Analysis of Conditional Genetic Effects and Variance Components in Developmental Genetics

PubMed Central

Zhu, J.

1995-01-01

A genetic model with additive-dominance effects and genotype X environment interactions is presented for quantitative traits with time-dependent measures. The genetic model for phenotypic means at time t conditional on phenotypic means measured at previous time (t - 1) is defined. Statistical methods are proposed for analyzing conditional genetic effects and conditional genetic variance components. Conditional variances can be estimated by minimum norm quadratic unbiased estimation (MINQUE) method. An adjusted unbiased prediction (AUP) procedure is suggested for predicting conditional genetic effects. A worked example from cotton fruiting data is given for comparison of unconditional and conditional genetic variances and additive effects. PMID:8601500

Estimating selection through male fitness: three complementary methods illuminate the nature and causes of selection on flowering time

PubMed Central

Austen, Emily J.; Weis, Arthur E.

2016-01-01

Our understanding of selection through male fitness is limited by the resource demands and indirect nature of the best available genetic techniques. Applying complementary, independent approaches to this problem can help clarify evolution through male function. We applied three methods to estimate selection on flowering time through male fitness in experimental populations of the annual plant Brassica rapa: (i) an analysis of mating opportunity based on flower production schedules, (ii) genetic paternity analysis, and (iii) a novel approach based on principles of experimental evolution. Selection differentials estimated by the first method disagreed with those estimated by the other two, indicating that mating opportunity was not the principal driver of selection on flowering time. The genetic and experimental evolution methods exhibited striking agreement overall, but a slight discrepancy between the two suggested that negative environmental covariance between age at flowering and male fitness may have contributed to phenotypic selection. Together, the three methods enriched our understanding of selection on flowering time, from mating opportunity to phenotypic selection to evolutionary response. The novel experimental evolution method may provide a means of examining selection through male fitness when genetic paternity analysis is not possible. PMID:26911957

Genome-wide association analysis of secondary imaging phenotypes from the Alzheimer's disease neuroimaging initiative study.

PubMed

Zhu, Wensheng; Yuan, Ying; Zhang, Jingwen; Zhou, Fan; Knickmeyer, Rebecca C; Zhu, Hongtu

2017-02-01

The aim of this paper is to systematically evaluate a biased sampling issue associated with genome-wide association analysis (GWAS) of imaging phenotypes for most imaging genetic studies, including the Alzheimer's Disease Neuroimaging Initiative (ADNI). Specifically, the original sampling scheme of these imaging genetic studies is primarily the retrospective case-control design, whereas most existing statistical analyses of these studies ignore such sampling scheme by directly correlating imaging phenotypes (called the secondary traits) with genotype. Although it has been well documented in genetic epidemiology that ignoring the case-control sampling scheme can produce highly biased estimates, and subsequently lead to misleading results and suspicious associations, such findings are not well documented in imaging genetics. We use extensive simulations and a large-scale imaging genetic data analysis of the Alzheimer's Disease Neuroimaging Initiative (ADNI) data to evaluate the effects of the case-control sampling scheme on GWAS results based on some standard statistical methods, such as linear regression methods, while comparing it with several advanced statistical methods that appropriately adjust for the case-control sampling scheme. Copyright © 2016 Elsevier Inc. All rights reserved.

Mendelian randomization with fine-mapped genetic data: Choosing from large numbers of correlated instrumental variables.

PubMed

Burgess, Stephen; Zuber, Verena; Valdes-Marquez, Elsa; Sun, Benjamin B; Hopewell, Jemma C

2017-12-01

Mendelian randomization uses genetic variants to make causal inferences about the effect of a risk factor on an outcome. With fine-mapped genetic data, there may be hundreds of genetic variants in a single gene region any of which could be used to assess this causal relationship. However, using too many genetic variants in the analysis can lead to spurious estimates and inflated Type 1 error rates. But if only a few genetic variants are used, then the majority of the data is ignored and estimates are highly sensitive to the particular choice of variants. We propose an approach based on summarized data only (genetic association and correlation estimates) that uses principal components analysis to form instruments. This approach has desirable theoretical properties: it takes the totality of data into account and does not suffer from numerical instabilities. It also has good properties in simulation studies: it is not particularly sensitive to varying the genetic variants included in the analysis or the genetic correlation matrix, and it does not have greatly inflated Type 1 error rates. Overall, the method gives estimates that are less precise than those from variable selection approaches (such as using a conditional analysis or pruning approach to select variants), but are more robust to seemingly arbitrary choices in the variable selection step. Methods are illustrated by an example using genetic associations with testosterone for 320 genetic variants to assess the effect of sex hormone related pathways on coronary artery disease risk, in which variable selection approaches give inconsistent inferences. © 2017 The Authors Genetic Epidemiology Published by Wiley Periodicals, Inc.

Quantitative genetic tools for insecticide resistance risk assessment: estimating the heritability of resistance

Treesearch

Michael J. Firko; Jane Leslie Hayes

1990-01-01

Quantitative genetic studies of resistance can provide estimates of genetic parameters not available with other types of genetic analyses. Three methods are discussed for estimating the amount of additive genetic variation in resistance to individual insecticides and subsequent estimation of heritability (h2) of resistance. Sibling analysis and...

Bio++: a set of C++ libraries for sequence analysis, phylogenetics, molecular evolution and population genetics.

PubMed

Dutheil, Julien; Gaillard, Sylvain; Bazin, Eric; Glémin, Sylvain; Ranwez, Vincent; Galtier, Nicolas; Belkhir, Khalid

2006-04-04

A large number of bioinformatics applications in the fields of bio-sequence analysis, molecular evolution and population genetics typically share input/output methods, data storage requirements and data analysis algorithms. Such common features may be conveniently bundled into re-usable libraries, which enable the rapid development of new methods and robust applications. We present Bio++, a set of Object Oriented libraries written in C++. Available components include classes for data storage and handling (nucleotide/amino-acid/codon sequences, trees, distance matrices, population genetics datasets), various input/output formats, basic sequence manipulation (concatenation, transcription, translation, etc.), phylogenetic analysis (maximum parsimony, markov models, distance methods, likelihood computation and maximization), population genetics/genomics (diversity statistics, neutrality tests, various multi-locus analyses) and various algorithms for numerical calculus. Implementation of methods aims at being both efficient and user-friendly. A special concern was given to the library design to enable easy extension and new methods development. We defined a general hierarchy of classes that allow the developer to implement its own algorithms while remaining compatible with the rest of the libraries. Bio++ source code is distributed free of charge under the CeCILL general public licence from its website http://kimura.univ-montp2.fr/BioPP.

Markov Logic Networks in the Analysis of Genetic Data

PubMed Central

Sakhanenko, Nikita A.

2010-01-01

Abstract Complex, non-additive genetic interactions are common and can be critical in determining phenotypes. Genome-wide association studies (GWAS) and similar statistical studies of linkage data, however, assume additive models of gene interactions in looking for genotype-phenotype associations. These statistical methods view the compound effects of multiple genes on a phenotype as a sum of influences of each gene and often miss a substantial part of the heritable effect. Such methods do not use any biological knowledge about underlying mechanisms. Modeling approaches from the artificial intelligence (AI) field that incorporate deterministic knowledge into models to perform statistical analysis can be applied to include prior knowledge in genetic analysis. We chose to use the most general such approach, Markov Logic Networks (MLNs), for combining deterministic knowledge with statistical analysis. Using simple, logistic regression-type MLNs we can replicate the results of traditional statistical methods, but we also show that we are able to go beyond finding independent markers linked to a phenotype by using joint inference without an independence assumption. The method is applied to genetic data on yeast sporulation, a complex phenotype with gene interactions. In addition to detecting all of the previously identified loci associated with sporulation, our method identifies four loci with smaller effects. Since their effect on sporulation is small, these four loci were not detected with methods that do not account for dependence between markers due to gene interactions. We show how gene interactions can be detected using more complex models, which can be used as a general framework for incorporating systems biology with genetics. PMID:20958249

Heuristic Identification of Biological Architectures for Simulating Complex Hierarchical Genetic Interactions

PubMed Central

Moore, Jason H; Amos, Ryan; Kiralis, Jeff; Andrews, Peter C

2015-01-01

Simulation plays an essential role in the development of new computational and statistical methods for the genetic analysis of complex traits. Most simulations start with a statistical model using methods such as linear or logistic regression that specify the relationship between genotype and phenotype. This is appealing due to its simplicity and because these statistical methods are commonly used in genetic analysis. It is our working hypothesis that simulations need to move beyond simple statistical models to more realistically represent the biological complexity of genetic architecture. The goal of the present study was to develop a prototype genotype–phenotype simulation method and software that are capable of simulating complex genetic effects within the context of a hierarchical biology-based framework. Specifically, our goal is to simulate multilocus epistasis or gene–gene interaction where the genetic variants are organized within the framework of one or more genes, their regulatory regions and other regulatory loci. We introduce here the Heuristic Identification of Biological Architectures for simulating Complex Hierarchical Interactions (HIBACHI) method and prototype software for simulating data in this manner. This approach combines a biological hierarchy, a flexible mathematical framework, a liability threshold model for defining disease endpoints, and a heuristic search strategy for identifying high-order epistatic models of disease susceptibility. We provide several simulation examples using genetic models exhibiting independent main effects and three-way epistatic effects. PMID:25395175

Ten-year experiences on initial genetic examination in childhood acute lymphoblastic leukaemia in Hungary (1993-2002). Technical approaches and clinical implementation.

PubMed

Olah, Eva; Balogh, Erzsebet; Pajor, Laszlo; Jakab, Zsuzsanna

2011-03-01

A nationwide study was started in 1993 to provide genetic diagnosis for all newly diagnosed childhood ALL cases in Hungary using cytogenetic examination, DNA-index determination, FISH (aneuploidy, ABL/BCR, TEL/AML1) and molecular genetic tests (ABL/BCR, MLL/AF4, TEL/AML1). Aim of the study was to assess the usefulness of different genetic methods, to study the frequency of various aberrations and their prognostic significance. Results were synthesized for genetic subgrouping of patients. To assess the prognostic value of genetic aberrations overall and event-free survival of genetic subgroups were compared using Kaplan-Meier method. Prognostic role of aberrations was investigated by multivariate analysis (Cox's regression) as well in comparison with other factors (age, sex, major congenital abnormalities, initial WBC, therapy, immunophenotype). Five hundred eighty-eight ALL cases were diagnosed between 1993-2002. Cytogenetic examination was performed in 537 (91%) (success rate 73%), DNA-index in 265 (45%), FISH in 74 (13%), TEL/AML1 RT-PCR in 219 (37%) cases producing genetic diagnosis in 457 patients (78%). Proportion of subgroups with good prognosis in prae-B-cell ALL was lower than expected: hyperdiploidB 18% (73/400), TEL/AML1+ 9% (36/400). Univariate analysis showed significantly better 5-year EFS in TEL/AML1+ (82%) and hyperdiploidB cases (78%) than in tetraploid (44%) or pseudodiploid (52%) subgroups. By multivariate analysis main negative prognostic factors were: congenital abnormalities, high WBC, delay in therapy, specific translocations. Complementary use of each of genetic methods used is necessary for reliable genetic diagnosis according to the algorithm presented. Specific genetic alterations proved to be of prognostic significance.

Pedigree data analysis with crossover interference.

PubMed Central

Browning, Sharon

2003-01-01

We propose a new method for calculating probabilities for pedigree genetic data that incorporates crossover interference using the chi-square models. Applications include relationship inference, genetic map construction, and linkage analysis. The method is based on importance sampling of unobserved inheritance patterns conditional on the observed genotype data and takes advantage of fast algorithms for no-interference models while using reweighting to allow for interference. We show that the method is effective for arbitrarily many markers with small pedigrees. PMID:12930760

Recruitment and Participation of Recreational Runners in a Large Epidemiological and Genetic Research Study: Retrospective Data Analysis

PubMed Central

Manzanero, Silvia; Kozlovskaia, Maria; Vlahovich, Nicole

2018-01-01

Background With the increasing capacity for remote collection of both data and samples for medical research, a thorough assessment is needed to determine the association of population characteristics and recruitment methodologies with response rates. Objective The aim of this research was to assess population representativeness in a two-stage study of health and injury in recreational runners, which consisted of an epidemiological arm and genetic analysis. Methods The cost and success of various classical and internet-based methods were analyzed, and demographic representativeness was assessed for recruitment to the epidemiological survey, reported willingness to participate in the genetic arm of the study, actual participation, sample return, and approval for biobank storage. Results A total of 4965 valid responses were received, of which 1664 were deemed eligible for genetic analysis. Younger age showed a negative association with initial recruitment rate, expressed willingness to participate in genetic analysis, and actual participation. Additionally, female sex was associated with higher initial recruitment rates, and ethnic origin impacted willingness to participate in the genetic analysis (all P<.001). Conclusions The sharp decline in retention through the different stages of the study in young respondents suggests the necessity to develop specific recruitment and retention strategies when investigating a young, physically active population. PMID:29792293

Genetic evaluation of mastitis liability and recovery through longitudinal analysis of transition probabilities

PubMed Central

2012-01-01

Background Many methods for the genetic analysis of mastitis use a cross-sectional approach, which omits information on, e.g., repeated mastitis cases during lactation, somatic cell count fluctuations, and recovery process. Acknowledging the dynamic behavior of mastitis during lactation and taking into account that there is more than one binary response variable to consider, can enhance the genetic evaluation of mastitis. Methods Genetic evaluation of mastitis was carried out by modeling the dynamic nature of somatic cell count (SCC) within the lactation. The SCC patterns were captured by modeling transition probabilities between assumed states of mastitis and non-mastitis. A widely dispersed SCC pattern generates high transition probabilities between states and vice versa. This method can model transitions to and from states of infection simultaneously, i.e. both the mastitis liability and the recovery process are considered. A multilevel discrete time survival model was applied to estimate breeding values on simulated data with different dataset sizes, mastitis frequencies, and genetic correlations. Results Correlations between estimated and simulated breeding values showed that the estimated accuracies for mastitis liability were similar to those from previously tested methods that used data of confirmed mastitis cases, while our results were based on SCC as an indicator of mastitis. In addition, unlike the other methods, our method also generates breeding values for the recovery process. Conclusions The developed method provides an effective tool for the genetic evaluation of mastitis when considering the whole disease course and will contribute to improving the genetic evaluation of udder health. PMID:22475575

Gene Level Meta-Analysis of Quantitative Traits by Functional Linear Models.

PubMed

Fan, Ruzong; Wang, Yifan; Boehnke, Michael; Chen, Wei; Li, Yun; Ren, Haobo; Lobach, Iryna; Xiong, Momiao

2015-08-01

Meta-analysis of genetic data must account for differences among studies including study designs, markers genotyped, and covariates. The effects of genetic variants may differ from population to population, i.e., heterogeneity. Thus, meta-analysis of combining data of multiple studies is difficult. Novel statistical methods for meta-analysis are needed. In this article, functional linear models are developed for meta-analyses that connect genetic data to quantitative traits, adjusting for covariates. The models can be used to analyze rare variants, common variants, or a combination of the two. Both likelihood-ratio test (LRT) and F-distributed statistics are introduced to test association between quantitative traits and multiple variants in one genetic region. Extensive simulations are performed to evaluate empirical type I error rates and power performance of the proposed tests. The proposed LRT and F-distributed statistics control the type I error very well and have higher power than the existing methods of the meta-analysis sequence kernel association test (MetaSKAT). We analyze four blood lipid levels in data from a meta-analysis of eight European studies. The proposed methods detect more significant associations than MetaSKAT and the P-values of the proposed LRT and F-distributed statistics are usually much smaller than those of MetaSKAT. The functional linear models and related test statistics can be useful in whole-genome and whole-exome association studies. Copyright © 2015 by the Genetics Society of America.

Genetic Analysis of 430 Chinese Cynodon dactylon Accessions Using Sequence-Related Amplified Polymorphism Markers

PubMed Central

Huang, Chunqiong; Liu, Guodao; Bai, Changjun; Wang, Wenqiang

2014-01-01

Although Cynodon dactylon (C. dactylon) is widely distributed in China, information on its genetic diversity within the germplasm pool is limited. The objective of this study was to reveal the genetic variation and relationships of 430 C. dactylon accessions collected from 22 Chinese provinces using sequence-related amplified polymorphism (SRAP) markers. Fifteen primer pairs were used to amplify specific C. dactylon genomic sequences. A total of 481 SRAP fragments were generated, with fragment sizes ranging from 260–1800 base pairs (bp). Genetic similarity coefficients (GSC) among the 430 accessions averaged 0.72 and ranged from 0.53–0.96. Cluster analysis conducted by two methods, namely the unweighted pair-group method with arithmetic averages (UPGMA) and principle coordinate analysis (PCoA), separated the accessions into eight distinct groups. Our findings verify that Chinese C. dactylon germplasms have rich genetic diversity, which is an excellent basis for C. dactylon breeding for new cultivars. PMID:25338051

A comparison of regression methods for model selection in individual-based landscape genetic analysis.

PubMed

Shirk, Andrew J; Landguth, Erin L; Cushman, Samuel A

2018-01-01

Anthropogenic migration barriers fragment many populations and limit the ability of species to respond to climate-induced biome shifts. Conservation actions designed to conserve habitat connectivity and mitigate barriers are needed to unite fragmented populations into larger, more viable metapopulations, and to allow species to track their climate envelope over time. Landscape genetic analysis provides an empirical means to infer landscape factors influencing gene flow and thereby inform such conservation actions. However, there are currently many methods available for model selection in landscape genetics, and considerable uncertainty as to which provide the greatest accuracy in identifying the true landscape model influencing gene flow among competing alternative hypotheses. In this study, we used population genetic simulations to evaluate the performance of seven regression-based model selection methods on a broad array of landscapes that varied by the number and type of variables contributing to resistance, the magnitude and cohesion of resistance, as well as the functional relationship between variables and resistance. We also assessed the effect of transformations designed to linearize the relationship between genetic and landscape distances. We found that linear mixed effects models had the highest accuracy in every way we evaluated model performance; however, other methods also performed well in many circumstances, particularly when landscape resistance was high and the correlation among competing hypotheses was limited. Our results provide guidance for which regression-based model selection methods provide the most accurate inferences in landscape genetic analysis and thereby best inform connectivity conservation actions. Published 2017. This article is a U.S. Government work and is in the public domain in the USA.

Integrative analysis of gene expression and copy number alterations using canonical correlation analysis.

PubMed

Soneson, Charlotte; Lilljebjörn, Henrik; Fioretos, Thoas; Fontes, Magnus

2010-04-15

With the rapid development of new genetic measurement methods, several types of genetic alterations can be quantified in a high-throughput manner. While the initial focus has been on investigating each data set separately, there is an increasing interest in studying the correlation structure between two or more data sets. Multivariate methods based on Canonical Correlation Analysis (CCA) have been proposed for integrating paired genetic data sets. The high dimensionality of microarray data imposes computational difficulties, which have been addressed for instance by studying the covariance structure of the data, or by reducing the number of variables prior to applying the CCA. In this work, we propose a new method for analyzing high-dimensional paired genetic data sets, which mainly emphasizes the correlation structure and still permits efficient application to very large data sets. The method is implemented by translating a regularized CCA to its dual form, where the computational complexity depends mainly on the number of samples instead of the number of variables. The optimal regularization parameters are chosen by cross-validation. We apply the regularized dual CCA, as well as a classical CCA preceded by a dimension-reducing Principal Components Analysis (PCA), to a paired data set of gene expression changes and copy number alterations in leukemia. Using the correlation-maximizing methods, regularized dual CCA and PCA+CCA, we show that without pre-selection of known disease-relevant genes, and without using information about clinical class membership, an exploratory analysis singles out two patient groups, corresponding to well-known leukemia subtypes. Furthermore, the variables showing the highest relevance to the extracted features agree with previous biological knowledge concerning copy number alterations and gene expression changes in these subtypes. Finally, the correlation-maximizing methods are shown to yield results which are more biologically interpretable than those resulting from a covariance-maximizing method, and provide different insight compared to when each variable set is studied separately using PCA. We conclude that regularized dual CCA as well as PCA+CCA are useful methods for exploratory analysis of paired genetic data sets, and can be efficiently implemented also when the number of variables is very large.

Alignment-free genetic sequence comparisons: a review of recent approaches by word analysis

PubMed Central

Steele, Joe; Bastola, Dhundy

2014-01-01

Modern sequencing and genome assembly technologies have provided a wealth of data, which will soon require an analysis by comparison for discovery. Sequence alignment, a fundamental task in bioinformatics research, may be used but with some caveats. Seminal techniques and methods from dynamic programming are proving ineffective for this work owing to their inherent computational expense when processing large amounts of sequence data. These methods are prone to giving misleading information because of genetic recombination, genetic shuffling and other inherent biological events. New approaches from information theory, frequency analysis and data compression are available and provide powerful alternatives to dynamic programming. These new methods are often preferred, as their algorithms are simpler and are not affected by synteny-related problems. In this review, we provide a detailed discussion of computational tools, which stem from alignment-free methods based on statistical analysis from word frequencies. We provide several clear examples to demonstrate applications and the interpretations over several different areas of alignment-free analysis such as base–base correlations, feature frequency profiles, compositional vectors, an improved string composition and the D2 statistic metric. Additionally, we provide detailed discussion and an example of analysis by Lempel–Ziv techniques from data compression. PMID:23904502

Coherent spectroscopic methods for monitoring pathogens, genetically modified products and nanostructured materials in colloidal solution

NASA Astrophysics Data System (ADS)

Moguilnaya, T.; Suminov, Y.; Botikov, A.; Ignatov, S.; Kononenko, A.; Agibalov, A.

2017-01-01

We developed the new automatic method that combines the method of forced luminescence and stimulated Brillouin scattering. This method is used for monitoring pathogens, genetically modified products and nanostructured materials in colloidal solution. We carried out the statistical spectral analysis of pathogens, genetically modified soy and nano-particles of silver in water from different regions in order to determine the statistical errors of the method. We studied spectral characteristics of these objects in water to perform the initial identification with 95% probability. These results were used for creation of the model of the device for monitor of pathogenic organisms and working model of the device to determine the genetically modified soy in meat.

A set of autosomal multiple InDel markers for forensic application and population genetic analysis in the Chinese Xinjiang Hui group.

PubMed

Xie, Tong; Guo, Yuxin; Chen, Ling; Fang, Yating; Tai, Yunchun; Zhou, Yongsong; Qiu, Pingming; Zhu, Bofeng

2018-07-01

In recent years, insertion/deletion (InDel) markers have become a promising and useful supporting tool in forensic identification cases and biogeographic research field. In this study, 30 InDel loci were explored to reveal the genetic diversities and genetic relationships between Chinese Xinjiang Hui group and the 25 previously reported populations using various biostatistics methods such as forensic statistical parameter analysis, phylogenetic reconstruction, multi-dimensional scaling, principal component analysis, and STRUCTURE analysis. No deviations from Hardy-Weinberg equilibrium tests were found at all 30 loci in the Chinese Xinjiang Hui group. The observed heterozygosity and expected heterozygosity ranged from 0.1971 (HLD118) to 0.5092 (HLD92), 0.2222 (HLD118) to 0.5000 (HLD6), respectively. The cumulative probability of exclusion and combined power of discrimination were 0.988849 and 0.99999999999378, respectively, which indicated that these 30 loci could be qualified for personal identification and used as complementary genetic markers for paternity tests in forensic cases. The results of present research based on the different methods of population genetic analysis revealed that the Chinese Xinjiang Hui group had close relationships with most Chinese groups, especially Han populations. In spite of this, for a better understanding of genetic background of the Chinese Xinjiang Hui group, more molecular genetic markers such as ancestry informative markers, single nucleotide polymorphisms (SNPs), and copy number variations will be conducted in future studies. Copyright © 2018 Elsevier B.V. All rights reserved.

Genetic Diversity and Association Studies in US Hispanic/Latino Populations: Applications in the Hispanic Community Health Study/Study of Latinos

PubMed Central

Conomos, Matthew P.; Laurie, Cecelia A.; Stilp, Adrienne M.; Gogarten, Stephanie M.; McHugh, Caitlin P.; Nelson, Sarah C.; Sofer, Tamar; Fernández-Rhodes, Lindsay; Justice, Anne E.; Graff, Mariaelisa; Young, Kristin L.; Seyerle, Amanda A.; Avery, Christy L.; Taylor, Kent D.; Rotter, Jerome I.; Talavera, Gregory A.; Daviglus, Martha L.; Wassertheil-Smoller, Sylvia; Schneiderman, Neil; Heiss, Gerardo; Kaplan, Robert C.; Franceschini, Nora; Reiner, Alex P.; Shaffer, John R.; Barr, R. Graham; Kerr, Kathleen F.; Browning, Sharon R.; Browning, Brian L.; Weir, Bruce S.; Avilés-Santa, M. Larissa; Papanicolaou, George J.; Lumley, Thomas; Szpiro, Adam A.; North, Kari E.; Rice, Ken; Thornton, Timothy A.; Laurie, Cathy C.

2016-01-01

US Hispanic/Latino individuals are diverse in genetic ancestry, culture, and environmental exposures. Here, we characterized and controlled for this diversity in genome-wide association studies (GWASs) for the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). We simultaneously estimated population-structure principal components (PCs) robust to familial relatedness and pairwise kinship coefficients (KCs) robust to population structure, admixture, and Hardy-Weinberg departures. The PCs revealed substantial genetic differentiation within and among six self-identified background groups (Cuban, Dominican, Puerto Rican, Mexican, and Central and South American). To control for variation among groups, we developed a multi-dimensional clustering method to define a “genetic-analysis group” variable that retains many properties of self-identified background while achieving substantially greater genetic homogeneity within groups and including participants with non-specific self-identification. In GWASs of 22 biomedical traits, we used a linear mixed model (LMM) including pairwise empirical KCs to account for familial relatedness, PCs for ancestry, and genetic-analysis groups for additional group-associated effects. Including the genetic-analysis group as a covariate accounted for significant trait variation in 8 of 22 traits, even after we fit 20 PCs. Additionally, genetic-analysis groups had significant heterogeneity of residual variance for 20 of 22 traits, and modeling this heteroscedasticity within the LMM reduced genomic inflation for 19 traits. Furthermore, fitting an LMM that utilized a genetic-analysis group rather than a self-identified background group achieved higher power to detect previously reported associations. We expect that the methods applied here will be useful in other studies with multiple ethnic groups, admixture, and relatedness. PMID:26748518

Disease Modeling via Large-Scale Network Analysis

DTIC Science & Technology

2015-05-20

SECURITY CLASSIFICATION OF: A central goal of genetics is to learn how the genotype of an organism determines its phenotype. We address the implicit...guarantees for the methods. In the past, we have developed predictive methods general enough to apply to potentially any genetic trait, varying from... genetics is to learn how the genotype of an organism determines its phenotype. We address the implicit problem of predicting the association of genes with

Querying Large Biological Network Datasets

ERIC Educational Resources Information Center

Gulsoy, Gunhan

2013-01-01

New experimental methods has resulted in increasing amount of genetic interaction data to be generated every day. Biological networks are used to store genetic interaction data gathered. Increasing amount of data available requires fast large scale analysis methods. Therefore, we address the problem of querying large biological network datasets.…

Understanding genetics: Analysis of secondary students' conceptual status

NASA Astrophysics Data System (ADS)

Tsui, Chi-Yan; Treagust, David F.

2007-02-01

This article explores the conceptual change of students in Grades 10 and 12 in three Australian senior high schools when the teachers included computer multimedia to a greater or lesser extent in their teaching of a genetics course. The study, underpinned by a multidimensional conceptual-change framework, used an interpretive approach and a case-based design with multiple data collection methods. Over 4-8 weeks, the students learned genetics in classroom lessons that included BioLogica activities, which feature multiple representations. Results of the online tests and interview tasks revealed that most students improved their understanding of genetics as evidenced in the development of genetics reasoning. However, using Thorley's (1990) status analysis categories, a cross-case analysis of the gene conceptions of 9 of the 26 students interviewed indicated that only 4 students' postinstructional conceptions were intelligible-plausible-fruitful. Students' conceptual change was consistent with classroom teaching and learning. Findings suggested that multiple representations supported conceptual understanding of genetics but not in all students. It was also shown that status can be a viable hallmark enabling researchers to identify students' conceptual change that would otherwise be less accessible. Thorley's method for analyzing conceptual status is discussed.

Genetic diversity and association analysis of leafminer (Liriomyza langei) resistance in spinach (Spinacia oleracea)

USDA-ARS?s Scientific Manuscript database

Leafminer (Liriomyza spp.) is a major insect pest of many important agricultural crops, including spinach (Spinacia oleracea). Use of genetic resistance is an efficient, economic and environment-friendly method to control this pest. The objective of this research was to conduct association analysis ...

Genetics and epidemiology, congenital anomalies and cancer

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

Friedman, J.M.

1997-03-01

Many of the basic statistical methods used in epidemiology - regression, analysis of variance, and estimation of relative risk, for example - originally were developed for the genetic analysis of biometric data. The familiarity that many geneticists have with this methodology has helped geneticists to understand and accept genetic epidemiology as a scientific discipline. It worth noting, however, that most of the work in genetic epidemiology during the past decade has been devoted to linkage and other family studies, rather than to population-based investigations of the type that characterize much of mainstream epidemiology. 30 refs., 2 tabs.

Pathway Analysis in Attention Deficit Hyperactivity Disorder: An Ensemble Approach

PubMed Central

Mooney, Michael A.; McWeeney, Shannon K.; Faraone, Stephen V.; Hinney, Anke; Hebebrand, Johannes; Nigg, Joel T.; Wilmot, Beth

2016-01-01

Despite a wealth of evidence for the role of genetics in attention deficit hyperactivity disorder (ADHD), specific and definitive genetic mechanisms have not been identified. Pathway analyses, a subset of gene-set analyses, extend the knowledge gained from genome-wide association studies (GWAS) by providing functional context for genetic associations. However, there are numerous methods for association testing of gene sets and no real consensus regarding the best approach. The present study applied six pathway analysis methods to identify pathways associated with ADHD in two GWAS datasets from the Psychiatric Genomics Consortium. Methods that utilize genotypes to model pathway-level effects identified more replicable pathway associations than methods using summary statistics. In addition, pathways implicated by more than one method were significantly more likely to replicate. A number of brain-relevant pathways, such as RhoA signaling, glycosaminoglycan biosynthesis, fibroblast growth factor receptor activity, and pathways containing potassium channel genes, were nominally significant by multiple methods in both datasets. These results support previous hypotheses about the role of regulation of neurotransmitter release, neurite outgrowth and axon guidance in contributing to the ADHD phenotype and suggest the value of cross-method convergence in evaluating pathway analysis results. PMID:27004716

Gene and genetic diagnostic method patent claims: a comparison under current European and US patent law

PubMed Central

Huys, Isabelle; Van Overwalle, Geertrui; Matthijs, Gert

2011-01-01

The paper focuses on the fundamental debate that is going on in Europe and the United States about whether genes and genetic diagnostic methods are to be regarded as inventions or subject matter eligible for patent protection, or whether they are discoveries or principles of nature and thus excluded from patentability. The study further explores some possible scenarios of American influences on European patent applications with respect to genetic diagnostic methods. Our analysis points out that patent eligibility for genes and genetic diagnostic methods, as discussed in the United States in the Association of Molecular Pathology versus US Patent and Trademark Office decision, is based on a different reasoning compared with the European Patent Convention. PMID:21654725

Gene and genetic diagnostic method patent claims: a comparison under current European and US patent law.

PubMed

Huys, Isabelle; Van Overwalle, Geertrui; Matthijs, Gert

2011-10-01

The paper focuses on the fundamental debate that is going on in Europe and the United States about whether genes and genetic diagnostic methods are to be regarded as inventions or subject matter eligible for patent protection, or whether they are discoveries or principles of nature and thus excluded from patentability. The study further explores some possible scenarios of American influences on European patent applications with respect to genetic diagnostic methods. Our analysis points out that patent eligibility for genes and genetic diagnostic methods, as discussed in the United States in the Association of Molecular Pathology versus US Patent and Trademark Office decision, is based on a different reasoning compared with the European Patent Convention.

Marker-based quantitative genetics in the wild?: the heritability and genetic correlation of chemical defenses in eucalyptus.

PubMed

Andrew, R L; Peakall, R; Wallis, I R; Wood, J T; Knight, E J; Foley, W J

2005-12-01

Marker-based methods for estimating heritability and genetic correlation in the wild have attracted interest because traditional methods may be impractical or introduce bias via G x E effects, mating system variation, and sampling effects. However, they have not been widely used, especially in plants. A regression-based approach, which uses a continuous measure of genetic relatedness, promises to be particularly appropriate for use in plants with mixed-mating systems and overlapping generations. Using this method, we found significant narrow-sense heritability of foliar defense chemicals in a natural population of Eucalyptus melliodora. We also demonstrated a genetic basis for the phenotypic correlation underlying an ecological example of conditioned flavor aversion involving different biosynthetic pathways. Our results revealed that heritability estimates depend on the spatial scale of the analysis in a way that offers insight into the distribution of genetic and environmental variance. This study is the first to successfully use a marker-based method to measure quantitative genetic parameters in a tree. We suggest that this method will prove to be a useful tool in other studies and offer some recommendations for future applications of the method.

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.

Effect of genetic algorithm as a variable selection method on different chemometric models applied for the analysis of binary mixture of amoxicillin and flucloxacillin: A comparative study

NASA Astrophysics Data System (ADS)

Attia, Khalid A. M.; Nassar, Mohammed W. I.; El-Zeiny, Mohamed B.; Serag, Ahmed

2016-03-01

Different chemometric models were applied for the quantitative analysis of amoxicillin (AMX), and flucloxacillin (FLX) in their binary mixtures, namely, partial least squares (PLS), spectral residual augmented classical least squares (SRACLS), concentration residual augmented classical least squares (CRACLS) and artificial neural networks (ANNs). All methods were applied with and without variable selection procedure (genetic algorithm GA). The methods were used for the quantitative analysis of the drugs in laboratory prepared mixtures and real market sample via handling the UV spectral data. Robust and simpler models were obtained by applying GA. The proposed methods were found to be rapid, simple and required no preliminary separation steps.

Alignment-free genetic sequence comparisons: a review of recent approaches by word analysis.

PubMed

Bonham-Carter, Oliver; Steele, Joe; Bastola, Dhundy

2014-11-01

Modern sequencing and genome assembly technologies have provided a wealth of data, which will soon require an analysis by comparison for discovery. Sequence alignment, a fundamental task in bioinformatics research, may be used but with some caveats. Seminal techniques and methods from dynamic programming are proving ineffective for this work owing to their inherent computational expense when processing large amounts of sequence data. These methods are prone to giving misleading information because of genetic recombination, genetic shuffling and other inherent biological events. New approaches from information theory, frequency analysis and data compression are available and provide powerful alternatives to dynamic programming. These new methods are often preferred, as their algorithms are simpler and are not affected by synteny-related problems. In this review, we provide a detailed discussion of computational tools, which stem from alignment-free methods based on statistical analysis from word frequencies. We provide several clear examples to demonstrate applications and the interpretations over several different areas of alignment-free analysis such as base-base correlations, feature frequency profiles, compositional vectors, an improved string composition and the D2 statistic metric. Additionally, we provide detailed discussion and an example of analysis by Lempel-Ziv techniques from data compression. © The Author 2013. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Genetic structure of populations and differentiation in forest trees

Treesearch

Raymond P. Guries; F. Thomas Ledig

1981-01-01

Electrophoretic techniques permit population biologists to analyze genetic structure of natural populations by using large numbers of allozyme loci. Several methods of analysis have been applied to allozyme data, including chi-square contingency tests, F-statistics, and genetic distance. This paper compares such statistics for pitch pine (Pinus rigida...

Some Conceptual Deficiencies in "Developmental" Behavior Genetics.

ERIC Educational Resources Information Center

Gottlieb, Gilbert

1995-01-01

Criticizes the application of the statistical procedures of the population-genetic approach within evolutionary biology to the study of psychological development. Argues that the application of the statistical methods of population genetics--primarily the analysis of variance--to the causes of psychological development is bound to result in a…

Evaluation of genetic diversity among soybean (Glycine max) genotypes using univariate and multivariate analysis.

PubMed

Oliveira, M M; Sousa, L B; Reis, M C; Silva Junior, E G; Cardoso, D B O; Hamawaki, O T; Nogueira, A P O

2017-05-31

The genetic diversity study has paramount importance in breeding programs; hence, it allows selection and choice of the parental genetic divergence, which have the agronomic traits desired by the breeder. This study aimed to characterize the genetic divergence between 24 soybean genotypes through their agronomic traits, using multivariate clustering methods to select the potential genitors for the promising hybrid combinations. Six agronomic traits evaluated were number of days to flowering and maturity, plant height at flowering and maturity, insertion height of the first pod, and yield. The genetic divergence evaluated by multivariate analysis that esteemed first the Mahalanobis' generalized distance (D 2 ), then the clustering using Tocher's optimization methods, and then the unweighted pair group method with arithmetic average (UPGMA). Tocher's optimization method and the UPGMA agreed with the groups' constitution between each other, the formation of eight distinct groups according Tocher's method and seven distinct groups using UPGMA. The trait number of days for flowering (45.66%) was the most efficient to explain dissimilarity between genotypes, and must be one of the main traits considered by the breeder in the moment of genitors choice in soybean-breeding programs. The genetic variability allowed the identification of dissimilar genotypes and with superior performances. The hybridizations UFU 18 x UFUS CARAJÁS, UFU 15 x UFU 13, and UFU 13 x UFUS CARAJÁS are promising to obtain superior segregating populations, which enable the development of more productive genotypes.

Ciona Genetics

PubMed Central

Veeman, Michael T.; Chiba, Shota; Smith, William C.

2010-01-01

Ascidians, such as Ciona, are invertebrate chordates with simple embryonic body plans and small, relatively non-redundant genomes. Ciona genetics is in its infancy compared to many other model systems, but it provides a powerful method for studying this important vertebrate outgroup. Here we give basic methods for genetic analysis of Ciona, including protocols for controlled crosses both by natural spawning and by the surgical isolation of gametes; the identification and propagation of mutant lines; and strategies for positional cloning. PMID:21805273

PICALM gene rs3851179 polymorphism contributes to Alzheimer's disease in an Asian population.

PubMed

Liu, Guiyou; Zhang, Shuyan; Cai, Zhiyou; Ma, Guoda; Zhang, Liangcai; Jiang, Yongshuai; Feng, Rennan; Liao, Mingzhi; Chen, Zugen; Zhao, Bin; Li, Keshen

2013-06-01

PICALM gene rs3851179 polymorphism was reported to an Alzheimer's disease (AD) susceptibility locus in a Caucasian population. However, recent studies reported consistent and inconsistent results in an Asian population. Four studies indicated no association between rs3851179 and AD in a Chinese population and one study reported weak association in a Japanese population. We consider that the failure to replicate the significant association between rs3851179 and AD may be caused by at least two reasons. The first reason may be the genetic heterogeneity in AD among different populations, and the second may be the relatively small sample size compared with large-scale GWAS in Caucasian ancestry. In order to confirm this view, in this research, we first evaluated the genetic heterogeneity of rs3851179 polymorphism in Caucasian and Asian populations. We then investigated rs3851179 polymorphism in an Asian population by a pooled analysis method and a meta-analysis method. We did not observe significant genetic heterogeneity of rs3851179 in the Caucasian and Asian populations. Our results indicate that rs3851179 polymorphism is significantly associated with AD in the Asian population by both pooled analysis and meta-analysis methods. We believe that our findings will be very useful for future genetic studies in AD.

Genetic interaction analysis of point mutations enables interrogation of gene function at a residue-level resolution

PubMed Central

Braberg, Hannes; Moehle, Erica A.; Shales, Michael; Guthrie, Christine; Krogan, Nevan J.

2014-01-01

We have achieved a residue-level resolution of genetic interaction mapping – a technique that measures how the function of one gene is affected by the alteration of a second gene – by analyzing point mutations. Here, we describe how to interpret point mutant genetic interactions, and outline key applications for the approach, including interrogation of protein interaction interfaces and active sites, and examination of post-translational modifications. Genetic interaction analysis has proven effective for characterizing cellular processes; however, to date, systematic high-throughput genetic interaction screens have relied on gene deletions or knockdowns, which limits the resolution of gene function analysis and poses problems for multifunctional genes. Our point mutant approach addresses these issues, and further provides a tool for in vivo structure-function analysis that complements traditional biophysical methods. We also discuss the potential for genetic interaction mapping of point mutations in human cells and its application to personalized medicine. PMID:24842270

Half of 23 Belgian dog breeds has a compromised genetic diversity, as revealed by genealogical and molecular data analysis.

PubMed

Wijnrocx, K; François, L; Stinckens, A; Janssens, S; Buys, N

2016-10-01

The genetic diversity in 23 dog breeds raised in Belgium was investigated using both genealogical analysis and microsatellite markers. Some of these breeds are native breeds, with only small populations maintained. Pedigree and molecular data, obtained from the Belgian kennel club, were used to calculate the inbreeding coefficients, realised effective population size as well as probabilities of gene origin and average observed heterozygosity. Inbreeding coefficients ranged from 0.8 to 44.7% and realised effective population size varied between 3.2 and 829.1, according to the used method and breed. Mean observed heterozygosity ranged from 0.47 to 0.73. Both pedigree and molecular methods reveal low genetic diversity and presence of bottlenecks, especially in native Belgian breeds with small population sizes. Furthermore, principal component analysis on the set of investigated diversity parameters revealed no groups of breeds that could be identified in which similar breeding strategies could be applied to maintain genetic diversity. © 2016 Blackwell Verlag GmbH.

Measurement of the distribution of non-structural carbohydrate composition in onion populations by a high-throughput microplate enzymatic assay.

PubMed

Revanna, Roopashree; Turnbull, Matthew H; Shaw, Martin L; Wright, Kathryn M; Butler, Ruth C; Jameson, Paula E; McCallum, John A

2013-08-15

Non-structural carbohydrate (NSC; glucose, fructose, sucrose and fructan) composition of onions (Allium cepa L.) varies widely and is a key determinant of market usage. To analyse the physiology and genetics of onion carbohydrate metabolism and to enable selective breeding, an inexpensive, reliable and practicable sugar assay is required to phenotype large numbers of samples. A rapid, reliable and cost-effective microplate-based assay was developed for NSC analysis in onions and used to characterise variation in tissue hexose, sucrose and fructan content in open-pollinated breeding populations and in mapping populations developed from a wide onion cross. Sucrose measured in microplates employing maltase as a hydrolytic enzyme was in agreement with HPLC-PAD results. The method revealed significant variation in bulb fructan content within open-pollinated 'Pukekohe Longkeeper' breeding populations over a threefold range. Very wide segregation from 80 to 600 g kg(-1) in fructan content was observed in bulbs of F2 genetic mapping populations from the wide onion cross 'Nasik Red × CUDH2150'. The microplate enzymatic assay is a reliable and practicable method for onion sugar analysis for genetics, breeding and food technology. Open-pollinated onion populations may harbour extensive within-population variability in carbohydrate content, which may be quantified and exploited using this method. The phenotypic data obtained from genetic mapping populations show that the method is well suited to detailed genetic and physiological analysis. © 2013 Society of Chemical Industry.

Melanocortin-1 receptor, skin cancer and phenotypic characteristics (M-SKIP) project: study design and methods for pooling results of genetic epidemiological studies

PubMed Central

2012-01-01

Background For complex diseases like cancer, pooled-analysis of individual data represents a powerful tool to investigate the joint contribution of genetic, phenotypic and environmental factors to the development of a disease. Pooled-analysis of epidemiological studies has many advantages over meta-analysis, and preliminary results may be obtained faster and with lower costs than with prospective consortia. Design and methods Based on our experience with the study design of the Melanocortin-1 receptor (MC1R) gene, SKin cancer and Phenotypic characteristics (M-SKIP) project, we describe the most important steps in planning and conducting a pooled-analysis of genetic epidemiological studies. We then present the statistical analysis plan that we are going to apply, giving particular attention to methods of analysis recently proposed to account for between-study heterogeneity and to explore the joint contribution of genetic, phenotypic and environmental factors in the development of a disease. Within the M-SKIP project, data on 10,959 skin cancer cases and 14,785 controls from 31 international investigators were checked for quality and recoded for standardization. We first proposed to fit the aggregated data with random-effects logistic regression models. However, for the M-SKIP project, a two-stage analysis will be preferred to overcome the problem regarding the availability of different study covariates. The joint contribution of MC1R variants and phenotypic characteristics to skin cancer development will be studied via logic regression modeling. Discussion Methodological guidelines to correctly design and conduct pooled-analyses are needed to facilitate application of such methods, thus providing a better summary of the actual findings on specific fields. PMID:22862891

A Method for Inferring an Individual’s Genetic Ancestry and Degree of Admixture Associated with Six Major Continental Populations

PubMed Central

Libiger, Ondrej; Schork, Nicholas J.

2013-01-01

The determination of the ancestry and genetic backgrounds of the subjects in genetic and general epidemiology studies is a crucial component in the analysis of relevant outcomes or associations. Although there are many methods for differentiating ancestral subgroups among individuals based on genetic markers only a few of these methods provide actual estimates of the fraction of an individual’s genome that is likely to be associated with different ancestral populations. We propose a method for assigning ancestry that works in stages to refine estimates of ancestral population contributions to individual genomes. The method leverages genotype data in the public domain obtained from individuals with known ancestries. Although we showcase the method in the assessment of ancestral genome proportions leveraging largely continental populations, the strategy can be used for assessing within-continent or more subtle ancestral origins with the appropriate data. PMID:23335941

The Information Content of Discrete Functions and Their Application in Genetic Data Analysis.

PubMed

Sakhanenko, Nikita A; Kunert-Graf, James; Galas, David J

2017-12-01

The complex of central problems in data analysis consists of three components: (1) detecting the dependence of variables using quantitative measures, (2) defining the significance of these dependence measures, and (3) inferring the functional relationships among dependent variables. We have argued previously that an information theory approach allows separation of the detection problem from the inference of functional form problem. We approach here the third component of inferring functional forms based on information encoded in the functions. We present here a direct method for classifying the functional forms of discrete functions of three variables represented in data sets. Discrete variables are frequently encountered in data analysis, both as the result of inherently categorical variables and from the binning of continuous numerical variables into discrete alphabets of values. The fundamental question of how much information is contained in a given function is answered for these discrete functions, and their surprisingly complex relationships are illustrated. The all-important effect of noise on the inference of function classes is found to be highly heterogeneous and reveals some unexpected patterns. We apply this classification approach to an important area of biological data analysis-that of inference of genetic interactions. Genetic analysis provides a rich source of real and complex biological data analysis problems, and our general methods provide an analytical basis and tools for characterizing genetic problems and for analyzing genetic data. We illustrate the functional description and the classes of a number of common genetic interaction modes and also show how different modes vary widely in their sensitivity to noise.

A weighted U statistic for association analyses considering genetic heterogeneity.

PubMed

Wei, Changshuai; Elston, Robert C; Lu, Qing

2016-07-20

Converging evidence suggests that common complex diseases with the same or similar clinical manifestations could have different underlying genetic etiologies. While current research interests have shifted toward uncovering rare variants and structural variations predisposing to human diseases, the impact of heterogeneity in genetic studies of complex diseases has been largely overlooked. Most of the existing statistical methods assume the disease under investigation has a homogeneous genetic effect and could, therefore, have low power if the disease undergoes heterogeneous pathophysiological and etiological processes. In this paper, we propose a heterogeneity-weighted U (HWU) method for association analyses considering genetic heterogeneity. HWU can be applied to various types of phenotypes (e.g., binary and continuous) and is computationally efficient for high-dimensional genetic data. Through simulations, we showed the advantage of HWU when the underlying genetic etiology of a disease was heterogeneous, as well as the robustness of HWU against different model assumptions (e.g., phenotype distributions). Using HWU, we conducted a genome-wide analysis of nicotine dependence from the Study of Addiction: Genetics and Environments dataset. The genome-wide analysis of nearly one million genetic markers took 7h, identifying heterogeneous effects of two new genes (i.e., CYP3A5 and IKBKB) on nicotine dependence. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

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.

A quadratically regularized functional canonical correlation analysis for identifying the global structure of pleiotropy with NGS data

PubMed Central

Zhu, Yun; Fan, Ruzong; Xiong, Momiao

2017-01-01

Investigating the pleiotropic effects of genetic variants can increase statistical power, provide important information to achieve deep understanding of the complex genetic structures of disease, and offer powerful tools for designing effective treatments with fewer side effects. However, the current multiple phenotype association analysis paradigm lacks breadth (number of phenotypes and genetic variants jointly analyzed at the same time) and depth (hierarchical structure of phenotype and genotypes). A key issue for high dimensional pleiotropic analysis is to effectively extract informative internal representation and features from high dimensional genotype and phenotype data. To explore correlation information of genetic variants, effectively reduce data dimensions, and overcome critical barriers in advancing the development of novel statistical methods and computational algorithms for genetic pleiotropic analysis, we proposed a new statistic method referred to as a quadratically regularized functional CCA (QRFCCA) for association analysis which combines three approaches: (1) quadratically regularized matrix factorization, (2) functional data analysis and (3) canonical correlation analysis (CCA). Large-scale simulations show that the QRFCCA has a much higher power than that of the ten competing statistics while retaining the appropriate type 1 errors. To further evaluate performance, the QRFCCA and ten other statistics are applied to the whole genome sequencing dataset from the TwinsUK study. We identify a total of 79 genes with rare variants and 67 genes with common variants significantly associated with the 46 traits using QRFCCA. The results show that the QRFCCA substantially outperforms the ten other statistics. PMID:29040274

A propensity score approach to correction for bias due to population stratification using genetic and non-genetic factors.

PubMed

Zhao, Huaqing; Rebbeck, Timothy R; Mitra, Nandita

2009-12-01

Confounding due to population stratification (PS) arises when differences in both allele and disease frequencies exist in a population of mixed racial/ethnic subpopulations. Genomic control, structured association, principal components analysis (PCA), and multidimensional scaling (MDS) approaches have been proposed to address this bias using genetic markers. However, confounding due to PS can also be due to non-genetic factors. Propensity scores are widely used to address confounding in observational studies but have not been adapted to deal with PS in genetic association studies. We propose a genomic propensity score (GPS) approach to correct for bias due to PS that considers both genetic and non-genetic factors. We compare the GPS method with PCA and MDS using simulation studies. Our results show that GPS can adequately adjust and consistently correct for bias due to PS. Under no/mild, moderate, and severe PS, GPS yielded estimated with bias close to 0 (mean=-0.0044, standard error=0.0087). Under moderate or severe PS, the GPS method consistently outperforms the PCA method in terms of bias, coverage probability (CP), and type I error. Under moderate PS, the GPS method consistently outperforms the MDS method in terms of CP. PCA maintains relatively high power compared to both MDS and GPS methods under the simulated situations. GPS and MDS are comparable in terms of statistical properties such as bias, type I error, and power. The GPS method provides a novel and robust tool for obtaining less-biased estimates of genetic associations that can consider both genetic and non-genetic factors. 2009 Wiley-Liss, Inc.

Genetic Classification of Populations Using Supervised Learning

PubMed Central

Bridges, Michael; Heron, Elizabeth A.; O'Dushlaine, Colm; Segurado, Ricardo; Morris, Derek; Corvin, Aiden; Gill, Michael; Pinto, Carlos

2011-01-01

There are many instances in genetics in which we wish to determine whether two candidate populations are distinguishable on the basis of their genetic structure. Examples include populations which are geographically separated, case–control studies and quality control (when participants in a study have been genotyped at different laboratories). This latter application is of particular importance in the era of large scale genome wide association studies, when collections of individuals genotyped at different locations are being merged to provide increased power. The traditional method for detecting structure within a population is some form of exploratory technique such as principal components analysis. Such methods, which do not utilise our prior knowledge of the membership of the candidate populations. are termed unsupervised. Supervised methods, on the other hand are able to utilise this prior knowledge when it is available. In this paper we demonstrate that in such cases modern supervised approaches are a more appropriate tool for detecting genetic differences between populations. We apply two such methods, (neural networks and support vector machines) to the classification of three populations (two from Scotland and one from Bulgaria). The sensitivity exhibited by both these methods is considerably higher than that attained by principal components analysis and in fact comfortably exceeds a recently conjectured theoretical limit on the sensitivity of unsupervised methods. In particular, our methods can distinguish between the two Scottish populations, where principal components analysis cannot. We suggest, on the basis of our results that a supervised learning approach should be the method of choice when classifying individuals into pre-defined populations, particularly in quality control for large scale genome wide association studies. PMID:21589856

Quantitative trait nucleotide analysis using Bayesian model selection.

PubMed

Blangero, John; Goring, Harald H H; Kent, Jack W; Williams, Jeff T; Peterson, Charles P; Almasy, Laura; Dyer, Thomas D

2005-10-01

Although much attention has been given to statistical genetic methods for the initial localization and fine mapping of quantitative trait loci (QTLs), little methodological work has been done to date on the problem of statistically identifying the most likely functional polymorphisms using sequence data. In this paper we provide a general statistical genetic framework, called Bayesian quantitative trait nucleotide (BQTN) analysis, for assessing the likely functional status of genetic variants. The approach requires the initial enumeration of all genetic variants in a set of resequenced individuals. These polymorphisms are then typed in a large number of individuals (potentially in families), and marker variation is related to quantitative phenotypic variation using Bayesian model selection and averaging. For each sequence variant a posterior probability of effect is obtained and can be used to prioritize additional molecular functional experiments. An example of this quantitative nucleotide analysis is provided using the GAW12 simulated data. The results show that the BQTN method may be useful for choosing the most likely functional variants within a gene (or set of genes). We also include instructions on how to use our computer program, SOLAR, for association analysis and BQTN analysis.

A new method for studying population genetics of cyst nematodes based on Pool-Seq and genomewide allele frequency analysis.

PubMed

Mimee, Benjamin; Duceppe, Marc-Olivier; Véronneau, Pierre-Yves; Lafond-Lapalme, Joël; Jean, Martine; Belzile, François; Bélair, Guy

2015-11-01

Cyst nematodes are important agricultural pests responsible for billions of dollars of losses each year. Plant resistance is the most effective management tool, but it requires a close monitoring of population genetics. Current technologies for pathotyping and genotyping cyst nematodes are time-consuming, expensive and imprecise. In this study, we capitalized on the reproduction mode of cyst nematodes to develop a simple population genetic analysis pipeline based on genotyping-by-sequencing and Pool-Seq. This method yielded thousands of SNPs and allowed us to study the relationships between populations of different origins or pathotypes. Validation of the method on well-characterized populations also demonstrated that it was a powerful and accurate tool for population genetics. The genomewide allele frequencies of 23 populations of golden nematode, from nine countries and representing the five known pathotypes, were compared. A clear separation of the pathotypes and fine genetic relationships between and among global populations were obtained using this method. In addition to being powerful, this tool has proven to be very time- and cost-efficient and could be applied to other cyst nematode species. © 2015 Her Majesty the Queen in Right of Canada Molecular Ecology Resources © 2015 John Wiley & Sons Ltd Reproduced with the permission of the Minister of Agriculture and Agri-food.

Sequence-related amplified polymorphism (SRAP) marker as a new method for identification of endophytic fungi from Taxus.

PubMed

Ren, Na; Liu, Jiajia; Yang, Dongliang; Chen, Jianhua; Luan, Mingbao; Hong, Juan

2012-01-01

A total of 20 endophytic fungi stains were classified into four groups using traditional morphological identification method, and were studied for genetic diversity by sequence-related amplified polymorphism (SRAP) technique. Genomic DNA (deoxyribonucleic acid) of these strains was extracted with CTAB method. SRAP analysis was done with 24 pairs of primers. All strains could be uniquely distinguished with 584 bands and 446 polymorphism bands which generated 76.4% of polymorphic ratio. Unweighted pair-group method with arithmetical averages cluster analysis enabled construction of a dendrogram for estimating genetic distances between different strains. All strains, which were just divided into four groups by traditional morphology identification, were clustered into four major groups at GS = 0.603 and further separated into eight sub-groups at GS = 0.921. Dendrogram also revealed a large genetic variation in 20 strains; different primer combinations allowed them distinctly distinguished one from others with relatively low genetic similarity. The results show that the SRAP technology is more efficient than traditional morphology identification. It is found that SRAP markers could more really reflect the genetic diversity of endophytic fungi strains from Taxus, and also could be used as a method for identification of endophytic fungi from Taxus. It also suggests that SRAP can be used to establish foundation for further screening of taxol-producing endophytic fungi strains which can produce high levels of paclitaxel.

Mendelian randomization analysis of a time-varying exposure for binary disease outcomes using functional data analysis methods.

PubMed

Cao, Ying; Rajan, Suja S; Wei, Peng

2016-12-01

A Mendelian randomization (MR) analysis is performed to analyze the causal effect of an exposure variable on a disease outcome in observational studies, by using genetic variants that affect the disease outcome only through the exposure variable. This method has recently gained popularity among epidemiologists given the success of genetic association studies. Many exposure variables of interest in epidemiological studies are time varying, for example, body mass index (BMI). Although longitudinal data have been collected in many cohort studies, current MR studies only use one measurement of a time-varying exposure variable, which cannot adequately capture the long-term time-varying information. We propose using the functional principal component analysis method to recover the underlying individual trajectory of the time-varying exposure from the sparsely and irregularly observed longitudinal data, and then conduct MR analysis using the recovered curves. We further propose two MR analysis methods. The first assumes a cumulative effect of the time-varying exposure variable on the disease risk, while the second assumes a time-varying genetic effect and employs functional regression models. We focus on statistical testing for a causal effect. Our simulation studies mimicking the real data show that the proposed functional data analysis based methods incorporating longitudinal data have substantial power gains compared to standard MR analysis using only one measurement. We used the Framingham Heart Study data to demonstrate the promising performance of the new methods as well as inconsistent results produced by the standard MR analysis that relies on a single measurement of the exposure at some arbitrary time point. © 2016 WILEY PERIODICALS, INC.

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

Genome-wide scans of genetic variants for psychophysiological endophenotypes: a methodological overview.

PubMed

Iacono, William G; Malone, Stephen M; Vaidyanathan, Uma; Vrieze, Scott I

2014-12-01

This article provides an introductory overview of the investigative strategy employed to evaluate the genetic basis of 17 endophenotypes examined as part of a 20-year data collection effort from the Minnesota Center for Twin and Family Research. Included are characterization of the study samples, descriptive statistics for key properties of the psychophysiological measures, and rationale behind the steps taken in the molecular genetic study design. The statistical approach included (a) biometric analysis of twin and family data, (b) heritability analysis using 527,829 single nucleotide polymorphisms (SNPs), (c) genome-wide association analysis of these SNPs and 17,601 autosomal genes, (d) follow-up analyses of candidate SNPs and genes hypothesized to have an association with each endophenotype, (e) rare variant analysis of nonsynonymous SNPs in the exome, and (f) whole genome sequencing association analysis using 27 million genetic variants. These methods were used in the accompanying empirical articles comprising this special issue, Genome-Wide Scans of Genetic Variants for Psychophysiological Endophenotypes. Copyright © 2014 Society for Psychophysiological Research.

Anger/Frustration, Task Persistence, and Conduct Problems in Childhood: A Behavioral Genetic Analysis

ERIC Educational Resources Information Center

Deater-Deckard, Kirby; Petrill, Stephen A.; Thompson, Lee A.

2007-01-01

Background: Individual differences in conduct problems arise in part from proneness to anger/frustration and poor self-regulation of behavior. However, the genetic and environmental etiology of these connections is not known. Method: Using a twin design, we examined genetic and environmental covariation underlying the well-documented correlations…

Pathway redundancy and protein essentiality revealed in the Saccharomyces cerevisiae interaction networks

PubMed Central

Ulitsky, Igor; Shamir, Ron

2007-01-01

The biological interpretation of genetic interactions is a major challenge. Recently, Kelley and Ideker proposed a method to analyze together genetic and physical networks, which explains many of the known genetic interactions as linking different pathways in the physical network. Here, we extend this method and devise novel analytic tools for interpreting genetic interactions in a physical context. Applying these tools on a large-scale Saccharomyces cerevisiae data set, our analysis reveals 140 between-pathway models that explain 3765 genetic interactions, roughly doubling those that were previously explained. Model genes tend to have short mRNA half-lives and many phosphorylation sites, suggesting that their stringent regulation is linked to pathway redundancy. We also identify ‘pivot' proteins that have many physical interactions with both pathways in our models, and show that pivots tend to be essential and highly conserved. Our analysis of models and pivots sheds light on the organization of the cellular machinery as well as on the roles of individual proteins. PMID:17437029

Navigating the Interface Between Landscape Genetics and Landscape Genomics.

PubMed

Storfer, Andrew; Patton, Austin; Fraik, Alexandra K

2018-01-01

As next-generation sequencing data become increasingly available for non-model organisms, a shift has occurred in the focus of studies of the geographic distribution of genetic variation. Whereas landscape genetics studies primarily focus on testing the effects of landscape variables on gene flow and genetic population structure, landscape genomics studies focus on detecting candidate genes under selection that indicate possible local adaptation. Navigating the transition between landscape genomics and landscape genetics can be challenging. The number of molecular markers analyzed has shifted from what used to be a few dozen loci to thousands of loci and even full genomes. Although genome scale data can be separated into sets of neutral loci for analyses of gene flow and population structure and putative loci under selection for inference of local adaptation, there are inherent differences in the questions that are addressed in the two study frameworks. We discuss these differences and their implications for study design, marker choice and downstream analysis methods. Similar to the rapid proliferation of analysis methods in the early development of landscape genetics, new analytical methods for detection of selection in landscape genomics studies are burgeoning. We focus on genome scan methods for detection of selection, and in particular, outlier differentiation methods and genetic-environment association tests because they are the most widely used. Use of genome scan methods requires an understanding of the potential mismatches between the biology of a species and assumptions inherent in analytical methods used, which can lead to high false positive rates of detected loci under selection. Key to choosing appropriate genome scan methods is an understanding of the underlying demographic structure of study populations, and such data can be obtained using neutral loci from the generated genome-wide data or prior knowledge of a species' phylogeographic history. To this end, we summarize recent simulation studies that test the power and accuracy of genome scan methods under a variety of demographic scenarios and sampling designs. We conclude with a discussion of additional considerations for future method development, and a summary of methods that show promise for landscape genomics studies but are not yet widely used.

Navigating the Interface Between Landscape Genetics and Landscape Genomics

PubMed Central

Storfer, Andrew; Patton, Austin; Fraik, Alexandra K.

2018-01-01

As next-generation sequencing data become increasingly available for non-model organisms, a shift has occurred in the focus of studies of the geographic distribution of genetic variation. Whereas landscape genetics studies primarily focus on testing the effects of landscape variables on gene flow and genetic population structure, landscape genomics studies focus on detecting candidate genes under selection that indicate possible local adaptation. Navigating the transition between landscape genomics and landscape genetics can be challenging. The number of molecular markers analyzed has shifted from what used to be a few dozen loci to thousands of loci and even full genomes. Although genome scale data can be separated into sets of neutral loci for analyses of gene flow and population structure and putative loci under selection for inference of local adaptation, there are inherent differences in the questions that are addressed in the two study frameworks. We discuss these differences and their implications for study design, marker choice and downstream analysis methods. Similar to the rapid proliferation of analysis methods in the early development of landscape genetics, new analytical methods for detection of selection in landscape genomics studies are burgeoning. We focus on genome scan methods for detection of selection, and in particular, outlier differentiation methods and genetic-environment association tests because they are the most widely used. Use of genome scan methods requires an understanding of the potential mismatches between the biology of a species and assumptions inherent in analytical methods used, which can lead to high false positive rates of detected loci under selection. Key to choosing appropriate genome scan methods is an understanding of the underlying demographic structure of study populations, and such data can be obtained using neutral loci from the generated genome-wide data or prior knowledge of a species' phylogeographic history. To this end, we summarize recent simulation studies that test the power and accuracy of genome scan methods under a variety of demographic scenarios and sampling designs. We conclude with a discussion of additional considerations for future method development, and a summary of methods that show promise for landscape genomics studies but are not yet widely used. PMID:29593776

Simple and Inexpensive but Highly Discriminating Method for Computer-Assisted DNA Fingerprinting of Pseudomonas aeruginosa

PubMed Central

Al-Samarrai, Taha H.; Zhang, Ningxin; Lamont, Iain L.; Martin, Lois; Kolbe, John; Wilsher, Margaret; Morris, Arthur J.; Schmid, Jan

2000-01-01

We describe here a method for computer-assisted fingerprinting of Pseudomonas aeruginosa. In this method, DNA is digested with SalI, and bands with molecular sizes of ≥9.7 kb are visually scored after electrophoresis on agarose gels. Pattern scores are entered into a Microsoft Excel database. In scoring, the number of bands within each of a set of molecular size ranges is scored, rather than the absolute molecular size of each band, substantially enhancing the speed and reproducibility of the method, while eliminating the need for using expensive gel scanning equipment and software. Pattern scores are used to generate matrices of genetic distance values, which can be visualized in neighbor-joining trees. The method reliably distinguishes two epidemiologically unrelated isolates in 99.3% of all comparisons. The genetic relationships between isolates observed with the method were consistent with those obtained by analysis of two P. aeruginosa genes, indicating that it provides valid estimates of genetic divergence between isolates. Using the method, respiratory tract isolates from cystic fibrosis patients in Green Lane Hospital in Auckland, New Zealand, were shown to be genetically less diverse than epidemiologically unrelated isolates from other patients. This finding was not due to the existence of clusters of related strains specialized toward colonization of the respiratory tract and thus was indicative of transmission between patients. Analysis of multiple isolates from individual cystic fibrosis patients suggested that up to five separate clusters of genetically related strains may simultaneously be present in a patient. The method described should significantly enhance our ability to investigate the epidemiology of P. aeruginosa. PMID:11101578

DNA-based methods of geochemical prospecting

DOEpatents

Ashby, Matthew [Mill Valley, CA

2011-12-06

The present invention relates to methods for performing surveys of the genetic diversity of a population. The invention also relates to methods for performing genetic analyses of a population. The invention further relates to methods for the creation of databases comprising the survey information and the databases created by these methods. The invention also relates to methods for analyzing the information to correlate the presence of nucleic acid markers with desired parameters in a sample. These methods have application in the fields of geochemical exploration, agriculture, bioremediation, environmental analysis, clinical microbiology, forensic science and medicine.

Web-Based Analysis for Student-Generated Complex Genetic Profiles

ERIC Educational Resources Information Center

Kass, David H.; LaRoe, Robert

2007-01-01

A simple, rapid method for generating complex genetic profiles using Alu-based markers was recently developed for students primarily at the undergraduate level to learn more about forensics and paternity analysis. On the basis of the Cold Spring Harbor Allele Server, which provides an excellent tool for analyzing a single Alu variant, we present a…

Genetic toxicology at the crossroads-from qualitative hazard evaluation to quantitative risk assessment.

PubMed

White, Paul A; Johnson, George E

2016-05-01

Applied genetic toxicology is undergoing a transition from qualitative hazard identification to quantitative dose-response analysis and risk assessment. To facilitate this change, the Health and Environmental Sciences Institute (HESI) Genetic Toxicology Technical Committee (GTTC) sponsored a workshop held in Lancaster, UK on July 10-11, 2014. The event included invited speakers from several institutions and the contents was divided into three themes-1: Point-of-departure Metrics for Quantitative Dose-Response Analysis in Genetic Toxicology; 2: Measurement and Estimation of Exposures for Better Extrapolation to Humans and 3: The Use of Quantitative Approaches in Genetic Toxicology for human health risk assessment (HHRA). A host of pertinent issues were discussed relating to the use of in vitro and in vivo dose-response data, the development of methods for in vitro to in vivo extrapolation and approaches to use in vivo dose-response data to determine human exposure limits for regulatory evaluations and decision-making. This Special Issue, which was inspired by the workshop, contains a series of papers that collectively address topics related to the aforementioned themes. The Issue includes contributions that collectively evaluate, describe and discuss in silico, in vitro, in vivo and statistical approaches that are facilitating the shift from qualitative hazard evaluation to quantitative risk assessment. The use and application of the benchmark dose approach was a central theme in many of the workshop presentations and discussions, and the Special Issue includes several contributions that outline novel applications for the analysis and interpretation of genetic toxicity data. Although the contents of the Special Issue constitutes an important step towards the adoption of quantitative methods for regulatory assessment of genetic toxicity, formal acceptance of quantitative methods for HHRA and regulatory decision-making will require consensus regarding the relationships between genetic damage and disease, and the concomitant ability to use genetic toxicity results per se. © Her Majesty the Queen in Right of Canada 2016. Reproduced with the permission of the Minister of Health.

Genetic variation and genetic structure of the endangered species Sinowilsonia henryi Hemsi. (Hamamelidaceae) revealed by amplified fragment length polymorphism (AFLP) markers.

PubMed

Zhang, H; Ji, W L; Li, M; Zhou, L Y

2015-10-14

Comprehensive research of genetic variation is crucial in designing conservation strategies for endangered and threatened species. Sinowilsonia henryi Hemsi. is a tertiary relic with a limited geographical distribution in the central and western areas of China. It is endangered because of climate change and habitat fragmentation over the last thousands of years. In this study, amplified fragment length polymorphism markers were utilized to estimate genetic diversity and genetic structure in and among S. henryi. In this study, Nei's genetic diversity and Shannon's information index were found to be 0.192 and 0.325 respectively, indicating a moderate-to-high genetic diversity in species. According to analysis of molecular variation results, 32% of the genetic variation was shown to be partitioned among populations, demonstrating a relatively high genetic divergence; this was supported by principal coordinate analysis and unweighted pair-group method with arithmetic average analysis. Moreover, the Mantel test showed that there was no significant correlation between genetic and geographical distances. The above results can be explained by the effects of habitat fragmentation, history traits, and gene drift. Based on the results, several implications were indicated and suggestions proposed for preservation strategies for this species.

[An ADAA model and its analysis method for agronomic traits based on the double-cross mating design].

PubMed

Xu, Z C; Zhu, J

2000-01-01

According to the double-cross mating design and using principles of Cockerham's general genetic model, a genetic model with additive, dominance and epistatic effects (ADAA model) was proposed for the analysis of agronomic traits. Components of genetic effects were derived for different generations. Monte Carlo simulation was conducted for analyzing the ADAA model and its reduced AD model by using different generations. It was indicated that genetic variance components could be estimated without bias by MINQUE(1) method and genetic effects could be predicted effectively by AUP method; at least three generations (including parent, F1 of single cross and F1 of double-cross) were necessary for analyzing the ADAA model and only two generations (including parent and F1 of double-cross) were enough for the reduced AD model. When epistatic effects were taken into account, a new approach for predicting the heterosis of agronomic traits of double-crosses was given on the basis of unbiased prediction of genotypic merits of parents and their crosses. In addition, genotype x environment interaction effects and interaction heterosis due to G x E interaction were discussed briefly.

Analysis of baseline, average, and longitudinally measured blood pressure data using linear mixed models.

PubMed

Hossain, Ahmed; Beyene, Joseph

2014-01-01

This article compares baseline, average, and longitudinal data analysis methods for identifying genetic variants in genome-wide association study using the Genetic Analysis Workshop 18 data. We apply methods that include (a) linear mixed models with baseline measures, (b) random intercept linear mixed models with mean measures outcome, and (c) random intercept linear mixed models with longitudinal measurements. In the linear mixed models, covariates are included as fixed effects, whereas relatedness among individuals is incorporated as the variance-covariance structure of the random effect for the individuals. The overall strategy of applying linear mixed models decorrelate the data is based on Aulchenko et al.'s GRAMMAR. By analyzing systolic and diastolic blood pressure, which are used separately as outcomes, we compare the 3 methods in identifying a known genetic variant that is associated with blood pressure from chromosome 3 and simulated phenotype data. We also analyze the real phenotype data to illustrate the methods. We conclude that the linear mixed model with longitudinal measurements of diastolic blood pressure is the most accurate at identifying the known single-nucleotide polymorphism among the methods, but linear mixed models with baseline measures perform best with systolic blood pressure as the outcome.

Genetics-based methods for detection of Salmonella spp. in foods.

PubMed

Mozola, Mark A

2006-01-01

Genetic methods are now at the forefront of foodborne pathogen testing. The sensitivity, specificity, and inclusivity advantages offered by deoxyribonucleic acid (DNA) probe technology have driven an intense effort in methods development over the past 20 years. DNA probe-based methods for Salmonella spp. and other pathogens have progressed from time-consuming procedures involving the use of radioisotopes to simple, high throughput, automated assays. The analytical sensitivity of nucleic acid amplification technology has facilitated a reduction in analysis time by allowing enriched samples to be tested for previously undetectable quantities of analyte. This article will trace the evolution of the development of genetic methods for detection of Salmonella in foods, review the basic assay formats and their advantages and limitations, and discuss method performance characteristics and considerations for selection of methods.

Reinventing the ames test as a quantitative lab that connects classical and molecular genetics.

PubMed

Goodson-Gregg, Nathan; De Stasio, Elizabeth A

2009-01-01

While many institutions use a version of the Ames test in the undergraduate genetics laboratory, students typically are not exposed to techniques or procedures beyond qualitative analysis of phenotypic reversion, thereby seriously limiting the scope of learning. We have extended the Ames test to include both quantitative analysis of reversion frequency and molecular analysis of revertant gene sequences. By giving students a role in designing their quantitative methods and analyses, students practice and apply quantitative skills. To help students connect classical and molecular genetic concepts and techniques, we report here procedures for characterizing the molecular lesions that confer a revertant phenotype. We suggest undertaking reversion of both missense and frameshift mutants to allow a more sophisticated molecular genetic analysis. These modifications and additions broaden the educational content of the traditional Ames test teaching laboratory, while simultaneously enhancing students' skills in experimental design, quantitative analysis, and data interpretation.

The Information Content of Discrete Functions and Their Application in Genetic Data Analysis

DOE PAGES

Sakhanenko, Nikita A.; Kunert-Graf, James; Galas, David J.

2017-10-13

The complex of central problems in data analysis consists of three components: (1) detecting the dependence of variables using quantitative measures, (2) defining the significance of these dependence measures, and (3) inferring the functional relationships among dependent variables. We have argued previously that an information theory approach allows separation of the detection problem from the inference of functional form problem. We approach here the third component of inferring functional forms based on information encoded in the functions. Here, we present here a direct method for classifying the functional forms of discrete functions of three variables represented in data sets. Discretemore » variables are frequently encountered in data analysis, both as the result of inherently categorical variables and from the binning of continuous numerical variables into discrete alphabets of values. The fundamental question of how much information is contained in a given function is answered for these discrete functions, and their surprisingly complex relationships are illustrated. The all-important effect of noise on the inference of function classes is found to be highly heterogeneous and reveals some unexpected patterns. We apply this classification approach to an important area of biological data analysis—that of inference of genetic interactions. Genetic analysis provides a rich source of real and complex biological data analysis problems, and our general methods provide an analytical basis and tools for characterizing genetic problems and for analyzing genetic data. Finally, we illustrate the functional description and the classes of a number of common genetic interaction modes and also show how different modes vary widely in their sensitivity to noise.« less

The Information Content of Discrete Functions and Their Application in Genetic Data Analysis

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

Sakhanenko, Nikita A.; Kunert-Graf, James; Galas, David J.

