Paul R H; Farrar, Kerrie; Gay, Alan P; Jensen, Elaine F; Clifton-Brown, John C; Donnison, Iain S

2013-05-01

Energy crops can provide a sustainable source of power and fuels, and mitigate the negative effects of CO2 emissions associated with fossil fuel use. Miscanthus is a perennial C4 energy crop capable of producing large biomass yields whilst requiring low levels of input. Miscanthus is largely unimproved and therefore there could be significant opportunities to increase yield. Further increases in yield will improve the economics, energy balance, and carbon mitigation of the crop, as well as reducing land-take. One strategy to increase yield in Miscanthus is to maximize the light captured through an extension of canopy duration. In this study, canopy duration was compared among a diverse collection of 244 Miscanthus genotypes. Canopy duration was determined by calculating the number of days between canopy establishment and senescence. Yield was positively correlated with canopy duration. Earlier establishment and later senescence were also both separately correlated with higher yield. However, although genotypes with short canopy durations were low yielding, not all genotypes with long canopy durations were high yielding. Differences of yield between genotypes with long canopy durations were associated with variation in stem and leaf traits. Different methodologies to assess canopy duration traits were investigated, including visual assessment, image analysis, light interception, and different trait thresholds. The highest correlation coefficients were associated with later assessments of traits and the use of quantum sensors for canopy establishment. A model for trait optimization to enable yield improvement in Miscanthus and other bioenergy crops is discussed.

QTL analysis of falling number and seed longevity in wheat (Triticum aestivum L.).

PubMed

Börner, Andreas; Nagel, Manuela; Agacka-Mołdoch, Monika; Gierke, Peter Ulrich; Oberforster, Michael; Albrecht, Theresa; Mohler, Volker

2018-02-01

Pre-harvest sprouting (PHS) and seed longevity (SL) are complex biological processes of major importance for agricultural production. In the present study, a recombinant inbred line (RIL) population derived from a cross between the German winter wheat (Triticum aestivum L.) cultivars History and Rubens was used to identify genetic factors controlling these two physiological seed traits. A falling number (FN) test was employed to evaluate PHS, while SL was measured using a germination test (and the speed of germination) after controlled deterioration. FN of the population was assessed in four environments; SL traits were measured in one environment. Four major quantitative trait loci (QTL) for FN were detected on chromosomes 4D, 5A, 5D, and 7B, whereas for SL traits, a major QTL was found on chromosome 1A. The FN QTL on chromosome 4D that coincided with the position of the dwarfing gene Rht-D1b only had effects in environments that were free of PHS. The remaining three QTL for FN were mostly pronounced under conditions conducive to PHS. The QTL on the long arm of chromosome 7B corresponded to the major gene locus controlling late maturity α-amylase (LMA) in wheat. The severity of the LMA phenotype became truly apparent under sprouting conditions. The position on the long arm of chromosome 1A of the QTL for SL points to a new QTL for this important regenerative seed trait.

Variation in canopy duration in the perennial biofuel crop Miscanthus reveals complex associations with yield

PubMed Central

Robson, Paul R.H.; Farrar, Kerrie; Gay, Alan P.; Jensen, Elaine F.; Clifton-Brown, John C.; Donnison, Iain S.

2013-01-01

Energy crops can provide a sustainable source of power and fuels, and mitigate the negative effects of CO2 emissions associated with fossil fuel use. Miscanthus is a perennial C4 energy crop capable of producing large biomass yields whilst requiring low levels of input. Miscanthus is largely unimproved and therefore there could be significant opportunities to increase yield. Further increases in yield will improve the economics, energy balance, and carbon mitigation of the crop, as well as reducing land-take. One strategy to increase yield in Miscanthus is to maximize the light captured through an extension of canopy duration. In this study, canopy duration was compared among a diverse collection of 244 Miscanthus genotypes. Canopy duration was determined by calculating the number of days between canopy establishment and senescence. Yield was positively correlated with canopy duration. Earlier establishment and later senescence were also both separately correlated with higher yield. However, although genotypes with short canopy durations were low yielding, not all genotypes with long canopy durations were high yielding. Differences of yield between genotypes with long canopy durations were associated with variation in stem and leaf traits. Different methodologies to assess canopy duration traits were investigated, including visual assessment, image analysis, light interception, and different trait thresholds. The highest correlation coefficients were associated with later assessments of traits and the use of quantum sensors for canopy establishment. A model for trait optimization to enable yield improvement in Miscanthus and other bioenergy crops is discussed. PMID:23599277

Novel pedigree analysis implicates DNA repair and chromatin remodeling in multiple myeloma risk

PubMed Central

Curtin, Karen; Rajamanickam, Venkatesh; Jayabalan, David; Atanackovic, Djordje; Rajkumar, S. Vincent; Kumar, Shaji; Slager, Susan; Galia, Perrine; Demangel, Delphine; Salama, Mohamed; Joseph, Vijai; Lipkin, Steven M.; Dumontet, Charles; Vachon, Celine M.

2018-01-01

The high-risk pedigree (HRP) design is an established strategy to discover rare, highly-penetrant, Mendelian-like causal variants. Its success, however, in complex traits has been modest, largely due to challenges of genetic heterogeneity and complex inheritance models. We describe a HRP strategy that addresses intra-familial heterogeneity, and identifies inherited segments important for mapping regulatory risk. We apply this new Shared Genomic Segment (SGS) method in 11 extended, Utah, multiple myeloma (MM) HRPs, and subsequent exome sequencing in SGS regions of interest in 1063 MM / MGUS (monoclonal gammopathy of undetermined significance–a precursor to MM) cases and 964 controls from a jointly-called collaborative resource, including cases from the initial 11 HRPs. One genome-wide significant 1.8 Mb shared segment was found at 6q16. Exome sequencing in this region revealed predicted deleterious variants in USP45 (p.Gln691* and p.Gln621Glu), a gene known to influence DNA repair through endonuclease regulation. Additionally, a 1.2 Mb segment at 1p36.11 is inherited in two Utah HRPs, with coding variants identified in ARID1A (p.Ser90Gly and p.Met890Val), a key gene in the SWI/SNF chromatin remodeling complex. Our results provide compelling statistical and genetic evidence for segregating risk variants for MM. In addition, we demonstrate a novel strategy to use large HRPs for risk-variant discovery more generally in complex traits. PMID:29389935

Novel pedigree analysis implicates DNA repair and chromatin remodeling in multiple myeloma risk.

PubMed

Waller, Rosalie G; Darlington, Todd M; Wei, Xiaomu; Madsen, Michael J; Thomas, Alun; Curtin, Karen; Coon, Hilary; Rajamanickam, Venkatesh; Musinsky, Justin; Jayabalan, David; Atanackovic, Djordje; Rajkumar, S Vincent; Kumar, Shaji; Slager, Susan; Middha, Mridu; Galia, Perrine; Demangel, Delphine; Salama, Mohamed; Joseph, Vijai; McKay, James; Offit, Kenneth; Klein, Robert J; Lipkin, Steven M; Dumontet, Charles; Vachon, Celine M; Camp, Nicola J

2018-02-01

The high-risk pedigree (HRP) design is an established strategy to discover rare, highly-penetrant, Mendelian-like causal variants. Its success, however, in complex traits has been modest, largely due to challenges of genetic heterogeneity and complex inheritance models. We describe a HRP strategy that addresses intra-familial heterogeneity, and identifies inherited segments important for mapping regulatory risk. We apply this new Shared Genomic Segment (SGS) method in 11 extended, Utah, multiple myeloma (MM) HRPs, and subsequent exome sequencing in SGS regions of interest in 1063 MM / MGUS (monoclonal gammopathy of undetermined significance-a precursor to MM) cases and 964 controls from a jointly-called collaborative resource, including cases from the initial 11 HRPs. One genome-wide significant 1.8 Mb shared segment was found at 6q16. Exome sequencing in this region revealed predicted deleterious variants in USP45 (p.Gln691* and p.Gln621Glu), a gene known to influence DNA repair through endonuclease regulation. Additionally, a 1.2 Mb segment at 1p36.11 is inherited in two Utah HRPs, with coding variants identified in ARID1A (p.Ser90Gly and p.Met890Val), a key gene in the SWI/SNF chromatin remodeling complex. Our results provide compelling statistical and genetic evidence for segregating risk variants for MM. In addition, we demonstrate a novel strategy to use large HRPs for risk-variant discovery more generally in complex traits.

Interrelationships between trait anxiety, situational stress and mental effort predict phonological processing efficiency, but not effectiveness.

PubMed

Edwards, Elizabeth J; Edwards, Mark S; Lyvers, Michael

2016-08-01

Attentional control theory (ACT) describes the mechanisms associated with the relationship between anxiety and cognitive performance. We investigated the relationship between cognitive trait anxiety, situational stress and mental effort on phonological performance using a simple (forward-) and complex (backward-) word span task. Ninety undergraduate students participated in the study. Predictor variables were cognitive trait anxiety, indexed using questionnaire scores; situational stress, manipulated using ego threat instructions; and perceived level of mental effort, measured using a visual analogue scale. Criterion variables (a) performance effectiveness (accuracy) and (b) processing efficiency (accuracy divided by response time) were analyzed in separate multiple moderated-regression analyses. The results revealed (a) no relationship between the predictors and performance effectiveness, and (b) a significant 3-way interaction on processing efficiency for both the simple and complex tasks, such that at higher effort, trait anxiety and situational stress did not predict processing efficiency, whereas at lower effort, higher trait anxiety was associated with lower efficiency at high situational stress, but not at low situational stress. Our results were in full support of the assumptions of ACT and implications for future research are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Floral trait variation and integration as a function of sexual deception in Gorteria diffusa

PubMed Central

Ellis, Allan G.; Brockington, Samuel F.; de Jager, Marinus L.; Mellers, Gregory; Walker, Rachel H.; Glover, Beverley J.

2014-01-01

Phenotypic integration, the coordinated covariance of suites of morphological traits, is critical for proper functioning of organisms. Angiosperm flowers are complex structures comprising suites of traits that function together to achieve effective pollen transfer. Floral integration could reflect shared genetic and developmental control of these traits, or could arise through pollinator-imposed stabilizing correlational selection on traits. We sought to expose mechanisms underlying floral trait integration in the sexually deceptive daisy, Gorteria diffusa, by testing the hypothesis that stabilizing selection imposed by male pollinators on floral traits involved in mimicry has resulted in tighter integration. To do this, we quantified patterns of floral trait variance and covariance in morphologically divergent G. diffusa floral forms representing a continuum in the levels of sexual deception. We show that integration of traits functioning in visual attraction of male pollinators increases with pollinator deception, and is stronger than integration of non-mimicry trait modules. Consistent patterns of within-population trait variance and covariance across floral forms suggest that integration has not been built by stabilizing correlational selection on genetically independent traits. Instead pollinator specialization has selected for tightened integration within modules of linked traits. Despite potentially strong constraint on morphological evolution imposed by developmental genetic linkages between traits, we demonstrate substantial divergence in traits across G. diffusa floral forms and show that divergence has often occurred without altering within-population patterns of trait correlations. PMID:25002705

What Explains Patterns of Diversification and Richness among Animal Phyla?

PubMed Central

Jezkova, Tereza; Wiens, John J.

2016-01-01

Animal phyla vary dramatically in species richness (from 1 species to >1.2 million), but the causes of this variation remain largely unknown. Animals have also evolved striking variation in morphology and ecology, including sessile marine taxa lacking heads, eyes, limbs, and complex organs (e.g. sponges), parasitic worms (e.g. nematodes, platyhelminths), and taxa with eyes, skeletons, limbs, and complex organs that dominate terrestrial ecosystems (arthropods, chordates). Relating this remarkable variation in traits to the diversification and richness of animal phyla is a fundamental yet unresolved problem in biology. Here, we test the impacts of 18 traits (including morphology, ecology, reproduction, and development) on diversification and richness of extant animal phyla. Using phylogenetic multiple regression, the best-fitting model includes five traits that explain ~74% of the variation in diversification rates (dioecy, parasitism, eyes/photoreceptors, a skeleton, non-marine habitat). However, a model including just three (skeleton, parasitism, habitat) explains nearly as much variation (~67%). Diversification rates then largely explain richness patterns. Our results also identify many striking traits that have surprisingly little impact on diversification (e.g. head, limbs, and complex circulatory and digestive systems). Overall, our results reveal the key factors that shape large-scale patterns of diversification and richness across >80% of all extant, described species. PMID:28221832

Epistasis interaction of QTL effects as a genetic parameter influencing estimation of the genetic additive effect.

PubMed

Bocianowski, Jan

2013-03-01

Epistasis, an additive-by-additive interaction between quantitative trait loci, has been defined as a deviation from the sum of independent effects of individual genes. Epistasis between QTLs assayed in populations segregating for an entire genome has been found at a frequency close to that expected by chance alone. Recently, epistatic effects have been considered by many researchers as important for complex traits. In order to understand the genetic control of complex traits, it is necessary to clarify additive-by-additive interactions among genes. Herein we compare estimates of a parameter connected with the additive gene action calculated on the basis of two models: a model excluding epistasis and a model with additive-by-additive interaction effects. In this paper two data sets were analysed: 1) 150 barley doubled haploid lines derived from the Steptoe × Morex cross, and 2) 145 DH lines of barley obtained from the Harrington × TR306 cross. The results showed that in cases when the effect of epistasis was different from zero, the coefficient of determination was larger for the model with epistasis than for the one excluding epistasis. These results indicate that epistatic interaction plays an important role in controlling the expression of complex traits.

What Explains Patterns of Diversification and Richness among Animal Phyla?

PubMed

Jezkova, Tereza; Wiens, John J

2017-03-01

Animal phyla vary dramatically in species richness (from one species to >1.2 million), but the causes of this variation remain largely unknown. Animals have also evolved striking variation in morphology and ecology, including sessile marine taxa lacking heads, eyes, limbs, and complex organs (e.g., sponges), parasitic worms (e.g., nematodes, platyhelminths), and taxa with eyes, skeletons, limbs, and complex organs that dominate terrestrial ecosystems (arthropods, chordates). Relating this remarkable variation in traits to the diversification and richness of animal phyla is a fundamental yet unresolved problem in biology. Here, we test the impacts of 18 traits (including morphology, ecology, reproduction, and development) on diversification and richness of extant animal phyla. Using phylogenetic multiple regression, the best-fitting model includes five traits that explain ∼74% of the variation in diversification rates (dioecy, parasitism, eyes/photoreceptors, a skeleton, nonmarine habitat). However, a model including just three (skeleton, parasitism, habitat) explains nearly as much variation (∼67%). Diversification rates then largely explain richness patterns. Our results also identify many striking traits that have surprisingly little impact on diversification (e.g., head, limbs, and complex circulatory and digestive systems). Overall, our results reveal the key factors that shape large-scale patterns of diversification and richness across >80% of all extant, described species.

The promise of genomics in the study of plant-pollinator interactions

PubMed Central

2013-01-01

Flowers exist in exceedingly complex fitness landscapes, in which subtle variation in each trait can affect the pollinators, herbivores and pleiotropically linked traits in other plant tissues. A whole-genome approach to flower evolution will help our understanding of plant-pollinator interactions. PMID:23796166

Enhancing Integrated Pest Management in GM Cotton Systems Using Host Plant Resistance

PubMed Central

Trapero, Carlos; Wilson, Iain W.; Stiller, Warwick N.; Wilson, Lewis J.

