Gene-Based Association Analysis for Censored Traits Via Fixed Effect Functional Regressions.

PubMed

Fan, Ruzong; Wang, Yifan; Yan, Qi; Ding, Ying; Weeks, Daniel E; Lu, Zhaohui; Ren, Haobo; Cook, Richard J; Xiong, Momiao; Swaroop, Anand; Chew, Emily Y; Chen, Wei

2016-02-01

Genetic studies of survival outcomes have been proposed and conducted recently, but statistical methods for identifying genetic variants that affect disease progression are rarely developed. Motivated by our ongoing real studies, here we develop Cox proportional hazard models using functional regression (FR) to perform gene-based association analysis of survival traits while adjusting for covariates. The proposed Cox models are fixed effect models where the genetic effects of multiple genetic variants are assumed to be fixed. We introduce likelihood ratio test (LRT) statistics to test for associations between the survival traits and multiple genetic variants in a genetic region. Extensive simulation studies demonstrate that the proposed Cox RF LRT statistics have well-controlled type I error rates. To evaluate power, we compare the Cox FR LRT with the previously developed burden test (BT) in a Cox model and sequence kernel association test (SKAT), which is based on mixed effect Cox models. The Cox FR LRT statistics have higher power than or similar power as Cox SKAT LRT except when 50%/50% causal variants had negative/positive effects and all causal variants are rare. In addition, the Cox FR LRT statistics have higher power than Cox BT LRT. The models and related test statistics can be useful in the whole genome and whole exome association studies. An age-related macular degeneration dataset was analyzed as an example. © 2016 WILEY PERIODICALS, INC.

Gene-based Association Analysis for Censored Traits Via Fixed Effect Functional Regressions

PubMed Central

Fan, Ruzong; Wang, Yifan; Yan, Qi; Ding, Ying; Weeks, Daniel E.; Lu, Zhaohui; Ren, Haobo; Cook, Richard J; Xiong, Momiao; Swaroop, Anand; Chew, Emily Y.; Chen, Wei

2015-01-01

Summary Genetic studies of survival outcomes have been proposed and conducted recently, but statistical methods for identifying genetic variants that affect disease progression are rarely developed. Motivated by our ongoing real studies, we develop here Cox proportional hazard models using functional regression (FR) to perform gene-based association analysis of survival traits while adjusting for covariates. The proposed Cox models are fixed effect models where the genetic effects of multiple genetic variants are assumed to be fixed. We introduce likelihood ratio test (LRT) statistics to test for associations between the survival traits and multiple genetic variants in a genetic region. Extensive simulation studies demonstrate that the proposed Cox RF LRT statistics have well-controlled type I error rates. To evaluate power, we compare the Cox FR LRT with the previously developed burden test (BT) in a Cox model and sequence kernel association test (SKAT) which is based on mixed effect Cox models. The Cox FR LRT statistics have higher power than or similar power as Cox SKAT LRT except when 50%/50% causal variants had negative/positive effects and all causal variants are rare. In addition, the Cox FR LRT statistics have higher power than Cox BT LRT. The models and related test statistics can be useful in the whole genome and whole exome association studies. An age-related macular degeneration dataset was analyzed as an example. PMID:26782979

Stakeholder perspectives on decision-analytic modeling frameworks to assess genetic services policy.

PubMed

Guzauskas, Gregory F; Garrison, Louis P; Stock, Jacquie; Au, Sylvia; Doyle, Debra Lochner; Veenstra, David L

2013-01-01

Genetic services policymakers and insurers often make coverage decisions in the absence of complete evidence of clinical utility and under budget constraints. We evaluated genetic services stakeholder opinions on the potential usefulness of decision-analytic modeling to inform coverage decisions, and asked them to identify genetic tests for decision-analytic modeling studies. We presented an overview of decision-analytic modeling to members of the Western States Genetic Services Collaborative Reimbursement Work Group and state Medicaid representatives and conducted directed content analysis and an anonymous survey to gauge their attitudes toward decision-analytic modeling. Participants also identified and prioritized genetic services for prospective decision-analytic evaluation. Participants expressed dissatisfaction with current processes for evaluating insurance coverage of genetic services. Some participants expressed uncertainty about their comprehension of decision-analytic modeling techniques. All stakeholders reported openness to using decision-analytic modeling for genetic services assessments. Participants were most interested in application of decision-analytic concepts to multiple-disorder testing platforms, such as next-generation sequencing and chromosomal microarray. Decision-analytic modeling approaches may provide a useful decision tool to genetic services stakeholders and Medicaid decision-makers.