The complex of central problems in data analysis consists of three components: (1) detecting the dependence of variables using quantitative measures, (2) defining the significance of these dependence measures, and (3) inferring the functional relationships among dependent variables. We have argued previously that an information theory approach allows separation of the detection problem from the inference of functional form problem. We approach here the third component of inferring functional forms based on information encoded in the functions. Here, we present here a direct method for classifying the functional forms of discrete functions of three variables represented in data sets. Discretemore » variables are frequently encountered in data analysis, both as the result of inherently categorical variables and from the binning of continuous numerical variables into discrete alphabets of values. The fundamental question of how much information is contained in a given function is answered for these discrete functions, and their surprisingly complex relationships are illustrated. The all-important effect of noise on the inference of function classes is found to be highly heterogeneous and reveals some unexpected patterns. We apply this classification approach to an important area of biological data analysis—that of inference of genetic interactions. Genetic analysis provides a rich source of real and complex biological data analysis problems, and our general methods provide an analytical basis and tools for characterizing genetic problems and for analyzing genetic data. Finally, we illustrate the functional description and the classes of a number of common genetic interaction modes and also show how different modes vary widely in their sensitivity to noise.« less

Statistical methods to detect novel genetic variants using publicly available GWAS summary data.

PubMed

Guo, Bin; Wu, Baolin

2018-03-01

We propose statistical methods to detect novel genetic variants using only genome-wide association studies (GWAS) summary data without access to raw genotype and phenotype data. With more and more summary data being posted for public access in the post GWAS era, the proposed methods are practically very useful to identify additional interesting genetic variants and shed lights on the underlying disease mechanism. We illustrate the utility of our proposed methods with application to GWAS meta-analysis results of fasting glucose from the international MAGIC consortium. We found several novel genome-wide significant loci that are worth further study. Copyright © 2018 Elsevier Ltd. All rights reserved.

Methods for Genome-Wide Analysis of Gene Expression Changes in Polyploids

PubMed Central

Wang, Jianlin; Lee, Jinsuk J.; Tian, Lu; Lee, Hyeon-Se; Chen, Meng; Rao, Sheetal; Wei, Edward N.; Doerge, R. W.; Comai, Luca; Jeffrey Chen, Z.

2007-01-01

Polyploidy is an evolutionary innovation, providing extra sets of genetic material for phenotypic variation and adaptation. It is predicted that changes of gene expression by genetic and epigenetic mechanisms are responsible for novel variation in nascent and established polyploids (Liu and Wendel, 2002; Osborn et al., 2003; Pikaard, 2001). Studying gene expression changes in allopolyploids is more complicated than in autopolyploids, because allopolyploids contain more than two sets of genomes originating from divergent, but related, species. Here we describe two methods that are applicable to the genome-wide analysis of gene expression differences resulting from genome duplication in autopolyploids or interactions between homoeologous genomes in allopolyploids. First, we describe an amplified fragment length polymorphism (AFLP)–complementary DNA (cDNA) display method that allows the discrimination of homoeologous loci based on restriction polymorphisms between the progenitors. Second, we describe microarray analyses that can be used to compare gene expression differences between the allopolyploids and respective progenitors using appropriate experimental design and statistical analysis. We demonstrate the utility of these two complementary methods and discuss the pros and cons of using the methods to analyze gene expression changes in autopolyploids and allopolyploids. Furthermore, we describe these methods in general terms to be of wider applicability for comparative gene expression in a variety of evolutionary, genetic, biological, and physiological contexts. PMID:15865985

Classification of cassava genotypes based on qualitative and quantitative data.

PubMed

Oliveira, E J; Oliveira Filho, O S; Santos, V S

2015-02-02

We evaluated the genetic variation of cassava accessions based on qualitative (binomial and multicategorical) and quantitative traits (continuous). We characterized 95 accessions obtained from the Cassava Germplasm Bank of Embrapa Mandioca e Fruticultura; we evaluated these accessions for 13 continuous, 10 binary, and 25 multicategorical traits. First, we analyzed the accessions based only on quantitative traits; next, we conducted joint analysis (qualitative and quantitative traits) based on the Ward-MLM method, which performs clustering in two stages. According to the pseudo-F, pseudo-t2, and maximum likelihood criteria, we identified five and four groups based on quantitative trait and joint analysis, respectively. The smaller number of groups identified based on joint analysis may be related to the nature of the data. On the other hand, quantitative data are more subject to environmental effects in the phenotype expression; this results in the absence of genetic differences, thereby contributing to greater differentiation among accessions. For most of the accessions, the maximum probability of classification was >0.90, independent of the trait analyzed, indicating a good fit of the clustering method. Differences in clustering according to the type of data implied that analysis of quantitative and qualitative traits in cassava germplasm might explore different genomic regions. On the other hand, when joint analysis was used, the means and ranges of genetic distances were high, indicating that the Ward-MLM method is very useful for clustering genotypes when there are several phenotypic traits, such as in the case of genetic resources and breeding programs.

Indel analysis by droplet digital PCR: a sensitive method for DNA mixture detection and chimerism analysis.

PubMed

Santurtún, Ana; Riancho, José A; Arozamena, Jana; López-Duarte, Mónica; Zarrabeitia, María T

2017-01-01

Several methods have been developed to determinate genetic profiles from a mixed samples and chimerism analysis in transplanted patients. The aim of this study was to explore the effectiveness of using the droplet digital PCR (ddPCR) for mixed chimerism detection (a mixture of genetic profiles resulting after allogeneic hematopoietic stem cell transplantation (HSCT)). We analyzed 25 DNA samples from patients who had undergone HSCT and compared the performance of ddPCR and two established methods for chimerism detection, based upon the Indel and STRs analysis, respectively. Additionally, eight artificial mixture DNA samples were created to evaluate the sensibility of ddPCR. Our results show that the chimerism percentages estimated by the analysis of a single Indel using ddPCR were very similar to those calculated by the amplification of 15 STRs (r 2  = 0.970) and with the results obtained by the amplification of 38 Indels (r 2  = 0.975). Moreover, the amplification of a single Indel by ddPCR was sensitive enough to detect a minor DNA contributor comprising down to 0.5 % of the sample. We conclude that ddPCR can be a powerful tool for the determination of a genetic profile of forensic mixtures and clinical chimerism analysis when traditional techniques are not sensitive enough.

Genetic particle swarm parallel algorithm analysis of optimization arrangement on mistuned blades

NASA Astrophysics Data System (ADS)

Zhao, Tianyu; Yuan, Huiqun; Yang, Wenjun; Sun, Huagang

2017-12-01

This article introduces a method of mistuned parameter identification which consists of static frequency testing of blades, dichotomy and finite element analysis. A lumped parameter model of an engine bladed-disc system is then set up. A bladed arrangement optimization method, namely the genetic particle swarm optimization algorithm, is presented. It consists of a discrete particle swarm optimization and a genetic algorithm. From this, the local and global search ability is introduced. CUDA-based co-evolution particle swarm optimization, using a graphics processing unit, is presented and its performance is analysed. The results show that using optimization results can reduce the amplitude and localization of the forced vibration response of a bladed-disc system, while optimization based on the CUDA framework can improve the computing speed. This method could provide support for engineering applications in terms of effectiveness and efficiency.

Pathway-based analyses.

PubMed

Kent, Jack W

2016-02-03

New technologies for acquisition of genomic data, while offering unprecedented opportunities for genetic discovery, also impose severe burdens of interpretation and penalties for multiple testing. The Pathway-based Analyses Group of the Genetic Analysis Workshop 19 (GAW19) sought reduction of multiple-testing burden through various approaches to aggregation of highdimensional data in pathways informed by prior biological knowledge. Experimental methods testedincluded the use of "synthetic pathways" (random sets of genes) to estimate power and false-positive error rate of methods applied to simulated data; data reduction via independent components analysis, single-nucleotide polymorphism (SNP)-SNP interaction, and use of gene sets to estimate genetic similarity; and general assessment of the efficacy of prior biological knowledge to reduce the dimensionality of complex genomic data. The work of this group explored several promising approaches to managing high-dimensional data, with the caveat that these methods are necessarily constrained by the quality of external bioinformatic annotation.

Discrimination of genetically modified sugar beets based on terahertz spectroscopy

NASA Astrophysics Data System (ADS)

Chen, Tao; Li, Zhi; Yin, Xianhua; Hu, Fangrong; Hu, Cong

2016-01-01

The objective of this paper was to apply terahertz (THz) spectroscopy combined with chemometrics techniques for discrimination of genetically modified (GM) and non-GM sugar beets. In this paper, the THz spectra of 84 sugar beet samples (36 GM sugar beets and 48 non-GM ones) were obtained by using terahertz time-domain spectroscopy (THz-TDS) system in the frequency range from 0.2 to 1.2 THz. Three chemometrics methods, principal component analysis (PCA), discriminant analysis (DA) and discriminant partial least squares (DPLS), were employed to classify sugar beet samples into two groups: genetically modified organisms (GMOs) and non-GMOs. The DPLS method yielded the best classification result, and the percentages of successful classification for GM and non-GM sugar beets were both 100%. Results of the present study demonstrate the usefulness of THz spectroscopy together with chemometrics methods as a powerful tool to distinguish GM and non-GM sugar beets.

GENETIC STRUCTURE OF CREEK CHUB (SEMOTILUS ATROMACULATUS) POPULATIONS IN COAL MINING-IMPACTED AREAS OF THE EASTERN UNITED STATES, AS DETERMINED BY MTDNA SEQUENCING AND AFLP ANALYSIS

EPA Science Inventory

Analysis of intraspecific patterns in genetic diversity of stream fishes provides a potentially powerful method for assessing the status and trends in the condition of aquatic ecosystems. We analyzed mitochondrial DNA (mtDNA) sequences (590 bases of cytochrome B) and nuclear DNA...

Social network analysis of the genetic structure of Pacific islanders.

PubMed

Terrell, John Edward

2010-05-01

Social network analysis (SNA) is a body of theory and a set of relatively new computer-aided techniques used in the analysis and study of relational data. Recent studies of autosomal markers from over 40 human populations in the south-western Pacific have further documented the remarkable degree of genetic diversity in this part of the world. I report additional analysis using SNA methods contributing new controlled observations on the structuring of genetic diversity among these islanders. These SNA mappings are then compared with model-based network expectations derived from the geographic distances among the same populations. Previous studies found that genetic divergence among island Melanesian populations is organised by island, island size/topography, and position (coastal vs. inland), and that similarities observed correlate only weakly with an isolation-by-distance model. Using SNA methods, however, improves the resolution of among population comparison, and suggests that isolation by distance constrained by social networks together with position (coastal/inland) accounts for much of the population structuring observed. The multilocus data now available is also in accord with current thinking on the impact of major biogeographical transformations on prehistoric colonisation and post-settlement human interaction in Oceania.

Interpreting findings from Mendelian randomization using the MR-Egger method.

PubMed

Burgess, Stephen; Thompson, Simon G

2017-05-01

Mendelian randomization-Egger (MR-Egger) is an analysis method for Mendelian randomization using summarized genetic data. MR-Egger consists of three parts: (1) a test for directional pleiotropy, (2) a test for a causal effect, and (3) an estimate of the causal effect. While conventional analysis methods for Mendelian randomization assume that all genetic variants satisfy the instrumental variable assumptions, the MR-Egger method is able to assess whether genetic variants have pleiotropic effects on the outcome that differ on average from zero (directional pleiotropy), as well as to provide a consistent estimate of the causal effect, under a weaker assumption-the InSIDE (INstrument Strength Independent of Direct Effect) assumption. In this paper, we provide a critical assessment of the MR-Egger method with regard to its implementation and interpretation. While the MR-Egger method is a worthwhile sensitivity analysis for detecting violations of the instrumental variable assumptions, there are several reasons why causal estimates from the MR-Egger method may be biased and have inflated Type 1 error rates in practice, including violations of the InSIDE assumption and the influence of outlying variants. The issues raised in this paper have potentially serious consequences for causal inferences from the MR-Egger approach. We give examples of scenarios in which the estimates from conventional Mendelian randomization methods and MR-Egger differ, and discuss how to interpret findings in such cases.

Application of the High Resolution Melting analysis for genetic mapping of Sequence Tagged Site markers in narrow-leafed lupin (Lupinus angustifolius L.).

PubMed

Kamel, Katarzyna A; Kroc, Magdalena; Święcicki, Wojciech

2015-01-01

Sequence tagged site (STS) markers are valuable tools for genetic and physical mapping that can be successfully used in comparative analyses among related species. Current challenges for molecular markers genotyping in plants include the lack of fast, sensitive and inexpensive methods suitable for sequence variant detection. In contrast, high resolution melting (HRM) is a simple and high-throughput assay, which has been widely applied in sequence polymorphism identification as well as in the studies of genetic variability and genotyping. The present study is the first attempt to use the HRM analysis to genotype STS markers in narrow-leafed lupin (Lupinus angustifolius L.). The sensitivity and utility of this method was confirmed by the sequence polymorphism detection based on melting curve profiles in the parental genotypes and progeny of the narrow-leafed lupin mapping population. Application of different approaches, including amplicon size and a simulated heterozygote analysis, has allowed for successful genetic mapping of 16 new STS markers in the narrow-leafed lupin genome.

Genetic analysis of floating Enteromorpha prolifera in the Yellow Sea with AFLP marker

NASA Astrophysics Data System (ADS)

Liu, Cui; Zhang, Jing; Sun, Xiaoyu; Li, Jian; Zhang, Xi; Liu, Tao

2011-09-01

Extremely large accumulation of green algae Enteromorpha prolifera floated along China' coastal region of the Yellow Sea ever since the summer of 2008. Amplified Fragment Length Polymorphism (AFLP) analysis was applied to assess the genetic diversity and relationships among E. prolifera samples collected from 9 affected areas of the Yellow Sea. Two hundred reproducible fragments were generated with 8 AFLP primer combinations, of which 194 (97%) were polymorphic. The average Nei's genetic diversity, the coefficiency of genetic differentiation (Gst), and the average gene flow estimated from Gst in the 9 populations were 0.4018, 0.6404 and 0.2807 respectively. Cluster analysis based on the unweighed pair group method with arithmetic averages (UPGMA) showed that the genetic relationships within one population or among different populations were all related to their collecting locations and sampling time. Large genetic differentiation was detected among the populations. The E. prolifera originated from different areas and were undergoing a course of mixing.

Functional Regression Models for Epistasis Analysis of Multiple Quantitative Traits.

PubMed

Zhang, Futao; Xie, Dan; Liang, Meimei; Xiong, Momiao

2016-04-01

To date, most genetic analyses of phenotypes have focused on analyzing single traits or analyzing each phenotype independently. However, joint epistasis analysis of multiple complementary traits will increase statistical power and improve our understanding of the complicated genetic structure of the complex diseases. Despite their importance in uncovering the genetic structure of complex traits, the statistical methods for identifying epistasis in multiple phenotypes remains fundamentally unexplored. To fill this gap, we formulate a test for interaction between two genes in multiple quantitative trait analysis as a multiple functional regression (MFRG) in which the genotype functions (genetic variant profiles) are defined as a function of the genomic position of the genetic variants. We use large-scale simulations to calculate Type I error rates for testing interaction between two genes with multiple phenotypes and to compare the power with multivariate pairwise interaction analysis and single trait interaction analysis by a single variate functional regression model. To further evaluate performance, the MFRG for epistasis analysis is applied to five phenotypes of exome sequence data from the NHLBI's Exome Sequencing Project (ESP) to detect pleiotropic epistasis. A total of 267 pairs of genes that formed a genetic interaction network showed significant evidence of epistasis influencing five traits. The results demonstrate that the joint interaction analysis of multiple phenotypes has a much higher power to detect interaction than the interaction analysis of a single trait and may open a new direction to fully uncovering the genetic structure of multiple phenotypes.

A kernel regression approach to gene-gene interaction detection for case-control studies.

PubMed

Larson, Nicholas B; Schaid, Daniel J

2013-11-01

Gene-gene interactions are increasingly being addressed as a potentially important contributor to the variability of complex traits. Consequently, attentions have moved beyond single locus analysis of association to more complex genetic models. Although several single-marker approaches toward interaction analysis have been developed, such methods suffer from very high testing dimensionality and do not take advantage of existing information, notably the definition of genes as functional units. Here, we propose a comprehensive family of gene-level score tests for identifying genetic elements of disease risk, in particular pairwise gene-gene interactions. Using kernel machine methods, we devise score-based variance component tests under a generalized linear mixed model framework. We conducted simulations based upon coalescent genetic models to evaluate the performance of our approach under a variety of disease models. These simulations indicate that our methods are generally higher powered than alternative gene-level approaches and at worst competitive with exhaustive SNP-level (where SNP is single-nucleotide polymorphism) analyses. Furthermore, we observe that simulated epistatic effects resulted in significant marginal testing results for the involved genes regardless of whether or not true main effects were present. We detail the benefits of our methods and discuss potential genome-wide analysis strategies for gene-gene interaction analysis in a case-control study design. © 2013 WILEY PERIODICALS, INC.

Endogenous Reference Genes and Their Quantitative Real-Time PCR Assays for Genetically Modified Bread Wheat (Triticum aestivum L.) Detection.

PubMed

Yang, Litao; Quan, Sheng; Zhang, Dabing

2017-01-01

Endogenous reference genes (ERG) and their derivate analytical methods are standard requirements for analysis of genetically modified organisms (GMOs). Development and validation of suitable ERGs is the primary step for establishing assays that monitoring the genetically modified (GM) contents in food/feed samples. Herein, we give a review of the ERGs currently used for GM wheat analysis, such as ACC1, PKABA1, ALMT1, and Waxy-D1, as well as their performances in GM wheat analysis. Also, we discussed one model for developing and validating one ideal RG for one plant species based on our previous research work.

Heritability of Nociception IV: Neuropathic pain assays are genetically distinct across methods of peripheral nerve injury

PubMed Central

Young, Erin E.; Costigan, Michael; Herbert, Teri A.; Lariviere, William R.

2013-01-01

Prior genetic correlation analysis of 22 heritable behavioral measures of nociception and hypersensitivity in the mouse identified five genetically distinct pain types. In the present study, we reanalyzed that dataset and included the results of an additional nine assays of nociception and hypersensitivity to: 1) replicate the previously identified five pain types; 2) test whether any of the newly added pain assays represent novel genetically distinct pain types; 3) test the level of genetic relatedness among nine commonly employed neuropathic pain assays. Multivariate analysis of pairwise correlations between assays shows that the newly added zymosan-induced heat hypersensitivity assay does not conform to the two previously identified groups of heat hypersensitivity assays and cyclophosphamide-induced cystitis, the first organ-specific visceral pain model examined, is genetically distinct from other inflammatory assays. The four included mechanical hypersensitivity assays are genetically distinct, and do not comprise a single pain type as previously reported. Among the nine neuropathic pain assays including autotomy, chemotherapy, nerve ligation and spared nerve injury assays, at least four genetically distinct types of neuropathic sensory abnormalities were identified, corresponding to differences in nerve injury method. In addition, two itch assays and Comt genotype were compared to the expanded set of nociception and hypersensitivity assays. Comt genotype was strongly related only to spontaneous inflammatory nociception assays. These results indicate the priority for continued investigation of genetic mechanisms in several assays newly identified to represent genetically distinct pain types. PMID:24071598

[Using IRAP markers for analysis of genetic variability in populations of resource and rare species of plants].

PubMed

Boronnikova, S V; Kalendar', R N

2010-01-01

Species-specific LTR retrotransposons were first cloned in five rare relic species of drug plants located in the Perm' region. Sequences of LTR retrotransposons were used for PCR analysis based on amplification of repeated sequences from LTR or other sites of retrotransposons (IRAP). Genetic diversity was studied in six populations of rare relic species of plants Adonis vernalis L. by means of the IRAP method; 125 polymorphic IRAP-markers were analyzed. Parameters for DNA polymorphism and genetic diversity of A. vernalis populations were determined.

Analysis of genetic effects of nuclear-cytoplasmic interaction on quantitative traits: genetic model for diploid plants.

PubMed

Han, Lide; Yang, Jian; Zhu, Jun

2007-06-01

A genetic model was proposed for simultaneously analyzing genetic effects of nuclear, cytoplasm, and nuclear-cytoplasmic interaction (NCI) as well as their genotype by environment (GE) interaction for quantitative traits of diploid plants. In the model, the NCI effects were further partitioned into additive and dominance nuclear-cytoplasmic interaction components. Mixed linear model approaches were used for statistical analysis. On the basis of diallel cross designs, Monte Carlo simulations showed that the genetic model was robust for estimating variance components under several situations without specific effects. Random genetic effects were predicted by an adjusted unbiased prediction (AUP) method. Data on four quantitative traits (boll number, lint percentage, fiber length, and micronaire) in Upland cotton (Gossypium hirsutum L.) were analyzed as a worked example to show the effectiveness of the model.

Development of a qualitative real-time PCR method to detect 19 targets for identification of genetically modified organisms.

PubMed

Peng, Cheng; Wang, Pengfei; Xu, Xiaoli; Wang, Xiaofu; Wei, Wei; Chen, Xiaoyun; Xu, Junfeng

2016-01-01

As the amount of commercially available genetically modified organisms (GMOs) grows recent years, the diversity of target sequences for molecular detection techniques are eagerly needed. Considered as the gold standard for GMO analysis, the real-time PCR technology was optimized to produce a high-throughput GMO screening method. With this method we can detect 19 transgenic targets. The specificity of the assays was demonstrated to be 100 % by the specific amplification of DNA derived from reference material from 20 genetically modified crops and 4 non modified crops. Furthermore, most assays showed a very sensitive detection, reaching the limit of ten copies. The 19 assays are the most frequently used genetic elements present in GM crops and theoretically enable the screening of the known GMO described in Chinese markets. Easy to use, fast and cost efficient, this method approach fits the purpose of GMO testing laboratories.

Population structure in Japanese rice population

PubMed Central

Yamasaki, Masanori; Ideta, Osamu

2013-01-01

It is essential to elucidate genetic diversity and relationships among even related individuals and populations for plant breeding and genetic analysis. Since Japanese rice breeding has improved agronomic traits such as yield and eating quality, modern Japanese rice cultivars originated from narrow genetic resource and closely related. To resolve the population structure and genetic diversity in Japanese rice population, we used a total of 706 alleles detected by 134 simple sequence repeat markers in a total of 114 cultivars composed of 94 improved varieties and 20 landraces, which are representative and important for Japanese rice breeding. The landraces exhibit greater gene diversity than improved lines, suggesting that landraces can provide additional genetic diversity for future breeding. Model-based Bayesian clustering analysis revealed six subgroups and admixture situation in the cultivars, showing good agreement with pedigree information. This method could be superior to phylogenetic method in classifying a related population. The leading Japanese rice cultivar, Koshihikari is unique due to the specific genome constitution. We defined Japanese rice diverse sets that capture the maximum number of alleles for given sample sizes. These sets are useful for a variety of genetic application in Japanese rice cultivars. PMID:23641181

Applications of modern statistical methods to analysis of data in physical science

NASA Astrophysics Data System (ADS)

Wicker, James Eric

Modern methods of statistical and computational analysis offer solutions to dilemmas confronting researchers in physical science. Although the ideas behind modern statistical and computational analysis methods were originally introduced in the 1970's, most scientists still rely on methods written during the early era of computing. These researchers, who analyze increasingly voluminous and multivariate data sets, need modern analysis methods to extract the best results from their studies. The first section of this work showcases applications of modern linear regression. Since the 1960's, many researchers in spectroscopy have used classical stepwise regression techniques to derive molecular constants. However, problems with thresholds of entry and exit for model variables plagues this analysis method. Other criticisms of this kind of stepwise procedure include its inefficient searching method, the order in which variables enter or leave the model and problems with overfitting data. We implement an information scoring technique that overcomes the assumptions inherent in the stepwise regression process to calculate molecular model parameters. We believe that this kind of information based model evaluation can be applied to more general analysis situations in physical science. The second section proposes new methods of multivariate cluster analysis. The K-means algorithm and the EM algorithm, introduced in the 1960's and 1970's respectively, formed the basis of multivariate cluster analysis methodology for many years. However, several shortcomings of these methods include strong dependence on initial seed values and inaccurate results when the data seriously depart from hypersphericity. We propose new cluster analysis methods based on genetic algorithms that overcomes the strong dependence on initial seed values. In addition, we propose a generalization of the Genetic K-means algorithm which can accurately identify clusters with complex hyperellipsoidal covariance structures. We then use this new algorithm in a genetic algorithm based Expectation-Maximization process that can accurately calculate parameters describing complex clusters in a mixture model routine. Using the accuracy of this GEM algorithm, we assign information scores to cluster calculations in order to best identify the number of mixture components in a multivariate data set. We will showcase how these algorithms can be used to process multivariate data from astronomical observations.

A comparative analysis of the effectiveness of cytogenetic and molecular genetic methods in the detection of Down syndrome.

PubMed

Mačkić-Đurović, Mirela; Projić, Petar; Ibrulj, Slavka; Cakar, Jasmina; Marjanović, Damir

2014-05-01

The goal of this study was to examine the effectiveness of 6 STR markers application (D21S1435, D21S11, D21S1270, D21S1411, D21S226 and IFNAR) in molecular genetic diagnostics of Down syndrome (DS) and to compare it with cytogenetic method. Testing was performed on 73 children, with the previously cytogenetically confirmed Down syndrome. DNA isolated from the buccal swab was used. Previously mentioned loci located on chromosome 21 were simultaneously amplified using quantitative fluorescence PCR (QF PCR). Using this method, 60 previously cytogenetically diagnosed DS with standard type of trisomy 21 were confirmed. Furthermore, six of eight children with mosaic type of DS were detected. Two false negative results for mosaic type of DS were obtained. Finally, five children with the translocation type of Down syndrome were also confirmed with this molecular test. In conclusion, molecular genetic analysis of STR loci is fast, cheap and simple method that could be used in detection of DS. Regarding possible false results detected for certain number of mosaic types, cytogenetic analysis should be used as a confirmatory test.

Clues to unraveling the coral species problem: distinguishing species from geographic variation in Porites across the Pacific with molecular markers and microskeletal traits

PubMed Central

Wellington, Gerrard M.; Fox, George E.; Toonen, Robert J.

2015-01-01

Morphological variation in the geographically widespread coral Porites lobata can make it difficult to distinguish from other massive congeneric species. This morphological variation could be attributed to geographic variability, phenotypic plasticity, or a combination of such factors. We examined genetic and microscopic morphological variability in P. lobata samples from the Galápagos, Easter Island, Tahiti, Fiji, Rarotonga, and Australia. Panamanian P. evermanni specimens were used as a previously established distinct outgroup against which to test genetic and morphological methods of discrimination. We employed a molecular analysis of variance (AMOVA) based on ribosomal internal transcribed spacer region (ITS) sequence, principal component analysis (PCA) of skeletal landmarks, and Mantel tests to compare genetic and morphological variation. Both genetic and morphometric methods clearly distinguished P. lobata and P. evermanni, while significant genetic and morphological variance was attributed to differences among geographic regions for P. lobata. Mantel tests indicate a correlation between genetic and morphological variation for P. lobata across the Pacific. Here we highlight landmark morphometric measures that correlate well with genetic differences, showing promise for resolving species of Porites, one of the most ubiquitous yet challenging to identify architects of coral reefs. PMID:25674364

Genetic Structure of Bluefin Tuna in the Mediterranean Sea Correlates with Environmental Variables

PubMed Central

Riccioni, Giulia; Stagioni, Marco; Landi, Monica; Ferrara, Giorgia; Barbujani, Guido; Tinti, Fausto

2013-01-01

Background Atlantic Bluefin Tuna (ABFT) shows complex demography and ecological variation in the Mediterranean Sea. Genetic surveys have detected significant, although weak, signals of population structuring; catch series analyses and tagging programs identified complex ABFT spatial dynamics and migration patterns. Here, we tested the hypothesis that the genetic structure of the ABFT in the Mediterranean is correlated with mean surface temperature and salinity. Methodology We used six samples collected from Western and Central Mediterranean integrated with a new sample collected from the recently identified easternmost reproductive area of Levantine Sea. To assess population structure in the Mediterranean we used a multidisciplinary framework combining classical population genetics, spatial and Bayesian clustering methods and a multivariate approach based on factor analysis. Conclusions FST analysis and Bayesian clustering methods detected several subpopulations in the Mediterranean, a result also supported by multivariate analyses. In addition, we identified significant correlations of genetic diversity with mean salinity and surface temperature values revealing that ABFT is genetically structured along two environmental gradients. These results suggest that a preference for some spawning habitat conditions could contribute to shape ABFT genetic structuring in the Mediterranean. However, further studies should be performed to assess to what extent ABFT spawning behaviour in the Mediterranean Sea can be affected by environmental variation. PMID:24260341

Comparative analysis of genetic diversity and differentiation of cauliflower (Brassica oleracea var. botrytis) accessions from two ex situ genebanks

PubMed Central

Börner, Andreas

2018-01-01

Cauliflower (Brassica oleracea var. botrytis) is an important vegetable crop for human nutrition. We characterized 192 cauliflower accessions from the USDA and IPK genebanks with genotyping by sequencing (GBS). They originated from 26 different countries and represent about 44% of all cauliflower accessions in both genebanks. The analysis of genetic diversity revealed that accessions formed two major groups that represented the two genebanks and were not related to the country of origin. This differentiation was robust with respect to the analysis methods that included principal component analysis, ADMIXTURE and neighbor-joining trees. Genetic diversity was higher in the USDA collection and significant phenotypic differences between the two genebanks were found in three out of six traits investigated. GBS data have a high proportion of missing data, but we observed that the exclusion of single nucleotide polymorphisms (SNPs) with missing data or the imputation of missing SNP alleles produced very similar results. The results indicate that the composition and type of accessions have a strong effect on the structure of genetic diversity of ex situ collections, although regeneration procedures and local adaptation to regeneration conditions may also contribute to a divergence. Fst-based outlier tests of genetic differentiation identified only a small proportion (<1%) of SNPs that are highly differentiated between the two genebanks, which indicates that selection during seed regeneration is not a major cause of differentiation between genebanks. Seed regeneration procedures of both genebanks do not result in different levels of genetic drift and loss of genetic variation. We therefore conclude that the composition and type of accessions mainly influence the level of genetic diversity and explain the strong genetic differentiation between the two ex situ collections. In summary, GBS is a useful method for characterizing genetic diversity in cauliflower genebank material and our results suggest that it may be useful to incorporate routine genotyping into accession management and seed regeneration to monitor the diversity present in ex situ collections and to reduce the loss of genetic diversity during seed regeneration. PMID:29420661

Comparative analysis of genetic diversity and differentiation of cauliflower (Brassica oleracea var. botrytis) accessions from two ex situ genebanks.

PubMed

Yousef, Eltohamy A A; Müller, Thomas; Börner, Andreas; Schmid, Karl J

2018-01-01

Cauliflower (Brassica oleracea var. botrytis) is an important vegetable crop for human nutrition. We characterized 192 cauliflower accessions from the USDA and IPK genebanks with genotyping by sequencing (GBS). They originated from 26 different countries and represent about 44% of all cauliflower accessions in both genebanks. The analysis of genetic diversity revealed that accessions formed two major groups that represented the two genebanks and were not related to the country of origin. This differentiation was robust with respect to the analysis methods that included principal component analysis, ADMIXTURE and neighbor-joining trees. Genetic diversity was higher in the USDA collection and significant phenotypic differences between the two genebanks were found in three out of six traits investigated. GBS data have a high proportion of missing data, but we observed that the exclusion of single nucleotide polymorphisms (SNPs) with missing data or the imputation of missing SNP alleles produced very similar results. The results indicate that the composition and type of accessions have a strong effect on the structure of genetic diversity of ex situ collections, although regeneration procedures and local adaptation to regeneration conditions may also contribute to a divergence. Fst-based outlier tests of genetic differentiation identified only a small proportion (<1%) of SNPs that are highly differentiated between the two genebanks, which indicates that selection during seed regeneration is not a major cause of differentiation between genebanks. Seed regeneration procedures of both genebanks do not result in different levels of genetic drift and loss of genetic variation. We therefore conclude that the composition and type of accessions mainly influence the level of genetic diversity and explain the strong genetic differentiation between the two ex situ collections. In summary, GBS is a useful method for characterizing genetic diversity in cauliflower genebank material and our results suggest that it may be useful to incorporate routine genotyping into accession management and seed regeneration to monitor the diversity present in ex situ collections and to reduce the loss of genetic diversity during seed regeneration.

PAQ: Partition Analysis of Quasispecies.

PubMed

Baccam, P; Thompson, R J; Fedrigo, O; Carpenter, S; Cornette, J L

2001-01-01

The complexities of genetic data may not be accurately described by any single analytical tool. Phylogenetic analysis is often used to study the genetic relationship among different sequences. Evolutionary models and assumptions are invoked to reconstruct trees that describe the phylogenetic relationship among sequences. Genetic databases are rapidly accumulating large amounts of sequences. Newly acquired sequences, which have not yet been characterized, may require preliminary genetic exploration in order to build models describing the evolutionary relationship among sequences. There are clustering techniques that rely less on models of evolution, and thus may provide nice exploratory tools for identifying genetic similarities. Some of the more commonly used clustering methods perform better when data can be grouped into mutually exclusive groups. Genetic data from viral quasispecies, which consist of closely related variants that differ by small changes, however, may best be partitioned by overlapping groups. We have developed an intuitive exploratory program, Partition Analysis of Quasispecies (PAQ), which utilizes a non-hierarchical technique to partition sequences that are genetically similar. PAQ was used to analyze a data set of human immunodeficiency virus type 1 (HIV-1) envelope sequences isolated from different regions of the brain and another data set consisting of the equine infectious anemia virus (EIAV) regulatory gene rev. Analysis of the HIV-1 data set by PAQ was consistent with phylogenetic analysis of the same data, and the EIAV rev variants were partitioned into two overlapping groups. PAQ provides an additional tool which can be used to glean information from genetic data and can be used in conjunction with other tools to study genetic similarities and genetic evolution of viral quasispecies.

[Progress in studies on the genetic risk factors for nonsyndromic cleft lip or palate in China].

PubMed

Huang, Y Q

2017-04-09

Cleft lip and palate is the most common congenital defects of oral and maxillofacial region in human beings. The etiology of this malformation is complex, with both genetic and environmental causal factors are involved. To provide a better understanding in the genetic etiology of cleft lip or palate, the author summarized recent years studies based on Chinese population. Those researches included validation of some candidate genes for cleft lip or palate, using genome wide association analysis which included six independent cohorts from China to elucidate the genetic architecture of non-syndromic cleft lip with or without cleft palate in Chinese population and finally found a new susceptibility locus. This locus was on the 16p13.3 (rs8049367) between CREBBP and ADCY9. It has been mentioned common methods of genetic analysis involved in the researches on cleft lip or palate in this paper. Furthermore, we try to discuss new methods to illustrate the etiology of cleft lip and palate that could provide more inspiration on future researches.

Detection and traceability of genetically modified organisms in the food production chain.

PubMed

Miraglia, M; Berdal, K G; Brera, C; Corbisier, P; Holst-Jensen, A; Kok, E J; Marvin, H J P; Schimmel, H; Rentsch, J; van Rie, J P P F; Zagon, J

2004-07-01

Both labelling and traceability of genetically modified organisms are current issues that are considered in trade and regulation. Currently, labelling of genetically modified foods containing detectable transgenic material is required by EU legislation. A proposed package of legislation would extend this labelling to foods without any traces of transgenics. These new legislations would also impose labelling and a traceability system based on documentation throughout the food and feed manufacture system. The regulatory issues of risk analysis and labelling are currently harmonised by Codex Alimentarius. The implementation and maintenance of the regulations necessitates sampling protocols and analytical methodologies that allow for accurate determination of the content of genetically modified organisms within a food and feed sample. Current methodologies for the analysis of genetically modified organisms are focused on either one of two targets, the transgenic DNA inserted- or the novel protein(s) expressed- in a genetically modified product. For most DNA-based detection methods, the polymerase chain reaction is employed. Items that need consideration in the use of DNA-based detection methods include the specificity, sensitivity, matrix effects, internal reference DNA, availability of external reference materials, hemizygosity versus homozygosity, extrachromosomal DNA, and international harmonisation. For most protein-based methods, enzyme-linked immunosorbent assays with antibodies binding the novel protein are employed. Consideration should be given to the selection of the antigen bound by the antibody, accuracy, validation, and matrix effects. Currently, validation of detection methods for analysis of genetically modified organisms is taking place. In addition, new methodologies are developed, including the use of microarrays, mass spectrometry, and surface plasmon resonance. Challenges for GMO detection include the detection of transgenic material in materials with varying chromosome numbers. The existing and proposed regulatory EU requirements for traceability of genetically modified products fit within a broader tendency towards traceability of foods in general and, commercially, towards products that can be distinguished from each other. Traceability systems document the history of a product and may serve the purpose of both marketing and health protection. In this framework, segregation and identity preservation systems allow for the separation of genetically modified and non-modified products from "farm to fork". Implementation of these systems comes with specific technical requirements for each particular step of the food processing chain. In addition, the feasibility of traceability systems depends on a number of factors, including unique identifiers for each genetically modified product, detection methods, permissible levels of contamination, and financial costs. In conclusion, progress has been achieved in the field of sampling, detection, and traceability of genetically modified products, while some issues remain to be solved. For success, much will depend on the threshold level for adventitious contamination set by legislation. Copryright 2004 Elsevier Ltd.

Databases for rRNA gene profiling of microbial communities

DOEpatents

Ashby, Matthew

2013-07-02

The present invention relates to methods for performing surveys of the genetic diversity of a population. The invention also relates to methods for performing genetic analyses of a population. The invention further relates to methods for the creation of databases comprising the survey information and the databases created by these methods. The invention also relates to methods for analyzing the information to correlate the presence of nucleic acid markers with desired parameters in a sample. These methods have application in the fields of geochemical exploration, agriculture, bioremediation, environmental analysis, clinical microbiology, forensic science and medicine.

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.

GMOMETHODS: the European Union database of reference methods for GMO analysis.

PubMed

Bonfini, Laura; Van den Bulcke, Marc H; Mazzara, Marco; Ben, Enrico; Patak, Alexandre

2012-01-01

In order to provide reliable and harmonized information on methods for GMO (genetically modified organism) analysis we have published a database called "GMOMETHODS" that supplies information on PCR assays validated according to the principles and requirements of ISO 5725 and/or the International Union of Pure and Applied Chemistry protocol. In addition, the database contains methods that have been verified by the European Union Reference Laboratory for Genetically Modified Food and Feed in the context of compliance with an European Union legislative act. The web application provides search capabilities to retrieve primers and probes sequence information on the available methods. It further supplies core data required by analytical labs to carry out GM tests and comprises information on the applied reference material and plasmid standards. The GMOMETHODS database currently contains 118 different PCR methods allowing identification of 51 single GM events and 18 taxon-specific genes in a sample. It also provides screening assays for detection of eight different genetic elements commonly used for the development of GMOs. The application is referred to by the Biosafety Clearing House, a global mechanism set up by the Cartagena Protocol on Biosafety to facilitate the exchange of information on Living Modified Organisms. The publication of the GMOMETHODS database can be considered an important step toward worldwide standardization and harmonization in GMO analysis.

Drosophila Melanogaster as an Experimental Organism.

ERIC Educational Resources Information Center

Rubin, Gerald M.

1988-01-01

Discusses the role of the fruit fly in genetics research requiring a multidisciplinary approach. Describes embryological and genetic methods used in the experimental analysis of this organism. Outlines the use of Drosophila in the study of the development and function of the nervous system. (RT)

Is a multivariate consensus representation of genetic relationships among populations always meaningful?

PubMed Central

Moazami-Goudarzi, K; Laloë, D

2002-01-01

To determine the relationships among closely related populations or species, two methods are commonly used in the literature: phylogenetic reconstruction or multivariate analysis. The aim of this article is to assess the reliability of multivariate analysis. We describe a method that is based on principal component analysis and Mantel correlations, using a two-step process: The first step consists of a single-marker analysis and the second step tests if each marker reveals the same typology concerning population differentiation. We conclude that if single markers are not congruent, the compromise structure is not meaningful. Our model is not based on any particular mutation process and it can be applied to most of the commonly used genetic markers. This method is also useful to determine the contribution of each marker to the typology of populations. We test whether our method is efficient with two real data sets based on microsatellite markers. Our analysis suggests that for closely related populations, it is not always possible to accept the hypothesis that an increase in the number of markers will increase the reliability of the typology analysis. PMID:12242255

[Diabetes and predictive medicine--parallax of the present time].

PubMed

Rybka, J

2010-04-01

Predictive genetics uses genetic testing to estimate the risk in asymptomatic persons. Since in the case of multifactorial diseases predictive genetic analysis deals with findings which allow wider interpretation, it has a higher predictive value in expressly qualified diseases (monogenous) with high penetration compared to multifactorial (polygenous) diseases with high participation of environmental factors. In most "civilisation" (multifactorial) diseases including diabetes, heredity and environmental factors do not play two separate, independent roles. Instead, their interactions play a principal role. The new classification of diabetes is based on the implementation of not only ethiopathogenetic, but also genetic research. Diabetes mellitus type 1 (DM1T) is a polygenous multifactorial disease with the genetic component carrying about one half of the risk, the non-genetic one the other half. The study of the autoimmune nature of DM1T in connection with genetic analysis is going to bring about new insights in DM1T prediction. The author presents new pieces of knowledge on molecular genetics concerning certain specific types of diabetes. Issues relating to heredity in diabetes mellitus type 2 (DM2T) are even more complex. The disease has a polygenous nature, and the phenotype of a patient with DM2T, in addition to environmental factors, involves at least three, perhaps even tens of different genetic variations. At present, results at the genom-wide level appear to be most promising. The current concept of prediabetes is a realistic foundation for our prediction and prevention of DM2T. A multifactorial, multimarker approach based on our understanding of new pathophysiological factors of DM2T, tries to outline a "map" of prediabetes physiology, and if these tests are combined with sophisticated methods of genetic forecasting of DM2T, this may represent a significant step in our methodology of diabetes prediction. So far however, predictive genetics is limited by the interpretation of genetic predisposition and individualisation of the level of risk. There is no doubt that interpretation calls for co-operation with clinicians, while results of genetic analyses should presently be not uncritically overestimated. Predictive medicine, however, unquestionably fulfills the preventive focus of modern medicine, and genetic analysis is a perspective diagnostic method.

[Analytic methods for seed models with genotype x environment interactions].

PubMed

Zhu, J

1996-01-01

Genetic models with genotype effect (G) and genotype x environment interaction effect (GE) are proposed for analyzing generation means of seed quantitative traits in crops. The total genetic effect (G) is partitioned into seed direct genetic effect (G0), cytoplasm genetic of effect (C), and maternal plant genetic effect (Gm). Seed direct genetic effect (G0) can be further partitioned into direct additive (A) and direct dominance (D) genetic components. Maternal genetic effect (Gm) can also be partitioned into maternal additive (Am) and maternal dominance (Dm) genetic components. The total genotype x environment interaction effect (GE) can also be partitioned into direct genetic by environment interaction effect (G0E), cytoplasm genetic by environment interaction effect (CE), and maternal genetic by environment interaction effect (GmE). G0E can be partitioned into direct additive by environment interaction (AE) and direct dominance by environment interaction (DE) genetic components. GmE can also be partitioned into maternal additive by environment interaction (AmE) and maternal dominance by environment interaction (DmE) genetic components. Partitions of genetic components are listed for parent, F1, F2 and backcrosses. A set of parents, their reciprocal F1 and F2 seeds is applicable for efficient analysis of seed quantitative traits. MINQUE(0/1) method can be used for estimating variance and covariance components. Unbiased estimation for covariance components between two traits can also be obtained by the MINQUE(0/1) method. Random genetic effects in seed models are predictable by the Adjusted Unbiased Prediction (AUP) approach with MINQUE(0/1) method. The jackknife procedure is suggested for estimation of sampling variances of estimated variance and covariance components and of predicted genetic effects, which can be further used in a t-test for parameter. Unbiasedness and efficiency for estimating variance components and predicting genetic effects are tested by Monte Carlo simulations.

Joint multi-population analysis for genetic linkage of bipolar disorder or "wellness" to chromosome 4p.

PubMed

Visscher, P M; Haley, C S; Ewald, H; Mors, O; Egeland, J; Thiel, B; Ginns, E; Muir, W; Blackwood, D H

2005-02-05

To test the hypothesis that the same genetic loci confer susceptibility to, or protection from, disease in different populations, and that a combined analysis would improve the map resolution of a common susceptibility locus, we analyzed data from three studies that had reported linkage to bipolar disorder in a small region on chromosome 4p. Data sets comprised phenotypic information and genetic marker data on Scottish, Danish, and USA extended pedigrees. Across the three data sets, 913 individuals appeared in the pedigrees, 462 were classified, either as unaffected (323) or affected (139) with unipolar or bipolar disorder. A consensus linkage map was created from 14 microsatellite markers in a 33 cM region. Phenotypic and genetic data were analyzed using a variance component (VC) and allele sharing method. All previously reported elevated test statistics in the region were confirmed with one or both analysis methods, indicating the presence of one or more susceptibility genes to bipolar disorder in the three populations in the studied chromosome segment. When the results from both the VC and allele sharing method were considered, there was strong evidence for a susceptibility locus in the data from Scotland, some evidence in the data from Denmark and relatively less evidence in the data from the USA. The test statistics from the Scottish data set dominated the test statistics from the other studies, and no improved map resolution for a putative genetic locus underlying susceptibility in all three studies was obtained. Studies reporting linkage to the same region require careful scrutiny and preferably joint or meta analysis on the same basis in order to ensure that the results are truly comparable. (c) 2004 Wiley-Liss, Inc.

Machine learning applications in genetics and genomics.

PubMed

Libbrecht, Maxwell W; Noble, William Stafford

2015-06-01

The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets. Here, we provide an overview of machine learning applications for the analysis of genome sequencing data sets, including the annotation of sequence elements and epigenetic, proteomic or metabolomic data. We present considerations and recurrent challenges in the application of supervised, semi-supervised and unsupervised machine learning methods, as well as of generative and discriminative modelling approaches. We provide general guidelines to assist in the selection of these machine learning methods and their practical application for the analysis of genetic and genomic data sets.

Image analysis of the blood cells for cytomorphodiagnostics and control of the effectiveness treatment

NASA Astrophysics Data System (ADS)

Zhukotsky, Alexander V.; Kogan, Emmanuil M.; Kopylov, Victor F.; Marchenko, Oleg V.; Lomakin, O. A.

1994-07-01

A new method for morphodensitometric analysis of blood cells was applied for medically screening some ecological influence and infection pathologies. A complex algorithm of computational image processing was created for supra molecular restructurings of interphase chromatin of lymphocytes research. It includes specific methods of staining and unifies different quantitative analysis methods. Our experience with the use of a television image analyzer in cytological and immunological studies made it possible to carry out some research in morphometric analysis of chromatin structure in interphase lymphocyte nuclei in genetic and virus pathologies. In our study to characterize lymphocytes as an image-forming system by a rigorous mathematical description we used an approach involving contaminant evaluation of the topography of chromatin network intact and victims' lymphocytes. It is also possible to digitize data, which revealed significant distinctions between control and experiment. The method allows us to observe the minute structural changes in chromatin, especially eu- and hetero-chromatin that were previously studied by genetics only in chromosomes.

Analysing malaria drug trials on a per-individual or per-clone basis: a comparison of methods.

PubMed

Jaki, Thomas; Parry, Alice; Winter, Katherine; Hastings, Ian

2013-07-30

There are a variety of methods used to estimate the effectiveness of antimalarial drugs in clinical trials, invariably on a per-person basis. A person, however, may have more than one malaria infection present at the time of treatment. We evaluate currently used methods for analysing malaria trials on a per-individual basis and introduce a novel method to estimate the cure rate on a per-infection (clone) basis. We used simulated and real data to highlight the differences of the various methods. We give special attention to classifying outcomes as cured, recrudescent (infections that never fully cleared) or ambiguous on the basis of genetic markers at three loci. To estimate cure rates on a per-clone basis, we used the genetic information within an individual before treatment to determine the number of clones present. We used the genetic information obtained at the time of treatment failure to classify clones as recrudescence or new infections. On the per-individual level, we find that the most accurate methods of classification label an individual as newly infected if all alleles are different at the beginning and at the time of failure and as a recrudescence if all or some alleles were the same. The most appropriate analysis method is survival analysis or alternatively for complete data/per-protocol analysis a proportion estimate that treats new infections as successes. We show that the analysis of drug effectiveness on a per-clone basis estimates the cure rate accurately and allows more detailed evaluation of the performance of the treatment. Copyright © 2012 John Wiley & Sons, Ltd.

Forensic genetics and genomics: Much more than just a human affair.

PubMed

Arenas, Miguel; Pereira, Filipe; Oliveira, Manuela; Pinto, Nadia; Lopes, Alexandra M; Gomes, Veronica; Carracedo, Angel; Amorim, Antonio

2017-09-01

While traditional forensic genetics has been oriented towards using human DNA in criminal investigation and civil court cases, it currently presents a much wider application range, including not only legal situations sensu stricto but also and, increasingly often, to preemptively avoid judicial processes. Despite some difficulties, current forensic genetics is progressively incorporating the analysis of nonhuman genetic material to a greater extent. The analysis of this material-including other animal species, plants, or microorganisms-is now broadly used, providing ancillary evidence in criminalistics in cases such as animal attacks, trafficking of species, bioterrorism and biocrimes, and identification of fraudulent food composition, among many others. Here, we explore how nonhuman forensic genetics is being revolutionized by the increasing variety of genetic markers, the establishment of faster, less error-burdened and cheaper sequencing technologies, and the emergence and improvement of models, methods, and bioinformatics facilities.

Forensic genetics and genomics: Much more than just a human affair

PubMed Central

Oliveira, Manuela; Pinto, Nadia; Carracedo, Angel

2017-01-01

While traditional forensic genetics has been oriented towards using human DNA in criminal investigation and civil court cases, it currently presents a much wider application range, including not only legal situations sensu stricto but also and, increasingly often, to preemptively avoid judicial processes. Despite some difficulties, current forensic genetics is progressively incorporating the analysis of nonhuman genetic material to a greater extent. The analysis of this material—including other animal species, plants, or microorganisms—is now broadly used, providing ancillary evidence in criminalistics in cases such as animal attacks, trafficking of species, bioterrorism and biocrimes, and identification of fraudulent food composition, among many others. Here, we explore how nonhuman forensic genetics is being revolutionized by the increasing variety of genetic markers, the establishment of faster, less error-burdened and cheaper sequencing technologies, and the emergence and improvement of models, methods, and bioinformatics facilities. PMID:28934201

Convergent evidence from systematic analysis of GWAS revealed genetic basis of esophageal cancer.

PubMed

Gao, Xue-Xin; Gao, Lei; Wang, Jiu-Qiang; Qu, Su-Su; Qu, Yue; Sun, Hong-Lei; Liu, Si-Dang; Shang, Ying-Li

2016-07-12

Recent genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with risk of esophageal cancer (EC). However, investigation of genetic basis from the perspective of systematic biology and integrative genomics remains scarce.In this study, we explored genetic basis of EC based on GWAS data and implemented a series of bioinformatics methods including functional annotation, expression quantitative trait loci (eQTL) analysis, pathway enrichment analysis and pathway grouped network analysis.Two hundred and thirteen risk SNPs were identified, in which 44 SNPs were found to have significantly differential gene expression in esophageal tissues by eQTL analysis. By pathway enrichment analysis, 170 risk genes mapped by risk SNPs were enriched into 38 significant GO terms and 17 significant KEGG pathways, which were significantly grouped into 9 sub-networks by pathway grouped network analysis. The 9 groups of interconnected pathways were mainly involved with muscle cell proliferation, cellular response to interleukin-6, cell adhesion molecules, and ethanol oxidation, which might participate in the development of EC.Our findings provide genetic evidence and new insight for exploring the molecular mechanisms of EC.

Integrative Analysis of Prognosis Data on Multiple Cancer Subtypes

PubMed Central

Liu, Jin; Huang, Jian; Zhang, Yawei; Lan, Qing; Rothman, Nathaniel; Zheng, Tongzhang; Ma, Shuangge

2014-01-01

Summary In cancer research, profiling studies have been extensively conducted, searching for genes/SNPs associated with prognosis. Cancer is diverse. Examining the similarity and difference in the genetic basis of multiple subtypes of the same cancer can lead to a better understanding of their connections and distinctions. Classic meta-analysis methods analyze each subtype separately and then compare analysis results across subtypes. Integrative analysis methods, in contrast, analyze the raw data on multiple subtypes simultaneously and can outperform meta-analysis methods. In this study, prognosis data on multiple subtypes of the same cancer are analyzed. An AFT (accelerated failure time) model is adopted to describe survival. The genetic basis of multiple subtypes is described using the heterogeneity model, which allows a gene/SNP to be associated with prognosis of some subtypes but not others. A compound penalization method is developed to identify genes that contain important SNPs associated with prognosis. The proposed method has an intuitive formulation and is realized using an iterative algorithm. Asymptotic properties are rigorously established. Simulation shows that the proposed method has satisfactory performance and outperforms a penalization-based meta-analysis method and a regularized thresholding method. An NHL (non-Hodgkin lymphoma) prognosis study with SNP measurements is analyzed. Genes associated with the three major subtypes, namely DLBCL, FL, and CLL/SLL, are identified. The proposed method identifies genes that are different from alternatives and have important implications and satisfactory prediction performance. PMID:24766212

HIV-1 Transmission during Early Infection in Men Who Have Sex with Men: A Phylodynamic Analysis

DOE PAGES

Volz, Erik M.; Ionides, Edward; Romero-Severson, Ethan O.; ...

2013-12-10

Conventional epidemiological surveillance of infectious diseases is focused on characterization of incident infections and estimation of the number of prevalent infections. Advances in methods for the analysis of the population-level genetic variation of viruses can potentially provide information about donors, not just recipients, of infection. Genetic sequences from many viruses are increasingly abundant, especially HIV, which is routinely sequenced for surveillance of drug resistance mutations. In this study, we conducted a phylodynamic analysis of HIV genetic sequence data and surveillance data from a US population of men who have sex with men (MSM) and estimated incidence and transmission rates bymore » stage of infection.« less

HIV-1 Transmission during Early Infection in Men Who Have Sex with Men: A Phylodynamic Analysis

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

Volz, Erik M.; Ionides, Edward; Romero-Severson, Ethan O.

Conventional epidemiological surveillance of infectious diseases is focused on characterization of incident infections and estimation of the number of prevalent infections. Advances in methods for the analysis of the population-level genetic variation of viruses can potentially provide information about donors, not just recipients, of infection. Genetic sequences from many viruses are increasingly abundant, especially HIV, which is routinely sequenced for surveillance of drug resistance mutations. In this study, we conducted a phylodynamic analysis of HIV genetic sequence data and surveillance data from a US population of men who have sex with men (MSM) and estimated incidence and transmission rates bymore » stage of infection.« less

Using ancestry matching to combine family-based and unrelated samples for genome-wide association studies‡

PubMed Central

Crossett, Andrew; Kent, Brian P.; Klei, Lambertus; Ringquist, Steven; Trucco, Massimo; Roeder, Kathryn; Devlin, Bernie

2015-01-01

We propose a method to analyze family-based samples together with unrelated cases and controls. The method builds on the idea of matched case–control analysis using conditional logistic regression (CLR). For each trio within the family, a case (the proband) and matched pseudo-controls are constructed, based upon the transmitted and untransmitted alleles. Unrelated controls, matched by genetic ancestry, supplement the sample of pseudo-controls; likewise unrelated cases are also paired with genetically matched controls. Within each matched stratum, the case genotype is contrasted with control pseudo-control genotypes via CLR, using a method we call matched-CLR (mCLR). Eigenanalysis of numerous SNP genotypes provides a tool for mapping genetic ancestry. The result of such an analysis can be thought of as a multidimensional map, or eigenmap, in which the relative genetic similarities and differences amongst individuals is encoded in the map. Once constructed, new individuals can be projected onto the ancestry map based on their genotypes. Successful differentiation of individuals of distinct ancestry depends on having a diverse, yet representative sample from which to construct the ancestry map. Once samples are well-matched, mCLR yields comparable power to competing methods while ensuring excellent control over Type I error. PMID:20862653

Reproducible MRI Measurement of Adipose Tissue Volumes in Genetic and Dietary Rodent Obesity Models

PubMed Central

Johnson, David H.; Flask, Chris A.; Ernsberger, Paul R.; Wong, Wilbur C. K.; Wilson, David L.

2010-01-01

Purpose To develop ratio MRI [lipid/(lipid+water)] methods for assessing lipid depots and compare measurement variability to biological differences in lean controls (spontaneously hypertensive rats, SHRs), dietary obese (SHR-DO), and genetic/dietary obese (SHROBs) animals. Materials and Methods Images with and without CHESS water-suppression were processed using a semi-automatic method accounting for relaxometry, chemical shift, receive coil sensitivity, and partial volume. Results Partial volume correction improved results by 10–15%. Over six operators, volume variation was reduced to 1.9 ml from 30.6 ml for single-image-analysis with intensity inhomogeneity. For three acquisitions on the same animal, volume reproducibility was <1%. SHROBs had 6X visceral and 8X subcutaneous adipose tissue than SHRs. SHR-DOs had enlarged visceral depots (3X SHRs). SHROB had significantly more subcutaneous adipose tissue, indicating a strong genetic component to this fat depot. Liver ratios in SHR-DO and SHROB were higher than SHR, indicating elevated fat content. Among SHROBs, evidence suggested a phenotype SHROB* having elevated liver ratios and visceral adipose tissue volumes. Conclusion Effects of diet and genetics on obesity were significantly larger than variations due to image acquisition and analysis, indicating that these methods can be used to assess accumulation/depletion of lipid depots in animal models of obesity. PMID:18821617

Genetic alterations of hepatocellular carcinoma by random amplified polymorphic DNA analysis and cloning sequencing of tumor differential DNA fragment

PubMed Central

Xian, Zhi-Hong; Cong, Wen-Ming; Zhang, Shu-Hui; Wu, Meng-Chao

2005-01-01

AIM: To study the genetic alterations and their association with clinicopathological characteristics of hepatocellular carcinoma (HCC), and to find the tumor related DNA fragments. METHODS: DNA isolated from tumors and corresponding noncancerous liver tissues of 56 HCC patients was amplified by random amplified polymorphic DNA (RAPD) with 10 random 10-mer arbitrary primers. The RAPD bands showing obvious differences in tumor tissue DNA corresponding to that of normal tissue were separated, purified, cloned and sequenced. DNA sequences were analyzed and compared with GenBank data. RESULTS: A total of 56 cases of HCC were demonstrated to have genetic alterations, which were detected by at least one primer. The detestability of genetic alterations ranged from 20% to 70% in each case, and 17.9% to 50% in each primer. Serum HBV infection, tumor size, histological grade, tumor capsule, as well as tumor intrahepatic metastasis, might be correlated with genetic alterations on certain primers. A band with a higher intensity of 480 bp or so amplified fragments in tumor DNA relative to normal DNA could be seen in 27 of 56 tumor samples using primer 4. Sequence analysis of these fragments showed 91% homology with Homo sapiens double homeobox protein DUX10 gene. CONCLUSION: Genetic alterations are a frequent event in HCC, and tumor related DNA fragments have been found in this study, which may be associated with hepatocarcin-ogenesis. RAPD is an effective method for the identification and analysis of genetic alterations in HCC, and may provide new information for further evaluating the molecular mechanism of hepatocarcinogenesis. PMID:15996039

The challenge for genetic epidemiologists: how to analyze large numbers of SNPs in relation to complex diseases.

PubMed

Heidema, A Geert; Boer, Jolanda M A; Nagelkerke, Nico; Mariman, Edwin C M; van der A, Daphne L; Feskens, Edith J M

2006-04-21

Genetic epidemiologists have taken the challenge to identify genetic polymorphisms involved in the development of diseases. Many have collected data on large numbers of genetic markers but are not familiar with available methods to assess their association with complex diseases. Statistical methods have been developed for analyzing the relation between large numbers of genetic and environmental predictors to disease or disease-related variables in genetic association studies. In this commentary we discuss logistic regression analysis, neural networks, including the parameter decreasing method (PDM) and genetic programming optimized neural networks (GPNN) and several non-parametric methods, which include the set association approach, combinatorial partitioning method (CPM), restricted partitioning method (RPM), multifactor dimensionality reduction (MDR) method and the random forests approach. The relative strengths and weaknesses of these methods are highlighted. Logistic regression and neural networks can handle only a limited number of predictor variables, depending on the number of observations in the dataset. Therefore, they are less useful than the non-parametric methods to approach association studies with large numbers of predictor variables. GPNN on the other hand may be a useful approach to select and model important predictors, but its performance to select the important effects in the presence of large numbers of predictors needs to be examined. Both the set association approach and random forests approach are able to handle a large number of predictors and are useful in reducing these predictors to a subset of predictors with an important contribution to disease. The combinatorial methods give more insight in combination patterns for sets of genetic and/or environmental predictor variables that may be related to the outcome variable. As the non-parametric methods have different strengths and weaknesses we conclude that to approach genetic association studies using the case-control design, the application of a combination of several methods, including the set association approach, MDR and the random forests approach, will likely be a useful strategy to find the important genes and interaction patterns involved in complex diseases.

[Detection of genetically modified soy (Roundup-Ready) in processed food products].

PubMed

Hagen, M; Beneke, B

2000-01-01

In this study, the application of a qualitative and a quantitative method of analysis to detect genetically modified RR-Soy (Roundup-Ready Soy) in processed foods is described. A total of 179 various products containing soy such as baby food and diet products, soy drinks and desserts, tofu and tofu products, soy based meat substitutes, soy protein, breads, flour, granules, cereals, noodles, soy bean sprouts, fats and oils as well as condiments were investigated following the pattern of the section 35 LMBG-method L 23.01.22-1. The DNA was extracted from the samples and analysed using a soybean specific lectin gene PCR as well as a PCR, specific for the genetic modification. Additional, by means of PCR in combination with fluorescence-detection (TaqMan 5'-Nuclease Assay), suspicious samples were subjected to a real-time quantification of the percentage of genetically modified RR-Soy. The methods of analysis proved to be extremely sensitive and specific in regard to the food groups checked. The fats and oils, as well as the condiments were the exceptions in which amplifiable soy DNA could not be detected. The genetic modification of RR-Soy was detected in 34 samples. Eight of these samples contained more than 1% of RR-Soy. It is necessary to determine the percentage of transgenic soy in order to assess whether genetically modified ingredients were deliberately added, or whether they were caused by technically unavoidable contamination (for example during transportation and processing).

Revealing barriers and facilitators to use a new genetic test: comparison of three user involvement methods.

PubMed

Rhebergen, Martijn D F; Visser, Maaike J; Verberk, Maarten M; Lenderink, Annet F; van Dijk, Frank J H; Kezic, Sanja; Hulshof, Carel T J

2012-10-01

We compared three common user involvement methods in revealing barriers and facilitators from intended users that might influence their use of a new genetic test. The study was part of the development of a new genetic test on the susceptibility to hand eczema for nurses. Eighty student nurses participated in five focus groups (n = 33), 15 interviews (n = 15) or questionnaires (n = 32). For each method, data were collected until saturation. We compared the mean number of items and relevant remarks that could influence the use of the genetic test obtained per method, divided by the number of participants in that method. Thematic content analysis was performed using MAXQDA software. The focus groups revealed 30 unique items compared to 29 in the interviews and 21 in the questionnaires. The interviews produced more items and relevant remarks per participant (1.9 and 8.4 pp) than focus groups (0.9 and 4.8 pp) or questionnaires (0.7 and 2.3 pp). All three involvement methods revealed relevant barriers and facilitators to use a new genetic test. Focus groups and interviews revealed substantially more items than questionnaires. Furthermore, this study suggests a preference for the use of interviews because the number of items per participant was higher than for focus groups and questionnaires. This conclusion may be valid for other genetic tests as well.

Perceptron Genetic to Recognize Openning Strategy Ruy Lopez

NASA Astrophysics Data System (ADS)

Azmi, Zulfian; Mawengkang, Herman

2018-01-01

The application of Perceptron method is not effective for coding on hardware based systems because it is not real time learning. With Genetic algorithm approach in calculating and searching the best weight (fitness value) system will do learning only one iteration. And the results of this analysis were tested in the case of the introduction of the opening pattern of chess Ruy Lopez. The Analysis with Perceptron Model with Algorithm Approach Genetics from group Artificial Neural Network for open Ruy Lopez. The data is processed with base open chess, with step eight a position white Pion from end open chess. Using perceptron method have many input and one output process many weight and refraction until output equal goal. Data trained and test with software Matlab and system can recognize the chess opening Ruy Lopez or Not open Ruy Lopez with Real time.

Genetic Counseling: Implications for Community Counselors.

ERIC Educational Resources Information Center

Bodenhorn, Nancy; Lawson, Gerard

2003-01-01

Special issue of the "Journal of Health Psychology" (Vol. 7, No. 2, 2002) was reviewed. Articles covered a variety of qualitative studies conducted using an interpretive phenomenological analysis method to examine the interviews with people who had received genetic testing and counseling. Implications for the broader counseling field…

Quantitative analysis of fatty-acid-based biofuels produced by wild-type and genetically engineered cyanobacteria by gas chromatography-mass spectrometry.

PubMed

Guan, Wenna; Zhao, Hui; Lu, Xuefeng; Wang, Cong; Yang, Menglong; Bai, Fali

2011-11-11

Simple and rapid quantitative determination of fatty-acid-based biofuels is greatly important for the study of genetic engineering progress for biofuels production by microalgae. Ideal biofuels produced from biological systems should be chemically similar to petroleum, like fatty-acid-based molecules including free fatty acids, fatty acid methyl esters, fatty acid ethyl esters, fatty alcohols and fatty alkanes. This study founded a gas chromatography-mass spectrometry (GC-MS) method for simultaneous quantification of seven free fatty acids, nine fatty acid methyl esters, five fatty acid ethyl esters, five fatty alcohols and three fatty alkanes produced by wild-type Synechocystis PCC 6803 and its genetically engineered strain. Data obtained from GC-MS analyses were quantified using internal standard peak area comparisons. The linearity, limit of detection (LOD) and precision (RSD) of the method were evaluated. The results demonstrated that fatty-acid-based biofuels can be directly determined by GC-MS without derivation. Therefore, rapid and reliable quantitative analysis of fatty-acid-based biofuels produced by wild-type and genetically engineered cyanobacteria can be achieved using the GC-MS method founded in this work. Copyright © 2011 Elsevier B.V. All rights reserved.

Analysis of mitochondrial genetic diversity of Ustilago maydis in Mexico.

PubMed

Jiménez-Becerril, María F; Hernández-Delgado, Sanjuana; Solís-Oba, Myrna; González Prieto, Juan M

2018-01-01

The current understanding of the genetic diversity of the phytopathogenic fungus Ustilago maydis is limited. To determine the genetic diversity and structure of U. maydis, 48 fungal isolates were analyzed using mitochondrial simple sequence repeats (SSRs). Tumours (corn smut or 'huitlacoche') were collected from different Mexican states with diverse environmental conditions. Using bioinformatic tools, five microsatellites were identified within intergenic regions of the U. maydis mitochondrial genome. SSRMUM4 was the most polymorphic marker. The most common repeats were hexanucleotides. A total of 12 allelic variants were identified, with a mean of 2.4 alleles per locus. An estimate of the genetic diversity using analysis of molecular variance (AMOVA) revealed that the highest variance component is within states (84%), with moderate genetic differentiation between states (16%) (F ST  = 0.158). A dendrogram generated using the unweighted paired-grouping method with arithmetic averages (UPGMA) and the Bayesian analysis of population structure grouped the U. maydis isolates into two subgroups (K = 2) based on their shared SSRs.

Irradiation influence on the detection of genetic-modified soybeans

NASA Astrophysics Data System (ADS)

Villavicencio, A. L. C. H.; Araújo, M. M.; Baldasso, J. G.; Aquino, S.; Konietzny, U.; Greiner, R.

2004-09-01

Three soybean varieties were analyzed to evaluate the irradiation influence on the detection of genetic modification. Samples were treated in a 60Co facility at dose levels of 0, 500, 800, and 1000Gy. The seeds were at first analyzed by Comet Assay as a rapid screening irradiation detection method. Secondly, germination test was performed to detect the viability of irradiated soybeans. Finally, because of its high sensitivity, its specificity and rapidity the polimerase chain reaction was the method applied for genetic modified organism detection. The analysis of DNA by the single technique of microgel electrophoresis of single cells (DNA Comet Assay) showed that DNA damage increased with increasing radiation doses. No negative influence of irradiation on the genetic modification detection was found.

Genetic algorithms as global random search methods

NASA Technical Reports Server (NTRS)

Peck, Charles C.; Dhawan, Atam P.

1995-01-01

Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that the schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solutions and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.

Genetic algorithms as global random search methods

NASA Technical Reports Server (NTRS)

Peck, Charles C.; Dhawan, Atam P.

1995-01-01

Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that that schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solution and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.

Attitudes to Gun Control in an American Twin Sample: Sex Differences in the Causes of Variation.

PubMed

Eaves, Lindon J; Silberg, Judy L

2017-10-01

The genetic and social causes of individual differences in attitudes to gun control are estimated in a sample of senior male and female twin pairs in the United States. Genetic and environmental parameters were estimated by weighted least squares applied to polychoric correlations for monozygotic (MZ) and dizygotic (DZ) twins of both sexes. The analysis suggests twin similarity for attitudes to gun control in men is entirely genetic while that in women is purely social. Although the volunteer sample is small, the analysis illustrates how the well-tested concepts and methods of genetic epidemiology may be a fertile resource for deepening our scientific understanding of biological and social pathways that affect individual risk to gun violence.

Genetic Diversity Analysis of Medicinally Important Horticultural Crop Aegle marmelos by ISSR Markers.

PubMed

Mujeeb, Farina; Bajpai, Preeti; Pathak, Neelam; Verma, Smita Rastogi

2017-01-01

Inter simple sequence repeat (ISSR) markers help in identifying and determining the extent of genetic diversity in cultivars. Here, we describe their application in determining the genetic diversity of bael (Aegle marmelos Corr.). Universal ISSR primers are selected and their marker characteristics such as polymorphism information content, effective multiplex ratio and marker index have been evaluated. ISSR-PCR is then performed using universal ISSR primers to generate polymorphic bands. This information is used to determine the degree of genetic similarity among the bael varieties/accessions by cluster analysis using unweighted pair-group method with arithmetic averages (UPGMA). This technology is valuable for biodiversity conservation and for making an efficient choice of parents in breeding programs.

Analysis of spatial heterogeneity in normal epithelium and preneoplastic alterations in mouse prostate tumor models

PubMed Central

Valkonen, Mira; Ruusuvuori, Pekka; Kartasalo, Kimmo; Nykter, Matti; Visakorpi, Tapio; Latonen, Leena

2017-01-01

Cancer involves histological changes in tissue, which is of primary importance in pathological diagnosis and research. Automated histological analysis requires ability to computationally separate pathological alterations from normal tissue with all its variables. On the other hand, understanding connections between genetic alterations and histological attributes requires development of enhanced analysis methods suitable also for small sample sizes. Here, we set out to develop computational methods for early detection and distinction of prostate cancer-related pathological alterations. We use analysis of features from HE stained histological images of normal mouse prostate epithelium, distinguishing the descriptors for variability between ventral, lateral, and dorsal lobes. In addition, we use two common prostate cancer models, Hi-Myc and Pten+/− mice, to build a feature-based machine learning model separating the early pathological lesions provoked by these genetic alterations. This work offers a set of computational methods for separation of early neoplastic lesions in the prostates of model mice, and provides proof-of-principle for linking specific tumor genotypes to quantitative histological characteristics. The results obtained show that separation between different spatial locations within the organ, as well as classification between histologies linked to different genetic backgrounds, can be performed with very high specificity and sensitivity. PMID:28317907

Comparison of genetic diversity and population structure of Pacific Coast whitebark pine across multiple markers

Treesearch

Andrew D. Bower; Bryce A. Richardson; Valerie Hipkins; Regina Rochefort; Carol Aubry

2011-01-01

Analysis of "neutral" molecular markers and "adaptive" quantitative traits are common methods of assessing genetic diversity and population structure. Molecular markers typically reflect the effects of demographic and stochastic processes but are generally assumed to not reflect natural selection. Conversely, quantitative (or "adaptive")...

DETERMINATION OF GENETIC DIVERSITY AND PATERNITY IN THE GRAY-TAILED VOLE (MICROTUS CANICAUDUS) BY RAPD-PCR

EPA Science Inventory

Genetic relatedness of gray-tailed voles (Microtus canicaudus) was determined by random amplified polymorphic DNA (RAPD). This work is the first reported use of the RAPD method for pedigree analysis of M. canicaudus and demonstrates the feasibility of RAPD for assessing paternity...

Shedding subspecies: The influence of genetics on reptile subspecies taxonomy.

PubMed

Torstrom, Shannon M; Pangle, Kevin L; Swanson, Bradley J

2014-07-01

The subspecies concept influences multiple aspects of biology and management. The 'molecular revolution' altered traditional methods (morphological traits) of subspecies classification by applying genetic analyses resulting in alternative or contradictory classifications. We evaluated recent reptile literature for bias in the recommendations regarding subspecies status when genetic data were included. Reviewing characteristics of the study, genetic variables, genetic distance values and noting the species concepts, we found that subspecies were more likely elevated to species when using genetic analysis. However, there was no predictive relationship between variables used and taxonomic recommendation. There was a significant difference between the median genetic distance values when researchers elevated or collapsed a subspecies. Our review found nine different concepts of species used when recommending taxonomic change, and studies incorporating multiple species concepts were more likely to recommend a taxonomic change. Since using genetic techniques significantly alter reptile taxonomy there is a need to establish a standard method to determine the species-subspecies boundary in order to effectively use the subspecies classification for research and conservation purposes. Copyright © 2014 Elsevier Inc. All rights reserved.

Multiple Phenotype Association Tests Using Summary Statistics in Genome-Wide Association Studies

PubMed Central

Liu, Zhonghua; Lin, Xihong

2017-01-01

Summary We study in this paper jointly testing the associations of a genetic variant with correlated multiple phenotypes using the summary statistics of individual phenotype analysis from Genome-Wide Association Studies (GWASs). We estimated the between-phenotype correlation matrix using the summary statistics of individual phenotype GWAS analyses, and developed genetic association tests for multiple phenotypes by accounting for between-phenotype correlation without the need to access individual-level data. Since genetic variants often affect multiple phenotypes differently across the genome and the between-phenotype correlation can be arbitrary, we proposed robust and powerful multiple phenotype testing procedures by jointly testing a common mean and a variance component in linear mixed models for summary statistics. We computed the p-values of the proposed tests analytically. This computational advantage makes our methods practically appealing in large-scale GWASs. We performed simulation studies to show that the proposed tests maintained correct type I error rates, and to compare their powers in various settings with the existing methods. We applied the proposed tests to a GWAS Global Lipids Genetics Consortium summary statistics data set and identified additional genetic variants that were missed by the original single-trait analysis. PMID:28653391

Multiple phenotype association tests using summary statistics in genome-wide association studies.