2016-01-01

Cotton has lost many ancestral defensive traits against key invertebrate pests. This is suggested by the levels of resistance to some pests found in wild cotton genotypes as well as in cultivated landraces and is a result of domestication and a long history of targeted breeding for yield and fiber quality, along with the capacity to control pests with pesticides. Genetic modification (GM) allowed integration of toxins from a bacteria into cotton to control key Lepidopteran pests. Since the mid-1990s, use of GM cotton cultivars has greatly reduced the amount of pesticides used in many cotton systems. However, pests not controlled by the GM traits have usually emerged as problems, especially the sucking bug complex. Control of this complex with pesticides often causes a reduction in beneficial invertebrate populations, allowing other secondary pests to increase rapidly and require control. Control of both sucking bug complex and secondary pests is problematic due to the cost of pesticides and/or high risk of selecting for pesticide resistance. Deployment of host plant resistance (HPR) provides an opportunity to manage these issues in GM cotton systems. Cotton cultivars resistant to the sucking bug complex and/or secondary pests would require fewer pesticide applications, reducing costs and risks to beneficial invertebrate populations and pesticide resistance. Incorporation of HPR traits into elite cotton cultivars with high yield and fiber quality offers the potential to further reduce pesticide use and increase the durability of pest management in GM cotton systems. We review the challenges that the identification and use of HPR against invertebrate pests brings to cotton breeding. We explore sources of resistance to the sucking bug complex and secondary pests, the mechanisms that control them and the approaches to incorporate these defense traits to commercial cultivars. PMID:27148323

Occlusal traits and orthodontic treatment need in 7‐ to 10‐year‐olds in Estonia

PubMed Central

Saag, Mare; Svedström‐Oristo, Anna‐Liisa; Peltomäki, Timo; Vinkka‐Puhakka, Heli

2017-01-01

Abstract The aim of this study was to evaluate the distribution of occlusal traits and orthodontic treatment need and complexity in Estonian 7‐ to 10‐year‐old children. This data provides solid information for planning of orthodontic care. Data of 392 Estonian children (198 girls and 194 boys, mean age 9.0 years, range 7.1–10.4 years) was analysed in this cross‐sectional study. Assessed traits included first molar and canine sagittal relationship, overjet, overbite, crowding, midline diastema, crossbite, and scissor bite. Orthodontic treatment need and complexity were assessed using the Index of Complexity, Outcome, and Need. Parents' opinion regarding their child's teeth was determined using a questionnaire. The most prevalent occlusal traits were canine class I sagittal relationship (73.7%), midline diastema (73.0%), molar class I sagittal relationship (57.4%), and overbite ≥3.5 mm (51.8%). According to the Index of Complexity, Outcome, and Need, 64.3% of Estonian elementary school children were in need of orthodontic treatment. Treatment complexity was simple in 12.5%, mild in 38.8%, moderate in 22.7%, difficult in 14.0%, and very difficult in 12.0% of the children. Approximately 66.4% of the parents felt that their child needed orthodontic treatment. This study confirms earlier findings indicating that the most frequent sagittal relationship is class I in the first molars and class I in the canines. However, the sagittal relationship was asymmetric in more than half of the children. Correlation between objectively defined treatment need and parents' desire for treatment was high in Estonia. PMID:29744185

Identification of female-specific QTLs affecting an emotionality-related behavior in rats.

PubMed

Ramos, A; Moisan, M P; Chaouloff, F; Mormède, C; Mormède, P

1999-09-01

The influence of genetic factors on psychological traits and disorders has been repeatedly demonstrated; however, the molecular mechanisms underlying such an influence remain largely unknown. Anxiety-related disorders constitute the most common class of mental disorder in humans, with women being diagnosed far more frequently than men. A better understanding of the genetic and gender-related mechanisms mediating anxiety traits should enable the development of more rational methods for preventing and treating anxiety disorders. In this study we have aimed to identify, for the first time, quantitative trait loci (QTL) influencing anxiety/emotionality-related traits in rats. To this end, two strains-Lewis (LEW) and Spontaneously Hypertensive Rats (SHR)-that differ for several behavioral measures of anxiety/emotionality were intercrossed. A QTL analysis of the F2 population revealed suggestive loci for various traits, including behaviors in the elevated plus-maze and blood pressure. In addition, one major QTL explaining 50.4% of the total variance (LOD = 7.22) was identified on chromosome 4 for the locomotion in the central and aversive area of the open field. Two other relevant QTLs have been recently mapped near this chromosomic region in the rat, which also harbors Tac1r, the gene encoding for the substance P receptor. Our major QTL affected females but not males and its effect depended on the type of cross (LEW or SHR grandmothers). The present results reveal a complex genetic basis underlying emotional behaviors and they confirm the existence of interactions between genetic factors and sex for this kind of trait. Further investigation of the loci identified herein may give clues to the pathophysiology of psychiatric disorders such as anxiety-related ones.

Major Contribution of Flowering Time and Vegetative Growth to Plant Production in Common Bean As Deduced from a Comparative Genetic Mapping.

PubMed

González, Ana M; Yuste-Lisbona, Fernando J; Saburido, Soledad; Bretones, Sandra; De Ron, Antonio M; Lozano, Rafael; Santalla, Marta

2016-01-01

Determinacy growth habit and accelerated flowering traits were selected during or after domestication in common bean. Both processes affect several presumed adaptive traits such as the rate of plant production. There is a close association between flowering initiation and vegetative growth; however, interactions among these two crucial developmental processes and their genetic bases remain unexplored. In this study, with the aim to establish the genetic relationships between these complex processes, a multi-environment quantitative trait locus (QTL) mapping approach was performed in two recombinant inbred line populations derived from inter-gene pool crosses between determinate and indeterminate genotypes. Additive and epistatic QTLs were found to regulate flowering time, vegetative growth, and rate of plant production. Moreover, the pleiotropic patterns of the identified QTLs evidenced that regions controlling time to flowering traits, directly or indirectly, are also involved in the regulation of plant production traits. Further QTL analysis highlighted one QTL, on the lower arm of the linkage group Pv01, harboring the Phvul.001G189200 gene, homologous to the Arabidopsis thaliana TERMINAL FLOWER1 ( TFL1 ) gene, which explained up to 32% of phenotypic variation for time to flowering, 66% for vegetative growth, and 19% for rate of plant production. This finding was consistent with previous results, which have also suggested Phvul.001G189200 (PvTFL1y ) as a candidate gene for determinacy locus. The information here reported can also be applied in breeding programs seeking to optimize key agronomic traits, such as time to flowering, plant height and an improved reproductive biomass, pods, and seed size, as well as yield.

Major Contribution of Flowering Time and Vegetative Growth to Plant Production in Common Bean As Deduced from a Comparative Genetic Mapping

PubMed Central

González, Ana M.; Yuste-Lisbona, Fernando J.; Saburido, Soledad; Bretones, Sandra; De Ron, Antonio M.; Lozano, Rafael; Santalla, Marta

2016-01-01

Determinacy growth habit and accelerated flowering traits were selected during or after domestication in common bean. Both processes affect several presumed adaptive traits such as the rate of plant production. There is a close association between flowering initiation and vegetative growth; however, interactions among these two crucial developmental processes and their genetic bases remain unexplored. In this study, with the aim to establish the genetic relationships between these complex processes, a multi-environment quantitative trait locus (QTL) mapping approach was performed in two recombinant inbred line populations derived from inter-gene pool crosses between determinate and indeterminate genotypes. Additive and epistatic QTLs were found to regulate flowering time, vegetative growth, and rate of plant production. Moreover, the pleiotropic patterns of the identified QTLs evidenced that regions controlling time to flowering traits, directly or indirectly, are also involved in the regulation of plant production traits. Further QTL analysis highlighted one QTL, on the lower arm of the linkage group Pv01, harboring the Phvul.001G189200 gene, homologous to the Arabidopsis thaliana TERMINAL FLOWER1 (TFL1) gene, which explained up to 32% of phenotypic variation for time to flowering, 66% for vegetative growth, and 19% for rate of plant production. This finding was consistent with previous results, which have also suggested Phvul.001G189200 (PvTFL1y) as a candidate gene for determinacy locus. The information here reported can also be applied in breeding programs seeking to optimize key agronomic traits, such as time to flowering, plant height and an improved reproductive biomass, pods, and seed size, as well as yield. PMID:28082996

Genetic Dissection of Maize Embryonic Callus Regenerative Capacity Using Multi-Locus Genome-Wide Association Studies

PubMed Central

Ma, Langlang; Liu, Min; Yan, Yuanyuan; Qing, Chunyan; Zhang, Xiaoling; Zhang, Yanling; Long, Yun; Wang, Lei; Pan, Lang; Zou, Chaoying; Li, Zhaoling; Wang, Yanli; Peng, Huanwei; Pan, Guangtang; Jiang, Zhou; Shen, Yaou

2018-01-01

The regenerative capacity of the embryonic callus, a complex quantitative trait, is one of the main limiting factors for maize transformation. This trait was decomposed into five traits, namely, green callus rate (GCR), callus differentiating rate (CDR), callus plantlet number (CPN), callus rooting rate (CRR), and callus browning rate (CBR). To dissect the genetic foundation of maize transformation, in this study multi-locus genome-wide association studies (GWAS) for the five traits were performed in a population of 144 inbred lines genotyped with 43,427 SNPs. Using the phenotypic values in three environments and best linear unbiased prediction (BLUP) values, as a result, a total of 127, 56, 160, and 130 significant quantitative trait nucleotides (QTNs) were identified by mrMLM, FASTmrEMMA, ISIS EM-BLASSO, and pLARmEB, respectively. Of these QTNs, 63 QTNs were commonly detected, including 15 across multiple environments and 58 across multiple methods. Allele distribution analysis showed that the proportion of superior alleles for 36 QTNs was <50% in 31 elite inbred lines. Meanwhile, these superior alleles had obviously additive effect on the regenerative capacity. This indicates that the regenerative capacity-related traits can be improved by proper integration of the superior alleles using marker-assisted selection. Moreover, a total of 40 candidate genes were found based on these common QTNs. Some annotated genes were previously reported to relate with auxin transport, cell fate, seed germination, or embryo development, especially, GRMZM2G108933 (WOX2) was found to promote maize transgenic embryonic callus regeneration. These identified candidate genes will contribute to a further understanding of the genetic foundation of maize embryonic callus regeneration. PMID:29755499

Genome Wide Association Study for Drought, Aflatoxin Resistance, and Important Agronomic Traits of Maize Hybrids in the Sub-Tropics

PubMed Central

Farfan, Ivan D. Barrero; De La Fuente, Gerald N.; Murray, Seth C.; Isakeit, Thomas; Huang, Pei-Cheng; Warburton, Marilyn; Williams, Paul; Windham, Gary L.; Kolomiets, Mike

2015-01-01

The primary maize (Zea mays L.) production areas are in temperate regions throughout the world and this is where most maize breeding is focused. Important but lower yielding maize growing regions such as the sub-tropics experience unique challenges, the greatest of which are drought stress and aflatoxin contamination. Here we used a diversity panel consisting of 346 maize inbred lines originating in temperate, sub-tropical and tropical areas testcrossed to stiff-stalk line Tx714 to investigate these traits. Testcross hybrids were evaluated under irrigated and non-irrigated trials for yield, plant height, ear height, days to anthesis, days to silking and other agronomic traits. Irrigated trials were also inoculated with Aspergillus flavus and evaluated for aflatoxin content. Diverse maize testcrosses out-yielded commercial checks in most trials, which indicated the potential for genetic diversity to improve sub-tropical breeding programs. To identify genomic regions associated with yield, aflatoxin resistance and other important agronomic traits, a genome wide association analysis was performed. Using 60,000 SNPs, this study found 10 quantitative trait variants for grain yield, plant and ear height, and flowering time after stringent multiple test corrections, and after fitting different models. Three of these variants explained 5–10% of the variation in grain yield under both water conditions. Multiple identified SNPs co-localized with previously reported QTL, which narrows the possible location of causal polymorphisms. Novel significant SNPs were also identified. This study demonstrated the potential to use genome wide association studies to identify major variants of quantitative and complex traits such as yield under drought that are still segregating between elite inbred lines. PMID:25714370

Quantitative trait loci and metabolic pathways: genetic control of the concentration of maysin, a corn earworm resistance factor, in maize silks.

PubMed Central

Byrne, P F; McMullen, M D; Snook, M E; Musket, T A; Theuri, J M; Widstrom, N W; Wiseman, B R; Coe, E H

1996-01-01

Interpretation of quantitative trait locus (QTL) studies of agronomic traits is limited by lack of knowledge of biochemical pathways leading to trait expression. To more fully elucidate the biological significance of detected QTL, we chose a trait that is the product of a well-characterized pathway, namely the concentration of maysin, a C-glycosyl flavone, in silks of maize, Zea mays L. Maysin is a host-plant resistance factor against the corn earworm, Helicoverpa zea (Boddie). We determined silk maysin concentrations and restriction fragment length polymorphism genotypes at flavonoid pathway loci or linked markers for 285 F2 plants derived from the cross of lines GT114 and GT119. Single-factor analysis of variance indicated that the p1 region on chromosome 1 accounted for 58.0% of the phenotypic variance and showed additive gene action. The p1 locus is a transcription activator for portions of the flavonoid pathway. A second QTL, represented by marker umc 105a near the brown pericarp1 locus on chromosome 9, accounted for 10.8% of the variance. Gene action of this region was dominant for low maysin, but was only expressed in the presence of a functional p1 allele. The model explaining the greatest proportion of phenotypic variance (75.9%) included p1, umc105a, umc166b (chromosome 1), r1 (chromosome 10), and two epistatic interaction terms, p1 x umc105a and p1 x r1. Our results provide evidence that regulatory loci have a central role and that there is a complex interplay among different branches of the flavonoid pathway in the expression of this trait. PMID:11607699

Identification of gene networks underlying dystocia in dairy cattle

USDA-ARS?s Scientific Manuscript database

Dystocia is a trait with a high impact in the dairy industry. Among its risk factors are calf weight, gestation length, breed and conformation. Biological networks have been proposed to capture the genetic architecture of complex traits, where GWAS show limitations. The objective of this study was t...

Successful Graduate Students: The Roles of Personality Traits and Emotional Intelligence

ERIC Educational Resources Information Center

Grehan, Patrick M.; Flanagan, Rosemary; Malgady, Robert G.

2011-01-01

Given the complex role of school psychologists, it is in the interest of stakeholders to identify characteristics related to student success in graduate training, which is suggestive of their effectiveness as practitioners. This study explores the relationship of personality traits and Emotional Intelligence (EI) to graduate students' performance…

QTLs detected for individual sugars and soluble solids content in apple

USDA-ARS?s Scientific Manuscript database

Sweetness is one of the most important fruit quality traits in breeding programs, determining the overall quality and flavor-perception of apples. Selecting for this trait using conventional breeding methods is challenging due to the complexity of its genetic control. In order to improve the efficie...

Dissecting genome-wide association signals for loss-of-function phenotypes in sorghum flavonoid pigmentation traits

USDA-ARS?s Scientific Manuscript database

Genome-wide association studies (GWAS) are a powerful method to dissect the genetic basis of traits, though in practice the effects of complex genetic architecture and population structure remain poorly understood. To compare mapping strategies we dissect the genetic control of flavonoid pigmentatio...

Ethnography in Educational Research: The Dynamics of Diffusion.

ERIC Educational Resources Information Center

Fetterman, David M.

1982-01-01

Ascribes difficulties associated with the use of ethnography in educational research to faulty or partial transmission of traits from one sociocultural system to another. Maintains that the appropriate use of ethnography demands that the whole trait complex be borrowed. Describes educational studies that used ethnographic methods. (Author/MJL)

Relationships between species feeding traits and environmental conditions in fish communities: a three-matrix approach.

PubMed

Brind'Amour, Anik; Boisclair, Daniel; Dray, Stéphane; Legendre, Pierre

2011-03-01

Understanding the relationships between species biological traits and the environment is crucial to predicting the effect of habitat perturbations on fish communities. It is also an essential step in the assessment of the functional diversity. Using two complementary three-matrix approaches (fourth-corner and RLQ analyses), we tested the hypothesis that feeding-oriented traits determine the spatial distributions of littoral fish species by assessing the relationship between fish spatial distributions, fish species traits, and habitat characteristics in two Laurentian Shield lakes. Significant associations between the feeding-oriented traits and the environmental characteristics suggested that fish communities in small lakes (displaying low species richness) can be spatially structured. Three groups of traits, mainly categorized by the species spatial and temporal feeding activity, were identified. The water column may be divided in two sections, each of them corresponding to a group of traits related to the vertical distribution of the prey coupled with the position of the mouth. Lake areas of low structural complexity were inhabited by functional assemblages dominated by surface feeders while structurally more complex areas were occupied by mid-water and benthic feeders. A third group referring to the time of feeding activity was observed. Our work could serve as a guideline study to evaluate species traits x environment associations at multiple spatial scales. Our results indicate that three-matrix statistical approaches are powerful tools that can be used to study such relationships. These recent statistical approaches open up new research directions such as the study of spatially based biological functions in lakes. They also provide new analytical tools for determining, for example, the potential size of freshwater protected areas.