Exploring Conceptual Change in Genetics Using a Multidimensional Interpretive Framework.

ERIC Educational Resources Information Center

Venville, Grady J.; Treagust, David F.

1998-01-01

Changes in grade 10 students' (n=79) conceptions of genes during genetics instruction was studied from multiple perspectives. Ontologically, most students moved from passive to active models of genes. Affectively, students were interested in genetics but unmotivated by microscopic mechanistic explanations; however, teaching approaches were…

Hierarchical, parallel computing strategies using component object model for process modelling responses of forest plantations to interacting multiple stresses

Treesearch

J. G. Isebrands; G. E. Host; K. Lenz; G. Wu; H. W. Stech

2000-01-01

Process models are powerful research tools for assessing the effects of multiple environmental stresses on forest plantations. These models are driven by interacting environmental variables and often include genetic factors necessary for assessing forest plantation growth over a range of different site, climate, and silvicultural conditions. However, process models are...

Background controlled QTL mapping in pure-line genetic populations derived from four-way crosses

PubMed Central

Zhang, S; Meng, L; Wang, J; Zhang, L

2017-01-01

Pure lines derived from multiple parents are becoming more important because of the increased genetic diversity, the possibility to conduct replicated phenotyping trials in multiple environments and potentially high mapping resolution of quantitative trait loci (QTL). In this study, we proposed a new mapping method for QTL detection in pure-line populations derived from four-way crosses, which is able to control the background genetic variation through a two-stage mapping strategy. First, orthogonal variables were created for each marker and used in an inclusive linear model, so as to completely absorb the genetic variation in the mapping population. Second, inclusive composite interval mapping approach was implemented for one-dimensional scanning, during which the inclusive linear model was employed to control the background variation. Simulation studies using different genetic models demonstrated that the new method is efficient when considering high detection power, low false discovery rate and high accuracy in estimating quantitative trait loci locations and effects. For illustration, the proposed method was applied in a reported wheat four-way recombinant inbred line population. PMID:28722705

Background controlled QTL mapping in pure-line genetic populations derived from four-way crosses.

PubMed

Zhang, S; Meng, L; Wang, J; Zhang, L

2017-10-01

Pure lines derived from multiple parents are becoming more important because of the increased genetic diversity, the possibility to conduct replicated phenotyping trials in multiple environments and potentially high mapping resolution of quantitative trait loci (QTL). In this study, we proposed a new mapping method for QTL detection in pure-line populations derived from four-way crosses, which is able to control the background genetic variation through a two-stage mapping strategy. First, orthogonal variables were created for each marker and used in an inclusive linear model, so as to completely absorb the genetic variation in the mapping population. Second, inclusive composite interval mapping approach was implemented for one-dimensional scanning, during which the inclusive linear model was employed to control the background variation. Simulation studies using different genetic models demonstrated that the new method is efficient when considering high detection power, low false discovery rate and high accuracy in estimating quantitative trait loci locations and effects. For illustration, the proposed method was applied in a reported wheat four-way recombinant inbred line population.

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.

Testing the structure of a hydrological model using Genetic Programming

NASA Astrophysics Data System (ADS)

Selle, Benny; Muttil, Nitin

2011-01-01

SummaryGenetic Programming is able to systematically explore many alternative model structures of different complexity from available input and response data. We hypothesised that Genetic Programming can be used to test the structure of hydrological models and to identify dominant processes in hydrological systems. To test this, Genetic Programming was used to analyse a data set from a lysimeter experiment in southeastern Australia. The lysimeter experiment was conducted to quantify the deep percolation response under surface irrigated pasture to different soil types, watertable depths and water ponding times during surface irrigation. Using Genetic Programming, a simple model of deep percolation was recurrently evolved in multiple Genetic Programming runs. This simple and interpretable model supported the dominant process contributing to deep percolation represented in a conceptual model that was published earlier. Thus, this study shows that Genetic Programming can be used to evaluate the structure of hydrological models and to gain insight about the dominant processes in hydrological systems.