PubMed

Liu, Zhonghua; Lin, Xihong

2018-03-01

We study in this article jointly testing the associations of a genetic variant with correlated multiple phenotypes using the summary statistics of individual phenotype analysis from Genome-Wide Association Studies (GWASs). We estimated the between-phenotype correlation matrix using the summary statistics of individual phenotype GWAS analyses, and developed genetic association tests for multiple phenotypes by accounting for between-phenotype correlation without the need to access individual-level data. Since genetic variants often affect multiple phenotypes differently across the genome and the between-phenotype correlation can be arbitrary, we proposed robust and powerful multiple phenotype testing procedures by jointly testing a common mean and a variance component in linear mixed models for summary statistics. We computed the p-values of the proposed tests analytically. This computational advantage makes our methods practically appealing in large-scale GWASs. We performed simulation studies to show that the proposed tests maintained correct type I error rates, and to compare their powers in various settings with the existing methods. We applied the proposed tests to a GWAS Global Lipids Genetics Consortium summary statistics data set and identified additional genetic variants that were missed by the original single-trait analysis. © 2017, The International Biometric Society.

A Spatial Statistical Model for Landscape Genetics

PubMed Central

Guillot, Gilles; Estoup, Arnaud; Mortier, Frédéric; Cosson, Jean François

2005-01-01

Landscape genetics is a new discipline that aims to provide information on how landscape and environmental features influence population genetic structure. The first key step of landscape genetics is the spatial detection and location of genetic discontinuities between populations. However, efficient methods for achieving this task are lacking. In this article, we first clarify what is conceptually involved in the spatial modeling of genetic data. Then we describe a Bayesian model implemented in a Markov chain Monte Carlo scheme that allows inference of the location of such genetic discontinuities from individual geo-referenced multilocus genotypes, without a priori knowledge on populational units and limits. In this method, the global set of sampled individuals is modeled as a spatial mixture of panmictic populations, and the spatial organization of populations is modeled through the colored Voronoi tessellation. In addition to spatially locating genetic discontinuities, the method quantifies the amount of spatial dependence in the data set, estimates the number of populations in the studied area, assigns individuals to their population of origin, and detects individual migrants between populations, while taking into account uncertainty on the location of sampled individuals. The performance of the method is evaluated through the analysis of simulated data sets. Results show good performances for standard data sets (e.g., 100 individuals genotyped at 10 loci with 10 alleles per locus), with high but also low levels of population differentiation (e.g., FST < 0.05). The method is then applied to a set of 88 individuals of wolverines (Gulo gulo) sampled in the northwestern United States and genotyped at 10 microsatellites. PMID:15520263

Systems genetics for drug target discovery

PubMed Central

Penrod, Nadia M.; Cowper-Sal_lari, Richard; Moore, Jason H.

2011-01-01

The collection and analysis of genomic data has the potential to reveal novel druggable targets by providing insight into the genetic basis of disease. However, the number of drugs, targeting new molecular entities, approved by the US Food and Drug Administration (FDA) has not increased in the years since the collection of genomic data has become commonplace. The paucity of translatable results can be partly attributed to conventional analysis methods that test one gene at a time in an effort to identify disease-associated factors as candidate drug targets. By disengaging genetic factors from their position within the genetic regulatory system, much of the information stored within the genomic data set is lost. Here we discuss how genomic data is used to identify disease-associated genes or genomic regions, how disease-associated regions are validated as functional targets, and the role network analysis can play in bridging the gap between data generation and effective drug target identification. PMID:21862141

Using microarray analysis to evaluate genetic polymorphisms involved in the metabolism of environmental chemicals.

PubMed

Ban, Susumu; Kondo, Tomoko; Ishizuka, Mayumi; Sasaki, Seiko; Konishi, Kanae; Washino, Noriaki; Fujita, Syoichi; Kishi, Reiko

2007-05-01

The field of molecular biology currently faces the need for a comprehensive method of evaluating individual differences derived from genetic variation in the form of single nucleotide polymorphisms (SNPs). SNPs in human genes are generally considered to be very useful in determining inherited genetic disorders, susceptibility to certain diseases, and cancer predisposition. Quick and accurate discrimination of SNPs is the key characteristic of technology used in DNA diagnostics. For this study, we first developed a DNA microarray and then evaluated its efficacy by determining the detection ability and validity of this method. Using DNA obtained from 380 pregnant Japanese women, we examined 13 polymorphisms of 9 genes, which are associated with the metabolism of environmental chemical compounds found in high frequency among Japanese populations. The ability to detect CYP1A1 I462V, CYP1B1 L432V, GSTP1 I105V and AhR R554K gene polymorphisms was above 98%, and agreement rates when compared with real time PCR analysis methods (kappa values) showed high validity: 0.98 (0.96), 0.97 (0.93), 0.90 (0.81), 0.90 (0.91), respectively. While this DNA microarray analysis should prove important as a method for initial screening, it is still necessary that we find better methods for improving the detection of other gene polymorphisms not part of this study.

GenomeFingerprinter: the genome fingerprint and the universal genome fingerprint analysis for systematic comparative genomics.

PubMed

Ai, Yuncan; Ai, Hannan; Meng, Fanmei; Zhao, Lei

2013-01-01

No attention has been paid on comparing a set of genome sequences crossing genetic components and biological categories with far divergence over large size range. We define it as the systematic comparative genomics and aim to develop the methodology. First, we create a method, GenomeFingerprinter, to unambiguously produce a set of three-dimensional coordinates from a sequence, followed by one three-dimensional plot and six two-dimensional trajectory projections, to illustrate the genome fingerprint of a given genome sequence. Second, we develop a set of concepts and tools, and thereby establish a method called the universal genome fingerprint analysis (UGFA). Particularly, we define the total genetic component configuration (TGCC) (including chromosome, plasmid, and phage) for describing a strain as a systematic unit, the universal genome fingerprint map (UGFM) of TGCC for differentiating strains as a universal system, and the systematic comparative genomics (SCG) for comparing a set of genomes crossing genetic components and biological categories. Third, we construct a method of quantitative analysis to compare two genomes by using the outcome dataset of genome fingerprint analysis. Specifically, we define the geometric center and its geometric mean for a given genome fingerprint map, followed by the Euclidean distance, the differentiate rate, and the weighted differentiate rate to quantitatively describe the difference between two genomes of comparison. Moreover, we demonstrate the applications through case studies on various genome sequences, giving tremendous insights into the critical issues in microbial genomics and taxonomy. We have created a method, GenomeFingerprinter, for rapidly computing, geometrically visualizing, intuitively comparing a set of genomes at genome fingerprint level, and hence established a method called the universal genome fingerprint analysis, as well as developed a method of quantitative analysis of the outcome dataset. These have set up the methodology of systematic comparative genomics based on the genome fingerprint analysis.

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.

Genetic methods improve accuracy of gender determination in beaver

USGS Publications Warehouse

Williams, C.L.; Breck, S.W.; Baker, B.W.

2004-01-01

Gender identification of sexually monomorphic mammals can be difficult. We used analysis of zinc-finger protein (Zfx and Zfy) DNA regions to determine gender of 96 beavers (Castor canadensis) from 3 areas and used these results to verify gender determined in the field. Gender was correctly determined for 86 (89.6%) beavers. Incorrect assignments were not attributed to errors in any one age or sex class. Although methods that can be used in the field (such as morphological methods) can provide reasonably accurate gender assignments in beavers, the genetic method might be preferred in certain situations.

Cluster ensemble based on Random Forests for genetic data.

PubMed

Alhusain, Luluah; Hafez, Alaaeldin M

2017-01-01

Clustering plays a crucial role in several application domains, such as bioinformatics. In bioinformatics, clustering has been extensively used as an approach for detecting interesting patterns in genetic data. One application is population structure analysis, which aims to group individuals into subpopulations based on shared genetic variations, such as single nucleotide polymorphisms. Advances in DNA sequencing technology have facilitated the obtainment of genetic datasets with exceptional sizes. Genetic data usually contain hundreds of thousands of genetic markers genotyped for thousands of individuals, making an efficient means for handling such data desirable. Random Forests (RFs) has emerged as an efficient algorithm capable of handling high-dimensional data. RFs provides a proximity measure that can capture different levels of co-occurring relationships between variables. RFs has been widely considered a supervised learning method, although it can be converted into an unsupervised learning method. Therefore, RF-derived proximity measure combined with a clustering technique may be well suited for determining the underlying structure of unlabeled data. This paper proposes, RFcluE, a cluster ensemble approach for determining the underlying structure of genetic data based on RFs. The approach comprises a cluster ensemble framework to combine multiple runs of RF clustering. Experiments were conducted on high-dimensional, real genetic dataset to evaluate the proposed approach. The experiments included an examination of the impact of parameter changes, comparing RFcluE performance against other clustering methods, and an assessment of the relationship between the diversity and quality of the ensemble and its effect on RFcluE performance. This paper proposes, RFcluE, a cluster ensemble approach based on RF clustering to address the problem of population structure analysis and demonstrate the effectiveness of the approach. The paper also illustrates that applying a cluster ensemble approach, combining multiple RF clusterings, produces more robust and higher-quality results as a consequence of feeding the ensemble with diverse views of high-dimensional genetic data obtained through bagging and random subspace, the two key features of the RF algorithm.

Touch imprint cytology with massively parallel sequencing (TIC-seq): a simple and rapid method to snapshot genetic alterations in tumors.

PubMed

Amemiya, Kenji; Hirotsu, Yosuke; Goto, Taichiro; Nakagomi, Hiroshi; Mochizuki, Hitoshi; Oyama, Toshio; Omata, Masao

2016-12-01

Identifying genetic alterations in tumors is critical for molecular targeting of therapy. In the clinical setting, formalin-fixed paraffin-embedded (FFPE) tissue is usually employed for genetic analysis. However, DNA extracted from FFPE tissue is often not suitable for analysis because of its low levels and poor quality. Additionally, FFPE sample preparation is time-consuming. To provide early treatment for cancer patients, a more rapid and robust method is required for precision medicine. We present a simple method for genetic analysis, called touch imprint cytology combined with massively paralleled sequencing (touch imprint cytology [TIC]-seq), to detect somatic mutations in tumors. We prepared FFPE tissues and TIC specimens from tumors in nine lung cancer patients and one patient with breast cancer. We found that the quality and quantity of TIC DNA was higher than that of FFPE DNA, which requires microdissection to enrich DNA from target tissues. Targeted sequencing using a next-generation sequencer obtained sufficient sequence data using TIC DNA. Most (92%) somatic mutations in lung primary tumors were found to be consistent between TIC and FFPE DNA. We also applied TIC DNA to primary and metastatic tumor tissues to analyze tumor heterogeneity in a breast cancer patient, and showed that common and distinct mutations among primary and metastatic sites could be classified into two distinct histological subtypes. TIC-seq is an alternative and feasible method to analyze genomic alterations in tumors by simply touching the cut surface of specimens to slides. © 2016 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

A Genome-Wide Association Meta-Analysis of Attention-Deficit/Hyperactivity Disorder Symptoms in Population-Based Paediatric Cohorts

PubMed Central

Groen-Blokhuis, Maria M.; Pourcain, Beate St.; Greven, Corina U.; Pappa, Irene; Tiesler, Carla M.T.; Ang, Wei; Nolte, Ilja M.; Vilor-Tejedor, Natalia; Bacelis, Jonas; Ebejer, Jane L.; Zhao, Huiying; Davies, Gareth E.; Ehli, Erik A.; Evans, David M.; Fedko, Iryna O.; Guxens, Mònica; Hottenga, Jouke-Jan; Hudziak, James J.; Jugessur, Astanand; Kemp, John P.; Krapohl, Eva; Martin, Nicholas G.; Murcia, Mario; Myhre, Ronny; Ormel, Johan; Ring, Susan M.; Standl, Marie; Stergiakouli, Evie; Stoltenberg, Camilla; Thiering, Elisabeth; Timpson, Nicholas J.; Trzaskowski, Maciej; van der Most, Peter J.; Wang, Carol; Nyholt, Dale R.; Medland, Sarah E.; Neale, Benjamin; Jacobsson, Bo; Sunyer, Jordi; Hartman, Catharina A.; Whitehouse, Andrew J.O.; Pennell, Craig E.; Heinrich, Joachim; Plomin, Robert; Smith, George Davey; Tiemeier, Henning; Posthuma, Danielle; Boomsma, Dorret I.

2016-01-01

Objective To elucidate the influence of common genetic variants on childhood attention-deficit/hyperactivity disorder (ADHD) symptoms, to identify genetic variants that explain its high heritability, and to investigate the genetic overlap of ADHD symptom scores with ADHD diagnosis. Method Within the EArly Genetics and Lifecourse Epidemiology (EAGLE) consortium, genome-wide single nucleotide polymorphisms (SNPs) and ADHD symptom scores were available for 17,666 children (< 13 years) from nine population-based cohorts. SNP-based heritability was estimated in data from the three largest cohorts. Meta-analysis based on genome-wide association (GWA) analyses with SNPs was followed by gene-based association tests, and the overlap in results with a meta-analysis in the Psychiatric Genomics Consortium (PGC) case-control ADHD study was investigated. Results SNP-based heritability ranged from 5% to 34%, indicating that variation in common genetic variants influences ADHD symptom scores. The meta-analysis did not detect genome-wide significant SNPs, but three genes, lying close to each other with SNPs in high linkage disequilibrium (LD), showed a gene-wide significant association (p values between 1.46×10-6 and 2.66×10-6). One gene, WASL, is involved in neuronal development. Both SNP- and gene-based analyses indicated overlap with the PGC meta-analysis results with the genetic correlation estimated at 0.96. Conclusion The SNP-based heritability for ADHD symptom scores indicates a polygenic architecture and genes involved in neurite outgrowth are possibly involved. Continuous and dichotomous measures of ADHD appear to assess a genetically common phenotype. A next step is to combine data from population-based and case-control cohorts in genetic association studies to increase sample size and improve statistical power for identifying genetic variants. PMID:27663945

WGCNA: an R package for weighted correlation network analysis.

PubMed

Langfelder, Peter; Horvath, Steve

2008-12-29

Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/Rpackages/WGCNA.

Development and interlaboratory validation of quantitative polymerase chain reaction method for screening analysis of genetically modified soybeans.

PubMed

Takabatake, Reona; Onishi, Mari; Koiwa, Tomohiro; Futo, Satoshi; Minegishi, Yasutaka; Akiyama, Hiroshi; Teshima, Reiko; Kurashima, Takeyo; Mano, Junichi; Furui, Satoshi; Kitta, Kazumi

2013-01-01

A novel real-time polymerase chain reaction (PCR)-based quantitative screening method was developed for three genetically modified soybeans: RRS, A2704-12, and MON89788. The 35S promoter (P35S) of cauliflower mosaic virus is introduced into RRS and A2704-12 but not MON89788. We then designed a screening method comprised of the combination of the quantification of P35S and the event-specific quantification of MON89788. The conversion factor (Cf) required to convert the amount of a genetically modified organism (GMO) from a copy number ratio to a weight ratio was determined experimentally. The trueness and precision were evaluated as the bias and reproducibility of relative standard deviation (RSDR), respectively. The determined RSDR values for the method were less than 25% for both targets. We consider that the developed method would be suitable for the simple detection and approximate quantification of GMO.

A novel method for finding the initial structure parameters of optical systems via a genetic algorithm

NASA Astrophysics Data System (ADS)

Jun, LIU; Huang, Wei; Hongjie, Fan

2016-02-01

A novel method for finding the initial structure parameters of an optical system via the genetic algorithm (GA) is proposed in this research. Usually, optical designers start their designs from the commonly used structures from a patent database; however, it is time consuming to modify the patented structures to meet the specification. A high-performance design result largely depends on the choice of the starting point. Accordingly, it would be highly desirable to be able to calculate the initial structure parameters automatically. In this paper, a method that combines a genetic algorithm and aberration analysis is used to determine an appropriate initial structure of an optical system. We use a three-mirror system as an example to demonstrate the validity and reliability of this method. On-axis and off-axis telecentric three-mirror systems are obtained based on this method.

Whole genome sequence analysis of unidentified genetically modified papaya for development of a specific detection method.

PubMed

Nakamura, Kosuke; Kondo, Kazunari; Akiyama, Hiroshi; Ishigaki, Takumi; Noguchi, Akio; Katsumata, Hiroshi; Takasaki, Kazuto; Futo, Satoshi; Sakata, Kozue; Fukuda, Nozomi; Mano, Junichi; Kitta, Kazumi; Tanaka, Hidenori; Akashi, Ryo; Nishimaki-Mogami, Tomoko

2016-08-15

Identification of transgenic sequences in an unknown genetically modified (GM) papaya (Carica papaya L.) by whole genome sequence analysis was demonstrated. Whole genome sequence data were generated for a GM-positive fresh papaya fruit commodity detected in monitoring using real-time polymerase chain reaction (PCR). The sequences obtained were mapped against an open database for papaya genome sequence. Transgenic construct- and event-specific sequences were identified as a GM papaya developed to resist infection from a Papaya ringspot virus. Based on the transgenic sequences, a specific real-time PCR detection method for GM papaya applicable to various food commodities was developed. Whole genome sequence analysis enabled identifying unknown transgenic construct- and event-specific sequences in GM papaya and development of a reliable method for detecting them in papaya food commodities. Copyright © 2016 Elsevier Ltd. All rights reserved.

Assessing genetic divergence in interspecific hybrids of Aechmea gomosepala and A. recurvata var. recurvata using inflorescence characteristics and sequence-related amplified polymorphism markers.

PubMed

Zhang, F; Ge, Y Y; Wang, W Y; Shen, X L; Yu, X Y

2012-12-03

Conventional hybridization and selection techniques have aided the development of new ornamental crop cultivars. However, little information is available on the genetic divergence of bromeliad hybrids. In the present study, we investigated the genetic variability in interspecific hybrids of Aechmea gomosepala and A. recurvata var. recurvata using inflorescence characteristics and sequence-related amplified polymorphism (SRAP) markers. The morphological analysis showed that the putative hybrids were intermediate between both parental species with respect to inflorescence characteristics. The 16 SRAP primer combinations yield 265 bands, among which 154 (57.72%) were polymorphic. The genetic similarity was an average of 0.59 and ranged from 0.21 to 0.87, indicating moderate genetic divergence among the hybrids. The unweighted pair group method with arithmetic average (UPGMA)-based cluster analysis distinguished the hybrids from their parents with a genetic distance coefficient of 0.54. The cophenetic correlation was 0.93, indicating a good fit between the dendrogram and the original distance matrix. The two-dimensional plot from the principal coordinate analysis showed that the hybrids were intermediately dispersed between both parents, corresponding to the results of the UPGMA cluster and the morphological analysis. These results suggest that SRAP markers could help to identify breeders, characterize F(1) hybrids of bromeliads at an early stage, and expedite genetic improvement of bromeliad cultivars.

Transcriptome analysis in non-model species: a new method for the analysis of heterologous hybridization on microarrays

PubMed Central

2010-01-01

Background Recent developments in high-throughput methods of analyzing transcriptomic profiles are promising for many areas of biology, including ecophysiology. However, although commercial microarrays are available for most common laboratory models, transcriptome analysis in non-traditional model species still remains a challenge. Indeed, the signal resulting from heterologous hybridization is low and difficult to interpret because of the weak complementarity between probe and target sequences, especially when no microarray dedicated to a genetically close species is available. Results We show here that transcriptome analysis in a species genetically distant from laboratory models is made possible by using MAXRS, a new method of analyzing heterologous hybridization on microarrays. This method takes advantage of the design of several commercial microarrays, with different probes targeting the same transcript. To illustrate and test this method, we analyzed the transcriptome of king penguin pectoralis muscle hybridized to Affymetrix chicken microarrays, two organisms separated by an evolutionary distance of approximately 100 million years. The differential gene expression observed between different physiological situations computed by MAXRS was confirmed by real-time PCR on 10 genes out of 11 tested. Conclusions MAXRS appears to be an appropriate method for gene expression analysis under heterologous hybridization conditions. PMID:20509979

The potential of genetic algorithms for conceptual design of rotor systems

NASA Technical Reports Server (NTRS)

Crossley, William A.; Wells, Valana L.; Laananen, David H.

1993-01-01

The capabilities of genetic algorithms as a non-calculus based, global search method make them potentially useful in the conceptual design of rotor systems. Coupling reasonably simple analysis tools to the genetic algorithm was accomplished, and the resulting program was used to generate designs for rotor systems to match requirements similar to those of both an existing helicopter and a proposed helicopter design. This provides a comparison with the existing design and also provides insight into the potential of genetic algorithms in design of new rotors.

Reliably Discriminating Stock Structure with Genetic Markers:Mixture Models with Robust and Fast Computation.

PubMed

Foster, Scott D; Feutry, Pierre; Grewe, Peter M; Berry, Oliver; Hui, Francis K C; Davies, Campbell R

2018-06-26

Delineating naturally occurring and self-sustaining sub-populations (stocks) of a species is an important task, especially for species harvested from the wild. Despite its central importance to natural resource management, analytical methods used to delineate stocks are often, and increasingly, borrowed from superficially similar analytical tasks in human genetics even though models specifically for stock identification have been previously developed. Unfortunately, the analytical tasks in resource management and human genetics are not identical { questions about humans are typically aimed at inferring ancestry (often referred to as 'admixture') rather than breeding stocks. In this article, we argue, and show through simulation experiments and an analysis of yellowfin tuna data, that ancestral analysis methods are not always appropriate for stock delineation. In this work, we advocate a variant of a previouslyintroduced and simpler model that identifies stocks directly. We also highlight that the computational aspects of the analysis, irrespective of the model, are difficult. We introduce some alternative computational methods and quantitatively compare these methods to each other and to established methods. We also present a method for quantifying uncertainty in model parameters and in assignment probabilities. In doing so, we demonstrate that point estimates can be misleading. One of the computational strategies presented here, based on an expectation-maximisation algorithm with judiciously chosen starting values, is robust and has a modest computational cost. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

A Genealogical Interpretation of Principal Components Analysis

PubMed Central

McVean, Gil

2009-01-01

Principal components analysis, PCA, is a statistical method commonly used in population genetics to identify structure in the distribution of genetic variation across geographical location and ethnic background. However, while the method is often used to inform about historical demographic processes, little is known about the relationship between fundamental demographic parameters and the projection of samples onto the primary axes. Here I show that for SNP data the projection of samples onto the principal components can be obtained directly from considering the average coalescent times between pairs of haploid genomes. The result provides a framework for interpreting PCA projections in terms of underlying processes, including migration, geographical isolation, and admixture. I also demonstrate a link between PCA and Wright's fst and show that SNP ascertainment has a largely simple and predictable effect on the projection of samples. Using examples from human genetics, I discuss the application of these results to empirical data and the implications for inference. PMID:19834557

Performance Analysis of Combined Methods of Genetic Algorithm and K-Means Clustering in Determining the Value of Centroid

NASA Astrophysics Data System (ADS)

Adya Zizwan, Putra; Zarlis, Muhammad; Budhiarti Nababan, Erna

2017-12-01

The determination of Centroid on K-Means Algorithm directly affects the quality of the clustering results. Determination of centroid by using random numbers has many weaknesses. The GenClust algorithm that combines the use of Genetic Algorithms and K-Means uses a genetic algorithm to determine the centroid of each cluster. The use of the GenClust algorithm uses 50% chromosomes obtained through deterministic calculations and 50% is obtained from the generation of random numbers. This study will modify the use of the GenClust algorithm in which the chromosomes used are 100% obtained through deterministic calculations. The results of this study resulted in performance comparisons expressed in Mean Square Error influenced by centroid determination on K-Means method by using GenClust method, modified GenClust method and also classic K-Means.

Further evidence for the increased power of LOD scores compared with nonparametric methods.

PubMed

Durner, M; Vieland, V J; Greenberg, D A

1999-01-01

In genetic analysis of diseases in which the underlying model is unknown, "model free" methods-such as affected sib pair (ASP) tests-are often preferred over LOD-score methods, although LOD-score methods under the correct or even approximately correct model are more powerful than ASP tests. However, there might be circumstances in which nonparametric methods will outperform LOD-score methods. Recently, Dizier et al. reported that, in some complex two-locus (2L) models, LOD-score methods with segregation analysis-derived parameters had less power to detect linkage than ASP tests. We investigated whether these particular models, in fact, represent a situation that ASP tests are more powerful than LOD scores. We simulated data according to the parameters specified by Dizier et al. and analyzed the data by using a (a) single locus (SL) LOD-score analysis performed twice, under a simple dominant and a recessive mode of inheritance (MOI), (b) ASP methods, and (c) nonparametric linkage (NPL) analysis. We show that SL analysis performed twice and corrected for the type I-error increase due to multiple testing yields almost as much linkage information as does an analysis under the correct 2L model and is more powerful than either the ASP method or the NPL method. We demonstrate that, even for complex genetic models, the most important condition for linkage analysis is that the assumed MOI at the disease locus being tested is approximately correct, not that the inheritance of the disease per se is correctly specified. In the analysis by Dizier et al., segregation analysis led to estimates of dominance parameters that were grossly misspecified for the locus tested in those models in which ASP tests appeared to be more powerful than LOD-score analyses.

Recruitment and Participation of Recreational Runners in a Large Epidemiological and Genetic Research Study: Retrospective Data Analysis.

PubMed

Manzanero, Silvia; Kozlovskaia, Maria; Vlahovich, Nicole; Hughes, David C

2018-05-23

With the increasing capacity for remote collection of both data and samples for medical research, a thorough assessment is needed to determine the association of population characteristics and recruitment methodologies with response rates. The aim of this research was to assess population representativeness in a two-stage study of health and injury in recreational runners, which consisted of an epidemiological arm and genetic analysis. The cost and success of various classical and internet-based methods were analyzed, and demographic representativeness was assessed for recruitment to the epidemiological survey, reported willingness to participate in the genetic arm of the study, actual participation, sample return, and approval for biobank storage. A total of 4965 valid responses were received, of which 1664 were deemed eligible for genetic analysis. Younger age showed a negative association with initial recruitment rate, expressed willingness to participate in genetic analysis, and actual participation. Additionally, female sex was associated with higher initial recruitment rates, and ethnic origin impacted willingness to participate in the genetic analysis (all P<.001). The sharp decline in retention through the different stages of the study in young respondents suggests the necessity to develop specific recruitment and retention strategies when investigating a young, physically active population. ©Silvia Manzanero, Maria Kozlovskaia, Nicole Vlahovich, David C Hughes. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 23.05.2018.

Defining the consequences of genetic variation on a proteome–wide scale

PubMed Central

Chick, Joel M.; Munger, Steven C.; Simecek, Petr; Huttlin, Edward L.; Choi, Kwangbom; Gatti, Daniel M.; Raghupathy, Narayanan; Svenson, Karen L.; Churchill, Gary A.; Gygi, Steven P.

2016-01-01

Genetic variation modulates protein expression through both transcriptional and post-transcriptional mechanisms. To characterize the consequences of natural genetic diversity on the proteome, here we combine a multiplexed, mass spectrometry-based method for protein quantification with an emerging outbred mouse model containing extensive genetic variation from eight inbred founder strains. By measuring genome-wide transcript and protein expression in livers from 192 Diversity outbred mice, we identify 2,866 protein quantitative trait loci (pQTL) with twice as many local as distant genetic variants. These data support distinct transcriptional and post-transcriptional models underlying the observed pQTL effects. Using a sensitive approach to mediation analysis, we often identified a second protein or transcript as the causal mediator of distant pQTL. Our analysis reveals an extensive network of direct protein–protein interactions. Finally, we show that local genotype can provide accurate predictions of protein abundance in an independent cohort of collaborative cross mice. PMID:27309819

Genetic diversity and structure of Atta robusta (Hymenoptera, Formicidae, Attini), an endangered species endemic to the restinga ecoregion

PubMed Central

dos Reis, Evelyze Pinheiro; Fernandes Salomão, Tânia Maria; de Oliveira Campos, Lucio Antonio; Tavares, Mara Garcia

2014-01-01

The genetic diversity and structure of the ant Atta robusta were assessed by ISSR (inter-simple sequence repeats) in 72 colonies collected from 10 localities in the Brazilian states of Espírito Santo (48 colonies) and Rio de Janeiro (24 colonies). The ISSR pattern included 67 bands, 51 of them (76.1%) polymorphic. Analysis of molecular variance (AMOVA) revealed a high level (57.4%) of inter-population variation, which suggested a high degree of genetic structure that was confirmed by UPGMA (unweighted pair-group method using an arithmetic average) cluster analysis. The significant correlation between genetic and geographic distances (r = 0.64, p < 0.05) indicated isolation that reflected the distance between locations. Overall, the populations were found to be genetically divergent. This finding indicates the need for management plans to preserve and reduce the risk of extinction of A. robusta. PMID:25249782

Genetic diversity and structure of Atta robusta (Hymenoptera, Formicidae, Attini), an endangered species endemic to the restinga ecoregion.

PubMed

Dos Reis, Evelyze Pinheiro; Fernandes Salomão, Tânia Maria; de Oliveira Campos, Lucio Antonio; Tavares, Mara Garcia

2014-09-01

The genetic diversity and structure of the ant Atta robusta were assessed by ISSR (inter-simple sequence repeats) in 72 colonies collected from 10 localities in the Brazilian states of Espírito Santo (48 colonies) and Rio de Janeiro (24 colonies). The ISSR pattern included 67 bands, 51 of them (76.1%) polymorphic. Analysis of molecular variance (AMOVA) revealed a high level (57.4%) of inter-population variation, which suggested a high degree of genetic structure that was confirmed by UPGMA (unweighted pair-group method using an arithmetic average) cluster analysis. The significant correlation between genetic and geographic distances (r = 0.64, p < 0.05) indicated isolation that reflected the distance between locations. Overall, the populations were found to be genetically divergent. This finding indicates the need for management plans to preserve and reduce the risk of extinction of A. robusta.

Genetic analysis of circulating tumor cells in pancreatic cancer patients: A pilot study.

PubMed

Görner, Karin; Bachmann, Jeannine; Holzhauer, Claudia; Kirchner, Roland; Raba, Katharina; Fischer, Johannes C; Martignoni, Marc E; Schiemann, Matthias; Alunni-Fabbroni, Marianna

2015-07-01

Pancreatic cancer is one of the most aggressive malignant tumors, mainly due to an aggressive metastasis spreading. In recent years, circulating tumor cells became associated to tumor metastasis. Little is known about their expression profiles. The aim of this study was to develop a complete workflow making it possible to isolate circulating tumor cells from patients with pancreatic cancer and their genetic characterization. We show that the proposed workflow offers a technical sensitivity and specificity high enough to detect and isolate single tumor cells. Moreover our approach makes feasible to genetically characterize single CTCs. Our work discloses a complete workflow to detect, count and genetically analyze individual CTCs isolated from blood samples. This method has a central impact on the early detection of metastasis development. The combination of cell quantification and genetic analysis provides the clinicians with a powerful tool not available so far. Copyright © 2015. Published by Elsevier Inc.

Computational Methods to Work as First-Pass Filter in Deleterious SNP Analysis of Alkaptonuria

PubMed Central

Magesh, R.; George Priya Doss, C.

2012-01-01

A major challenge in the analysis of human genetic variation is to distinguish functional from nonfunctional SNPs. Discovering these functional SNPs is one of the main goals of modern genetics and genomics studies. There is a need to effectively and efficiently identify functionally important nsSNPs which may be deleterious or disease causing and to identify their molecular effects. The prediction of phenotype of nsSNPs by computational analysis may provide a good way to explore the function of nsSNPs and its relationship with susceptibility to disease. In this context, we surveyed and compared variation databases along with in silico prediction programs to assess the effects of deleterious functional variants on protein functions. In other respects, we attempted these methods to work as first-pass filter to identify the deleterious substitutions worth pursuing for further experimental research. In this analysis, we used the existing computational methods to explore the mutation-structure-function relationship in HGD gene causing alkaptonuria. PMID:22606059

Mapping heritability and molecular genetic associations with cortical features using probabilistic brain atlases: methods and applications to schizophrenia.

PubMed

Cannon, Tyrone D; Thompson, Paul M; van Erp, Theo G M; Huttunen, Matti; Lonnqvist, Jouko; Kaprio, Jaakko; Toga, Arthur W

2006-01-01

There is an urgent need to decipher the complex nature of genotype-phenotype relationships within the multiple dimensions of brain structure and function that are compromised in neuropsychiatric syndromes such as schizophrenia. Doing so requires sophisticated methodologies to represent population variability in neural traits and to probe their heritable and molecular genetic bases. We have recently developed and applied computational algorithms to map the heritability of, as well as genetic linkage and association to, neural features encoded using brain imaging in the context of three-dimensional (3D), populationbased, statistical brain atlases. One set of algorithms builds on our prior work using classical twin study methods to estimate heritability by fitting biometrical models for additive genetic, unique, and common environmental influences. Another set of algorithms performs regression-based (Haseman-Elston) identical-bydescent linkage analysis and genetic association analysis of DNA polymorphisms in relation to neural traits of interest in the same 3D population-based brain atlas format. We demonstrate these approaches using samples of healthy monozygotic (MZ) and dizygotic (DZ) twin pairs, as well as MZ and DZ twin pairs discordant for schizophrenia, but the methods can be generalized to other classes of relatives and to other diseases. The results confirm prior evidence of genetic influences on gray matter density in frontal brain regions. They also provide converging evidence that the chromosome 1q42 region is relevant to schizophrenia by demonstrating linkage and association of markers of the Transelin-Associated-Factor-X and Disrupted-In- Schizophrenia-1 genes with prefrontal cortical gray matter deficits in twins discordant for schizophrenia.

Genetic Diversity and Genetic Relationships of Purple Willow (Salix purpurea L.) from Natural Locations

PubMed Central

Prinz, Kathleen; Przyborowski, Jerzy A.

2017-01-01

In this study, the genetic diversity and structure of 13 natural locations of Salix purpurea were determined with the use of AFLP (amplified length polymorphism), RAPD (randomly amplified polymorphic DNA) and ISSR (inter-simple sequence repeats). The genetic relationships between 91 examined S. purpurea genotypes were evaluated by analyses of molecular variance (AMOVA), principal coordinates analyses (PCoA) and UPGMA (unweighted pair group method with arithmetic mean) dendrograms for both single marker types and a combination of all marker systems. The locations were assigned to distinct regions and the analysis of AMOVA (analysis of molecular variance) revealed a high genetic diversity within locations. The genetic diversity between both regions and locations was relatively low, but typical for many woody plant species. The results noted for the analyzed marker types were generally comparable with few differences in the genetic relationships among S. purpurea locations. A combination of several marker systems could thus be ideally suited to understand genetic diversity patterns of the species. This study makes the first attempt to broaden our knowledge of the genetic parameters of the purple willow (S. purpurea) from natural location for research and several applications, inter alia breeding purposes. PMID:29301207

Genetic diversity of Pinus nigra Arn. populations in Southern Spain and Northern Morocco revealed by inter-simple sequence repeat profiles.

PubMed

Rubio-Moraga, Angela; Candel-Perez, David; Lucas-Borja, Manuel E; Tiscar, Pedro A; Viñegla, Benjamin; Linares, Juan C; Gómez-Gómez, Lourdes; Ahrazem, Oussama

2012-01-01

Eight Pinus nigra Arn. populations from Southern Spain and Northern Morocco were examined using inter-simple sequence repeat markers to characterize the genetic variability amongst populations. Pair-wise population genetic distance ranged from 0.031 to 0.283, with a mean of 0.150 between populations. The highest inter-population average distance was between PaCU from Cuenca and YeCA from Cazorla, while the lowest distance was between TaMO from Morocco and MA Sierra Mágina populations. Analysis of molecular variance (AMOVA) and Nei's genetic diversity analyses revealed higher genetic variation within the same population than among different populations. Genetic differentiation (Gst) was 0.233. Cuenca showed the highest Nei's genetic diversity followed by the Moroccan region, Sierra Mágina, and Cazorla region. However, clustering of populations was not in accordance with their geographical locations. Principal component analysis showed the presence of two major groups-Group 1 contained all populations from Cuenca while Group 2 contained populations from Cazorla, Sierra Mágina and Morocco-while Bayesian analysis revealed the presence of three clusters. The low genetic diversity observed in PaCU and YeCA is probably a consequence of inappropriate management since no estimation of genetic variability was performed before the silvicultural treatments. Data indicates that the inter-simple sequence repeat (ISSR) method is sufficiently informative and powerful to assess genetic variability among populations of P. nigra.

Genetic Diversity of Pinus nigra Arn. Populations in Southern Spain and Northern Morocco Revealed By Inter-Simple Sequence Repeat Profiles †

PubMed Central

Rubio-Moraga, Angela; Candel-Perez, David; Lucas-Borja, Manuel E.; Tiscar, Pedro A.; Viñegla, Benjamin; Linares, Juan C.; Gómez-Gómez, Lourdes; Ahrazem, Oussama

2012-01-01

Eight Pinus nigra Arn. populations from Southern Spain and Northern Morocco were examined using inter-simple sequence repeat markers to characterize the genetic variability amongst populations. Pair-wise population genetic distance ranged from 0.031 to 0.283, with a mean of 0.150 between populations. The highest inter-population average distance was between PaCU from Cuenca and YeCA from Cazorla, while the lowest distance was between TaMO from Morocco and MA Sierra Mágina populations. Analysis of molecular variance (AMOVA) and Nei’s genetic diversity analyses revealed higher genetic variation within the same population than among different populations. Genetic differentiation (Gst) was 0.233. Cuenca showed the highest Nei’s genetic diversity followed by the Moroccan region, Sierra Mágina, and Cazorla region. However, clustering of populations was not in accordance with their geographical locations. Principal component analysis showed the presence of two major groups—Group 1 contained all populations from Cuenca while Group 2 contained populations from Cazorla, Sierra Mágina and Morocco—while Bayesian analysis revealed the presence of three clusters. The low genetic diversity observed in PaCU and YeCA is probably a consequence of inappropriate management since no estimation of genetic variability was performed before the silvicultural treatments. Data indicates that the inter-simple sequence repeat (ISSR) method is sufficiently informative and powerful to assess genetic variability among populations of P. nigra. PMID:22754321

Genetic diversity of the Andean tuber-bearing species, oca (Oxalis tuberosa Mol.), investigated by inter-simple sequence repeats.

PubMed

Pissard, A; Ghislain, M; Bertin, P

2006-01-01

The Andean tuber-bearing species, Oxalis tuberosa Mol., is a vegetatively propagated crop cultivated in the uplands of the Andes. Its genetic diversity was investigated in the present study using the inter-simple sequence repeat (ISSR) technique. Thirty-two accessions originating from South America (Argentina, Bolivia, Chile, and Peru) and maintained in vitro were chosen to represent the ecogeographic diversity of its cultivation area. Twenty-two primers were tested and 9 were selected according to fingerprinting quality and reproducibility. Genetic diversity analysis was performed with 90 markers. Jaccard's genetic distance between accessions ranged from 0 to 0.49 with an average of 0.28 +/- 0.08 (mean +/- SD). Dendrogram (UPGMA (unweighted pair-group method with arithmetic averaging)) and factorial correspondence analysis (FCA) showed that the genetic structure was influenced by the collection site. The two most distant clusters contained all of the Peruvian accessions, one from Bolivia, none from Argentina or Chile. Analysis by country revealed that Peru presented the greatest genetic distances from the other countries and possessed the highest intra-country genetic distance (0.30 +/- 0.08). This suggests that the Peruvian oca accessions form a distinct genetic group. The relatively low level of genetic diversity in the oca species may be related to its predominating reproduction strategy, i.e., vegetative propagation. The extent and structure of the genetic diversity of the species detailed here should help the establishment of conservation strategies.

Ancestry estimation and control of population stratification for sequence-based association studies.

PubMed

Wang, Chaolong; Zhan, Xiaowei; Bragg-Gresham, Jennifer; Kang, Hyun Min; Stambolian, Dwight; Chew, Emily Y; Branham, Kari E; Heckenlively, John; Fulton, Robert; Wilson, Richard K; Mardis, Elaine R; Lin, Xihong; Swaroop, Anand; Zöllner, Sebastian; Abecasis, Gonçalo R

2014-04-01

Estimating individual ancestry is important in genetic association studies where population structure leads to false positive signals, although assigning ancestry remains challenging with targeted sequence data. We propose a new method for the accurate estimation of individual genetic ancestry, based on direct analysis of off-target sequence reads, and implement our method in the publicly available LASER software. We validate the method using simulated and empirical data and show that the method can accurately infer worldwide continental ancestry when used with sequencing data sets with whole-genome shotgun coverage as low as 0.001×. For estimates of fine-scale ancestry within Europe, the method performs well with coverage of 0.1×. On an even finer scale, the method improves discrimination between exome-sequenced study participants originating from different provinces within Finland. Finally, we show that our method can be used to improve case-control matching in genetic association studies and to reduce the risk of spurious findings due to population structure.

Application of different spectrophotometric methods for simultaneous determination of elbasvir and grazoprevir in pharmaceutical preparation

NASA Astrophysics Data System (ADS)

Attia, Khalid A. M.; El-Abasawi, Nasr M.; El-Olemy, Ahmed; Abdelazim, Ahmed H.

2018-01-01

The first three UV spectrophotometric methods have been developed of simultaneous determination of two new FDA approved drugs namely; elbasvir and grazoprevir in their combined pharmaceutical dosage form. These methods include simultaneous equation, partial least squares with and without variable selection procedure (genetic algorithm). For simultaneous equation method, the absorbance values at 369 (λmax of elbasvir) and 253 nm (λmax of grazoprevir) have been selected for the formation of two simultaneous equations required for the mathematical processing and quantitative analysis of the studied drugs. Alternatively, the partial least squares with and without variable selection procedure (genetic algorithm) have been applied in the spectra analysis because the synchronous inclusion of many unreal wavelengths rather than by using a single or dual wavelength which greatly increases the precision and predictive ability of the methods. Successfully assay of the drugs in their pharmaceutical formulation has been done by the proposed methods. Statistically comparative analysis for the obtained results with the manufacturing methods has been performed. It is noteworthy to mention that there was no significant difference between the proposed methods and the manufacturing one with respect to the validation parameters.

A practical guide to environmental association analysis in landscape genomics.

PubMed

Rellstab, Christian; Gugerli, Felix; Eckert, Andrew J; Hancock, Angela M; Holderegger, Rolf

2015-09-01

Landscape genomics is an emerging research field that aims to identify the environmental factors that shape adaptive genetic variation and the gene variants that drive local adaptation. Its development has been facilitated by next-generation sequencing, which allows for screening thousands to millions of single nucleotide polymorphisms in many individuals and populations at reasonable costs. In parallel, data sets describing environmental factors have greatly improved and increasingly become publicly accessible. Accordingly, numerous analytical methods for environmental association studies have been developed. Environmental association analysis identifies genetic variants associated with particular environmental factors and has the potential to uncover adaptive patterns that are not discovered by traditional tests for the detection of outlier loci based on population genetic differentiation. We review methods for conducting environmental association analysis including categorical tests, logistic regressions, matrix correlations, general linear models and mixed effects models. We discuss the advantages and disadvantages of different approaches, provide a list of dedicated software packages and their specific properties, and stress the importance of incorporating neutral genetic structure in the analysis. We also touch on additional important aspects such as sampling design, environmental data preparation, pooled and reduced-representation sequencing, candidate-gene approaches, linearity of allele-environment associations and the combination of environmental association analyses with traditional outlier detection tests. We conclude by summarizing expected future directions in the field, such as the extension of statistical approaches, environmental association analysis for ecological gene annotation, and the need for replication and post hoc validation studies. © 2015 John Wiley & Sons Ltd.

An efficient Bayesian meta-analysis approach for studying cross-phenotype genetic associations

PubMed Central

Majumdar, Arunabha; Haldar, Tanushree; Bhattacharya, Sourabh; Witte, John S.

2018-01-01

Simultaneous analysis of genetic associations with multiple phenotypes may reveal shared genetic susceptibility across traits (pleiotropy). For a locus exhibiting overall pleiotropy, it is important to identify which specific traits underlie this association. We propose a Bayesian meta-analysis approach (termed CPBayes) that uses summary-level data across multiple phenotypes to simultaneously measure the evidence of aggregate-level pleiotropic association and estimate an optimal subset of traits associated with the risk locus. This method uses a unified Bayesian statistical framework based on a spike and slab prior. CPBayes performs a fully Bayesian analysis by employing the Markov Chain Monte Carlo (MCMC) technique Gibbs sampling. It takes into account heterogeneity in the size and direction of the genetic effects across traits. It can be applied to both cohort data and separate studies of multiple traits having overlapping or non-overlapping subjects. Simulations show that CPBayes can produce higher accuracy in the selection of associated traits underlying a pleiotropic signal than the subset-based meta-analysis ASSET. We used CPBayes to undertake a genome-wide pleiotropic association study of 22 traits in the large Kaiser GERA cohort and detected six independent pleiotropic loci associated with at least two phenotypes. This includes a locus at chromosomal region 1q24.2 which exhibits an association simultaneously with the risk of five different diseases: Dermatophytosis, Hemorrhoids, Iron Deficiency, Osteoporosis and Peripheral Vascular Disease. We provide an R-package ‘CPBayes’ implementing the proposed method. PMID:29432419

Genetic Approaches to Study Meiosis and Meiosis-Specific Gene Expression in Saccharomyces cerevisiae.

PubMed

Kassir, Yona; Stuart, David T

2017-01-01

The budding yeast Saccharomyces cerevisiae has a long history as a model organism for studies of meiosis and the cell cycle. The popularity of this yeast as a model is in large part due to the variety of genetic and cytological approaches that can be effectively performed with the cells. Cultures of the cells can be induced to synchronously progress through meiosis and sporulation allowing large-scale gene expression and biochemical studies to be performed. Additionally, the spore tetrads resulting from meiosis make it possible to characterize the haploid products of meiosis allowing investigation of meiotic recombination and chromosome segregation. Here we describe genetic methods for analysis progression of S. cerevisiae through meiosis and sporulation with an emphasis on strategies for the genetic analysis of regulators of meiosis-specific genes.

Identification of Genetic Loci Underlying the Phenotypic Constructs of Autism Spectrum Disorders

ERIC Educational Resources Information Center

Liu, Xiao-Qing; Georgiades, Stelios; Duku, Eric; Thompson, Ann; Devlin, Bernie; Cook, Edwin H.; Wijsman, Ellen M.; Paterson, Andrew D.; Szatmari, Peter

2011-01-01

Objective: To investigate the underlying phenotypic constructs in autism spectrum disorders (ASD) and to identify genetic loci that are linked to these empirically derived factors. Method: Exploratory factor analysis was applied to two datasets with 28 selected Autism Diagnostic Interview-Revised (ADI-R) algorithm items. The first dataset was from…

ENVIRONMENTAL INFLUENCES ON GENETIC DIVERSITY OF CREEK CHUBS IN THE MID-ATLANTIC REGION OF THE USA

EPA Science Inventory

Analysis of genetic diversity within and among populations of stream fishes may provide a powerful method for assessing the status and trends in the condition of aquatic ecosystems. We analyzed mitochondrial DNA sequences (590 bases of cytochrome B) and nuclear DNA loci (109 amp...

Genetic variance of tolerance and the toxicant threshold model.

PubMed

Tanaka, Yoshinari; Mano, Hiroyuki; Tatsuta, Haruki

2012-04-01

A statistical genetics method is presented for estimating the genetic variance (heritability) of tolerance to pollutants on the basis of a standard acute toxicity test conducted on several isofemale lines of cladoceran species. To analyze the genetic variance of tolerance in the case when the response is measured as a few discrete states (quantal endpoints), the authors attempted to apply the threshold character model in quantitative genetics to the threshold model separately developed in ecotoxicology. The integrated threshold model (toxicant threshold model) assumes that the response of a particular individual occurs at a threshold toxicant concentration and that the individual tolerance characterized by the individual's threshold value is determined by genetic and environmental factors. As a case study, the heritability of tolerance to p-nonylphenol in the cladoceran species Daphnia galeata was estimated by using the maximum likelihood method and nested analysis of variance (ANOVA). Broad-sense heritability was estimated to be 0.199 ± 0.112 by the maximum likelihood method and 0.184 ± 0.089 by ANOVA; both results implied that the species examined had the potential to acquire tolerance to this substance by evolutionary change. Copyright © 2012 SETAC.

RNA isolation from bloodstains collected on FTA cards - application in clinical and forensic genetics.

PubMed

Skonieczna, Katarzyna; Styczyński, Jan; Krenska, Anna; Wysocki, Mariusz; Jakubowska, Aneta; Grzybowski, Tomasz

2016-01-01

Aim of the study: In recent years, RNA analysis has been increasingly used in clinical and forensic genetics. Nevertheless, a major limitation of RNA-based applications is very low RNA stability in biological material, due to the RNAse activity. This highlights the need for improving the methods of RNA collection and storage. Technological approaches such as FTA Classic Cards (Whatman) could provide a solution for the problem of RNA degradation. However, different methods of RNA isolation from FTA cards could have diverse effects on RNA quantity and quality. The purpose of this research was to analyze the utility of three different methods of RNA isolation from peripheral blood collected on FTA Classic Cards (Whatman). The study also aimed at assessing RNA stability in bloodstains deposited on FTA cards. Material and methods: The study was performed on peripheral bloodstains collected from 59 individuals on FTA Classic Cards (Whatman). RNA was isolated with High Pure RNA Isolation Kit (Roche Diagnostics), Universal RNA/miRNA Purification (EURx) and TRIzol Reagent (Life Technologies). RNA was subjected to quantitative analysis followed by reverse transcription and Real - Time PCR reaction. Results: The study has shown that FTA Classic Cards (Whatman) are useful tools for storing bloodstains at room temperature for RNA analysis. Moreover, the method of RNA extraction employing TRIzol Reagent (Life Technologies) provides the highest efficiency and reproducibility for samples stored for no more than 2 years. Conclusions: The FTA cards are suitable for collecting and storing bloodstains for RNA analysis in clinical and forensic genetics.

Clinical application of high throughput molecular screening techniques for pharmacogenomics

PubMed Central

Wiita, Arun P; Schrijver, Iris

2011-01-01

Genetic analysis is one of the fastest-growing areas of clinical diagnostics. Fortunately, as our knowledge of clinically relevant genetic variants rapidly expands, so does our ability to detect these variants in patient samples. Increasing demand for genetic information may necessitate the use of high throughput diagnostic methods as part of clinically validated testing. Here we provide a general overview of our current and near-future abilities to perform large-scale genetic testing in the clinical laboratory. First we review in detail molecular methods used for high throughput mutation detection, including techniques able to monitor thousands of genetic variants for a single patient or to genotype a single genetic variant for thousands of patients simultaneously. These methods are analyzed in the context of pharmacogenomic testing in the clinical laboratories, with a focus on tests that are currently validated as well as those that hold strong promise for widespread clinical application in the near future. We further discuss the unique economic and clinical challenges posed by pharmacogenomic markers. Our ability to detect genetic variants frequently outstrips our ability to accurately interpret them in a clinical context, carrying implications both for test development and introduction into patient management algorithms. These complexities must be taken into account prior to the introduction of any pharmacogenomic biomarker into routine clinical testing. PMID:23226057

Prioritizing individual genetic variants after kernel machine testing using variable selection.

PubMed

He, Qianchuan; Cai, Tianxi; Liu, Yang; Zhao, Ni; Harmon, Quaker E; Almli, Lynn M; Binder, Elisabeth B; Engel, Stephanie M; Ressler, Kerry J; Conneely, Karen N; Lin, Xihong; Wu, Michael C

2016-12-01

Kernel machine learning methods, such as the SNP-set kernel association test (SKAT), have been widely used to test associations between traits and genetic polymorphisms. In contrast to traditional single-SNP analysis methods, these methods are designed to examine the joint effect of a set of related SNPs (such as a group of SNPs within a gene or a pathway) and are able to identify sets of SNPs that are associated with the trait of interest. However, as with many multi-SNP testing approaches, kernel machine testing can draw conclusion only at the SNP-set level, and does not directly inform on which one(s) of the identified SNP set is actually driving the associations. A recently proposed procedure, KerNel Iterative Feature Extraction (KNIFE), provides a general framework for incorporating variable selection into kernel machine methods. In this article, we focus on quantitative traits and relatively common SNPs, and adapt the KNIFE procedure to genetic association studies and propose an approach to identify driver SNPs after the application of SKAT to gene set analysis. Our approach accommodates several kernels that are widely used in SNP analysis, such as the linear kernel and the Identity by State (IBS) kernel. The proposed approach provides practically useful utilities to prioritize SNPs, and fills the gap between SNP set analysis and biological functional studies. Both simulation studies and real data application are used to demonstrate the proposed approach. © 2016 WILEY PERIODICALS, INC.

New application of intelligent agents in sporadic amyotrophic lateral sclerosis identifies unexpected specific genetic background.

PubMed

Penco, Silvana; Buscema, Massimo; Patrosso, Maria Cristina; Marocchi, Alessandro; Grossi, Enzo

2008-05-30

Few genetic factors predisposing to the sporadic form of amyotrophic lateral sclerosis (ALS) have been identified, but the pathology itself seems to be a true multifactorial disease in which complex interactions between environmental and genetic susceptibility factors take place. The purpose of this study was to approach genetic data with an innovative statistical method such as artificial neural networks to identify a possible genetic background predisposing to the disease. A DNA multiarray panel was applied to genotype more than 60 polymorphisms within 35 genes selected from pathways of lipid and homocysteine metabolism, regulation of blood pressure, coagulation, inflammation, cellular adhesion and matrix integrity, in 54 sporadic ALS patients and 208 controls. Advanced intelligent systems based on novel coupling of artificial neural networks and evolutionary algorithms have been applied. The results obtained have been compared with those derived from the use of standard neural networks and classical statistical analysis Advanced intelligent systems based on novel coupling of artificial neural networks and evolutionary algorithms have been applied. The results obtained have been compared with those derived from the use of standard neural networks and classical statistical analysis. An unexpected discovery of a strong genetic background in sporadic ALS using a DNA multiarray panel and analytical processing of the data with advanced artificial neural networks was found. The predictive accuracy obtained with Linear Discriminant Analysis and Standard Artificial Neural Networks ranged from 70% to 79% (average 75.31%) and from 69.1 to 86.2% (average 76.6%) respectively. The corresponding value obtained with Advanced Intelligent Systems reached an average of 96.0% (range 94.4 to 97.6%). This latter approach allowed the identification of seven genetic variants essential to differentiate cases from controls: apolipoprotein E arg158cys; hepatic lipase -480 C/T; endothelial nitric oxide synthase 690 C/T and glu298asp; vitamin K-dependent coagulation factor seven arg353glu, glycoprotein Ia/IIa 873 G/A and E-selectin ser128arg. This study provides an alternative and reliable method to approach complex diseases. Indeed, the application of a novel artificial intelligence-based method offers a new insight into genetic markers of sporadic ALS pointing out the existence of a strong genetic background.

MEGA-CC: computing core of molecular evolutionary genetics analysis program for automated and iterative data analysis.

PubMed

Kumar, Sudhir; Stecher, Glen; Peterson, Daniel; Tamura, Koichiro

2012-10-15

There is a growing need in the research community to apply the molecular evolutionary genetics analysis (MEGA) software tool for batch processing a large number of datasets and to integrate it into analysis workflows. Therefore, we now make available the computing core of the MEGA software as a stand-alone executable (MEGA-CC), along with an analysis prototyper (MEGA-Proto). MEGA-CC provides users with access to all the computational analyses available through MEGA's graphical user interface version. This includes methods for multiple sequence alignment, substitution model selection, evolutionary distance estimation, phylogeny inference, substitution rate and pattern estimation, tests of natural selection and ancestral sequence inference. Additionally, we have upgraded the source code for phylogenetic analysis using the maximum likelihood methods for parallel execution on multiple processors and cores. Here, we describe MEGA-CC and outline the steps for using MEGA-CC in tandem with MEGA-Proto for iterative and automated data analysis. http://www.megasoftware.net/.

An overview of STRUCTURE: applications, parameter settings, and supporting software

PubMed Central

Porras-Hurtado, Liliana; Ruiz, Yarimar; Santos, Carla; Phillips, Christopher; Carracedo, Ángel; Lareu, Maria V.

2013-01-01

Objectives: We present an up-to-date review of STRUCTURE software: one of the most widely used population analysis tools that allows researchers to assess patterns of genetic structure in a set of samples. STRUCTURE can identify subsets of the whole sample by detecting allele frequency differences within the data and can assign individuals to those sub-populations based on analysis of likelihoods. The review covers STRUCTURE's most commonly used ancestry and frequency models, plus an overview of the main applications of the software in human genetics including case-control association studies (CCAS), population genetics, and forensic analysis. The review is accompanied by supplementary material providing a step-by-step guide to running STRUCTURE. Methods: With reference to a worked example, we explore the effects of changing the principal analysis parameters on STRUCTURE results when analyzing a uniform set of human genetic data. Use of the supporting software: CLUMPP and distruct is detailed and we provide an overview and worked example of STRAT software, applicable to CCAS. Conclusion: The guide offers a simplified view of how STRUCTURE, CLUMPP, distruct, and STRAT can be applied to provide researchers with an informed choice of parameter settings and supporting software when analyzing their own genetic data. PMID:23755071

Congruent population structure inferred from dispersal behaviour and intensive genetic surveys of the threatened Florida scrub-jay (Aphelocoma cœrulescens)

USGS Publications Warehouse

Coulon, A.; Fitzpatrick, J.W.; Bowman, R.; Stith, B.M.; Makarewich, C.A.; Stenzler, L.M.; Lovette, I.J.

2008-01-01

The delimitation of populations, defined as groups of individuals linked by gene flow, is possible by the analysis of genetic markers and also by spatial models based on dispersal probabilities across a landscape. We combined these two complimentary methods to define the spatial pattern of genetic structure among remaining populations of the threatened Florida scrub-jay, a species for which dispersal ability is unusually well-characterized. The range-wide population was intensively censused in the 1990s, and a metapopulation model defined population boundaries based on predicted dispersal-mediated demographic connectivity. We subjected genotypes from more than 1000 individual jays screened at 20 microsatellite loci to two Bayesian clustering methods. We describe a consensus method for identifying common features across many replicated clustering runs. Ten genetically differentiated groups exist across the present-day range of the Florida scrub-jay. These groups are largely consistent with the dispersal-defined metapopulations, which assume very limited dispersal ability. Some genetic groups comprise more than one metapopulation, likely because these genetically similar metapopulations were sundered only recently by habitat alteration. The combined reconstructions of population structure based on genetics and dispersal-mediated demographic connectivity provide a robust depiction of the current genetic and demographic organization of this species, reflecting past and present levels of dispersal among occupied habitat patches. The differentiation of populations into 10 genetic groups adds urgency to management efforts aimed at preserving what remains of genetic variation in this dwindling species, by maintaining viable populations of all genetically differentiated and geographically isolated populations.

Population genetic structure in a social landscape: barley in a traditional Ethiopian agricultural system

PubMed Central

Samberg, Leah H; Fishman, Lila; Allendorf, Fred W

2013-01-01

Conservation strategies are increasingly driven by our understanding of the processes and patterns of gene flow across complex landscapes. The expansion of population genetic approaches into traditional agricultural systems requires understanding how social factors contribute to that landscape, and thus to gene flow. This study incorporates extensive farmer interviews and population genetic analysis of barley landraces (Hordeum vulgare) to build a holistic picture of farmer-mediated geneflow in an ancient, traditional agricultural system in the highlands of Ethiopia. We analyze barley samples at 14 microsatellite loci across sites at varying elevations and locations across a contiguous mountain range, and across farmer-identified barley types and management strategies. Genetic structure is analyzed using population-based and individual-based methods, including measures of population differentiation and genetic distance, multivariate Principal Coordinate Analysis, and Bayesian assignment tests. Phenotypic analysis links genetic patterns to traits identified by farmers. We find that differential farmer management strategies lead to markedly different patterns of population structure across elevation classes and barley types. The extent to which farmer seed management appears as a stronger determinant of spatial structure than the physical landscape highlights the need for incorporation of social, landscape, and genetic data for the design of conservation strategies in human-influenced landscapes. PMID:24478796

Optimisation of shock absorber process parameters using failure mode and effect analysis and genetic algorithm

NASA Astrophysics Data System (ADS)

Mariajayaprakash, Arokiasamy; Senthilvelan, Thiyagarajan; Vivekananthan, Krishnapillai Ponnambal

2013-07-01

The various process parameters affecting the quality characteristics of the shock absorber during the process were identified using the Ishikawa diagram and by failure mode and effect analysis. The identified process parameters are welding process parameters (squeeze, heat control, wheel speed, and air pressure), damper sealing process parameters (load, hydraulic pressure, air pressure, and fixture height), washing process parameters (total alkalinity, temperature, pH value of rinsing water, and timing), and painting process parameters (flowability, coating thickness, pointage, and temperature). In this paper, the process parameters, namely, painting and washing process parameters, are optimized by Taguchi method. Though the defects are reasonably minimized by Taguchi method, in order to achieve zero defects during the processes, genetic algorithm technique is applied on the optimized parameters obtained by Taguchi method.

Genetic diversity and population structure analysis to construct a core collection from a large Capsicum germplasm.

PubMed

Lee, Hea-Young; Ro, Na-Young; Jeong, Hee-Jin; Kwon, Jin-Kyung; Jo, Jinkwan; Ha, Yeaseong; Jung, Ayoung; Han, Ji-Woong; Venkatesh, Jelli; Kang, Byoung-Cheorl

2016-11-14

Conservation of genetic diversity is an essential prerequisite for developing new cultivars with desirable agronomic traits. Although a large number of germplasm collections have been established worldwide, many of them face major difficulties due to large size and a lack of adequate information about population structure and genetic diversity. Core collection with a minimum number of accessions and maximum genetic diversity of pepper species and its wild relatives will facilitate easy access to genetic material as well as the use of hidden genetic diversity in Capsicum. To explore genetic diversity and population structure, we investigated patterns of molecular diversity using a transcriptome-based 48 single nucleotide polymorphisms (SNPs) in a large germplasm collection comprising 3,821 accessions. Among the 11 species examined, Capsicum annuum showed the highest genetic diversity (H E  = 0.44, I = 0.69), whereas the wild species C. galapagoense showed the lowest genetic diversity (H E  = 0.06, I = 0.07). The Capsicum germplasm collection was divided into 10 clusters (cluster 1 to 10) based on population structure analysis, and five groups (group A to E) based on phylogenetic analysis. Capsicum accessions from the five distinct groups in an unrooted phylogenetic tree showed taxonomic distinctness and reflected their geographic origins. Most of the accessions from European countries are distributed in the A and B groups, whereas the accessions from Asian countries are mainly distributed in C and D groups. Five different sampling strategies with diverse genetic clustering methods were used to select the optimal method for constructing the core collection. Using a number of allelic variations based on 48 SNP markers and 32 different phenotypic/morphological traits, a core collection 'CC240' with a total of 240 accessions (5.2 %) was selected from within the entire Capsicum germplasm. Compared to the other core collections, CC240 displayed higher genetic diversity (I = 0.95) and genetic evenness (J' = 0.80), and represented a wider range of phenotypic variation (MD = 9.45 %, CR = 98.40 %). A total of 240 accessions were selected from 3,821 Capsicum accessions based on transcriptome-based 48 SNP markers with genome-wide distribution and 32 traits using a systematic approach. This core collection will be a primary resource for pepper breeders and researchers for further genetic association and functional analyses.

Multiple testing and power calculations in genetic association studies.

PubMed

So, Hon-Cheong; Sham, Pak C

2011-01-01

Modern genetic association studies typically involve multiple single-nucleotide polymorphisms (SNPs) and/or multiple genes. With the development of high-throughput genotyping technologies and the reduction in genotyping cost, investigators can now assay up to a million SNPs for direct or indirect association with disease phenotypes. In addition, some studies involve multiple disease or related phenotypes and use multiple methods of statistical analysis. The combination of multiple genetic loci, multiple phenotypes, and multiple methods of evaluating associations between genotype and phenotype means that modern genetic studies often involve the testing of an enormous number of hypotheses. When multiple hypothesis tests are performed in a study, there is a risk of inflation of the type I error rate (i.e., the chance of falsely claiming an association when there is none). Several methods for multiple-testing correction are in popular use, and they all have strengths and weaknesses. Because no single method is universally adopted or always appropriate, it is important to understand the principles, strengths, and weaknesses of the methods so that they can be applied appropriately in practice. In this article, we review the three principle methods for multiple-testing correction and provide guidance for calculating statistical power.

A Groupwise Association Test for Rare Mutations Using a Weighted Sum Statistic

PubMed Central

Madsen, Bo Eskerod; Browning, Sharon R.

2009-01-01

Resequencing is an emerging tool for identification of rare disease-associated mutations. Rare mutations are difficult to tag with SNP genotyping, as genotyping studies are designed to detect common variants. However, studies have shown that genetic heterogeneity is a probable scenario for common diseases, in which multiple rare mutations together explain a large proportion of the genetic basis for the disease. Thus, we propose a weighted-sum method to jointly analyse a group of mutations in order to test for groupwise association with disease status. For example, such a group of mutations may result from resequencing a gene. We compare the proposed weighted-sum method to alternative methods and show that it is powerful for identifying disease-associated genes, both on simulated and Encode data. Using the weighted-sum method, a resequencing study can identify a disease-associated gene with an overall population attributable risk (PAR) of 2%, even when each individual mutation has much lower PAR, using 1,000 to 7,000 affected and unaffected individuals, depending on the underlying genetic model. This study thus demonstrates that resequencing studies can identify important genetic associations, provided that specialised analysis methods, such as the weighted-sum method, are used. PMID:19214210

Genetic Architecture of the Delis-Kaplan Executive Function System Trail Making Test: Evidence for Distinct Genetic Influences on Executive Function

PubMed Central

Vasilopoulos, Terrie; Franz, Carol E.; Panizzon, Matthew S.; Xian, Hong; Grant, Michael D.; Lyons, Michael J; Toomey, Rosemary; Jacobson, Kristen C.; Kremen, William S.

2012-01-01

Objective To examine how genes and environments contribute to relationships among Trail Making test conditions and the extent to which these conditions have unique genetic and environmental influences. Method Participants included 1237 middle-aged male twins from the Vietnam-Era Twin Study of Aging (VESTA). The Delis-Kaplan Executive Function System Trail Making test included visual searching, number and letter sequencing, and set-shifting components. Results Phenotypic correlations among Trails conditions ranged from 0.29 – 0.60, and genes accounted for the majority (58–84%) of each correlation. Overall heritability ranged from 0.34 to 0.62 across conditions. Phenotypic factor analysis suggested a single factor. In contrast, genetic models revealed a single common genetic factor but also unique genetic influences separate from the common factor. Genetic variance (i.e., heritability) of number and letter sequencing was completely explained by the common genetic factor while unique genetic influences separate from the common factor accounted for 57% and 21% of the heritabilities of visual search and set-shifting, respectively. After accounting for general cognitive ability, unique genetic influences accounted for 64% and 31% of those heritabilities. Conclusions A common genetic factor, most likely representing a combination of speed and sequencing accounted for most of the correlation among Trails 1–4. Distinct genetic factors, however, accounted for a portion of variance in visual scanning and set-shifting. Thus, although traditional phenotypic shared variance analysis techniques suggest only one general factor underlying different neuropsychological functions in non-patient populations, examining the genetic underpinnings of cognitive processes with twin analysis can uncover more complex etiological processes. PMID:22201299

Two-locus maximum lod score analysis of a multifactorial trait: joint consideration of IDDM2 and IDDM4 with IDDM1 in type 1 diabetes.

PubMed

Cordell, H J; Todd, J A; Bennett, S T; Kawaguchi, Y; Farrall, M

1995-10-01

To investigate the genetic component of multifactorial diseases such as type 1 (insulin-dependent) diabetes mellitus (IDDM), models involving the joint action of several disease loci are important. These models can give increased power to detect an effect and a greater understanding of etiological mechanisms. Here, we present an extension of the maximum lod score method of N. Risch, which allows the simultaneous detection and modeling of two unlinked disease loci. Genetic constraints on the identical-by-descent sharing probabilities, analogous to the "triangle" restrictions in the single-locus method, are derived, and the size and power of the test statistics are investigated. The method is applied to affected-sib-pair data, and the joint effects of IDDM1 (HLA) and IDDM2 (the INS VNTR) and of IDDM1 and IDDM4 (FGF3-linked) are assessed with relation to the development of IDDM. In the presence of genetic heterogeneity, there is seen to be a significant advantage in analyzing more than one locus simultaneously. Analysis of these families indicates that the effects at IDDM1 and IDDM2 are well described by a multiplicative genetic model, while those at IDDM1 and IDDM4 follow a heterogeneity model.

Two-locus maximum lod score analysis of a multifactorial trait: joint consideration of IDDM2 and IDDM4 with IDDM1 in type 1 diabetes.

PubMed Central

Cordell, H J; Todd, J A; Bennett, S T; Kawaguchi, Y; Farrall, M

1995-01-01

To investigate the genetic component of multifactorial diseases such as type 1 (insulin-dependent) diabetes mellitus (IDDM), models involving the joint action of several disease loci are important. These models can give increased power to detect an effect and a greater understanding of etiological mechanisms. Here, we present an extension of the maximum lod score method of N. Risch, which allows the simultaneous detection and modeling of two unlinked disease loci. Genetic constraints on the identical-by-descent sharing probabilities, analogous to the "triangle" restrictions in the single-locus method, are derived, and the size and power of the test statistics are investigated. The method is applied to affected-sib-pair data, and the joint effects of IDDM1 (HLA) and IDDM2 (the INS VNTR) and of IDDM1 and IDDM4 (FGF3-linked) are assessed with relation to the development of IDDM. In the presence of genetic heterogeneity, there is seen to be a significant advantage in analyzing more than one locus simultaneously. Analysis of these families indicates that the effects at IDDM1 and IDDM2 are well described by a multiplicative genetic model, while those at IDDM1 and IDDM4 follow a heterogeneity model. PMID:7573054

Breeding of Acrocomia aculeata using genetic diversity parameters and correlations to select accessions based on vegetative, phenological, and reproductive characteristics.

PubMed

Coser, S M; Motoike, S Y; Corrêa, T R; Pires, T P; Resende, M D V

2016-10-17

Macaw palm (Acrocomia aculeata) is a promising species for use in biofuel production, and establishing breeding programs is important for the development of commercial plantations. The aim of the present study was to analyze genetic diversity, verify correlations between traits, estimate genetic parameters, and select different accessions of A. aculeata in the Macaw Palm Germplasm Bank located in Universidade Federal de Viçosa, to develop a breeding program for this species. Accessions were selected based on precocity (PREC), total spathe (TS), diameter at breast height (DBH), height of the first spathe (HFS), and canopy area (CA). The traits were evaluated in 52 accessions during the 2012/2013 season and analyzed by restricted estimation maximum likelihood/best linear unbiased predictor procedures. Genetic diversity resulted in the formation of four groups by Tocher's clustering method. The correlation analysis showed it was possible to have indirect and early selection for the traits PREC and DBH. Estimated genetic parameters strengthened the genetic variability verified by cluster analysis. Narrow-sense heritability was classified as moderate (PREC, TS, and CA) to high (HFS and DBH), resulting in strong genetic control of the traits and success in obtaining genetic gains by selection. Accuracy values were classified as moderate (PREC and CA) to high (TS, HFS, and DBH), reinforcing the success of the selection process. Selection of accessions for PREC, TS, and HFS by the rank-average method permits selection gains of over 100%, emphasizing the successful use of the accessions in breeding programs and obtaining superior genotypes for commercial plantations.

Mapping the Schizophrenia Genes by Neuroimaging: The Opportunities and the Challenges

PubMed Central

2018-01-01

Schizophrenia (SZ) is a heritable brain disease originating from a complex interaction of genetic and environmental factors. The genes underpinning the neurobiology of SZ are largely unknown but recent data suggest strong evidence for genetic variations, such as single nucleotide polymorphisms, making the brain vulnerable to the risk of SZ. Structural and functional brain mapping of these genetic variations are essential for the development of agents and tools for better diagnosis, treatment and prevention of SZ. Addressing this, neuroimaging methods in combination with genetic analysis have been increasingly used for almost 20 years. So-called imaging genetics, the opportunities of this approach along with its limitations for SZ research will be outlined in this invited paper. While the problems such as reproducibility, genetic effect size, specificity and sensitivity exist, opportunities such as multivariate analysis, development of multisite consortia for large-scale data collection, emergence of non-candidate gene (hypothesis-free) approach of neuroimaging genetics are likely to contribute to a rapid progress for gene discovery besides to gene validation studies that are related to SZ. PMID:29324666

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.

Robust inference of population structure for ancestry prediction and correction of stratification in the presence of relatedness.

PubMed

Conomos, Matthew P; Miller, Michael B; Thornton, Timothy A

2015-05-01

Population structure inference with genetic data has been motivated by a variety of applications in population genetics and genetic association studies. Several approaches have been proposed for the identification of genetic ancestry differences in samples where study participants are assumed to be unrelated, including principal components analysis (PCA), multidimensional scaling (MDS), and model-based methods for proportional ancestry estimation. Many genetic studies, however, include individuals with some degree of relatedness, and existing methods for inferring genetic ancestry fail in related samples. We present a method, PC-AiR, for robust population structure inference in the presence of known or cryptic relatedness. PC-AiR utilizes genome-screen data and an efficient algorithm to identify a diverse subset of unrelated individuals that is representative of all ancestries in the sample. The PC-AiR method directly performs PCA on the identified ancestry representative subset and then predicts components of variation for all remaining individuals based on genetic similarities. In simulation studies and in applications to real data from Phase III of the HapMap Project, we demonstrate that PC-AiR provides a substantial improvement over existing approaches for population structure inference in related samples. We also demonstrate significant efficiency gains, where a single axis of variation from PC-AiR provides better prediction of ancestry in a variety of structure settings than using 10 (or more) components of variation from widely used PCA and MDS approaches. Finally, we illustrate that PC-AiR can provide improved population stratification correction over existing methods in genetic association studies with population structure and relatedness. © 2015 WILEY PERIODICALS, INC.

Highly polygenic architecture of antidepressant treatment response: Comparative analysis of SSRI and NRI treatment in an animal model of depression.