Pedigree- and SNP-Associated Genetics and Recent Environment are the Major Contributors to Anthropometric and Cardiometabolic Trait Variation.

PubMed

Xia, Charley; Amador, Carmen; Huffman, Jennifer; Trochet, Holly; Campbell, Archie; Porteous, David; Hastie, Nicholas D; Hayward, Caroline; Vitart, Veronique; Navarro, Pau; Haley, Chris S

2016-02-01

Genome-wide association studies have successfully identified thousands of loci for a range of human complex traits and diseases. The proportion of phenotypic variance explained by significant associations is, however, limited. Given the same dense SNP panels, mixed model analyses capture a greater proportion of phenotypic variance than single SNP analyses but the total is generally still less than the genetic variance estimated from pedigree studies. Combining information from pedigree relationships and SNPs, we examined 16 complex anthropometric and cardiometabolic traits in a Scottish family-based cohort comprising up to 20,000 individuals genotyped for ~520,000 common autosomal SNPs. The inclusion of related individuals provides the opportunity to also estimate the genetic variance associated with pedigree as well as the effects of common family environment. Trait variation was partitioned into SNP-associated and pedigree-associated genetic variation, shared nuclear family environment, shared couple (partner) environment and shared full-sibling environment. Results demonstrate that trait heritabilities vary widely but, on average across traits, SNP-associated and pedigree-associated genetic effects each explain around half the genetic variance. For most traits the recently-shared environment of couples is also significant, accounting for ~11% of the phenotypic variance on average. On the other hand, the environment shared largely in the past by members of a nuclear family or by full-siblings, has a more limited impact. Our findings point to appropriate models to use in future studies as pedigree-associated genetic effects and couple environmental effects have seldom been taken into account in genotype-based analyses. Appropriate description of the trait variation could help understand causes of intra-individual variation and in the detection of contributing loci and environmental factors.

Advances in biotechnology and linking outputs to variation in complex traits: Plant and Animal Genome meeting January 2012.

PubMed

Appels, R; Barrero, R; Bellgard, M

2012-03-01

The Plant and Animal Genome (PAG, held annually) meeting in January 2012 provided insights into the advances in plant, animal, and microbe genome studies particularly as they impact on our understanding of complex biological systems. The diverse areas of biology covered included the advances in technologies, variation in complex traits, genome change in evolution, and targeting phenotypic changes, across the broad spectrum of life forms. This overview aims to summarize the major advances in research areas presented in the plenary lectures and does not attempt to summarize the diverse research activities covered throughout the PAG in workshops, posters, presentations, and displays by suppliers of cutting-edge technologies.

Natural selection and inheritance of breeding time and clutch size in the collared flycatcher.

PubMed

Sheldon, B C; Kruuk, L E B; Merilä, J

2003-02-01

Many characteristics of organisms in free-living populations appear to be under directional selection, possess additive genetic variance, and yet show no evolutionary response to selection. Avian breeding time and clutch size are often-cited examples of such characters. We report analyses of inheritance of, and selection on, these traits in a long-term study of a wild population of the collared flycatcher Ficedula albicollis. We used mixed model analysis with REML estimation ("animal models") to make full use of the information in complex multigenerational pedigrees. Heritability of laying date, but not clutch size, was lower than that estimated previously using parent-offspring regressions, although for both traits there was evidence of substantial additive genetic variance (h2 = 0.19 and 0.29, respectively). Laying date and clutch size were negatively genetically correlated (rA = -0.41 +/- 0.09), implying that selection on one of the traits would cause a correlated response in the other, but there was little evidence to suggest that evolution of either trait would be constrained by correlations with other phenotypic characters. Analysis of selection on these traits in females revealed consistent strong directional fecundity selection for earlier breeding at the level of the phenotype (beta = -0.28 +/- 0.03), but little evidence for stabilising selection on breeding time. We found no evidence that clutch size was independently under selection. Analysis of fecundity selection on breeding values for laying date, estimated from an animal model, indicated that selection acts directly on additive genetic variance underlying breeding time (beta = -0.20 +/- 0.04), but not on clutch size (beta = 0.03 +/- 0.05). In contrast, selection on laying date via adult female survival fluctuated in sign between years, and was opposite in sign for selection on phenotypes (negative) and breeding values (positive). Our data thus suggest that any evolutionary response to selection on laying date is partially constrained by underlying life-history trade-offs, and illustrate the difficulties in using purely phenotypic measures and incomplete fitness estimates to assess evolution of life-history trade-offs. We discuss some of the difficulties associated with understanding the evolution of laying date and clutch size in natural populations.

Genetic Architecture of Atherosclerosis in Mice: A Systems Genetics Analysis of Common Inbred Strains

PubMed Central

Bennett, Brian J.; Davis, Richard C.; Civelek, Mete; Orozco, Luz; Wu, Judy; Qi, Hannah; Pan, Calvin; Packard, René R. Sevag; Eskin, Eleazar; Yan, Mujing; Kirchgessner, Todd; Wang, Zeneng; Li, Xinmin; Gregory, Jill C.; Hazen, Stanley L.; Gargalovic, Peter S.; Lusis, Aldons J.

2015-01-01

Common forms of atherosclerosis involve multiple genetic and environmental factors. While human genome-wide association studies have identified numerous loci contributing to coronary artery disease and its risk factors, these studies are unable to control environmental factors or examine detailed molecular traits in relevant tissues. We now report a study of natural variations contributing to atherosclerosis and related traits in over 100 inbred strains of mice from the Hybrid Mouse Diversity Panel (HMDP). The mice were made hyperlipidemic by transgenic expression of human apolipoprotein E-Leiden (APOE-Leiden) and human cholesteryl ester transfer protein (CETP). The mice were examined for lesion size and morphology as well as plasma lipid, insulin and glucose levels, and blood cell profiles. A subset of mice was studied for plasma levels of metabolites and cytokines. We also measured global transcript levels in aorta and liver. Finally, the uptake of acetylated LDL by macrophages from HMDP mice was quantitatively examined. Loci contributing to the traits were mapped using association analysis, and relationships among traits were examined using correlation and statistical modeling. A number of conclusions emerged. First, relationships among atherosclerosis and the risk factors in mice resemble those found in humans. Second, a number of trait-loci were identified, including some overlapping with previous human and mouse studies. Third, gene expression data enabled enrichment analysis of pathways contributing to atherosclerosis and prioritization of candidate genes at associated loci in both mice and humans. Fourth, the data provided a number of mechanistic inferences; for example, we detected no association between macrophage uptake of acetylated LDL and atherosclerosis. Fifth, broad sense heritability for atherosclerosis was much larger than narrow sense heritability, indicating an important role for gene-by-gene interactions. Sixth, stepwise linear regression showed that the combined variations in plasma metabolites, including LDL/VLDL-cholesterol, trimethylamine N-oxide (TMAO), arginine, glucose and insulin, account for approximately 30 to 40% of the variation in atherosclerotic lesion area. Overall, our data provide a rich resource for studies of complex interactions underlying atherosclerosis. PMID:26694027

Detection of expression quantitative trait Loci in complex mouse crosses: impact and alleviation of data quality and complex population substructure.

PubMed

Iancu, Ovidiu D; Darakjian, Priscila; Kawane, Sunita; Bottomly, Daniel; Hitzemann, Robert; McWeeney, Shannon

2012-01-01

Complex Mus musculus crosses, e.g., heterogeneous stock (HS), provide increased resolution for quantitative trait loci detection. However, increased genetic complexity challenges detection methods, with discordant results due to low data quality or complex genetic architecture. We quantified the impact of theses factors across three mouse crosses and two different detection methods, identifying procedures that greatly improve detection quality. Importantly, HS populations have complex genetic architectures not fully captured by the whole genome kinship matrix, calling for incorporating chromosome specific relatedness information. We analyze three increasingly complex crosses, using gene expression levels as quantitative traits. The three crosses were an F(2) intercross, a HS formed by crossing four inbred strains (HS4), and a HS (HS-CC) derived from the eight lines found in the collaborative cross. Brain (striatum) gene expression and genotype data were obtained using the Illumina platform. We found large disparities between methods, with concordance varying as genetic complexity increased; this problem was more acute for probes with distant regulatory elements (trans). A suite of data filtering steps resulted in substantial increases in reproducibility. Genetic relatedness between samples generated overabundance of detected eQTLs; an adjustment procedure that includes the kinship matrix attenuates this problem. However, we find that relatedness between individuals is not evenly distributed across the genome; information from distinct chromosomes results in relatedness structure different from the whole genome kinship matrix. Shared polymorphisms from distinct chromosomes collectively affect expression levels, confounding eQTL detection. We suggest that considering chromosome specific relatedness can result in improved eQTL detection.

Software errors and complexity: An empirical investigation

NASA Technical Reports Server (NTRS)

Basili, Victor R.; Perricone, Berry T.

1983-01-01

The distributions and relationships derived from the change data collected during the development of a medium scale satellite software project show that meaningful results can be obtained which allow an insight into software traits and the environment in which it is developed. Modified and new modules were shown to behave similarly. An abstract classification scheme for errors which allows a better understanding of the overall traits of a software project is also shown. Finally, various size and complexity metrics are examined with respect to errors detected within the software yielding some interesting results.

Software errors and complexity: An empirical investigation

NASA Technical Reports Server (NTRS)

Basili, V. R.; Perricone, B. T.

1982-01-01

The distributions and relationships derived from the change data collected during the development of a medium scale satellite software project show that meaningful results can be obtained which allow an insight into software traits and the environment in which it is developed. Modified and new modules were shown to behave similarly. An abstract classification scheme for errors which allows a better understanding of the overall traits of a software project is also shown. Finally, various size and complexity metrics are examined with respect to errors detected within the software yielding some interesting results.

Congenital Chromosomal Syndromes—A Model for Pathogenesis

PubMed Central

Rohde, Russell A.

1965-01-01

The origin of anomalies in the chromosomal syndromes is believed to be both polyetiologic and polypathogenetic. Whereas some malformations quite clearly appear to result from single gene mutations or from genic imbalance due to individual monosomic or trisomic loci, other anomalies (singly or in complex patterns) are better interpreted as originating from disturbances in particular biochemical pathways which affect the development of a variety of traits. Additional phenogenetic studies and the use of sophisticated biochemical analysis in persons with complex patterns of anomalies should provide a truer understanding of disease mechanisms and provide guidance for future studies which are aimed at the treatment and prevention of these intriguing misadventures of Nature. PMID:5318572

Life-history traits and energetic status in relation to vulnerability to angling in an experimentally selected teleost fish.

PubMed

Redpath, Tara D; Cooke, Steven J; Arlinghaus, Robert; Wahl, David H; Philipp, David P

2009-08-01

In recreational fisheries, a correlation has been established between fishing-induced selection pressures and the metabolic traits of individual fish. This study used a population of largemouth bass (Micropterus salmoides) with lines of low vulnerability fish (LVF) and high vulnerability fish (HVF) that were previously established through artificial truncation selection experiments. The main objective was to evaluate if differential vulnerability to angling was correlated with growth, energetics and nutritional condition during the sub-adult stage. Absolute growth rate was found to be between 9% and 17% higher for LVF compared with HVF over a 6-month period in three experimental ponds. The gonadosomatic index in females was lower for LVF compared with HVF in one experimental pond. No significant differences in energy stores (measured using body constituent analysis) were observed between LVF and HVF. In addition, both groups were consuming the same prey items as evidenced by stomach content analysis. The inherent reasons behind differential vulnerability to angling are complex, and selection for these opposing phenotypes appears to select for differing growth rates, although the driving factors remain unclear. These traits are important from a life-history perspective, and alterations to their frequency as a result of fishing-induced selection could alter fish population structure. These findings further emphasize the need to incorporate evolutionary principles into fisheries management activities.

Personality dimensions of the captive California sea lion (Zalophus californianus).

PubMed

Ciardelli, Lillian E; Weiss, Alexander; Powell, David M; Reiss, Diana

2017-02-01

Although the field of animal personality research is growing, information on sea lion personality is lacking. This is surprising as sea lions are charismatic, cognitively advanced, and relatively accessible for research. In addition, their presence in captivity and frequent interactions with humans allow for them to be closely observed in various contexts. These interactions provide a valuable and unique opportunity to assess dimensions of their personality. This study created a personality survey for captive California sea lions (Zalophus californianus) using a 3-step approach that balances comprehensiveness and comparability to other species. Zookeepers (N = 43) at 5 zoological parks rated sea lions (N = 16) on 52 personality traits and 7 training traits. A principal components analysis and regularized exploratory factor analysis revealed 3 dimensions (Extraversion/Impulsivity, Dominance/Confidence, and Reactivity/Undependability). Each dimension was significantly correlated with at least 1 training trait. Pups and juveniles scored significantly higher on Extraversion/Impulsivity than adults. No other age or sex effects were present on this or any other dimension. Sea lions are cognitively complex marine mammals that represent a valuable addition to the group of species in which personality structure and function have been studied. The unique behavioral and ecological characteristics of sea lions offer another vantage point for understanding how personality varies between disparate species. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Genetic architecture of adiposity and organ weight using combined generation QTL analysis.

PubMed

Fawcett, Gloria L; Roseman, Charles C; Jarvis, Joseph P; Wang, Bing; Wolf, Jason B; Cheverud, James M

2008-08-01

We present here a detailed study of the genetic contributions to adult body size and adiposity in the LG,SM advanced intercross line (AIL), an obesity model. This study represents a first step in fine-mapping obesity quantitative trait loci (QTLs) in an AIL. QTLs for adiposity in this model were previously isolated to chromosomes 1, 6, 7, 8, 9, 12, 13, and 18. This study focuses on heritable contributions and the genetic architecture of fatpad and organ weights. We analyzed both the F(2) and F(3) generations of the LG,SM AIL population single-nucleotide polymorphism (SNP) genotyped with a marker density of approximately 4 cM. We replicate 88% of the previously identified obesity QTLs and identify 13 new obesity QTLs. Nearly half of the single-trait QTLs were sex-specific. Several broad QTL regions were resolved into multiple, narrower peaks. The 113 single-trait QTLs for organs and body weight clustered into 27 pleiotropic loci. A large number of epistatic interactions are described which begin to elucidate potential interacting molecular networks. We present a relatively rapid means to obtain fine-mapping details from AILs using dense marker maps and consecutive generations. Analysis of the complex genetic architecture underlying fatpad and organ weights in this model may eventually help to elucidate not only heritable contributions to obesity but also common gene sets for obesity and its comorbidities.

Life-history traits and energetic status in relation to vulnerability to angling in an experimentally selected teleost fish

PubMed Central

Redpath, Tara D; Cooke, Steven J; Arlinghaus, Robert; Wahl, David H; Philipp, David P

2009-01-01

In recreational fisheries, a correlation has been established between fishing-induced selection pressures and the metabolic traits of individual fish. This study used a population of largemouth bass (Micropterus salmoides) with lines of low vulnerability fish (LVF) and high vulnerability fish (HVF) that were previously established through artificial truncation selection experiments. The main objective was to evaluate if differential vulnerability to angling was correlated with growth, energetics and nutritional condition during the sub-adult stage. Absolute growth rate was found to be between 9% and 17% higher for LVF compared with HVF over a 6-month period in three experimental ponds. The gonadosomatic index in females was lower for LVF compared with HVF in one experimental pond. No significant differences in energy stores (measured using body constituent analysis) were observed between LVF and HVF. In addition, both groups were consuming the same prey items as evidenced by stomach content analysis. The inherent reasons behind differential vulnerability to angling are complex, and selection for these opposing phenotypes appears to select for differing growth rates, although the driving factors remain unclear. These traits are important from a life-history perspective, and alterations to their frequency as a result of fishing-induced selection could alter fish population structure. These findings further emphasize the need to incorporate evolutionary principles into fisheries management activities. PMID:25567883

SNP-based association analysis for seedling traits in durum wheat (Triticum turgidum L. durum (Desf.)).