Multivariate Methods for Meta-Analysis of Genetic Association Studies.

PubMed

Dimou, Niki L; Pantavou, Katerina G; Braliou, Georgia G; Bagos, Pantelis G

2018-01-01

Multivariate meta-analysis of genetic association studies and genome-wide association studies has received a remarkable attention as it improves the precision of the analysis. Here, we review, summarize and present in a unified framework methods for multivariate meta-analysis of genetic association studies and genome-wide association studies. Starting with the statistical methods used for robust analysis and genetic model selection, we present in brief univariate methods for meta-analysis and we then scrutinize multivariate methodologies. Multivariate models of meta-analysis for a single gene-disease association studies, including models for haplotype association studies, multiple linked polymorphisms and multiple outcomes are discussed. The popular Mendelian randomization approach and special cases of meta-analysis addressing issues such as the assumption of the mode of inheritance, deviation from Hardy-Weinberg Equilibrium and gene-environment interactions are also presented. All available methods are enriched with practical applications and methodologies that could be developed in the future are discussed. Links for all available software implementing multivariate meta-analysis methods are also provided.

[Application of genetic algorithm in blending technology for extractions of Cortex Fraxini].

PubMed

Yang, Ming; Zhou, Yinmin; Chen, Jialei; Yu, Minying; Shi, Xiufeng; Gu, Xijun

2009-10-01

To explore the feasibility of genetic algorithm (GA) on multiple objective blending technology for extractions of Cortex Fraxini. According to that the optimization objective was the combination of fingerprint similarity and the root-mean-square error of multiple key constituents, a new multiple objective optimization model of 10 batches extractions of Cortex Fraxini was built. The blending coefficient was obtained by genetic algorithm. The quality of 10 batches extractions of Cortex Fraxini that after blending was evaluated with the finger print similarity and root-mean-square error as indexes. The quality of 10 batches extractions of Cortex Fraxini that after blending was well improved. Comparing with the fingerprint of the control sample, the similarity was up, but the degree of variation is down. The relative deviation of the key constituents was less than 10%. It is proved that genetic algorithm works well on multiple objective blending technology for extractions of Cortex Fraxini. This method can be a reference to control the quality of extractions of Cortex Fraxini. Genetic algorithm in blending technology for extractions of Chinese medicines is advisable.

Spatial scaling and multi-model inference in landscape genetics: Martes americana in northern Idaho

Treesearch

Tzeidle N. Wasserman; Samuel A. Cushman; Michael K. Schwartz; David O. Wallin

2010-01-01

Individual-based analyses relating landscape structure to genetic distances across complex landscapes enable rigorous evaluation of multiple alternative hypotheses linking landscape structure to gene flow. We utilize two extensions to increase the rigor of the individual-based causal modeling approach to inferring relationships between landscape patterns and gene flow...

Generation of gene-targeted mice using embryonic stem cells derived from a transgenic mouse model of Alzheimer's disease.

PubMed

Yamamoto, Satoshi; Ooshima, Yuki; Nakata, Mitsugu; Yano, Takashi; Matsuoka, Kunio; Watanabe, Sayuri; Maeda, Ryouta; Takahashi, Hideki; Takeyama, Michiyasu; Matsumoto, Yoshio; Hashimoto, Tadatoshi