PubMed

Malki, Karim; Tosto, Maria Grazia; Mouriño-Talín, Héctor; Rodríguez-Lorenzo, Sabela; Pain, Oliver; Jumhaboy, Irfan; Liu, Tina; Parpas, Panos; Newman, Stuart; Malykh, Artem; Carboni, Lucia; Uher, Rudolf; McGuffin, Peter; Schalkwyk, Leonard C; Bryson, Kevin; Herbster, Mark

2017-04-01

Response to antidepressant (AD) treatment may be a more polygenic trait than previously hypothesized, with many genetic variants interacting in yet unclear ways. In this study we used methods that can automatically learn to detect patterns of statistical regularity from a sparsely distributed signal across hippocampal transcriptome measurements in a large-scale animal pharmacogenomic study to uncover genomic variations associated with AD. The study used four inbred mouse strains of both sexes, two drug treatments, and a control group (escitalopram, nortriptyline, and saline). Multi-class and binary classification using Machine Learning (ML) and regularization algorithms using iterative and univariate feature selection methods, including InfoGain, mRMR, ANOVA, and Chi Square, were used to uncover genomic markers associated with AD response. Relevant genes were selected based on Jaccard distance and carried forward for gene-network analysis. Linear association methods uncovered only one gene associated with drug treatment response. The implementation of ML algorithms, together with feature reduction methods, revealed a set of 204 genes associated with SSRI and 241 genes associated with NRI response. Although only 10% of genes overlapped across the two drugs, network analysis shows that both drugs modulated the CREB pathway, through different molecular mechanisms. Through careful implementation and optimisations, the algorithms detected a weak signal used to predict whether an animal was treated with nortriptyline (77%) or escitalopram (67%) on an independent testing set. The results from this study indicate that the molecular signature of AD treatment may include a much broader range of genomic markers than previously hypothesized, suggesting that response to medication may be as complex as the pathology. The search for biomarkers of antidepressant treatment response could therefore consider a higher number of genetic markers and their interactions. Through predominately different molecular targets and mechanisms of action, the two drugs modulate the same Creb1 pathway which plays a key role in neurotrophic responses and in inflammatory processes. © 2016 The Authors. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Published by Wiley Periodicals, Inc. © 2016 The Authors. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Published by Wiley Periodicals, Inc.

Enhanced genetic analysis of single human bioparticles recovered by simplified micromanipulation from forensic 'touch DNA' evidence.

PubMed

Farash, Katherine; Hanson, Erin K; Ballantyne, Jack

2015-03-09

DNA profiles can be obtained from 'touch DNA' evidence, which comprises microscopic traces of human biological material. Current methods for the recovery of trace DNA employ cotton swabs or adhesive tape to sample an area of interest. However, such a 'blind-swabbing' approach will co-sample cellular material from the different individuals, even if the individuals' cells are located in geographically distinct locations on the item. Thus, some of the DNA mixtures encountered in touch DNA samples are artificially created by the swabbing itself. In some instances, a victim's DNA may be found in significant excess thus masking any potential perpetrator's DNA. In order to circumvent the challenges with standard recovery and analysis methods, we have developed a lower cost, 'smart analysis' method that results in enhanced genetic analysis of touch DNA evidence. We describe an optimized and efficient micromanipulation recovery strategy for the collection of bio-particles present in touch DNA samples, as well as an enhanced amplification strategy involving a one-step 5 µl microvolume lysis/STR amplification to permit the recovery of STR profiles from the bio-particle donor(s). The use of individual or few (i.e., "clumps") bioparticles results in the ability to obtain single source profiles. These procedures represent alternative enhanced techniques for the isolation and analysis of single bioparticles from forensic touch DNA evidence. While not necessary in every forensic investigation, the method could be highly beneficial for the recovery of a single source perpetrator DNA profile in cases involving physical assault (e.g., strangulation) that may not be possible using standard analysis techniques. Additionally, the strategies developed here offer an opportunity to obtain genetic information at the single cell level from a variety of other non-forensic trace biological material.

Genetic diversity, linkage disequilibrium, population structure and construction of a core collection of Prunus avium L. landraces and bred cultivars.

PubMed

Campoy, José Antonio; Lerigoleur-Balsemin, Emilie; Christmann, Hélène; Beauvieux, Rémi; Girollet, Nabil; Quero-García, José; Dirlewanger, Elisabeth; Barreneche, Teresa

2016-02-24

Depiction of the genetic diversity, linkage disequilibrium (LD) and population structure is essential for the efficient organization and exploitation of genetic resources. The objectives of this study were to (i) to evaluate the genetic diversity and to detect the patterns of LD, (ii) to estimate the levels of population structure and (iii) to identify a 'core collection' suitable for association genetic studies in sweet cherry. A total of 210 genotypes including modern cultivars and landraces from 16 countries were genotyped using the RosBREED cherry 6 K SNP array v1. Two groups, mainly bred cultivars and landraces, respectively, were first detected using STRUCTURE software and confirmed by Principal Coordinate Analysis (PCoA). Further analyses identified nine subgroups using STRUCTURE and Discriminant Analysis of Principal Components (DAPC). Several sub-groups correspond to different eco-geographic regions of landraces distribution. Linkage disequilibrium was evaluated showing lower values than in peach, the reference Prunus species. A 'core collection' containing 156 accessions was selected using the maximum length sub tree method. The present study constitutes the first population genetics analysis in cultivated sweet cherry using a medium-density SNP (single nucleotide polymorphism) marker array. We provided estimations of linkage disequilibrium, genetic structure and the definition of a first INRA's Sweet Cherry core collection useful for breeding programs, germplasm management and association genetics studies.

Evolutionary Meta-Analysis of Association Studies Reveals Ancient Constraints Affecting Disease Marker Discovery

PubMed Central

Dudley, Joel T.; Chen, Rong; Sanderford, Maxwell; Butte, Atul J.; Kumar, Sudhir

2012-01-01

Genome-wide disease association studies contrast genetic variation between disease cohorts and healthy populations to discover single nucleotide polymorphisms (SNPs) and other genetic markers revealing underlying genetic architectures of human diseases. Despite scores of efforts over the past decade, many reproducible genetic variants that explain substantial proportions of the heritable risk of common human diseases remain undiscovered. We have conducted a multispecies genomic analysis of 5,831 putative human risk variants for more than 230 disease phenotypes reported in 2,021 studies. We find that the current approaches show a propensity for discovering disease-associated SNPs (dSNPs) at conserved genomic positions because the effect size (odds ratio) and allelic P value of genetic association of an SNP relates strongly to the evolutionary conservation of their genomic position. We propose a new measure for ranking SNPs that integrates evolutionary conservation scores and the P value (E-rank). Using published data from a large case-control study, we demonstrate that E-rank method prioritizes SNPs with a greater likelihood of bona fide and reproducible genetic disease associations, many of which may explain greater proportions of genetic variance. Therefore, long-term evolutionary histories of genomic positions offer key practical utility in reassessing data from existing disease association studies, and in the design and analysis of future studies aimed at revealing the genetic basis of common human diseases. PMID:22389448

Identification of a Heterozygous SPG11 Mutation by Clinical Exome Sequencing in a Patient With Hereditary Spastic Paraplegia: A Case Report.

PubMed

Oh, Ja-Young; Do, Hyun Jung; Lee, Seungok; Jang, Ja-Hyun; Cho, Eun-Hae; Jang, Dae-Hyun

2016-12-01

Next-generation sequencing, such as whole-genome sequencing, whole-exome sequencing, and targeted panel sequencing have been applied for diagnosis of many genetic diseases, and are in the process of replacing the traditional methods of genetic analysis. Clinical exome sequencing (CES), which provides not only sequence variation data but also clinical interpretation, aids in reaching a final conclusion with regards to genetic diagnosis. Sequencing of genes with clinical relevance rather than whole exome sequencing might be more suitable for the diagnosis of known hereditary disease with genetic heterogeneity. Here, we present the clinical usefulness of CES for the diagnosis of hereditary spastic paraplegia (HSP). We report a case of patient who was strongly suspected of having HSP based on her clinical manifestations. HSP is one of the diseases with high genetic heterogeneity, the 72 different loci and 59 discovered genes identified so far. Therefore, traditional approach for diagnosis of HSP with genetic analysis is very challenging and time-consuming. CES with TruSight One Sequencing Panel, which enriches about 4,800 genes with clinical relevance, revealed compound heterozygous mutations in SPG11 . One workflow and one procedure can provide the results of genetic analysis, and CES with enrichment of clinically relevant genes is a cost-effective and time-saving diagnostic tool for diseases with genetic heterogeneity, including HSP.

FARVATX: FAmily-based Rare Variant Association Test for X-linked genes

PubMed Central

Choi, Sungkyoung; Lee, Sungyoung; Qiao, Dandi; Hardin, Megan; Cho, Michael H.; Silverman, Edwin K; Park, Taesung; Won, Sungho

2016-01-01

Although the X chromosome has many genes that are functionally related to human diseases, the complicated biological properties of the X chromosome have prevented efficient genetic association analyses, and only a few significantly associated X-linked variants have been reported for complex traits. For instance, dosage compensation of X-linked genes is often achieved via the inactivation of one allele in each X-linked variant in females; however, some X-linked variants can escape this X chromosome inactivation. Efficient genetic analyses cannot be conducted without prior knowledge about the gene expression process of X-linked variants, and misspecified information can lead to power loss. In this report, we propose new statistical methods for rare X-linked variant genetic association analysis of dichotomous phenotypes with family-based samples. The proposed methods are computationally efficient and can complete X-linked analyses within a few hours. Simulation studies demonstrate the statistical efficiency of the proposed methods, which were then applied to rare-variant association analysis of the X chromosome in chronic obstructive pulmonary disease (COPD). Some promising significant X-linked genes were identified, illustrating the practical importance of the proposed methods. PMID:27325607

FARVATX: Family-Based Rare Variant Association Test for X-Linked Genes.

PubMed

Choi, Sungkyoung; Lee, Sungyoung; Qiao, Dandi; Hardin, Megan; Cho, Michael H; Silverman, Edwin K; Park, Taesung; Won, Sungho

2016-09-01

Although the X chromosome has many genes that are functionally related to human diseases, the complicated biological properties of the X chromosome have prevented efficient genetic association analyses, and only a few significantly associated X-linked variants have been reported for complex traits. For instance, dosage compensation of X-linked genes is often achieved via the inactivation of one allele in each X-linked variant in females; however, some X-linked variants can escape this X chromosome inactivation. Efficient genetic analyses cannot be conducted without prior knowledge about the gene expression process of X-linked variants, and misspecified information can lead to power loss. In this report, we propose new statistical methods for rare X-linked variant genetic association analysis of dichotomous phenotypes with family-based samples. The proposed methods are computationally efficient and can complete X-linked analyses within a few hours. Simulation studies demonstrate the statistical efficiency of the proposed methods, which were then applied to rare-variant association analysis of the X chromosome in chronic obstructive pulmonary disease. Some promising significant X-linked genes were identified, illustrating the practical importance of the proposed methods. © 2016 WILEY PERIODICALS, INC.

A novel computational approach for simultaneous tracking and feature extraction of C. elegans populations in fluid environments.

PubMed

Tsechpenakis, Gabriel; Bianchi, Laura; Metaxas, Dimitris; Driscoll, Monica

2008-05-01

The nematode Caenorhabditis elegans (C. elegans) is a genetic model widely used to dissect conserved basic biological mechanisms of development and nervous system function. C. elegans locomotion is under complex neuronal regulation and is impacted by genetic and environmental factors; thus, its analysis is expected to shed light on how genetic, environmental, and pathophysiological processes control behavior. To date, computer-based approaches have been used for analysis of C. elegans locomotion; however, none of these is both high resolution and high throughput. We used computer vision methods to develop a novel automated approach for analyzing the C. elegans locomotion. Our method provides information on the position, trajectory, and body shape during locomotion and is designed to efficiently track multiple animals (C. elegans) in cluttered images and under lighting variations. We used this method to describe in detail C. elegans movement in liquid for the first time and to analyze six unc-8, one mec-4, and one odr-1 mutants. We report features of nematode swimming not previously noted and show that our method detects differences in the swimming profile of mutants that appear at first glance similar.

Analysis of conservation priorities of Iberoamerican cattle based on autosomal microsatellite markers

PubMed Central

2013-01-01

Background Determining the value of livestock breeds is essential to define conservation priorities, manage genetic diversity and allocate funds. Within- and between-breed genetic diversity need to be assessed to preserve the highest intra-specific variability. Information on genetic diversity and risk status is still lacking for many Creole cattle breeds from the Americas, despite their distinct evolutionary trajectories and adaptation to extreme environmental conditions. Methods A comprehensive genetic analysis of 67 Iberoamerican cattle breeds was carried out with 19 FAO-recommended microsatellites to assess conservation priorities. Contributions to global diversity were investigated using alternative methods, with different weights given to the within- and between-breed components of genetic diversity. Information on Iberoamerican plus 15 worldwide cattle breeds was used to investigate the contribution of geographical breed groups to global genetic diversity. Results Overall, Creole cattle breeds showed a high level of genetic diversity with the highest level found in breeds admixed with zebu cattle, which were clearly differentiated from all other breeds. Within-breed kinships revealed seven highly inbred Creole breeds for which measures are needed to avoid further genetic erosion. However, if contribution to heterozygosity was the only criterion considered, some of these breeds had the lowest priority for conservation decisions. The Weitzman approach prioritized highly differentiated breeds, such as Guabalá, Romosinuano, Cr. Patagonico, Siboney and Caracú, while kinship-based methods prioritized mainly zebu-related breeds. With the combined approaches, breed ranking depended on the weights given to the within- and between-breed components of diversity. Overall, the Creole groups of breeds were generally assigned a higher priority for conservation than the European groups of breeds. Conclusions Conservation priorities differed significantly according to the weight given to within- and between-breed genetic diversity. Thus, when establishing conservation programs, it is necessary to also take into account other features. Creole cattle and local isolated breeds retain a high level of genetic diversity. The development of sustainable breeding and crossbreeding programs for Creole breeds, and the added value resulting from their products should be taken into consideration to ensure their long-term survival. PMID:24079454

Genetic Simulation Tools for Post-Genome Wide Association Studies of Complex Diseases

PubMed Central

Amos, Christopher I.; Bafna, Vineet; Hauser, Elizabeth R.; Hernandez, Ryan D.; Li, Chun; Liberles, David A.; McAllister, Kimberly; Moore, Jason H.; Paltoo, Dina N.; Papanicolaou, George J.; Peng, Bo; Ritchie, Marylyn D.; Rosenfeld, Gabriel; Witte, John S.

2014-01-01

Genetic simulation programs are used to model data under specified assumptions to facilitate the understanding and study of complex genetic systems. Standardized data sets generated using genetic simulation are essential for the development and application of novel analytical tools in genetic epidemiology studies. With continuing advances in high-throughput genomic technologies and generation and analysis of larger, more complex data sets, there is a need for updating current approaches in genetic simulation modeling. To provide a forum to address current and emerging challenges in this area, the National Cancer Institute (NCI) sponsored a workshop, entitled “Genetic Simulation Tools for Post-Genome Wide Association Studies of Complex Diseases” at the National Institutes of Health (NIH) in Bethesda, Maryland on March 11-12, 2014. The goals of the workshop were to: (i) identify opportunities, challenges and resource needs for the development and application of genetic simulation models; (ii) improve the integration of tools for modeling and analysis of simulated data; and (iii) foster collaborations to facilitate development and applications of genetic simulation. During the course of the meeting the group identified challenges and opportunities for the science of simulation, software and methods development, and collaboration. This paper summarizes key discussions at the meeting, and highlights important challenges and opportunities to advance the field of genetic simulation. PMID:25371374

Comparative genetic analysis of trichome-less and normal pod genotypes of Mucuna pruriens (Fabaceae).

PubMed

Dhawan, S S; Rai, G K; Darokar, M P; Lal, R K; Misra, H O; Khanuja, S P S

2011-09-15

Velvet bean (Mucuna pruriens) seeds contain the catecholic amino acid L-DoPA (L-3,4-dihydroxyphenylalanine), which is a neurotransmitter precursor and used for the treatment of Parkinson's disease and mental disorders. The great demand for L-DoPA is largely met by the pharmaceutical industry through extraction of the compound from wild populations of this plant; commercial exploitation of this compound is hampered because of its limited availability. The trichomes present on the pods can cause severe itching, blisters and dermatitis, discouraging cultivation. We screened genetic stocks of velvet bean for the trichome-less trait, along with high seed yield and L-DoPA content. The highest yielding trichome-less elite strain was selected and indentified on the basis of a PCR-based DNA fingerprinting method (RAPD), using deca-nucleotide primers. A genetic similarity index matrix was obtained through multivariant analysis using Nei and Li's coefficient. The similarity coefficients were used to generate a tree for cluster analysis using the UPGMA method. Analysis of amplification spectra of 408 bands obtained with 56 primers allowed us to distinguish a trichome-less elite strain of M. pruriens.

Testing Genetic Pleiotropy with GWAS Summary Statistics for Marginal and Conditional Analyses.

PubMed

Deng, Yangqing; Pan, Wei

2017-12-01

There is growing interest in testing genetic pleiotropy, which is when a single genetic variant influences multiple traits. Several methods have been proposed; however, these methods have some limitations. First, all the proposed methods are based on the use of individual-level genotype and phenotype data; in contrast, for logistical, and other, reasons, summary statistics of univariate SNP-trait associations are typically only available based on meta- or mega-analyzed large genome-wide association study (GWAS) data. Second, existing tests are based on marginal pleiotropy, which cannot distinguish between direct and indirect associations of a single genetic variant with multiple traits due to correlations among the traits. Hence, it is useful to consider conditional analysis, in which a subset of traits is adjusted for another subset of traits. For example, in spite of substantial lowering of low-density lipoprotein cholesterol (LDL) with statin therapy, some patients still maintain high residual cardiovascular risk, and, for these patients, it might be helpful to reduce their triglyceride (TG) level. For this purpose, in order to identify new therapeutic targets, it would be useful to identify genetic variants with pleiotropic effects on LDL and TG after adjusting the latter for LDL; otherwise, a pleiotropic effect of a genetic variant detected by a marginal model could simply be due to its association with LDL only, given the well-known correlation between the two types of lipids. Here, we develop a new pleiotropy testing procedure based only on GWAS summary statistics that can be applied for both marginal analysis and conditional analysis. Although the main technical development is based on published union-intersection testing methods, care is needed in specifying conditional models to avoid invalid statistical estimation and inference. In addition to the previously used likelihood ratio test, we also propose using generalized estimating equations under the working independence model for robust inference. We provide numerical examples based on both simulated and real data, including two large lipid GWAS summary association datasets based on ∼100,000 and ∼189,000 samples, respectively, to demonstrate the difference between marginal and conditional analyses, as well as the effectiveness of our new approach. Copyright © 2017 by the Genetics Society of America.

Application of neural networks with novel independent component analysis methodologies to a Prussian blue modified glassy carbon electrode array.

PubMed

Wang, Liang; Yang, Die; Fang, Cheng; Chen, Zuliang; Lesniewski, Peter J; Mallavarapu, Megharaj; Naidu, Ravendra

2015-01-01

Sodium potassium absorption ratio (SPAR) is an important measure of agricultural water quality, wherein four exchangeable cations (K(+), Na(+), Ca(2+) and Mg(2+)) should be simultaneously determined. An ISE-array is suitable for this application because its simplicity, rapid response characteristics and lower cost. However, cross-interferences caused by the poor selectivity of ISEs need to be overcome using multivariate chemometric methods. In this paper, a solid contact ISE array, based on a Prussian blue modified glassy carbon electrode (PB-GCE), was applied with a novel chemometric strategy. One of the most popular independent component analysis (ICA) methods, the fast fixed-point algorithm for ICA (fastICA), was implemented by the genetic algorithm (geneticICA) to avoid the local maxima problem commonly observed with fastICA. This geneticICA can be implemented as a data preprocessing method to improve the prediction accuracy of the Back-propagation neural network (BPNN). The ISE array system was validated using 20 real irrigation water samples from South Australia, and acceptable prediction accuracies were obtained. Copyright © 2014 Elsevier B.V. All rights reserved.

Whole-exome sequencing analysis of Waardenburg syndrome in a Chinese family.

PubMed

Chen, Dezhong; Zhao, Na; Wang, Jing; Li, Zhuoyu; Wu, Changxin; Fu, Jie; Xiao, Han

2017-01-01

Waardenburg syndrome (WS) is a dominantly inherited, genetically heterogeneous auditory-pigmentary syndrome characterized by non-progressive sensorineural hearing loss and iris discoloration. By whole-exome sequencing (WES), we identified a nonsense mutation (c.598C>T) in PAX3 gene, predicted to be disease causing by in silico analysis. This is the first report of genetically diagnosed case of WS PAX3 c.598C>T nonsense mutation in Chinese ethnic origin by WES and in silico functional prediction methods.

Whole-exome sequencing analysis of Waardenburg syndrome in a Chinese family

PubMed Central

Chen, Dezhong; Zhao, Na; Wang, Jing; Li, Zhuoyu; Wu, Changxin; Fu, Jie; Xiao, Han

2017-01-01

Waardenburg syndrome (WS) is a dominantly inherited, genetically heterogeneous auditory-pigmentary syndrome characterized by non-progressive sensorineural hearing loss and iris discoloration. By whole-exome sequencing (WES), we identified a nonsense mutation (c.598C>T) in PAX3 gene, predicted to be disease causing by in silico analysis. This is the first report of genetically diagnosed case of WS PAX3 c.598C>T nonsense mutation in Chinese ethnic origin by WES and in silico functional prediction methods. PMID:28690861

Sieve analysis in HIV-1 vaccine efficacy trials

PubMed Central

Edlefsen, Paul T.; Gilbert, Peter B.; Rolland, Morgane

2013-01-01

Purpose of review The genetic characterization of HIV-1 breakthrough infections in vaccine and placebo recipients offers new ways to assess vaccine efficacy trials. Statistical and sequence analysis methods provide opportunities to mine the mechanisms behind the effect of an HIV vaccine. Recent findings The release of results from two HIV-1 vaccine efficacy trials, Step/HVTN-502 and RV144, led to numerous studies in the last five years, including efforts to sequence HIV-1 breakthrough infections and compare viral characteristics between the vaccine and placebo groups. Novel genetic and statistical analysis methods uncovered features that distinguished founder viruses isolated from vaccinees from those isolated from placebo recipients, and identified HIV-1 genetic targets of vaccine-induced immune responses. Summary Studies of HIV-1 breakthrough infections in vaccine efficacy trials can provide an independent confirmation to correlates of risk studies, as they take advantage of vaccine/placebo comparisons while correlates of risk analyses are limited to vaccine recipients. Through the identification of viral determinants impacted by vaccine-mediated host immune responses, sieve analyses can shed light on potential mechanisms of vaccine protection. PMID:23719202

Sieve analysis in HIV-1 vaccine efficacy trials.

PubMed

Edlefsen, Paul T; Gilbert, Peter B; Rolland, Morgane

2013-09-01

The genetic characterization of HIV-1 breakthrough infections in vaccine and placebo recipients offers new ways to assess vaccine efficacy trials. Statistical and sequence analysis methods provide opportunities to mine the mechanisms behind the effect of an HIV vaccine. The release of results from two HIV-1 vaccine efficacy trials, Step/HVTN-502 (HIV Vaccine Trials Network-502) and RV144, led to numerous studies in the last 5 years, including efforts to sequence HIV-1 breakthrough infections and compare viral characteristics between the vaccine and placebo groups. Novel genetic and statistical analysis methods uncovered features that distinguished founder viruses isolated from vaccinees from those isolated from placebo recipients, and identified HIV-1 genetic targets of vaccine-induced immune responses. Studies of HIV-1 breakthrough infections in vaccine efficacy trials can provide an independent confirmation to correlates of risk studies, as they take advantage of vaccine/placebo comparisons, whereas correlates of risk analyses are limited to vaccine recipients. Through the identification of viral determinants impacted by vaccine-mediated host immune responses, sieve analyses can shed light on potential mechanisms of vaccine protection.

Genetically engineering adenoviral vectors for gene therapy.

PubMed

Coughlan, Lynda

2014-01-01

Adenoviral (Ad) vectors are commonly used for various gene therapy applications. Significant advances in the genetic engineering of Ad vectors in recent years has highlighted their potential for the treatment of metastatic disease. There are several methods to genetically modify the Ad genome to incorporate retargeting peptides which will redirect the natural tropism of the viruses, including homologous recombination in bacteria or yeast. However, homologous recombination in yeast is highly efficient and can be achieved without the need for extensive cloning strategies. In addition, the method does not rely on the presence of unique restriction sites within the Ad genome and the reagents required for this method are widely available and inexpensive. Large plasmids containing the entire adenoviral genome (~36 kbp) can be modified within Saccharomyces cerevisiae yeast and genomes easily rescued in Escherichia coli hosts for analysis or amplification. A method for two-step homologous recombination in yeast is described in this chapter.

Genetic variability in Jatropha curcas L. from diallel crossing.

PubMed

Ribeiro, D O; Silva-Mann, R; Alvares-Carvalho, S V; Souza, E M S; Vasconcelos, M C; Blank, A F

2017-05-18

Physic nut (Jatropha curcas L.) presents high oilseed yield and low production cost. However, technical-scientific knowledge on this crop is still limited. This study aimed to evaluate and estimate the genetic variability of hybrids obtained from dialell crossing. Genetic variability was carried out using ISSR molecular markers. For genetic variability, nine primers were used, and six were selected with 80.7% polymorphism. Genetic similarity was obtained using the NTSYS pc. 2.1 software, and cluster analysis was obtained by the UPGMA method. Mean genetic similarity was 58.4% among hybrids; the most divergent pair was H1 and H10 and the most similar pair was H9 and H10. ISSR PCR markers provided a quick and highly informative system for DNA fingerprinting, and also allowed establishing genetic relationships of Jatropha hybrids.

Fully automated analysis of multi-resolution four-channel micro-array genotyping data

NASA Astrophysics Data System (ADS)

Abbaspour, Mohsen; Abugharbieh, Rafeef; Podder, Mohua; Tebbutt, Scott J.

2006-03-01

We present a fully-automated and robust microarray image analysis system for handling multi-resolution images (down to 3-micron with sizes up to 80 MBs per channel). The system is developed to provide rapid and accurate data extraction for our recently developed microarray analysis and quality control tool (SNP Chart). Currently available commercial microarray image analysis applications are inefficient, due to the considerable user interaction typically required. Four-channel DNA microarray technology is a robust and accurate tool for determining genotypes of multiple genetic markers in individuals. It plays an important role in the state of the art trend where traditional medical treatments are to be replaced by personalized genetic medicine, i.e. individualized therapy based on the patient's genetic heritage. However, fast, robust, and precise image processing tools are required for the prospective practical use of microarray-based genetic testing for predicting disease susceptibilities and drug effects in clinical practice, which require a turn-around timeline compatible with clinical decision-making. In this paper we have developed a fully-automated image analysis platform for the rapid investigation of hundreds of genetic variations across multiple genes. Validation tests indicate very high accuracy levels for genotyping results. Our method achieves a significant reduction in analysis time, from several hours to just a few minutes, and is completely automated requiring no manual interaction or guidance.

A methodological overview on molecular preimplantation genetic diagnosis and screening: a genomic future?

PubMed

Vendrell, Xavier; Bautista-Llácer, Rosa

2012-12-01

The genetic diagnosis and screening of preimplantation embryos generated by assisted reproduction technology has been consolidated in the prenatal care framework. The rapid evolution of DNA technologies is tending to molecular approaches. Our intention is to present a detailed methodological view, showing different diagnostic strategies based on molecular techniques that are currently applied in preimplantation genetic diagnosis. The amount of DNA from one single, or a few cells, obtained by embryo biopsy is a limiting factor for the molecular analysis. In this sense, genetic laboratories have developed molecular protocols considering this restrictive condition. Nevertheless, the development of whole-genome amplification methods has allowed preimplantation genetic diagnosis for two or more indications simultaneously, like the selection of histocompatible embryos plus detection of monogenic diseases or aneuploidies. Moreover, molecular techniques have permitted preimplantation genetic screening to progress, by implementing microarray-based comparative genome hybridization. Finally, a future view of the embryo-genetics field based on molecular advances is proposed. The normalization, cost-effectiveness analysis, and new technological tools are the next topics for preimplantation genetic diagnosis and screening. Concomitantly, these additions to assisted reproduction technologies could have a positive effect on the schedules of preimplantation studies.

Improving power and robustness for detecting genetic association with extreme-value sampling design.

PubMed

Chen, Hua Yun; Li, Mingyao

2011-12-01

Extreme-value sampling design that samples subjects with extremely large or small quantitative trait values is commonly used in genetic association studies. Samples in such designs are often treated as "cases" and "controls" and analyzed using logistic regression. Such a case-control analysis ignores the potential dose-response relationship between the quantitative trait and the underlying trait locus and thus may lead to loss of power in detecting genetic association. An alternative approach to analyzing such data is to model the dose-response relationship by a linear regression model. However, parameter estimation from this model can be biased, which may lead to inflated type I errors. We propose a robust and efficient approach that takes into consideration of both the biased sampling design and the potential dose-response relationship. Extensive simulations demonstrate that the proposed method is more powerful than the traditional logistic regression analysis and is more robust than the linear regression analysis. We applied our method to the analysis of a candidate gene association study on high-density lipoprotein cholesterol (HDL-C) which includes study subjects with extremely high or low HDL-C levels. Using our method, we identified several SNPs showing a stronger evidence of association with HDL-C than the traditional case-control logistic regression analysis. Our results suggest that it is important to appropriately model the quantitative traits and to adjust for the biased sampling when dose-response relationship exists in extreme-value sampling designs. © 2011 Wiley Periodicals, Inc.

Genetic Diversity Analysis of Highly Incomplete SNP Genotype Data with Imputations: An Empirical Assessment

PubMed Central

Fu, Yong-Bi

2014-01-01

Genotyping by sequencing (GBS) recently has emerged as a promising genomic approach for assessing genetic diversity on a genome-wide scale. However, concerns are not lacking about the uniquely large unbalance in GBS genotype data. Although some genotype imputation has been proposed to infer missing observations, little is known about the reliability of a genetic diversity analysis of GBS data, with up to 90% of observations missing. Here we performed an empirical assessment of accuracy in genetic diversity analysis of highly incomplete single nucleotide polymorphism genotypes with imputations. Three large single-nucleotide polymorphism genotype data sets for corn, wheat, and rice were acquired, and missing data with up to 90% of missing observations were randomly generated and then imputed for missing genotypes with three map-independent imputation methods. Estimating heterozygosity and inbreeding coefficient from original, missing, and imputed data revealed variable patterns of bias from assessed levels of missingness and genotype imputation, but the estimation biases were smaller for missing data without genotype imputation. The estimates of genetic differentiation were rather robust up to 90% of missing observations but became substantially biased when missing genotypes were imputed. The estimates of topology accuracy for four representative samples of interested groups generally were reduced with increased levels of missing genotypes. Probabilistic principal component analysis based imputation performed better in terms of topology accuracy than those analyses of missing data without genotype imputation. These findings are not only significant for understanding the reliability of the genetic diversity analysis with respect to large missing data and genotype imputation but also are instructive for performing a proper genetic diversity analysis of highly incomplete GBS or other genotype data. PMID:24626289

Microchip capillary gel electrophoresis using programmed field strength gradients for the ultra-fast analysis of genetically modified organisms in soybeans.

PubMed

Kim, Yun-Jeong; Chae, Joon-Seok; Chang, Jun Keun; Kang, Seong Ho

2005-08-12

We have developed a novel method for the ultra-fast analysis of genetically modified organisms (GMOs) in soybeans by microchip capillary gel electrophoresis (MCGE) using programmed field strength gradients (PFSG) in a conventional glass double-T microchip. Under the programmed electric field strength and 0.3% poly(ethylene oxide) sieving matrix, the GMO in soybeans was analyzed within only 11 s of the microchip. The MCGE-PFSG method was a program that changes the electric field strength during GMO analysis, and was also applied to the ultra-fast analysis of PCR products. Compared to MCGE using a conventional and constantly applied electric field, the MCGE-PFSG analysis generated faster results without the loss of resolving power and reproducibility for specific DNA fragments (100- and 250-bp DNA) of GM-soybeans. The MCGE-PFSG technique may prove to be a new tool in the GMO analysis due to its speed, simplicity, and high efficiency.

A proposal for clinical genetics (genetics in medicine) education for medical technologists and other health professionals in Japan.

PubMed

Kohzaki, Hidetsugu

2014-01-01

Since the completion of the Human Genome Project, technology has developed markedly in fields such as medical genetics and genetic counseling in the medical arena. In particular, this technology has advanced the discovery of and ways of understanding various genes responsible for genetic diseases, and genetic polymorphisms thought to be associated with disease. Some have been implicated as factors in common lifestyle diseases and have increased the significance of genetic testing. In Japan, doctors and other health professionals, such as nurse and medical technologists have been engaged in genetic testing and genetic disease treatment. Chromosomal and gene aberrations were detected mainly by medical technologists. However, due to the nature of medical technologists who have to provide various clinical tests, such as blood test, pre-medical technology students are required to cover tremendous knowledge of different academic fields to pass the national exam. Therefore, the time allowed for such students to study chromosomal and gene analysis is quite limited. Moreover, they are forced to enter the medical setting without receiving sufficient training. Among them, only few medical technologists specialize in chromosomal and gene analysis. However, with the advancement of clinical genetics and development of chromosomal and gene analysis, conducting clinical practice is becoming more and more difficult for medical technologists who just passed the national exam. Also, doctors and other health professionals have not been able to keep up with service demands either. This paper attempts to address knowledge and skills gaps (especially clinical genetics, English, and ICT literacy) of medical technologists and we propose educational methods to prepare medical genetics professionals in Japan to meet these gaps.

Stochastic modelling of shifts in allele frequencies reveals a strongly polygynous mating system in the re-introduced Asiatic wild ass.

PubMed

Renan, Sharon; Greenbaum, Gili; Shahar, Naama; Templeton, Alan R; Bouskila, Amos; Bar-David, Shirli

2015-04-01

Small populations are prone to loss of genetic variation and hence to a reduction in their evolutionary potential. Therefore, studying the mating system of small populations and its potential effects on genetic drift and genetic diversity is of high importance for their viability assessments. The traditional method for studying genetic mating systems is paternity analysis. Yet, as small populations are often rare and elusive, the genetic data required for paternity analysis are frequently unavailable. The endangered Asiatic wild ass (Equus hemionus), like all equids, displays a behaviourally polygynous mating system; however, the level of polygyny has never been measured genetically in wild equids. Combining noninvasive genetic data with stochastic modelling of shifts in allele frequencies, we developed an alternative approach to paternity analysis for studying the genetic mating system of the re-introduced Asiatic wild ass in the Negev Desert, Israel. We compared the shifts in allele frequencies (as a measure of genetic drift) that have occurred in the wild ass population since re-introduction onset to simulated scenarios under different proportions of mating males. We revealed a strongly polygynous mating system in which less than 25% of all males participate in the mating process each generation. This strongly polygynous mating system and its potential effect on the re-introduced population's genetic diversity could have significant consequences for the long-term persistence of the population in the Negev. The stochastic modelling approach and the use of allele-frequency shifts can be further applied to systems that are affected by genetic drift and for which genetic data are limited. © 2015 John Wiley & Sons Ltd.

Parity among interpretation methods of MLEE patterns and disparity among clustering methods in epidemiological typing of Candida albicans.

PubMed

Boriollo, Marcelo Fabiano Gomes; Rosa, Edvaldo Antonio Ribeiro; Gonçalves, Reginaldo Bruno; Höfling, José Francisco

2006-03-01

The typing of C. albicans by MLEE (multilocus enzyme electrophoresis) is dependent on the interpretation of enzyme electrophoretic patterns, and the study of the epidemiological relationships of these yeasts can be conducted by cluster analysis. Therefore, the aims of the present study were to first determine the discriminatory power of genetic interpretation (deduction of the allelic composition of diploid organisms) and numerical interpretation (mere determination of the presence and absence of bands) of MLEE patterns, and then to determine the concordance (Pearson product-moment correlation coefficient) and similarity (Jaccard similarity coefficient) of the groups of strains generated by three cluster analysis models, and the discriminatory power of such models as well [model A: genetic interpretation, genetic distance matrix of Nei (d(ij)) and UPGMA dendrogram; model B: genetic interpretation, Dice similarity matrix (S(D1)) and UPGMA dendrogram; model C: numerical interpretation, Dice similarity matrix (S(D2)) and UPGMA dendrogram]. MLEE was found to be a powerful and reliable tool for the typing of C. albicans due to its high discriminatory power (>0.9). Discriminatory power indicated that numerical interpretation is a method capable of discriminating a greater number of strains (47 versus 43 subtypes), but also pointed to model B as a method capable of providing a greater number of groups, suggesting its use for the typing of C. albicans by MLEE and cluster analysis. Very good agreement was only observed between the elements of the matrices S(D1) and S(D2), but a large majority of the groups generated in the three UPGMA dendrograms showed similarity S(J) between 4.8% and 75%, suggesting disparities in the conclusions obtained by the cluster assays.

Kernel methods for large-scale genomic data analysis

PubMed Central

Xing, Eric P.; Schaid, Daniel J.

2015-01-01

Machine learning, particularly kernel methods, has been demonstrated as a promising new tool to tackle the challenges imposed by today’s explosive data growth in genomics. They provide a practical and principled approach to learning how a large number of genetic variants are associated with complex phenotypes, to help reveal the complexity in the relationship between the genetic markers and the outcome of interest. In this review, we highlight the potential key role it will have in modern genomic data processing, especially with regard to integration with classical methods for gene prioritizing, prediction and data fusion. PMID:25053743

Going forward with genetics: recent technological advances and forward genetics in mice.

PubMed

Moresco, Eva Marie Y; Li, Xiaohong; Beutler, Bruce

2013-05-01

Forward genetic analysis is an unbiased approach for identifying genes essential to defined biological phenomena. When applied to mice, it is one of the most powerful methods to facilitate understanding of the genetic basis of human biology and disease. The speed at which disease-causing mutations can be identified in mutagenized mice has been markedly increased by recent advances in DNA sequencing technology. Creating and analyzing mutant phenotypes may therefore become rate-limiting in forward genetic experimentation. We review the forward genetic approach and its future in the context of recent technological advances, in particular massively parallel DNA sequencing, induced pluripotent stem cells, and haploid embryonic stem cells. Copyright © 2013 American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.

The Importance of Source: A Mixed Methods Analysis of Undergraduate Students' Attitudes toward Genetically Modified Food

ERIC Educational Resources Information Center

Ruth, Taylor K.; Rumble, Joy N.; Gay, Keegan D.; Rodriguez, Mary T.

2016-01-01

Even though science says genetically modified (GM) foods are safe, many consumers remain skeptical of the technology. Additionally, the scientific community has trouble communicating to the public, causing consumers to make uninformed decisions. The Millennial Generation will have more buying power than any other generation before them, and more…

From genes to ecosystems: Measuring evolutionary diversity and community structure with Forest Inventory and Analysis (FIA) data

Treesearch

Kevin M. Potter

2009-01-01

Forest genetic sustainability is an important component of forest health because genetic diversity and evolutionary processes allow for the adaptation of species and for the maintenance of ecosystem functionality and resilience. Phylogenetic community analyses, a set of new statistical methods for describing the evolutionary relationships among species, offer an...

MONITORING MYCOTOXIN PRODUCTION AT THE GENETIC LEVEL ON VARIOUS GROWTH SUBSTRATES USING QUANTITATIVE REVERSE TRANSCRIPTION POLYMERASE CHAIN REACTION?EXPERIMENT DESIGN

EPA Science Inventory

The paper describes a method of analyzing the production of mycotoxins at the genetic level by monitoring the intracellular levels of messenger RNA (mRNA). Initial work will focus on threshing out the mycotoxin gene clusters in Stachybotrys chartarum followed by analysis of toxin...

Palmar dermatoglyphic patterns in twins.

PubMed

Jacques, S M; Salzano, F M; Penña, H F

1977-01-01

The role of genetic factors in the determination of palmar dermatoglyphic patterns was investigated in a series of 49 MZ and 51 DZ twins, using Spearman's rank correlation and analysis of variance. Both methods indicated that the genetic effect in the distribution of patterns is highest in the interdigital III and lowest in the interdigital IV regions, the hypothenar and thenar showing intermediate values. As for interdigital II, no evaluation of genetic effects was possible using the nonparametric test, but the estimates of genetic variance indicate that inherited factors may play a relatively minor role in the pattern distribution of this area.

Logistic regression trees for initial selection of interesting loci in case-control studies

PubMed Central

Nickolov, Radoslav Z; Milanov, Valentin B

2007-01-01

Modern genetic epidemiology faces the challenge of dealing with hundreds of thousands of genetic markers. The selection of a small initial subset of interesting markers for further investigation can greatly facilitate genetic studies. In this contribution we suggest the use of a logistic regression tree algorithm known as logistic tree with unbiased selection. Using the simulated data provided for Genetic Analysis Workshop 15, we show how this algorithm, with incorporation of multifactor dimensionality reduction method, can reduce an initial large pool of markers to a small set that includes the interesting markers with high probability. PMID:18466557

Genetic diversity revealed by single nucleotide polymorphism markers in a worldwide germplasm collection of durum wheat.

PubMed

Ren, Jing; Sun, Daokun; Chen, Liang; You, Frank M; Wang, Jirui; Peng, Yunliang; Nevo, Eviatar; Sun, Dongfa; Luo, Ming-Cheng; Peng, Junhua

2013-03-28

Evaluation of genetic diversity and genetic structure in crops has important implications for plant breeding programs and the conservation of genetic resources. Newly developed single nucleotide polymorphism (SNP) markers are effective in detecting genetic diversity. In the present study, a worldwide durum wheat collection consisting of 150 accessions was used. Genetic diversity and genetic structure were investigated using 946 polymorphic SNP markers covering the whole genome of tetraploid wheat. Genetic structure was greatly impacted by multiple factors, such as environmental conditions, breeding methods reflected by release periods of varieties, and gene flows via human activities. A loss of genetic diversity was observed from landraces and old cultivars to the modern cultivars released during periods of the Early Green Revolution, but an increase in cultivars released during the Post Green Revolution. Furthermore, a comparative analysis of genetic diversity among the 10 mega ecogeographical regions indicated that South America, North America, and Europe possessed the richest genetic variability, while the Middle East showed moderate levels of genetic diversity.

General Framework for Meta-analysis of Rare Variants in Sequencing Association Studies

PubMed Central

Lee, Seunggeun; Teslovich, Tanya M.; Boehnke, Michael; Lin, Xihong

2013-01-01

We propose a general statistical framework for meta-analysis of gene- or region-based multimarker rare variant association tests in sequencing association studies. In genome-wide association studies, single-marker meta-analysis has been widely used to increase statistical power by combining results via regression coefficients and standard errors from different studies. In analysis of rare variants in sequencing studies, region-based multimarker tests are often used to increase power. We propose meta-analysis methods for commonly used gene- or region-based rare variants tests, such as burden tests and variance component tests. Because estimation of regression coefficients of individual rare variants is often unstable or not feasible, the proposed method avoids this difficulty by calculating score statistics instead that only require fitting the null model for each study and then aggregating these score statistics across studies. Our proposed meta-analysis rare variant association tests are conducted based on study-specific summary statistics, specifically score statistics for each variant and between-variant covariance-type (linkage disequilibrium) relationship statistics for each gene or region. The proposed methods are able to incorporate different levels of heterogeneity of genetic effects across studies and are applicable to meta-analysis of multiple ancestry groups. We show that the proposed methods are essentially as powerful as joint analysis by directly pooling individual level genotype data. We conduct extensive simulations to evaluate the performance of our methods by varying levels of heterogeneity across studies, and we apply the proposed methods to meta-analysis of rare variant effects in a multicohort study of the genetics of blood lipid levels. PMID:23768515

[The development of molecular human genetics and its significance for perspectives of modern medicine].

PubMed

Coutelle, C; Speer, A; Grade, K; Rosenthal, A; Hunger, H D

1989-01-01

The introduction of molecular human genetics has become a paradigma for the application of genetic engineering in medicine. The main principles of this technology are the isolation of molecular probes, their application in hybridization reactions, specific gene-amplification by the polymerase chain reaction, and DNA sequencing reactions. These methods are used for the analysis of monogenic diseases by linkage studies and the elucidation of the molecular defect causing these conditions, respectively. They are also the basis for genomic diagnosis of monogenic diseases, introduced into the health care system of the GDR by a national project on Duchenne/Becker muscular dystrophy, Cystic Fibrosis and Phenylketonuria. The rapid development of basic research on the molecular analysis of the human genome and genomic diagnosis indicates, that human molecular genetics is becoming a decisive basic discipline of modern medicine.

The association between intelligence and lifespan is mostly genetic

PubMed Central

Arden, Rosalind; Deary, Ian J; Reynolds, Chandra A; Pedersen, Nancy L; Plassman, Brenda L; McGue, Matt; Christensen, Kaare; Visscher, Peter M

2016-01-01

Abstract Background: Several studies in the new field of cognitive epidemiology have shown that higher intelligence predicts longer lifespan. This positive correlation might arise from socioeconomic status influencing both intelligence and health; intelligence leading to better health behaviours; and/or some shared genetic factors influencing both intelligence and health. Distinguishing among these hypotheses is crucial for medicine and public health, but can only be accomplished by studying a genetically informative sample. Methods: We analysed data from three genetically informative samples containing information on intelligence and mortality: Sample 1, 377 pairs of male veterans from the NAS-NRC US World War II Twin Registry; Sample 2, 246 pairs of twins from the Swedish Twin Registry; and Sample 3, 784 pairs of twins from the Danish Twin Registry. The age at which intelligence was measured differed between the samples. We used three methods of genetic analysis to examine the relationship between intelligence and lifespan: we calculated the proportion of the more intelligent twins who outlived their co-twin; we regressed within-twin-pair lifespan differences on within-twin-pair intelligence differences; and we used the resulting regression coefficients to model the additive genetic covariance. We conducted a meta-analysis of the regression coefficients across the three samples. Results: The combined (and all three individual samples) showed a small positive phenotypic correlation between intelligence and lifespan. In the combined sample observed r  = .12 (95% confidence interval .06 to .18). The additive genetic covariance model supported a genetic relationship between intelligence and lifespan. In the combined sample the genetic contribution to the covariance was 95%; in the US study, 84%; in the Swedish study, 86%, and in the Danish study, 85%. Conclusions: The finding of common genetic effects between lifespan and intelligence has important implications for public health, and for those interested in the genetics of intelligence, lifespan or inequalities in health outcomes including lifespan. PMID:26213105

Diversity in phenotypic and nutritional traits in vegetable amaranth (Amaranthus tricolor), a nutritionally underutilised crop.

PubMed

Shukla, Sudhir; Bhargava, Atul; Chatterjee, Avijeet; Pandey, Avinash Chandra; Mishra, Brij K

2010-01-15

Assessment of genetic diversity in a crop-breeding programme helps in the identification of diverse parental combinations to create segregating progenies with maximum genetic variability and facilitates introgression of desirable genes from diverse germplasm into the available genetic base. In the present study, 39 strains of vegetable amaranth (Amaranthus tricolor) were evaluated for eight morphological and seven quality traits for two test seasons to study the extent of genetic divergence among the strains. Multivariate analysis showed that the first four principal components contributed 67.55% of the variability. Cluster analysis grouped the strains into six clusters that displayed a wide range of diversity for most of the traits. Cluster analysis has proved to be an effective method in grouping strains that may facilitate effective management and utilisation in crop-breeding programmes. The diverse strains falling in different clusters were identified, which can be utilised in different hybridisation programmes to develop high-foliage-yielding varieties rich in nutritional components. Copyright (c) 2009 Society of Chemical Industry.

Integrated single-cell genetic and transcriptional analysis suggests novel drivers of chronic lymphocytic leukemia

PubMed Central

Wang, Lili; Fan, Jean; Francis, Joshua M.; Georghiou, George; Hergert, Sarah; Li, Shuqiang; Gambe, Rutendo; Zhou, Chensheng W.; Yang, Chunxiao; Xiao, Sheng; Cin, Paola Dal; Bowden, Michaela; Kotliar, Dylan; Shukla, Sachet A.; Brown, Jennifer R.; Neuberg, Donna; Alessi, Dario R.; Zhang, Cheng-Zhong; Kharchenko, Peter V.; Livak, Kenneth J.; Wu, Catherine J.

2017-01-01

Intra-tumoral genetic heterogeneity has been characterized across cancers by genome sequencing of bulk tumors, including chronic lymphocytic leukemia (CLL). In order to more accurately identify subclones, define phylogenetic relationships, and probe genotype–phenotype relationships, we developed methods for targeted mutation detection in DNA and RNA isolated from thousands of single cells from five CLL samples. By clearly resolving phylogenic relationships, we uncovered mutated LCP1 and WNK1 as novel CLL drivers, supported by functional evidence demonstrating their impact on CLL pathways. Integrative analysis of somatic mutations with transcriptional states prompts the idea that convergent evolution generates phenotypically similar cells in distinct genetic branches, thus creating a cohesive expression profile in each CLL sample despite the presence of genetic heterogeneity. Our study highlights the potential for single-cell RNA-based targeted analysis to sensitively determine transcriptional and mutational profiles of individual cancer cells, leading to increased understanding of driving events in malignancy. PMID:28679620

A High-throughput AFLP-based Method for Constructing Integrated Genetic and Physical Maps: Progress Toward a Sorghum Genome Map

PubMed Central

Klein, Patricia E.; Klein, Robert R.; Cartinhour, Samuel W.; Ulanch, Paul E.; Dong, Jianmin; Obert, Jacque A.; Morishige, Daryl T.; Schlueter, Shannon D.; Childs, Kevin L.; Ale, Melissa; Mullet, John E.

2000-01-01

Sorghum is an important target for plant genomic mapping because of its adaptation to harsh environments, diverse germplasm collection, and value for comparing the genomes of grass species such as corn and rice. The construction of an integrated genetic and physical map of the sorghum genome (750 Mbp) is a primary goal of our sorghum genome project. To help accomplish this task, we have developed a new high-throughput PCR-based method for building BAC contigs and locating BAC clones on the sorghum genetic map. This task involved pooling 24,576 sorghum BAC clones (∼4× genome equivalents) in six different matrices to create 184 pools of BAC DNA. DNA fragments from each pool were amplified using amplified fragment length polymorphism (AFLP) technology, resolved on a LI-COR dual-dye DNA sequencing system, and analyzed using Bionumerics software. On average, each set of AFLP primers amplified 28 single-copy DNA markers that were useful for identifying overlapping BAC clones. Data from 32 different AFLP primer combinations identified ∼2400 BACs and ordered ∼700 BAC contigs. Analysis of a sorghum RIL mapping population using the same primer pairs located ∼200 of the BAC contigs on the sorghum genetic map. Restriction endonuclease fingerprinting of the entire collection of sorghum BAC clones was applied to test and extend the contigs constructed using this PCR-based methodology. Analysis of the fingerprint data allowed for the identification of 3366 contigs each containing an average of 5 BACs. BACs in ∼65% of the contigs aligned by AFLP analysis had sufficient overlap to be confirmed by DNA fingerprint analysis. In addition, 30% of the overlapping BACs aligned by AFLP analysis provided information for merging contigs and singletons that could not be joined using fingerprint data alone. Thus, the combination of fingerprinting and AFLP-based contig assembly and mapping provides a reliable, high-throughput method for building an integrated genetic and physical map of the sorghum genome. [The sequence data described in this paper have been submitted to the GenBank data library under accession no. AF218263.] PMID:10854411

Locating Critical Circular and Unconstrained Failure Surface in Slope Stability Analysis with Tailored Genetic Algorithm

NASA Astrophysics Data System (ADS)

Pasik, Tomasz; van der Meij, Raymond

2017-12-01

This article presents an efficient search method for representative circular and unconstrained slip surfaces with the use of the tailored genetic algorithm. Searches for unconstrained slip planes with rigid equilibrium methods are yet uncommon in engineering practice, and little publications regarding truly free slip planes exist. The proposed method presents an effective procedure being the result of the right combination of initial population type, selection, crossover and mutation method. The procedure needs little computational effort to find the optimum, unconstrained slip plane. The methodology described in this paper is implemented using Mathematica. The implementation, along with further explanations, is fully presented so the results can be reproduced. Sample slope stability calculations are performed for four cases, along with a detailed result interpretation. Two cases are compared with analyses described in earlier publications. The remaining two are practical cases of slope stability analyses of dikes in Netherlands. These four cases show the benefits of analyzing slope stability with a rigid equilibrium method combined with a genetic algorithm. The paper concludes by describing possibilities and limitations of using the genetic algorithm in the context of the slope stability problem.

Dealing with AFLP genotyping errors to reveal genetic structure in Plukenetia volubilis (Euphorbiaceae) in the Peruvian Amazon

PubMed Central

Vašek, Jakub; Viehmannová, Iva; Ocelák, Martin; Cachique Huansi, Danter; Vejl, Pavel

2017-01-01

An analysis of the population structure and genetic diversity for any organism often depends on one or more molecular marker techniques. Nonetheless, these techniques are not absolutely reliable because of various sources of errors arising during the genotyping process. Thus, a complex analysis of genotyping error was carried out with the AFLP method in 169 samples of the oil seed plant Plukenetia volubilis L. from small isolated subpopulations in the Peruvian Amazon. Samples were collected in nine localities from the region of San Martin. Analysis was done in eight datasets with a genotyping error from 0 to 5%. Using eleven primer combinations, 102 to 275 markers were obtained according to the dataset. It was found that it is only possible to obtain the most reliable and robust results through a multiple-level filtering process. Genotyping error and software set up influence both the estimation of population structure and genetic diversity, where in our case population number (K) varied between 2–9 depending on the dataset and statistical method used. Surprisingly, discrepancies in K number were caused more by statistical approaches than by genotyping errors themselves. However, for estimation of genetic diversity, the degree of genotyping error was critical because descriptive parameters (He, FST, PLP 5%) varied substantially (by at least 25%). Due to low gene flow, P. volubilis mostly consists of small isolated subpopulations (ΦPT = 0.252–0.323) with some degree of admixture given by socio-economic connectivity among the sites; a direct link between the genetic and geographic distances was not confirmed. The study illustrates the successful application of AFLP to infer genetic structure in non-model plants. PMID:28910307

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.

The contributions of admixture and genetic drift to diversity among post-contact populations in the Americas.

PubMed

Koehl, Anthony J; Long, Jeffrey C

2018-02-01

We present a model that partitions Nei's minimum genetic distance between admixed populations into components of admixture and genetic drift. We applied this model to 17 admixed populations in the Americas to examine how admixture and drift have contributed to the patterns of genetic diversity. We analyzed 618 short tandem repeat loci in 949 individuals from 49 population samples. Thirty-two samples serve as proxies for continental ancestors. Seventeen samples represent admixed populations: (4) African-American and (13) Latin American. We partition genetic distance, and then calculate fixation indices and principal coordinates to interpret our results. A computer simulation confirms that our method correctly estimates drift and admixture components of genetic distance when the assumptions of the model are met. The partition of genetic distance shows that both admixture and genetic drift contribute to patterns of genetic diversity. The admixture component of genetic distance provides evidence for two distinct axes of continental ancestry. However, the genetic distances show that ancestry contributes to only one axis of genetic differentiation. The genetic distances among the 13 Latin American populations in this analysis show contributions from both differences in ancestry and differences in genetic drift. By contrast, the genetic distances among the four African American populations in this analysis owe mostly to genetic drift because these groups have similar fractions of European and African ancestry. The genetic structure of admixed populations in the Americas reflects more than admixture. We show that the history of serial founder effects constrains the impact of admixture on allele frequencies to a single dimension. Genetic drift in the admixed populations imposed a new level of genetic structure onto that created by admixture. © 2017 Wiley Periodicals, Inc.

High-performance single cell genetic analysis using microfluidic emulsion generator arrays.

PubMed

Zeng, Yong; Novak, Richard; Shuga, Joe; Smith, Martyn T; Mathies, Richard A

2010-04-15

High-throughput genetic and phenotypic analysis at the single cell level is critical to advance our understanding of the molecular mechanisms underlying cellular function and dysfunction. Here we describe a high-performance single cell genetic analysis (SCGA) technique that combines high-throughput microfluidic emulsion generation with single cell multiplex polymerase chain reaction (PCR). Microfabricated emulsion generator array (MEGA) devices containing 4, 32, and 96 channels are developed to confer a flexible capability of generating up to 3.4 x 10(6) nanoliter-volume droplets per hour. Hybrid glass-polydimethylsiloxane diaphragm micropumps integrated into the MEGA chips afford uniform droplet formation, controlled generation frequency, and effective transportation and encapsulation of primer functionalized microbeads and cells. A multiplex single cell PCR method is developed to detect and quantify both wild type and mutant/pathogenic cells. In this method, microbeads functionalized with multiple forward primers targeting specific genes from different cell types are used for solid-phase PCR in droplets. Following PCR, the droplets are lysed and the beads are pooled and rapidly analyzed by multicolor flow cytometry. Using Escherichia coli bacterial cells as a model, we show that this technique enables digital detection of pathogenic E. coli O157 cells in a high background of normal K12 cells, with a detection limit on the order of 1/10(5). This result demonstrates that multiplex SCGA is a promising tool for high-throughput quantitative digital analysis of genetic variation in complex populations.

High-Performance Single Cell Genetic Analysis Using Microfluidic Emulsion Generator Arrays

PubMed Central

Zeng, Yong; Novak, Richard; Shuga, Joe; Smith, Martyn T.; Mathies, Richard A.

2010-01-01

High-throughput genetic and phenotypic analysis at the single cell level is critical to advance our understanding of the molecular mechanisms underlying cellular function and dysfunction. Here we describe a high-performance single cell genetic analysis (SCGA) technique that combines high-throughput microfluidic emulsion generation with single cell multiplex PCR. Microfabricated emulsion generator array (MEGA) devices containing 4, 32 and 96 channels are developed to confer a flexible capability of generating up to 3.4 × 106 nanoliter-volume droplets per hour. Hybrid glass-polydimethylsiloxane diaphragm micropumps integrated into the MEGA chips afford uniform droplet formation, controlled generation frequency, and effective transportation and encapsulation of primer functionalized microbeads and cells. A multiplex single cell PCR method is developed to detect and quantify both wild type and mutant/pathogenic cells. In this method, microbeads functionalized with multiple forward primers targeting specific genes from different cell types are used for solid-phase PCR in droplets. Following PCR, the droplets are lysed, the beads are pooled and rapidly analyzed by multi-color flow cytometry. Using E. coli bacterial cells as a model, we show that this technique enables digital detection of pathogenic E. coli O157 cells in a high background of normal K12 cells, with a detection limit on the order of 1:105. This result demonstrates that multiplex SCGA is a promising tool for high-throughput quantitative digital analysis of genetic variation in complex populations. PMID:20192178

Improved score statistics for meta-analysis in single-variant and gene-level association studies.

PubMed

Yang, Jingjing; Chen, Sai; Abecasis, Gonçalo

2018-06-01

Meta-analysis is now an essential tool for genetic association studies, allowing them to combine large studies and greatly accelerating the pace of genetic discovery. Although the standard meta-analysis methods perform equivalently as the more cumbersome joint analysis under ideal settings, they result in substantial power loss under unbalanced settings with various case-control ratios. Here, we investigate the power loss problem by the standard meta-analysis methods for unbalanced studies, and further propose novel meta-analysis methods performing equivalently to the joint analysis under both balanced and unbalanced settings. We derive improved meta-score-statistics that can accurately approximate the joint-score-statistics with combined individual-level data, for both linear and logistic regression models, with and without covariates. In addition, we propose a novel approach to adjust for population stratification by correcting for known population structures through minor allele frequencies. In the simulated gene-level association studies under unbalanced settings, our method recovered up to 85% power loss caused by the standard methods. We further showed the power gain of our methods in gene-level tests with 26 unbalanced studies of age-related macular degeneration . In addition, we took the meta-analysis of three unbalanced studies of type 2 diabetes as an example to discuss the challenges of meta-analyzing multi-ethnic samples. In summary, our improved meta-score-statistics with corrections for population stratification can be used to construct both single-variant and gene-level association studies, providing a useful framework for ensuring well-powered, convenient, cross-study analyses. © 2018 WILEY PERIODICALS, INC.

Genetic Simulation Resources: a website for the registration and discovery of genetic data simulators

PubMed Central

Peng, Bo; Chen, Huann-Sheng; Mechanic, Leah E.; Racine, Ben; Clarke, John; Clarke, Lauren; Gillanders, Elizabeth; Feuer, Eric J.

2013-01-01

Summary: Many simulation methods and programs have been developed to simulate genetic data of the human genome. These data have been widely used, for example, to predict properties of populations retrospectively or prospectively according to mathematically intractable genetic models, and to assist the validation, statistical inference and power analysis of a variety of statistical models. However, owing to the differences in type of genetic data of interest, simulation methods, evolutionary features, input and output formats, terminologies and assumptions for different applications, choosing the right tool for a particular study can be a resource-intensive process that usually involves searching, downloading and testing many different simulation programs. Genetic Simulation Resources (GSR) is a website provided by the National Cancer Institute (NCI) that aims to help researchers compare and choose the appropriate simulation tools for their studies. This website allows authors of simulation software to register their applications and describe them with well-defined attributes, thus allowing site users to search and compare simulators according to specified features. Availability: http://popmodels.cancercontrol.cancer.gov/gsr. Contact: gsr@mail.nih.gov PMID:23435068

A Simple and Computationally Efficient Sampling Approach to Covariate Adjustment for Multifactor Dimensionality Reduction Analysis of Epistasis

PubMed Central

Gui, Jiang; Andrew, Angeline S.; Andrews, Peter; Nelson, Heather M.; Kelsey, Karl T.; Karagas, Margaret R.; Moore, Jason H.

2010-01-01

Epistasis or gene-gene interaction is a fundamental component of the genetic architecture of complex traits such as disease susceptibility. Multifactor dimensionality reduction (MDR) was developed as a nonparametric and model-free method to detect epistasis when there are no significant marginal genetic effects. However, in many studies of complex disease, other covariates like age of onset and smoking status could have a strong main effect and may potentially interfere with MDR's ability to achieve its goal. In this paper, we present a simple and computationally efficient sampling method to adjust for covariate effects in MDR. We use simulation to show that after adjustment, MDR has sufficient power to detect true gene-gene interactions. We also compare our method with the state-of-art technique in covariate adjustment. The results suggest that our proposed method performs similarly, but is more computationally efficient. We then apply this new method to an analysis of a population-based bladder cancer study in New Hampshire. PMID:20924193

Identification of rheumatoid arthritis biomarkers based on single nucleotide polymorphisms and haplotype blocks: A systematic review and meta-analysis

PubMed Central

Saad, Mohamed N.; Mabrouk, Mai S.; Eldeib, Ayman M.; Shaker, Olfat G.

2015-01-01

Genetics of autoimmune diseases represent a growing domain with surpassing biomarker results with rapid progress. The exact cause of Rheumatoid Arthritis (RA) is unknown, but it is thought to have both a genetic and an environmental bases. Genetic biomarkers are capable of changing the supervision of RA by allowing not only the detection of susceptible individuals, but also early diagnosis, evaluation of disease severity, selection of therapy, and monitoring of response to therapy. This review is concerned with not only the genetic biomarkers of RA but also the methods of identifying them. Many of the identified genetic biomarkers of RA were identified in populations of European and Asian ancestries. The study of additional human populations may yield novel results. Most of the researchers in the field of identifying RA biomarkers use single nucleotide polymorphism (SNP) approaches to express the significance of their results. Although, haplotype block methods are expected to play a complementary role in the future of that field. PMID:26843965

Bayes factors based on robust TDT-type tests for family trio design.

PubMed

Yuan, Min; Pan, Xiaoqing; Yang, Yaning

2015-06-01

Adaptive transmission disequilibrium test (aTDT) and MAX3 test are two robust-efficient association tests for case-parent family trio data. Both tests incorporate information of common genetic models including recessive, additive and dominant models and are efficient in power and robust to genetic model specifications. The aTDT uses information of departure from Hardy-Weinberg disequilibrium to identify the potential genetic model underlying the data and then applies the corresponding TDT-type test, and the MAX3 test is defined as the maximum of the absolute value of three TDT-type tests under the three common genetic models. In this article, we propose three robust Bayes procedures, the aTDT based Bayes factor, MAX3 based Bayes factor and Bayes model averaging (BMA), for association analysis with case-parent trio design. The asymptotic distributions of aTDT under the null and alternative hypothesis are derived in order to calculate its Bayes factor. Extensive simulations show that the Bayes factors and the p-values of the corresponding tests are generally consistent and these Bayes factors are robust to genetic model specifications, especially so when the priors on the genetic models are equal. When equal priors are used for the underlying genetic models, the Bayes factor method based on aTDT is more powerful than those based on MAX3 and Bayes model averaging. When the prior placed a small (large) probability on the true model, the Bayes factor based on aTDT (BMA) is more powerful. Analysis of a simulation data about RA from GAW15 is presented to illustrate applications of the proposed methods.

Genetic diversity in populations of Isatis glauca Aucher ex Boiss. ssp. from Central Anatolia in Turkey, as revealed by AFLP analysis.

PubMed

Özbek, Özlem; Görgülü, Elçin; Yıldırımlı, Şinasi

2013-12-01

Isatidae L. is a complex and systematically difficult genus in Brassicaceae. The genus displays great morphological polymorphism, which makes the classification of species and subspecies difficult as it is observed in Isatis glauca Aucher ex Boiss. The aim of this study is characterization of the genetic diversity in subspecies of Isatis glauca Aucher ex Boiss. distributed widely in Central Anatolia, in Turkey by using Amplified Fragment Length Polymorphism (AFLP) technique. Eight different Eco RI-Mse I primer combinations produced 805 AFLP loci, 793 (98.5%) of which were polymorphic in 67 accessions representing nine different populations. The data obtained by AFLP was computed with using GDA (Genetic Data Analysis) and STRUCTURE (version 2.3.3) software programs for population genetics. The mean proportion of the polymorphic locus (P), the mean number of alleles (A), the number of unique alleles (U) and the mean value of gene diversity (He) were 0.59, 1.59, 20, and 0.23 respectively. The coancestry coefficient (ϴ) was 0.24. The optimal number of K was identified as seven. The principal component analysis (PCA) explained 85.61% of the total genetic variation. Isatis glauca ssp. populations showed a high level of genetic diversity, and the AFLP analysis revealed that high polymorphism and differentiated subspecies could be used conveniently for population genetic studies. The principal coordinate analysis (PCoA) based on the dissimilarity matrix, the dendrogram drawn with UPGMA method and STRUCTURE cluster analysis distinguished the accessions successfully. The accessions formed distinctive population structures for populations AA, AB, E, K, and S. Populations AG1 and AG2 seemed to have similar genetic content, in addition, in both populations several hybrid individuals were observed. The accessions did not formed distinctive population structures for both populations AI and ANP. Consequently, Ankara province might be the area, where species Isatis glauca Aucher ex Boiss. originated.

Theory, Method, and Triangulation in the Study of Street Children.

ERIC Educational Resources Information Center

Lucchini, Riccardo

1996-01-01

Describes how a comparative study of street children in Montevideo (Uruguay), Rio de Janeiro, and Mexico City contributes to a synergism between theory and method. Notes how theoretical approaches of symbolic interactionism, genetic structuralism, and habitus theory complement interview, participant observation, and content analysis methods;…

Analysis of health trait data from on-farm computer systems in the U.S. II: Comparison of genomic analyses including two-stage and single-step methods

USDA-ARS?s Scientific Manuscript database

The development of genomic selection methodology, with accompanying substantial gains in reliability for low-heritability traits, may dramatically improve the feasibility of genetic improvement of dairy cow health. Many methods for genomic analysis have now been developed, including the “Bayesian Al...

[ISSR analysis for genetic polymorphism of Aconitum leucostomum from different habitats].

PubMed

Gao, Fu-chun; Sun, Yun; Zhang, Jing; Zhang, Fan

2014-01-01

To investigate the genetic diversities and variations of Aconitum leucostomum,and to supply essential characteristics for identifying Aconitum crude drugs. Plant genome extraction kit was applied to extract DNA,and ultraviolet spectrophotometer was used to detect the concentrations and purity of DNA. 60 ISSR primers were screened to analyze the DNA of Aconitum leucostomum from 10 habitats. Biosoftwares including POPGEN32 and NTSYS-PC were used to analyze the polymorphic bands obtained, and hence to yield the genetic similarity coefficient of the 10 habitats and map the related graphics, and cluster analysis were performed by UPGMA method. 11 primers selected from 60 ISSR primers were used for amplification and a total of 101 DNA bands were obtained, including 89 polymorphic bands,the average percentage of polymorphic bands (PPB) was 88.1%. Shannon information index (I) was 0.5298, the genetic similarity coefficient (H) was 0.3648, observed number of alleles was 1.8911, and effective number of alleles was 1.6555. The genetic identity was from 0.4950 to 0.6931, and the genetic distances were from 0.3666 to 0.7031. According to cluster analysis result of ISSR, the 10 habitats of Aconitum leucostomum were classified into five groups. Germplasm resources of Aconitum leucostomum show abundant polymorphism and higher genetic variation, which might supply molecular level basis, and provide basis for building DNA fingerprint.

[The estimation of possibilities for the application of the laser capture microdissection technology for the molecular-genetic expert analysis (genotyping) of human chromosomal DNA].

PubMed

Ivanov, P L; Leonov, S N; Zemskova, E Iu

2012-01-01

The present study was designed to estimate the possibilities of application of the laser capture microdissection (LCM) technology for the molecular-genetic expert analysis (genotyping) of human chromosomal DNA. The experimental method employed for the purpose was the multiplex multilocus analysis of autosomal DNA polymorphism in the preparations of buccal epitheliocytes obtained by LCM. The key principles of the study were the application of physical methods for contrast enhancement of the micropreparations (such as phase-contrast microscopy and dark-field microscopy) and PCR-compatible cell lysis. Genotyping was carried out with the use of AmpFISTR Minifiler TM PCR Amplification Kits ("Applied Biosynthesis", USA). It was shown that the technique employed in the present study ensures reliable genotyping of human chromosomal DNA in the pooled preparations containing 10-20 dissected diploid cells each. This result fairly well agrees with the calculated sensitivity of the method. A few practical recommendations are offered.

Significant population genetic structure detected in the rock bream Oplegnathus fasciatus (Temminck & Schlegel, 1844) inferred from fluorescent-AFLP analysis

NASA Astrophysics Data System (ADS)

Xiao, Yongshuang; Ma, Daoyuan; Xu, Shihong; Liu, Qinghua; Wang, Yanfeng; Xiao, Zhizhong; Li, Jun

2016-05-01

Oplegnathus fasciatus (rock bream) is a commercial rocky reef fish species in East Asia that has been considered for aquaculture. We estimated the population genetic diversity and population structure of the species along the coastal waters of China using fluorescent-amplified fragment length polymorphisms technology. Using 53 individuals from three populations and four pairs of selective primers, we amplified 1 264 bands, 98.73% of which were polymorphic. The Zhoushan population showed the highest Nei's genetic diversity and Shannon genetic diversity. The results of analysis of molecular variance (AMOVA) showed that 59.55% of genetic variation existed among populations and 40.45% occurred within populations, which indicated that a significant population genetic structure existed in the species. The pairwise fixation index F st ranged from 0.20 to 0.63 and were significant after sequential Bonferroni correction. The topology of an unweighted pair group method with arithmetic mean tree showed two significant genealogical branches corresponding to the sampling locations of North and South China. The AMOVA and STRUCTURE analyses suggested that the O. fasciatus populations examined should comprise two stocks.

The relationship between genetic and chemotypic diversity in American ginseng (Panax quinquefolius L.).

PubMed

Schlag, Erin M; McIntosh, Marla S

2013-09-01

Ginseng is one of the world's most important herbals used as an adaptogen and a cure for an impressively large range of ailments. Differences in the medicinal properties of ginseng roots have been attributed to variation in ginsenoside composition. In this study, the association between genetic and chemotypic profiles of wild and cultivated American ginseng (Panax quinquefolius L.) roots grown in Maryland was investigated. Ginseng roots were classified into chemotypes based on their relative composition of Re and Rg1. Genetic profiles of these roots were determined from the analysis of 38 polymorphic RAPD markers and used for a cluster analysis of genetic similarities. The close correspondence between chemotype and genetic cluster provides the first DNA-based evidence for the genetic basis of ginsenoside composition. Results of this research are significant for plant breeding and conservation, phytochemical research, and clinical and pharmacological studies. Also, the correlation between RAPD markers and chemotype indicates the potential to use RAPD markers as a reliable and practical method for identification and certification of ginseng roots. Copyright © 2013 Elsevier Ltd. All rights reserved.

Clinical relevance of cytogenetics to pediatric practice. Postnatal findings of Patau syndrome – Review of 5 cases

PubMed Central

PLAIASU, Vasilica; OCHIANA, Diana; MOTEI, Gabriela; ANCA, Ioana; GEORGESCU, Adrian

2010-01-01

ABSTRACT Introduction: Patau syndrome (trisomy 13) is one of the most common chromosomal anomalies clinically characterized by the presence of numerous malformations with a limited survival rate for most cases. Babies are usually identified at birth and the diagnosis is confirmed with genetic testing. Materials and methods: In this review we outline the clinical and cytogenetic aspects of trisomy 13 and associated phenotypes for 5 cases analyzed in the last 3 years, referred to our Clinical Genetics Department. For each child cytogenetic analysis was performed to determine the genetic variant; also, the patients were investigated for other associated malformations (cardiac, cerebral, renal, ocular anomalies). Discussion: All 5 cases presented multiple malformations, including some but not all signs of the classical clinical triad suggestive of Patau syndrome. The cytogenetic investigation confirmed for each case the suspected diagnosis and also indicated the specific genetic variant, this being a valuable information for the genetic counselling of the families. Conclusion: The application of genetic analysis can increase diagnosis and prognosis accuracy and have an impact on clinical management. PMID:21977150

Analysis of the genetic diversity of Chinese native Cannabis sativa cultivars by using ISSR and chromosome markers.

PubMed

Zhang, L G; Chang, Y; Zhang, X F; Guan, F Z; Yuan, H M; Yu, Y; Zhao, L J

2014-12-12

Hemp (Cannabis sativa) is an important fiber crop, and native cultivars exist widely throughout China. In the present study, we analyzed the genetic diversity of 27 important Chinese native hemp cultivars, by using inter-simple sequence repeats (ISSR) and chromosome markers. We determined the following chromosome formulas: 2n = 20 = 14m + 6sm; 2n = 20 = 20m; 2n = 20 = 18m + 2sm; 2n = 20 = 16m + 4sm; and 2n = 20 = 12m + 8sm. The results of our ISSR analysis revealed the genetic relationships among the 27 cultivars; these relationships were analyzed by using the unweighted pair-group method based on DNA polymorphism. Our results revealed that all of the native cultivars showed considerable genetic diversity. At a genetic distance of 0.324, the 27 varieties could be classified into five categories; this grouping corresponded well with the chromosome formulas. All of the investigated hemp cultivars represent relatively primitive types; moreover, the genetic distances show a geographical distribution, with a small amount of regional hybridity.