PubMed

Sabiel, Salih A I; Huang, Sisi; Hu, Xin; Ren, Xifeng; Fu, Chunjie; Peng, Junhua; Sun, Dongfa

2017-03-01

In the present study, 150 accessions of worldwide originated durum wheat germplasm ( Triticum turgidum spp. durum ) were observed for major seedling traits and their growth. The accessions were evaluated for major seedling traits under controlled conditions of hydroponics at the 13 th , 20 th , 27 th and 34 th day-after germination. Biomass traits were measured at the 34 th day-after germination. Correlation analysis was conducted among the seedling traits and three field traits at maturity, plant height, grain weight and 1000-grain weight observed in four consecutive years. Associations of the measured seedling traits and SNP markers were analyzed based on the mixed linear model (MLM). The results indicated that highly significant genetic variation and robust heritability were found for the seedling and field mature traits. In total, 259 significant associations were detected for all the traits and four growth stages. The phenotypic variation explained (R2) by a single SNP marker is higher than 10% for most (84%) of the significant SNP markers. Forty-six SNP markers associated with multiple traits, indicating non-neglectable pleiotropy in seedling stage. The associated SNP markers could be helpful for genetic analysis of seedling traits, and marker-assisted breeding of new wheat varieties with strong seedling vigor.

Prioritization of Candidate Genes in QTL Regions for Physiological and Biochemical Traits Underlying Drought Response in Barley (Hordeum vulgare L.)

PubMed Central

Gudys, Kornelia; Guzy-Wrobelska, Justyna; Janiak, Agnieszka; Dziurka, Michał A.; Ostrowska, Agnieszka; Hura, Katarzyna; Jurczyk, Barbara; Żmuda, Katarzyna; Grzybkowska, Daria; Śróbka, Joanna; Urban, Wojciech; Biesaga-Koscielniak, Jolanta; Filek, Maria; Koscielniak, Janusz; Mikołajczak, Krzysztof; Ogrodowicz, Piotr; Krystkowiak, Karolina; Kuczyńska, Anetta; Krajewski, Paweł; Szarejko, Iwona

2018-01-01

Drought is one of the most adverse abiotic factors limiting growth and productivity of crops. Among them is barley, ranked fourth cereal worldwide in terms of harvested acreage and production. Plants have evolved various mechanisms to cope with water deficit at different biological levels, but there is an enormous challenge to decipher genes responsible for particular complex phenotypic traits, in order to develop drought tolerant crops. This work presents a comprehensive approach for elucidation of molecular mechanisms of drought tolerance in barley at the seedling stage of development. The study includes mapping of QTLs for physiological and biochemical traits associated with drought tolerance on a high-density function map, projection of QTL confidence intervals on barley physical map, and the retrievement of positional candidate genes (CGs), followed by their prioritization based on Gene Ontology (GO) enrichment analysis. A total of 64 QTLs for 25 physiological and biochemical traits that describe plant water status, photosynthetic efficiency, osmoprotectant and hormone content, as well as antioxidant activity, were positioned on a consensus map, constructed using RIL populations developed from the crosses between European and Syrian genotypes. The map contained a total of 875 SNP, SSR and CGs, spanning 941.86 cM with resolution of 1.1 cM. For the first time, QTLs for ethylene, glucose, sucrose, maltose, raffinose, α-tocopherol, γ-tocotrienol content, and catalase activity, have been mapped in barley. Based on overlapping confidence intervals of QTLs, 11 hotspots were identified that enclosed more than 60% of mapped QTLs. Genetic and physical map integration allowed the identification of 1,101 positional CGs within the confidence intervals of drought response-specific QTLs. Prioritization resulted in the designation of 143 CGs, among them were genes encoding antioxidants, carboxylic acid biosynthesis enzymes, heat shock proteins, small auxin up-regulated RNAs, nitric oxide synthase, ATP sulfurylases, and proteins involved in regulation of flowering time. This global approach may be proposed for identification of new CGs that underlies QTLs responsible for complex traits. PMID:29946328

Genetic Basis of Haloperidol Resistance in Saccharomyces cerevisiae Is Complex and Dose Dependent

PubMed Central

Wang, Xin; Kruglyak, Leonid

2014-01-01

The genetic basis of most heritable traits is complex. Inhibitory compounds and their effects in model organisms have been used in many studies to gain insights into the genetic architecture underlying quantitative traits. However, the differential effect of compound concentration has not been studied in detail. In this study, we used a large segregant panel from a cross between two genetically divergent yeast strains, BY4724 (a laboratory strain) and RM11_1a (a vineyard strain), to study the genetic basis of variation in response to different doses of a drug. Linkage analysis revealed that the genetic architecture of resistance to the small-molecule therapeutic drug haloperidol is highly dose-dependent. Some of the loci identified had effects only at low doses of haloperidol, while other loci had effects primarily at higher concentrations of the drug. We show that a major QTL affecting resistance across all concentrations of haloperidol is caused by polymorphisms in SWH1, a homologue of human oxysterol binding protein. We identify a complex set of interactions among the alleles of the genes SWH1, MKT1, and IRA2 that are most pronounced at a haloperidol dose of 200 µM and are only observed when the remainder of the genome is of the RM background. Our results provide further insight into the genetic basis of drug resistance. PMID:25521586

Pre and Post-copulatory Selection Favor Similar Genital Phenotypes in the Male Broad Horned Beetle.

PubMed

House, Clarissa M; Sharma, M D; Okada, Kensuke; Hosken, David J

2016-10-01

Sexual selection can operate before and after copulation and the same or different trait(s) can be targeted during these episodes of selection. The direction and form of sexual selection imposed on characters prior to mating has been relatively well described, but the same is not true after copulation. In general, when male-male competition and female choice favor the same traits then there is the expectation of reinforcing selection on male sexual traits that improve competitiveness before and after copulation. However, when male-male competition overrides pre-copulatory choice then the opposite could be true. With respect to studies of selection on genitalia there is good evidence that male genital morphology influences mating and fertilization success. However, whether genital morphology affects reproductive success in more than one context (i.e., mating versus fertilization success) is largely unknown. Here we use multivariate analysis to estimate linear and nonlinear selection on male body size and genital morphology in the flour beetle Gnatocerus cornutus, simulated in a non-competitive (i.e., monogamous) setting. This analysis estimates the form of selection on multiple traits and typically, linear (directional) selection is easiest to detect, while nonlinear selection is more complex and can be stabilizing, disruptive, or correlational. We find that mating generates stabilizing selection on male body size and genitalia, and fertilization causes a blend of directional and stabilizing selection. Differences in the form of selection across these bouts of selection result from a significant alteration of nonlinear selection on body size and a marginally significant difference in nonlinear selection on a component of genital shape. This suggests that both bouts of selection favor similar genital phenotypes, whereas the strong stabilizing selection imposed on male body size during mate acquisition is weak during fertilization. © The Author 2016. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology.

Advantages of continuous genotype values over genotype classes for GWAS in higher polyploids: a comparative study in hexaploid chrysanthemum.

PubMed

Grandke, Fabian; Singh, Priyanka; Heuven, Henri C M; de Haan, Jorn R; Metzler, Dirk

2016-08-24

Association studies are an essential part of modern plant breeding, but are limited for polyploid crops. The increased number of possible genotype classes complicates the differentiation between them. Available methods are limited with respect to the ploidy level or data producing technologies. While genotype classification is an established noise reduction step in diploids, it gains complexity with increasing ploidy levels. Eventually, the errors produced by misclassifications exceed the benefits of genotype classes. Alternatively, continuous genotype values can be used for association analysis in higher polyploids. We associated continuous genotypes to three different traits and compared the results to the output of the genotype caller SuperMASSA. Linear, Bayesian and partial least squares regression were applied, to determine if the use of continuous genotypes is limited to a specific method. A disease, a flowering and a growth trait with h (2) of 0.51, 0.78 and 0.91 were associated with a hexaploid chrysanthemum genotypes. The data set consisted of 55,825 probes and 228 samples. We were able to detect associating probes using continuous genotypes for multiple traits, using different regression methods. The identified probe sets were overlapping, but not identical between the methods. Baysian regression was the most restrictive method, resulting in ten probes for one trait and none for the others. Linear and partial least squares regression led to numerous associating probes. Association based on genotype classes resulted in similar values, but missed several significant probes. A simulation study was used to successfully validate the number of associating markers. Association of various phenotypic traits with continuous genotypes is successful with both uni- and multivariate regression methods. Genotype calling does not improve the association and shows no advantages in this study. Instead, use of continuous genotypes simplifies the analysis, saves computational time and results more potential markers.

Elucidation of the genetic basis of variation for stem strength characteristics in bread wheat by Associative Transcriptomics.

PubMed

Miller, Charlotte N; Harper, Andrea L; Trick, Martin; Werner, Peter; Waldron, Keith; Bancroft, Ian

2016-07-16

The current approach to reducing the tendency for wheat grown under high fertilizer conditions to collapse (lodge) under the weight of its grain is based on reducing stem height via the introduction of Rht genes. However, these reduce the yield of straw (itself an important commodity) and introduce other undesirable characteristics. Identification of alternative height-control loci is therefore of key interest. In addition, the improvement of stem mechanical strength provides a further way through which lodging can be reduced. To investigate the prospects for genetic alternatives to Rht, we assessed variation for plant height and stem strength properties in a training genetic diversity panel of 100 wheat accessions fixed for Rht. Using mRNAseq data derived from RNA purified from leaves, functional genotypes were developed for the panel comprising 42,066 Single Nucleotide Polymorphism (SNP) markers and 94,060 Gene Expression Markers (GEMs). In the first application in wheat of the recently-developed method of Associative Transcriptomics, we identified associations between trait variation and both SNPs and GEMs. Analysis of marker-trait associations revealed candidates for the causative genes underlying the trait variation, implicating xylan acetylation and the COP9 signalosome as contributing to stem strength and auxin in the control of the observed variation for plant height. Predictive capabilities of key markers for stem strength were validated using a test genetic diversity panel of 30 further wheat accessions. This work illustrates the power of Associative Transcriptomics for the exploration of complex traits of high agronomic importance in wheat. The careful selection of genotypes included in the analysis, allowed for high resolution mapping of novel trait-controlling loci in this staple crop. The use of Gene Expression markers coupled with the more traditional sequence-based markers, provides the power required to understand the biological context of the marker-trait associations observed. This not only adds to the wealth of knowledge that we strive to accumulate regarding gene function and plant adaptation, but also provides breeders with the information required to make more informed decisions regarding the potential consequences of incorporating the use of particular markers into future breeding programmes.

Genome-wide copy number variation analysis identified deletions in SFMBT1 associated with fasting plasma glucose in a Han Chinese population.

PubMed

Chung, Ren-Hua; Chiu, Yen-Feng; Hung, Yi-Jen; Lee, Wen-Jane; Wu, Kwan-Dun; Chen, Hui-Ling; Lin, Ming-Wei; Chen, Yii-Der I; Quertermous, Thomas; Hsiung, Chao A

2017-08-08

Fasting glucose and fasting insulin are glycemic traits closely related to diabetes, and understanding the role of genetic factors in these traits can help reveal the etiology of type 2 diabetes. Although single nucleotide polymorphisms (SNPs) in several candidate genes have been found to be associated with fasting glucose and fasting insulin, copy number variations (CNVs), which have been reported to be associated with several complex traits, have not been reported for association with these two traits. We aimed to identify CNVs associated with fasting glucose and fasting insulin. We conducted a genome-wide CNV association analysis for fasting plasma glucose (FPG) and fasting plasma insulin (FPI) using a family-based genome-wide association study sample from a Han Chinese population in Taiwan. A family-based CNV association test was developed in this study to identify common CNVs (i.e., CNVs with frequencies ≥ 5%), and a generalized estimating equation approach was used to test the associations between the traits and counts of global rare CNVs (i.e., CNVs with frequencies <5%). We found a significant genome-wide association for common deletions with a frequency of 5.2% in the Scm-like with four mbt domains 1 (SFMBT1) gene with FPG (association p-value = 2×10 -4 and an adjusted p-value = 0.0478 for multiple testing). No significant association was observed between global rare CNVs and FPG or FPI. The deletions in 20 individuals with DNA samples available were successfully validated using PCR-based amplification. The association of the deletions in SFMBT1 with FPG was further evaluated using an independent population-based replication sample obtained from the Taiwan Biobank. An association p-value of 0.065, which was close to the significance level of 0.05, for FPG was obtained by testing 9 individuals with CNVs in the SFMBT1 gene region and 11,692 individuals with normal copies in the replication cohort. Previous studies have found that SNPs in SFMBT1 are associated with blood pressure and serum urate concentration, suggesting that SFMBT1 may have functional implications in some metabolic-related traits.

A Genome-Wide mQTL Analysis in Human Adipose Tissue Identifies Genetic Variants Associated with DNA Methylation, Gene Expression and Metabolic Traits

PubMed Central

Volkov, Petr; Olsson, Anders H.; Gillberg, Linn; Jørgensen, Sine W.; Brøns, Charlotte; Eriksson, Karl-Fredrik; Groop, Leif; Jansson, Per-Anders; Nilsson, Emma; Rönn, Tina; Vaag, Allan; Ling, Charlotte

2016-01-01

Little is known about the extent to which interactions between genetics and epigenetics may affect the risk of complex metabolic diseases and/or their intermediary phenotypes. We performed a genome-wide DNA methylation quantitative trait locus (mQTL) analysis in human adipose tissue of 119 men, where 592,794 single nucleotide polymorphisms (SNPs) were related to DNA methylation of 477,891 CpG sites, covering 99% of RefSeq genes. SNPs in significant mQTLs were further related to gene expression in adipose tissue and obesity related traits. We found 101,911 SNP-CpG pairs (mQTLs) in cis and 5,342 SNP-CpG pairs in trans showing significant associations between genotype and DNA methylation in adipose tissue after correction for multiple testing, where cis is defined as distance less than 500 kb between a SNP and CpG site. These mQTLs include reported obesity, lipid and type 2 diabetes loci, e.g. ADCY3/POMC, APOA5, CETP, FADS2, GCKR, SORT1 and LEPR. Significant mQTLs were overrepresented in intergenic regions meanwhile underrepresented in promoter regions and CpG islands. We further identified 635 SNPs in significant cis-mQTLs associated with expression of 86 genes in adipose tissue including CHRNA5, G6PC2, GPX7, RPL27A, THNSL2 and ZFP57. SNPs in significant mQTLs were also associated with body mass index (BMI), lipid traits and glucose and insulin levels in our study cohort and public available consortia data. Importantly, the Causal Inference Test (CIT) demonstrates how genetic variants mediate their effects on metabolic traits (e.g. BMI, cholesterol, high-density lipoprotein (HDL), hemoglobin A1c (HbA1c) and homeostatic model assessment of insulin resistance (HOMA-IR)) via altered DNA methylation in human adipose tissue. This study identifies genome-wide interactions between genetic and epigenetic variation in both cis and trans positions influencing gene expression in adipose tissue and in vivo (dys)metabolic traits associated with the development of obesity and diabetes. PMID:27322064

We can be heroes: MLA's leadership journey(s).