On the validity of within-nuclear-family genetic association analysis in samples of extended families.

PubMed

Bureau, Alexandre; Duchesne, Thierry

2015-12-01

Splitting extended families into their component nuclear families to apply a genetic association method designed for nuclear families is a widespread practice in familial genetic studies. Dependence among genotypes and phenotypes of nuclear families from the same extended family arises because of genetic linkage of the tested marker with a risk variant or because of familial specificity of genetic effects due to gene-environment interaction. This raises concerns about the validity of inference conducted under the assumption of independence of the nuclear families. We indeed prove theoretically that, in a conditional logistic regression analysis applicable to disease cases and their genotyped parents, the naive model-based estimator of the variance of the coefficient estimates underestimates the true variance. However, simulations with realistic effect sizes of risk variants and variation of this effect from family to family reveal that the underestimation is negligible. The simulations also show the greater efficiency of the model-based variance estimator compared to a robust empirical estimator. Our recommendation is therefore, to use the model-based estimator of variance for inference on effects of genetic variants.

Teaching Human Genetics with Mustard: Rapid Cycling Brassica rapa (Fast Plants Type) as a Model for Human Genetics in the Classroom Laboratory

PubMed Central

Pickard, Dawn

2007-01-01

We have developed experiments and materials to model human genetics using rapid cycling Brassica rapa, also known as Fast Plants. Because of their self-incompatibility for pollination and the genetic diversity within strains, B. rapa can serve as a relevant model for human genetics in teaching laboratory experiments. The experiment presented here is a paternity exclusion project in which a child is born with a known mother but two possible alleged fathers. Students use DNA markers (microsatellites) to perform paternity exclusion on these subjects. Realistic DNA marker analysis can be challenging to implement within the limitations of an instructional lab, but we have optimized the experimental methods to work in a teaching lab environment and to maximize the “hands-on” experience for the students. The genetic individuality of each B. rapa plant, revealed by analysis of polymorphic microsatellite markers, means that each time students perform this project, they obtain unique results that foster independent thinking in the process of data interpretation. PMID:17548880

A novel structure-aware sparse learning algorithm for brain imaging genetics.

PubMed

Du, Lei; Jingwen, Yan; Kim, Sungeun; Risacher, Shannon L; Huang, Heng; Inlow, Mark; Moore, Jason H; Saykin, Andrew J; Shen, Li

2014-01-01

Brain imaging genetics is an emergent research field where the association between genetic variations such as single nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is evaluated. Sparse canonical correlation analysis (SCCA) is a bi-multivariate analysis method that has the potential to reveal complex multi-SNP-multi-QT associations. Most existing SCCA algorithms are designed using the soft threshold strategy, which assumes that the features in the data are independent from each other. This independence assumption usually does not hold in imaging genetic data, and thus inevitably limits the capability of yielding optimal solutions. We propose a novel structure-aware SCCA (denoted as S2CCA) algorithm to not only eliminate the independence assumption for the input data, but also incorporate group-like structure in the model. Empirical comparison with a widely used SCCA implementation, on both simulated and real imaging genetic data, demonstrated that S2CCA could yield improved prediction performance and biologically meaningful findings.

Integrating genome-wide association studies and gene expression data highlights dysregulated multiple sclerosis risk pathways.

PubMed

Liu, Guiyou; Zhang, Fang; Jiang, Yongshuai; Hu, Yang; Gong, Zhongying; Liu, Shoufeng; Chen, Xiuju; Jiang, Qinghua; Hao, Junwei

2017-02-01

Much effort has been expended on identifying the genetic determinants of multiple sclerosis (MS). Existing large-scale genome-wide association study (GWAS) datasets provide strong support for using pathway and network-based analysis methods to investigate the mechanisms underlying MS. However, no shared genetic pathways have been identified to date. We hypothesize that shared genetic pathways may indeed exist in different MS-GWAS datasets. Here, we report results from a three-stage analysis of GWAS and expression datasets. In stage 1, we conducted multiple pathway analyses of two MS-GWAS datasets. In stage 2, we performed a candidate pathway analysis of the large-scale MS-GWAS dataset. In stage 3, we performed a pathway analysis using the dysregulated MS gene list from seven human MS case-control expression datasets. In stage 1, we identified 15 shared pathways. In stage 2, we successfully replicated 14 of these 15 significant pathways. In stage 3, we found that dysregulated MS genes were significantly enriched in 10 of 15 MS risk pathways identified in stages 1 and 2. We report shared genetic pathways in different MS-GWAS datasets and highlight some new MS risk pathways. Our findings provide new insights on the genetic determinants of MS.

High genetic diversity in the offshore island populations of the tephritid fruit fly Bactrocera dorsalis.

PubMed

Yi, Chunyan; Zheng, Chunyan; Zeng, Ling; Xu, Yijuan

2016-10-13

Geographic isolation is an important factor that limit species dispersal and thereby affects genetic diversity. Because islands are often small and surrounded by a natural water barrier to dispersal, they generally form discrete isolated habitats. Therefore, islands may play a key role in the distribution of the genetic diversity of insects, including flies. To characterize the genetic structure of island populations of Bactrocera dorsalis, we analyzed a dataset containing both microsatellite and mtDNA loci of B. dorsalis samples collected from six offshore islands in Southern China. The microsatellite data revealed a high level of genetic diversity among these six island populations based on observed heterozygosity (Ho), expected heterozygosity (H E ), Nei's standard genetic distance (D), genetic identity (I) and the percentage of polymorphic loci (PIC). These island populations had low F ST values (F ST  = 0.04161), and only 4.16 % of the total genetic variation in the species was found on these islands, as determined by an analysis of molecular variance. Based on the mtDNA COI data, high nucleotide diversity (0.9655) and haplotype diversity (0.00680) were observed in all six island populations. F-statistics showed that the six island populations exhibited low or medium levels of genetic differentiation among some island populations. To investigate the population differentiation between the sampled locations, a factorial correspondence analysis and both the unweighted pair-group method with arithmetic mean and Bayesian clustering methods were used to analyze the microsatellite data. The results showed that Hebao Island, Weizhou Island and Dong'ao Island were grouped together in one clade. Another clade consisted of Shangchuan Island and Naozhou Island, and a final, separate clade contained only the Wailingding Island population. Phylogenetic analysis of the mtDNA COI sequences revealed that the populations on each of these six islands were closely related to different populations on mainland China. Our study suggests that these island populations have high genetic diversity, experience frequent gene flow and exhibit low or medium levels of genetic differentiation among some island populations. Therefore, the geographic isolation of the six islands does not appear to be a major dispersal barrier to B. dorsalis. Such knowledge is helpful for a better understanding of evolutionary processes of the species of island populations.

Genetic evidence for an East Asian origin of Chinese Muslim populations Dongxiang and Hui

PubMed Central

Yao, Hong-Bing; Wang, Chuan-Chao; Tao, Xiaolan; Shang, Lei; Wen, Shao-Qing; Zhu, Bofeng; Kang, Longli; Jin, Li; Li, Hui

2016-01-01

There is a long-going debate on the genetic origin of Chinese Muslim populations, such as Uygur, Dongxiang, and Hui. However, genetic information for those Muslim populations except Uygur is extremely limited. In this study, we investigated the genetic structure and ancestry of Chinese Muslims by analyzing 15 autosomal short tandem repeats in 652 individuals from Dongxiang, Hui, and Han Chinese populations in Gansu province. Both genetic distance and Bayesian-clustering methods showed significant genetic homogeneity between the two Muslim populations and East Asian populations, suggesting a common genetic ancestry. Our analysis found no evidence of substantial gene flow from Middle East or Europe into Dongxiang and Hui people during their Islamization. The dataset generated in present study are also valuable for forensic identification and paternity tests in China. PMID:27924949

Solving deterministic non-linear programming problem using Hopfield artificial neural network and genetic programming techniques

NASA Astrophysics Data System (ADS)

Vasant, P.; Ganesan, T.; Elamvazuthi, I.

2012-11-01

A fairly reasonable result was obtained for non-linear engineering problems using the optimization techniques such as neural network, genetic algorithms, and fuzzy logic independently in the past. Increasingly, hybrid techniques are being used to solve the non-linear problems to obtain better output. This paper discusses the use of neuro-genetic hybrid technique to optimize the geological structure mapping which is known as seismic survey. It involves the minimization of objective function subject to the requirement of geophysical and operational constraints. In this work, the optimization was initially performed using genetic programming, and followed by hybrid neuro-genetic programming approaches. Comparative studies and analysis were then carried out on the optimized results. The results indicate that the hybrid neuro-genetic hybrid technique produced better results compared to the stand-alone genetic programming method.

Interlaboratory validation of quantitative duplex real-time PCR method for screening analysis of genetically modified maize.

PubMed

Takabatake, Reona; Koiwa, Tomohiro; Kasahara, Masaki; Takashima, Kaori; Futo, Satoshi; Minegishi, Yasutaka; Akiyama, Hiroshi; Teshima, Reiko; Oguchi, Taichi; Mano, Junichi; Furui, Satoshi; Kitta, Kazumi

2011-01-01

To reduce the cost and time required to routinely perform the genetically modified organism (GMO) test, we developed a duplex quantitative real-time PCR method for a screening analysis simultaneously targeting an event-specific segment for GA21 and Cauliflower Mosaic Virus 35S promoter (P35S) segment [Oguchi et al., J. Food Hyg. Soc. Japan, 50, 117-125 (2009)]. To confirm the validity of the method, an interlaboratory collaborative study was conducted. In the collaborative study, conversion factors (Cfs), which are required to calculate the GMO amount (%), were first determined for two real-time PCR instruments, the ABI PRISM 7900HT and the ABI PRISM 7500. A blind test was then conducted. The limit of quantitation for both GA21 and P35S was estimated to be 0.5% or less. The trueness and precision were evaluated as the bias and reproducibility of the relative standard deviation (RSD(R)). The determined bias and RSD(R) were each less than 25%. We believe the developed method would be useful for the practical screening analysis of GM maize.

Genetic diversity of popcorn genotypes using molecular analysis.

PubMed

Resh, F S; Scapim, C A; Mangolin, C A; Machado, M F P S; do Amaral, A T; Ramos, H C C; Vivas, M

2015-08-19

In this study, we analyzed dominant molecular markers to estimate the genetic divergence of 26 popcorn genotypes and evaluate whether using various dissimilarity coefficients with these dominant markers influences the results of cluster analysis. Fifteen random amplification of polymorphic DNA primers produced 157 amplified fragments, of which 65 were monomorphic and 92 were polymorphic. To calculate the genetic distances among the 26 genotypes, the complements of the Jaccard, Dice, and Rogers and Tanimoto similarity coefficients were used. A matrix of Dij values (dissimilarity matrix) was constructed, from which the genetic distances among genotypes were represented in a more simplified manner as a dendrogram generated using the unweighted pair-group method with arithmetic average. Clusters determined by molecular analysis generally did not group material from the same parental origin together. The largest genetic distance was between varieties 17 (UNB-2) and 18 (PA-091). In the identification of genotypes with the smallest genetic distance, the 3 coefficients showed no agreement. The 3 dissimilarity coefficients showed no major differences among their grouping patterns because agreement in determining the genotypes with large, medium, and small genetic distances was high. The largest genetic distances were observed for the Rogers and Tanimoto dissimilarity coefficient (0.74), followed by the Jaccard coefficient (0.65) and the Dice coefficient (0.48). The 3 coefficients showed similar estimations for the cophenetic correlation coefficient. Correlations among the matrices generated using the 3 coefficients were positive and had high magnitudes, reflecting strong agreement among the results obtained using the 3 evaluated dissimilarity coefficients.

Genetic structure of lake whitefish, Coregonus clupeaformis, populations in the northern main basin of Lake Huron

USGS Publications Warehouse

Stott, Wendylee; Ebener, Mark P.; Mohr, Lloyd; Schaeffer, Jeff; Roseman, Edward F.; Harford, William J.; Johnson, James E.; Fietsch, Cherie-Lee

2012-01-01

Genetic analysis of spawning lake whitefish (Coregonus clupeaformis) from six sites in the main basin of Lake Huron was conducted to determine population structure. Samples from fisheryindependent assessment surveys in the northwest main basin were analyzed to determine the relative contributions of lake whitefish genetic populations. Genetic population structure was identified using data from seven microsatellite DNA loci. One population was identified at Manitoulin Island, one to two were observed in the east-central main basin (Fishing Island and Douglas Point), and one to two populations were found in the northwest (Thunder Bay and Duncan Bay). The genetic identity of collections from Duncan Bay and Thunder Bay was not consistent among methods used to analyze population structure. Low genetic distances suggested that they comprised one population, but genic differences indicated that they may constitute separate populations. Simulated data indicated that the genetic origins of samples from a mixed-fishery could be accurately identified, but accuracy could be improved by incorporating additional microsatellite loci. Mixture analysis and individual assignment tests performed on mixed-stock samples collected from the western main basin suggested that genetic populations from the east-central main basin contributed less than those from the western main basin and that the proportional contribution of each baseline population was similar in each assessment sample. Analysis of additional microsatellite DNA loci may be useful to help improve the precision of the estimates, thus increasing our ability to manage and protect this valuable resource.

The chemotaxonomic classification of Rhodiola plants and its correlation with morphological characteristics and genetic taxonomy.

PubMed

Liu, Zhenli; Liu, Yuanyan; Liu, Chunsheng; Song, Zhiqian; Li, Qing; Zha, Qinglin; Lu, Cheng; Wang, Chun; Ning, Zhangchi; Zhang, Yuxin; Tian, Cheng; Lu, Aiping

2013-07-12

Rhodiola plants are used as a natural remedy in the western world and as a traditional herbal medicine in China, and are valued for their ability to enhance human resistance to stress or fatigue and to promote longevity. Due to the morphological similarities among different species, the identification of the genus remains somewhat controversial, which may affect their safety and effectiveness in clinical use. In this paper, 47 Rhodiola samples of seven species were collected from thirteen local provinces of China. They were identified by their morphological characteristics and genetic and phytochemical taxonomies. Eight bioactive chemotaxonomic markers from four chemical classes (phenylpropanoids, phenylethanol derivatives, flavonoids and phenolic acids) were determined to evaluate and distinguish the chemotaxonomy of Rhodiola samples using an HPLC-DAD/UV method. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were applied to compare the two classification methods between genetic and phytochemical taxonomy. The established chemotaxonomic classification could be effectively used for Rhodiola species identification.

The chemotaxonomic classification of Rhodiola plants and its correlation with morphological characteristics and genetic taxonomy

PubMed Central

2013-01-01

Background Rhodiola plants are used as a natural remedy in the western world and as a traditional herbal medicine in China, and are valued for their ability to enhance human resistance to stress or fatigue and to promote longevity. Due to the morphological similarities among different species, the identification of the genus remains somewhat controversial, which may affect their safety and effectiveness in clinical use. Results In this paper, 47 Rhodiola samples of seven species were collected from thirteen local provinces of China. They were identified by their morphological characteristics and genetic and phytochemical taxonomies. Eight bioactive chemotaxonomic markers from four chemical classes (phenylpropanoids, phenylethanol derivatives, flavonoids and phenolic acids) were determined to evaluate and distinguish the chemotaxonomy of Rhodiola samples using an HPLC-DAD/UV method. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were applied to compare the two classification methods between genetic and phytochemical taxonomy. Conclusions The established chemotaxonomic classification could be effectively used for Rhodiola species identification. PMID:23844866

Proteomics analysis reveals novel host molecular mechanisms that could be associated with heat-induced resistance to ‘Ca. Liberibacter asiaticus’ in lemon plants

USDA-ARS?s Scientific Manuscript database

Sustainable long-term measures to combat HLB via breeding or genetic engineering methods are hampered by the fact that no true genetic resistance has been found in citrus germplasm. All cultivated citrus species or citrus relatives are susceptible to the disease. However, the degree of HLB susceptib...

Genetic and Environmental Influences on Developmental Milestones and Movement: Results from the Gemini Cohort Study

ERIC Educational Resources Information Center

Smith, Lee; van Jaarsveld, Cornelia H. M.; Llewellyn, Clare H.; Fildes, Alison; López Sánchez, Guillermo Felipe; Wardle, Jane; Fisher, Abigail

2017-01-01

Purpose: Variability in the timing of infant developmental milestones is poorly understood. We used a twin analysis to estimate genetic and environmental influences on motor development and activity levels in infancy. Method: Data were from the Gemini Study, a twin birth cohort of 2,402 families with twins born in the United Kingdom in 2007.…

Genetic diversity analysis of tree peony germplasm using iPBS markers.

PubMed

Duan, Y B; Guo, D L; Guo, L L; Wei, D F; Hou, X G

2015-07-06

We examined the genetic diversity of 10 wild species (populations) and 55 varieties of tree peony using inter-primer binding site (iPBS) markers. From a total of 36 iPBS primers, 16 were selected based on polymorphic amplification. The number of bands amplified by each primer ranged from 9 to 19, with an average of 12.88 bands per primer. The length of bands ranged from 100 to 2000 bp, concentrated at 200 to 1800 bp. Sixteen primers amplified 206 bands in total, of which 173 bands were polymorphic with a polymorphism ratio of 83.98%. Each primer amplified 10.81 polymorphic bands on average. The data were then used to construct a phylogenetic tree using unweighted pair group method with arithmetic mean methods. Clustering analysis showed that the genetic relationships among the varieties were not only related to the genetic background or geographic origin, but also to the flowering phase, flower color, and flower type. Our data also indicated that iPBS markers were useful tools for classifying tree peony germplasms and for tree peony breeding, and the specific bands were helpful for molecular identification of tree peony varieties.

Mutation scanning analysis of genetic variation within and among Echinococcus species: implications and future prospects.

PubMed

Jabbar, Abdul; Gasser, Robin B

2013-07-01

Adult tapeworms of the genus Echinococcus (family Taeniidae) occur in the small intestines of carnivorous definitive hosts and are transmitted to particular intermediate mammalian hosts, in which they develop as fluid-filled larvae (cysts) in internal organs (usually lung and liver), causing the disease echinococcosis. Echinococcus species are of major medical importance and also cause losses to the meat and livestock industries, mainly due to the condemnation of infected offal. Decisions regarding the treatment and control of echinococcosis rely on the accurate identification of species and population variants (strains). Conventional, phenetic methods for specific identification have some significant limitations. Despite advances in the development of molecular tools, there has been limited application of mutation scanning methods to species of Echinococcus. Here, we briefly review key genetic markers used for the identification of Echinococcus species and techniques for the analysis of genetic variation within and among populations, and the diagnosis of echinococcosis. We also discuss the benefits of utilizing mutation scanning approaches to elucidate the population genetics and epidemiology of Echinococcus species. These benefits are likely to become more evident following the complete characterization of the genomes of E. granulosus and E. multilocularis.

Genetic diversity and relationships among different tomato varieties revealed by EST-SSR markers.

PubMed

Korir, N K; Diao, W; Tao, R; Li, X; Kayesh, E; Li, A; Zhen, W; Wang, S

2014-01-08

The genetic diversity and relationship of 42 tomato varieties sourced from different geographic regions was examined with EST-SSR markers. The genetic diversity was between 0.18 and 0.77, with a mean of 0.49; the polymorphic information content ranged from 0.17 to 0.74, with a mean of 0.45. This indicates a fairly high degree of diversity among these tomato varieties. Based on the cluster analysis using unweighted pair-group method with arithmetic average (UPGMA), all the tomato varieties fell into 5 groups, with no obvious geographical distribution characteristics despite their diverse sources. The principal component analysis (PCA) supported the clustering result; however, relationships among varieties were more complex in the PCA scatterplot than in the UPGMA dendrogram. This information about the genetic relationships between these tomato lines helps distinguish these 42 varieties and will be useful for tomato variety breeding and selection. We confirm that the EST-SSR marker system is useful for studying genetic diversity among tomato varieties. The high degree of polymorphism and the large number of bands obtained per assay shows that SSR is the most informative marker system for tomato genotyping for purposes of rights/protection and for the tomato industry in general. It is recommended that these varieties be subjected to identification using an SSR-based manual cultivar identification diagram strategy or other easy-to-use and referable methods so as to provide a complete set of information concerning genetic relationships and a readily usable means of identifying these varieties.

Evaluating methods to visualize patterns of genetic differentiation on a landscape.

PubMed

House, Geoffrey L; Hahn, Matthew W

2018-05-01

With advances in sequencing technology, research in the field of landscape genetics can now be conducted at unprecedented spatial and genomic scales. This has been especially evident when using sequence data to visualize patterns of genetic differentiation across a landscape due to demographic history, including changes in migration. Two recent model-based visualization methods that can highlight unusual patterns of genetic differentiation across a landscape, SpaceMix and EEMS, are increasingly used. While SpaceMix's model can infer long-distance migration, EEMS' model is more sensitive to short-distance changes in genetic differentiation, and it is unclear how these differences may affect their results in various situations. Here, we compare SpaceMix and EEMS side by side using landscape genetics simulations representing different migration scenarios. While both methods excel when patterns of simulated migration closely match their underlying models, they can produce either un-intuitive or misleading results when the simulated migration patterns match their models less well, and this may be difficult to assess in empirical data sets. We also introduce unbundled principal components (un-PC), a fast, model-free method to visualize patterns of genetic differentiation by combining principal components analysis (PCA), which is already used in many landscape genetics studies, with the locations of sampled individuals. Un-PC has characteristics of both SpaceMix and EEMS and works well with simulated and empirical data. Finally, we introduce msLandscape, a collection of tools that streamline the creation of customizable landscape-scale simulations using the popular coalescent simulator ms and conversion of the simulated data for use with un-PC, SpaceMix and EEMS. © 2017 John Wiley & Sons Ltd.

Identification of important image features for pork and turkey ham classification using colour and wavelet texture features and genetic selection.

PubMed

Jackman, Patrick; Sun, Da-Wen; Allen, Paul; Valous, Nektarios A; Mendoza, Fernando; Ward, Paddy

2010-04-01

A method to discriminate between various grades of pork and turkey ham was developed using colour and wavelet texture features. Image analysis methods originally developed for predicting the palatability of beef were applied to rapidly identify the ham grade. With high quality digital images of 50-94 slices per ham it was possible to identify the greyscale that best expressed the differences between the various ham grades. The best 10 discriminating image features were then found with a genetic algorithm. Using the best 10 image features, simple linear discriminant analysis models produced 100% correct classifications for both pork and turkey on both calibration and validation sets. 2009 Elsevier Ltd. All rights reserved.

Characterization of the genetic profile of five Danish dog breeds.

PubMed

Pertoldi, C; Kristensen, T N; Loeschcke, V; Berg, P; Praebel, A; Stronen, A V; Proschowsky, H F; Fredholm, M

2013-11-01

This investigation presents results from a genetic characterization of 5 Danish dog breeds genotyped on the CanineHD BeadChip microarray with 170,000 SNP. The breeds investigated were 1) Danish Spitz (DS; n=8), 2) Danish-Swedish Farm Dog (DSF; n=18), 3) Broholmer (BR; n=22), 4) Old Danish Pointing Dog (ODP; n=24), and 5) Greenland Dog (GD; n=23). The aims of the investigation were to characterize the genetic profile of the abovementioned dog breeds by quantifying the genetic differentiation among them and the degree of genetic homogeneity within breeds. The genetic profile was determined by means of principal component analysis (PCA) and through a Bayesian clustering method. Both the PCA and the Bayesian clustering method revealed a clear genetic separation of the 5 breeds. The level of genetic variation within the breeds varied. The expected heterozygosity (HE) as well as the degree of polymorphism (P%) ranked the dog breeds in the order DS>DSF>BR>ODP>GD. Interestingly, the breed with a tenfold higher census population size compared to the other breeds, the Greenland Dog, had the lowest within-breed genetic variation, emphasizing that census size is a poor predictor of genetic variation. The observed differences in variation among and within dog breeds may be related to factors such as genetic drift, founder effects, genetic admixture, and population bottlenecks. We further examined whether the observed genetic patterns in the 5 dog breeds can be used to design breeding strategies for the preservation of the genetic pool of these dog breeds.

Geographical structuring of Trypanosoma cruzi populations from Chilean Triatoma infestans triatomines and their genetic relationship with other Latino American counterparts

PubMed Central

Venegas, J; Rojas, T; DÍaz, F; Miranda, S; Jercic, M I; González, C; Coñoepán, W; Pichuantes, S; RodrÍguez, J; Gajardo, M; Sánchez, G

2011-01-01

In order to obtain more information about the population structure of Chilean Trypanosoma cruzi, and their genetic relationship with other Latino American counterparts, we performed the study of T. cruzi samples detected in the midgut content of Triatoma infestans insects from three endemic regions of Chile. The genetic characteristics of these samples were analysed using microsatellite markers and PCR conditions that allow the detection of predominant T. cruzi clones directly in triatomine midgut content. Population genetic analyses using the Fisher’s exact method, analysis of molecular variance (AMOVA) and the determination of FST showed that the northern T. cruzi population sample was genetically differentiated from the two southern population counterparts. Further analysis showed that the cause of this genetic differentiation was the asymmetrical distribution of TcIII T. cruzi predominant clones. Considering all triatomines from the three regions, the most frequent predominant lineages were TcIII (38%), followed by TcI (34%) and hybrid (8%). No TcII lineage was observed along the predominant T. cruzi clones. The best phylogenetic reconstruction using the shared allelic genetic distance was concordant with the population genetic analysis and tree topology previously described studying foreign samples. The correlation studies showed that the lineage TcIII from the III region was genetically differentiated from the other two, and this differentiation was correlated with geographical distance including Chilean and mainly Brazilian samples. It will be interesting to investigate whether this geographical structure may be related with different clinical manifestation of Chagas disease. PMID:22325822

Genetic mixed linear models for twin survival data.

PubMed

Ha, Il Do; Lee, Youngjo; Pawitan, Yudi

2007-07-01

Twin studies are useful for assessing the relative importance of genetic or heritable component from the environmental component. In this paper we develop a methodology to study the heritability of age-at-onset or lifespan traits, with application to analysis of twin survival data. Due to limited period of observation, the data can be left truncated and right censored (LTRC). Under the LTRC setting we propose a genetic mixed linear model, which allows general fixed predictors and random components to capture genetic and environmental effects. Inferences are based upon the hierarchical-likelihood (h-likelihood), which provides a statistically efficient and unified framework for various mixed-effect models. We also propose a simple and fast computation method for dealing with large data sets. The method is illustrated by the survival data from the Swedish Twin Registry. Finally, a simulation study is carried out to evaluate its performance.

Draft genome sequence and genetic transformation of the oleaginous alga Nannochloropis gaditana

PubMed Central

Radakovits, Randor; Jinkerson, Robert E.; Fuerstenberg, Susan I.; Tae, Hongseok; Settlage, Robert E.; Boore, Jeffrey L.; Posewitz, Matthew C.

2012-01-01

The potential use of algae in biofuels applications is receiving significant attention. However, none of the current algal model species are competitive production strains. Here we present a draft genome sequence and a genetic transformation method for the marine microalga Nannochloropsis gaditana CCMP526. We show that N. gaditana has highly favourable lipid yields, and is a promising production organism. The genome assembly includes nuclear (~29 Mb) and organellar genomes, and contains 9,052 gene models. We define the genes required for glycerolipid biogenesis and detail the differential regulation of genes during nitrogen-limited lipid biosynthesis. Phylogenomic analysis identifies genetic attributes of this organism, including unique stramenopile photosynthesis genes and gene expansions that may explain the distinguishing photoautotrophic phenotypes observed. The availability of a genome sequence and transformation methods will facilitate investigations into N. gaditana lipid biosynthesis and permit genetic engineering strategies to further improve this naturally productive alga. PMID:22353717

Draft genome sequence and genetic transformation of the oleaginous alga Nannochloropis gaditana.

PubMed

Radakovits, Randor; Jinkerson, Robert E; Fuerstenberg, Susan I; Tae, Hongseok; Settlage, Robert E; Boore, Jeffrey L; Posewitz, Matthew C

2012-02-21

The potential use of algae in biofuels applications is receiving significant attention. However, none of the current algal model species are competitive production strains. Here we present a draft genome sequence and a genetic transformation method for the marine microalga Nannochloropsis gaditana CCMP526. We show that N. gaditana has highly favourable lipid yields, and is a promising production organism. The genome assembly includes nuclear (~29 Mb) and organellar genomes, and contains 9,052 gene models. We define the genes required for glycerolipid biogenesis and detail the differential regulation of genes during nitrogen-limited lipid biosynthesis. Phylogenomic analysis identifies genetic attributes of this organism, including unique stramenopile photosynthesis genes and gene expansions that may explain the distinguishing photoautotrophic phenotypes observed. The availability of a genome sequence and transformation methods will facilitate investigations into N. gaditana lipid biosynthesis and permit genetic engineering strategies to further improve this naturally productive alga.

Diallel analysis for sex-linked and maternal effects.

PubMed

Zhu, J; Weir, B S

1996-01-01

Genetic models including sex-linked and maternal effects as well as autosomal gene effects are described. Monte Carlo simulations were conducted to compare efficiencies of estimation by minimum norm quadratic unbiased estimation (MINQUE) and restricted maximum likelihood (REML) methods. MINQUE(1), which has 1 for all prior values, has a similar efficiency to MINQUE(θ), which requires prior estimates of parameter values. MINQUE(1) has the advantage over REML of unbiased estimation and convenient computation. An adjusted unbiased prediction (AUP) method is developed for predicting random genetic effects. AUP is desirable for its easy computation and unbiasedness of both mean and variance of predictors. The jackknife procedure is appropriate for estimating the sampling variances of estimated variances (or covariances) and of predicted genetic effects. A t-test based on jackknife variances is applicable for detecting significance of variation. Worked examples from mice and silkworm data are given in order to demonstrate variance and covariance estimation and genetic effect prediction.

Integrative Analysis of High-throughput Cancer Studies with Contrasted Penalization

PubMed Central

Shi, Xingjie; Liu, Jin; Huang, Jian; Zhou, Yong; Shia, BenChang; Ma, Shuangge

2015-01-01

In cancer studies with high-throughput genetic and genomic measurements, integrative analysis provides a way to effectively pool and analyze heterogeneous raw data from multiple independent studies and outperforms “classic” meta-analysis and single-dataset analysis. When marker selection is of interest, the genetic basis of multiple datasets can be described using the homogeneity model or the heterogeneity model. In this study, we consider marker selection under the heterogeneity model, which includes the homogeneity model as a special case and can be more flexible. Penalization methods have been developed in the literature for marker selection. This study advances from the published ones by introducing the contrast penalties, which can accommodate the within- and across-dataset structures of covariates/regression coefficients and, by doing so, further improve marker selection performance. Specifically, we develop a penalization method that accommodates the across-dataset structures by smoothing over regression coefficients. An effective iterative algorithm, which calls an inner coordinate descent iteration, is developed. Simulation shows that the proposed method outperforms the benchmark with more accurate marker identification. The analysis of breast cancer and lung cancer prognosis studies with gene expression measurements shows that the proposed method identifies genes different from those using the benchmark and has better prediction performance. PMID:24395534

Analysis of genetic diversity and population structure of oil palm (Elaeis guineensis) from China and Malaysia based on species-specific simple sequence repeat markers.

PubMed

Zhou, L X; Xiao, Y; Xia, W; Yang, Y D

2015-12-08

Genetic diversity and patterns of population structure of the 94 oil palm lines were investigated using species-specific simple sequence repeat (SSR) markers. We designed primers for 63 SSR loci based on their flanking sequences and conducted amplification in 94 oil palm DNA samples. The amplification result showed that a relatively high level of genetic diversity was observed between oil palm individuals according a set of 21 polymorphic microsatellite loci. The observed heterozygosity (Ho) was 0.3683 and 0.4035, with an average of 0.3859. The Ho value was a reliable determinant of the discriminatory power of the SSR primer combinations. The principal component analysis and unweighted pair-group method with arithmetic averaging cluster analysis showed the 94 oil palm lines were grouped into one cluster. These results demonstrated that the oil palm in Hainan Province of China and the germplasm introduced from Malaysia may be from the same source. The SSR protocol was effective and reliable for assessing the genetic diversity of oil palm. Knowledge of the genetic diversity and population structure will be crucial for establishing appropriate management stocks for this species.

Gene-by-Psychosocial Factor Interactions Influence Diastolic Blood Pressure in European and African Ancestry Populations: Meta-Analysis of Four Cohort Studies.

PubMed

Smith, Jennifer A; Zhao, Wei; Yasutake, Kalyn; August, Carmella; Ratliff, Scott M; Faul, Jessica D; Boerwinkle, Eric; Chakravarti, Aravinda; Diez Roux, Ana V; Gao, Yan; Griswold, Michael E; Heiss, Gerardo; Kardia, Sharon L R; Morrison, Alanna C; Musani, Solomon K; Mwasongwe, Stanford; North, Kari E; Rose, Kathryn M; Sims, Mario; Sun, Yan V; Weir, David R; Needham, Belinda L

2017-12-18

Inter-individual variability in blood pressure (BP) is influenced by both genetic and non-genetic factors including socioeconomic and psychosocial stressors. A deeper understanding of the gene-by-socioeconomic/psychosocial factor interactions on BP may help to identify individuals that are genetically susceptible to high BP in specific social contexts. In this study, we used a genomic region-based method for longitudinal analysis, Longitudinal Gene-Environment-Wide Interaction Studies (LGEWIS), to evaluate the effects of interactions between known socioeconomic/psychosocial and genetic risk factors on systolic and diastolic BP in four large epidemiologic cohorts of European and/or African ancestry. After correction for multiple testing, two interactions were significantly associated with diastolic BP. In European ancestry participants, outward/trait anger score had a significant interaction with the C10orf107 genomic region ( p = 0.0019). In African ancestry participants, depressive symptom score had a significant interaction with the HFE genomic region ( p = 0.0048). This study provides a foundation for using genomic region-based longitudinal analysis to identify subgroups of the population that may be at greater risk of elevated BP due to the combined influence of genetic and socioeconomic/psychosocial risk factors.

The SNPforID Assay as a Supplementary Method in Kinship and Trace Analysis

PubMed Central

Schwark, Thorsten; Meyer, Patrick; Harder, Melanie; Modrow, Jan-Hendrick; von Wurmb-Schwark, Nicole

2012-01-01

Objective Short tandem repeat (STR) analysis using commercial multiplex PCR kits is the method of choice for kinship testing and trace analysis. However, under certain circumstances (deficiency testing, mutations, minute DNA amounts), STRs alone may not suffice. Methods We present a 50-plex single nucleotide polymorphism (SNP) assay based on the SNPs chosen by the SNPforID consortium as an additional method for paternity and for trace analysis. The new assay was applied to selected routine paternity and trace cases from our laboratory. Results and Conclusions Our investigation shows that the new SNP multiplex assay is a valuable method to supplement STR analysis, and is a powerful means to solve complicated genetic analyses. PMID:22851934

Development of melting temperature-based SYBR Green I polymerase chain reaction methods for multiplex genetically modified organism detection.

PubMed

Hernández, Marta; Rodríguez-Lázaro, David; Esteve, Teresa; Prat, Salomé; Pla, Maria

2003-12-15

Commercialization of several genetically modified crops has been approved worldwide to date. Uniplex polymerase chain reaction (PCR)-based methods to identify these different insertion events have been developed, but their use in the analysis of all commercially available genetically modified organisms (GMOs) is becoming progressively insufficient. These methods require a large number of assays to detect all possible GMOs present in the sample and thereby the development of multiplex PCR systems using combined probes and primers targeted to sequences specific to various GMOs is needed for detection of this increasing number of GMOs. Here we report on the development of a multiplex real-time PCR suitable for multiple GMO identification, based on the intercalating dye SYBR Green I and the analysis of the melting curves of the amplified products. Using this method, different amplification products specific for Maximizer 176, Bt11, MON810, and GA21 maize and for GTS 40-3-2 soybean were obtained and identified by their specific Tm. We have combined amplification of these products in a number of multiplex reactions and show the suitability of the methods for identification of GMOs with a sensitivity of 0.1% in duplex reactions. The described methods offer an economic and simple alternative to real-time PCR systems based on sequence-specific probes (i.e., TaqMan chemistry). These methods can be used as selection tests and further optimized for uniplex GMO quantification.

A probabilistic method for testing and estimating selection differences between populations

PubMed Central

He, Yungang; Wang, Minxian; Huang, Xin; Li, Ran; Xu, Hongyang; Xu, Shuhua; Jin, Li

2015-01-01

Human populations around the world encounter various environmental challenges and, consequently, develop genetic adaptations to different selection forces. Identifying the differences in natural selection between populations is critical for understanding the roles of specific genetic variants in evolutionary adaptation. Although numerous methods have been developed to detect genetic loci under recent directional selection, a probabilistic solution for testing and quantifying selection differences between populations is lacking. Here we report the development of a probabilistic method for testing and estimating selection differences between populations. By use of a probabilistic model of genetic drift and selection, we showed that logarithm odds ratios of allele frequencies provide estimates of the differences in selection coefficients between populations. The estimates approximate a normal distribution, and variance can be estimated using genome-wide variants. This allows us to quantify differences in selection coefficients and to determine the confidence intervals of the estimate. Our work also revealed the link between genetic association testing and hypothesis testing of selection differences. It therefore supplies a solution for hypothesis testing of selection differences. This method was applied to a genome-wide data analysis of Han and Tibetan populations. The results confirmed that both the EPAS1 and EGLN1 genes are under statistically different selection in Han and Tibetan populations. We further estimated differences in the selection coefficients for genetic variants involved in melanin formation and determined their confidence intervals between continental population groups. Application of the method to empirical data demonstrated the outstanding capability of this novel approach for testing and quantifying differences in natural selection. PMID:26463656

MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms.

PubMed

Kumar, Sudhir; Stecher, Glen; Li, Michael; Knyaz, Christina; Tamura, Koichiro

2018-06-01

The Molecular Evolutionary Genetics Analysis (Mega) software implements many analytical methods and tools for phylogenomics and phylomedicine. Here, we report a transformation of Mega to enable cross-platform use on Microsoft Windows and Linux operating systems. Mega X does not require virtualization or emulation software and provides a uniform user experience across platforms. Mega X has additionally been upgraded to use multiple computing cores for many molecular evolutionary analyses. Mega X is available in two interfaces (graphical and command line) and can be downloaded from www.megasoftware.net free of charge.

[Algorithm of toxigenic genetically altered Vibrio cholerae El Tor biovar strain identification].

PubMed

Smirnova, N I; Agafonov, D A; Zadnova, S P; Cherkasov, A V; Kutyrev, V V

2014-01-01

Development of an algorithm of genetically altered Vibrio cholerae biovar El Tor strai identification that ensures determination of serogroup, serovar and biovar of the studied isolate based on pheno- and genotypic properties, detection of genetically altered cholera El Tor causative agents, their differentiation by epidemic potential as well as evaluation of variability of key pathogenicity genes. Complex analysis of 28 natural V. cholerae strains was carried out by using traditional microbiological methods, PCR and fragmentary sequencing. An algorithm of toxigenic genetically altered V. cholerae biovar El Tor strain identification was developed that includes 4 stages: determination of serogroup, serovar and biovar based on phenotypic properties, confirmation of serogroup and biovar based on molecular-genetic properties determination of strains as genetically altered, differentiation of genetically altered strains by their epidemic potential and detection of ctxB and tcpA key pathogenicity gene polymorphism. The algorithm is based on the use of traditional microbiological methods, PCR and sequencing of gene fragments. The use of the developed algorithm will increase the effectiveness of detection of genetically altered variants of the cholera El Tor causative agent, their differentiation by epidemic potential and will ensure establishment of polymorphism of genes that code key pathogenicity factors for determination of origins of the strains and possible routes of introduction of the infection.

Computational, Integrative, and Comparative Methods for the Elucidation of Genetic Coexpression Networks

DOE PAGES

Baldwin, Nicole E.; Chesler, Elissa J.; Kirov, Stefan; ...

2005-01-01

Gene expression microarray data can be used for the assembly of genetic coexpression network graphs. Using mRNA samples obtained from recombinant inbred Mus musculus strains, it is possible to integrate allelic variation with molecular and higher-order phenotypes. The depth of quantitative genetic analysis of microarray data can be vastly enhanced utilizing this mouse resource in combination with powerful computational algorithms, platforms, and data repositories. The resulting network graphs transect many levels of biological scale. This approach is illustrated with the extraction of cliques of putatively co-regulated genes and their annotation using gene ontology analysis and cis -regulatory element discovery. Themore » causal basis for co-regulation is detected through the use of quantitative trait locus mapping.« less

EPS-LASSO: Test for High-Dimensional Regression Under Extreme Phenotype Sampling of Continuous Traits.

PubMed

Xu, Chao; Fang, Jian; Shen, Hui; Wang, Yu-Ping; Deng, Hong-Wen

2018-01-25

Extreme phenotype sampling (EPS) is a broadly-used design to identify candidate genetic factors contributing to the variation of quantitative traits. By enriching the signals in extreme phenotypic samples, EPS can boost the association power compared to random sampling. Most existing statistical methods for EPS examine the genetic factors individually, despite many quantitative traits have multiple genetic factors underlying their variation. It is desirable to model the joint effects of genetic factors, which may increase the power and identify novel quantitative trait loci under EPS. The joint analysis of genetic data in high-dimensional situations requires specialized techniques, e.g., the least absolute shrinkage and selection operator (LASSO). Although there are extensive research and application related to LASSO, the statistical inference and testing for the sparse model under EPS remain unknown. We propose a novel sparse model (EPS-LASSO) with hypothesis test for high-dimensional regression under EPS based on a decorrelated score function. The comprehensive simulation shows EPS-LASSO outperforms existing methods with stable type I error and FDR control. EPS-LASSO can provide a consistent power for both low- and high-dimensional situations compared with the other methods dealing with high-dimensional situations. The power of EPS-LASSO is close to other low-dimensional methods when the causal effect sizes are small and is superior when the effects are large. Applying EPS-LASSO to a transcriptome-wide gene expression study for obesity reveals 10 significant body mass index associated genes. Our results indicate that EPS-LASSO is an effective method for EPS data analysis, which can account for correlated predictors. The source code is available at https://github.com/xu1912/EPSLASSO. hdeng2@tulane.edu. Supplementary data are available at Bioinformatics online. © The Author (2018). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Summarizing techniques that combine three non-parametric scores to detect disease-associated 2-way SNP-SNP interactions.

PubMed

Sengupta Chattopadhyay, Amrita; Hsiao, Ching-Lin; Chang, Chien Ching; Lian, Ie-Bin; Fann, Cathy S J

2014-01-01

Identifying susceptibility genes that influence complex diseases is extremely difficult because loci often influence the disease state through genetic interactions. Numerous approaches to detect disease-associated SNP-SNP interactions have been developed, but none consistently generates high-quality results under different disease scenarios. Using summarizing techniques to combine a number of existing methods may provide a solution to this problem. Here we used three popular non-parametric methods-Gini, absolute probability difference (APD), and entropy-to develop two novel summary scores, namely principle component score (PCS) and Z-sum score (ZSS), with which to predict disease-associated genetic interactions. We used a simulation study to compare performance of the non-parametric scores, the summary scores, the scaled-sum score (SSS; used in polymorphism interaction analysis (PIA)), and the multifactor dimensionality reduction (MDR). The non-parametric methods achieved high power, but no non-parametric method outperformed all others under a variety of epistatic scenarios. PCS and ZSS, however, outperformed MDR. PCS, ZSS and SSS displayed controlled type-I-errors (<0.05) compared to GS, APDS, ES (>0.05). A real data study using the genetic-analysis-workshop 16 (GAW 16) rheumatoid arthritis dataset identified a number of interesting SNP-SNP interactions. © 2013 Elsevier B.V. All rights reserved.

Genetic source tracking of an anthrax outbreak in Shaanxi province, China.

PubMed

Liu, Dong-Li; Wei, Jian-Chun; Chen, Qiu-Lan; Guo, Xue-Jun; Zhang, En-Min; He, Li; Liang, Xu-Dong; Ma, Guo-Zhu; Zhou, Ti-Cao; Yin, Wen-Wu; Liu, Wei; Liu, Kai; Shi, Yi; Ji, Jian-Jun; Zhang, Hui-Juan; Ma, Lin; Zhang, Fa-Xin; Zhang, Zhi-Kai; Zhou, Hang; Yu, Hong-Jie; Kan, Biao; Xu, Jian-Guo; Liu, Feng; Li, Wei

2017-01-17

Anthrax is an acute zoonotic infectious disease caused by the bacterium known as Bacillus anthracis. From 26 July to 8 August 2015, an outbreak with 20 suspected cutaneous anthrax cases was reported in Ganquan County, Shaanxi province in China. The genetic source tracking analysis of the anthrax outbreak was performed by molecular epidemiological methods in this study. Three molecular typing methods, namely canonical single nucleotide polymorphisms (canSNP), multiple-locus variable-number tandem repeat analysis (MLVA), and single nucleotide repeat (SNR) analysis, were used to investigate the possible source of transmission and identify the genetic relationship among the strains isolated from human cases and diseased animals during the outbreak. Five strains isolated from diseased mules were clustered together with patients' isolates using canSNP typing and MLVA. The causative B. anthracis lineages in this outbreak belonged to the A.Br.001/002 canSNP subgroup and the MLVA15-31 genotype (the 31 genotype in MLVA15 scheme). Because nine isolates from another four provinces in China were clustered together with outbreak-related strains by the canSNP (A.Br.001/002 subgroup) and MLVA15 method (MLVA15-31 genotype), still another SNR analysis (CL10, CL12, CL33, and CL35) was used to source track the outbreak, and the results suggesting that these patients in the anthrax outbreak were probably infected by the same pathogen clone. It was deduced that the anthrax outbreak occurred in Shaanxi province, China in 2015 was a local occurrence.

Chemometrics Methods for Specificity, Authenticity and Traceability Analysis of Olive Oils: Principles, Classifications and Applications.

PubMed

Messai, Habib; Farman, Muhammad; Sarraj-Laabidi, Abir; Hammami-Semmar, Asma; Semmar, Nabil

2016-11-17

Olive oils (OOs) show high chemical variability due to several factors of genetic, environmental and anthropic types. Genetic and environmental factors are responsible for natural compositions and polymorphic diversification resulting in different varietal patterns and phenotypes. Anthropic factors, however, are at the origin of different blends' preparation leading to normative, labelled or adulterated commercial products. Control of complex OO samples requires their (i) characterization by specific markers; (ii) authentication by fingerprint patterns; and (iii) monitoring by traceability analysis. These quality control and management aims require the use of several multivariate statistical tools: specificity highlighting requires ordination methods; authentication checking calls for classification and pattern recognition methods; traceability analysis implies the use of network-based approaches able to separate or extract mixed information and memorized signals from complex matrices. This chapter presents a review of different chemometrics methods applied for the control of OO variability from metabolic and physical-chemical measured characteristics. The different chemometrics methods are illustrated by different study cases on monovarietal and blended OO originated from different countries. Chemometrics tools offer multiple ways for quantitative evaluations and qualitative control of complex chemical variability of OO in relation to several intrinsic and extrinsic factors.

DHLAS: A web-based information system for statistical genetic analysis of HLA population data.

PubMed

Thriskos, P; Zintzaras, E; Germenis, A

2007-03-01

DHLAS (database HLA system) is a user-friendly, web-based information system for the analysis of human leukocyte antigens (HLA) data from population studies. DHLAS has been developed using JAVA and the R system, it runs on a Java Virtual Machine and its user-interface is web-based powered by the servlet engine TOMCAT. It utilizes STRUTS, a Model-View-Controller framework and uses several GNU packages to perform several of its tasks. The database engine it relies upon for fast access is MySQL, but others can be used a well. The system estimates metrics, performs statistical testing and produces graphs required for HLA population studies: (i) Hardy-Weinberg equilibrium (calculated using both asymptotic and exact tests), (ii) genetics distances (Euclidian or Nei), (iii) phylogenetic trees using the unweighted pair group method with averages and neigbor-joining method, (iv) linkage disequilibrium (pairwise and overall, including variance estimations), (v) haplotype frequencies (estimate using the expectation-maximization algorithm) and (vi) discriminant analysis. The main merit of DHLAS is the incorporation of a database, thus, the data can be stored and manipulated along with integrated genetic data analysis procedures. In addition, it has an open architecture allowing the inclusion of other functions and procedures.

Evidence for cryptic northern refugia in the last glacial period in Cryptomeria japonica

PubMed Central

Kimura, Megumi K.; Uchiyama, Kentaro; Nakao, Katsuhiro; Moriguchi, Yoshinari; San Jose-Maldia, Lerma; Tsumura, Yoshihiko

2014-01-01

Background and Aims Distribution shifts and natural selection during past climatic changes are important factors in determining the genetic structure of forest species. In particular, climatic fluctuations during the Quaternary appear to have caused changes in the distribution ranges of plants, and thus strongly affected their genetic structure. This study was undertaken to identify the responses of the conifer Cryptomeria japonica, endemic to the Japanese Archipelago, to past climatic changes using a combination of phylogeography and species distribution modelling (SDM) methods. Specifically, this study focused on the locations of refugia during the last glacial maximum (LGM). Methods Genetic diversity and structure were examined using 20 microsatellite markers in 37 populations of C. japonica. The locations of glacial refugia were assessed using STRUCTURE analysis, and potential habitats under current and past climate conditions were predicted using SDM. The process of genetic divergence was also examined using the approximate Bayesian computation procedure (ABC) in DIY ABC to test the divergence time between the gene pools detected by the STRUCTURE analysis. Key Results STRUCTURE analysis identified four gene pools: northern Tohoku district; from Chubu to Chugoku district; from Tohoku to Shikoku district on the Pacific Ocean side of the Archipelago; and Yakushima Island. DIY ABC analysis indicated that the four gene pools diverged at the same time before the LGM. SDM also indicated potential northern cryptic refugia. Conclusions The combined evidence from microsatellites and SDM clearly indicates that climatic changes have shaped the genetic structure of C. japonica. The gene pool detected in northern Tohoku district is likely to have been established by cryptic northern refugia on the coast of the Japan Sea to the west of the Archipelago. The gene pool in Yakushima Island can probably be explained simply by long-term isolation from the other gene pools since the LGM. These results are supported by those of SDM and the predicted divergence time determined using ABC analysis. PMID:25355521

Construction of a high-density genetic map by specific locus amplified fragment sequencing (SLAF-seq) and its application to Quantitative Trait Loci (QTL) analysis for boll weight in upland cotton (Gossypium hirsutum.).

PubMed

Zhang, Zhen; Shang, Haihong; Shi, Yuzhen; Huang, Long; Li, Junwen; Ge, Qun; Gong, Juwu; Liu, Aiying; Chen, Tingting; Wang, Dan; Wang, Yanling; Palanga, Koffi Kibalou; Muhammad, Jamshed; Li, Weijie; Lu, Quanwei; Deng, Xiaoying; Tan, Yunna; Song, Weiwu; Cai, Juan; Li, Pengtao; Rashid, Harun or; Gong, Wankui; Yuan, Youlu

2016-04-11

Upland Cotton (Gossypium hirsutum) is one of the most important worldwide crops it provides natural high-quality fiber for the industrial production and everyday use. Next-generation sequencing is a powerful method to identify single nucleotide polymorphism markers on a large scale for the construction of a high-density genetic map for quantitative trait loci mapping. In this research, a recombinant inbred lines population developed from two upland cotton cultivars 0-153 and sGK9708 was used to construct a high-density genetic map through the specific locus amplified fragment sequencing method. The high-density genetic map harbored 5521 single nucleotide polymorphism markers which covered a total distance of 3259.37 cM with an average marker interval of 0.78 cM without gaps larger than 10 cM. In total 18 quantitative trait loci of boll weight were identified as stable quantitative trait loci and were detected in at least three out of 11 environments and explained 4.15-16.70 % of the observed phenotypic variation. In total, 344 candidate genes were identified within the confidence intervals of these stable quantitative trait loci based on the cotton genome sequence. These genes were categorized based on their function through gene ontology analysis, Kyoto Encyclopedia of Genes and Genomes analysis and eukaryotic orthologous groups analysis. This research reported the first high-density genetic map for Upland Cotton (Gossypium hirsutum) with a recombinant inbred line population using single nucleotide polymorphism markers developed by specific locus amplified fragment sequencing. We also identified quantitative trait loci of boll weight across 11 environments and identified candidate genes within the quantitative trait loci confidence intervals. The results of this research would provide useful information for the next-step work including fine mapping, gene functional analysis, pyramiding breeding of functional genes as well as marker-assisted selection.

Induction of atherosclerosis in mice and hamsters without germline genetic engineering.

PubMed

Bjørklund, Martin Maeng; Hollensen, Anne Kruse; Hagensen, Mette Kallestrup; Dagnaes-Hansen, Frederik; Christoffersen, Christina; Mikkelsen, Jacob Giehm; Bentzon, Jacob Fog

2014-05-23

Atherosclerosis can be achieved in animals by germline genetic engineering, leading to hypercholesterolemia, but such models are constrained to few species and strains, and they are difficult to combine with other powerful techniques involving genetic manipulation or variation. To develop a method for induction of atherosclerosis without germline genetic engineering. Recombinant adeno-associated viral vectors were engineered to encode gain-of-function proprotein convertase subtilisin/kexin type 9 mutants, and mice were given a single intravenous vector injection followed by high-fat diet feeding. Plasma proprotein convertase subtilisin/kexin type 9 and total cholesterol increased rapidly and were maintained at high levels, and after 12 weeks, mice had atherosclerotic lesions in the aorta. Histology of the aortic root showed progression of lesions to the fibroatheromatous stage. To demonstrate the applicability of this method for rapid analysis of the atherosclerosis susceptibility of a mouse strain and for providing temporal control over disease induction, we demonstrated the accelerated atherosclerosis of mature diabetic Akita mice. Furthermore, the versatility of this approach for creating atherosclerosis models also in nonmurine species was demonstrated by inducing hypercholesterolemia and early atherosclerosis in Golden Syrian hamsters. Single injections of proprotein convertase subtilisin/kexin type 9-encoding recombinant adeno-associated viral vectors are a rapid and versatile method to induce atherosclerosis in animals. This method should prove useful for experiments that are high-throughput or involve genetic techniques, strains, or species that do not combine well with current genetically engineered models. © 2014 American Heart Association, Inc.

High-resolution DNA melting analysis in plant research

USDA-ARS?s Scientific Manuscript database

Genetic and genomic studies provide valuable insight into the inheritance, structure, organization, and function of genes. The knowledge gained from the analysis of plant genes is beneficial to all aspects of plant research, including crop improvement. New methods and tools are continually developed...

Revealing gene regulation and association through biological networks

USDA-ARS?s Scientific Manuscript database

This review had first summarized traditional methods used by plant breeders for genetic improvement, such as QTL analysis and transcriptomic analysis. With accumulating data, we can draw a network that comprises all possible links between members of a community, including protein–protein interaction...

Variability among Capsicum baccatum accessions from Goiás, Brazil, assessed by morphological traits and molecular markers.

PubMed

Martinez, A L A; Araújo, J S P; Ragassi, C F; Buso, G S C; Reifschneider, F J B

2017-07-06

Capsicum peppers are native to the Americas, with Brazil being a significant diversity center. Capsicum baccatum accessions at Instituto Federal (IF) Goiano represent a portion of the species genetic resources from central Brazil. We aimed to characterize a C. baccatum working collection comprising 27 accessions and 3 commercial cultivars using morphological traits and molecular markers to describe its genetic and morphological variability and verify the occurrence of duplicates. This set included 1 C. baccatum var. praetermissum and 29 C. baccatum var. pendulum with potential for use in breeding programs. Twenty-two morphological descriptors, 57 inter-simple sequence repeat, and 34 random amplified polymorphic DNA markers were used. Genetic distance was calculated through the Jaccard similarity index and genetic variability through cluster analysis using the unweighted pair group method with arithmetic mean, resulting in dendrograms for both morphological analysis and molecular analysis. Genetic variability was found among C. baccatum var. pendulum accessions, and the distinction between the two C. baccatum varieties was evident in both the morphological and molecular analyses. The 29 C. baccatum var. pendulum genotypes clustered in four groups according to fruit type in the morphological analysis. They formed seven groups in the molecular analysis, without a clear correspondence with morphology. No duplicates were found. The results describe the genetic and morphological variability, provide a detailed characterization of genotypes, and discard the possibility of duplicates within the IF Goiano C. baccatum L. collection. This study will foment the use of this germplasm collection in C. baccatum breeding programs.

A multifactorial analysis of obesity as CVD risk factor: use of neural network based methods in a nutrigenetics context.

PubMed

Valavanis, Ioannis K; Mougiakakou, Stavroula G; Grimaldi, Keith A; Nikita, Konstantina S

2010-09-08

Obesity is a multifactorial trait, which comprises an independent risk factor for cardiovascular disease (CVD). The aim of the current work is to study the complex etiology beneath obesity and identify genetic variations and/or factors related to nutrition that contribute to its variability. To this end, a set of more than 2300 white subjects who participated in a nutrigenetics study was used. For each subject a total of 63 factors describing genetic variants related to CVD (24 in total), gender, and nutrition (38 in total), e.g. average daily intake in calories and cholesterol, were measured. Each subject was categorized according to body mass index (BMI) as normal (BMI ≤ 25) or overweight (BMI > 25). Two artificial neural network (ANN) based methods were designed and used towards the analysis of the available data. These corresponded to i) a multi-layer feed-forward ANN combined with a parameter decreasing method (PDM-ANN), and ii) a multi-layer feed-forward ANN trained by a hybrid method (GA-ANN) which combines genetic algorithms and the popular back-propagation training algorithm. PDM-ANN and GA-ANN were comparatively assessed in terms of their ability to identify the most important factors among the initial 63 variables describing genetic variations, nutrition and gender, able to classify a subject into one of the BMI related classes: normal and overweight. The methods were designed and evaluated using appropriate training and testing sets provided by 3-fold Cross Validation (3-CV) resampling. Classification accuracy, sensitivity, specificity and area under receiver operating characteristics curve were utilized to evaluate the resulted predictive ANN models. The most parsimonious set of factors was obtained by the GA-ANN method and included gender, six genetic variations and 18 nutrition-related variables. The corresponding predictive model was characterized by a mean accuracy equal of 61.46% in the 3-CV testing sets. The ANN based methods revealed factors that interactively contribute to obesity trait and provided predictive models with a promising generalization ability. In general, results showed that ANNs and their hybrids can provide useful tools for the study of complex traits in the context of nutrigenetics.

Methods for identifying SNP interactions: a review on variations of Logic Regression, Random Forest and Bayesian logistic regression.

PubMed

Chen, Carla Chia-Ming; Schwender, Holger; Keith, Jonathan; Nunkesser, Robin; Mengersen, Kerrie; Macrossan, Paula

2011-01-01

Due to advancements in computational ability, enhanced technology and a reduction in the price of genotyping, more data are being generated for understanding genetic associations with diseases and disorders. However, with the availability of large data sets comes the inherent challenges of new methods of statistical analysis and modeling. Considering a complex phenotype may be the effect of a combination of multiple loci, various statistical methods have been developed for identifying genetic epistasis effects. Among these methods, logic regression (LR) is an intriguing approach incorporating tree-like structures. Various methods have built on the original LR to improve different aspects of the model. In this study, we review four variations of LR, namely Logic Feature Selection, Monte Carlo Logic Regression, Genetic Programming for Association Studies, and Modified Logic Regression-Gene Expression Programming, and investigate the performance of each method using simulated and real genotype data. We contrast these with another tree-like approach, namely Random Forests, and a Bayesian logistic regression with stochastic search variable selection.

A rapid generalized least squares model for a genome-wide quantitative trait association analysis in families.

PubMed

Li, Xiang; Basu, Saonli; Miller, Michael B; Iacono, William G; McGue, Matt

2011-01-01

Genome-wide association studies (GWAS) using family data involve association analyses between hundreds of thousands of markers and a trait for a large number of related individuals. The correlations among relatives bring statistical and computational challenges when performing these large-scale association analyses. Recently, several rapid methods accounting for both within- and between-family variation have been proposed. However, these techniques mostly model the phenotypic similarities in terms of genetic relatedness. The familial resemblances in many family-based studies such as twin studies are not only due to the genetic relatedness, but also derive from shared environmental effects and assortative mating. In this paper, we propose 2 generalized least squares (GLS) models for rapid association analysis of family-based GWAS, which accommodate both genetic and environmental contributions to familial resemblance. In our first model, we estimated the joint genetic and environmental variations. In our second model, we estimated the genetic and environmental components separately. Through simulation studies, we demonstrated that our proposed approaches are more powerful and computationally efficient than a number of existing methods are. We show that estimating the residual variance-covariance matrix in the GLS models without SNP effects does not lead to an appreciable bias in the p values as long as the SNP effect is small (i.e. accounting for no more than 1% of trait variance). Copyright © 2011 S. Karger AG, Basel.

Establishing an efficient way to utilize the drought resistance germplasm population in wheat.

PubMed

Wang, Jiancheng; Guan, Yajing; Wang, Yang; Zhu, Liwei; Wang, Qitian; Hu, Qijuan; Hu, Jin

2013-01-01

Drought resistance breeding provides a hopeful way to improve yield and quality of wheat in arid and semiarid regions. Constructing core collection is an efficient way to evaluate and utilize drought-resistant germplasm resources in wheat. In the present research, 1,683 wheat varieties were divided into five germplasm groups (high resistant, HR; resistant, R; moderate resistant, MR; susceptible, S; and high susceptible, HS). The least distance stepwise sampling (LDSS) method was adopted to select core accessions. Six commonly used genetic distances (Euclidean distance, Euclid; Standardized Euclidean distance, Seuclid; Mahalanobis distance, Mahal; Manhattan distance, Manhat; Cosine distance, Cosine; and Correlation distance, Correlation) were used to assess genetic distances among accessions. Unweighted pair-group average (UPGMA) method was used to perform hierarchical cluster analysis. Coincidence rate of range (CR) and variable rate of coefficient of variation (VR) were adopted to evaluate the representativeness of the core collection. A method for selecting the ideal constructing strategy was suggested in the present research. A wheat core collection for the drought resistance breeding programs was constructed by the strategy selected in the present research. The principal component analysis showed that the genetic diversity was well preserved in that core collection.

Large-scale genome-wide analysis identifies genetic variants associated with cardiac structure and function

PubMed Central

Wild, Philipp S.; Felix, Janine F.; Schillert, Arne; Chen, Ming-Huei; Leening, Maarten J.G.; Völker, Uwe; Großmann, Vera; Brody, Jennifer A.; Irvin, Marguerite R.; Shah, Sanjiv J.; Pramana, Setia; Lieb, Wolfgang; Schmidt, Reinhold; Stanton, Alice V.; Malzahn, Dörthe; Lyytikäinen, Leo-Pekka; Tiller, Daniel; Smith, J. Gustav; Di Tullio, Marco R.; Musani, Solomon K.; Morrison, Alanna C.; Pers, Tune H.; Morley, Michael; Kleber, Marcus E.; Aragam, Jayashri; Bis, Joshua C.; Bisping, Egbert; Broeckel, Ulrich; Cheng, Susan; Deckers, Jaap W.; Del Greco M, Fabiola; Edelmann, Frank; Fornage, Myriam; Franke, Lude; Friedrich, Nele; Harris, Tamara B.; Hofer, Edith; Hofman, Albert; Huang, Jie; Hughes, Alun D.; Kähönen, Mika; investigators, KNHI; Kruppa, Jochen; Lackner, Karl J.; Lannfelt, Lars; Laskowski, Rafael; Launer, Lenore J.; Lindgren, Cecilia M.; Loley, Christina; Mayet, Jamil; Medenwald, Daniel; Morris, Andrew P.; Müller, Christian; Müller-Nurasyid, Martina; Nappo, Stefania; Nilsson, Peter M.; Nuding, Sebastian; Nutile, Teresa; Peters, Annette; Pfeufer, Arne; Pietzner, Diana; Pramstaller, Peter P.; Raitakari, Olli T.; Rice, Kenneth M.; Rotter, Jerome I.; Ruohonen, Saku T.; Sacco, Ralph L.; Samdarshi, Tandaw E.; Sharp, Andrew S.P.; Shields, Denis C.; Sorice, Rossella; Sotoodehnia, Nona; Stricker, Bruno H.; Surendran, Praveen; Töglhofer, Anna M.; Uitterlinden, André G.; Völzke, Henry; Ziegler, Andreas; Münzel, Thomas; März, Winfried; Cappola, Thomas P.; Hirschhorn, Joel N.; Mitchell, Gary F.; Smith, Nicholas L.; Fox, Ervin R.; Dueker, Nicole D.; Jaddoe, Vincent W.V.; Melander, Olle; Lehtimäki, Terho; Ciullo, Marina; Hicks, Andrew A.; Lind, Lars; Gudnason, Vilmundur; Pieske, Burkert; Barron, Anthony J.; Zweiker, Robert; Schunkert, Heribert; Ingelsson, Erik; Liu, Kiang; Arnett, Donna K.; Psaty, Bruce M.; Blankenberg, Stefan; Larson, Martin G.; Felix, Stephan B.; Franco, Oscar H.; Zeller, Tanja; Vasan, Ramachandran S.; Dörr, Marcus

2017-01-01

BACKGROUND. Understanding the genetic architecture of cardiac structure and function may help to prevent and treat heart disease. This investigation sought to identify common genetic variations associated with inter-individual variability in cardiac structure and function. METHODS. A GWAS meta-analysis of echocardiographic traits was performed, including 46,533 individuals from 30 studies (EchoGen consortium). The analysis included 16 traits of left ventricular (LV) structure, and systolic and diastolic function. RESULTS. The discovery analysis included 21 cohorts for structural and systolic function traits (n = 32,212) and 17 cohorts for diastolic function traits (n = 21,852). Replication was performed in 5 cohorts (n = 14,321) and 6 cohorts (n = 16,308), respectively. Besides 5 previously reported loci, the combined meta-analysis identified 10 additional genome-wide significant SNPs: rs12541595 near MTSS1 and rs10774625 in ATXN2 for LV end-diastolic internal dimension; rs806322 near KCNRG, rs4765663 in CACNA1C, rs6702619 near PALMD, rs7127129 in TMEM16A, rs11207426 near FGGY, rs17608766 in GOSR2, and rs17696696 in CFDP1 for aortic root diameter; and rs12440869 in IQCH for Doppler transmitral A-wave peak velocity. Findings were in part validated in other cohorts and in GWAS of related disease traits. The genetic loci showed associations with putative signaling pathways, and with gene expression in whole blood, monocytes, and myocardial tissue. CONCLUSION. The additional genetic loci identified in this large meta-analysis of cardiac structure and function provide insights into the underlying genetic architecture of cardiac structure and warrant follow-up in future functional studies. FUNDING. For detailed information per study, see Acknowledgments. PMID:28394258

Inferring Population Genetic Structure in Widely and Continuously Distributed Carnivores: The Stone Marten (Martes foina) as a Case Study

PubMed Central

Vergara, María; Basto, Mafalda P.; Madeira, María José; Gómez-Moliner, Benjamín J.; Santos-Reis, Margarida; Fernandes, Carlos; Ruiz-González, Aritz

2015-01-01

The stone marten is a widely distributed mustelid in the Palaearctic region that exhibits variable habitat preferences in different parts of its range. The species is a Holocene immigrant from southwest Asia which, according to fossil remains, followed the expansion of the Neolithic farming cultures into Europe and possibly colonized the Iberian Peninsula during the Early Neolithic (ca. 7,000 years BP). However, the population genetic structure and historical biogeography of this generalist carnivore remains essentially unknown. In this study we have combined mitochondrial DNA (mtDNA) sequencing (621 bp) and microsatellite genotyping (23 polymorphic markers) to infer the population genetic structure of the stone marten within the Iberian Peninsula. The mtDNA data revealed low haplotype and nucleotide diversities and a lack of phylogeographic structure, most likely due to a recent colonization of the Iberian Peninsula by a few mtDNA lineages during the Early Neolithic. The microsatellite data set was analysed with a) spatial and non-spatial Bayesian individual-based clustering (IBC) approaches (STRUCTURE, TESS, BAPS and GENELAND), and b) multivariate methods [discriminant analysis of principal components (DAPC) and spatial principal component analysis (sPCA)]. Additionally, because isolation by distance (IBD) is a common spatial genetic pattern in mobile and continuously distributed species and it may represent a challenge to the performance of the above methods, the microsatellite data set was tested for its presence. Overall, the genetic structure of the stone marten in the Iberian Peninsula was characterized by a NE-SW spatial pattern of IBD, and this may explain the observed disagreement between clustering solutions obtained by the different IBC methods. However, there was significant indication for contemporary genetic structuring, albeit weak, into at least three different subpopulations. The detected subdivision could be attributed to the influence of the rivers Ebro, Tagus and Guadiana, suggesting that main watercourses in the Iberian Peninsula may act as semi-permeable barriers to gene flow in stone martens. To our knowledge, this is the first phylogeographic and population genetic study of the species at a broad regional scale. We also wanted to make the case for the importance and benefits of using and comparing multiple different clustering and multivariate methods in spatial genetic analyses of mobile and continuously distributed species. PMID:26222680

Inferring Population Genetic Structure in Widely and Continuously Distributed Carnivores: The Stone Marten (Martes foina) as a Case Study.

PubMed

Vergara, María; Basto, Mafalda P; Madeira, María José; Gómez-Moliner, Benjamín J; Santos-Reis, Margarida; Fernandes, Carlos; Ruiz-González, Aritz

2015-01-01

The stone marten is a widely distributed mustelid in the Palaearctic region that exhibits variable habitat preferences in different parts of its range. The species is a Holocene immigrant from southwest Asia which, according to fossil remains, followed the expansion of the Neolithic farming cultures into Europe and possibly colonized the Iberian Peninsula during the Early Neolithic (ca. 7,000 years BP). However, the population genetic structure and historical biogeography of this generalist carnivore remains essentially unknown. In this study we have combined mitochondrial DNA (mtDNA) sequencing (621 bp) and microsatellite genotyping (23 polymorphic markers) to infer the population genetic structure of the stone marten within the Iberian Peninsula. The mtDNA data revealed low haplotype and nucleotide diversities and a lack of phylogeographic structure, most likely due to a recent colonization of the Iberian Peninsula by a few mtDNA lineages during the Early Neolithic. The microsatellite data set was analysed with a) spatial and non-spatial Bayesian individual-based clustering (IBC) approaches (STRUCTURE, TESS, BAPS and GENELAND), and b) multivariate methods [discriminant analysis of principal components (DAPC) and spatial principal component analysis (sPCA)]. Additionally, because isolation by distance (IBD) is a common spatial genetic pattern in mobile and continuously distributed species and it may represent a challenge to the performance of the above methods, the microsatellite data set was tested for its presence. Overall, the genetic structure of the stone marten in the Iberian Peninsula was characterized by a NE-SW spatial pattern of IBD, and this may explain the observed disagreement between clustering solutions obtained by the different IBC methods. However, there was significant indication for contemporary genetic structuring, albeit weak, into at least three different subpopulations. The detected subdivision could be attributed to the influence of the rivers Ebro, Tagus and Guadiana, suggesting that main watercourses in the Iberian Peninsula may act as semi-permeable barriers to gene flow in stone martens. To our knowledge, this is the first phylogeographic and population genetic study of the species at a broad regional scale. We also wanted to make the case for the importance and benefits of using and comparing multiple different clustering and multivariate methods in spatial genetic analyses of mobile and continuously distributed species.

Assessment of pleiotropic transcriptome perturbations in Arabidopsis engineered for indirect insect defence.

PubMed

Houshyani, Benyamin; van der Krol, Alexander R; Bino, Raoul J; Bouwmeester, Harro J

2014-06-19

Molecular characterization is an essential step of risk/safety assessment of genetically modified (GM) crops. Holistic approaches for molecular characterization using omics platforms can be used to confirm the intended impact of the genetic engineering, but can also reveal the unintended changes at the omics level as a first assessment of potential risks. The potential of omics platforms for risk assessment of GM crops has rarely been used for this purpose because of the lack of a consensus reference and statistical methods to judge the significance or importance of the pleiotropic changes in GM plants. Here we propose a meta data analysis approach to the analysis of GM plants, by measuring the transcriptome distance to untransformed wild-types. In the statistical analysis of the transcriptome distance between GM and wild-type plants, values are compared with naturally occurring transcriptome distances in non-GM counterparts obtained from a database. Using this approach we show that the pleiotropic effect of genes involved in indirect insect defence traits is substantially equivalent to the variation in gene expression occurring naturally in Arabidopsis. Transcriptome distance is a useful screening method to obtain insight in the pleiotropic effects of genetic modification.

Hybridization in the section Mentha (Lamiaceae) inferred from AFLP markers.

PubMed

Gobert, V; Moja, S; Colson, M; Taberlet, P

2002-12-01

The amplified fragment length polymorphism (AFLP) method was used to evaluate genetic diversity and to assess genetic relationships within the section Mentha in order to clarify the taxonomy of several interspecific mint hybrids with molecular markers. To this end, genetic diversity of 62 Mentha accessions from different geographic origins, representing five species and three hybrids, was assessed. Three EcoRI/MseI AFLP primer combinations generated an average of 40 AFLP markers per primer combination, ranging in size from 50 to 500 base pairs (bp). The percentage of markers polymorphic ranged from 50% to 60% across all accessions studied. According to phenetic and cladistic analysis, the 62 mint accessions were grouped into two major clusters. Principal coordinates analysis separated species into well-defined groups, and clear relationships between species and hybrids could be described. Our AFLP analysis supports taxonomic classification established among Mentha species by conventional (morphological, cytological, and chemical) methods. It allows the assessment of phenetic relationships between species and the hybrids M. spicata and M. × piperita, largely cultivated all over the world for their menthol source, and provides new insights into the subdivision of M. spicata, based for the first time on molecular markers.

Descent graphs in pedigree analysis: applications to haplotyping, location scores, and marker-sharing statistics.

PubMed Central

Sobel, E.; Lange, K.

1996-01-01

The introduction of stochastic methods in pedigree analysis has enabled geneticists to tackle computations intractable by standard deterministic methods. Until now these stochastic techniques have worked by running a Markov chain on the set of genetic descent states of a pedigree. Each descent state specifies the paths of gene flow in the pedigree and the founder alleles dropped down each path. The current paper follows up on a suggestion by Elizabeth Thompson that genetic descent graphs offer a more appropriate space for executing a Markov chain. A descent graph specifies the paths of gene flow but not the particular founder alleles traveling down the paths. This paper explores algorithms for implementing Thompson's suggestion for codominant markers in the context of automatic haplotyping, estimating location scores, and computing gene-clustering statistics for robust linkage analysis. Realistic numerical examples demonstrate the feasibility of the algorithms. PMID:8651310

Approaches in Characterizing Genetic Structure and Mapping in a Rice Multiparental Population.