PubMed

Tooey, Mary Joan M J

2017-01-01

Are there key attributes of leaders? Extrovert versus introvert? Charismatic? Detail oriented? Visionary? How do past leaders of the Medical Library Association (MLA) stack up? What leadership skills will MLA's leaders need in a complex information future? Leadership attributes of MLA's past and current presidents were studied to determine the common characteristics shared among these leaders. An examination of the leadership literature identified critical leadership characteristics essential to successful future leaders. MLA's past, current, and future leadership development efforts were examined. Finally, all members were encouraged to consider leadership with a small "l" and become leaders based on their own strengths, interests, and environments. A text analysis was performed on past presidential profiles, the past twenty-five years of MLA presidents were surveyed, and conversations with MLA's current presidents were held to determine commonalities among leadership characteristics. These were compared and contrasted with characteristics in the current leadership literature regarding the qualities of future leaders. The text analysis of past presidential profiles was not particularly revelatory regarding leadership qualities of early MLA presidents although several generalized traits emerged including collaborative traits; management traits such as effectiveness and efficiency, innovation, and vision; personal traits such as humor and energy; and finally, a passion for the work were revealed. These aligned with traits identified in the survey of the past twenty-five years of MLA presidents and with the thoughts of the president-elect, president, and past president. Additional qualities identified were communication skills, political acumen, creativity, courage, and respect for the opinions and concerns of all members. MLA's current leadership programs were reviewed in the context of examining traits needed by leaders of the future. A lack of focus on the needs of middle managers and the development of individual leadership skills was identified. As an organization, MLA should focus on leadership development in contrast to management training to prepare members as leaders in careers and work that may be vastly different than current situations. Equipping members with the skills enabling them to lead and thrive in these diverse situations, whether as the heads of programs or middle managers, or exploring and empowering individual leadership development while maintaining a passion for the profession, will be essential.

A comparative meta-analysis of QTL between intraspecific Gossypium hirsutum interspecific populations and Gossypium hirsutum x Gossypium barbadense populations

USDA-ARS?s Scientific Manuscript database

Recent Meta-analysis of quantitative trait loci (QTL) in tetraploid cotton (Gossypium spp.) has identified regions of the genome with high concentrations of various trait QTL called clusters, and specific trait QTL called hotspots. The Meta-analysis included all population types of Gossypium mixing ...

Genome Wide Analysis of Fertility and Production Traits in Italian Holstein Cattle

PubMed Central

Stella, Alessandra; Biffani, Stefano; Negrini, Riccardo; Lazzari, Barbara; Ajmone-Marsan, Paolo; Williams, John L .

2013-01-01

A genome wide scan was performed on a total of 2093 Italian Holstein proven bulls genotyped with 50K single nucleotide polymorphisms (SNPs), with the objective of identifying loci associated with fertility related traits and to test their effects on milk production traits. The analysis was carried out using estimated breeding values for the aggregate fertility index and for each trait contributing to the index: angularity, calving interval, non-return rate at 56 days, days to first service, and 305 day first parity lactation. In addition, two production traits not included in the aggregate fertility index were analysed: fat yield and protein yield. Analyses were carried out using all SNPs treated separately, further the most significant marker on BTA14 associated to milk quality located in the DGAT1 region was treated as fixed effect. Genome wide association analysis identified 61 significant SNPs and 75 significant marker-trait associations. Eight additional SNP associations were detected when SNP located near DGAT1 was included as a fixed effect. As there were no obvious common SNPs between the traits analyzed independently in this study, a network analysis was carried out to identify unforeseen relationships that may link production and fertility traits. PMID:24265800

Floral trait variation and integration as a function of sexual deception in Gorteria diffusa.

PubMed

Ellis, Allan G; Brockington, Samuel F; de Jager, Marinus L; Mellers, Gregory; Walker, Rachel H; Glover, Beverley J

2014-08-19

Phenotypic integration, the coordinated covariance of suites of morphological traits, is critical for proper functioning of organisms. Angiosperm flowers are complex structures comprising suites of traits that function together to achieve effective pollen transfer. Floral integration could reflect shared genetic and developmental control of these traits, or could arise through pollinator-imposed stabilizing correlational selection on traits. We sought to expose mechanisms underlying floral trait integration in the sexually deceptive daisy, Gorteria diffusa, by testing the hypothesis that stabilizing selection imposed by male pollinators on floral traits involved in mimicry has resulted in tighter integration. To do this, we quantified patterns of floral trait variance and covariance in morphologically divergent G. diffusa floral forms representing a continuum in the levels of sexual deception. We show that integration of traits functioning in visual attraction of male pollinators increases with pollinator deception, and is stronger than integration of non-mimicry trait modules. Consistent patterns of within-population trait variance and covariance across floral forms suggest that integration has not been built by stabilizing correlational selection on genetically independent traits. Instead pollinator specialization has selected for tightened integration within modules of linked traits. Despite potentially strong constraint on morphological evolution imposed by developmental genetic linkages between traits, we demonstrate substantial divergence in traits across G. diffusa floral forms and show that divergence has often occurred without altering within-population patterns of trait correlations. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

New genomic resources for switchgrass: a BAC library and comparative analysis of homoeologous genomic regions harboring bioenergy traits

PubMed Central

2011-01-01

Background Switchgrass, a C4 species and a warm-season grass native to the prairies of North America, has been targeted for development into an herbaceous biomass fuel crop. Genetic improvement of switchgrass feedstock traits through marker-assisted breeding and biotechnology approaches calls for genomic tools development. Establishment of integrated physical and genetic maps for switchgrass will accelerate mapping of value added traits useful to breeding programs and to isolate important target genes using map based cloning. The reported polyploidy series in switchgrass ranges from diploid (2X = 18) to duodecaploid (12X = 108). Like in other large, repeat-rich plant genomes, this genomic complexity will hinder whole genome sequencing efforts. An extensive physical map providing enough information to resolve the homoeologous genomes would provide the necessary framework for accurate assembly of the switchgrass genome. Results A switchgrass BAC library constructed by partial digestion of nuclear DNA with EcoRI contains 147,456 clones covering the effective genome approximately 10 times based on a genome size of 3.2 Gigabases (~1.6 Gb effective). Restriction digestion and PFGE analysis of 234 randomly chosen BACs indicated that 95% of the clones contained inserts, ranging from 60 to 180 kb with an average of 120 kb. Comparative sequence analysis of two homoeologous genomic regions harboring orthologs of the rice OsBRI1 locus, a low-copy gene encoding a putative protein kinase and associated with biomass, revealed that orthologous clones from homoeologous chromosomes can be unambiguously distinguished from each other and correctly assembled to respective fingerprint contigs. Thus, the data obtained not only provide genomic resources for further analysis of switchgrass genome, but also improve efforts for an accurate genome sequencing strategy. Conclusions The construction of the first switchgrass BAC library and comparative analysis of homoeologous harboring OsBRI1 orthologs present a glimpse into the switchgrass genome structure and complexity. Data obtained demonstrate the feasibility of using HICF fingerprinting to resolve the homoeologous chromosomes of the two distinct genomes in switchgrass, providing a robust and accurate BAC-based physical platform for this species. The genomic resources and sequence data generated will lay the foundation for deciphering the switchgrass genome and lead the way for an accurate genome sequencing strategy. PMID:21767393

New genomic resources for switchgrass: a BAC library and comparative analysis of homoeologous genomic regions harboring bioenergy traits.

PubMed

Saski, Christopher A; Li, Zhigang; Feltus, Frank A; Luo, Hong

2011-07-18

Switchgrass, a C4 species and a warm-season grass native to the prairies of North America, has been targeted for development into an herbaceous biomass fuel crop. Genetic improvement of switchgrass feedstock traits through marker-assisted breeding and biotechnology approaches calls for genomic tools development. Establishment of integrated physical and genetic maps for switchgrass will accelerate mapping of value added traits useful to breeding programs and to isolate important target genes using map based cloning. The reported polyploidy series in switchgrass ranges from diploid (2X = 18) to duodecaploid (12X = 108). Like in other large, repeat-rich plant genomes, this genomic complexity will hinder whole genome sequencing efforts. An extensive physical map providing enough information to resolve the homoeologous genomes would provide the necessary framework for accurate assembly of the switchgrass genome. A switchgrass BAC library constructed by partial digestion of nuclear DNA with EcoRI contains 147,456 clones covering the effective genome approximately 10 times based on a genome size of 3.2 Gigabases (~1.6 Gb effective). Restriction digestion and PFGE analysis of 234 randomly chosen BACs indicated that 95% of the clones contained inserts, ranging from 60 to 180 kb with an average of 120 kb. Comparative sequence analysis of two homoeologous genomic regions harboring orthologs of the rice OsBRI1 locus, a low-copy gene encoding a putative protein kinase and associated with biomass, revealed that orthologous clones from homoeologous chromosomes can be unambiguously distinguished from each other and correctly assembled to respective fingerprint contigs. Thus, the data obtained not only provide genomic resources for further analysis of switchgrass genome, but also improve efforts for an accurate genome sequencing strategy. The construction of the first switchgrass BAC library and comparative analysis of homoeologous harboring OsBRI1 orthologs present a glimpse into the switchgrass genome structure and complexity. Data obtained demonstrate the feasibility of using HICF fingerprinting to resolve the homoeologous chromosomes of the two distinct genomes in switchgrass, providing a robust and accurate BAC-based physical platform for this species. The genomic resources and sequence data generated will lay the foundation for deciphering the switchgrass genome and lead the way for an accurate genome sequencing strategy.

Twin methodology in epigenetic studies.

PubMed

Tan, Qihua; Christiansen, Lene; von Bornemann Hjelmborg, Jacob; Christensen, Kaare

2015-01-01

Since the final decades of the last century, twin studies have made a remarkable contribution to the genetics of human complex traits and diseases. With the recent rapid development in modern biotechnology of high-throughput genetic and genomic analyses, twin modelling is expanding from analysis of diseases to molecular phenotypes in functional genomics especially in epigenetics, a thriving field of research that concerns the environmental regulation of gene expression through DNA methylation, histone modification, microRNA and long non-coding RNA expression, etc. The application of the twin method to molecular phenotypes offers new opportunities to study the genetic (nature) and environmental (nurture) contributions to epigenetic regulation of gene activity during developmental, ageing and disease processes. Besides the classical twin model, the case co-twin design using identical twins discordant for a trait or disease is becoming a popular and powerful design for epigenome-wide association study in linking environmental exposure to differential epigenetic regulation and to disease status while controlling for individual genetic make-up. It can be expected that novel uses of twin methods in epigenetic studies are going to help with efficiently unravelling the genetic and environmental basis of epigenomics in human complex diseases. © 2015. Published by The Company of Biologists Ltd.

The bidirectional associations between state anger and rumination and the role of trait mindfulness.

PubMed

Borders, Ashley; Lu, Shou-En

2017-07-01

Rumination is associated with exacerbated angry mood. Angry moods may also trigger rumination. However, research has not empirically tested the bidirectional associations of state rumination and anger, as experience sampling methodology can do. We predicted that state anger and rumination would be bi-directionally associated, both concurrently and over time, even controlling for trait anger and rumination. In addition, because mindfulness is associated with rumination and anger at the bivariate level, we examined the effect of trait mindfulness on the bidirectional association between state rumination and anger. We examined two hypotheses: (i) state rumination mediates the effect of trait mindfulness on state anger; and (ii) trait mindfulness weakens, or moderates, the bidirectional associations between state rumination and anger. In an experience-sampling study, 200 college students reported their current ruminative thinking and angry mood several times a day for 7 days. Mixed model analyses indicated that state anger and rumination predicted each other concurrently. In cross-lagged analyses, previous anger did not uniquely predict current rumination; previous rumination predicted current anger, although the effect was small. In support of our hypothesis, state rumination mediated the association between trait mindfulness and state anger. Additionally, trait mindfulness moderated the concurrent and cross-lagged associations between state rumination and anger, although the results were complex. This study contributes new information about the complex interplay of rumination and anger. Findings also add support to the theory that mindfulness decreases emotional reactivity. Aggr. Behav. 43:342-351, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Trait-based plant ecology: moving towards a unifying species coexistence theory : Features of the Special Section.

PubMed

Escudero, Adrián; Valladares, Fernando

2016-04-01

Functional traits are the center of recent attempts to unify key ecological theories on species coexistence and assembling in populations and communities. While the plethora of studies on the role of functional traits to explain patterns and dynamics of communities has rendered a complex picture due to the idiosyncrasies of each study system and approach, there is increasing evidence on their actual relevance when aspects such as different spatial scales, intraspecific variability and demography are considered.

The interplay of trait worry and trait anxiety in determining episodic retrieval: The role of cognitive control.

PubMed

Pajkossy, Péter; Keresztes, Attila; Racsmány, Mihály

2017-11-01

Worrying is a key concept in describing the complex relationship between anxiety and cognitive control. On the one hand, cognitive control processes might underlie the specific tendency to engage in worrying (i.e., trait worry), conceptualized as a future-oriented mental problem-solving activity. On the other hand, the general tendency to experience the signs and symptoms of anxiety (i.e., trait anxiety) is suggested to impair cognitive control because worrisome thoughts interfere with task-relevant processing. Based on these opposing tendencies, we predicted that the effect of the two related constructs, trait anxiety and trait worry, might cancel out one another. In statistics, such instances have been termed suppressor situations. In four experiments, we found evidence for such a suppressor situation: When their shared variance was controlled, trait worry was positively whereas trait anxiety was negatively related to performance in a memory task requiring strategic, effortful retrieval. We also showed that these opposing effects are related to temporal context reinstatement. Our results suggest that trait worry and trait anxiety possess unique sources of variance, which differently relate to performance in memory tasks requiring cognitive control.

Multivariate synaptic and behavioral profiling reveals new developmental endophenotypes in the prefrontal cortex

PubMed Central

Iafrati, Jillian; Malvache, Arnaud; Gonzalez Campo, Cecilia; Orejarena, M. Juliana; Lassalle, Olivier; Bouamrane, Lamine; Chavis, Pascale

2016-01-01

The postnatal maturation of the prefrontal cortex (PFC) represents a period of increased vulnerability to risk factors and emergence of neuropsychiatric disorders. To disambiguate the pathophysiological mechanisms contributing to these disorders, we revisited the endophenotype approach from a developmental viewpoint. The extracellular matrix protein reelin which contributes to cellular and network plasticity, is a risk factor for several psychiatric diseases. We mapped the aggregate effect of the RELN risk allele on postnatal development of PFC functions by cross-sectional synaptic and behavioral analysis of reelin-haploinsufficient mice. Multivariate analysis of bootstrapped datasets revealed subgroups of phenotypic traits specific to each maturational epoch. The preeminence of synaptic AMPA/NMDA receptor content to pre-weaning and juvenile endophenotypes shifts to long-term potentiation and memory renewal during adolescence followed by NMDA-GluN2B synaptic content in adulthood. Strikingly, multivariate analysis shows that pharmacological rehabilitation of reelin haploinsufficient dysfunctions is mediated through induction of new endophenotypes rather than reversion to wild-type traits. By delineating previously unknown developmental endophenotypic sequences, we conceived a promising general strategy to disambiguate the molecular underpinnings of complex psychiatric disorders and for the rational design of pharmacotherapies in these disorders. PMID:27765946

Mapping in an apple (Malus x domestica) F1 segregating population based on physical clustering of differentially expressed genes

PubMed Central

2014-01-01

Background Apple tree breeding is slow and difficult due to long generation times, self-incompatibility, and complex genetics. The identification of molecular markers linked to traits of interest is a way to expedite the breeding process. In the present study, we aimed to identify genes whose steady-state transcript abundance was associated with inheritance of specific traits segregating in an apple (Malus × domestica) rootstock F1 breeding population, including resistance to powdery mildew (Podosphaera leucotricha) disease and woolly apple aphid (Eriosoma lanigerum). Results Transcription profiling was performed for 48 individual F1 apple trees from a cross of two highly heterozygous parents, using RNA isolated from healthy, actively-growing shoot tips and a custom apple DNA oligonucleotide microarray representing 26,000 unique transcripts. Genome-wide expression profiles were not clear indicators of powdery mildew or woolly apple aphid resistance phenotype. However, standard differential gene expression analysis between phenotypic groups of trees revealed relatively small sets of genes with trait-associated expression levels. For example, thirty genes were identified that were differentially expressed between trees resistant and susceptible to powdery mildew. Interestingly, the genes encoding twenty-four of these transcripts were physically clustered on chromosome 12. Similarly, seven genes were identified that were differentially expressed between trees resistant and susceptible to woolly apple aphid, and the genes encoding five of these transcripts were also clustered, this time on chromosome 17. In each case, the gene clusters were in the vicinity of previously identified major quantitative trait loci for the corresponding trait. Similar results were obtained for a series of molecular traits. Several of the differentially expressed genes were used to develop DNA polymorphism markers linked to powdery mildew disease and woolly apple aphid resistance. Conclusions Gene expression profiling and trait-associated transcript analysis using an apple F1 population readily identified genes physically linked to powdery mildew disease resistance and woolly apple aphid resistance loci. This result was especially useful in apple, where extreme levels of heterozygosity make the development of reliable DNA markers quite difficult. The results suggest that this approach could prove effective in crops with complicated genetics, or for which few genomic information resources are available. PMID:24708064

Enriched pathways for major depressive disorder identified from a genome-wide association study.

PubMed

Kao, Chung-Feng; Jia, Peilin; Zhao, Zhongming; Kuo, Po-Hsiu

2012-11-01

Major depressive disorder (MDD) has caused a substantial burden of disease worldwide with moderate heritability. Despite efforts through conducting numerous association studies and now, genome-wide association (GWA) studies, the success of identifying susceptibility loci for MDD has been limited, which is partially attributed to the complex nature of depression pathogenesis. A pathway-based analytic strategy to investigate the joint effects of various genes within specific biological pathways has emerged as a powerful tool for complex traits. The present study aimed to identify enriched pathways for depression using a GWA dataset for MDD. For each gene, we estimated its gene-wise p value using combined and minimum p value, separately. Canonical pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and BioCarta were used. We employed four pathway-based analytic approaches (gene set enrichment analysis, hypergeometric test, sum-square statistic, sum-statistic). We adjusted for multiple testing using Benjamini & Hochberg's method to report significant pathways. We found 17 significantly enriched pathways for depression, which presented low-to-intermediate crosstalk. The top four pathways were long-term depression (p⩽1×10-5), calcium signalling (p⩽6×10-5), arrhythmogenic right ventricular cardiomyopathy (p⩽1.6×10-4) and cell adhesion molecules (p⩽2.2×10-4). In conclusion, our comprehensive pathway analyses identified promising pathways for depression that are related to neurotransmitter and neuronal systems, immune system and inflammatory response, which may be involved in the pathophysiological mechanisms underlying depression. We demonstrated that pathway enrichment analysis is promising to facilitate our understanding of complex traits through a deeper interpretation of GWA data. Application of this comprehensive analytic strategy in upcoming GWA data for depression could validate the findings reported in this study.

Network-Based Identification of Adaptive Pathways in Evolved Ethanol-Tolerant Bacterial Populations.

PubMed

Swings, Toon; Weytjens, Bram; Schalck, Thomas; Bonte, Camille; Verstraeten, Natalie; Michiels, Jan; Marchal, Kathleen

2017-11-01

Efficient production of ethanol for use as a renewable fuel requires organisms with a high level of ethanol tolerance. However, this trait is complex and increased tolerance therefore requires mutations in multiple genes and pathways. Here, we use experimental evolution for a system-level analysis of adaptation of Escherichia coli to high ethanol stress. As adaptation to extreme stress often results in complex mutational data sets consisting of both causal and noncausal passenger mutations, identifying the true adaptive mutations in these settings is not trivial. Therefore, we developed a novel method named IAMBEE (Identification of Adaptive Mutations in Bacterial Evolution Experiments). IAMBEE exploits the temporal profile of the acquisition of mutations during evolution in combination with the functional implications of each mutation at the protein level. These data are mapped to a genome-wide interaction network to search for adaptive mutations at the level of pathways. The 16 evolved populations in our data set together harbored 2,286 mutated genes with 4,470 unique mutations. Analysis by IAMBEE significantly reduced this number and resulted in identification of 90 mutated genes and 345 unique mutations that are most likely to be adaptive. Moreover, IAMBEE not only enabled the identification of previously known pathways involved in ethanol tolerance, but also identified novel systems such as the AcrAB-TolC efflux pump and fatty acids biosynthesis and even allowed to gain insight into the temporal profile of adaptation to ethanol stress. Furthermore, this method offers a solid framework for identifying the molecular underpinnings of other complex traits as well. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Detecting genotypic changes associated with selective mortality at sea in Atlantic salmon: polygenic multilocus analysis surpasses genome scan.

PubMed

Bourret, Vincent; Dionne, Mélanie; Bernatchez, Louis

2014-09-01

Wild populations of Atlantic salmon have declined worldwide. While the causes for this decline may be complex and numerous, increased mortality at sea is predicted to be one of the major contributing factors. Examining the potential changes occurring in the genome-wide composition of populations during this migration has the potential to tease apart some of the factors influencing marine mortality. Here, we genotyped 5568 SNPs in Atlantic salmon populations representing two distinct regional genetic groups and across two cohorts to test for differential allelic and genotypic frequencies between juveniles (smolts) migrating to sea and adults (grilses) returning to freshwater after 1 year at sea. Given the complexity of the traits potentially associated with sea mortality, we contrasted the outcomes of a single-locus F(ST) based genome scan method with a new multilocus framework to test for genetically based differential mortality at sea. While numerous outliers were identified by the single-locus analysis, no evidence for parallel, temporally repeated selection was found. In contrast, the multilocus approach detected repeated patterns of selection for a multilocus group of 34 covarying SNPs in one of the two populations. No significant pattern of selective mortality was detected in the other population, suggesting different causes of mortality among populations. These results first support the hypothesis that selection mainly causes small changes in allele frequencies among many covarying loci rather than a small number of changes in loci with large effects. They also point out that moving away from the a strict 'selective sweep paradigm' towards a multilocus genetics framework may be a more useful approach for studying the genomic signatures of natural selection on complex traits in wild populations. © 2014 John Wiley & Sons Ltd.

Reducing complexity when studying seed dispersal at community scales: a functional classification of vertebrate seed dispersers in tropical forests.

PubMed

Dennis, Andrew J; Westcott, David A

2006-10-01

The process of seed dispersal has a profound effect on vegetation structure and diversity in tropical forests. However, our understanding of the process and our ability to predict its outcomes at a community scale are limited by the frequently large number of interactions associated with it. Here, we outline an approach to dealing with this complexity that reduces the number of unique interactions considered by classifying the participants according to their functional similarity. We derived a classification of dispersers based on the nature of the dispersal service they provide to plants. We described the quantities of fruit handled, the quality of handling and the diversity of plants to which the service is provided. We used ten broad disperser traits to group 26 detailed measures for each disperser. We then applied this approach to vertebrate dispersers in Australia's tropical forests. Using this we also develop a classification that may be more generally applicable. For each disperser, data relating to each trait was obtained either from the field or published literature. First, we identified dispersers whose service outcomes were so distinct that statistical analysis was not required and assigned them to functional groups. The remaining dispersers were assigned to functional groups using cluster analysis. The combined processes created 15 functional groups from 65 vertebrate dispersers in Australian tropical forests. Our approach--grouping dispersers on the basis of the type of dispersal service provided and the fruit types it is provided to--represents a means of reducing the complexity encountered in tropical seed dispersal systems and could be effectively applied in community level studies. It also represents a useful tool for exploring changes in dispersal services when the distribution and abundance of animal populations change due to human impacts.

Impact of the Carbon and Nitrogen Supply on Relationships and Connectivity between Metabolism and Biomass in a Broad Panel of Arabidopsis Accessions1[W][OA

PubMed Central

Sulpice, Ronan; Nikoloski, Zoran; Tschoep, Hendrik; Antonio, Carla; Kleessen, Sabrina; Larhlimi, Abdelhalim; Selbig, Joachim; Ishihara, Hirofumi; Gibon, Yves; Fernie, Alisdair R.; Stitt, Mark

2013-01-01

Natural genetic diversity provides a powerful tool to study the complex interrelationship between metabolism and growth. Profiling of metabolic traits combined with network-based and statistical analyses allow the comparison of conditions and identification of sets of traits that predict biomass. However, it often remains unclear why a particular set of metabolites is linked with biomass and to what extent the predictive model is applicable beyond a particular growth condition. A panel of 97 genetically diverse Arabidopsis (Arabidopsis thaliana) accessions was grown in near-optimal carbon and nitrogen supply, restricted carbon supply, and restricted nitrogen supply and analyzed for biomass and 54 metabolic traits. Correlation-based metabolic networks were generated from the genotype-dependent variation in each condition to reveal sets of metabolites that show coordinated changes across accessions. The networks were largely specific for a single growth condition. Partial least squares regression from metabolic traits allowed prediction of biomass within and, slightly more weakly, across conditions (cross-validated Pearson correlations in the range of 0.27–0.58 and 0.21–0.51 and P values in the range of <0.001–<0.13 and <0.001–<0.023, respectively). Metabolic traits that correlate with growth or have a high weighting in the partial least squares regression were mainly condition specific and often related to the resource that restricts growth under that condition. Linear mixed-model analysis using the combined metabolic traits from all growth conditions as an input indicated that inclusion of random effects for the conditions improves predictions of biomass. Thus, robust prediction of biomass across a range of conditions requires condition-specific measurement of metabolic traits to take account of environment-dependent changes of the underlying networks. PMID:23515278

Dissection of complicate genetic architecture and breeding perspective of cottonseed traits by genome-wide association study.

PubMed

Du, Xiongming; Liu, Shouye; Sun, Junling; Zhang, Gengyun; Jia, Yinhua; Pan, Zhaoe; Xiang, Haitao; He, Shoupu; Xia, Qiuju; Xiao, Songhua; Shi, Weijun; Quan, Zhiwu; Liu, Jianguang; Ma, Jun; Pang, Baoyin; Wang, Liru; Sun, Gaofei; Gong, Wenfang; Jenkins, Johnie N; Lou, Xiangyang; Zhu, Jun; Xu, Haiming

2018-06-13

Cottonseed is one of the most important raw materials for plant protein, oil and alternative biofuel for diesel engines. Understanding the complex genetic basis of cottonseed traits is requisite for achieving efficient genetic improvement of the traits. However, it is not yet clear about their genetic architecture in genomic level. GWAS has been an effective way to explore genetic basis of quantitative traits in human and many crops. This study aims to dissect genetic mechanism seven cottonseed traits by a GWAS for genetic improvement. A genome-wide association study (GWAS) based on a full gene model with gene effects as fixed and gene-environment interaction as random, was conducted for protein, oil and 5 fatty acids using 316 accessions and ~ 390 K SNPs. Totally, 124 significant quantitative trait SNPs (QTSs), consisting of 16, 21, 87 for protein, oil and fatty acids (palmitic, linoleic, oleic, myristic, stearic), respectively, were identified and the broad-sense heritability was estimated from 71.62 to 93.43%; no QTS-environment interaction was detected for the protein, the palmitic and the oleic contents; the protein content was predominantly controlled by epistatic effects accounting for 65.18% of the total variation, but the oil content and the fatty acids except the palmitic were mainly determined by gene main effects and no epistasis was detected for the myristic and the stearic. Prediction of superior pure line and hybrid revealed the potential of the QTSs in the improvement of cottonseed traits, and the hybrid could achieve higher or lower genetic values compared with pure lines. This study revealed complex genetic architecture of seven cottonseed traits at whole genome-wide by mixed linear model approach; the identified genetic variants and estimated genetic component effects of gene, gene-gene and gene-environment interaction provide cotton geneticist or breeders new knowledge on the genetic mechanism of the traits and the potential molecular breeding design strategy.

Effect of urbanization on the structure and functional traits of remnant subtropical evergreen broad-leaved forests in South China.

PubMed

Huang, Liujing; Chen, Hongfeng; Ren, Hai; Wang, Jun; Guo, Qinfeng

2013-06-01

We investigated the effects of major environmental drivers associated with urbanization on species diversity and plant functional traits (PFTs) in the remnant subtropical evergreen broad-leaved forests in Metropolitan Guangzhou (Guangdong, China). Twenty environmental factors including topography, light, and soil properties were used to quantify the effects of urbanization. Vegetation data and soil properties were collected from 30 400-m(2) plots at 6 study sites in urban and rural areas. The difference of plant species diversity and PFTs of remnant forests between urban and rural areas were analyzed. To discern the complex relationships, multivariate statistical analyses (e.g., canonical correspondence analysis and regression analysis) were employed. Pioneer species and stress-tolerant species can survive and vigorously establish their population dominance in the urban environment. The native herb diversity was lower in urban forests than in rural forests. Urban forests tend to prefer the species with Mesophanerophyte life form. In contrast, species in rural forests possessed Chamaephyte and Nanophanerophyte life forms and gravity/clonal growth dispersal mode. Soil pH and soil nutrients (K, Na, and TN) were positively related to herb diversity, while soil heavy metal concentrations (Cu) were negatively correlated with herb diversity. The herb plant species diversity declines and the species in the remnant forests usually have stress-tolerant functional traits in response to urbanization. The factors related to urbanization such as soil acidification, nutrient leaching, and heavy metal pollution were important in controlling the plant diversity in the forests along the urban-rural gradients. Urbanization affects the structure and functional traits of remnant subtropical evergreen broad-leaved forests.

Exploring and Harnessing Haplotype Diversity to Improve Yield Stability in Crops.

PubMed

Qian, Lunwen; Hickey, Lee T; Stahl, Andreas; Werner, Christian R; Hayes, Ben; Snowdon, Rod J; Voss-Fels, Kai P

2017-01-01

In order to meet future food, feed, fiber, and bioenergy demands, global yields of all major crops need to be increased significantly. At the same time, the increasing frequency of extreme weather events such as heat and drought necessitates improvements in the environmental resilience of modern crop cultivars. Achieving sustainably increase yields implies rapid improvement of quantitative traits with a very complex genetic architecture and strong environmental interaction. Latest advances in genome analysis technologies today provide molecular information at an ultrahigh resolution, revolutionizing crop genomic research, and paving the way for advanced quantitative genetic approaches. These include highly detailed assessment of population structure and genotypic diversity, facilitating the identification of selective sweeps and signatures of directional selection, dissection of genetic variants that underlie important agronomic traits, and genomic selection (GS) strategies that not only consider major-effect genes. Single-nucleotide polymorphism (SNP) markers today represent the genotyping system of choice for crop genetic studies because they occur abundantly in plant genomes and are easy to detect. SNPs are typically biallelic, however, hence their information content compared to multiallelic markers is low, limiting the resolution at which SNP-trait relationships can be delineated. An efficient way to overcome this limitation is to construct haplotypes based on linkage disequilibrium, one of the most important features influencing genetic analyses of crop genomes. Here, we give an overview of the latest advances in genomics-based haplotype analyses in crops, highlighting their importance in the context of polyploidy and genome evolution, linkage drag, and co-selection. We provide examples of how haplotype analyses can complement well-established quantitative genetics frameworks, such as quantitative trait analysis and GS, ultimately providing an effective tool to equip modern crops with environment-tailored characteristics.