PubMed

Raghavan, Chitra; Mauleon, Ramil; Lacorte, Vanica; Jubay, Monalisa; Zaw, Hein; Bonifacio, Justine; Singh, Rakesh Kumar; Huang, B Emma; Leung, Hei

2017-06-07

Multi-parent Advanced Generation Intercross (MAGIC) populations are fast becoming mainstream tools for research and breeding, along with the technology and tools for analysis. This paper demonstrates the analysis of a rice MAGIC population from data filtering to imputation and processing of genetic data to characterizing genomic structure, and finally quantitative trait loci (QTL) mapping. In this study, 1316 S6:8 indica MAGIC (MI) lines and the eight founders were sequenced using Genotyping by Sequencing (GBS). As the GBS approach often includes missing data, the first step was to impute the missing SNPs. The observable number of recombinations in the population was then explored. Based on this case study, a general outline of procedures for a MAGIC analysis workflow is provided, as well as for QTL mapping of agronomic traits and biotic and abiotic stress, using the results from both association and interval mapping approaches. QTL for agronomic traits (yield, flowering time, and plant height), physical (grain length and grain width) and cooking properties (amylose content) of the rice grain, abiotic stress (submergence tolerance), and biotic stress (brown spot disease) were mapped. Through presenting this extensive analysis in the MI population in rice, we highlight important considerations when choosing analytical approaches. The methods and results reported in this paper will provide a guide to future genetic analysis methods applied to multi-parent populations. Copyright © 2017 Raghavan et al.

Targeted Analysis of Whole Genome Sequence Data to Diagnose Genetic Cardiomyopathy

DOE PAGES

Golbus, Jessica R.; Puckelwartz, Megan J.; Dellefave-Castillo, Lisa; ...

2014-09-01

Background—Cardiomyopathy is highly heritable but genetically diverse. At present, genetic testing for cardiomyopathy uses targeted sequencing to simultaneously assess the coding regions of more than 50 genes. New genes are routinely added to panels to improve the diagnostic yield. With the anticipated $1000 genome, it is expected that genetic testing will shift towards comprehensive genome sequencing accompanied by targeted gene analysis. Therefore, we assessed the reliability of whole genome sequencing and targeted analysis to identify cardiomyopathy variants in 11 subjects with cardiomyopathy. Methods and Results—Whole genome sequencing with an average of 37× coverage was combined with targeted analysis focused onmore » 204 genes linked to cardiomyopathy. Genetic variants were scored using multiple prediction algorithms combined with frequency data from public databases. This pipeline yielded 1-14 potentially pathogenic variants per individual. Variants were further analyzed using clinical criteria and/or segregation analysis. Three of three previously identified primary mutations were detected by this analysis. In six subjects for whom the primary mutation was previously unknown, we identified mutations that segregated with disease, had clinical correlates, and/or had additional pathological correlation to provide evidence for causality. For two subjects with previously known primary mutations, we identified additional variants that may act as modifiers of disease severity. In total, we identified the likely pathological mutation in 9 of 11 (82%) subjects. We conclude that these pilot data demonstrate that ~30-40× coverage whole genome sequencing combined with targeted analysis is feasible and sensitive to identify rare variants in cardiomyopathy-associated genes.« less

Childhood adiposity and risk of type 1 diabetes: A Mendelian randomization study

PubMed Central

Todd, John A.

2017-01-01

Background The incidence of type 1 diabetes (T1D) is increasing globally. One hypothesis is that increasing childhood obesity rates may explain part of this increase, but, as T1D is rare, intervention studies are challenging to perform. The aim of this study was to assess this hypothesis with a Mendelian randomization approach that uses genetic variants as instrumental variables to test for causal associations. Methods and findings We created a genetic instrument of 23 single nucleotide polymorphisms (SNPs) associated with childhood adiposity in children aged 2–10 years. Summary-level association results for these 23 SNPs with childhood-onset (<17 years) T1D were extracted from a meta-analysis of genome-wide association study with 5,913 T1D cases and 8,828 reference samples. Using inverse-variance weighted Mendelian randomization analysis, we found support for an effect of childhood adiposity on T1D risk (odds ratio 1.32, 95% CI 1.06–1.64 per standard deviation score in body mass index [SDS-BMI]). A sensitivity analysis provided evidence of horizontal pleiotropy bias (p = 0.04) diluting the estimates towards the null. We therefore applied Egger regression and multivariable Mendelian randomization methods to control for this type of bias and found evidence in support of a role of childhood adiposity in T1D (odds ratio in Egger regression, 2.76, 95% CI 1.40–5.44). Limitations of our study include that underlying genes and their mechanisms for most of the genetic variants included in the score are not known. Mendelian randomization requires large sample sizes, and power was limited to provide precise estimates. This research has been conducted using data from the Early Growth Genetics (EGG) Consortium, the Genetic Investigation of Anthropometric Traits (GIANT) Consortium, the Tobacco and Genetics (TAG) Consortium, and the Social Science Genetic Association Consortium (SSGAC), as well as meta-analysis results from a T1D genome-wide association study. Conclusions This study provides genetic support for a link between childhood adiposity and T1D risk. Together with evidence from observational studies, our findings further emphasize the importance of measures to reduce the global epidemic of childhood obesity and encourage mechanistic studies. PMID:28763444

Sequence analysis of tau 3'untranslated region and saitohin gene in sporadic progressive supranuclear palsy

PubMed Central

Ezquerra, M; Campdelacreu, J; Munoz, E; Oliva, R; Tolosa, E

2004-01-01

Objectives: To search for genetic changes in the 3'untranslated region (3'UTR) of tau and adjacent sequence LOC147077, and in the coding region of STH in PSP patients. Methods: The study included 57 PSP patients and 83 healthy controls. The genetic analysis of each region was performed through sequencing. The Q7R polymorphism was studied through restriction enzyme and electrophoresis analysis. Results: No mutations were found in the regions analysed. The QQ genotype of the STH polymorphism was over-represented in participants with PSP (91.5%) compared with control subjects (47%) (p⩽0.00001). This genotype co-segregated with the H1/H1 haplotype in our PSP cases. Conclusions: Our results do not support a major role for the tau 3'UTR in PSP genetics. The QQ genotype of STH confers susceptibility for PSP and is in linkage disequilibrium with the H1/H1 haplotype. PMID:14707330

Analysis of Plasmodium falciparum diversity in natural infections by deep sequencing

PubMed Central

Manske, Magnus; Miotto, Olivo; Campino, Susana; Auburn, Sarah; Almagro-Garcia, Jacob; Maslen, Gareth; O’Brien, Jack; Djimde, Abdoulaye; Doumbo, Ogobara; Zongo, Issaka; Ouedraogo, Jean-Bosco; Michon, Pascal; Mueller, Ivo; Siba, Peter; Nzila, Alexis; Borrmann, Steffen; Kiara, Steven M.; Marsh, Kevin; Jiang, Hongying; Su, Xin-Zhuan; Amaratunga, Chanaki; Fairhurst, Rick; Socheat, Duong; Nosten, Francois; Imwong, Mallika; White, Nicholas J.; Sanders, Mandy; Anastasi, Elisa; Alcock, Dan; Drury, Eleanor; Oyola, Samuel; Quail, Michael A.; Turner, Daniel J.; Rubio, Valentin Ruano; Jyothi, Dushyanth; Amenga-Etego, Lucas; Hubbart, Christina; Jeffreys, Anna; Rowlands, Kate; Sutherland, Colin; Roper, Cally; Mangano, Valentina; Modiano, David; Tan, John C.; Ferdig, Michael T.; Amambua-Ngwa, Alfred; Conway, David J.; Takala-Harrison, Shannon; Plowe, Christopher V.; Rayner, Julian C.; Rockett, Kirk A.; Clark, Taane G.; Newbold, Chris I.; Berriman, Matthew; MacInnis, Bronwyn; Kwiatkowski, Dominic P.

2013-01-01

Malaria elimination strategies require surveillance of the parasite population for genetic changes that demand a public health response, such as new forms of drug resistance. 1,2 Here we describe methods for large-scale analysis of genetic variation in Plasmodium falciparum by deep sequencing of parasite DNA obtained from the blood of patients with malaria, either directly or after short term culture. Analysis of 86,158 exonic SNPs that passed genotyping quality control in 227 samples from Africa, Asia and Oceania provides genome-wide estimates of allele frequency distribution, population structure and linkage disequilibrium. By comparing the genetic diversity of individual infections with that of the local parasite population, we derive a metric of within-host diversity that is related to the level of inbreeding in the population. An open-access web application has been established for exploration of regional differences in allele frequency and of highly differentiated loci in the P. falciparum genome. PMID:22722859

Spatio-temporal dynamics of genetic diversity in Sorghum bicolor in Niger.

PubMed

Deu, Monique; Sagnard, F; Chantereau, J; Calatayud, C; Vigouroux, Y; Pham, J L; Mariac, C; Kapran, I; Mamadou, A; Gérard, B; Ndjeunga, J; Bezançon, G

2010-05-01

The dynamics of crop genetic diversity need to be assessed to draw up monitoring and conservation priorities. However, few surveys have been conducted in centres of diversity. Sub-Saharan Africa is the centre of origin of sorghum. Most Sahel countries have been faced with major human, environmental and social changes in recent decades, which are suspected to cause genetic erosion. Sorghum is the second staple cereal in Niger, a centre of diversity for this crop. Niger was submitted to recurrent drought period and to major social changes during these last decades. We report here on a spatio-temporal analysis of sorghum genetic diversity, conducted in 71 villages covering the rainfall gradient and range of agro-ecological conditions in Niger's agricultural areas. We used 28 microsatellite markers and applied spatial and genetic clustering methods to investigate change in genetic diversity over a 26-year period (1976-2003). Global genetic differentiation between the two collections was very low (F (st) = 0.0025). Most of the spatial clusters presented no major differentiation, as measured by F (st), and showed stability or an increase in allelic richness, except for two of them located in eastern Niger. The genetic clusters identified by Bayesian analysis did not show a major change between the two collections in the distribution of accessions between them or in their spatial location. These results suggest that farmers' management has globally preserved sorghum genetic diversity in Niger.

Estimating Information Processing in a Memory System: The Utility of Meta-analytic Methods for Genetics.

PubMed

Yildizoglu, Tugce; Weislogel, Jan-Marek; Mohammad, Farhan; Chan, Edwin S-Y; Assam, Pryseley N; Claridge-Chang, Adam

2015-12-01

Genetic studies in Drosophila reveal that olfactory memory relies on a brain structure called the mushroom body. The mainstream view is that each of the three lobes of the mushroom body play specialized roles in short-term aversive olfactory memory, but a number of studies have made divergent conclusions based on their varying experimental findings. Like many fields, neurogenetics uses null hypothesis significance testing for data analysis. Critics of significance testing claim that this method promotes discrepancies by using arbitrary thresholds (α) to apply reject/accept dichotomies to continuous data, which is not reflective of the biological reality of quantitative phenotypes. We explored using estimation statistics, an alternative data analysis framework, to examine published fly short-term memory data. Systematic review was used to identify behavioral experiments examining the physiological basis of olfactory memory and meta-analytic approaches were applied to assess the role of lobular specialization. Multivariate meta-regression models revealed that short-term memory lobular specialization is not supported by the data; it identified the cellular extent of a transgenic driver as the major predictor of its effect on short-term memory. These findings demonstrate that effect sizes, meta-analysis, meta-regression, hierarchical models and estimation methods in general can be successfully harnessed to identify knowledge gaps, synthesize divergent results, accommodate heterogeneous experimental design and quantify genetic mechanisms.

Molecular – genetic variance of RH blood group system within human population of Bosnia and Herzegovina

PubMed Central

Lasić, Lejla; Lojo-Kadrić, Naida; Silajdžić, Elma; Pojskić, Lejla; Hadžiselimović, Rifat; Pojskić, Naris

2013-01-01

There are two major theories for inheritance of Rh blood group system: Fisher – Race theory and Wiener theory. Aim of this study was identifying frequency of RHDCE alleles in Bosnian – Herzegovinian population and introduction of this method in screening for Rh phenotype in B&H since this type of analysis was not used for blood typing in B&H before. Rh blood group was typed by Polymerase Chain Reaction, using the protocols and primers previously established by other authors, then carrying out electrophoresis in 2-3% agarose gel. Percentage of Rh positive individuals in our sample is 84.48%, while the percentage of Rh negative individuals is 15.52%. Inter-rater agreement statistic showed perfect agreement (K=1) between the results of Rh blood system detection based on serological and molecular-genetics methods. In conclusion, molecular – genetic methods are suitable for prenatal genotyping and specific cases while standard serological method is suitable for high-throughput of samples. PMID:23448604

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.

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.

Comparative Study on Prediction Effects of Short Fatigue Crack Propagation Rate by Two Different Calculation Methods

NASA Astrophysics Data System (ADS)

Yang, Bing; Liao, Zhen; Qin, Yahang; Wu, Yayun; Liang, Sai; Xiao, Shoune; Yang, Guangwu; Zhu, Tao

2017-05-01

To describe the complicated nonlinear process of the fatigue short crack evolution behavior, especially the change of the crack propagation rate, two different calculation methods are applied. The dominant effective short fatigue crack propagation rates are calculated based on the replica fatigue short crack test with nine smooth funnel-shaped specimens and the observation of the replica films according to the effective short fatigue cracks principle. Due to the fast decay and the nonlinear approximation ability of wavelet analysis, the self-learning ability of neural network, and the macroscopic searching and global optimization of genetic algorithm, the genetic wavelet neural network can reflect the implicit complex nonlinear relationship when considering multi-influencing factors synthetically. The effective short fatigue cracks and the dominant effective short fatigue crack are simulated and compared by the Genetic Wavelet Neural Network. The simulation results show that Genetic Wavelet Neural Network is a rational and available method for studying the evolution behavior of fatigue short crack propagation rate. Meanwhile, a traditional data fitting method for a short crack growth model is also utilized for fitting the test data. It is reasonable and applicable for predicting the growth rate. Finally, the reason for the difference between the prediction effects by these two methods is interpreted.

Isolation and genetic analysis of pure cells from forensic biological mixtures: The precision of a digital approach.

PubMed

Fontana, F; Rapone, C; Bregola, G; Aversa, R; de Meo, A; Signorini, G; Sergio, M; Ferrarini, A; Lanzellotto, R; Medoro, G; Giorgini, G; Manaresi, N; Berti, A

2017-07-01

Latest genotyping technologies allow to achieve a reliable genetic profile for the offender identification even from extremely minute biological evidence. The ultimate challenge occurs when genetic profiles need to be retrieved from a mixture, which is composed of biological material from two or more individuals. In this case, DNA profiling will often result in a complex genetic profile, which is then subject matter for statistical analysis. In principle, when more individuals contribute to a mixture with different biological fluids, their single genetic profiles can be obtained by separating the distinct cell types (e.g. epithelial cells, blood cells, sperm), prior to genotyping. Different approaches have been investigated for this purpose, such as fluorescent-activated cell sorting (FACS) or laser capture microdissection (LCM), but currently none of these methods can guarantee the complete separation of different type of cells present in a mixture. In other fields of application, such as oncology, DEPArray™ technology, an image-based, microfluidic digital sorter, has been widely proven to enable the separation of pure cells, with single-cell precision. This study investigates the applicability of DEPArray™ technology to forensic samples analysis, focusing on the resolution of the forensic mixture problem. For the first time, we report here the development of an application-specific DEPArray™ workflow enabling the detection and recovery of pure homogeneous cell pools from simulated blood/saliva and semen/saliva mixtures, providing full genetic match with genetic profiles of corresponding donors. In addition, we assess the performance of standard forensic methods for DNA quantitation and genotyping on low-count, DEPArray™-isolated cells, showing that pure, almost complete profiles can be obtained from as few as ten haploid cells. Finally, we explore the applicability in real casework samples, demonstrating that the described approach provides complete separation of cells with outstanding precision. In all examined cases, DEPArray™ technology proves to be a groundbreaking technology for the resolution of forensic biological mixtures, through the precise isolation of pure cells for an incontrovertible attribution of the obtained genetic profiles. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Genetic divergence through joint analysis of morphoagronomic and molecular characters in accessions of Jatropha curcas.

PubMed

Pestana-Caldas, C N; Silva, S A; Machado, E L; de Souza, D R; Cerqueira-Pereira, E C; Silva, M S

2016-10-05

The aim of this study was to investigate the genetic divergence between accessions of Jatropha curcas through joint analysis of morphoagronomic and molecular characters. To this end, we investigated 11 morphoagronomic characters and performed molecular genotyping, using 23 inter-simple sequence repeat (ISSR) primers in 46 accessions of J. curcas. We calculated the contribution of each character on divergence using analysis of variance. The grouping among accessions was performed using the Ward-MLM (modified location model) method, using morphoagronomic and molecular data, whereas the cophenetic correlation was obtained based on Gower's algorithm. There were significant differences in all growth-related characteristics: number of primary and secondary branches per plant, plant height, and stem diameter. For characters related to grain production, differences were found for number of fruit clusters per plant and number of inflorescence clusters per plant and average number of seeds per fruit. The greatest phenotypic variation was found in plant height (59.67- 222.33 cm), whereas the smallest variation was found in average number of seeds per fruit (0-2.90), followed by the number of fruit clusters per plant (0-8.67). In total, 94 polymorphic ISSR fragments were obtained. The genotypic grouping identified six groups, indicating that there is genetic divergence among the accessions. The most promising crossings for future hybridization were identified among accessions UFRB60 and UFVJC45, and UFRB61 and UFVJC18. In conclusion, the joint analysis of morphoagronomic characters and ISSR markers is an efficient method to assess the genetic divergence in J. curcas.

Advertisement call and genetic structure conservatism: good news for an endangered Neotropical frog

PubMed Central

Costa, William P.; Martins, Lucas B.; Nunes-de-Almeida, Carlos H. L.; Toledo, Luís Felipe

2016-01-01

Background: Many amphibian species are negatively affected by habitat change due to anthropogenic activities. Populations distributed over modified landscapes may be subject to local extinction or may be relegated to the remaining—likely isolated and possibly degraded—patches of available habitat. Isolation without gene flow could lead to variability in phenotypic traits owing to differences in local selective pressures such as environmental structure, microclimate, or site-specific species assemblages. Methods: Here, we tested the microevolution hypothesis by evaluating the acoustic parameters of 349 advertisement calls from 15 males from six populations of the endangered amphibian species Proceratophrys moratoi. In addition, we analyzed the genetic distances among populations and the genetic diversity with a haplotype network analysis. We performed cluster analysis on acoustic data based on the Bray-Curtis index of similarity, using the UPGMA method. We correlated acoustic dissimilarities (calculated by Euclidean distance) with geographical and genetic distances among populations. Results: Spectral traits of the advertisement call of P. moratoi presented lower coefficients of variation than did temporal traits, both within and among males. Cluster analyses placed individuals without congruence in population or geographical distance, but recovered the species topology in relation to sister species. The genetic distance among populations was low; it did not exceed 0.4% for the most distant populations, and was not correlated with acoustic distance. Discussion: Both acoustic features and genetic sequences are highly conserved, suggesting that populations could be connected by recent migrations, and that they are subject to stabilizing selective forces. Although further studies are required, these findings add to a growing body of literature suggesting that this species would be a good candidate for a reintroduction program without negative effects on communication or genetic impact. PMID:27190717

Forensic molecular genetic diversity analysis of Chinese Hui ethnic group based on a novel STR panel.

PubMed

Fang, Yating; Guo, Yuxin; Xie, Tong; Jin, Xiaoye; Lan, Qiong; Zhou, Yongsong; Zhu, Bofeng

2018-03-26

In present study, the genetic polymorphisms of 22 autosomal short tandem repeat (STR) loci were analyzed in 496 unrelated Chinese Xinjiang Hui individuals. These autosomal STR loci were multiplex amplified and genotyped based on a novel STR panel. There were 246 observed alleles with the allele frequencies ranging from 0.0010 to 0.3609. All polymorphic information content values were higher than 0.7. The combined power of discrimination and the combined probability of exclusion were 0.999999999999999999999999999426766 and 0.999999999860491, respectively. Based on analysis of molecular variance method, genetic differentiation analysis between the Xinjiang Hui and other reported groups were conducted at these 22 loci. The results indicated that there were no significant differences in statistics between Hui group and Northern Han group (including Han groups from Hebei, Henan, Shaanxi provinces), and significant deviations with Southern Han group (including those from Guangdong, Guangxi provinces) at 7 loci, and Uygur group at 10 loci. To sum up, these 22 autosomal STR loci were high genetic polymorphic in Xinjiang Hui group.

SecureMA: protecting participant privacy in genetic association meta-analysis.

PubMed

Xie, Wei; Kantarcioglu, Murat; Bush, William S; Crawford, Dana; Denny, Joshua C; Heatherly, Raymond; Malin, Bradley A

2014-12-01

Sharing genomic data is crucial to support scientific investigation such as genome-wide association studies. However, recent investigations suggest the privacy of the individual participants in these studies can be compromised, leading to serious concerns and consequences, such as overly restricted access to data. We introduce a novel cryptographic strategy to securely perform meta-analysis for genetic association studies in large consortia. Our methodology is useful for supporting joint studies among disparate data sites, where privacy or confidentiality is of concern. We validate our method using three multisite association studies. Our research shows that genetic associations can be analyzed efficiently and accurately across substudy sites, without leaking information on individual participants and site-level association summaries. Our software for secure meta-analysis of genetic association studies, SecureMA, is publicly available at http://github.com/XieConnect/SecureMA. Our customized secure computation framework is also publicly available at http://github.com/XieConnect/CircuitService. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Chemometrics Methods for Specificity, Authenticity and Traceability Analysis of Olive Oils: Principles, Classifications and Applications

PubMed Central

Messai, Habib; Farman, Muhammad; Sarraj-Laabidi, Abir; Hammami-Semmar, Asma; Semmar, Nabil

2016-01-01

Background. Olive oils (OOs) show high chemical variability due to several factors of genetic, environmental and anthropic types. Genetic and environmental factors are responsible for natural compositions and polymorphic diversification resulting in different varietal patterns and phenotypes. Anthropic factors, however, are at the origin of different blends’ preparation leading to normative, labelled or adulterated commercial products. Control of complex OO samples requires their (i) characterization by specific markers; (ii) authentication by fingerprint patterns; and (iii) monitoring by traceability analysis. Methods. These quality control and management aims require the use of several multivariate statistical tools: specificity highlighting requires ordination methods; authentication checking calls for classification and pattern recognition methods; traceability analysis implies the use of network-based approaches able to separate or extract mixed information and memorized signals from complex matrices. Results. This chapter presents a review of different chemometrics methods applied for the control of OO variability from metabolic and physical-chemical measured characteristics. The different chemometrics methods are illustrated by different study cases on monovarietal and blended OO originated from different countries. Conclusion. Chemometrics tools offer multiple ways for quantitative evaluations and qualitative control of complex chemical variability of OO in relation to several intrinsic and extrinsic factors. PMID:28231172

Genetic diversity analysis of Chrysopidae family (Insecta, Neuroptera) via molecular markers.

PubMed

Yari, Kheirollah; Mirmoayedi, Alinaghi; Marami, Marzieh; Kazemi, Elham; Kahrizi, Danial

2014-09-01

In entomology, improvement of molecular methods would be beneficial tools for accurate identification and detecting the genetic diversity of insect species to discover a corroborative evidence for the traditional classification based on morphology. The aim of this study was focused on RAPD-PCR method for distinguishing the genetic diversity between eight species of Chrysopidae family. In current research, many specimens were collected in different locations of Tehran province (Iran), between them 24 specimens were identified. The wing venation, male genitalia and other morphological characters were used for identification and also the sexing of species was recognized with study of external genitalia. Then, the DNA was extracted with CTAB method. The RAPD-PCR method was carried out with twenty random primers. The agarose gel electrophoresis was used for separation of the PCR products. Based on electrophoresis results, 133 bands were amplified and between them, 126 bands were poly-morph and others were mono-morph. Also, among the applied primers, the primers OPA02 with 19 bands and OPA03 with 8 bands were amplified the maximum and minimum of bands, respectively. The results showed that 80.35 and 73.21 % of genetic similarity existed between Chrysopa pallens-Chrysopa dubitans, and between the Chrysoperla kolthoffi and Chrysoperla carnea, respectively. The minimum (45.53 %) of genetic similarity was observed between C. kolthoffi and C. dubitans, and the maximum (0.80 %) was seen between C. pallens and C. dubitans.

Genetic diversity and geographic differentiation analysis of duckweed using inter-simple sequence repeat markers.

PubMed

Xue, Huiling; Xiao, Yao; Jin, Yanling; Li, Xinbo; Fang, Yang; Zhao, Hai; Zhao, Yun; Guan, Jiafa

2012-01-01

Duckweed, with rapid growth rate and high starch content, is a new alternate feedstock for bioethanol production. The genetic diversity among 27 duckweed populations of seven species in genus Lemna and Spirodela from China and Vietnam was analyzed by ISSR-PCR. Eight ISSR primers generating a reproducible amplification banding pattern had been screened. 89 polymorphic bands were scored out of the 92 banding patterns of 16 Lemna populations, accounting for 96.74% of the polymorphism. 98 polymorphic bands of 11 Spirodela populations were scored out of 99 banding patterns, and the polymorphism was 98.43%. The genetic distance of Lemna varied from 0.127 to 0.784, and from 0.138 to 0.902 for Spirodela, which indicated a high level of genetic variation among the populations studied. The unweighted pair group method with arithmetic average (UPGMA) cluster analysis corresponded well with the genetic distance. Populations from Sichuan China grouped together and so did the populations from Vietnam, which illuminated populations collected from the same region clustered into one group. Especially, the only one population from Tibet was included in subgroup A2 alone. Clustering analysis indicated that the geographic differentiation of collected sites correlated closely with the genetic differentiation of duckweeds. The results suggested that geographic differentiation had great influence on genetic diversity of duckweed in China and Vietnam at the regional scale. This study provided primary guidelines for collection, conservation, characterization of duckweed resources for bioethanol production etc.

Readiness of adolescents to use genetically modified organisms according to their knowledge and emotional attitude towards GMOs.

PubMed

Lachowski, Stanisław; Jurkiewicz, Anna; Choina, Piotr; Florek-Łuszczki, Magdalena; Buczaj, Agnieszka; Goździewska, Małgorzata

2017-06-07

Agriculture based on genetically modified organisms plays an increasingly important role in feeding the world population, which is evidenced by a considerable growth in the size of land under genetically modified crops (GM). Uncertainty and controversy around GM products are mainly due to the lack of accurate and reliable information, and lack of knowledge concerning the essence of genetic modifications, and the effect of GM food on the human organism, and consequently, a negative emotional attitude towards what is unknown. The objective of the presented study was to discover to what extent knowledge and the emotional attitude of adolescents towards genetically modified organisms is related with acceptance of growing genetically modified plants or breeding GM animals on own farm or allotment garden, and the purchase and consumption of GM food, as well as the use of GMOs in medicine. The study was conducted by the method of a diagnostic survey using a questionnaire designed by the author, which covered a group of 500 adolescents completing secondary school on the level of maturity examination. The collected material was subjected to statistical analysis. Research hypotheses were verified using chi-square test (χ ² ), t-Student test, and stepwise regression analysis. Stepwise regression analysis showed that the readiness of adolescents to use genetically modified organisms as food or for the production of pharmaceuticals, the production of GM plants or animals on own farm, depends on an emotional-evaluative attitude towards GMOs, and the level of knowledge concerning the essence of genetic modifications.

[Genetic diversity and genetic structure of endangered wild Sinopodophyllum emodi by start codon targeted polymorphism].

PubMed

Chen, Da-Xia; Zhao, Ji-Feng; Liu, Xiang; Wang, Chang-Hua; Zhang, Zhi-Wei; Qin, Song-Yun; Zhong, Guo-Yue

2013-01-01

Revealed the genetic diversity level and genetic structure characteristics in Sinopodophyllum emodi, a rare and endangered species in China. We detected the genetic polymorphism within and among six wild populations (45 individuals) by the approach of Start Codon Targeted (SCoT) Polymorphism. The associated genetic parameters were calculated by POP-GENE1.31 and the relationship was constructed based on UPGMA method. A total of 350 bands were scored by 27 primers and 284 bands of them were polymorphic. The average polymorphic bands of each primer were 10.52. At species level, there was a high level of genetic diversity among six populations (PPB = 79.27%, N(e) = 1.332 7, H = 0.210 9 and H(sp) = 0.328 6). At population level, the genetic diversity level was low (PPB = 10.48% (4.00% -23.71%), N(e) = 1.048 7 (1.020 7-1.103 7), H = 0.029 7 (0.012 9-0.063 1), H(pop) = 0.046 2 (0.019 9-0.098 6). The Nei's coefficient of genetic differentiation was 0.841 1, which was consistent with the Shannon's coefficient of genetic differentiation (0.849 4). Two calculated methods all showed that most of the genetic variation existed among populations. The gene flow (N(m) = 0.094 4) was less among populations, indicating that the degree of genetic differentiation was higher. Genetic similarity coefficient were changed from 0.570 8 to 0.978 7. By clustering analysis, the tested populations were divided into two classes and had a tendency that the same geographical origin or material of similar habitats clustered into one group. The genetic diversity of samples of S. emodi is high,which laid a certain foundation for effective protection and improvement of germplasm resources.

Population analysis of clinical and environmental Vibrio parahaemolyticus isolated from eastern provinces in China by removing the recombinant SNPs in the MLST loci.

PubMed

Lu, Xin; Zhou, Haijian; Du, Xiaoli; Liu, Sha; Xu, Jialiang; Cui, Zhigang; Pang, Bo; Kan, Biao

2016-11-01

Vibrio parahaemolyticus is a common seafood-borne pathogenic bacterium which causes gastroenteritis in humans. Continuous surveillance on the molecular characters of the clinical and environmental V. parahaemolyticus strains needs to be conducted for the epidemiological and genetic purposes. To generate a picture of the population distribution of V. parahaemolyticus in eastern China isolated from clinical cases of gastroenteritis and environmental samples, we investigated the genetic and evolutionary relationships of the strains using the commonly used multi-locus sequence typing (MLST, in which seven house-keeping genes are used in the protocol). A highly genetic diversity within the V. parahaemolyticus population was observed but ST3 was still dominant in the clinical strains, and 103 new sequence types (ST) were found in the clinical strains by searching in the global V. parahaemolyticus MLST database. With these genetically diverse strains, we estimated the recombination rates of the loci in MLST analysis. The locus recA was found to be subject to exceptionally high rate of recombination, and the recombinant single nucleotide polymorphisms (SNPs) were also identified within the seven loci. The phylogenetic tree of the strains was re-constructed using the maximum likelihood method by removing the recombination SNPs of the seven loci, and the minimum spanning tree was re-constructed with the six loci without recA. Some changes were observed in comparison with the previously used methods, suggesting that the homologous recombination has roles in shaping the clonal structure of V. parahaemolyticus. We propose the recombination-free SNPs strategy in the clonality analysis of V. parahaemolyticus, especially when using the maximum likelihood method. Copyright © 2016. Published by Elsevier B.V.

Valuing the benefits of genetic testing for retinitis pigmentosa: a pilot application of the contingent valuation method.

PubMed

Eden, Martin; Payne, Katherine; Combs, Ryan M; Hall, Georgina; McAllister, Marion; Black, Graeme C M

2013-08-01

Technological advances present an opportunity for more people with, or at risk of, developing retinitis pigmentosa (RP) to be offered genetic testing. Valuation of these tests using current evaluative frameworks is problematic since benefits may be derived from diagnostic information rather than improvements in health. This pilot study aimed to explore if contingent valuation method (CVM) can be used to value the benefits of genetic testing for RP. CVM was used to elicit willingness-to-pay (WTP) values for (1) genetic counselling and (2) genetic counselling with genetic testing. Telephone and face-to-face interviews with a purposive sample of individuals with (n=25), and without (n=27), prior experience of RP were used to explore the feasibility and validity of CVM in this context. Faced with a hypothetical scenario, the majority of participants stated that they would seek genetic counselling and testing in the context of RP. Between participant groups, respondents offered similar justifications for stated WTP values. Overall stated WTP was higher for genetic counselling plus testing (median=£524.00) compared with counselling alone (median=£224.50). Between-group differences in stated WTP were statistically significant; participants with prior knowledge of the condition were willing to pay more for genetic ophthalmology services. Participants were able to attach a monetary value to the perceived potential benefit that genetic testing offered regardless of prior experience of the condition. This exploratory work represents an important step towards evaluating these services using formal cost-benefit analysis.

Genetic diversity and structure of Sinopodophyllum hexandrum (Royle) Ying in the Qinling Mountains, China.

PubMed

Liu, Wei; Yin, Dongxue; Liu, Jianjun; Li, Na

2014-01-01

Sinopodophyllum hexandrum is an important medicinal plant whose genetic diversity must be conserved because it is endangered. The Qinling Mts. are a S. hexandrum distribution area that has unique environmental features that highly affect the evolution of the species. To provide the reference data for evolutionary and conservation studies, the genetic diversity and population structure of S. hexandrum in its overall natural distribution areas in the Qinling Mts. were investigated through inter-simple sequence repeats analysis of 32 natural populations. The 11 selected primers generated a total of 135 polymorphic bands. S. hexandrum genetic diversity was low within populations (average He = 0.0621), but higher at the species level (He = 0.1434). Clear structure and high genetic differentiation among populations were detected by using the unweighted pair group method for arithmetic averages, principle coordinate analysis and Bayesian clustering. The clustering approaches supported a division of the 32 populations into three major groups, for which analysis of molecular variance confirmed significant variation (63.27%) among populations. The genetic differentiation may have been attributed to the limited gene flow (Nm = 0.3587) in the species. Isolation by distance among populations was determined by comparing genetic distance versus geographic distance by using the Mantel test. Result was insignificant (r = 0.212, P = 0.287) at 0.05, showing that their spatial pattern and geographic locations are not correlated. Given the low within-population genetic diversity, high differentiation among populations and the increasing anthropogenic pressure on the species, in situ conservation measures were recommended to preserve S. hexandrum in Qinling Mts., and other populations must be sampled to retain as much genetic diversity of the species to achieve ex situ preservation as a supplement to in situ conservation.

Genetic Diversity and Structure of Sinopodophyllum hexandrum (Royle) Ying in the Qinling Mountains, China

PubMed Central

Liu, Wei; Yin, Dongxue; Liu, Jianjun; Li, Na

2014-01-01

Sinopodophyllum hexandrum is an important medicinal plant whose genetic diversity must be conserved because it is endangered. The Qinling Mts. are a S. hexandrum distribution area that has unique environmental features that highly affect the evolution of the species. To provide the reference data for evolutionary and conservation studies, the genetic diversity and population structure of S. hexandrum in its overall natural distribution areas in the Qinling Mts. were investigated through inter-simple sequence repeats analysis of 32 natural populations. The 11 selected primers generated a total of 135 polymorphic bands. S. hexandrum genetic diversity was low within populations (average He = 0.0621), but higher at the species level (He = 0.1434). Clear structure and high genetic differentiation among populations were detected by using the unweighted pair group method for arithmetic averages, principle coordinate analysis and Bayesian clustering. The clustering approaches supported a division of the 32 populations into three major groups, for which analysis of molecular variance confirmed significant variation (63.27%) among populations. The genetic differentiation may have been attributed to the limited gene flow (Nm = 0.3587) in the species. Isolation by distance among populations was determined by comparing genetic distance versus geographic distance by using the Mantel test. Result was insignificant (r = 0.212, P = 0.287) at 0.05, showing that their spatial pattern and geographic locations are not correlated. Given the low within-population genetic diversity, high differentiation among populations and the increasing anthropogenic pressure on the species, in situ conservation measures were recommended to preserve S. hexandrum in Qinling Mts., and other populations must be sampled to retain as much genetic diversity of the species to achieve ex situ preservation as a supplement to in situ conservation. PMID:25333788

Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: Comparing meta and megaanalytical approaches for data pooling.

PubMed

Kochunov, Peter; Jahanshad, Neda; Sprooten, Emma; Nichols, Thomas E; Mandl, René C; Almasy, Laura; Booth, Tom; Brouwer, Rachel M; Curran, Joanne E; de Zubicaray, Greig I; Dimitrova, Rali; Duggirala, Ravi; Fox, Peter T; Hong, L Elliot; Landman, Bennett A; Lemaitre, Hervé; Lopez, Lorna M; Martin, Nicholas G; McMahon, Katie L; Mitchell, Braxton D; Olvera, Rene L; Peterson, Charles P; Starr, John M; Sussmann, Jessika E; Toga, Arthur W; Wardlaw, Joanna M; Wright, Margaret J; Wright, Susan N; Bastin, Mark E; McIntosh, Andrew M; Boomsma, Dorret I; Kahn, René S; den Braber, Anouk; de Geus, Eco J C; Deary, Ian J; Hulshoff Pol, Hilleke E; Williamson, Douglas E; Blangero, John; van 't Ent, Dennis; Thompson, Paul M; Glahn, David C

2014-07-15

Combining datasets across independent studies can boost statistical power by increasing the numbers of observations and can achieve more accurate estimates of effect sizes. This is especially important for genetic studies where a large number of observations are required to obtain sufficient power to detect and replicate genetic effects. There is a need to develop and evaluate methods for joint-analytical analyses of rich datasets collected in imaging genetics studies. The ENIGMA-DTI consortium is developing and evaluating approaches for obtaining pooled estimates of heritability through meta-and mega-genetic analytical approaches, to estimate the general additive genetic contributions to the intersubject variance in fractional anisotropy (FA) measured from diffusion tensor imaging (DTI). We used the ENIGMA-DTI data harmonization protocol for uniform processing of DTI data from multiple sites. We evaluated this protocol in five family-based cohorts providing data from a total of 2248 children and adults (ages: 9-85) collected with various imaging protocols. We used the imaging genetics analysis tool, SOLAR-Eclipse, to combine twin and family data from Dutch, Australian and Mexican-American cohorts into one large "mega-family". We showed that heritability estimates may vary from one cohort to another. We used two meta-analytical (the sample-size and standard-error weighted) approaches and a mega-genetic analysis to calculate heritability estimates across-population. We performed leave-one-out analysis of the joint estimates of heritability, removing a different cohort each time to understand the estimate variability. Overall, meta- and mega-genetic analyses of heritability produced robust estimates of heritability. Copyright © 2014 Elsevier Inc. All rights reserved.

Association Between Coronary Artery Disease Genetic Variants and Subclinical Atherosclerosis: An Association Study and Meta-analysis.

PubMed

Zabalza, Michel; Subirana, Isaac; Lluis-Ganella, Carla; Sayols-Baixeras, Sergi; de Groot, Eric; Arnold, Roman; Cenarro, Ana; Ramos, Rafel; Marrugat, Jaume; Elosua, Roberto

2015-10-01

Recent studies have identified several genetic variants associated with coronary artery disease. Some of these genetic variants are not associated with classical cardiovascular risk factors and the mechanism of such associations is unclear. The aim of the study was to determine whether these genetic variants are related to subclinical atherosclerosis measured by carotid intima media thickness, carotid stiffness, and ankle brachial index. A cross-sectional study nested in the follow-up of the REGICOR cohort was undertaken. The study included 2667 individuals. Subclinical atherosclerosis measurements were performed with standardized methods. Nine genetic variants were genotyped to assess associations with subclinical atherosclerosis, individually and in a weighted genetic risk score. A systematic review and meta-analysis of previous studies that analyzed these associations was undertaken. Neither the selected genetic variants nor the genetic risk score were significantly associated with subclinical atherosclerosis. In the meta-analysis, the rs1746048 (CXCL12; n = 10581) risk allele was directly associated with carotid intima-media thickness (β = 0.008; 95% confidence interval, 0.001-0.015), whereas the rs6725887 (WDR12; n = 7801) risk allele was inversely associated with this thickness (β = -0.013; 95% confidence interval, -0.024 to -0.003). The analyzed genetic variants seem to mediate their association with coronary artery disease through different mechanisms. Our results generate the hypothesis that the CXCL12 variant appears to influence coronary artery disease risk through arterial remodeling and thickening, whereas the WDR12 risk variant could be related to higher plaque vulnerability. Copyright © 2014 Sociedad Española de Cardiología. Published by Elsevier España, S.L.U. All rights reserved.

Evaluation of Genetic Diversity of Candida spp. and Klebsiella spp. Isolated from the Denture Plaque of COPD Patients.

PubMed

Przybyłowska, D; Piskorska, K; Gołaś, M; Sikora, M; Swoboda-Kopeć, E; Kostrzewa-Janicka, J; Mierzwińska-Nastalska, E

2017-01-01

Yeast-like fungi and gram-negative bacilli are the most frequent potential pathogens of the respiratory tract isolated from the denture plaque of patients with chronic obstructive pulmonary disease (COPD). Dominant species among yeast-like fungi are Candida albicans and Candida tropicalis. Significant frequency is also exhibited by Klebsiella pneumoniae and Klebsiella oxytoca. The purpose of this study was to analyze genetic diversity of the strains of C. albicans, C. tropicalis, and Klebsiella spp. present in patients in stable phases of COPD. The analysis was conducted by the random amplified polymorphic DNA (RAPD) method on clinical strains isolated from patients with COPD and control patients in overall good health. Forty one strains of Candida albicans, 12 of Candida tropicalis, as well as 9 strains of K. pneumoniae and 7 of K. oxytoca were scrutinized. The dominant species in clinical material from COPD patients was Candida albicans with a substantial degree of variations of genetic profiles. On the basis of affinity analysis, 19 genetic types were identified within this strain. An analysis of the banding patterns among C. tropicalis strains indicated the existence of 6 genetic types. A considerable diversity of genetic profiles among Klebsiella spp. also was established. The genotype diversity of Klebsiella spp. strains may indicate the endogenic character of the majority of infections, regardless of the therapy applied for the underlying condition.

Integrating Genetic, Neuropsychological and Neuroimaging Data to Model Early-Onset Obsessive Compulsive Disorder Severity

PubMed Central

Mas, Sergi; Gassó, Patricia; Morer, Astrid; Calvo, Anna; Bargalló, Nuria; Lafuente, Amalia; Lázaro, Luisa

2016-01-01

We propose an integrative approach that combines structural magnetic resonance imaging data (MRI), diffusion tensor imaging data (DTI), neuropsychological data, and genetic data to predict early-onset obsessive compulsive disorder (OCD) severity. From a cohort of 87 patients, 56 with complete information were used in the present analysis. First, we performed a multivariate genetic association analysis of OCD severity with 266 genetic polymorphisms. This association analysis was used to select and prioritize the SNPs that would be included in the model. Second, we split the sample into a training set (N = 38) and a validation set (N = 18). Third, entropy-based measures of information gain were used for feature selection with the training subset. Fourth, the selected features were fed into two supervised methods of class prediction based on machine learning, using the leave-one-out procedure with the training set. Finally, the resulting model was validated with the validation set. Nine variables were used for the creation of the OCD severity predictor, including six genetic polymorphisms and three variables from the neuropsychological data. The developed model classified child and adolescent patients with OCD by disease severity with an accuracy of 0.90 in the testing set and 0.70 in the validation sample. Above its clinical applicability, the combination of particular neuropsychological, neuroimaging, and genetic characteristics could enhance our understanding of the neurobiological basis of the disorder. PMID:27093171

WISARD: workbench for integrated superfast association studies for related datasets.

PubMed

Lee, Sungyoung; Choi, Sungkyoung; Qiao, Dandi; Cho, Michael; Silverman, Edwin K; Park, Taesung; Won, Sungho

2018-04-20

A Mendelian transmission produces phenotypic and genetic relatedness between family members, giving family-based analytical methods an important role in genetic epidemiological studies-from heritability estimations to genetic association analyses. With the advance in genotyping technologies, whole-genome sequence data can be utilized for genetic epidemiological studies, and family-based samples may become more useful for detecting de novo mutations. However, genetic analyses employing family-based samples usually suffer from the complexity of the computational/statistical algorithms, and certain types of family designs, such as incorporating data from extended families, have rarely been used. We present a Workbench for Integrated Superfast Association studies for Related Data (WISARD) programmed in C/C++. WISARD enables the fast and a comprehensive analysis of SNP-chip and next-generation sequencing data on extended families, with applications from designing genetic studies to summarizing analysis results. In addition, WISARD can automatically be run in a fully multithreaded manner, and the integration of R software for visualization makes it more accessible to non-experts. Comparison with existing toolsets showed that WISARD is computationally suitable for integrated analysis of related subjects, and demonstrated that WISARD outperforms existing toolsets. WISARD has also been successfully utilized to analyze the large-scale massive sequencing dataset of chronic obstructive pulmonary disease data (COPD), and we identified multiple genes associated with COPD, which demonstrates its practical value.

Research on rolling element bearing fault diagnosis based on genetic algorithm matching pursuit

NASA Astrophysics Data System (ADS)

Rong, R. W.; Ming, T. F.

2017-12-01

In order to solve the problem of slow computation speed, matching pursuit algorithm is applied to rolling bearing fault diagnosis, and the improvement are conducted from two aspects that are the construction of dictionary and the way to search for atoms. To be specific, Gabor function which can reflect time-frequency localization characteristic well is used to construct the dictionary, and the genetic algorithm to improve the searching speed. A time-frequency analysis method based on genetic algorithm matching pursuit (GAMP) algorithm is proposed. The way to set property parameters for the improvement of the decomposition results is studied. Simulation and experimental results illustrate that the weak fault feature of rolling bearing can be extracted effectively by this proposed method, at the same time, the computation speed increases obviously.

Recent advances in genetic testing for familial hypercholesterolemia.

PubMed

Iacocca, Michael A; Hegele, Robert A

2017-07-01

Familial hypercholesterolemia (FH) is a common genetic cause of premature coronary heart disease that is widely underdiagnosed and undertreated. To improve the identification of FH and initiate timely and appropriate treatment strategies, genetic testing is becoming increasingly offered worldwide as a central part of diagnosis. Areas covered: Recent advances have been propelled by an improved understanding of the genetic determinants of FH together with substantially reduced costs of appropriate screening strategies. Here we review the various methods available for obtaining a molecular diagnosis of FH, and highlight the particular advantages of targeted next-generation sequencing (NGS) platforms as the most robust approach. Furthermore, we note the importance of screening for copy number variants and common polymorphisms to aid in molecularly defining suspected FH cases. Expert commentary: The need for genetic analysis of FH will increase, both for diagnosis and reimbursement of new therapies. An effective molecular diagnostic method must detect: 1) molecular and gene locus heterogeneity; 2) a wide range of mutation types; and 3) the polygenic component of FH. As availability of genetic testing for FH expands, standardization of variant curation, maintenance of clinical databases and registries, and wider health care provider education all assume greater importance.

Molecular marker-based genetic diversity analysis of scantly studied Brazilian accessions of a medicinal plant, Morinda citrifolia L. (noni).

PubMed

Bordallo, P N; Monteiro, A M R; Sousa, J A; Aragão, F A S

2017-02-23

Morinda citrifolia L., commonly known as noni, has been used for the treatment of various diseases for over two centuries. It was introduced and widely disseminated in Brazil because of its high market value and ease of adaptation to the soil and climatic conditions of the country. The aim of this study was to estimate the genetic variability of noni accessions from the collection of Embrapa Agroindústria Tropical in Brazil. We evaluated 36 plants of the 13 accessions of noni from the germplasm collection of M. citrifolia. Several methods of DNA extraction were tested. After definition of the method, the DNA of each sample was subjected to polymerase chain reactions using 20 random amplified polymorphic DNA primers. The band patterns on agarose gel were converted into a binary data matrix, which was used to estimate the genetic distances between the plants and to perform the cluster analyses. Of the total number of markers used in this study, 125 (81.1%) were polymorphic. The genetic distances between the genotypes ranged from 0.04 to 0.49. Regardless of the high number of polymorphic bands, the genetic variability of the noni plants evaluated was low since most of the genotypes belonged to the same cluster as shown by the dendrogram and Tocher's cluster analysis. The low genetic diversity among the studied noni individuals indicates that additional variability should be introduced in the germplasm collection of noni by gathering new individuals and/or by hybridizing contrasting individuals.

Optofluidic Cell Selection from Complex Microbial Communities for Single-Genome Analysis

PubMed Central

Landry, Zachary C.; Giovanonni, Stephen J.; Quake, Stephen R.; Blainey, Paul C.

2013-01-01

Genetic analysis of single cells is emerging as a powerful approach for studies of heterogeneous cell populations. Indeed, the notion of homogeneous cell populations is receding as approaches to resolve genetic and phenotypic variation between single cells are applied throughout the life sciences. A key step in single-cell genomic analysis today is the physical isolation of individual cells from heterogeneous populations, particularly microbial populations, which often exhibit high diversity. Here, we detail the construction and use of instrumentation for optical trapping inside microfluidic devices to select individual cells for analysis by methods including nucleic acid sequencing. This approach has unique advantages for analyses of rare community members, cells with irregular morphologies, small quantity samples, and studies that employ advanced optical microscopy. PMID:24060116

The emerging potential for network analysis to inform precision cancer medicine.

PubMed

Ozturk, Kivilcim; Dow, Michelle; Carlin, Daniel E; Bejar, Rafael; Carter, Hannah

2018-06-14

Precision cancer medicine promises to tailor clinical decisions to patients using genomic information. Indeed, successes of drugs targeting genetic alterations in tumors, such as imatinib that targets BCR-ABL in chronic myelogenous leukemia, have demonstrated the power of this approach. However biological systems are complex, and patients may differ not only by the specific genetic alterations in their tumor, but by more subtle interactions among such alterations. Systems biology and more specifically, network analysis, provides a framework for advancing precision medicine beyond clinical actionability of individual mutations. Here we discuss applications of network analysis to study tumor biology, early methods for N-of-1 tumor genome analysis and the path for such tools to the clinic. Copyright © 2018. Published by Elsevier Ltd.

Study of amyloids using yeast

PubMed Central

Wickner, Reed B.; Kryndushkin, Dmitry; Shewmaker, Frank; McGlinchey, Ryan; Edskes, Herman K.

2012-01-01

Summary Saccharomyces cerevisiae has been a useful model organism in such fields as the cell cycle, regulation of transcription, protein trafficking and cell biology, primarily because of its ease of genetic manipulation. This is no less so in the area of amyloid studies. The endogenous yeast amyloids described to date include prions, infectious proteins (Table 1), and some cell wall proteins (1). and amyloids of humans and a fungal prion have also been studied using the yeast system. Accordingly, the emphasis of this chapter will be on genetic, biochemical, cell biological and physical methods particularly useful in the study of yeast prions and other amyloids studied in yeast. We limit our description of these methods to those aspects which have been most useful in studying yeast prions, citing more detailed expositions in the literature. Volumes on yeast genetics methods (2–4), and on amyloids and prions (5, 6) are useful, and Masison has edited a volume of Methods on “Identification, analysis and characterization of fungal prions” which covers some of this territory (7). We also outline some useful physical methods, pointing the reader to more extensive and authoratative descriptions. PMID:22528100

Endogenous allergens and compositional analysis in the allergenicity assessment of genetically modified plants.

PubMed

Fernandez, A; Mills, E N C; Lovik, M; Spoek, A; Germini, A; Mikalsen, A; Wal, J M

2013-12-01

Allergenicity assessment of genetically modified (GM) plants is one of the key pillars in the safety assessment process of these products. As part of this evaluation, one of the concerns is to assess that unintended effects (e.g. over-expression of endogenous allergens) relevant for the food safety have not occurred due to the genetic modification. Novel technologies are now available and could be used as complementary and/or alternative methods to those based on human sera for the assessment of endogenous allergenicity. In view of these developments and as a step forward in the allergenicity assessment of GM plants, it is recommended that known endogenous allergens are included in the compositional analysis as additional parameters to be measured. Copyright © 2013 Elsevier Ltd. All rights reserved.

Molecular analysis of genetic diversity among vine accessions using DNA markers.

PubMed

da Costa, A F; Teodoro, P E; Bhering, L L; Tardin, F D; Daher, R F; Campos, W F; Viana, A P; Pereira, M G

2017-04-13

Viticulture presents a number of economic and social advantages, such as increasing employment levels and fixing the labor force in rural areas. With the aim of initiating a program of genetic improvement in grapevine from the State University of the state of Rio de Janeiro North Darcy Ribeiro, genetic diversity between 40 genotypes (varieties, rootstock, and species of different subgenera) was evaluated using Random amplified polymorphic DNA (RAPD) molecular markers. We built a matrix of binary data, whereby the presence of a band was assigned as "1" and the absence of a band was assigned as "0." The genetic distance was calculated between pairs of genotypes based on the arithmetic complement from the Jaccard Index. The results revealed the presence of considerable variability in the collection. Analysis of the genetic dissimilarity matrix revealed that the most dissimilar genotypes were Rupestris du Lot and Vitis rotundifolia because they were the most genetically distant (0.5972). The most similar were genotypes 31 (unidentified) and Rupestris du lot, which showed zero distance, confirming the results of field observations. A duplicate was confirmed, consistent with field observations, and a short distance was found between the variety 'Italy' and its mutation, 'Ruby'. The grouping methods used were somewhat concordant.

Genetic Variation and Geographic Differentiation Among Populations of the Nonmigratory Agricultural Pest Oedaleus infernalis (Orthoptera: Acridoidea) in China

PubMed Central

Sun, Wei; Dong, Hui; Gao, Yue-Bo; Su, Qian-Fu; Qian, Hai-Tao; Bai, Hong-Yan; Zhang, Zhu-Ting; Cong, Bin

2015-01-01

The nonmigratory grasshopper Oedaleus infernalis Saussure (Orthoptera : Acridoidea) is an agricultural pest to crops and forage grasses over a wide natural geographical distribution in China. The genetic diversity and genetic variation among 10 geographically separated populations of O. infernalis was assessed using polymerase chain reaction-based molecular markers, including the intersimple sequence repeat and mitochondrial cytochrome oxidase sequences. A high level of genetic diversity was detected among these populations from the intersimple sequence repeat (H: 0.2628, I: 0.4129, Hs: 0.2130) and cytochrome oxidase analyses (Hd: 0.653). There was no obvious geographical structure based on an unweighted pair group method analysis and median-joining network. The values of FST, θII, and Gst estimated in this study are low, and the gene flow is high (Nm > 4). Analysis of the molecular variance suggested that most of the genetic variation occurs within populations, whereas only a small variation takes place between populations. No significant correlation was found between the genetic distance and geographical distance. Overall, our results suggest that the geographical distance plays an unimpeded role in the gene flow among O. infernalis populations. PMID:26496789

Heritability of mandibular cephalometric variables in twins with completed craniofacial growth.

PubMed

Šidlauskas, Mantas; Šalomskienė, Loreta; Andriuškevičiūtė, Irena; Šidlauskienė, Monika; Labanauskas, Žygimantas; Vasiliauskas, Arūnas; Kupčinskas, Limas; Juzėnas, Simonas; Šidlauskas, Antanas

2016-10-01

To determine genetic and environmental impact on mandibular morphology using lateral cephalometric analysis of twins with completed mandibular growth and deoxyribonucleic acid (DNA) based zygosity determination. The 39 cephalometric variables of 141 same gender adult pair of twins were analysed. Zygosity was determined using 15 specific DNA markers and cervical vertebral maturation method was used to assess completion of the mandibular growth. A genetic analysis was performed using maximum likelihood genetic structural equation modelling (GSEM). The genetic heritability estimates of angular variables describing horizontal mandibular position in relationship to cranial base and maxilla were considerably higher than in those describing vertical position. The mandibular skeletal cephalometric variables also showed high heritability estimates with angular measurements being considerably higher than linear ones. Results of this study indicate that the angular measurements representing mandibular skeletal morphology (mandibular form) have greater genetic determination than the linear measurements (mandibular size). The shape and sagittal position of the mandible is under stronger genetic control, than is its size and vertical relationship to cranial base. © The Author 2015. Published by Oxford University Press on behalf of the European Orthodontic Society. All rights reserved. For permissions, please email: journals.permissions@oup.com.

A probabilistic method for testing and estimating selection differences between populations.

PubMed

He, Yungang; Wang, Minxian; Huang, Xin; Li, Ran; Xu, Hongyang; Xu, Shuhua; Jin, Li

2015-12-01

Human populations around the world encounter various environmental challenges and, consequently, develop genetic adaptations to different selection forces. Identifying the differences in natural selection between populations is critical for understanding the roles of specific genetic variants in evolutionary adaptation. Although numerous methods have been developed to detect genetic loci under recent directional selection, a probabilistic solution for testing and quantifying selection differences between populations is lacking. Here we report the development of a probabilistic method for testing and estimating selection differences between populations. By use of a probabilistic model of genetic drift and selection, we showed that logarithm odds ratios of allele frequencies provide estimates of the differences in selection coefficients between populations. The estimates approximate a normal distribution, and variance can be estimated using genome-wide variants. This allows us to quantify differences in selection coefficients and to determine the confidence intervals of the estimate. Our work also revealed the link between genetic association testing and hypothesis testing of selection differences. It therefore supplies a solution for hypothesis testing of selection differences. This method was applied to a genome-wide data analysis of Han and Tibetan populations. The results confirmed that both the EPAS1 and EGLN1 genes are under statistically different selection in Han and Tibetan populations. We further estimated differences in the selection coefficients for genetic variants involved in melanin formation and determined their confidence intervals between continental population groups. Application of the method to empirical data demonstrated the outstanding capability of this novel approach for testing and quantifying differences in natural selection. © 2015 He et al.; Published by Cold Spring Harbor Laboratory Press.

EYE DEVELOPMENT

PubMed Central

Baker, Nicholas E.; Li, Ke; Quiquand, Manon; Ruggiero, Robert; Wang, Lan-Hsin

2014-01-01

The eye has been one of the most intensively studied organs in Drosophila. The wealth of knowledge about its development, as well as the reagents that have been developed, and the fact that the eye is dispensable for survival, also make the eye suitable for genetic interaction studies and genetic screens. This chapter provides a brief overview of the methods developed to image and probe eye development at multiple developmental stages, including live imaging, immunostaining of fixed tissues, in situ hybridizations, and scanning electron microscopy and color photography of adult eyes. Also summarized are genetic approaches that can be performed in the eye, including mosaic analysis and conditional mutation, gene misexpression and knockdown, and forward genetic and modifier screens. PMID:24784530

The GMOseek matrix: a decision support tool for optimizing the detection of genetically modified plants.

PubMed

Block, Annette; Debode, Frédéric; Grohmann, Lutz; Hulin, Julie; Taverniers, Isabel; Kluga, Linda; Barbau-Piednoir, Elodie; Broeders, Sylvia; Huber, Ingrid; Van den Bulcke, Marc; Heinze, Petra; Berben, Gilbert; Busch, Ulrich; Roosens, Nancy; Janssen, Eric; Žel, Jana; Gruden, Kristina; Morisset, Dany

2013-08-22

Since their first commercialization, the diversity of taxa and the genetic composition of transgene sequences in genetically modified plants (GMOs) are constantly increasing. To date, the detection of GMOs and derived products is commonly performed by PCR-based methods targeting specific DNA sequences introduced into the host genome. Information available regarding the GMOs' molecular characterization is dispersed and not appropriately organized. For this reason, GMO testing is very challenging and requires more complex screening strategies and decision making schemes, demanding in return the use of efficient bioinformatics tools relying on reliable information. The GMOseek matrix was built as a comprehensive, online open-access tabulated database which provides a reliable, comprehensive and user-friendly overview of 328 GMO events and 247 different genetic elements (status: 18/07/2013). The GMOseek matrix is aiming to facilitate GMO detection from plant origin at different phases of the analysis. It assists in selecting the targets for a screening analysis, interpreting the screening results, checking the occurrence of a screening element in a group of selected GMOs, identifying gaps in the available pool of GMO detection methods, and designing a decision tree. The GMOseek matrix is an independent database with effective functionalities in a format facilitating transferability to other platforms. Data were collected from all available sources and experimentally tested where detection methods and certified reference materials (CRMs) were available. The GMOseek matrix is currently a unique and very valuable tool with reliable information on GMOs from plant origin and their present genetic elements that enables further development of appropriate strategies for GMO detection. It is flexible enough to be further updated with new information and integrated in different applications and platforms.

The GMOseek matrix: a decision support tool for optimizing the detection of genetically modified plants

PubMed Central

2013-01-01

Background Since their first commercialization, the diversity of taxa and the genetic composition of transgene sequences in genetically modified plants (GMOs) are constantly increasing. To date, the detection of GMOs and derived products is commonly performed by PCR-based methods targeting specific DNA sequences introduced into the host genome. Information available regarding the GMOs’ molecular characterization is dispersed and not appropriately organized. For this reason, GMO testing is very challenging and requires more complex screening strategies and decision making schemes, demanding in return the use of efficient bioinformatics tools relying on reliable information. Description The GMOseek matrix was built as a comprehensive, online open-access tabulated database which provides a reliable, comprehensive and user-friendly overview of 328 GMO events and 247 different genetic elements (status: 18/07/2013). The GMOseek matrix is aiming to facilitate GMO detection from plant origin at different phases of the analysis. It assists in selecting the targets for a screening analysis, interpreting the screening results, checking the occurrence of a screening element in a group of selected GMOs, identifying gaps in the available pool of GMO detection methods, and designing a decision tree. The GMOseek matrix is an independent database with effective functionalities in a format facilitating transferability to other platforms. Data were collected from all available sources and experimentally tested where detection methods and certified reference materials (CRMs) were available. Conclusions The GMOseek matrix is currently a unique and very valuable tool with reliable information on GMOs from plant origin and their present genetic elements that enables further development of appropriate strategies for GMO detection. It is flexible enough to be further updated with new information and integrated in different applications and platforms. PMID:23965170

Direct-to-consumer genetic testing: an assessment of genetic counselors' knowledge and beliefs

PubMed Central

Hock, Kathryn T.; Christensen, Kurt D.; Yashar, Beverly M.; Roberts, J. Scott; Gollust, Sarah E.; Uhlmann, Wendy R.

2013-01-01

Purpose Direct-to-consumer genetic testing is a new means of obtaining genetic testing outside of a traditional clinical setting. This study assesses genetic counselors’ experience, knowledge, and beliefs regarding direct-to-consumer genetic testing for tests that would currently be offered in genetics clinics. Methods Members of the National Society of Genetic Counselors completed a web-administered survey in February 2008. Results Response rate was 36%; the final data analysis included 312 respondents. Eighty-three percent of respondents had two or fewer inquiries about direct-to-consumer genetic testing, and 14% had received requests for test interpretation or discussion. Respondents believed that genetic counselors have a professional obligation to be knowledgeable about direct-to-consumer genetic testing (55%) and interpret results (48%). Fifty-one percent of respondents thought genetic testing should be limited to a clinical setting; 56% agreed direct-to-consumer genetic testing is acceptable if genetic counseling is provided. More than 70% of respondents would definitely or possibly consider direct-to-consumer testing for patients who (1) have concerns about genetic discrimination, (2) want anonymous testing, or (3) have geographic constraints. Conclusions Results indicate that genetic counselors have limited patient experiences with direct-to-consumer genetic testing and are cautiously considering if and under what circumstances this approach should be used PMID:21233722

Understanding the genetic diversity and population structure of yam (Dioscorea alata L.) using microsatellite markers

PubMed Central

Arnau, Gemma; MN, Sheela; Chair, Hana; Lebot, Vincent; K, Abraham; Perrier, Xavier; Petro, Dalila; Penet, Laurent; Pavis, Claudie

2017-01-01

Yams (Dioscorea sp.) are staple food crops for millions of people in tropical and subtropical regions. Dioscorea alata, also known as greater yam, is one of the major cultivated species and most widely distributed throughout the tropics. Despite its economic and cultural importance, very little is known about its origin, diversity and genetics. As a consequence, breeding efforts for resistance to its main disease, anthracnose, have been fairly limited. The objective of this study was to contribute to the understanding of D. alata genetic diversity by genotyping 384 accessions from different geographical regions (South Pacific, Asia, Africa and the Caribbean), using 24 microsatellite markers. Diversity structuration was assessed via Principal Coordinate Analysis, UPGMA analysis and the Bayesian approach implemented in STRUCTURE. Our results revealed the existence of a wide genetic diversity and a significant structuring associated with geographic origin, ploidy levels and morpho-agronomic characteristics. Seventeen major groups of genetically close cultivars have been identified, including eleven groups of diploid cultivars, four groups of triploids and two groups of tetraploids. STRUCTURE revealed the existence of six populations in the diploid genetic pool and a few admixed cultivars. These results will be very useful for rationalizing D. alata genetic resources in breeding programs across different regions and for improving germplasm conservation methods. PMID:28355293

Detecting Coevolution in Mammalian Sperm–Egg Fusion Proteins

PubMed Central

CLAW, KATRINA G.; GEORGE, RENEE D.; SWANSON, WILLIE J.

2018-01-01

SUMMARY Interactions between sperm and egg proteins can occur physically between gamete surface-binding proteins, and genetically between gamete proteins that work in complementary pathways in which they may not physically interact. Physically interacting sperm–egg proteins have been functionally identified in only a few species, and none have been verified within mammals. Candidate genes on both the sperm and egg surfaces exist, but gene deletion studies do not support functional interactions between these sperm–egg proteins; interacting sperm–egg proteins thus remain elusive. Cooperative gamete proteins undergo rapid evolution, and it is predicted that these sperm–egg proteins will also have correlated evolutionary rates due to compensatory changes on both the sperm and egg. To explore potential physical and genetic interactions in sperm–egg proteins, we sequenced four candidate genes from diverse primate species, and used regression and likelihood methods to test for signatures of coevolution between sperm–egg gene pairs. With both methods, we found that the egg protein CD9 coevolves with the sperm protein IZUMO1, suggesting a physical or genetic interaction occurs between them. With regression analysis, we found that CD9 and CRISP2 have correlated rates of evolution, and with likelihood analysis, that CD9 and CRISP1 have correlated rates. This suggests that the different tests may reflect different levels of interaction, be it physical or genetic. Coevolution tests thus provide an exploratory method for detecting potentially interacting sperm–egg protein pairs. PMID:24644026

Detecting coevolution in mammalian sperm-egg fusion proteins.

PubMed

Claw, Katrina G; George, Renee D; Swanson, Willie J

2014-06-01

Interactions between sperm and egg proteins can occur physically between gamete surface-binding proteins, and genetically between gamete proteins that work in complementary pathways in which they may not physically interact. Physically interacting sperm-egg proteins have been functionally identified in only a few species, and none have been verified within mammals. Candidate genes on both the sperm and egg surfaces exist, but gene deletion studies do not support functional interactions between these sperm-egg proteins; interacting sperm-egg proteins thus remain elusive. Cooperative gamete proteins undergo rapid evolution, and it is predicted that these sperm-egg proteins will also have correlated evolutionary rates due to compensatory changes on both the sperm and egg. To explore potential physical and genetic interactions in sperm-egg proteins, we sequenced four candidate genes from diverse primate species, and used regression and likelihood methods to test for signatures of coevolution between sperm-egg gene pairs. With both methods, we found that the egg protein CD9 coevolves with the sperm protein IZUMO1, suggesting a physical or genetic interaction occurs between them. With regression analysis, we found that CD9 and CRISP2 have correlated rates of evolution, and with likelihood analysis, that CD9 and CRISP1 have correlated rates. This suggests that the different tests may reflect different levels of interaction, be it physical or genetic. Coevolution tests thus provide an exploratory method for detecting potentially interacting sperm-egg protein pairs. © 2014 Wiley Periodicals, Inc.

Online health communication about human genetics: perceptions and preferences of internet users.

PubMed

Bernhardt, Jay M; McClain, Jacqueline; Parrott, Roxanne L

2004-12-01

Unprecedented advancements in human genetics research necessitate keeping the public abreast of new information, applications, and implications and the Internet represents an important method of communicating with the public. Our research used cross-sectional self-report survey data collected from a diverse convenience sample of 780 Internet users in two states. Multivariate regression analysis explored the relationships between experiences, perceptions, and preferences for online health and genetics communication. Online health information seeking was associated with previous genetic information seeking, comfort with online genetic communication, perceived risk for genetic abnormality, being female, and having more education. Comfort with online genetics communication was associated with a preference for online genetic information, previous online health and off-line genetics information seeking, having a healthy lifestyle, believing in the positive impact of human genetics research, and being female. Perceiving online health information to be accurate was associated with preferring the Internet for genetics communication, being older, less educated, and perceiving Internet use as anonymous. Preferring online genetics communication to other communication channels was associated with perceiving online health information as accurate, being comfortable receiving online genetics information, having lower intrinsic religiosity, and being male. The implications of findings for Web-based health message design are discussed.

An analysis of mobile genetic elements in three Plasmodium species and their potential impact on the nucleotide composition of the P. falciparum genome.

PubMed

Durand, Pierre M; Oelofse, Andries J; Coetzer, Theresa L

2006-11-04

The completed genome sequences of the malaria parasites P. falciparum, P. y. yoelii and P. vivax have revealed some unusual features. P. falciparum contains the most AT rich genome sequenced so far--over 90% in some regions. In comparison, P. y. yoelii is approximately 77% and P. vivax is approximately 55% AT rich. The evolutionary reasons for these findings are unknown. Mobile genetic elements have a considerable impact on genome evolution but a thorough investigation of these elements in Plasmodium has not been undertaken. We therefore performed a comprehensive genome analysis of these elements and their derivatives in the three Plasmodium species. Whole genome analysis was performed using bioinformatic methods. Forty potential protein encoding sequences with features of transposable elements were identified in P. vivax, eight in P. y. yoelii and only six in P. falciparum. Further investigation of the six open reading frames in P. falciparum revealed that only one is potentially an active mobile genetic element. Most of the open reading frames identified in all three species are hypothetical proteins. Some represent annotated host proteins such as the putative telomerase reverse transcriptase genes in P. y. yoelii and P. falciparum. One of the P. vivax open reading frames identified in this study demonstrates similarity to telomerase reverse transcriptase and we conclude it to be the orthologue of this gene. There is a divergence in the frequencies of mobile genetic elements in the three Plasmodium species investigated. Despite the limitations of whole genome analytical methods, it is tempting to speculate that mobile genetic elements might have been a driving force behind the compositional bias of the P. falciparum genome.

The association between lower educational attainment and depression owing to shared genetic effects? Results in ~25,000 subjects.

PubMed

Peyrot, W J; Lee, S H; Milaneschi, Y; Abdellaoui, A; Byrne, E M; Esko, T; de Geus, E J C; Hemani, G; Hottenga, J J; Kloiber, S; Levinson, D F; Lucae, S; Martin, N G; Medland, S E; Metspalu, A; Milani, L; Noethen, M M; Potash, J B; Rietschel, M; Rietveld, C A; Ripke, S; Shi, J; Willemsen, G; Zhu, Z; Boomsma, D I; Wray, N R; Penninx, B W J H

2015-06-01

An association between lower educational attainment (EA) and an increased risk for depression has been confirmed in various western countries. This study examines whether pleiotropic genetic effects contribute to this association. Therefore, data were analyzed from a total of 9662 major depressive disorder (MDD) cases and 14,949 controls (with no lifetime MDD diagnosis) from the Psychiatric Genomics Consortium with additional Dutch and Estonian data. The association of EA and MDD was assessed with logistic regression in 15,138 individuals indicating a significantly negative association in our sample with an odds ratio for MDD 0.78 (0.75-0.82) per standard deviation increase in EA. With data of 884,105 autosomal common single-nucleotide polymorphisms (SNPs), three methods were applied to test for pleiotropy between MDD and EA: (i) genetic profile risk scores (GPRS) derived from training data for EA (independent meta-analysis on ~120,000 subjects) and MDD (using a 10-fold leave-one-out procedure in the current sample), (ii) bivariate genomic-relationship-matrix restricted maximum likelihood (GREML) and (iii) SNP effect concordance analysis (SECA). With these methods, we found (i) that the EA-GPRS did not predict MDD status, and MDD-GPRS did not predict EA, (ii) a weak negative genetic correlation with bivariate GREML analyses, but this correlation was not consistently significant, (iii) no evidence for concordance of MDD and EA SNP effects with SECA analysis. To conclude, our study confirms an association of lower EA and MDD risk, but this association was not because of measurable pleiotropic genetic effects, which suggests that environmental factors could be involved, for example, socioeconomic status.

Clinical evaluation incorporating a personal genome

PubMed Central

Ashley, Euan A.; Butte, Atul J.; Wheeler, Matthew T.; Chen, Rong; Klein, Teri E.; Dewey, Frederick E.; Dudley, Joel T.; Ormond, Kelly E.; Pavlovic, Aleksandra; Hudgins, Louanne; Gong, Li; Hodges, Laura M.; Berlin, Dorit S.; Thorn, Caroline F.; Sangkuhl, Katrin; Hebert, Joan M.; Woon, Mark; Sagreiya, Hersh; Whaley, Ryan; Morgan, Alexander A.; Pushkarev, Dmitry; Neff, Norma F; Knowles, Joshua W.; Chou, Mike; Thakuria, Joseph; Rosenbaum, Abraham; Zaranek, Alexander Wait; Church, George; Greely, Henry T.; Quake, Stephen R.; Altman, Russ B.

2010-01-01

Background The cost of genomic information has fallen steeply but the path to clinical translation of risk estimates for common variants found in genome wide association studies remains unclear. Since the speed and cost of sequencing complete genomes is rapidly declining, more comprehensive means of analyzing these data in concert with rare variants for genetic risk assessment and individualisation of therapy are required. Here, we present the first integrated analysis of a complete human genome in a clinical context. Methods An individual with a family history of vascular disease and early sudden death was evaluated. Clinical assessment included risk prediction for coronary artery disease, screening for causes of sudden cardiac death, and genetic counselling. Genetic analysis included the development of novel methods for the integration of whole genome sequence data including 2.6 million single nucleotide polymorphisms and 752 copy number variations. The algorithm focused on predicting genetic risk of genes associated with known Mendelian disease, recognised drug responses, and pathogenicity for novel variants. In addition, since integration of risk ratios derived from case control studies is challenging, we estimated posterior probabilities from age and sex appropriate prior probability and likelihood ratios derived for each genotype. In addition, we developed a visualisation approach to account for gene-environment interactions and conditionally dependent risks. Findings We found increased genetic risk for myocardial infarction, type II diabetes and certain cancers. Rare variants in LPA are consistent with the family history of coronary artery disease. Pharmacogenomic analysis suggested a positive response to lipid lowering therapy, likely clopidogrel resistance, and a low initial dosing requirement for warfarin. Many variants of uncertain significance were reported. Interpretation Although challenges remain, our results suggest that whole genome sequencing can yield useful and clinically relevant information for individual patients, especially for those with a strong family history of significant disease. PMID:20435227

Improvement of tissue culture, genetic transformation, and applications of biotechnology to Brassica.

PubMed

Ravanfar, Seyed Ali; Orbovic, Vladimir; Moradpour, Mahdi; Abdul Aziz, Maheran; Karan, Ratna; Wallace, Simon; Parajuli, Saroj

2017-04-01

Development of in vitro plant regeneration method from Brassica explants via organogenesis and somatic embryogenesis is influenced by many factors such as culture environment, culture medium composition, explant sources, and genotypes which are reviewed in this study. An efficient in vitro regeneration system to allow genetic transformation of Brassica is a crucial tool for improving its economical value. Methods to optimize transformation protocols for the efficient introduction of desirable traits, and a comparative analysis of these methods are also reviewed. Hence, binary vectors, selectable marker genes, minimum inhibitory concentration of selection agents, reporter marker genes, preculture media, Agrobacterium concentration and regeneration ability of putative transformants for improvement of Agrobacterium-mediated transformation of Brassica are discussed.

Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data

PubMed Central

2017-01-01

Mapping gene expression as a quantitative trait using whole genome-sequencing and transcriptome analysis allows to discover the functional consequences of genetic variation. We developed a novel method and ultra-fast software Findr for higly accurate causal inference between gene expression traits using cis-regulatory DNA variations as causal anchors, which improves current methods by taking into consideration hidden confounders and weak regulations. Findr outperformed existing methods on the DREAM5 Systems Genetics challenge and on the prediction of microRNA and transcription factor targets in human lymphoblastoid cells, while being nearly a million times faster. Findr is publicly available at https://github.com/lingfeiwang/findr. PMID:28821014

Dew inspired breathing-based detection of genetic point mutation visualized by naked eye

PubMed Central

Xie, Liping; Wang, Tongzhou; Huang, Tianqi; Hou, Wei; Huang, Guoliang; Du, Yanan

2014-01-01

A novel label-free method based on breathing-induced vapor condensation was developed for detection of genetic point mutation. The dew-inspired detection was realized by integration of target-induced DNA ligation with rolling circle amplification (RCA). The vapor condensation induced by breathing transduced the RCA-amplified variances in DNA contents into visible contrast. The image could be recorded by a cell phone for further or even remote analysis. This green assay offers a naked-eye-reading method potentially applied for point-of-care liver cancer diagnosis in resource-limited regions. PMID:25199907

Dew inspired breathing-based detection of genetic point mutation visualized by naked eye

NASA Astrophysics Data System (ADS)

Xie, Liping; Wang, Tongzhou; Huang, Tianqi; Hou, Wei; Huang, Guoliang; Du, Yanan

2014-09-01

A novel label-free method based on breathing-induced vapor condensation was developed for detection of genetic point mutation. The dew-inspired detection was realized by integration of target-induced DNA ligation with rolling circle amplification (RCA). The vapor condensation induced by breathing transduced the RCA-amplified variances in DNA contents into visible contrast. The image could be recorded by a cell phone for further or even remote analysis. This green assay offers a naked-eye-reading method potentially applied for point-of-care liver cancer diagnosis in resource-limited regions.

Dew inspired breathing-based detection of genetic point mutation visualized by naked eye.

PubMed

Xie, Liping; Wang, Tongzhou; Huang, Tianqi; Hou, Wei; Huang, Guoliang; Du, Yanan

2014-09-09

A novel label-free method based on breathing-induced vapor condensation was developed for detection of genetic point mutation. The dew-inspired detection was realized by integration of target-induced DNA ligation with rolling circle amplification (RCA). The vapor condensation induced by breathing transduced the RCA-amplified variances in DNA contents into visible contrast. The image could be recorded by a cell phone for further or even remote analysis. This green assay offers a naked-eye-reading method potentially applied for point-of-care liver cancer diagnosis in resource-limited regions.

Genetic variation in the midcontinental population of sandhill cranes, Grus canadensis.

PubMed

Petersen, Jessica L; Bischof, Richard; Krapu, Gary L; Szalanski, Allen L

2003-02-01

Three subspecies of sandhill crane (Grus canadensis) are recognized in the Midcontinental population, the lesser (Grus c. canadensis), Canadian (G. c. rowani), and greater (G. c. tabida). Blood samples collected on the population's primary spring staging area in Nebraska, U.S.A., were used to resolve the genetic relationship among these subspecies. Phylogenetic analysis of 27 G. canadensis, by DNA sequencing of a 675 bp region of the mtDNA, supports the subspecies designations of G. c. canadensis and G. c. tabida. G. c. rowani individuals were intermediate with each of the other two subspecies. Genetic divergence ranged from 6.5 to 14.5% between G. c. canadensis and G. c. tabida, 0.5 to 6.6% within G. c. canadensis, and 0.1 to 6.0% within G. c. tabida. Sufficient DNA for analysis was obtained from shed feathers indicating a source of genetic material that does not require the capture or sacrifice of the birds. Other genetic markers and methods, including satellite telemetry, are required for obtaining detailed information on crane distributions as needed to establish effective management units for the MCP.

Cluster analysis of Pinus taiwanensis for its ex situ conservation in China.

PubMed

Gao, X; Shi, L; Wu, Z

2015-06-01

Pinus taiwanensis Hayata is one of the most famous sights in the Huangshan Scenic Resort, China, because of its strong adaptability and ability to survive; however, this endemic species is currently under threat in China. Relationships between different P. taiwanensis populations have been well-documented; however, few studies have been conducted on how to protect this rare pine. In the present study, we propose the ex situ conservation of this species using geographical information system (GIS) cluster and genetic diversity analyses. The GIS cluster method was conducted as a preliminary analysis for establishing a sampling site category based on climatic factors. Genetic diversity was analyzed using morphological and genetic traits. By combining geographical information with genetic data, we demonstrate that growing conditions, morphological traits, and the genetic make-up of the population in the Huangshan Scenic Resort were most similar to conditions on Tianmu Mountain. Therefore, we suggest that Tianmu Mountain is the best choice for the ex situ conservation of P. taiwanensis. Our results provide a molecular basis for the sustainable management, utilization, and conservation of this species in Huangshan Scenic Resort.

Optimized hyperspectral band selection using hybrid genetic algorithm and gravitational search algorithm

NASA Astrophysics Data System (ADS)

Zhang, Aizhu; Sun, Genyun; Wang, Zhenjie

2015-12-01

The serious information redundancy in hyperspectral images (HIs) cannot contribute to the data analysis accuracy, instead it require expensive computational resources. Consequently, to identify the most useful and valuable information from the HIs, thereby improve the accuracy of data analysis, this paper proposed a novel hyperspectral band selection method using the hybrid genetic algorithm and gravitational search algorithm (GA-GSA). In the proposed method, the GA-GSA is mapped to the binary space at first. Then, the accuracy of the support vector machine (SVM) classifier and the number of selected spectral bands are utilized to measure the discriminative capability of the band subset. Finally, the band subset with the smallest number of spectral bands as well as covers the most useful and valuable information is obtained. To verify the effectiveness of the proposed method, studies conducted on an AVIRIS image against two recently proposed state-of-the-art GSA variants are presented. The experimental results revealed the superiority of the proposed method and indicated that the method can indeed considerably reduce data storage costs and efficiently identify the band subset with stable and high classification precision.

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

Genetic Analysis of Aspartate Aminotransferase Isozymes from Hybrids between DROSOPHILA MELANOGASTER and DROSOPHILA SIMULANS and Mutagen-Induced Isozyme Variants

PubMed Central

Grell, E. H.

1976-01-01

The aspartate aminotransferases (designated GOT1 and GOT2) are two enzymes of Drosophila melanogaster for which naturally occurring electrophoretic variants were not found. There is an electrophoretic difference between D. melanogaster and D. simulans. Since the F 1 hybrid offspring of these species are sterile, a genetic analysis of the ordinary type cannot be done on differences between the two species. A method was devised to make "partial hybrids" in which one chromosome arm is homozygous for melanogaster genes in an otherwise hybrid background. By using this method, Got1 was localized to 2R and Got2 to 2L. Once a gene can be assigned to a chromosome, it may be followed in crossing schemes and mutations from mutagen treatments may be looked for. At the locus of Got1 a mutation with low activity was recovered and designated Got1lo. It was located at a genetic map position of 75 on 2R. A Got2 mutant with a greater migration to the anode was recovered and designated Got2 J. It was located at a genetic map position of 3.0, and in the salivary chromosome was between 22B1 and 22B4 inclusive. PMID:823072

The Effect of Pleistocene Climate Fluctuations on Distribution of European Abalone (Haliotis tuberculata), Revealed by Combined Mitochondrial and Nuclear Marker Analyses.

PubMed

Roussel, Valérie; Van Wormhoudt, Alain

2017-04-01

The genetic differentiation among the populations of the European abalone Haliotis tuberculata was investigated using different markers to better understand the evolutionary history and exchanges between populations. Three markers were used: mitochondrial cytochrome oxidase I (COI), the sperm lysin nuclear gene, and eight nuclear microsatellites. These markers present different characteristics concerning mutation rate and inheritance, which provided complementary information about abalone history and gene diversity. Genetic diversity and relationships among subspecies were calculated from a sample of approximately 500 individuals, collected from 17 different locations in the north-eastern Atlantic Ocean, Macaronesia, and Mediterranean Sea. COI marker was used to explore the phylogeny of the species with a network analysis and two phylogenetic methods. The analysis revealed 18 major haplotypes grouped into two distinct clades with a pairwise sequence divergence up to 3.5 %. These clades do not correspond to subspecies but revealed many contacts along Atlantic coast during the Pleistocene interglaciations. The sperm lysin gene analysis separated two different subtaxa: one associated to Macaronesian islands, and the other to all other populations. Moreover, a small population of the northern subtaxon was isolated in the Adriatic Sea-probably before the separation of the two lineages-and evolved independently. Microsatellites were analyzed by different genetics methods, including the Bayesian clustering method and migration patterns analysis. It revealed genetically distinct microsatellite patterns among populations from Mediterranean Sea, Brittany and Normandy, Morocco, and Canary and Balearic islands. Gene flow is asymmetric among the regions; the Azores and the Canary Islands are particularly isolated and have low effective population sizes. Our results support the hypothesis that climate changes since the Pleistocene glaciations have played a major role in the geographic distribution of the European abalone. Traces of these events related to maternal inheritance were shown on COI marker.

Neurosphere and adherent culture conditions are equivalent for malignant glioma stem cell lines.

PubMed

Rahman, Maryam; Reyner, Karina; Deleyrolle, Loic; Millette, Sebastien; Azari, Hassan; Day, Bryan W; Stringer, Brett W; Boyd, Andrew W; Johns, Terrance G; Blot, Vincent; Duggal, Rohit; Reynolds, Brent A

2015-03-01

Certain limitations of the neurosphere assay (NSA) have resulted in a search for alternative culture techniques for brain tumor-initiating cells (TICs). Recently, reports have described growing glioblastoma (GBM) TICs as a monolayer using laminin. We performed a side-by-side analysis of the NSA and laminin (adherent) culture conditions to compare the growth and expansion of GBM TICs. GBM cells were grown using the NSA and adherent culture conditions. Comparisons were made using growth in culture, apoptosis assays, protein expression, limiting dilution clonal frequency assay, genetic affymetrix analysis, and tumorigenicity in vivo. In vitro expansion curves for the NSA and adherent culture conditions were virtually identical (P=0.24) and the clonogenic frequencies (5.2% for NSA vs. 5.0% for laminin, P=0.9) were similar as well. Likewise, markers of differentiation (glial fibrillary acidic protein and beta tubulin III) and proliferation (Ki67 and MCM2) revealed no statistical difference between the sphere and attachment methods. Several different methods were used to determine the numbers of dead or dying cells (trypan blue, DiIC, caspase-3, and annexin V) with none of the assays noting a meaningful variance between the two methods. In addition, genetic expression analysis with microarrays revealed no significant differences between the two groups. Finally, glioma cells derived from both methods of expansion formed large invasive tumors exhibiting GBM features when implanted in immune-compromised animals. A detailed functional, protein and genetic characterization of human GBM cells cultured in serum-free defined conditions demonstrated no statistically meaningful differences when grown using sphere (NSA) or adherent conditions. Hence, both methods are functionally equivalent and remain suitable options for expanding primary high-grade gliomas in tissue culture.

Neurosphere and adherent culture conditions are equivalent for malignant glioma stem cell lines

PubMed Central

Reyner, Karina; Deleyrolle, Loic; Millette, Sebastien; Azari, Hassan; Day, Bryan W.; Stringer, Brett W.; Boyd, Andrew W.; Johns, Terrance G.; Blot, Vincent; Duggal, Rohit; Reynolds, Brent A.

2015-01-01

Certain limitations of the neurosphere assay (NSA) have resulted in a search for alternative culture techniques for brain tumor-initiating cells (TICs). Recently, reports have described growing glioblastoma (GBM) TICs as a monolayer using laminin. We performed a side-by-side analysis of the NSA and laminin (adherent) culture conditions to compare the growth and expansion of GBM TICs. GBM cells were grown using the NSA and adherent culture conditions. Comparisons were made using growth in culture, apoptosis assays, protein expression, limiting dilution clonal frequency assay, genetic affymetrix analysis, and tumorigenicity in vivo. In vitro expansion curves for the NSA and adherent culture conditions were virtually identical (P=0.24) and the clonogenic frequencies (5.2% for NSA vs. 5.0% for laminin, P=0.9) were similar as well. Likewise, markers of differentiation (glial fibrillary acidic protein and beta tubulin III) and proliferation (Ki67 and MCM2) revealed no statistical difference between the sphere and attachment methods. Several different methods were used to determine the numbers of dead or dying cells (trypan blue, DiIC, caspase-3, and annexin V) with none of the assays noting a meaningful variance between the two methods. In addition, genetic expression analysis with microarrays revealed no significant differences between the two groups. Finally, glioma cells derived from both methods of expansion formed large invasive tumors exhibiting GBM features when implanted in immune-compromised animals. A detailed functional, protein and genetic characterization of human GBM cells cultured in serum-free defined conditions demonstrated no statistically meaningful differences when grown using sphere (NSA) or adherent conditions. Hence, both methods are functionally equivalent and remain suitable options for expanding primary high-grade gliomas in tissue culture. PMID:25806119

Landscape genomics and pathway analysis to understand genetic adaptation of South African indigenous goat populations.

PubMed

Mdladla, K; Dzomba, E F; Muchadeyi, F C

2018-04-01

In Africa, extensively raised livestock populations in most smallholder farming communities are exposed to harsh and heterogeneous climatic conditions and disease pathogens that they adapt to in order to survive. Majority of these livestock species, including goats, are of non-descript and uncharacterized breeds and their response to natural selection presented by heterogeneous environments is still unresolved. This study investigated genetic diversity and its association with environmental and geographic conditions in 194 South African indigenous goats from different geographic locations genotyped on the Illumina goat SNP50K panel. Population structure analysis revealed a homogeneous genetic cluster of the Tankwa goats, restricted to the Northern Cape province. Overall, the Boer, Kalahari Red, and Savanna showed a wide geographic spread of shared genetic components, whereas the village ecotypes revealed a longitudinal distribution. The relative importance of environmental factors on genetic variation of goat populations was assessed using redundancy analysis (RDA). Climatic and geographic variables explained 22% of the total variation while climatic variables alone accounted for 17% of the diversity. Geographic variables solitarily explained 1% of the total variation. The first axis (Model I) of the RDA analysis revealed 329 outlier SNPs. Landscape genomic approaches of spatial analysis method (SAM) identified a total of 843 (1.75%) SNPs, while latent factor mixed models (LFMM) identified 714 (1.48%) SNPs significantly associated with environmental variables. Significant markers were within genes involved in biological functions potentially important for environmental adaptation. Overall, the study suggested environmental factors to have some effect in shaping the genetic variation of South African indigenous goat populations. Loci observed to be significant and under selection may be responsible for the adaption of the goat populations to local production systems.

Genetic diversity and population structure of Chinese natural bermudagrass [Cynodon dactylon (L.) Pers.] germplasm based on SRAP markers.

PubMed

Zheng, Yiqi; Xu, Shaojun; Liu, Jing; Zhao, Yan; Liu, Jianxiu

2017-01-01

Bermudagrass [Cynodon dactylon (L.) Pers.], an important turfgrass used in public parks, home lawns, golf courses and sports fields, is widely distributed in China. In the present study, sequence-related amplified polymorphism (SRAP) markers were used to assess genetic diversity and population structure among 157 indigenous bermudagrass genotypes from 20 provinces in China. The application of 26 SRAP primer pairs produced 340 bands, of which 328 (96.58%) were polymorphic. The polymorphic information content (PIC) ranged from 0.36 to 0.49 with a mean of 0.44. Genetic distance coefficients among accessions ranged from 0.04 to 0.61, with an average of 0.32. The results of STRUCTURE analysis suggested that 157 bermudagrass accessions can be grouped into three subpopulations. Moreover, according to clustering based on the unweighted pair-group method of arithmetic averages (UPGMA), accessions were divided into three major clusters. The UPGMA dendrogram revealed that accessions from identical or adjacent areas were generally, but not entirely, clustered into the same cluster. Comparison of the UPGMA dendrogram and the Bayesian STRUCTURE analysis showed general agreement between the population subdivisions and the genetic relationships among accessions. Principal coordinate analysis (PCoA) with SRAP markers revealed a similar grouping of accessions to the UPGMA dendrogram and STRUCTUE analysis. Analysis of molecular variance (AMOVA) indicated that 18% of total molecular variance was attributed to diversity among subpopulations, while 82% of variance was associated with differences within subpopulations. Our study represents the most comprehensive investigation of the genetic diversity and population structure of bermudagrass in China to date, and provides valuable information for the germplasm collection, genetic improvement, and systematic utilization of bermudagrass.

Genetic diversity and population structure of Chinese natural bermudagrass [Cynodon dactylon (L.) Pers.] germplasm based on SRAP markers

PubMed Central

Xu, Shaojun; Liu, Jing; Zhao, Yan; Liu, Jianxiu

2017-01-01

Bermudagrass [Cynodon dactylon (L.) Pers.], an important turfgrass used in public parks, home lawns, golf courses and sports fields, is widely distributed in China. In the present study, sequence-related amplified polymorphism (SRAP) markers were used to assess genetic diversity and population structure among 157 indigenous bermudagrass genotypes from 20 provinces in China. The application of 26 SRAP primer pairs produced 340 bands, of which 328 (96.58%) were polymorphic. The polymorphic information content (PIC) ranged from 0.36 to 0.49 with a mean of 0.44. Genetic distance coefficients among accessions ranged from 0.04 to 0.61, with an average of 0.32. The results of STRUCTURE analysis suggested that 157 bermudagrass accessions can be grouped into three subpopulations. Moreover, according to clustering based on the unweighted pair-group method of arithmetic averages (UPGMA), accessions were divided into three major clusters. The UPGMA dendrogram revealed that accessions from identical or adjacent areas were generally, but not entirely, clustered into the same cluster. Comparison of the UPGMA dendrogram and the Bayesian STRUCTURE analysis showed general agreement between the population subdivisions and the genetic relationships among accessions. Principal coordinate analysis (PCoA) with SRAP markers revealed a similar grouping of accessions to the UPGMA dendrogram and STRUCTUE analysis. Analysis of molecular variance (AMOVA) indicated that 18% of total molecular variance was attributed to diversity among subpopulations, while 82% of variance was associated with differences within subpopulations. Our study represents the most comprehensive investigation of the genetic diversity and population structure of bermudagrass in China to date, and provides valuable information for the germplasm collection, genetic improvement, and systematic utilization of bermudagrass. PMID:28493962

Genetic Candidate Variants in Two Multigenerational Families with Childhood Apraxia of Speech

PubMed Central

Wijsman, Ellen M.; Nato, Alejandro Q.; Matsushita, Mark M.; Chapman, Kathy L.; Stanaway, Ian B.; Wolff, John; Oda, Kaori; Gabo, Virginia B.; Raskind, Wendy H.

2016-01-01

Childhood apraxia of speech (CAS) is a severe and socially debilitating form of speech sound disorder with suspected genetic involvement, but the genetic etiology is not yet well understood. Very few known or putative causal genes have been identified to date, e.g., FOXP2 and BCL11A. Building a knowledge base of the genetic etiology of CAS will make it possible to identify infants at genetic risk and motivate the development of effective very early intervention programs. We investigated the genetic etiology of CAS in two large multigenerational families with familial CAS. Complementary genomic methods included Markov chain Monte Carlo linkage analysis, copy-number analysis, identity-by-descent sharing, and exome sequencing with variant filtering. No overlaps in regions with positive evidence of linkage between the two families were found. In one family, linkage analysis detected two chromosomal regions of interest, 5p15.1-p14.1, and 17p13.1-q11.1, inherited separately from the two founders. Single-point linkage analysis of selected variants identified CDH18 as a primary gene of interest and additionally, MYO10, NIPBL, GLP2R, NCOR1, FLCN, SMCR8, NEK8, and ANKRD12, possibly with additive effects. Linkage analysis in the second family detected five regions with LOD scores approaching the highest values possible in the family. A gene of interest was C4orf21 (ZGRF1) on 4q25-q28.2. Evidence for previously described causal copy-number variations and validated or suspected genes was not found. Results are consistent with a heterogeneous CAS etiology, as is expected in many neurogenic disorders. Future studies will investigate genome variants in these and other families with CAS. PMID:27120335

Analysis of the Multi Strategy Goal Programming for Micro-Grid Based on Dynamic ant Genetic Algorithm

NASA Astrophysics Data System (ADS)

Qiu, J. P.; Niu, D. X.

Micro-grid is one of the key technologies of the future energy supplies. Take economic planning. reliability, and environmental protection of micro grid as a basis for the analysis of multi-strategy objective programming problems for micro grid which contains wind power, solar power, and battery and micro gas turbine. Establish the mathematical model of each power generation characteristics and energy dissipation. and change micro grid planning multi-objective function under different operating strategies to a single objective model based on AHP method. Example analysis shows that in combination with dynamic ant mixed genetic algorithm can get the optimal power output of this model.

Genetic diversity and relationship analysis of Gossypium arboreum accessions.

PubMed

Liu, F; Zhou, Z L; Wang, C Y; Wang, Y H; Cai, X Y; Wang, X X; Zhang, Z S; Wang, K B

2015-11-19

Simple sequence repeat techniques were used to identify the genetic diversity of 101 Gossypium arboreum accessions collected from India, Vietnam, and the southwest of China (Guizhou, Guangxi, and Yunnan provinces). Twenty-six pairs of SSR primers produced a total of 103 polymorphic loci with an average of 3.96 polymorphic loci per primer. The average of the effective number of alleles, Nei's gene diversity, and Shannon's information index were 0.59, 0.2835, and 0.4361, respectively. The diversity varied among different geographic regions. The result of principal component analysis was consistent with that of unweighted pair group method with arithmetic mean clustering analysis. The 101 G. arboreum accessions were clustered into 2 groups.

High Resolution Melt analysis for mutation screening in PKD1 and PKD2

PubMed Central

2011-01-01

Background Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary kidney disorder. It is characterized by focal development and progressive enlargement of renal cysts leading to end-stage renal disease. PKD1 and PKD2 have been implicated in ADPKD pathogenesis but genetic features and the size of PKD1 make genetic diagnosis tedious. Methods We aim to prove that high resolution melt analysis (HRM), a recent technique in molecular biology, can facilitate molecular diagnosis of ADPKD. We screened for mutations in PKD1 and PKD2 with HRM in 37 unrelated patients with ADPKD. Results We identified 440 sequence variants in the 37 patients. One hundred and thirty eight were different. We found 28 pathogenic mutations (25 in PKD1 and 3 in PKD2 ) within 28 different patients, which is a diagnosis rate of 75% consistent with literature mean direct sequencing diagnosis rate. We describe 52 new sequence variants in PKD1 and two in PKD2. Conclusion HRM analysis is a sensitive and specific method for molecular diagnosis of ADPKD. HRM analysis is also costless and time sparing. Thus, this method is efficient and might be used for mutation pre-screening in ADPKD genes. PMID:22008521

A combined analysis of D22S278 marker alleles in affected sib-pairs: Support for a susceptibility locus for schizophrenia at chromosome 22q12

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

Gill, M.; Vallada, H.; Collier, D.

1996-02-16

Several groups have reported weak evidence for linkage between schizophrenia and genetic markers located on chromosome 22q using the lod score method of analysis. However these findings involved different genetic markers and methods of analysis, and so were not directly comparable. To resolve this issue we have performed a combined analysis of genotypic data from the marker D22S278 in multiply affected schizophrenic families derived from 11 independent research groups worldwide. This marker was chosen because it showed maximum evidence for linkage in three independent datasets. Using the affected sib-pair method as implemented by the program ESPA, the combined dataset showedmore » 252 alleles shared compared with 188 alleles not shared (chi-square 9.31, 1df, P = 0.001) where parental genotype data was completely known. When sib-pairs for whom parental data was assigned according to probability were included the number of alleles shared was 514.1 compared with 437.8 not shared (chi-square 6.12, 1df, P = 0.006). Similar results were obtained when a likelihood ratio method for sib-pair analysis was used. These results indicate that there may be a susceptibility locus for schizophrenia at 22q12. 27 refs., 3 tabs.« less

The historical role of species from the Solanaceae plant family in genetic research.

PubMed

Gebhardt, Christiane

2016-12-01

This article evaluates the main contributions of tomato, tobacco, petunia, potato, pepper and eggplant to classical and molecular plant genetics and genomics since the beginning of the twentieth century. Species from the Solanaceae family form integral parts of human civilizations as food sources and drugs since thousands of years, and, more recently, as ornamentals. Some Solanaceous species were subjects of classical and molecular genetic research over the last 100 years. The tomato was one of the principal models in twentieth century classical genetics and a pacemaker of genome analysis in plants including molecular linkage maps, positional cloning of disease resistance genes and quantitative trait loci (QTL). Besides that, tomato is the model for the genetics of fruit development and composition. Tobacco was the major model used to establish the principals and methods of plant somatic cell genetics including in vitro propagation of cells and tissues, totipotency of somatic cells, doubled haploid production and genetic transformation. Petunia was a model for elucidating the biochemical and genetic basis of flower color and development. The cultivated potato is the economically most important Solanaceous plant and ranks third after wheat and rice as one of the world's great food crops. Potato is the model for studying the genetic basis of tuber development. Molecular genetics and genomics of potato, in particular association genetics, made valuable contributions to the genetic dissection of complex agronomic traits and the development of diagnostic markers for breeding applications. Pepper and eggplant are horticultural crops of worldwide relevance. Genetic and genomic research in pepper and eggplant mostly followed the tomato model. Comparative genome analysis of tomato, potato, pepper and eggplant contributed to the understanding of plant genome evolution.

Genetic Determinants of Thrombin Generation and Their Relation to Venous Thrombosis: Results from the GAIT-2 Project

PubMed Central

Martin-Fernandez, Laura; Ziyatdinov, Andrey; Carrasco, Marina; Millon, Juan Antonio; Martinez-Perez, Angel; Vilalta, Noelia; Brunel, Helena; Font, Montserrat; Hamsten, Anders; Souto, Juan Carlos; Soria, José Manuel

2016-01-01

Background Venous thromboembolism (VTE) is a common disease where known genetic risk factors explain only a small portion of the genetic variance. Then, the analysis of intermediate phenotypes, such as thrombin generation assay, can be used to identify novel genetic risk factors that contribute to VTE. Objectives To investigate the genetic basis of distinct quantitative phenotypes of thrombin generation and its relationship to the risk of VTE. Patients/Methods Lag time, thrombin peak and endogenous thrombin potential (ETP) were measured in the families of the Genetic Analysis of Idiopathic Thrombophilia 2 (GAIT-2) Project. This sample consisted of 935 individuals in 35 extended families selected through a proband with idiopathic thrombophilia. We performed also genome wide association studies (GWAS) with thrombin generation phenotypes. Results The results showed that 67% of the variation in the risk of VTE is attributable to genetic factors. The heritabilities of lag time, thrombin peak and ETP were 49%, 54% and 52%, respectively. More importantly, we demonstrated also the existence of positive genetic correlations between thrombin peak or ETP and the risk of VTE. Moreover, the major genetic determinant of thrombin generation was the F2 gene. However, other suggestive signals were observed. Conclusions The thrombin generation phenotypes are strongly genetically determined. The thrombin peak and ETP are significantly genetically correlated with the risk of VTE. In addition, F2 was identified as a major determinant of thrombin generation. We reported suggestive signals that might increase our knowledge to explain the variability of this important phenotype. Validation and functional studies are required to confirm GWAS results. PMID:26784699

Tendency for interlaboratory precision in the GMO analysis method based on real-time PCR.

PubMed

Kodama, Takashi; Kurosawa, Yasunori; Kitta, Kazumi; Naito, Shigehiro

2010-01-01

The Horwitz curve estimates interlaboratory precision as a function only of concentration, and is frequently used as a method performance criterion in food analysis with chemical methods. The quantitative biochemical methods based on real-time PCR require an analogous criterion to progressively promote method validation. We analyzed the tendency of precision using a simplex real-time PCR technique in 53 collaborative studies of seven genetically modified (GM) crops. Reproducibility standard deviation (SR) and repeatability standard deviation (Sr) of the genetically modified organism (GMO) amount (%) was more or less independent of GM crops (i.e., maize, soybean, cotton, oilseed rape, potato, sugar beet, and rice) and evaluation procedure steps. Some studies evaluated whole steps consisting of DNA extraction and PCR quantitation, whereas others focused only on the PCR quantitation step by using DNA extraction solutions. Therefore, SR and Sr for GMO amount (%) are functions only of concentration similar to the Horwitz curve. We proposed S(R) = 0.1971C 0.8685 and S(r) = 0.1478C 0.8424, where C is the GMO amount (%). We also proposed a method performance index in GMO quantitative methods that is analogous to the Horwitz Ratio.

Genetic diversity of Y-short tandem repeats in Chinese native cattle breeds.

PubMed

Xin, Y P; Zan, L S; Liu, Y F; Tian, W Q; Wang, H B; Cheng, G; Li, A N; Yang, W C

2014-11-14

The aim of this study is to use Y-chromosome gene polymorphism method to investigate regional differences in genetic variation and population evolution history of the Chinese native cattle breeds. Six Y-chromosome short tandem repeat (Y-STR) loci (UMN0929, UMN0108, UMN0920, INRA124, UMN2404, and UMN0103) were analyzed using 1016 healthy and heterogenetic males and 90 females of 9 native cattle breeds (Qinchuan, Jinnan, Zaosheng, Luxi, Nanyang, Jiaxian, Dabieshan, Yanbian, and Menggu) in China. Allele frequency and gene diversity were calculated for the various populations. The results indicated that Y-STRs in the 6 loci have polymorphisms and genetic diversity in Chinese cattle populations. The genetic diversity analysis revealed that the Chinese cattle populations have a close genetic relationship. The analysis of INRA124, UMN2404, and UMN0103 loci revealed the original history of Chinese cattle because of which cattle belonging to Bos taurus or Bos indicus could be determined. Interestingly, a declining zebu introgression was displayed from South to North and from East to West in the Chinese geographical distribution, which implied that cattle population from various regions of China had been subjected to somewhat different evolutionary history. This conclusion supported other evidences such as earlier archaeological, historical research, and blood protein polymorphism analysis.

The genetic regulatory network centered on Pto-Wuschela and its targets involved in wood formation revealed by association studies.

PubMed

Yang, Xiaohui; Wei, Zunzheng; Du, Qingzhang; Chen, Jinhui; Wang, Qingshi; Quan, Mingyang; Song, Yuepeng; Xie, Jianbo; Zhang, Deqiang

2015-11-09

Transcription factors (TFs) regulate gene expression and can strongly affect phenotypes. However, few studies have examined TF variants and TF interactions with their targets in plants. Here, we used genetic association in 435 unrelated individuals of Populus tomentosa to explore the variants in Pto-Wuschela and its targets to decipher the genetic regulatory network of Pto-Wuschela. Our bioinformatics and co-expression analysis identified 53 genes with the motif TCACGTGA as putative targets of Pto-Wuschela. Single-marker association analysis showed that Pto-Wuschela was associated with wood properties, which is in agreement with the observation that it has higher expression in stem vascular tissues in Populus. Also, SNPs in the 53 targets were associated with growth or wood properties under additive or dominance effects, suggesting these genes and Pto-Wuschela may act in the same genetic pathways that affect variation in these quantitative traits. Epistasis analysis indicated that 75.5% of these genes directly or indirectly interacted Pto-Wuschela, revealing the coordinated genetic regulatory network formed by Pto-Wuschela and its targets. Thus, our study provides an alternative method for dissection of the interactions between a TF and its targets, which will strength our understanding of the regulatory roles of TFs in complex traits in plants.

An advanced analysis method of initial orbit determination with too short arc data

NASA Astrophysics Data System (ADS)

Li, Binzhe; Fang, Li

2018-02-01

This paper studies the initial orbit determination (IOD) based on space-based angle measurement. Commonly, these space-based observations have short durations. As a result, classical initial orbit determination algorithms give poor results, such as Laplace methods and Gauss methods. In this paper, an advanced analysis method of initial orbit determination is developed for space-based observations. The admissible region and triangulation are introduced in the method. Genetic algorithm is also used for adding some constraints of parameters. Simulation results show that the algorithm can successfully complete the initial orbit determination.

Genetic algorithm to optimize the design of main combustor and gas generator in liquid rocket engines

NASA Astrophysics Data System (ADS)

Son, Min; Ko, Sangho; Koo, Jaye

2014-06-01

A genetic algorithm was used to develop optimal design methods for the regenerative cooled combustor and fuel-rich gas generator of a liquid rocket engine. For the combustor design, a chemical equilibrium analysis was applied, and the profile was calculated using Rao's method. One-dimensional heat transfer was assumed along the profile, and cooling channels were designed. For the gas-generator design, non-equilibrium properties were derived from a counterflow analysis, and a vaporization model for the fuel droplet was adopted to calculate residence time. Finally, a genetic algorithm was adopted to optimize the designs. The combustor and gas generator were optimally designed for 30-tonf, 75-tonf, and 150-tonf engines. The optimized combustors demonstrated superior design characteristics when compared with previous non-optimized results. Wall temperatures at the nozzle throat were optimized to satisfy the requirement of 800 K, and specific impulses were maximized. In addition, the target turbine power and a burned-gas temperature of 1000 K were obtained from the optimized gas-generator design.

The efficacy of wire and glue hair snares in identifying mesocarnivores

Treesearch

William J. Zielinski; Fredrick V. Schlexer; Kristine L. Pilgrim; Michael K. Schwartz

2006-01-01

Track plates and cameras are proven methods for detecting and identifying fishers (Martes pennant) and other mesocarnivores. But these methods are inadequate to achieve demographic and population-monitoring objectives that require identifying sex and individuals. Although noninvasive collection of biological material for genetic analysis (i.e.,...

An improved partial least-squares regression method for Raman spectroscopy

NASA Astrophysics Data System (ADS)

Momenpour Tehran Monfared, Ali; Anis, Hanan

2017-10-01

It is known that the performance of partial least-squares (PLS) regression analysis can be improved using the backward variable selection method (BVSPLS). In this paper, we further improve the BVSPLS based on a novel selection mechanism. The proposed method is based on sorting the weighted regression coefficients, and then the importance of each variable of the sorted list is evaluated using root mean square errors of prediction (RMSEP) criterion in each iteration step. Our Improved BVSPLS (IBVSPLS) method has been applied to leukemia and heparin data sets and led to an improvement in limit of detection of Raman biosensing ranged from 10% to 43% compared to PLS. Our IBVSPLS was also compared to the jack-knifing (simpler) and Genetic Algorithm (more complex) methods. Our method was consistently better than the jack-knifing method and showed either a similar or a better performance compared to the genetic algorithm.

A robust multifactor dimensionality reduction method for detecting gene-gene interactions with application to the genetic analysis of bladder cancer susceptibility

PubMed Central

Gui, Jiang; Andrew, Angeline S.; Andrews, Peter; Nelson, Heather M.; Kelsey, Karl T.; Karagas, Margaret R.; Moore, Jason H.

2010-01-01

A central goal of human genetics is to identify and characterize susceptibility genes for common complex human diseases. An important challenge in this endeavor is the modeling of gene-gene interaction or epistasis that can result in non-additivity of genetic effects. The multifactor dimensionality reduction (MDR) method was developed as machine learning alternative to parametric logistic regression for detecting interactions in absence of significant marginal effects. The goal of MDR is to reduce the dimensionality inherent in modeling combinations of polymorphisms using a computational approach called constructive induction. Here, we propose a Robust Multifactor Dimensionality Reduction (RMDR) method that performs constructive induction using a Fisher’s Exact Test rather than a predetermined threshold. The advantage of this approach is that only those genotype combinations that are determined to be statistically significant are considered in the MDR analysis. We use two simulation studies to demonstrate that this approach will increase the success rate of MDR when there are only a few genotype combinations that are significantly associated with case-control status. We show that there is no loss of success rate when this is not the case. We then apply the RMDR method to the detection of gene-gene interactions in genotype data from a population-based study of bladder cancer in New Hampshire. PMID:21091664

Phylogeography and genetic structure of a Tertiary relict tree species, Tapiscia sinensis (Tapisciaceae): implications for conservation

PubMed Central

Zhang, Jinju; Li, Zuozhou; Fritsch, Peter W.; Tian, Hua; Yang, Aihong; Yao, Xiaohong

2015-01-01

Background and Aims The phylogeography of plant species in sub-tropical China remains largely unclear. This study used Tapiscia sinensis, an endemic and endangered tree species widely but disjunctly distributed in sub-tropical China, as a model to reveal the patterns of genetic diversity and phylogeographical history of Tertiary relict plant species in this region. The implications of the results are discussed in relation to its conservation management. Methods Samples were taken from 24 populations covering the natural geographical distribution of T. sinensis. Genetic structure was investigated by analysis of molecular variance (AMOVA) and spatial analysis of molecular variance (SAMOVA). Phylogenetic relationships among haplotypes were constructed with maximum parsimony and haplotype network methods. Historical population expansion events were tested with pairwise mismatch distribution analysis and neutrality tests. Species potential range was deduced by ecological niche modelling (ENM). Key Results A low level of genetic diversity was detected at the population level. A high level of genetic differentiation and a significant phylogeographical structure were revealed. The mean divergence time of the haplotypes was approx. 1·33 million years ago. Recent range expansion in this species is suggested by a star-like haplotype network and by the results from the mismatch distribution analysis and neutrality tests. Conclusions Climatic oscillations during the Pleistocene have had pronounced effects on the extant distribution of Tapiscia relative to the Last Glacial Maximum (LGM). Spatial patterns of molecular variation and ENM suggest that T. sinensis may have retreated in south-western and central China and colonized eastern China prior to the LGM. Multiple montane refugia for T. sinense existing during the LGM are inferred in central and western China. The populations adjacent to or within these refugia of T. sinense should be given high priority in the development of conservation policies and management strategies for this endangered species. PMID:26187222

Construction of high-quality recombination maps with low-coverage genomic sequencing for joint linkage analysis in maize

USDA-ARS?s Scientific Manuscript database

A genome-wide association study (GWAS) is the foremost strategy used for finding genes that control human diseases and agriculturally important traits, but it often reports false positives. In contrast, its complementary method, linkage analysis, provides direct genetic confirmation, but with limite...

[A survey of willingness about genetic counseling and tests in patients of epithelial ovarian cancer].

PubMed

Li, L; Qiu, L; Wu, M

2017-11-21

Objective: To analyze patients' tendency towards genetics counseling and tests based on a prospective cohort study on hereditary ovarian cancer. Methods: From February 2017 to June 2017, among 220 cases of epithelial ovarian cancer in Peking Union Medical College Hospital, we collected epidemiological, pathological and tendency towards genetics counseling and tests via medical records and questionnaire.All patients would get education about hereditary ovarian cancer by pamphlets and WeChat.If they would receive further counseling, a face to face interview and tests will be given. Results: Among all 220 patients, 10 (4.5%) denied further counseling.For 210 patients receiving genetic counseling, 170 (81%) accepted genetic tests.In multivariate analysis, risk factors relevant to acceptance of genetic tests included: being charged by physicians of gynecologic oncology for diagnosis and treatment, receiving counseling in genetic counseling clinics, and having family history of breast cancer.For patients denying genetic tests, there were many subjective reasons, among which, "still not understanding genetic tests" (25%) and "unable bear following expensive targeting medicine" . Conclusions: High proportion patients of epithelial ovarian cancer would accept genetic counseling and tests.Genetic counseling clinics for gynecologic oncology would further improve genetic tests for patients.

German Ethics Council on genetic diagnostics: trend setting?

PubMed

Buechner, Bianca

2014-06-01

On 30 April 2013, the German Ethics Council ('Council') published its opinion on 'The future of genetic diagnostics--from research to clinical application' ('the Opinion'). The Council was asked by the German government to discuss the future of genetic diagnostic methods in relation to the current applicable laws and regulations as well as the ethical stand points. The Council's 23 recommendations show that the existing regulations in Germany, and indirectly on a European level, lack in protecting consumers sufficiently. Consumer protection built the major focus of the Council's opinion. However, the opinion misses a critical overall analysis of genetic testing and, for example, the potential misuse of genetic test results by insures or the risk of disclosure toward employers. The Council missed an opportunity to discuss which barriers are necessary from a legal and ethical perspective but which still do not prohibit genetic testing and research.

Higher criticism approach to detect rare variants using whole genome sequencing data

PubMed Central

2014-01-01

Because of low statistical power of single-variant tests for whole genome sequencing (WGS) data, the association test for variant groups is a key approach for genetic mapping. To address the features of sparse and weak genetic effects to be detected, the higher criticism (HC) approach has been proposed and theoretically has proven optimal for detecting sparse and weak genetic effects. Here we develop a strategy to apply the HC approach to WGS data that contains rare variants as the majority. By using Genetic Analysis Workshop 18 "dose" genetic data with simulated phenotypes, we assess the performance of HC under a variety of strategies for grouping variants and collapsing rare variants. The HC approach is compared with the minimal p-value method and the sequence kernel association test. The results show that the HC approach is preferred for detecting weak genetic effects. PMID:25519367

Mechanisms and impact of genetic recombination in the evolution of Streptococcus pneumoniae

PubMed Central

Chaguza, Chrispin; Cornick, Jennifer E.; Everett, Dean B.

2015-01-01

Streptococcus pneumoniae (the pneumococcus) is a highly recombinogenic bacterium responsible for a high burden of human disease globally. Genetic recombination, a process in which exogenous DNA is acquired and incorporated into its genome, is a key evolutionary mechanism employed by the pneumococcus to rapidly adapt to selective pressures. The rate at which the pneumococcus acquires genetic variation through recombination is much higher than the rate at which the organism acquires variation through spontaneous mutations. This higher rate of variation allows the pneumococcus to circumvent the host innate and adaptive immune responses, escape clinical interventions, including antibiotic therapy and vaccine introduction. The rapid influx of whole genome sequence (WGS) data and the advent of novel analysis methods and powerful computational tools for population genetics and evolution studies has transformed our understanding of how genetic recombination drives pneumococcal adaptation and evolution. Here we discuss how genetic recombination has impacted upon the evolution of the pneumococcus. PMID:25904996

Mechanisms and impact of genetic recombination in the evolution of Streptococcus pneumoniae.

PubMed

Chaguza, Chrispin; Cornick, Jennifer E; Everett, Dean B

2015-01-01

Streptococcus pneumoniae (the pneumococcus) is a highly recombinogenic bacterium responsible for a high burden of human disease globally. Genetic recombination, a process in which exogenous DNA is acquired and incorporated into its genome, is a key evolutionary mechanism employed by the pneumococcus to rapidly adapt to selective pressures. The rate at which the pneumococcus acquires genetic variation through recombination is much higher than the rate at which the organism acquires variation through spontaneous mutations. This higher rate of variation allows the pneumococcus to circumvent the host innate and adaptive immune responses, escape clinical interventions, including antibiotic therapy and vaccine introduction. The rapid influx of whole genome sequence (WGS) data and the advent of novel analysis methods and powerful computational tools for population genetics and evolution studies has transformed our understanding of how genetic recombination drives pneumococcal adaptation and evolution. Here we discuss how genetic recombination has impacted upon the evolution of the pneumococcus.

MIG-seq: an effective PCR-based method for genome-wide single-nucleotide polymorphism genotyping using the next-generation sequencing platform

PubMed Central

Suyama, Yoshihisa; Matsuki, Yu

2015-01-01

Restriction-enzyme (RE)-based next-generation sequencing methods have revolutionized marker-assisted genetic studies; however, the use of REs has limited their widespread adoption, especially in field samples with low-quality DNA and/or small quantities of DNA. Here, we developed a PCR-based procedure to construct reduced representation libraries without RE digestion steps, representing de novo single-nucleotide polymorphism discovery, and its genotyping using next-generation sequencing. Using multiplexed inter-simple sequence repeat (ISSR) primers, thousands of genome-wide regions were amplified effectively from a wide variety of genomes, without prior genetic information. We demonstrated: 1) Mendelian gametic segregation of the discovered variants; 2) reproducibility of genotyping by checking its applicability for individual identification; and 3) applicability in a wide variety of species by checking standard population genetic analysis. This approach, called multiplexed ISSR genotyping by sequencing, should be applicable to many marker-assisted genetic studies with a wide range of DNA qualities and quantities. PMID:26593239

Using imputed genotype data in the joint score tests for genetic association and gene-environment interactions in case-control studies.

PubMed

Song, Minsun; Wheeler, William; Caporaso, Neil E; Landi, Maria Teresa; Chatterjee, Nilanjan

2018-03-01

Genome-wide association studies (GWAS) are now routinely imputed for untyped single nucleotide polymorphisms (SNPs) based on various powerful statistical algorithms for imputation trained on reference datasets. The use of predicted allele counts for imputed SNPs as the dosage variable is known to produce valid score test for genetic association. In this paper, we investigate how to best handle imputed SNPs in various modern complex tests for genetic associations incorporating gene-environment interactions. We focus on case-control association studies where inference for an underlying logistic regression model can be performed using alternative methods that rely on varying degree on an assumption of gene-environment independence in the underlying population. As increasingly large-scale GWAS are being performed through consortia effort where it is preferable to share only summary-level information across studies, we also describe simple mechanisms for implementing score tests based on standard meta-analysis of "one-step" maximum-likelihood estimates across studies. Applications of the methods in simulation studies and a dataset from GWAS of lung cancer illustrate ability of the proposed methods to maintain type-I error rates for the underlying testing procedures. For analysis of imputed SNPs, similar to typed SNPs, the retrospective methods can lead to considerable efficiency gain for modeling of gene-environment interactions under the assumption of gene-environment independence. Methods are made available for public use through CGEN R software package. © 2017 WILEY PERIODICALS, INC.

Prioritization for conservation of northern European cattle breeds based on analysis of microsatellite data.

PubMed

Tapio, I; Värv, S; Bennewitz, J; Maleviciute, J; Fimland, E; Grislis, Z; Meuwissen, T H E; Miceikiene, I; Olsaker, I; Viinalass, H; Vilkki, J; Kantanen, J

2006-12-01

Northern European indigenous cattle breeds are currently endangered and at a risk of becoming extinct. We analyzed variation at 20 microsatellite loci in 23 indigenous, 3 old imported, and 9 modern commercial cattle breeds that are presently distributed in northern Europe. We measured the breeds' allelic richness and heterozygosity, and studied their genetic relationships with a neighbor-joining tree based on the Chord genetic distance matrix. We used the Weitzman approach and the core set diversity measure of Eding et al. (2002) to quantify the contribution of each breed to the maximum amount of genetic diversity and to identify breeds important for the conservation of genetic diversity. We defined 11 breeds as a "safe set" of breeds (not endangered) and estimated a reduction in genetic diversity if all nonsafe (endangered) breeds were lost. We then calculated the increase in genetic diversity by adding one by one each of the nonsafe breeds to the safe set (the safe-set-plus-one approach). The neighbor-joining tree grouped the northern European cattle breeds into Black-and-White type, Baltic Red, and Nordic cattle groups. Väne cattle, Bohus Poll, and Danish Jersey had the highest relative contribution to the maximum amount of genetic diversity when the diversity was quantified by the Weitzman diversity measure. These breeds not only showed phylogenetic distinctiveness but also low within-population variation. When the Eding et al. method was applied, Eastern Finncattle and Lithuanian White Backed cattle contributed most of the genetic variation. If the loss of the nonsafe set of breeds happens, the reduction in genetic diversity would be substantial (72%) based on the Weitzman approach, but relatively small (1.81%) based on the Eding et al. method. The safe set contained only 66% of the observed microsatellite alleles. The safe-set-plus-one approach indicated that Bohus Poll and Väne cattle contributed most to the Weitzman diversity, whereas the Eastern Finncattle contribution was the highest according to the Eding et al. method. Our results indicate that both methods of Weitzman and Eding et al. recognize the importance of local populations as a valuable resource of genetic variation.

A comparison of cosegregation analysis methods for the clinical setting.

PubMed

Rañola, John Michael O; Liu, Quanhui; Rosenthal, Elisabeth A; Shirts, Brian H

2018-04-01

Quantitative cosegregation analysis can help evaluate the pathogenicity of genetic variants. However, genetics professionals without statistical training often use simple methods, reporting only qualitative findings. We evaluate the potential utility of quantitative cosegregation in the clinical setting by comparing three methods. One thousand pedigrees each were simulated for benign and pathogenic variants in BRCA1 and MLH1 using United States historical demographic data to produce pedigrees similar to those seen in the clinic. These pedigrees were analyzed using two robust methods, full likelihood Bayes factors (FLB) and cosegregation likelihood ratios (CSLR), and a simpler method, counting meioses. Both FLB and CSLR outperform counting meioses when dealing with pathogenic variants, though counting meioses is not far behind. For benign variants, FLB and CSLR greatly outperform as counting meioses is unable to generate evidence for benign variants. Comparing FLB and CSLR, we find that the two methods perform similarly, indicating that quantitative results from either of these methods could be combined in multifactorial calculations. Combining quantitative information will be important as isolated use of cosegregation in single families will yield classification for less than 1% of variants. To encourage wider use of robust cosegregation analysis, we present a website ( http://www.analyze.myvariant.org ) which implements the CSLR, FLB, and Counting Meioses methods for ATM, BRCA1, BRCA2, CHEK2, MEN1, MLH1, MSH2, MSH6, and PMS2. We also present an R package, CoSeg, which performs the CSLR analysis on any gene with user supplied parameters. Future variant classification guidelines should allow nuanced inclusion of cosegregation evidence against pathogenicity.

Identifying Multimodal Intermediate Phenotypes between Genetic Risk Factors and Disease Status in Alzheimer’s Disease

PubMed Central

Hao, Xiaoke; Yao, Xiaohui; Yan, Jingwen; Risacher, Shannon L.; Saykin, Andrew J.; Zhang, Daoqiang; Shen, Li

2016-01-01

Neuroimaging genetics has attracted growing attention and interest, which is thought to be a powerful strategy to examine the influence of genetic variants (i.e., single nucleotide polymorphisms (SNPs)) on structures or functions of human brain. In recent studies, univariate or multivariate regression analysis methods are typically used to capture the effective associations between genetic variants and quantitative traits (QTs) such as brain imaging phenotypes. The identified imaging QTs, although associated with certain genetic markers, may not be all disease specific. A useful, but underexplored, scenario could be to discover only those QTs associated with both genetic markers and disease status for revealing the chain from genotype to phenotype to symptom. In addition, multimodal brain imaging phenotypes are extracted from different perspectives and imaging markers consistently showing up in multimodalities may provide more insights for mechanistic understanding of diseases (i.e., Alzheimer’s disease (AD)). In this work, we propose a general framework to exploit multi-modal brain imaging phenotypes as intermediate traits that bridge genetic risk factors and multi-class disease status. We applied our proposed method to explore the relation between the well-known AD risk SNP APOE rs429358 and three baseline brain imaging modalities (i.e., structural magnetic resonance imaging (MRI), fluorodeoxyglucose positron emission tomography (FDG-PET) and F-18 florbetapir PET scans amyloid imaging (AV45)) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The empirical results demonstrate that our proposed method not only helps improve the performances of imaging genetic associations, but also discovers robust and consistent regions of interests (ROIs) across multi-modalities to guide the disease-induced interpretation. PMID:27277494

A functional U-statistic method for association analysis of sequencing data.

PubMed

Jadhav, Sneha; Tong, Xiaoran; Lu, Qing

2017-11-01

Although sequencing studies hold great promise for uncovering novel variants predisposing to human diseases, the high dimensionality of the sequencing data brings tremendous challenges to data analysis. Moreover, for many complex diseases (e.g., psychiatric disorders) multiple related phenotypes are collected. These phenotypes can be different measurements of an underlying disease, or measurements characterizing multiple related diseases for studying common genetic mechanism. Although jointly analyzing these phenotypes could potentially increase the power of identifying disease-associated genes, the different types of phenotypes pose challenges for association analysis. To address these challenges, we propose a nonparametric method, functional U-statistic method (FU), for multivariate analysis of sequencing data. It first constructs smooth functions from individuals' sequencing data, and then tests the association of these functions with multiple phenotypes by using a U-statistic. The method provides a general framework for analyzing various types of phenotypes (e.g., binary and continuous phenotypes) with unknown distributions. Fitting the genetic variants within a gene using a smoothing function also allows us to capture complexities of gene structure (e.g., linkage disequilibrium, LD), which could potentially increase the power of association analysis. Through simulations, we compared our method to the multivariate outcome score test (MOST), and found that our test attained better performance than MOST. In a real data application, we apply our method to the sequencing data from Minnesota Twin Study (MTS) and found potential associations of several nicotine receptor subunit (CHRN) genes, including CHRNB3, associated with nicotine dependence and/or alcohol dependence. © 2017 WILEY PERIODICALS, INC.

[Use of multiple locus variable number tandem repeats analysis for the Brucella systematization].

PubMed

Kulakov, Iu K; Kovalev, D A; Misetova, E N; Golovneva, S I; Liapustina, L V; Zheludkov, M M

2012-01-01

The methods of molecular-genetic differentiation to strain level acquire increasing significance in the current system of struggle with brucellosis. MLVA (multiple locus variable number tandem repeats analysis) was selected for molecular-genetic differentiation to strain level and simultaneous establishment of the genetic relationship of investigated Brucella strains. The goal of this work was MLVA typing of three pathogenic Brucella species strains with the analysis of stability of chosen loci, discrimination power and concordance to conventional phenotypic methods of the Brucella differentiation for use in systematization of brucellosis causing agents. Twenty six Brucella strains representing reference (n = 15), vaccine (n = 2) and field strains of three pathogenic Brucella species were tested: B. melitensis (n = 3), B. abortus (n = 2), B. suis (n = 2), and isolates (n = 2) with unidentified taxonomic position using MLVA with 9 pairs primers on known variable loci of Brucella genome. The analysis of the stability of chosen loci, discrimination power on Hunter-Gaston discrimination index (HGDI) and consistency to phenotypic methods of identification was performed. MLVA was confirmed for the results of phenotypic methods of identification, stability of the chosen loci in majority reference, and vaccine strains with a high index of variability HGDI 0.9969 for all loci. A dendrogram was plotted on the basis of MLVA data on distributed Brucella strains in related clusters according to its taxonomic species and biovar positions and construction of 25 genotypes. B. melitensis strains formed cluster related to the reference strain of B. melitensis 63/9 biovar 2. Australian isolates of Brucella 83-4 and Brucella 83-6 isolated from rodents formed a cluster distant from other strains of Brucella. MLVA is a promising method for differentiation of Brucella strains with known and unresolved taxonomic status for their systematization and creation of MLVA genotype catalogue that will promote qualitative improvement of brucellosis surveillance system in Russia.

[Genetic variation of some varieties of common juniper Juniperus communis L. inferred from analysis of allozyme loci].

PubMed

Khantemirova, E V; Semerikov, V L

2010-05-01

Using the method of allozyme analysis, genetic variation, diversity, and population structure of Juniperus communis L. var. communis and J. communis L. var. saxatilis Pall. (= J. sibirica Burgsd. = J. nana Wild), growing on the territory of Russia, J. c. var. communis from Sweden, and J. c. var. depressa Pursh from Northern America (Alaska), was investigated. The total level of genetic variation of these varieties was found to be higher than the values obtained for the other conifers. The population samples of J. c. var. depressa from Alaska and J. c. var. saxatilis from Sakhalin were noticeably different from all other populations examined. Between the other samples, no substantial genetic differences were observed. These populations were characterized by weak interpopulation differentiation along with the absence of expressed geographical pattern of the allele frequency spatial distribution. The only exception was the procumbent form of common juniper from the high mountain populations of South and North Ural, which was somewhat different from the others.

Genome-Wide Association Analysis of Adaptation Using Environmentally Predicted Traits.

PubMed

van Heerwaarden, Joost; van Zanten, Martijn; Kruijer, Willem

2015-10-01

Current methods for studying the genetic basis of adaptation evaluate genetic associations with ecologically relevant traits or single environmental variables, under the implicit assumption that natural selection imposes correlations between phenotypes, environments and genotypes. In practice, observed trait and environmental data are manifestations of unknown selective forces and are only indirectly associated with adaptive genetic variation. In theory, improved estimation of these forces could enable more powerful detection of loci under selection. Here we present an approach in which we approximate adaptive variation by modeling phenotypes as a function of the environment and using the predicted trait in multivariate and univariate genome-wide association analysis (GWAS). Based on computer simulations and published flowering time data from the model plant Arabidopsis thaliana, we find that environmentally predicted traits lead to higher recovery of functional loci in multivariate GWAS and are more strongly correlated to allele frequencies at adaptive loci than individual environmental variables. Our results provide an example of the use of environmental data to obtain independent and meaningful information on adaptive genetic variation.

A simplified fractional order impedance model and parameter identification method for lithium-ion batteries

PubMed Central

Yang, Qingxia; Xu, Jun; Cao, Binggang; Li, Xiuqing

2017-01-01

Identification of internal parameters of lithium-ion batteries is a useful tool to evaluate battery performance, and requires an effective model and algorithm. Based on the least square genetic algorithm, a simplified fractional order impedance model for lithium-ion batteries and the corresponding parameter identification method were developed. The simplified model was derived from the analysis of the electrochemical impedance spectroscopy data and the transient response of lithium-ion batteries with different states of charge. In order to identify the parameters of the model, an equivalent tracking system was established, and the method of least square genetic algorithm was applied using the time-domain test data. Experiments and computer simulations were carried out to verify the effectiveness and accuracy of the proposed model and parameter identification method. Compared with a second-order resistance-capacitance (2-RC) model and recursive least squares method, small tracing voltage fluctuations were observed. The maximum battery voltage tracing error for the proposed model and parameter identification method is within 0.5%; this demonstrates the good performance of the model and the efficiency of the least square genetic algorithm to estimate the internal parameters of lithium-ion batteries. PMID:28212405

Optimized mtDNA Control Region Primer Extension Capture Analysis for Forensically Relevant Samples and Highly Compromised mtDNA of Different Age and Origin

PubMed Central

Eduardoff, Mayra; Xavier, Catarina; Strobl, Christina; Casas-Vargas, Andrea; Parson, Walther

2017-01-01

The analysis of mitochondrial DNA (mtDNA) has proven useful in forensic genetics and ancient DNA (aDNA) studies, where specimens are often highly compromised and DNA quality and quantity are low. In forensic genetics, the mtDNA control region (CR) is commonly sequenced using established Sanger-type Sequencing (STS) protocols involving fragment sizes down to approximately 150 base pairs (bp). Recent developments include Massively Parallel Sequencing (MPS) of (multiplex) PCR-generated libraries using the same amplicon sizes. Molecular genetic studies on archaeological remains that harbor more degraded aDNA have pioneered alternative approaches to target mtDNA, such as capture hybridization and primer extension capture (PEC) methods followed by MPS. These assays target smaller mtDNA fragment sizes (down to 50 bp or less), and have proven to be substantially more successful in obtaining useful mtDNA sequences from these samples compared to electrophoretic methods. Here, we present the modification and optimization of a PEC method, earlier developed for sequencing the Neanderthal mitochondrial genome, with forensic applications in mind. Our approach was designed for a more sensitive enrichment of the mtDNA CR in a single tube assay and short laboratory turnaround times, thus complying with forensic practices. We characterized the method using sheared, high quantity mtDNA (six samples), and tested challenging forensic samples (n = 2) as well as compromised solid tissue samples (n = 15) up to 8 kyrs of age. The PEC MPS method produced reliable and plausible mtDNA haplotypes that were useful in the forensic context. It yielded plausible data in samples that did not provide results with STS and other MPS techniques. We addressed the issue of contamination by including four generations of negative controls, and discuss the results in the forensic context. We finally offer perspectives for future research to enable the validation and accreditation of the PEC MPS method for final implementation in forensic genetic laboratories. PMID:28934125

Enhanced Genetic Analysis of Single Human Bioparticles Recovered by Simplified Micromanipulation from Forensic ‘Touch DNA’ Evidence

PubMed Central

Farash, Katherine; Hanson, Erin K.; Ballantyne, Jack

2015-01-01

DNA profiles can be obtained from ‘touch DNA’ evidence, which comprises microscopic traces of human biological material. Current methods for the recovery of trace DNA employ cotton swabs or adhesive tape to sample an area of interest. However, such a ‘blind-swabbing’ approach will co-sample cellular material from the different individuals, even if the individuals’ cells are located in geographically distinct locations on the item. Thus, some of the DNA mixtures encountered in touch DNA samples are artificially created by the swabbing itself. In some instances, a victim’s DNA may be found in significant excess thus masking any potential perpetrator’s DNA. In order to circumvent the challenges with standard recovery and analysis methods, we have developed a lower cost, ‘smart analysis’ method that results in enhanced genetic analysis of touch DNA evidence. We describe an optimized and efficient micromanipulation recovery strategy for the collection of bio-particles present in touch DNA samples, as well as an enhanced amplification strategy involving a one-step 5 µl microvolume lysis/STR amplification to permit the recovery of STR profiles from the bio-particle donor(s). The use of individual or few (i.e., “clumps”) bioparticles results in the ability to obtain single source profiles. These procedures represent alternative enhanced techniques for the isolation and analysis of single bioparticles from forensic touch DNA evidence. While not necessary in every forensic investigation, the method could be highly beneficial for the recovery of a single source perpetrator DNA profile in cases involving physical assault (e.g., strangulation) that may not be possible using standard analysis techniques. Additionally, the strategies developed here offer an opportunity to obtain genetic information at the single cell level from a variety of other non-forensic trace biological material. PMID:25867046

On the use of sibling recurrence risks to select environmental factors liable to interact with genetic risk factors.

PubMed

Kazma, Rémi; Bonaïti-Pellié, Catherine; Norris, Jill M; Génin, Emmanuelle

2010-01-01

Gene-environment interactions are likely to be involved in the susceptibility to multifactorial diseases but are difficult to detect. Available methods usually concentrate on some particular genetic and environmental factors. In this paper, we propose a new method to determine whether a given exposure is susceptible to interact with unknown genetic factors. Rather than focusing on a specific genetic factor, the degree of familial aggregation is used as a surrogate for genetic factors. A test comparing the recurrence risks in sibs according to the exposure of indexes is proposed and its power is studied for varying values of model parameters. The Exposed versus Unexposed Recurrence Analysis (EURECA) is valuable for common diseases with moderate familial aggregation, only when the role of exposure has been clearly outlined. Interestingly, accounting for a sibling correlation for the exposure increases the power of EURECA. An application on a sample ascertained through one index affected with type 2 diabetes is presented where gene-environment interactions involving obesity and physical inactivity are investigated. Association of obesity with type 2 diabetes is clearly evidenced and a potential interaction involving this factor is suggested in Hispanics (P=0.045), whereas a clear gene-environment interaction is evidenced involving physical inactivity only in non-Hispanic whites (P=0.028). The proposed method might be of particular interest before genetic studies to help determine the environmental risk factors that will need to be accounted for to increase the power to detect genetic risk factors and to select the most appropriate samples to genotype.

Gene ontology analysis of pairwise genetic associations in two genome-wide studies of sporadic ALS.

PubMed

Kim, Nora Chung; Andrews, Peter C; Asselbergs, Folkert W; Frost, H Robert; Williams, Scott M; Harris, Brent T; Read, Cynthia; Askland, Kathleen D; Moore, Jason H

2012-07-28

It is increasingly clear that common human diseases have a complex genetic architecture characterized by both additive and nonadditive genetic effects. The goal of the present study was to determine whether patterns of both additive and nonadditive genetic associations aggregate in specific functional groups as defined by the Gene Ontology (GO). We first estimated all pairwise additive and nonadditive genetic effects using the multifactor dimensionality reduction (MDR) method that makes few assumptions about the underlying genetic model. Statistical significance was evaluated using permutation testing in two genome-wide association studies of ALS. The detection data consisted of 276 subjects with ALS and 271 healthy controls while the replication data consisted of 221 subjects with ALS and 211 healthy controls. Both studies included genotypes from approximately 550,000 single-nucleotide polymorphisms (SNPs). Each SNP was mapped to a gene if it was within 500 kb of the start or end. Each SNP was assigned a p-value based on its strongest joint effect with the other SNPs. We then used the Exploratory Visual Analysis (EVA) method and software to assign a p-value to each gene based on the overabundance of significant SNPs at the α = 0.05 level in the gene. We also used EVA to assign p-values to each GO group based on the overabundance of significant genes at the α = 0.05 level. A GO category was determined to replicate if that category was significant at the α = 0.05 level in both studies. We found two GO categories that replicated in both studies. The first, 'Regulation of Cellular Component Organization and Biogenesis', a GO Biological Process, had p-values of 0.010 and 0.014 in the detection and replication studies, respectively. The second, 'Actin Cytoskeleton', a GO Cellular Component, had p-values of 0.040 and 0.046 in the detection and replication studies, respectively. Pathway analysis of pairwise genetic associations in two GWAS of sporadic ALS revealed a set of genes involved in cellular component organization and actin cytoskeleton, more specifically, that were not reported by prior GWAS. However, prior biological studies have implicated actin cytoskeleton in ALS and other motor neuron diseases. This study supports the idea that pathway-level analysis of GWAS data may discover important associations not revealed using conventional one-SNP-at-a-time approaches.

Pathway-based discovery of genetic interactions in breast cancer

PubMed Central

Xu, Zack Z.; Boone, Charles; Lange, Carol A.

2017-01-01

Breast cancer is the second largest cause of cancer death among U.S. women and the leading cause of cancer death among women worldwide. Genome-wide association studies (GWAS) have identified several genetic variants associated with susceptibility to breast cancer, but these still explain less than half of the estimated genetic contribution to the disease. Combinations of variants (i.e. genetic interactions) may play an important role in breast cancer susceptibility. However, due to a lack of statistical power, the current tests for genetic interactions from GWAS data mainly leverage prior knowledge to focus on small sets of genes or SNPs that are known to have an association with breast cancer. Thus, many genetic interactions, particularly among novel variants, remain understudied. Reverse-genetic interaction screens in model organisms have shown that genetic interactions frequently cluster into highly structured motifs, where members of the same pathway share similar patterns of genetic interactions. Based on this key observation, we recently developed a method called BridGE to search for such structured motifs in genetic networks derived from GWAS studies and identify pathway-level genetic interactions in human populations. We applied BridGE to six independent breast cancer cohorts and identified significant pathway-level interactions in five cohorts. Joint analysis across all five cohorts revealed a high confidence consensus set of genetic interactions with support in multiple cohorts. The discovered interactions implicated the glutathione conjugation, vitamin D receptor, purine metabolism, mitotic prometaphase, and steroid hormone biosynthesis pathways as major modifiers of breast cancer risk. Notably, while many of the pathways identified by BridGE show clear relevance to breast cancer, variants in these pathways had not been previously discovered by traditional single variant association tests, or single pathway enrichment analysis that does not consider SNP-SNP interactions. PMID:28957314

A robust and efficient statistical method for genetic association studies using case and control samples from multiple cohorts

PubMed Central

2013-01-01

Background The theoretical basis of genome-wide association studies (GWAS) is statistical inference of linkage disequilibrium (LD) between any polymorphic marker and a putative disease locus. Most methods widely implemented for such analyses are vulnerable to several key demographic factors and deliver a poor statistical power for detecting genuine associations and also a high false positive rate. Here, we present a likelihood-based statistical approach that accounts properly for non-random nature of case–control samples in regard of genotypic distribution at the loci in populations under study and confers flexibility to test for genetic association in presence of different confounding factors such as population structure, non-randomness of samples etc. Results We implemented this novel method together with several popular methods in the literature of GWAS, to re-analyze recently published Parkinson’s disease (PD) case–control samples. The real data analysis and computer simulation show that the new method confers not only significantly improved statistical power for detecting the associations but also robustness to the difficulties stemmed from non-randomly sampling and genetic structures when compared to its rivals. In particular, the new method detected 44 significant SNPs within 25 chromosomal regions of size < 1 Mb but only 6 SNPs in two of these regions were previously detected by the trend test based methods. It discovered two SNPs located 1.18 Mb and 0.18 Mb from the PD candidates, FGF20 and PARK8, without invoking false positive risk. Conclusions We developed a novel likelihood-based method which provides adequate estimation of LD and other population model parameters by using case and control samples, the ease in integration of these samples from multiple genetically divergent populations and thus confers statistically robust and powerful analyses of GWAS. On basis of simulation studies and analysis of real datasets, we demonstrated significant improvement of the new method over the non-parametric trend test, which is the most popularly implemented in the literature of GWAS. PMID:23394771

Intelligent design optimization of a shape-memory-alloy-actuated reconfigurable wing

NASA Astrophysics Data System (ADS)

Lagoudas, Dimitris C.; Strelec, Justin K.; Yen, John; Khan, Mohammad A.

2000-06-01

The unique thermal and mechanical properties offered by shape memory alloys (SMAs) present exciting possibilities in the field of aerospace engineering. When properly trained, SMA wires act as linear actuators by contracting when heated and returning to their original shape when cooled. It has been shown experimentally that the overall shape of an airfoil can be altered by activating several attached SMA wire actuators. This shape-change can effectively increase the efficiency of a wing in flight at several different flow regimes. To determine the necessary placement of these wire actuators within the wing, an optimization method that incorporates a fully-coupled structural, thermal, and aerodynamic analysis has been utilized. Due to the complexity of the fully-coupled analysis, intelligent optimization methods such as genetic algorithms have been used to efficiently converge to an optimal solution. The genetic algorithm used in this case is a hybrid version with global search and optimization capabilities augmented by the simplex method as a local search technique. For the reconfigurable wing, each chromosome represents a realizable airfoil configuration and its genes are the SMA actuators, described by their location and maximum transformation strain. The genetic algorithm has been used to optimize this design problem to maximize the lift-to-drag ratio for a reconfigured airfoil shape.

The Novel Application of Non-Lethal Citizen Science Tissue Sampling in Recreational Fisheries.

PubMed

Williams, Samuel M; Holmes, Bonnie J; Pepperell, Julian G

2015-01-01

Increasing fishing pressure and uncertainty surrounding recreational fishing catch and effort data promoted the development of alternative methods for conducting fisheries research. A pilot investigation was undertaken to engage the Australian game fishing community and promote the non-lethal collection of tissue samples from the black marlin Istiompax indica, a valuable recreational-only species in Australian waters, for the purpose of future genetic research. Recruitment of recreational anglers was achieved by publicizing the project in magazines, local newspapers, social media, blogs, websites and direct communication workshops at game fishing tournaments. The Game Fishing Association of Australia and the Queensland Game Fishing Association were also engaged to advertise the project and recruit participants with a focus on those anglers already involved in the tag-and-release of marlin. Participants of the program took small tissue samples using non-lethal methods which were stored for future genetic analysis. The program resulted in 165 samples from 49 participants across the known distribution of I. indica within Australian waters which was a sufficient number to facilitate a downstream population genetic analysis. The project demonstrated the potential for the development of citizen science sampling programs to collect tissue samples using non-lethal methods in order to achieve targeted research objects in recreationally caught species.

Characterization of Movement Disorder Phenomenology in Genetically Proven, Familial Frontotemporal Lobar Degeneration: A Systematic Review and Meta-Analysis

PubMed Central

Gasca-Salas, Carmen; Masellis, Mario; Khoo, Edwin; Shah, Binit B.; Fisman, David; Lang, Anthony E.; Kleiner-Fisman, Galit

2016-01-01

Background Mutations in granulin (PGRN) and tau (MAPT), and hexanucleotide repeat expansions near the C9orf72 genes are the most prevalent genetic causes of frontotemporal lobar degeneration. Although behavior, language and movement presentations are common, the relationship between genetic subgroup and movement disorder phenomenology is unclear. Objective We conducted a systematic review and meta-analysis of the literature characterizing the spectrum and prevalence of movement disorders in genetic frontotemporal lobar degeneration. Methods Electronic databases were searched using terms related to frontotemporal lobar degeneration and movement disorders. Articles were included when cases had a proven genetic cause. Study-specific prevalence estimates for clinical features were transformed using Freeman-Tukey arcsine transformation, allowing for pooled estimates of prevalence to be generated using random-effects models. Results The mean age at onset was earlier in those with MAPT mutations compared to PGRN (p<0.001) and C9orf72 (p = 0.024). 66.5% of subjects had an initial non-movement presentation that was most likely a behavioral syndrome (35.7%). At any point during the disease, parkinsonism was the most common movement syndrome reported in 79.8% followed by progressive supranuclear palsy (PSPS) and corticobasal (CBS) syndromes in 12.2% and 10.7%, respectively. The prevalence of movement disorder as initial presentation was higher in MAPT subjects (35.8%) compared to PGRN subjects (10.1). In those with a non-movement presentation, language disorder was more common in PGRN subjects (18.7%) compared to MAPT subjects (5.4%). Summary This represents the first systematic review and meta-analysis of the occurrence of movement disorder phenomenology in genetic frontotemporal lobar degeneration. Standardized prospective collection of clinical information in conjunction with genetic characterization will be crucial for accurate clinico-genetic correlation. PMID:27100392

Applying Multivariate Discrete Distributions to Genetically Informative Count Data.

PubMed

Kirkpatrick, Robert M; Neale, Michael C

2016-03-01

We present a novel method of conducting biometric analysis of twin data when the phenotypes are integer-valued counts, which often show an L-shaped distribution. Monte Carlo simulation is used to compare five likelihood-based approaches to modeling: our multivariate discrete method, when its distributional assumptions are correct, when they are incorrect, and three other methods in common use. With data simulated from a skewed discrete distribution, recovery of twin correlations and proportions of additive genetic and common environment variance was generally poor for the Normal, Lognormal and Ordinal models, but good for the two discrete models. Sex-separate applications to substance-use data from twins in the Minnesota Twin Family Study showed superior performance of two discrete models. The new methods are implemented using R and OpenMx and are freely available.

Two-trait-locus linkage analysis: A powerful strategy for mapping complex genetic traits

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

Schork, N.J.; Boehnke, M.; Terwilliger, J.D.