Iranian and Swedish adolescents: differences in personality traits and well-being

PubMed Central

Nima, Ali A.; Sikström, Sverker; Archer, Trevor

2013-01-01

Introduction. This study addresses the need to further contextualize research on well-being (e.g., Kjell, 2011) in terms of cross-cultural aspects of personality traits among adolescents and by examining two different conceptualizations of well-being: subjective well-being (i.e., life satisfaction, positive and negative affect) and psychological well-being (i.e., positive relations with others, environmental mastery, self-acceptance, autonomy, personal growth, and life purpose). Methods. Iranian (N = 122, mean age 15.23 years) and Swedish (N = 109, mean age 16.69 years) adolescents were asked to fill out a Big Five personality test, as well as questionnaires assessing subjective well-being and psychological well-being. Results. Swedes reported higher subjective and psychological well-being, while Iranians reported higher degree of Agreeableness, Openness and Conscientiousness. Neuroticism and Extraversion did not differ between cultures. Neuroticism was related to well-being within both cultures. Openness was related to well-being only among Iranians, and Extraversion only among Swedes. A mediation analysis within the Swedish sample, the only sample meeting statistical criteria for mediation analysis to be conducted, demonstrated that psychological well-being mediated the relationship between Neuroticism and subjective well-being as well as between Extraversion and subjective well-being. Conclusions. Certain personality traits, such as Extraversion, Openness, and Conscientiousness, relate differently to well-being measures across cultures. Meanwhile, Neuroticism seems to relate similarly across cultures at least with regard to subjective well-being. Furthermore, the results give an indication on how psychological well-being might mediate the relationship between certain personality traits and subjective well-being. Overall, the complexity of the results illustrates the need for more research whilst supporting the importance of contextualizing well-being research. PMID:24255812

Catalog of MicroRNA Seed Polymorphisms in Vertebrates

PubMed Central

Calin, George Adrian; Horvat, Simon; Jiang, Zhihua; Dovc, Peter; Kunej, Tanja

2012-01-01

MicroRNAs (miRNAs) are a class of non-coding RNA that plays an important role in posttranscriptional regulation of mRNA. Evidence has shown that miRNA gene variability might interfere with its function resulting in phenotypic variation and disease susceptibility. A major role in miRNA target recognition is ascribed to complementarity with the miRNA seed region that can be affected by polymorphisms. In the present study, we developed an online tool for the detection of miRNA polymorphisms (miRNA SNiPer) in vertebrates (http://www.integratomics-time.com/miRNA-SNiPer) and generated a catalog of miRNA seed region polymorphisms (miR-seed-SNPs) consisting of 149 SNPs in six species. Although a majority of detected polymorphisms were due to point mutations, two consecutive nucleotide substitutions (double nucleotide polymorphisms, DNPs) were also identified in nine miRNAs. We determined that miR-SNPs are frequently located within the quantitative trait loci (QTL), chromosome fragile sites, and cancer susceptibility loci, indicating their potential role in the genetic control of various complex traits. To test this further, we performed an association analysis between the mmu-miR-717 seed SNP rs30372501, which is polymorphic in a large number of standard inbred strains, and all phenotypic traits in these strains deposited in the Mouse Phenome Database. Analysis showed a significant association between the mmu-miR-717 seed SNP and a diverse array of traits including behavior, blood-clinical chemistry, body weight size and growth, and immune system suggesting that seed SNPs can indeed have major pleiotropic effects. The bioinformatics analyses, data and tools developed in the present study can serve researchers as a starting point in testing more targeted hypotheses and designing experiments using optimal species or strains for further mechanistic studies. PMID:22303453

Combining field performance with controlled environment plant imaging to identify the genetic control of growth and transpiration underlying yield response to water-deficit stress in wheat.

PubMed

Parent, Boris; Shahinnia, Fahimeh; Maphosa, Lance; Berger, Bettina; Rabie, Huwaida; Chalmers, Ken; Kovalchuk, Alex; Langridge, Peter; Fleury, Delphine

2015-09-01

Crop yield in low-rainfall environments is a complex trait under multigenic control that shows significant genotype×environment (G×E) interaction. One way to understand and track this trait is to link physiological studies to genetics by using imaging platforms to phenotype large segregating populations. A wheat population developed from parental lines contrasting in their mechanisms of yield maintenance under water deficit was studied in both an imaging platform and in the field. We combined phenotyping methods in a common analysis pipeline to estimate biomass and leaf area from images and then inferred growth and relative growth rate, transpiration, and water-use efficiency, and applied these to genetic analysis. From the 20 quantitative trait loci (QTLs) found for several traits in the platform, some showed strong effects, accounting for between 26 and 43% of the variation on chromosomes 1A and 1B, indicating that the G×E interaction could be reduced in a controlled environment and by using dynamic variables. Co-location of QTLs identified in the platform and in the field showed a possible common genetic basis at some loci. Co-located QTLs were found for average growth rate, leaf expansion rate, transpiration rate, and water-use efficiency from the platform with yield, spike number, grain weight, grain number, and harvest index in the field. These results demonstrated that imaging platforms are a suitable alternative to field-based screening and may be used to phenotype recombinant lines for positional cloning. © The Author 2015. Published by Oxford University Press on behalf of the Society for Experimental Biology.

The Birth of a Black Rice Gene and Its Local Spread by Introgression

PubMed Central

Oikawa, Tetsuo; Maeda, Hiroaki; Oguchi, Taichi; Yamaguchi, Takuya; Tanabe, Noriko; Ebana, Kaworu; Yano, Masahiro; Izawa, Takeshi

2015-01-01

The origin and spread of novel agronomic traits during crop domestication are complex events in plant evolution. Wild rice (Oryza rufipogon) has red grains due to the accumulation of proanthocyanidins, whereas most cultivated rice (Oryza sativa) varieties have white grains induced by a defective allele in the Rc basic helix-loop-helix (bHLH) gene. Although the events surrounding the origin and spread of black rice traits remain unknown, varieties with black grains due to anthocyanin accumulation are distributed in various locations throughout Asia. Here, we show that the black grain trait originated from ectopic expression of the Kala4 bHLH gene due to rearrangement in the promoter region. Both the Rc and Kala4 genes activate upstream flavonol biosynthesis genes, such as chalcone synthase and dihydroflavonol-4-reductase, and downstream genes, such as leucoanthocyanidin reductase and leucoanthocyanidin dioxygenase, to produce the respective specific pigments. Genome analysis of 21 black rice varieties as well as red- and white-grained landraces demonstrated that black rice arose in tropical japonica and its subsequent spread to the indica subspecies can be attributed to the causal alleles of Kala4. The relatively small size of genomic fragments of tropical japonica origin in some indica varieties indicates that refined introgression must have occurred by natural crossbreeding in the course of evolution of the black trait in rice. PMID:26362607

The Birth of a Black Rice Gene and Its Local Spread by Introgression.

PubMed

Oikawa, Tetsuo; Maeda, Hiroaki; Oguchi, Taichi; Yamaguchi, Takuya; Tanabe, Noriko; Ebana, Kaworu; Yano, Masahiro; Ebitani, Takeshi; Izawa, Takeshi

2015-09-01

The origin and spread of novel agronomic traits during crop domestication are complex events in plant evolution. Wild rice (Oryza rufipogon) has red grains due to the accumulation of proanthocyanidins, whereas most cultivated rice (Oryza sativa) varieties have white grains induced by a defective allele in the Rc basic helix-loop-helix (bHLH) gene. Although the events surrounding the origin and spread of black rice traits remain unknown, varieties with black grains due to anthocyanin accumulation are distributed in various locations throughout Asia. Here, we show that the black grain trait originated from ectopic expression of the Kala4 bHLH gene due to rearrangement in the promoter region. Both the Rc and Kala4 genes activate upstream flavonol biosynthesis genes, such as chalcone synthase and dihydroflavonol-4-reductase, and downstream genes, such as leucoanthocyanidin reductase and leucoanthocyanidin dioxygenase, to produce the respective specific pigments. Genome analysis of 21 black rice varieties as well as red- and white-grained landraces demonstrated that black rice arose in tropical japonica and its subsequent spread to the indica subspecies can be attributed to the causal alleles of Kala4. The relatively small size of genomic fragments of tropical japonica origin in some indica varieties indicates that refined introgression must have occurred by natural crossbreeding in the course of evolution of the black trait in rice. © 2015 American Society of Plant Biologists. All rights reserved.

GWAS and fine-mapping of 35 production, reproduction and conformation traits with imputed sequences of 27K Holstein bulls

USDA-ARS?s Scientific Manuscript database

Fine-mapping of causal variants is becoming feasible for complex traits in livestock GWAS, as an increasing number of animals are sequenced. Imputation has been routinely applied to ascertain sequence variants in large genotyped populations based on small reference populations of sequenced animals. ...

The Relationship between Confidence and Self-Concept--Towards a Model of Response Confidence

ERIC Educational Resources Information Center

Kroner, Stephan; Biermann, Antje

2007-01-01

According to Stankov [Stankov, L. (2000). Complexity, metacognition and fluid intelligence. Intelligence, 28, 121-143.] response confidence in cognitive tests reflects a trait on the boundary of personality and abilities. However, several studies failed in relating confidence scores to other known traits, including self-concept. A model of…

Approaches for geospatial processing of field-based high-throughput plant phenomics data from ground vehicle platforms

USDA-ARS?s Scientific Manuscript database

Understanding the genetic basis of complex plant traits requires connecting genotype to phenotype information, known as the “G2P question.” In the last three decades, genotyping methods have become highly developed. Much less innovation has occurred for measuring plant traits (phenotyping), particul...

Multimedia Case-Based Instruction in Literacy: Pedagogy, Effectiveness, and Perceptions

ERIC Educational Resources Information Center

Baker, Elizabeth A.

2009-01-01

Effective literacy teachers share a variety of instructional traits such as the ability to kidwatch, pedagogically reflect, and make informed decisions based on ill-structured and complex data. Teacher educators face the challenge of helping preservice teachers develop such traits so as to prepare them to be successful literacy teachers. One…

Association genetics in Pinus taeda L. I. wood property traits

Treesearch

Santiago C. Gonzalez-Martinez; Nicholas C. Wheeler; Elhan Ersoz; C. Dana Nelson; David B. Neale

2007-01-01

Genetic association is a powerful method for dissecting complex adaptive traits due to (i) fine-scale mapping resulting from historical recombination, (ii) wide coverage of phenotypic and genotypic variation within a single experiment, and (iii) the simultaneous discovery of loci and alleles. In this article, genetic association among single nucleotide polymorphisms (...

Accounting for genotype–by-environment interactions and non-additive genetic variation in genomic selection for water-soluble carbohydrate concentration in wheat

USDA-ARS?s Scientific Manuscript database

Abiotic stress tolerance traits are often complex and recalcitrant targets for conventional breeding improvement in many crop species. This study evaluated the potential of genomic selection to predict water-soluble carbohydrate concentration (WSCC), an important drought tolerance trait, in wheat un...

Individual differences in components of impulsivity and effortful control moderate the relation between borderline personality disorder traits and emotion recognition in a sample of university students.

PubMed

Preti, Emanuele; Richetin, Juliette; Suttora, Chiara; Pisani, Alberto

2016-04-30

Dysfunctions in social cognition characterize personality disorders. However, mixed results emerged from literature on emotion processing. Borderline Personality Disorder (BPD) traits are either associated with enhanced emotion recognition, impairments, or equal functioning compared to controls. These apparent contradictions might result from the complexity of emotion recognition tasks used and from individual differences in impulsivity and effortful control. We conducted a study in a sample of undergraduate students (n=80), assessing BPD traits, using an emotion recognition task that requires the processing of only visual information or both visual and acoustic information. We also measured individual differences in impulsivity and effortful control. Results demonstrated the moderating role of some components of impulsivity and effortful control on the capability of BPD traits in predicting anger and happiness recognition. We organized the discussion around the interaction between different components of regulatory functioning and task complexity for a better understanding of emotion recognition in BPD samples. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A Tricky Trait: Applying the Fruits of the “Function Debate” in the Philosophy of Biology to the “Venom Debate” in the Science of Toxinology

PubMed Central

Jackson, Timothy N. W.; Fry, Bryan G.

2016-01-01

The “function debate” in the philosophy of biology and the “venom debate” in the science of toxinology are conceptually related. Venom systems are complex multifunctional traits that have evolved independently numerous times throughout the animal kingdom. No single concept of function, amongst those popularly defended, appears adequate to describe these systems in all their evolutionary contexts and extant variations. As such, a pluralistic view of function, previously defended by some philosophers of biology, is most appropriate. Venom systems, like many other functional traits, exist in nature as points on a continuum and the boundaries between “venomous” and “non-venomous” species may not always be clearly defined. This paper includes a brief overview of the concept of function, followed by in-depth discussion of its application to venom systems. A sound understanding of function may aid in moving the venom debate forward. Similarly, consideration of a complex functional trait such as venom may be of interest to philosophers of biology. PMID:27618098

Association genetics and transcriptome analysis reveal a gibberellin-responsive pathway involved in regulating photosynthesis.

PubMed

Xie, Jianbo; Tian, Jiaxing; Du, Qingzhang; Chen, Jinhui; Li, Ying; Yang, Xiaohui; Li, Bailian; Zhang, Deqiang

2016-05-01

Gibberellins (GAs) regulate a wide range of important processes in plant growth and development, including photosynthesis. However, the mechanism by which GAs regulate photosynthesis remains to be understood. Here, we used multi-gene association to investigate the effect of genes in the GA-responsive pathway, as constructed by RNA sequencing, on photosynthesis, growth, and wood property traits, in a population of 435 Populus tomentosa By analyzing changes in the transcriptome following GA treatment, we identified many key photosynthetic genes, in agreement with the observed increase in measurements of photosynthesis. Regulatory motif enrichment analysis revealed that 37 differentially expressed genes related to photosynthesis shared two essential GA-related cis-regulatory elements, the GA response element and the pyrimidine box. Thus, we constructed a GA-responsive pathway consisting of 47 genes involved in regulating photosynthesis, including GID1, RGA, GID2, MYBGa, and 37 photosynthetic differentially expressed genes. Single nucleotide polymorphism (SNP)-based association analysis showed that 142 SNPs, representing 40 candidate genes in this pathway, were significantly associated with photosynthesis, growth, and wood property traits. Epistasis analysis uncovered interactions between 310 SNP-SNP pairs from 37 genes in this pathway, revealing possible genetic interactions. Moreover, a structural gene-gene matrix based on a time-course of transcript abundances provided a better understanding of the multi-gene pathway affecting photosynthesis. The results imply a functional role for these genes in mediating photosynthesis, growth, and wood properties, demonstrating the potential of combining transcriptome-based regulatory pathway construction and genetic association approaches to detect the complex genetic networks underlying quantitative traits. © The Author 2016. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Using extreme phenotype sampling to identify the rare causal variants of quantitative traits in association studies.

PubMed

Li, Dalin; Lewinger, Juan Pablo; Gauderman, William J; Murcray, Cassandra Elizabeth; Conti, David

2011-12-01

Variants identified in recent genome-wide association studies based on the common-disease common-variant hypothesis are far from fully explaining the hereditability of complex traits. Rare variants may, in part, explain some of the missing hereditability. Here, we explored the advantage of the extreme phenotype sampling in rare-variant analysis and refined this design framework for future large-scale association studies on quantitative traits. We first proposed a power calculation approach for a likelihood-based analysis method. We then used this approach to demonstrate the potential advantages of extreme phenotype sampling for rare variants. Next, we discussed how this design can influence future sequencing-based association studies from a cost-efficiency (with the phenotyping cost included) perspective. Moreover, we discussed the potential of a two-stage design with the extreme sample as the first stage and the remaining nonextreme subjects as the second stage. We demonstrated that this two-stage design is a cost-efficient alternative to the one-stage cross-sectional design or traditional two-stage design. We then discussed the analysis strategies for this extreme two-stage design and proposed a corresponding design optimization procedure. To address many practical concerns, for example measurement error or phenotypic heterogeneity at the very extremes, we examined an approach in which individuals with very extreme phenotypes are discarded. We demonstrated that even with a substantial proportion of these extreme individuals discarded, an extreme-based sampling can still be more efficient. Finally, we expanded the current analysis and design framework to accommodate the CMC approach where multiple rare-variants in the same gene region are analyzed jointly. © 2011 Wiley Periodicals, Inc.

Using Extreme Phenotype Sampling to Identify the Rare Causal Variants of Quantitative Traits in Association Studies

PubMed Central

Li, Dalin; Lewinger, Juan Pablo; Gauderman, William J.; Murcray, Cassandra Elizabeth; Conti, David

2014-01-01

Variants identified in recent genome-wide association studies based on the common-disease common-variant hypothesis are far from fully explaining the hereditability of complex traits. Rare variants may, in part, explain some of the missing hereditability. Here, we explored the advantage of the extreme phenotype sampling in rare-variant analysis and refined this design framework for future large-scale association studies on quantitative traits. We first proposed a power calculation approach for a likelihood-based analysis method. We then used this approach to demonstrate the potential advantages of extreme phenotype sampling for rare variants. Next, we discussed how this design can influence future sequencing-based association studies from a cost-efficiency (with the phenotyping cost included) perspective. Moreover, we discussed the potential of a two-stage design with the extreme sample as the first stage and the remaining nonextreme subjects as the second stage. We demonstrated that this two-stage design is a cost-efficient alternative to the one-stage cross-sectional design or traditional two-stage design. We then discussed the analysis strategies for this extreme two-stage design and proposed a corresponding design optimization procedure. To address many practical concerns, for example measurement error or phenotypic heterogeneity at the very extremes, we examined an approach in which individuals with very extreme phenotypes are discarded. We demonstrated that even with a substantial proportion of these extreme individuals discarded, an extreme-based sampling can still be more efficient. Finally, we expanded the current analysis and design framework to accommodate the CMC approach where multiple rare-variants in the same gene region are analyzed jointly. PMID:21922541

Analysis of the Gli-D2 locus identifies a genetic target for simultaneously improving the breadmaking and health-related traits of common wheat.

PubMed

Li, Da; Jin, Huaibing; Zhang, Kunpu; Wang, Zhaojun; Wang, Faming; Zhao, Yue; Huo, Naxin; Liu, Xin; Gu, Yong Q; Wang, Daowen; Dong, Lingli

2018-05-11

Gliadins are a major component of wheat seed proteins. However, the complex homoeologous Gli-2 loci (Gli-A2, -B2 and -D2) that encode the α-gliadins in commercial wheat are still poorly understood. Here we analyzed the Gli-D2 locus of Xiaoyan 81 (Xy81), a winter wheat cultivar. A total of 421.091 kb of the Gli-D2 sequence was assembled from sequencing multiple bacterial artificial clones, and 10 α-gliadin genes were annotated. Comparative genomic analysis showed that Xy81 carried only eight of the α-gliadin genes of the D genome donor Aegilops tauschii, with two of them each experiencing a tandem duplication. A mutant line lacking Gli-D2 (DLGliD2) consistently exhibited better breadmaking quality and dough functionalities than its progenitor Xy81, but without penalties in other agronomic traits. It also had an elevated lysine content in the grains. Transcriptome analysis verified the lack of Gli-D2 α-gliadin gene expression in DLGliD2. Furthermore, the transcript and protein levels of protein disulfide isomerase were both upregulated in DLGliD2 grains. Consistent with this finding, DLGliD2 had increased disulfide content in the flour. Our work sheds light on the structure and function of Gli-D2 in commercial wheat, and suggests that the removal of Gli-D2 and the gliadins specified by it is likely to be useful for simultaneously enhancing the end-use and health-related traits of common wheat. Because gliadins and homologous proteins are widely present in grass species, the strategy and information reported here may be broadly useful for improving the quality traits of diverse cereal crops. © 2018 The Authors The Plant Journal © 2018 John Wiley & Sons Ltd.

Comparative Polygenic Analysis of Maximal Ethanol Accumulation Capacity and Tolerance to High Ethanol Levels of Cell Proliferation in Yeast

PubMed Central

Pais, Thiago M.; Foulquié-Moreno, María R.; Hubmann, Georg; Duitama, Jorge; Swinnen, Steve; Goovaerts, Annelies; Yang, Yudi; Dumortier, Françoise; Thevelein, Johan M.

2013-01-01

The yeast Saccharomyces cerevisiae is able to accumulate ≥17% ethanol (v/v) by fermentation in the absence of cell proliferation. The genetic basis of this unique capacity is unknown. Up to now, all research has focused on tolerance of yeast cell proliferation to high ethanol levels. Comparison of maximal ethanol accumulation capacity and ethanol tolerance of cell proliferation in 68 yeast strains showed a poor correlation, but higher ethanol tolerance of cell proliferation clearly increased the likelihood of superior maximal ethanol accumulation capacity. We have applied pooled-segregant whole-genome sequence analysis to identify the polygenic basis of these two complex traits using segregants from a cross of a haploid derivative of the sake strain CBS1585 and the lab strain BY. From a total of 301 segregants, 22 superior segregants accumulating ≥17% ethanol in small-scale fermentations and 32 superior segregants growing in the presence of 18% ethanol, were separately pooled and sequenced. Plotting SNP variant frequency against chromosomal position revealed eleven and eight Quantitative Trait Loci (QTLs) for the two traits, respectively, and showed that the genetic basis of the two traits is partially different. Fine-mapping and Reciprocal Hemizygosity Analysis identified ADE1, URA3, and KIN3, encoding a protein kinase involved in DNA damage repair, as specific causative genes for maximal ethanol accumulation capacity. These genes, as well as the previously identified MKT1 gene, were not linked in this genetic background to tolerance of cell proliferation to high ethanol levels. The superior KIN3 allele contained two SNPs, which are absent in all yeast strains sequenced up to now. This work provides the first insight in the genetic basis of maximal ethanol accumulation capacity in yeast and reveals for the first time the importance of DNA damage repair in yeast ethanol tolerance. PMID:23754966

Matching consumer feeding behaviours and resource traits: a fourth-corner problem in food-web theory.

PubMed

Monteiro, Angelo Barbosa; Faria, Lucas Del Bianco

2018-06-06

For decades, food web theory has proposed phenomenological models for the underlying structure of ecological networks. Generally, these models rely on latent niche variables that match the feeding behaviour of consumers with their resource traits. In this paper, we used a comprehensive database to evaluate different hypotheses on the best dependency structure of trait-matching patterns between consumers and resource traits. We found that consumer feeding behaviours had complex interactions with resource traits; however, few dimensions (i.e. latent variables) could reproduce the trait-matching patterns. We discuss our findings in the light of three food web models designed to reproduce the multidimensionality of food web data; additionally, we discuss how using species traits clarify food webs beyond species pairwise interactions and enable studies to infer ecological generality at larger scales, despite potential taxonomic differences, variations in ecological conditions and differences in species abundance between communities. © 2018 John Wiley & Sons Ltd/CNRS.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Snijders, Antoine M.; Langley, Sasha A.; Kim, Young-Mo

Although the gut microbiome plays important roles in host physiology, health and disease1, we lack understanding of the complex interplay between host genetics and early life environment on the microbial and metabolic composition of the gut.We used the genetically diverse Collaborative Cross mouse system2 to discover that early life history impacts themicrobiome composition, whereas dietary changes have only a moderate effect. By contrast, the gut metabolome was shaped mostly by diet, with specific non-dietary metabolites explained by microbial metabolism. Quantitative trait analysis identified mouse genetic trait loci (QTL) that impact the abundances of specific microbes. Human orthologues of genes inmore » the mouse QTL are implicated in gastrointestinal cancer. Additionally, genes located in mouse QTL for Lactobacillales abundance are implicated in arthritis, rheumatic disease and diabetes. Furthermore, Lactobacillales abundance was predictive of higher host T-helper cell counts, suggesting an important link between Lactobacillales and host adaptive immunity.« less

Deletion Analysis of the Tumorous-Head (tuh–3) Gene in DROSOPHILA MELANOGASTER

PubMed Central

Kuhn, David T.; Woods, Daniel F.; Andrew, Deborah J.

1981-01-01

In the presence of the naturally occurring maternal-effect alleles tuh-1h or tuh-1g, the tuh-3 mutant gene can cause the tumorous-head trait or the sac-testis trait. The tuh-3 gene functions as a semidominant in the presence of the tuh-1h maternal effect. Eye-antennal structures are replaced by posterior abdominal tergites and genital structures. If tuh-1h is replaced by its naturally occurring allele tuh-1g, tuh-3 functions as a recessive hypomorph and the defect switches from anterior to posterior structures, with a male genital-disc defect appearing with variable penetrance. Function and regulation of tuh-3+ may better be understood in light of the cytological localization of tuh-3 either adjacent to or as part of the bithorax complex. The tuh-3+ gene product appears to be essential for normal development, at least in the posterior end of the embryo. PMID:6804305

Congenic Mice Provide In Vivo Evidence for a Genetic Locus that Modulates Intrinsic TGF-b1-mediated Signaling and Bone Acquisition

PubMed Central

Mukherjee, Aditi; Larson, Emily A.; Carlos, Amy S.; Belknap, John K.; Rotwein, Peter; Klein, Robert F.

2016-01-01

Osteoporosis, the most common skeletal disorder, is characterized by low bone mineral density (BMD) and an increased risk of fragility fractures. BMD is the best clinical predictor of future osteoporotic fracture risk, but is a complex trait controlled by multiple environmental and genetic determinants with individually modest effects. Quantitative trait locus (QTL) mapping is a powerful method for identifying chromosomal regions encompassing genes involved in shaping complex phenotypes, such as BMD. Here we have applied QTL analysis to male and female genetically-heterogeneous F2 mice derived from a cross between C57BL/6 and DBA/2 strains, and have identified 11 loci contributing to femoral BMD. Further analysis of a QTL on mouse chromosome 7 following the generation of reciprocal congenic strains has allowed us to determine that the high BMD trait, which tracks with the DBA/2 chromosome and exerts equivalent effects on male and female mice, is manifested by enhanced osteogenic differentiation of mesenchymal stem cells (MSCs) in vitro and by increased growth of metatarsal bones in short-term primary culture. An insertion/deletion DNA polymorphism in Ltbp4 exon 12 that causes the in-frame removal of 12 codons in the DBA/2-derived gene maps within 0.6 Mb of the marker most tightly linked to the QTL. LTBP4, one of four paralogous mouse proteins that modify the bioavailability of the TGF-b family of growth factors, is expressed in differentiating MSC-derived osteoblasts and in long bones, and reduced responsiveness to TGF-b1 is observed in MSCs of mice homozygous for the DBA/2 chromosome 7. Taken together, our results identify a potential genetic and biochemical relationship between decreased TGF-b1-mediated signaling and enhanced femoral BMD that may be regulated by a variant LTBP4 molecule. PMID:22407846

Analyzing Association Mapping in Pedigree-Based GWAS Using a Penalized Multitrait Mixed Model

PubMed Central

Liu, Jin; Yang, Can; Shi, Xingjie; Li, Cong; Huang, Jian; Zhao, Hongyu; Ma, Shuangge

2017-01-01

Genome-wide association studies (GWAS) have led to the identification of many genetic variants associated with complex diseases in the past 10 years. Penalization methods, with significant numerical and statistical advantages, have been extensively adopted in analyzing GWAS. This study has been partly motivated by the analysis of Genetic Analysis Workshop (GAW) 18 data, which have two notable characteristics. First, the subjects are from a small number of pedigrees and hence related. Second, for each subject, multiple correlated traits have been measured. Most of the existing penalization methods assume independence between subjects and traits and can be suboptimal. There are a few methods in the literature based on mixed modeling that can accommodate correlations. However, they cannot fully accommodate the two types of correlations while conducting effective marker selection. In this study, we develop a penalized multitrait mixed modeling approach. It accommodates the two different types of correlations and includes several existing methods as special cases. Effective penalization is adopted for marker selection. Simulation demonstrates its satisfactory performance. The GAW 18 data are analyzed using the proposed method. PMID:27247027

The genetic architecture of pediatric cognitive abilities in the Philadelphia Neurodevelopmental Cohort

PubMed Central

Robinson, Elise B.; Kirby, Andrew; Ruparel, Kosha; Yang, Jian; McGrath, Lauren; Anttila, Verneri; Neale, Benjamin M.; Merikangas, Kathleen; Lehner, Thomas; Sleiman, Patrick M.A.; Daly, Mark J.; Gur, Ruben; Gur, Raquel; Hakonarson, Hakon

2014-01-01

The objective of this analysis was to examine the genetic architecture of diverse cognitive abilities in children and adolescents, including the magnitude of common genetic effects and patterns of shared and unique genetic influences. Subjects included 3,689 members of the Philadelphia Neurodevelopmental Cohort, a general population sample of ages 8-21 years who completed an extensive battery of cognitive tests. We used genome-wide complex trait analysis (GCTA) to estimate the SNP-based heritability of each domain, as well as the genetic correlation between all domains that showed significant genetic influence. Several of the individual domains suggested strong influence of common genetic variants (e.g. reading ability, h2g=0.43, p=4e-06; emotion identification, h2g=0.36, p=1e-05; verbal memory, h2g=0.24, p=0.005). The genetic correlations highlighted trait domains that are candidates for joint interrogation in future genetic studies (e.g. language reasoning and spatial reasoning, r(g)=0.72, p=0.007). These results can be used to structure future genetic and neuropsychiatric investigations of diverse cognitive abilities. PMID:25023143

Selection and explosive growth alter genetic architecture and hamper the detection of causal rare variants.

PubMed

Uricchio, Lawrence H; Zaitlen, Noah A; Ye, Chun Jimmie; Witte, John S; Hernandez, Ryan D

2016-07-01

The role of rare alleles in complex phenotypes has been hotly debated, but most rare variant association tests (RVATs) do not account for the evolutionary forces that affect genetic architecture. Here, we use simulation and numerical algorithms to show that explosive population growth, as experienced by human populations, can dramatically increase the impact of very rare alleles on trait variance. We then assess the ability of RVATs to detect causal loci using simulations and human RNA-seq data. Surprisingly, we find that statistical performance is worst for phenotypes in which genetic variance is due mainly to rare alleles, and explosive population growth decreases power. Although many studies have attempted to identify causal rare variants, few have reported novel associations. This has sometimes been interpreted to mean that rare variants make negligible contributions to complex trait heritability. Our work shows that RVATs are not robust to realistic human evolutionary forces, so general conclusions about the impact of rare variants on complex traits may be premature. © 2016 Uricchio et al.; Published by Cold Spring Harbor Laboratory Press.

The curvilinear relationship between work pressure and momentary task performance: the role of state and trait core self-evaluations.

PubMed

Hofmans, Joeri; Debusscher, Jonas; Dóci, Edina; Spanouli, Andromachi; De Fruyt, Filip

2015-01-01

Whereas several studies have demonstrated that core self-evaluations (CSE)-or one's appraisals about one's own self-worth, capabilities, and competences-relate to job outcomes, less is known about the mechanisms underlying these relationships. In the present study, we address this issue by examining the role of within- and between-person variation in CSE in the relationship between work pressure and task performance. We hypothesized that (a) work pressure relates to task performance in a curvilinear way, (b) state CSE mediates the curvilinear relationship between work pressure and task performance, and (c) the relationship between work pressure and state CSE is moderated by trait CSE. Our hypotheses were tested via a 10-day daily diary study with 55 employees in which trait CSE was measured at baseline, while work pressure, task performance, and state CSE were assessed on a daily basis. Bayesian multilevel path analysis showed that work pressure affects task performance via state CSE, with state CSE increasing as long as the employee feels that (s)he is able to handle the work pressure, while it decreases when the level of work pressure exceeds the employees' coping abilities. Moreover, we found that for people low on trait CSE, the depleting effect of work pressure via state CSE happens for low levels of work pressure, while for people high in trait CSE the depleting effect is located at high levels of work pressure. Together, our findings suggest that the impact of work pressure on task performance is driven by a complex interplay of between- and within-person differences in CSE.

Taking off the training wheels: the properties of a dynamic vegetation model without climate envelopes

DOE PAGES

Fisher, R. A.; Muszala, S.; Verteinstein, M.; ...

2015-04-29

We describe an implementation of the Ecosystem Demography (ED) concept in the Community Land Model. The structure of CLM(ED) and the physiological and structural modifications applied to the CLM are presented. A major motivation of this development is to allow the prediction of biome boundaries directly from plant physiological traits via their competitive interactions. Here we investigate the performance of the model for an example biome boundary in Eastern North America. We explore the sensitivity of the predicted biome boundaries and ecosystem properties to the variation of leaf properties determined by the parameter space defined by the GLOPNET global leafmore » trait database. Further, we investigate the impact of four sequential alterations to the structural assumptions in the model governing the relative carbon economy of deciduous and evergreen plants. The default assumption is that the costs and benefits of deciduous vs. evergreen leaf strategies, in terms of carbon assimilation and expenditure, can reproduce the geographical structure of biome boundaries and ecosystem functioning. We find some support for this assumption, but only under particular combinations of model traits and structural assumptions. Many questions remain regarding the preferred methods for deployment of plant trait information in land surface models. In some cases, plant traits might best be closely linked with each other, but we also find support for direct linkages to environmental conditions. We advocate for intensified study of the costs and benefits of plant life history strategies in different environments, and for the increased use of parametric and structural ensembles in the development and analysis of complex vegetation models.« less