1993-11-01

Nearly all diseases mapped to date follow clear Mendelian, single-locus segregation patterns. In contrast, many common familial diseases such as diabetes, psoriasis, several forms of cancer, and schizophrenia are familial and appear to have a genetic component but do not exhibit simple Mendelian transmission. More complex models are required to explain the genetics of these important diseases. In this paper, the authors explore two-trait-locus, two-marker-locus linkage analysis in which two trait loci are mapped simultaneously to separate genetic markers. The authors compare the utility of this approach to standard one-trait-locus, one-marker-locus linkage analysis with and without allowance for heterogeneity. Themore » authors also compare the utility of the two-trait-locus, two-marker-locus analysis to two-trait-locus, one-marker-locus linkage analysis. For common diseases, pedigrees are often bilineal, with disease genes entering via two or more unrelated pedigree members. Since such pedigrees often are avoided in linkage studies, the authors also investigate the relative information content of unilineal and bilineal pedigrees. For the dominant-or-recessive and threshold models that the authors consider, the authors find that two-trait-locus, two-marker-locus linkage analysis can provide substantially more linkage information, as measured by expected maximum lod score, than standard one-trait-locus, one-marker-locus methods, even allowing for heterogeneity, while, for a dominant-or-dominant generating model, one-locus models that allow for heterogeneity extract essentially as much information as the two-trait-locus methods. For these three models, the authors also find that bilineal pedigrees provide sufficient linkage information to warrant their inclusion in such studies. The authors discuss strategies for assessing the significance of the two linkages assumed in two-trait-locus, two-marker-locus models. 37 refs., 1 fig., 4 tabs.« less

Different differences: The use of ‘genetic ancestry’ versus race in biomedical human genetic research

PubMed Central

Fujimura, Joan H.; Rajagopalan, Ramya

2011-01-01

This article presents findings from our ethnographic research on biomedical scientists’ studies of human genetic variation and common complex disease. We examine the socio-material work involved in genome-wide association studies (GWAS) and discuss whether, how, and when notions of race and ethnicity are or are not used. We analyze how researchers produce simultaneously different kinds of populations and population differences. Although many geneticists use race in their analyses, we find some who have invented a statistical genetics method and associated software that they use specifically to avoid using categories of race in their genetics analysis. Their method allows them to operationalize their concept of ‘genetic ancestry’ without resorting to notions of race and ethnicity. We focus on the construction and implementation of the software’s algorithms, and discuss the consequences and implications of the software technology for debates and policies around the use of race in genetics research. We also demonstrate that the production and use of their method involves a dynamic and fluid assemblage of actors in various disciplines responding to disciplinary and sociopolitical contexts and concerns. This assemblage also includes particular discourses on human history and geography as they become entangled with research on genetic markers and disease. We introduce the concept of ‘genome geography’, to analyze how some researchers studying human genetic variation ‘locate’ stretches of DNA in different places and times. The concept of genetic ancestry and the practice of genome geography rely on old discourses, but they also incorporate new technologies, infrastructures, and political and scientific commitments. Some of these new technologies provide opportunities to change some of our institutional and cultural forms and frames around notions of difference and similarity. Neverthless, we also highlight the slipperiness of genome geography and the tenacity of race and race concepts. PMID:21553638

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

OPATs: Omnibus P-value association tests.

PubMed

Chen, Chia-Wei; Yang, Hsin-Chou

2017-07-10

Combining statistical significances (P-values) from a set of single-locus association tests in genome-wide association studies is a proof-of-principle method for identifying disease-associated genomic segments, functional genes and biological pathways. We review P-value combinations for genome-wide association studies and introduce an integrated analysis tool, Omnibus P-value Association Tests (OPATs), which provides popular analysis methods of P-value combinations. The software OPATs programmed in R and R graphical user interface features a user-friendly interface. In addition to analysis modules for data quality control and single-locus association tests, OPATs provides three types of set-based association test: window-, gene- and biopathway-based association tests. P-value combinations with or without threshold and rank truncation are provided. The significance of a set-based association test is evaluated by using resampling procedures. Performance of the set-based association tests in OPATs has been evaluated by simulation studies and real data analyses. These set-based association tests help boost the statistical power, alleviate the multiple-testing problem, reduce the impact of genetic heterogeneity, increase the replication efficiency of association tests and facilitate the interpretation of association signals by streamlining the testing procedures and integrating the genetic effects of multiple variants in genomic regions of biological relevance. In summary, P-value combinations facilitate the identification of marker sets associated with disease susceptibility and uncover missing heritability in association studies, thereby establishing a foundation for the genetic dissection of complex diseases and traits. OPATs provides an easy-to-use and statistically powerful analysis tool for P-value combinations. OPATs, examples, and user guide can be downloaded from http://www.stat.sinica.edu.tw/hsinchou/genetics/association/OPATs.htm. © The Author 2017. Published by Oxford University Press.

PCR amplification and genetic analysis in a microwell cell culturing chip.

PubMed

Lindström, Sara; Hammond, Maria; Brismar, Hjalmar; Andersson-Svahn, Helene; Ahmadian, Afshin

2009-12-21

We have previously described a microwell chip designed for high throughput, long-term single-cell culturing and clonal analysis in individual wells providing a controlled way of studying high numbers of individual adherent or non-adherent cells. Here we present a method for the genetic analysis of cells cultured on-chip by PCR and minisequencing, demonstrated using two human adherent cell lines: one wild type and one with a single-base mutation in the p53 gene. Five wild type or mutated cells were seeded per well (in a defined set of wells, each holding 500 nL of culture medium) in a 672-microwell chip. The cell chip was incubated overnight, or cultured for up to five days, depending on the desired colony size, after which the cells were lysed and subjected to PCR directly in the wells. PCR products were detected, in the wells, using a biotinylated primer and a fluorescently labelled primer, allowing the products to be captured on streptavidin-coated magnetic beads and detected by a fluorescence microscope. In addition, to enable genetic analysis by minisequencing, the double-stranded PCR products were denatured and the immobilized strands were kept in the wells by applying a magnetic field from the bottom of the wells while the wells were washed, a minisequencing reaction mixture was added, and after incubation in appropriate conditions the expected genotypes were detected in the investigated microwells, simultaneously, by an array scanner. We anticipate that the technique could be used in mutation frequency screening, providing the ability to correlate cells' proliferative heterogeneity to their genetic heterogeneity, in hundreds of samples simultaneously. The presented method of single-cell culture and DNA amplification thus offers a potentially powerful alternative to single-cell PCR, with advantageous robustness and sensitivity.

Reparametrization-based estimation of genetic parameters in multi-trait animal model using Integrated Nested Laplace Approximation.

PubMed

Mathew, Boby; Holand, Anna Marie; Koistinen, Petri; Léon, Jens; Sillanpää, Mikko J

2016-02-01

A novel reparametrization-based INLA approach as a fast alternative to MCMC for the Bayesian estimation of genetic parameters in multivariate animal model is presented. Multi-trait genetic parameter estimation is a relevant topic in animal and plant breeding programs because multi-trait analysis can take into account the genetic correlation between different traits and that significantly improves the accuracy of the genetic parameter estimates. Generally, multi-trait analysis is computationally demanding and requires initial estimates of genetic and residual correlations among the traits, while those are difficult to obtain. In this study, we illustrate how to reparametrize covariance matrices of a multivariate animal model/animal models using modified Cholesky decompositions. This reparametrization-based approach is used in the Integrated Nested Laplace Approximation (INLA) methodology to estimate genetic parameters of multivariate animal model. Immediate benefits are: (1) to avoid difficulties of finding good starting values for analysis which can be a problem, for example in Restricted Maximum Likelihood (REML); (2) Bayesian estimation of (co)variance components using INLA is faster to execute than using Markov Chain Monte Carlo (MCMC) especially when realized relationship matrices are dense. The slight drawback is that priors for covariance matrices are assigned for elements of the Cholesky factor but not directly to the covariance matrix elements as in MCMC. Additionally, we illustrate the concordance of the INLA results with the traditional methods like MCMC and REML approaches. We also present results obtained from simulated data sets with replicates and field data in rice.

A network-based method to evaluate quality of reproducibility of differential expression in cancer genomics studies

PubMed Central

Geng, Haijiang; Li, Zhihui; Li, Jiabing; Lu, Tao; Yan, Fangrong

2015-01-01

BACKGROUND Personalized cancer treatments depend on the determination of a patient's genetic status according to known genetic profiles for which targeted treatments exist. Such genetic profiles must be scientifically validated before they is applied to general patient population. Reproducibility of findings that support such genetic profiles is a fundamental challenge in validation studies. The percentage of overlapping genes (POG) criterion and derivative methods produce unstable and misleading results. Furthermore, in a complex disease, comparisons between different tumor subtypes can produce high POG scores that do not capture the consistencies in the functions. RESULTS We focused on the quality rather than the quantity of the overlapping genes. We defined the rank value of each gene according to importance or quality by PageRank on basis of a particular topological structure. Then, we used the p-value of the rank-sum of the overlapping genes (PRSOG) to evaluate the quality of reproducibility. Though the POG scores were low in different studies of the same disease, the PRSOG was statistically significant, which suggests that sets of differentially expressed genes might be highly reproducible. CONCLUSIONS Evaluations of eight datasets from breast cancer, lung cancer and four other disorders indicate that quality-based PRSOG method performs better than a quantity-based method. Our analysis of the components of the sets of overlapping genes supports the utility of the PRSOG method. PMID:26556852

Pitfalls in genetic analysis of pheochromocytomas/paragangliomas-case report.

PubMed

Canu, Letizia; Rapizzi, Elena; Zampetti, Benedetta; Fucci, Rossella; Nesi, Gabriella; Richter, Susan; Qin, Nan; Giachè, Valentino; Bergamini, Carlo; Parenti, Gabriele; Valeri, Andrea; Ercolino, Tonino; Eisenhofer, Graeme; Mannelli, Massimo

2014-07-01

About 35% of patients with pheochromocytoma/paraganglioma carry a germline mutation in one of the 10 main susceptibility genes. The recent introduction of next-generation sequencing will allow the analysis of all these genes in one run. When positive, the analysis is generally unequivocal due to the association between a germline mutation and a concordant clinical presentation or positive family history. When genetic analysis reveals a novel mutation with no clinical correlates, particularly in the presence of a missense variant, the question arises whether the mutation is pathogenic or a rare polymorphism. We report the case of a 35-year-old patient operated for a pheochromocytoma who turned out to be a carrier of a novel SDHD (succinate dehydrogenase subunit D) missense mutation. With no positive family history or clinical correlates, we decided to perform additional analyses to test the clinical significance of the mutation. We performed in silico analysis, tissue loss of heterozygosity analysis, immunohistochemistry, Western blot analysis, SDH enzymatic assay, and measurement of the succinate/fumarate concentration ratio in the tumor tissue by tandem mass spectrometry. Although the in silico analysis gave contradictory results according to the different methods, all the other tests demonstrated that the SDH complex was conserved and normally active. We therefore came to the conclusion that the variant was a nonpathogenic polymorphism. Advancements in technology facilitate genetic analysis of patients with pheochromocytoma but also offer new challenges to the clinician who, in some cases, needs clinical correlates and/or functional tests to give significance to the results of the genetic assay.

[Methods of high-throughput plant phenotyping for large-scale breeding and genetic experiments].

PubMed

Afonnikov, D A; Genaev, M A; Doroshkov, A V; Komyshev, E G; Pshenichnikova, T A

2016-07-01

Phenomics is a field of science at the junction of biology and informatics which solves the problems of rapid, accurate estimation of the plant phenotype; it was rapidly developed because of the need to analyze phenotypic characteristics in large scale genetic and breeding experiments in plants. It is based on using the methods of computer image analysis and integration of biological data. Owing to automation, new approaches make it possible to considerably accelerate the process of estimating the characteristics of a phenotype, to increase its accuracy, and to remove a subjectivism (inherent to humans). The main technologies of high-throughput plant phenotyping in both controlled and field conditions, their advantages and disadvantages, and also the prospects of their use for the efficient solution of problems of plant genetics and breeding are presented in the review.

eSensor: an electrochemical detection-based DNA microarray technology enabling sample-to-answer molecular diagnostics

NASA Astrophysics Data System (ADS)

Liu, Robin H.; Longiaru, Mathew

2009-05-01

DNA microarrays are becoming a widespread tool used in life science and drug screening due to its many benefits of miniaturization and integration. Microarrays permit a highly multiplexed DNA analysis. Recently, the development of new detection methods and simplified methodologies has rapidly expanded the use of microarray technologies from predominantly gene expression analysis into the arena of diagnostics. Osmetech's eSensor® is an electrochemical detection platform based on a low-to- medium density DNA hybridization array on a cost-effective printed circuit board substrate. eSensor® has been cleared by FDA for Warfarin sensitivity test and Cystic Fibrosis Carrier Detection. Other genetic-based diagnostic and infectious disease detection tests are under development. The eSensor® platform eliminates the need for an expensive laser-based optical system and fluorescent reagents. It allows one to perform hybridization and detection in a single and small instrument without any fluidic processing and handling. Furthermore, the eSensor® platform is readily adaptable to on-chip sample-to-answer genetic analyses using microfluidics technology. The eSensor® platform provides a cost-effective solution to direct sample-to-answer genetic analysis, and thus have a potential impact in the fields of point-of-care genetic analysis, environmental testing, and biological warfare agent detection.

The fine-scale genetic structure and evolution of the Japanese population.

PubMed

Takeuchi, Fumihiko; Katsuya, Tomohiro; Kimura, Ryosuke; Nabika, Toru; Isomura, Minoru; Ohkubo, Takayoshi; Tabara, Yasuharu; Yamamoto, Ken; Yokota, Mitsuhiro; Liu, Xuanyao; Saw, Woei-Yuh; Mamatyusupu, Dolikun; Yang, Wenjun; Xu, Shuhua; Teo, Yik-Ying; Kato, Norihiro

2017-01-01

The contemporary Japanese populations largely consist of three genetically distinct groups-Hondo, Ryukyu and Ainu. By principal-component analysis, while the three groups can be clearly separated, the Hondo people, comprising 99% of the Japanese, form one almost indistinguishable cluster. To understand fine-scale genetic structure, we applied powerful haplotype-based statistical methods to genome-wide single nucleotide polymorphism data from 1600 Japanese individuals, sampled from eight distinct regions in Japan. We then combined the Japanese data with 26 other Asian populations data to analyze the shared ancestry and genetic differentiation. We found that the Japanese could be separated into nine genetic clusters in our dataset, showing a marked concordance with geography; and that major components of ancestry profile of Japanese were from the Korean and Han Chinese clusters. We also detected and dated admixture in the Japanese. While genetic differentiation between Ryukyu and Hondo was suggested to be caused in part by positive selection, genetic differentiation among the Hondo clusters appeared to result principally from genetic drift. Notably, in Asians, we found the possibility that positive selection accentuated genetic differentiation among distant populations but attenuated genetic differentiation among close populations. These findings are significant for studies of human evolution and medical genetics.

Population Genetic Effects of Urban Habitat Fragmentation in the Perennial Herb Viola pubescens (Violaceae) using ISSR Markers

PubMed Central

Culley, Theresa M.; Sbita, Sarah J.; Wick, Anne

2007-01-01

Background and Aims Fragmentation of natural habitats can negatively impact plant populations by leading to reduced genetic variation and increased genetic distance as populations become geographically and genetically isolated from one another. To test whether such detrimental effects occur within an urban landscape, the genetic structure of six populations of the perennial herb Viola pubescens was characterized in the metropolitan area of Greater Cincinnati in southwestern Ohio, USA. Methods Using three inter-simple sequence repeat (ISSR) markers, 51 loci amplified across all urban populations. For reference, four previously examined agricultural populations in central/northern Ohio and a geographically distant population in Michigan were also included in the analysis. Key Results Urban populations retained high levels of genetic variation (percentage of polymorphic loci, Pp = 80·7 %) with similar genetic distances among populations and an absence of unique alleles. Geographic and genetic distances were correlated with one another, and all populations grouped according to region. Individuals from urban populations clustered together and away from individuals from agricultural populations and from the Michigan population in a principle coordinates analysis. Hierarchical analysis of molecular variance (AMOVA) revealed that most of the genetic variability was partitioned within populations (69·1 %) and among groups (22·2 %) of southwestern Ohio, central/northern Ohio and Michigan groups. Mean Fst was 0·308, indicating substantial population differentiation. Conclusions It is concluded that urban fragmentation does not appear to impede gene flow in V. pubescens in southwestern Ohio. These results are consistent with life history traits of this species and the possibility of high insect abundance in urban habitats due to diverse floral resources and nesting sites. Combined with the cleistogamous breeding system of this species, pollinator availability in the urban matrix may buffer populations against detrimental effects of habitat fragmentation, at least in larger forest fragments. Consequently, it may be inappropriate to generalize about genetic effects of fragmentation across landscapes or even across plant species with different pollination systems. PMID:17556381

An independent component analysis confounding factor correction framework for identifying broad impact expression quantitative trait loci

PubMed Central

Ju, Jin Hyun; Crystal, Ronald G.

2017-01-01

Genome-wide expression Quantitative Trait Loci (eQTL) studies in humans have provided numerous insights into the genetics of both gene expression and complex diseases. While the majority of eQTL identified in genome-wide analyses impact a single gene, eQTL that impact many genes are particularly valuable for network modeling and disease analysis. To enable the identification of such broad impact eQTL, we introduce CONFETI: Confounding Factor Estimation Through Independent component analysis. CONFETI is designed to address two conflicting issues when searching for broad impact eQTL: the need to account for non-genetic confounding factors that can lower the power of the analysis or produce broad impact eQTL false positives, and the tendency of methods that account for confounding factors to model broad impact eQTL as non-genetic variation. The key advance of the CONFETI framework is the use of Independent Component Analysis (ICA) to identify variation likely caused by broad impact eQTL when constructing the sample covariance matrix used for the random effect in a mixed model. We show that CONFETI has better performance than other mixed model confounding factor methods when considering broad impact eQTL recovery from synthetic data. We also used the CONFETI framework and these same confounding factor methods to identify eQTL that replicate between matched twin pair datasets in the Multiple Tissue Human Expression Resource (MuTHER), the Depression Genes Networks study (DGN), the Netherlands Study of Depression and Anxiety (NESDA), and multiple tissue types in the Genotype-Tissue Expression (GTEx) consortium. These analyses identified both cis-eQTL and trans-eQTL impacting individual genes, and CONFETI had better or comparable performance to other mixed model confounding factor analysis methods when identifying such eQTL. In these analyses, we were able to identify and replicate a few broad impact eQTL although the overall number was small even when applying CONFETI. In light of these results, we discuss the broad impact eQTL that have been previously reported from the analysis of human data and suggest that considerable caution should be exercised when making biological inferences based on these reported eQTL. PMID:28505156

An independent component analysis confounding factor correction framework for identifying broad impact expression quantitative trait loci.

PubMed

Ju, Jin Hyun; Shenoy, Sushila A; Crystal, Ronald G; Mezey, Jason G

2017-05-01

Genome-wide expression Quantitative Trait Loci (eQTL) studies in humans have provided numerous insights into the genetics of both gene expression and complex diseases. While the majority of eQTL identified in genome-wide analyses impact a single gene, eQTL that impact many genes are particularly valuable for network modeling and disease analysis. To enable the identification of such broad impact eQTL, we introduce CONFETI: Confounding Factor Estimation Through Independent component analysis. CONFETI is designed to address two conflicting issues when searching for broad impact eQTL: the need to account for non-genetic confounding factors that can lower the power of the analysis or produce broad impact eQTL false positives, and the tendency of methods that account for confounding factors to model broad impact eQTL as non-genetic variation. The key advance of the CONFETI framework is the use of Independent Component Analysis (ICA) to identify variation likely caused by broad impact eQTL when constructing the sample covariance matrix used for the random effect in a mixed model. We show that CONFETI has better performance than other mixed model confounding factor methods when considering broad impact eQTL recovery from synthetic data. We also used the CONFETI framework and these same confounding factor methods to identify eQTL that replicate between matched twin pair datasets in the Multiple Tissue Human Expression Resource (MuTHER), the Depression Genes Networks study (DGN), the Netherlands Study of Depression and Anxiety (NESDA), and multiple tissue types in the Genotype-Tissue Expression (GTEx) consortium. These analyses identified both cis-eQTL and trans-eQTL impacting individual genes, and CONFETI had better or comparable performance to other mixed model confounding factor analysis methods when identifying such eQTL. In these analyses, we were able to identify and replicate a few broad impact eQTL although the overall number was small even when applying CONFETI. In light of these results, we discuss the broad impact eQTL that have been previously reported from the analysis of human data and suggest that considerable caution should be exercised when making biological inferences based on these reported eQTL.

An efficient genome-wide association test for mixed binary and continuous phenotypes with applications to substance abuse research.

PubMed

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

2018-03-01

We propose a new genome-wide association test for mixed binary and continuous phenotypes that uses an efficient numerical method to estimate the empirical distribution of the Fisher's combination statistic under the null hypothesis. Our simulation study shows that the proposed method controls the type I error rate and also maintains its power at the level of the permutation method. More importantly, the computational efficiency of the proposed method is much higher than the one of the permutation method. The simulation results also indicate that the power of the test increases when the genetic effect increases, the minor allele frequency increases, and the correlation between responses decreases. The statistical analysis on the database of the Study of Addiction: Genetics and Environment demonstrates that the proposed method combining multiple phenotypes can increase the power of identifying markers that may not be, otherwise, chosen using marginal tests.

Genetic diversity in wild populations of Paulownia fortune.

PubMed

Li, H Y; Ru, G X; Zhang, J; Lu, Y Y

2014-11-01

The genetic diversities of 16 Paulownia fortunei populations involving 143 individuals collected from 6 provinces in China were analyzed using amplified fragment length polymorphism (AFLP). A total of 9 primer pairs with 1169 polymorphic loci were screened out, and each pair possessed 132 bands on average. The percentage of polymorphic bands (98.57%), the effective number of alleles (1.2138-1.2726), Nei's genetic diversity (0.1566-0.1887), and Shannon's information index (0.2692-0.3117) indicated a plentiful genetic diversity and different among Paulownia fortunei populations. The genetic differentiation coefficient between populations was 0.2386, while the gene flow was 1.0954, and the low gene exchange promoted genetic differentiation. Analysis of variance indicated that genetic variation mainly occurred within populations (81.62% of total variation) rather than among populations (18.38%). The 16 populations were divided by unweighted pair-group method with arithmetic means (UPGMA) into 4 groups with obvious regionalism, in which the populations with close geographical locations (latitude) were clustered together.

Genetic and environmental contributions to children's prosocial behavior: brief review and new evidence from a reanalysis of experimental twin data.

PubMed

Knafo-Noam, Ariel; Vertsberger, Dana; Israel, Salomon

2018-04-01

Children's prosocial behaviors show considerable variability. Here we discuss the genetic and environmental contributions to individual differences in children's prosocial behavior. Twin research systematically shows, at least from the age of 3 years, a genetic contribution to individual differences in prosocial behavior, both questionnaire-based and observed. This finding is demonstrated across a wide variety of cultures. We discuss the possibility that different prosocial behaviors have different genetic etiologies. A re-analysis of past twin data shows that sharing and comforting are affected by overlapping genetic factors at age 3.5 years. In contrast, the association between helping and comforting is attributed to environmental factors. The few molecular genetic studies of children's prosocial behavior are reviewed, and we point out genome-wide and polygenic methods as a key future direction. Finally, we discuss the interplay of genetic and environmental factors, focusing on both gene×environment interactions and gene-environment correlations. Copyright © 2017 Elsevier Ltd. All rights reserved.

How do clinical genetics consent forms address the familial approach to confidentiality and incidental findings? A mixed-methods study.

PubMed

Dheensa, Sandi; Crawford, Gillian; Salter, Claire; Parker, Michael; Fenwick, Angela; Lucassen, Anneke

2018-01-01

Genetic test results can be relevant to patients and their relatives. Questions thus arise around whether clinicians regard genetic information as confidential to individuals or to families, and about how they broach this and other issues, including the potential for incidental findings, in consent (forms) for genetic testing. We conducted a content analysis of UK-wide genetic testing consent forms and interviewed 128 clinicians/laboratory scientists. We found that almost all genetic services offered patients multiple, sometimes unworkable, choices on forms, including an option to veto the use of familial genetic information to benefit relatives. Participants worried that documented choices were overriding professional judgement and cautioned against any future forms dictating practice around incidental findings. We conclude that 'tick-box' forms, which do little to enhance autonomy, are masking valid consent processes in clinical practice. As genome-wide testing becomes commonplace, we must re-consider consent processes, so that they protects patients'-and relatives'-interests.

Instantiating the multiple levels of analysis perspective in a program of study on externalizing behavior

PubMed Central

Beauchaine, Theodore P.; Gatzke-Kopp, Lisa M.

2014-01-01

During the last quarter century, developmental psychopathology has become increasingly inclusive and now spans disciplines ranging from psychiatric genetics to primary prevention. As a result, developmental psychopathologists have extended traditional diathesis–stress and transactional models to include causal processes at and across all relevant levels of analysis. Such research is embodied in what is known as the multiple levels of analysis perspective. We describe how multiple levels of analysis research has informed our current thinking about antisocial and borderline personality development among trait impulsive and therefore vulnerable individuals. Our approach extends the multiple levels of analysis perspective beyond simple Biology × Environment interactions by evaluating impulsivity across physiological systems (genetic, autonomic, hormonal, neural), psychological constructs (social, affective, motivational), developmental epochs (preschool, middle childhood, adolescence, adulthood), sexes (male, female), and methods of inquiry (self-report, informant report, treatment outcome, cardiovascular, electrophysiological, neuroimaging). By conducting our research using any and all available methods across these levels of analysis, we have arrived at a developmental model of trait impulsivity that we believe confers a greater understanding of this highly heritable trait and captures at least some heterogeneity in key behavioral outcomes, including delinquency and suicide. PMID:22781868

A Novel ‘Gene Insertion/Marker Out’ (GIMO) Method for Transgene Expression and Gene Complementation in Rodent Malaria Parasites

PubMed Central

Sajid, Mohammed; Chevalley-Maurel, Séverine; Ramesar, Jai; Klop, Onny; Franke-Fayard, Blandine M. D.; Janse, Chris J.; Khan, Shahid M.

2011-01-01

Research on the biology of malaria parasites has greatly benefited from the application of reverse genetic technologies, in particular through the analysis of gene deletion mutants and studies on transgenic parasites that express heterologous or mutated proteins. However, transfection in Plasmodium is limited by the paucity of drug-selectable markers that hampers subsequent genetic modification of the same mutant. We report the development of a novel ‘gene insertion/marker out’ (GIMO) method for two rodent malaria parasites, which uses negative selection to rapidly generate transgenic mutants ready for subsequent modifications. We have created reference mother lines for both P. berghei ANKA and P. yoelii 17XNL that serve as recipient parasites for GIMO-transfection. Compared to existing protocols GIMO-transfection greatly simplifies and speeds up the generation of mutants expressing heterologous proteins, free of drug-resistance genes, and requires far fewer laboratory animals. In addition we demonstrate that GIMO-transfection is also a simple and fast method for genetic complementation of mutants with a gene deletion or mutation. The implementation of GIMO-transfection procedures should greatly enhance Plasmodium reverse-genetic research. PMID:22216235

A multifactorial analysis of obesity as CVD risk factor: Use of neural network based methods in a nutrigenetics context

PubMed Central

2010-01-01

Background Obesity is a multifactorial trait, which comprises an independent risk factor for cardiovascular disease (CVD). The aim of the current work is to study the complex etiology beneath obesity and identify genetic variations and/or factors related to nutrition that contribute to its variability. To this end, a set of more than 2300 white subjects who participated in a nutrigenetics study was used. For each subject a total of 63 factors describing genetic variants related to CVD (24 in total), gender, and nutrition (38 in total), e.g. average daily intake in calories and cholesterol, were measured. Each subject was categorized according to body mass index (BMI) as normal (BMI ≤ 25) or overweight (BMI > 25). Two artificial neural network (ANN) based methods were designed and used towards the analysis of the available data. These corresponded to i) a multi-layer feed-forward ANN combined with a parameter decreasing method (PDM-ANN), and ii) a multi-layer feed-forward ANN trained by a hybrid method (GA-ANN) which combines genetic algorithms and the popular back-propagation training algorithm. Results PDM-ANN and GA-ANN were comparatively assessed in terms of their ability to identify the most important factors among the initial 63 variables describing genetic variations, nutrition and gender, able to classify a subject into one of the BMI related classes: normal and overweight. The methods were designed and evaluated using appropriate training and testing sets provided by 3-fold Cross Validation (3-CV) resampling. Classification accuracy, sensitivity, specificity and area under receiver operating characteristics curve were utilized to evaluate the resulted predictive ANN models. The most parsimonious set of factors was obtained by the GA-ANN method and included gender, six genetic variations and 18 nutrition-related variables. The corresponding predictive model was characterized by a mean accuracy equal of 61.46% in the 3-CV testing sets. Conclusions The ANN based methods revealed factors that interactively contribute to obesity trait and provided predictive models with a promising generalization ability. In general, results showed that ANNs and their hybrids can provide useful tools for the study of complex traits in the context of nutrigenetics. PMID:20825661

Ethical and professional challenges posed by patients with genetic concerns: a report of focus group discussions with genetic counselors, physicians, and nurses.

PubMed

Veach, P M; Bartels, D M; LeRoy, B S

2001-04-01

Ninety-seven physicians, nurses, and genetic counselors from four regions within the United States participated in focus groups to identify the types of ethical and professional challenges that arise when their patients have genetic concerns. Responses were taped and transcribed and then analyzed using the Hill et al. (1997, Counsel Psychol 25:517-522) Consensual Qualitative Research method of analysis. Sixteen major ethical and professional domains and 63 subcategories were identified. Major domains are informed consent; withholding information; facing uncertainty; resource allocation; value conflicts, directiveness/nondirectiveness; determining the primary patient; professional identity issues; emotional responses; diversity issues; confidentiality; attaining/maintaining proficiency; professional misconduct; discrimination; colleague error; and documentation. Implications for practitioners who deal with genetic issues and recommendations for additional research are given.

Efficient Bayesian mixed model analysis increases association power in large cohorts

PubMed Central

Loh, Po-Ru; Tucker, George; Bulik-Sullivan, Brendan K; Vilhjálmsson, Bjarni J; Finucane, Hilary K; Salem, Rany M; Chasman, Daniel I; Ridker, Paul M; Neale, Benjamin M; Berger, Bonnie; Patterson, Nick; Price, Alkes L

2014-01-01

Linear mixed models are a powerful statistical tool for identifying genetic associations and avoiding confounding. However, existing methods are computationally intractable in large cohorts, and may not optimize power. All existing methods require time cost O(MN2) (where N = #samples and M = #SNPs) and implicitly assume an infinitesimal genetic architecture in which effect sizes are normally distributed, which can limit power. Here, we present a far more efficient mixed model association method, BOLT-LMM, which requires only a small number of O(MN)-time iterations and increases power by modeling more realistic, non-infinitesimal genetic architectures via a Bayesian mixture prior on marker effect sizes. We applied BOLT-LMM to nine quantitative traits in 23,294 samples from the Women’s Genome Health Study (WGHS) and observed significant increases in power, consistent with simulations. Theory and simulations show that the boost in power increases with cohort size, making BOLT-LMM appealing for GWAS in large cohorts. PMID:25642633

[Reconstruction of Vehicle-human Crash Accident and Injury Analysis Based on 3D Laser Scanning, Multi-rigid-body Reconstruction and Optimized Genetic Algorithm].

PubMed

Sun, J; Wang, T; Li, Z D; Shao, Y; Zhang, Z Y; Feng, H; Zou, D H; Chen, Y J

2017-12-01

To reconstruct a vehicle-bicycle-cyclist crash accident and analyse the injuries using 3D laser scanning technology, multi-rigid-body dynamics and optimized genetic algorithm, and to provide biomechanical basis for the forensic identification of death cause. The vehicle was measured by 3D laser scanning technology. The multi-rigid-body models of cyclist, bicycle and vehicle were developed based on the measurements. The value range of optimal variables was set. A multi-objective genetic algorithm and the nondominated sorting genetic algorithm were used to find the optimal solutions, which were compared to the record of the surveillance video around the accident scene. The reconstruction result of laser scanning on vehicle was satisfactory. In the optimal solutions found by optimization method of genetic algorithm, the dynamical behaviours of dummy, bicycle and vehicle corresponded to that recorded by the surveillance video. The injury parameters of dummy were consistent with the situation and position of the real injuries on the cyclist in accident. The motion status before accident, damage process by crash and mechanical analysis on the injury of the victim can be reconstructed using 3D laser scanning technology, multi-rigid-body dynamics and optimized genetic algorithm, which have application value in the identification of injury manner and analysis of death cause in traffic accidents. Copyright© by the Editorial Department of Journal of Forensic Medicine

Plasminogen activator inhibitor type 1 gene is located at region q21. 3-q22 of chromosome 7 and genetically linked with cystic fibrosis

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

Klinger, K.W.; Winqvist, R.; Riccio, A.

1987-12-01

The regional chromosomal location of the human gene for plasminogen activator inhibitor type 1 (PAI1) was determined by three independent methods of gene mapping. PAI1 was localized first to 7cen-q32 and then to 7q21.3-q22 by Southern blot hybridization analysis of a panel of human and mouse somatic cell hybrids with a PAI1 cDNA probe and in situ hybridization, respectively. The authors frequent HindIII restriction fragment length polymorphism (RFLP) of the PAI1 gene with an information content of 0.369. In family studies using this polymorphism, genetic linkage was found between PAI1 and the loci for erythropoietin (EPO), paraoxonase (PON), the metmore » protooncogene (MET), and cystic fibrosis (CF), all previously assigned to the middle part of the long arm of chromosome 7. The linkage with EPO was closest with an estimated genetic distance of 3 centimorgans, whereas that to CF was 20 centimorgans. A three-point genetic linkage analysis and data from previous studies showed that the most likely order of these loci is EPO, PAI1, PON, (MET, CF), with PAI1 being located centromeric to CF. The PAI1 RFLP may prove to be valuable in ordering genetic markers in the CF-linkage group and may also be valuable in genetic analysis of plasminogen activation-related diseases, such as certain thromboembolic disorders and cancer.« less

Analysis of genetic diversity of Tunisian pistachio (Pistacia vera L.) using sequence-related amplified polymorphism (SRAP) markers.

PubMed

Guenni, K; Aouadi, M; Chatti, K; Salhi-Hannachi, A

2016-10-17

Sequence-related amplified polymorphism (SRAP) markers preferentially amplify open reading frames and were used to study the genetic diversity of Tunisian pistachio. In the present study, 43 Pistacia vera accessions were screened using seven SRAP primer pairs. A total of 78 markers was revealed (95.12%) with an average polymorphic information content of 0.850. The results suggest that there is strong genetic differentiation, which characterizes the local resources (G ST = 0.307). High gene flow (N m = 1.127) among groups was explained by the exchange of plant material among regions. Analysis of molecular variance revealed significant differences within groups and showed that 73.88% of the total genetic diversity occurred within groups, whereas the remaining 26.12% occurred among groups. Bayesian clustering and principal component analysis identified three pools, El Guettar, Pollenizers, and the rest of the pistachios belonging to the Gabès, Kasserine, and Sfax localities. Bayesian analysis revealed that El Guettar and male genotypes were assigned with more than 80% probability. The BayeScan method proposed that locus 59 (F13-R9) could be used in the development of sex-linked SCAR markers from SRAP since it is a commonly detected locus in comparisons involving the Pollenizers group. This is the first application of SRAP markers for the assessment of genetic diversity in Tunisian germplasm of P. vera. Such information will be useful to define conservation strategies and improvement programs for this species.

High-density genetic map construction and comparative genome analysis in asparagus bean.

PubMed

Huang, Haitao; Tan, Huaqiang; Xu, Dongmei; Tang, Yi; Niu, Yisong; Lai, Yunsong; Tie, Manman; Li, Huanxiu

2018-03-19

Genetic maps are a prerequisite for quantitative trait locus (QTL) analysis, marker-assisted selection (MAS), fine gene mapping, and assembly of genome sequences. So far, several asparagus bean linkage maps have been established using various kinds of molecular markers. However, these maps were all constructed by gel- or array-based markers. No maps based on sequencing method have been reported. In this study, an NGS-based strategy, SLAF-seq, was applied to create a high-density genetic map for asparagus bean. Through SLAF library construction and Illumina sequencing of two parents and 100 F2 individuals, a total of 55,437 polymorphic SLAF markers were developed and mined for SNP markers. The map consisted of 5,225 SNP markers in 11 LGs, spanning a total distance of 1,850.81 cM, with an average distance between markers of 0.35 cM. Comparative genome analysis with four other legume species, soybean, common bean, mung bean and adzuki bean showed that asparagus bean is genetically more related to adzuki bean. The results will provide a foundation for future genomic research, such as QTL fine mapping, comparative mapping in pulses, and offer support for assembling asparagus bean genome sequence.

Imaging genetics in attention-deficit/hyperactivity disorder and related neurodevelopmental domains: state of the art.

PubMed

Vilor-Tejedor, Natàlia; Cáceres, Alejandro; Pujol, Jesús; Sunyer, Jordi; González, Juan R

2017-12-01

Joint analysis of genetic and neuroimaging data, known as Imaging Genetics (IG), offers an opportunity to deepen our knowledge of the biological mechanisms of neurodevelopmental domains. There has been exponential growth in the literature on IG studies, which challenges the standardization of analysis methods in this field. In this review we give a complete up-to-date account of IG studies on attention deficit hyperactivity disorder (ADHD) and related neurodevelopmental domains, which serves as a reference catalog for researchers working on this neurological disorder. We searched MEDLINE/Pubmed and identified 37 articles on IG of ADHD that met our eligibility criteria. We carefully cataloged these articles according to imaging technique, genes and brain region, and summarized the main results and characteristics of each study. We found that IG studies on ADHD generally focus on dopaminergic genes and the structure of basal ganglia using structural Magnetic Resonance Imaging (MRI). We found little research involving multiple genetic factors and brain regions because of the scarce use of multivariate strategies in data analysis. IG of ADHD and related neurodevelopmental domains is still in its early stages, and a lack of replicated findings is one of the most pressing challenges in the field.

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.

Genealogical data in population medical genetics: Field guidelines

PubMed Central

Poletta, Fernando A.; Orioli, Ieda M.; Castilla, Eduardo E.

2014-01-01

This is a guide for fieldwork in Population Medical Genetics research projects. Data collection, handling, and analysis from large pedigrees require the use of specific tools and methods not widely familiar to human geneticists, unfortunately leading to ineffective graphic pedigrees. Initially, the objective of the pedigree must be decided, and the available information sources need to be identified and validated. Data collection and recording by the tabulated method is advocated, and the involved techniques are presented. Genealogical and personal information are the two main components of pedigree data. While the latter is unique to each investigation project, the former is solely represented by gametic links between persons. The triad of a given pedigree member and its two parents constitutes the building unit of a genealogy. Likewise, three ID numbers representing those three elements of the triad is the record field required for any pedigree analysis. Pedigree construction, as well as pedigree and population data analysis, varies according to the pre-established objectives, the existing information, and the available resources. PMID:24764752

Genetic diversity analysis of Capparis spinosa L. populations by using ISSR markers.

PubMed

Liu, C; Xue, G P; Cheng, B; Wang, X; He, J; Liu, G H; Yang, W J

2015-12-09

Capparis spinosa L. is an important medicinal species in the Xinjiang Province of China. Ten natural populations of C. spinosa from 3 locations in North, Central, and South Xinjiang were studied using morphological trait inter simple sequence repeat (ISSR) molecular markers to assess the genetic diversity and population structure. In this study, the 10 ISSR primers produced 313 amplified DNA fragments, with 52% of fragments being polymorphic. Unweighted pair-group method with arithmetic average (UPGMA) cluster analysis indicated that 10 C. spinosa populations were clustered into 3 geographically distinct groups. The Nei gene of C. spinosa populations in different regions had Diversity and Shannon's information index ranges of 0.1312-0.2001 and 0.1004-0.1875, respectively. The 362 markers were used to construct the dendrogram based on the UPGMA cluster analysis. The dendrogram indicated that 10 populations of C. spinosa were clustered into 3 geographically distinct groups. The results showed these genotypes have high genetic diversity, and can be used for an alternative breeding program.

Systematic exploration of essential yeast gene function with temperature-sensitive mutants

PubMed Central

Li, Zhijian; Vizeacoumar, Franco J; Bahr, Sondra; Li, Jingjing; Warringer, Jonas; Vizeacoumar, Frederick S; Min, Renqiang; VanderSluis, Benjamin; Bellay, Jeremy; DeVit, Michael; Fleming, James A; Stephens, Andrew; Haase, Julian; Lin, Zhen-Yuan; Baryshnikova, Anastasia; Lu, Hong; Yan, Zhun; Jin, Ke; Barker, Sarah; Datti, Alessandro; Giaever, Guri; Nislow, Corey; Bulawa, Chris; Myers, Chad L; Costanzo, Michael; Gingras, Anne-Claude; Zhang, Zhaolei; Blomberg, Anders; Bloom, Kerry; Andrews, Brenda; Boone, Charles

2012-01-01

Conditional temperature-sensitive (ts) mutations are valuable reagents for studying essential genes in the yeast Saccharomyces cerevisiae. We constructed 787 ts strains, covering 497 (~45%) of the 1,101 essential yeast genes, with ~30% of the genes represented by multiple alleles. All of the alleles are integrated into their native genomic locus in the S288C common reference strain and are linked to a kanMX selectable marker, allowing further genetic manipulation by synthetic genetic array (SGA)–based, high-throughput methods. We show two such manipulations: barcoding of 440 strains, which enables chemical-genetic suppression analysis, and the construction of arrays of strains carrying different fluorescent markers of subcellular structure, which enables quantitative analysis of phenotypes using high-content screening. Quantitative analysis of a GFP-tubulin marker identified roles for cohesin and condensin genes in spindle disassembly. This mutant collection should facilitate a wide range of systematic studies aimed at understanding the functions of essential genes. PMID:21441928

An investigation of natural genetic variation in the circadian system of Drosophila melanogaster: rhythm characteristics and methods of quantification.

PubMed

Emery, P T; Morgan, E; Birley, A J

1994-04-01

Variation in four characteristics of the circadian locomotor activity rhythm was investigated in 24 true-breeding strains of Drosophila melanogaster with a view to establishing methods of phenotypic measurement sufficiently robust to allow subsequent biometric analysis. Between them, these strains formed a representative sample of the genetic variability of a natural population. Period, phase, definition (the degree to which a rhythmic signal was obscured by noise), and rhythm waveform were all found to vary continuously among the strains, although within each strain the rhythm phenotype was remarkably consistent. Each characteristic was found to be sufficiently robust to permit objective measurement using several different methods of quantification, which were then compared.

Improved diagnostic yield compared with targeted gene sequencing panels suggests a role for whole-genome sequencing as a first-tier genetic test

PubMed Central

Lionel, Anath C; Costain, Gregory; Monfared, Nasim; Walker, Susan; Reuter, Miriam S; Hosseini, S Mohsen; Thiruvahindrapuram, Bhooma; Merico, Daniele; Jobling, Rebekah; Nalpathamkalam, Thomas; Pellecchia, Giovanna; Sung, Wilson W L; Wang, Zhuozhi; Bikangaga, Peter; Boelman, Cyrus; Carter, Melissa T; Cordeiro, Dawn; Cytrynbaum, Cheryl; Dell, Sharon D; Dhir, Priya; Dowling, James J; Heon, Elise; Hewson, Stacy; Hiraki, Linda; Inbar-Feigenberg, Michal; Klatt, Regan; Kronick, Jonathan; Laxer, Ronald M; Licht, Christoph; MacDonald, Heather; Mercimek-Andrews, Saadet; Mendoza-Londono, Roberto; Piscione, Tino; Schneider, Rayfel; Schulze, Andreas; Silverman, Earl; Siriwardena, Komudi; Snead, O Carter; Sondheimer, Neal; Sutherland, Joanne; Vincent, Ajoy; Wasserman, Jonathan D; Weksberg, Rosanna; Shuman, Cheryl; Carew, Chris; Szego, Michael J; Hayeems, Robin Z; Basran, Raveen; Stavropoulos, Dimitri J; Ray, Peter N; Bowdin, Sarah; Meyn, M Stephen; Cohn, Ronald D; Scherer, Stephen W; Marshall, Christian R

2018-01-01

Purpose Genetic testing is an integral diagnostic component of pediatric medicine. Standard of care is often a time-consuming stepwise approach involving chromosomal microarray analysis and targeted gene sequencing panels, which can be costly and inconclusive. Whole-genome sequencing (WGS) provides a comprehensive testing platform that has the potential to streamline genetic assessments, but there are limited comparative data to guide its clinical use. Methods We prospectively recruited 103 patients from pediatric non-genetic subspecialty clinics, each with a clinical phenotype suggestive of an underlying genetic disorder, and compared the diagnostic yield and coverage of WGS with those of conventional genetic testing. Results WGS identified diagnostic variants in 41% of individuals, representing a significant increase over conventional testing results (24% P = 0.01). Genes clinically sequenced in the cohort (n = 1,226) were well covered by WGS, with a median exonic coverage of 40 × ±8 × (mean ±SD). All the molecular diagnoses made by conventional methods were captured by WGS. The 18 new diagnoses made with WGS included structural and non-exonic sequence variants not detectable with whole-exome sequencing, and confirmed recent disease associations with the genes PIGG, RNU4ATAC, TRIO, and UNC13A. Conclusion WGS as a primary clinical test provided a higher diagnostic yield than conventional genetic testing in a clinically heterogeneous cohort. PMID:28771251

Improved diagnostic yield compared with targeted gene sequencing panels suggests a role for whole-genome sequencing as a first-tier genetic test.

PubMed

Lionel, Anath C; Costain, Gregory; Monfared, Nasim; Walker, Susan; Reuter, Miriam S; Hosseini, S Mohsen; Thiruvahindrapuram, Bhooma; Merico, Daniele; Jobling, Rebekah; Nalpathamkalam, Thomas; Pellecchia, Giovanna; Sung, Wilson W L; Wang, Zhuozhi; Bikangaga, Peter; Boelman, Cyrus; Carter, Melissa T; Cordeiro, Dawn; Cytrynbaum, Cheryl; Dell, Sharon D; Dhir, Priya; Dowling, James J; Heon, Elise; Hewson, Stacy; Hiraki, Linda; Inbar-Feigenberg, Michal; Klatt, Regan; Kronick, Jonathan; Laxer, Ronald M; Licht, Christoph; MacDonald, Heather; Mercimek-Andrews, Saadet; Mendoza-Londono, Roberto; Piscione, Tino; Schneider, Rayfel; Schulze, Andreas; Silverman, Earl; Siriwardena, Komudi; Snead, O Carter; Sondheimer, Neal; Sutherland, Joanne; Vincent, Ajoy; Wasserman, Jonathan D; Weksberg, Rosanna; Shuman, Cheryl; Carew, Chris; Szego, Michael J; Hayeems, Robin Z; Basran, Raveen; Stavropoulos, Dimitri J; Ray, Peter N; Bowdin, Sarah; Meyn, M Stephen; Cohn, Ronald D; Scherer, Stephen W; Marshall, Christian R

2018-04-01

PurposeGenetic testing is an integral diagnostic component of pediatric medicine. Standard of care is often a time-consuming stepwise approach involving chromosomal microarray analysis and targeted gene sequencing panels, which can be costly and inconclusive. Whole-genome sequencing (WGS) provides a comprehensive testing platform that has the potential to streamline genetic assessments, but there are limited comparative data to guide its clinical use.MethodsWe prospectively recruited 103 patients from pediatric non-genetic subspecialty clinics, each with a clinical phenotype suggestive of an underlying genetic disorder, and compared the diagnostic yield and coverage of WGS with those of conventional genetic testing.ResultsWGS identified diagnostic variants in 41% of individuals, representing a significant increase over conventional testing results (24%; P = 0.01). Genes clinically sequenced in the cohort (n = 1,226) were well covered by WGS, with a median exonic coverage of 40 × ±8 × (mean ±SD). All the molecular diagnoses made by conventional methods were captured by WGS. The 18 new diagnoses made with WGS included structural and non-exonic sequence variants not detectable with whole-exome sequencing, and confirmed recent disease associations with the genes PIGG, RNU4ATAC, TRIO, and UNC13A.ConclusionWGS as a primary clinical test provided a higher diagnostic yield than conventional genetic testing in a clinically heterogeneous cohort.

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. ...

Spatial genetic analyses reveal cryptic population structure and migration patterns in a continuously harvested grey wolf (Canis lupus) population in north-eastern Europe.

PubMed

Hindrikson, Maris; Remm, Jaanus; Männil, Peep; Ozolins, Janis; Tammeleht, Egle; Saarma, Urmas

2013-01-01

Spatial genetics is a relatively new field in wildlife and conservation biology that is becoming an essential tool for unravelling the complexities of animal population processes, and for designing effective strategies for conservation and management. Conceptual and methodological developments in this field are therefore critical. Here we present two novel methodological approaches that further the analytical possibilities of STRUCTURE and DResD. Using these approaches we analyse structure and migrations in a grey wolf (Canislupus) population in north-eastern Europe. We genotyped 16 microsatellite loci in 166 individuals sampled from the wolf population in Estonia and Latvia that has been under strong and continuous hunting pressure for decades. Our analysis demonstrated that this relatively small wolf population is represented by four genetic groups. We also used a novel methodological approach that uses linear interpolation to statistically test the spatial separation of genetic groups. The new method, which is capable of using program STRUCTURE output, can be applied widely in population genetics to reveal both core areas and areas of low significance for genetic groups. We also used a recently developed spatially explicit individual-based method DResD, and applied it for the first time to microsatellite data, revealing a migration corridor and barriers, and several contact zones.

Comparative artificial neural network and partial least squares models for analysis of Metronidazole, Diloxanide, Spiramycin and Cliquinol in pharmaceutical preparations.

PubMed

Elkhoudary, Mahmoud M; Abdel Salam, Randa A; Hadad, Ghada M

2014-09-15

Metronidazole (MNZ) is a widely used antibacterial and amoebicide drug. Therefore, it is important to develop a rapid and specific analytical method for the determination of MNZ in mixture with Spiramycin (SPY), Diloxanide (DIX) and Cliquinol (CLQ) in pharmaceutical preparations. This work describes simple, sensitive and reliable six multivariate calibration methods, namely linear and nonlinear artificial neural networks preceded by genetic algorithm (GA-ANN) and principle component analysis (PCA-ANN) as well as partial least squares (PLS) either alone or preceded by genetic algorithm (GA-PLS) for UV spectrophotometric determination of MNZ, SPY, DIX and CLQ in pharmaceutical preparations with no interference of pharmaceutical additives. The results manifest the problem of nonlinearity and how models like ANN can handle it. Analytical performance of these methods was statistically validated with respect to linearity, accuracy, precision and specificity. The developed methods indicate the ability of the previously mentioned multivariate calibration models to handle and solve UV spectra of the four components' mixtures using easy and widely used UV spectrophotometer. Copyright © 2014 Elsevier B.V. All rights reserved.

Comparative artificial neural network and partial least squares models for analysis of Metronidazole, Diloxanide, Spiramycin and Cliquinol in pharmaceutical preparations

NASA Astrophysics Data System (ADS)

Elkhoudary, Mahmoud M.; Abdel Salam, Randa A.; Hadad, Ghada M.

2014-09-01

Metronidazole (MNZ) is a widely used antibacterial and amoebicide drug. Therefore, it is important to develop a rapid and specific analytical method for the determination of MNZ in mixture with Spiramycin (SPY), Diloxanide (DIX) and Cliquinol (CLQ) in pharmaceutical preparations. This work describes simple, sensitive and reliable six multivariate calibration methods, namely linear and nonlinear artificial neural networks preceded by genetic algorithm (GA-ANN) and principle component analysis (PCA-ANN) as well as partial least squares (PLS) either alone or preceded by genetic algorithm (GA-PLS) for UV spectrophotometric determination of MNZ, SPY, DIX and CLQ in pharmaceutical preparations with no interference of pharmaceutical additives. The results manifest the problem of nonlinearity and how models like ANN can handle it. Analytical performance of these methods was statistically validated with respect to linearity, accuracy, precision and specificity. The developed methods indicate the ability of the previously mentioned multivariate calibration models to handle and solve UV spectra of the four components’ mixtures using easy and widely used UV spectrophotometer.

Sensitivity to Structure in the Speech Signal by Children with Speech Sound Disorder and Reading Disability

ERIC Educational Resources Information Center

Johnson, Erin Phinney; Pennington, Bruce F.; Lowenstein, Joanna H.; Nittrouer, Susan

2011-01-01

Research Design;Intervention;Biology;Biotechnology;Teaching Methods;Hands on Science;Professional Development;Comparative Analysis;Genetics;Evaluation;Pretests Posttests;Control Groups;Science Education;Science Instruction;Pedagogical Content Knowledge;

Quantitative analysis of terahertz spectra for illicit drugs using adaptive-range micro-genetic algorithm

NASA Astrophysics Data System (ADS)

Chen, Yi; Ma, Yong; Lu, Zheng; Peng, Bei; Chen, Qin

2011-08-01

In the field of anti-illicit drug applications, many suspicious mixture samples might consist of various drug components—for example, a mixture of methamphetamine, heroin, and amoxicillin—which makes spectral identification very difficult. A terahertz spectroscopic quantitative analysis method using an adaptive range micro-genetic algorithm with a variable internal population (ARVIPɛμGA) has been proposed. Five mixture cases are discussed using ARVIPɛμGA driven quantitative terahertz spectroscopic analysis in this paper. The devised simulation results show agreement with the previous experimental results, which suggested that the proposed technique has potential applications for terahertz spectral identifications of drug mixture components. The results show agreement with the results obtained using other experimental and numerical techniques.

Toward a systems-level analysis of infection biology: a new method for conducting genetic screens in human cells.

PubMed

Stanley, Sarah A; Hung, Deborah T

2009-12-16

Loss-of-function genetic screens have facilitated great strides in our understanding of the biology of model organisms but have not been possible in diploid human cells. A recent report by Brummelkamp's group in Science describes the use of insertional mutagenesis to generate loss-of-function alleles in a largely haploid human cell line and demonstrates the versatility of this method in screens designed to investigate the host/pathogen interaction. This approach has strengths that are complementary to existing strategies and will facilitate progress toward a systems-level understanding of infectious disease and ultimately the development of new therapeutics.

Mendelian randomization analysis associates increased serum urate, due to genetic variation in uric acid transporters, with improved renal function.

PubMed

Hughes, Kim; Flynn, Tanya; de Zoysa, Janak; Dalbeth, Nicola; Merriman, Tony R

2014-02-01

Increased serum urate predicts chronic kidney disease independent of other risk factors. The use of xanthine oxidase inhibitors coincides with improved renal function. Whether this is due to reduced serum urate or reduced production of oxidants by xanthine oxidase or another physiological mechanism remains unresolved. Here we applied Mendelian randomization, a statistical genetics approach allowing disentangling of cause and effect in the presence of potential confounding, to determine whether lowering of serum urate by genetic modulation of renal excretion benefits renal function using data from 7979 patients of the Atherosclerosis Risk in Communities and Framingham Heart studies. Mendelian randomization by the two-stage least squares method was done with serum urate as the exposure, a uric acid transporter genetic risk score as instrumental variable, and estimated glomerular filtration rate and serum creatinine as the outcomes. Increased genetic risk score was associated with significantly improved renal function in men but not in women. Analysis of individual genetic variants showed the effect size associated with serum urate did not correlate with that associated with renal function in the Mendelian randomization model. This is consistent with the possibility that the physiological action of these genetic variants in raising serum urate correlates directly with improved renal function. Further studies are required to understand the mechanism of the potential renal function protection mediated by xanthine oxidase inhibitors.

Application of a multilocus variable number of tandem repeats analysis to regional outbreak surveillance of Enterohemorrhagic Escherichia coli O157:H7 infections.

PubMed

Konno, Takayuki; Yatsuyanagi, Jun; Saito, Shioko

2011-01-01

A total of 18 strains of EHEC O157:H7 were isolated from distinct cases in Akita Prefecture, Japan from July to September 2007. The genetic relatedness of these isolates was investigated by performing a multilocus variable number of tandem repeats analysis (MLVA) and a pulsed-field gel electrophoresis (PFGE) analysis using XbaI. The PFGE analyses allowed us to group these 18 isolates into three major clusters. The MLVA results correlated closely with those obtained by PFGE, although some variants were found within the clusters obtained by PFGE, thus highlighting the utility of this technique for determining a precise classification when it is difficult to differentiate between isolates with indistinguishable or very similar PFGE patterns. In addition, MLVA is a much easier and more rapid method than PFGE for analysis of the genetic relatedness of strains. Thus, as a second molecular epidemiological subtyping method, MLVA is useful for the regional outbreak surveillance of EHEC O157:H7 infections.

THREaD Mapper Studio: a novel, visual web server for the estimation of genetic linkage maps

PubMed Central

Cheema, Jitender; Ellis, T. H. Noel; Dicks, Jo

2010-01-01

The estimation of genetic linkage maps is a key component in plant and animal research, providing both an indication of the genetic structure of an organism and a mechanism for identifying candidate genes associated with traits of interest. Because of this importance, several computational solutions to genetic map estimation exist, mostly implemented as stand-alone software packages. However, the estimation process is often largely hidden from the user. Consequently, problems such as a program crashing may occur that leave a user baffled. THREaD Mapper Studio (http://cbr.jic.ac.uk/threadmapper) is a new web site that implements a novel, visual and interactive method for the estimation of genetic linkage maps from DNA markers. The rationale behind the web site is to make the estimation process as transparent and robust as possible, while also allowing users to use their expert knowledge during analysis. Indeed, the 3D visual nature of the tool allows users to spot features in a data set, such as outlying markers and potential structural rearrangements that could cause problems with the estimation procedure and to account for them in their analysis. Furthermore, THREaD Mapper Studio facilitates the visual comparison of genetic map solutions from third party software, aiding users in developing robust solutions for their data sets. PMID:20494977

Rapid identification of Enterobacter hormaechei and Enterobacter cloacae genetic cluster III.

PubMed

Ohad, S; Block, C; Kravitz, V; Farber, A; Pilo, S; Breuer, R; Rorman, E

2014-05-01

Enterobacter cloacae complex bacteria are of both clinical and environmental importance. Phenotypic methods are unable to distinguish between some of the species in this complex, which often renders their identification incomplete. The goal of this study was to develop molecular assays to identify Enterobacter hormaechei and Ent. cloacae genetic cluster III which are relatively frequently encountered in clinical material. The molecular assays developed in this study are qPCR technology based and served to identify both Ent. hormaechei and Ent. cloacae genetic cluster III. qPCR results were compared to hsp60 sequence analysis. Most clinical isolates were assigned to Ent. hormaechei subsp. steigerwaltii and Ent. cloacae genetic cluster III. The latter was proportionately more frequently isolated from bloodstream infections than from other material (P < 0·05). The qPCR assays detecting Ent. hormaechei and Ent. cloacae genetic cluster III demonstrated high sensitivity and specificity. The presented qPCR assays allow accurate and rapid identification of clinical isolates of the Ent. cloacae complex. The improved identifications obtained can specifically assist analysis of Ent. hormaechei and Ent. cloacae genetic cluster III in nosocomial outbreaks and can promote rapid environmental monitoring. An association was observed between Ent. cloacae cluster III and systemic infection that deserves further attention. © 2014 The Society for Applied Microbiology.

General quantitative genetic methods for comparative biology: phylogenies, taxonomies and multi-trait models for continuous and categorical characters.

PubMed

Hadfield, J D; Nakagawa, S

2010-03-01

Although many of the statistical techniques used in comparative biology were originally developed in quantitative genetics, subsequent development of comparative techniques has progressed in relative isolation. Consequently, many of the new and planned developments in comparative analysis already have well-tested solutions in quantitative genetics. In this paper, we take three recent publications that develop phylogenetic meta-analysis, either implicitly or explicitly, and show how they can be considered as quantitative genetic models. We highlight some of the difficulties with the proposed solutions, and demonstrate that standard quantitative genetic theory and software offer solutions. We also show how results from Bayesian quantitative genetics can be used to create efficient Markov chain Monte Carlo algorithms for phylogenetic mixed models, thereby extending their generality to non-Gaussian data. Of particular utility is the development of multinomial models for analysing the evolution of discrete traits, and the development of multi-trait models in which traits can follow different distributions. Meta-analyses often include a nonrandom collection of species for which the full phylogenetic tree has only been partly resolved. Using missing data theory, we show how the presented models can be used to correct for nonrandom sampling and show how taxonomies and phylogenies can be combined to give a flexible framework with which to model dependence.

Genetic determinants of prepubertal and pubertal growth and development.

PubMed

Thomis, Martine A; Towne, Bradford

2006-12-01

This article surveys the current general understanding of genetic influences on within- and between-population variation in growth and development in the context of establishing an International Growth Standard for Preadolescent and Adolescent Children. Traditional genetic epidemiologic analysis methods are reviewed, and evidence from family studies for genetic effects on different measures of growth and development is then presented. Findings from linkage and association studies seeking to identify specific genomic locations and allelic variants of genes influencing variation in growth and maturation are then summarized. Special mention is made of the need to study the interactions between genes and environments. At present, specific genes and polymorphisms contributing to variation in growth and maturation are only beginning to be identified. Larger genetic epidemiologic studies are needed in different parts of the world to better explore population differences in gene frequencies and gene-environment interactions. As advances continue to be made in molecular and statistical genetic methods, the genetic architecture of complex processes, including those of growth and development, will become better elucidated. For now, it can only be concluded that although the fundamental genetic underpinnings of the growth and development of children worldwide are likely to be essentially the same, there are also likely to be differences between populations in the frequencies of allelic gene variants that influence growth and maturation and in the nature of gene-environment interactions. This does not necessarily preclude an international growth reference, but it does have important implications for the form that such a reference might ultimately take.

A simple capillary gel electrophoresis approach for efficient and reproducible DNA separations. Analysis of genetically modified soy and maize.

PubMed

Sánchez, Laura; González, Ramón; Crego, Antonio L; Cifuentes, Alejandro

2007-03-01

It is generally assumed that in order to achieve suitable separations of DNA fragments, capillary gel electrophoresis (CGE)-coated capillaries should be used. In this work, a new method is presented that allows to obtain reproducible CGE separations of DNA fragments using bare fused-silica capillaries without any previous coating step. The proposed method only requires: (i) a capillary washing with 0.1 M hydrochloric acid between injections and (ii) a running buffer composed of Tris-phosphate-ethylenediamine tetraacetic acid (EDTA) and 4.5% of 2-hydroxyethyl cellulose (HEC) as sieving polymer. The use of this new CGE procedure gives highly resolved and reproducible separations of DNA fragments ranging from 50 to 750 bp. The separation of these DNA fragments is accomplished in less than 30 min with efficiencies up to 1.7 x 10(6) plates/m. Reproducibility values of migration times (given as %RSD) for the analyzed DNA fragments are better than 1.0% (n = 4) for the same day, 2.2% (n = 16) for four different days, and 2.3% (n = 16) for four different capillaries. The usefulness of this separation method is demonstrated by detecting genetically modified maize and genetically modified soy after DNA amplification by PCR. This new CGE procedure together with LIF as detector provides sensitive analysis of 0.9% of Bt11 maize, Mon810 maize, and Roundup Ready soy in flours with S/ N up to 542. These results demonstrate the usefulness of this procedure to fulfill the European regulation on detection of genetically modified organisms in foods.

Genetic divergence of physiological-quality traits of seeds in a population of peppers.

PubMed

Pessoa, A M S; Barroso, P A; do Rêgo, E R; Medeiros, G D A; Bruno, R L A; do Rêgo, M M

2015-10-16

Brazil has a great diversity of Capsicum peppers that can be used in breeding programs. The objective of this study was to evaluate genetic variation in traits related to the physiological quality of seeds of Capsicum annuum L. in a segregating F2 population and its parents. A total of 250 seeds produced by selfing in the F1 generation resulting from crosses between UFPB 77.3 and UFPB 76 were used, with 100 seeds of both parents used as additional controls, totaling 252 genotypes. The seeds were germinated in gerboxes containing substrate blotting paper moistened with distilled water. Germination and the following vigor tests were evaluated: first count, germination velocity index, and root and shoot lengths. Data were subjected to analysis of variance, and means were compared by Scott and Knott's method at 1% probability. Tocher's clustering based on Mahalanobis distance and canonical variable analysis with graphic dispersion of genotypes were performed, and genetic parameters were estimated. All variables were found to be significant by the F test (P ≤ 0.01) and showed high heritability and a CVg/CVe ratio higher than 1.0, indicating genetic differences among genotypes. Parents (genotypes 1 and 2) formed distinct groups in all clustering methods. Genotypes 3, 104, 153, and 232 were found to be the most divergent according to Tocher's clustering method, and this was mainly due to early germination, which was observed on day 14, and would therefore be selected. Understanding the phenotypic variability among these 252 genotypes will serve as a basis for continuing the breeding program within this family.

Development of microsatellite markers for the rapid and reliable genotyping of Brettanomyces bruxellensis at strain level.

PubMed

Albertin, Warren; Panfili, Aurélie; Miot-Sertier, Cécile; Goulielmakis, Aurélie; Delcamp, Adline; Salin, Franck; Lonvaud-Funel, Aline; Curtin, Chris; Masneuf-Pomarede, Isabelle

2014-09-01

Although many yeasts are useful for food production and beverage, some species may cause spoilage with important economic loss. This is the case of Dekkera/Brettanomyces bruxellensis, a contaminant species that is mainly associated with fermented beverages (wine, beer, cider and traditional drinks). To better control Brettanomyces spoilage, rapid and reliable genotyping methods are necessary to determine the origins of the spoilage, to assess the effectiveness of preventive treatments and to develop new control strategies. Despite several previously published typing methods, ranging from classical molecular methods (RAPD, AFLP, REA-PFGE, mtDNA restriction analysis) to more engineered technologies (infrared spectroscopy), there is still a lack of a rapid, reliable and universal genotyping approach. In this work, we developed eight polymorphic microsatellites markers for the Brettanomyces/Dekkera bruxellensis species. Microsatellite typing was applied to the genetic analysis of wine and beer isolates from Europe, Australia and South Africa. Our results suggest that B. bruxellensis is a highly disseminated species, with some strains isolated from different continents being closely related at the genetic level. We also focused on strains isolated from two Bordeaux wineries on different substrates (grapes, red wines) and for different vintages (over half a century). We showed that all B. bruxellensis strains within a cellar are strongly related at the genetic level, suggesting that one clonal population may cause spoilage over decades. The microsatellite tool now paves the way for future population genetics research of the B. bruxellensis species. Copyright © 2014 Elsevier Ltd. All rights reserved.

A detailed view on Model-Based Multifactor Dimensionality Reduction for detecting gene-gene interactions in case-control data in the absence and presence of noise

PubMed Central

CATTAERT, TOM; CALLE, M. LUZ; DUDEK, SCOTT M.; MAHACHIE JOHN, JESTINAH M.; VAN LISHOUT, FRANÇOIS; URREA, VICTOR; RITCHIE, MARYLYN D.; VAN STEEN, KRISTEL

2010-01-01

SUMMARY Analyzing the combined effects of genes and/or environmental factors on the development of complex diseases is a great challenge from both the statistical and computational perspective, even using a relatively small number of genetic and non-genetic exposures. Several data mining methods have been proposed for interaction analysis, among them, the Multifactor Dimensionality Reduction Method (MDR), which has proven its utility in a variety of theoretical and practical settings. Model-Based Multifactor Dimensionality Reduction (MB-MDR), a relatively new MDR-based technique that is able to unify the best of both non-parametric and parametric worlds, was developed to address some of the remaining concerns that go along with an MDR-analysis. These include the restriction to univariate, dichotomous traits, the absence of flexible ways to adjust for lower-order effects and important confounders, and the difficulty to highlight epistasis effects when too many multi-locus genotype cells are pooled into two new genotype groups. Whereas the true value of MB-MDR can only reveal itself by extensive applications of the method in a variety of real-life scenarios, here we investigate the empirical power of MB-MDR to detect gene-gene interactions in the absence of any noise and in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity. For the considered simulation settings, we show that the power is generally higher for MB-MDR than for MDR, in particular in the presence of genetic heterogeneity, phenocopy, or low minor allele frequencies. PMID:21158747

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

Golbus, Jessica R.; Puckelwartz, Megan J.; Dellefave-Castillo, Lisa

Background—Cardiomyopathy is highly heritable but genetically diverse. At present, genetic testing for cardiomyopathy uses targeted sequencing to simultaneously assess the coding regions of more than 50 genes. New genes are routinely added to panels to improve the diagnostic yield. With the anticipated $1000 genome, it is expected that genetic testing will shift towards comprehensive genome sequencing accompanied by targeted gene analysis. Therefore, we assessed the reliability of whole genome sequencing and targeted analysis to identify cardiomyopathy variants in 11 subjects with cardiomyopathy. Methods and Results—Whole genome sequencing with an average of 37× coverage was combined with targeted analysis focused onmore » 204 genes linked to cardiomyopathy. Genetic variants were scored using multiple prediction algorithms combined with frequency data from public databases. This pipeline yielded 1-14 potentially pathogenic variants per individual. Variants were further analyzed using clinical criteria and/or segregation analysis. Three of three previously identified primary mutations were detected by this analysis. In six subjects for whom the primary mutation was previously unknown, we identified mutations that segregated with disease, had clinical correlates, and/or had additional pathological correlation to provide evidence for causality. For two subjects with previously known primary mutations, we identified additional variants that may act as modifiers of disease severity. In total, we identified the likely pathological mutation in 9 of 11 (82%) subjects. We conclude that these pilot data demonstrate that ~30-40× coverage whole genome sequencing combined with targeted analysis is feasible and sensitive to identify rare variants in cardiomyopathy-associated genes.« less

Genetic Variation in the Raptor Gene Is Associated With Overweight But Not Hypertension in American Men of Japanese Ancestry

PubMed Central

Carnes, Bruce A.; Chen, Randi; Donlon, Timothy A.; He, Qimei; Grove, John S.; Masaki, Kamal H.; Elliott, Ayako; Willcox, Donald C.; Allsopp, Richard; Willcox, Bradley J.

2015-01-01

BACKGROUND The mechanistic target of rapamycin (mTOR) pathway is pivotal for cell growth. Regulatory associated protein of mTOR complex I (Raptor) is a unique component of this pro-growth complex. The present study tested whether variation across the raptor gene (RPTOR) is associated with overweight and hypertension. METHODS We tested 61 common (allele frequency ≥ 0.1) tagging single nucleotide polymorphisms (SNPs) that captured most of the genetic variation across RPTOR in 374 subjects of normal lifespan and 439 subjects with a lifespan exceeding 95 years for association with overweight/obesity, essential hypertension, and isolated systolic hypertension. Subjects were drawn from the Honolulu Heart Program, a homogeneous population of American men of Japanese ancestry, well characterized for phenotypes relevant to conditions of aging. Hypertension status was ascertained when subjects were 45–68 years old. Statistical evaluation involved contingency table analysis, logistic regression, and the powerful method of recursive partitioning. RESULTS After analysis of RPTOR genotypes by each statistical approach, we found no significant association between genetic variation in RPTOR and either essential hypertension or isolated systolic hypertension. Models generated by recursive partitioning analysis showed that RPTOR SNPs significantly enhanced the ability of the model to accurately assign individuals to either the overweight/obese or the non-overweight/obese groups (P = 0.008 by 1-tailed Z test). CONCLUSION Common genetic variation in RPTOR is associated with overweight/obesity but does not discernibly contribute to either essential hypertension or isolated systolic hypertension in the population studied. PMID:25249372

Efficient genotype compression and analysis of large genetic variation datasets

PubMed Central

Layer, Ryan M.; Kindlon, Neil; Karczewski, Konrad J.; Quinlan, Aaron R.

2015-01-01

Genotype Query Tools (GQT) is a new indexing strategy that expedites analyses of genome variation datasets in VCF format based on sample genotypes, phenotypes and relationships. GQT’s compressed genotype index minimizes decompression for analysis, and performance relative to existing methods improves with cohort size. We show substantial (up to 443 fold) performance gains over existing methods and demonstrate GQT’s utility for exploring massive datasets involving thousands to millions of genomes. PMID:26550772

Genetic diversity and origin of weedy rice (Oryza sativa f. spontanea) populations found in North-eastern China revealed by simple sequence repeat (SSR) markers.

PubMed

Cao, Qianjin; Lu, Bao-Rong; Xia, Hui; Rong, Jun; Sala, Francesco; Spada, Alberto; Grassi, Fabrizio

2006-12-01

Weedy rice (Oryza sativa f. spontanea) is one of the most notorious weeds occurring in rice-planting areas worldwide. The objectives of this study are to determine the genetic diversity and differentiation of weedy rice populations from Liaoning Province in North-eastern China and to explore the possible origin of these weedy populations by comparing their genetic relationships with rice varieties (O. sativa) and wild rice (O. rufipogon) from different sources. Simple sequence repeat (SSR) markers were used to estimate the genetic diversity of 30 weedy rice populations from Liaoning, each containing about 30 individuals, selected rice varieties and wild O. rufipogon. Genetic differentiation and the relationships of weedy rice populations were analysed using cluster analysis (UPGMA) and principle component analysis (PCA). The overall genetic diversity of weedy rice populations from Liaoning was relatively high (H(e) = 0.313, I = 0.572), with about 35 % of the genetic variation found among regions. The Liaoning weedy rice populations were closely related to rice varieties from Liaoning and japonica varieties from other regions but distantly related to indica rice varieties and wild O. rufipogon. Weedy rice populations from Liaoning are considerably variable genetically and most probably originated from Liaoning rice varieties by mutation and intervarietal hybrids. Recent changes in farming practices and cultivation methods along with less weed management may have promoted the re-emergence and divergence of weedy rice in North-eastern China.

A Multivariate Twin Study of the DSM-IV Criteria for Antisocial Personality Disorder

PubMed Central

Kendler, Kenneth S.; Aggen, Steven H.; Patrick, Christopher J.

2012-01-01

BACKGROUND Many assessment instruments for psychopathy are multidimensional, suggesting that distinguishable factors are needed to effectively capture variation in this personality domain. However, no prior study has examined the factor structure of the DSM-IV criteria for antisocial personality disorder (ASPD). METHODS Self-report questionnaire items reflecting all A criteria for DSM-IV ASPD were available from 4,291 twins (including both members of 1,647 pairs) from the Virginia Adult Study of Psychiatric and Substance Use Disorders. Exploratory factor analysis and twin model fitting were performed using, respectively, Mplus and Mx. RESULTS Phenotypic factor analysis produced evidence for 2 correlated factors: aggressive-disregard and disinhibition. The best-fitting multivariate twin model included two genetic and one unique environmental common factor, along with criteria-specific genetic and environmental effects. The two genetic factors closely resembled the phenotypic factors and varied in their prediction of a range of relevant criterion variables. Scores on the genetic aggressive-disregard factor score were more strongly associated with risk for conduct disorder, early and heavy alcohol use, and low educational status, whereas scores on the genetic disinhibition factor score were more strongly associated with younger age, novelty seeking, and major depression. CONCLUSION From a genetic perspective, the DSM-IV criteria for ASPD do not reflect a single dimension of liability but rather are influenced by two dimensions of genetic risk reflecting aggressive-disregard and disinhibition. The phenotypic structure of the ASPD criteria results largely from genetic and not from environmental influences. PMID:21762879