2013-06-01

Gene-targeting technology using mouse embryonic stem (ES) cells has become the "gold standard" for analyzing gene functions and producing disease models. Recently, genetically modified mice with multiple mutations have increasingly been produced to study the interaction between proteins and polygenic diseases. However, introduction of an additional mutation into mice already harboring several mutations by conventional natural crossbreeding is an extremely time- and labor-intensive process. Moreover, to do so in mice with a complex genetic background, several years may be required if the genetic background is to be retained. Establishing ES cells from multiple-mutant mice, or disease-model mice with a complex genetic background, would offer a possible solution. Here, we report the establishment and characterization of novel ES cell lines from a mouse model of Alzheimer's disease (3xTg-AD mouse, Oddo et al. in Neuron 39:409-421, 2003) harboring 3 mutated genes (APPswe, TauP301L, and PS1M146V) and a complex genetic background. Thirty blastocysts were cultured and 15 stable ES cell lines (male: 11; female: 4) obtained. By injecting these ES cells into diploid or tetraploid blastocysts, we generated germline-competent chimeras. Subsequently, we confirmed that F1 mice derived from these animals showed similar biochemical and behavioral characteristics to the original 3xTg-AD mice. Furthermore, we introduced a gene-targeting vector into the ES cells and successfully obtained gene-targeted ES cells, which were then used to generate knockout mice for the targeted gene. These results suggest that the present methodology is effective for introducing an additional mutation into mice already harboring multiple mutated genes and/or a complex genetic background.

Computers in Biological Education: Simulation Approaches. Genetics and Evolution. CAL Research Group Technical Report No. 13.

ERIC Educational Resources Information Center

Murphy, P. J.

Three examples of genetics and evolution simulation concerning Mendelian inheritance, genetic mapping, and natural selection are used to illustrate the use of simulations in modeling scientific/natural processes. First described is the HERED series, which illustrates such phenomena as incomplete dominance, multiple alleles, lethal alleles,…

Genetic and Environmental Influences of General Cognitive Ability: Is g a valid latent construct?

PubMed Central

Panizzon, Matthew S.; Vuoksimaa, Eero; Spoon, Kelly M.; Jacobson, Kristen C.; Lyons, Michael J.; Franz, Carol E.; Xian, Hong; Vasilopoulos, Terrie; Kremen, William S.

2014-01-01

Despite an extensive literature, the “g” construct remains a point of debate. Different models explaining the observed relationships among cognitive tests make distinct assumptions about the role of g in relation to those tests and specific cognitive domains. Surprisingly, these different models and their corresponding assumptions are rarely tested against one another. In addition to the comparison of distinct models, a multivariate application of the twin design offers a unique opportunity to test whether there is support for g as a latent construct with its own genetic and environmental influences, or whether the relationships among cognitive tests are instead driven by independent genetic and environmental factors. Here we tested multiple distinct models of the relationships among cognitive tests utilizing data from the Vietnam Era Twin Study of Aging (VETSA), a study of middle-aged male twins. Results indicated that a hierarchical (higher-order) model with a latent g phenotype, as well as specific cognitive domains, was best supported by the data. The latent g factor was highly heritable (86%), and accounted for most, but not all, of the genetic effects in specific cognitive domains and elementary cognitive tests. By directly testing multiple competing models of the relationships among cognitive tests in a genetically-informative design, we are able to provide stronger support than in prior studies for g being a valid latent construct. PMID:24791031

Drag reduction of a car model by linear genetic programming control

NASA Astrophysics Data System (ADS)

Li, Ruiying; Noack, Bernd R.; Cordier, Laurent; Borée, Jacques; Harambat, Fabien

2017-08-01

We investigate open- and closed-loop active control for aerodynamic drag reduction of a car model. Turbulent flow around a blunt-edged Ahmed body is examined at ReH≈ 3× 105 based on body height. The actuation is performed with pulsed jets at all trailing edges (multiple inputs) combined with a Coanda deflection surface. The flow is monitored with 16 pressure sensors distributed at the rear side (multiple outputs). We apply a recently developed model-free control strategy building on genetic programming in Dracopoulos and Kent (Neural Comput Appl 6:214-228, 1997) and Gautier et al. (J Fluid Mech 770:424-441, 2015). The optimized control laws comprise periodic forcing, multi-frequency forcing and sensor-based feedback including also time-history information feedback and combinations thereof. Key enabler is linear genetic programming (LGP) as powerful regression technique for optimizing the multiple-input multiple-output control laws. The proposed LGP control can select the best open- or closed-loop control in an unsupervised manner. Approximately 33% base pressure recovery associated with 22% drag reduction is achieved in all considered classes of control laws. Intriguingly, the feedback actuation emulates periodic high-frequency forcing. In addition, the control identified automatically the only sensor which listens to high-frequency flow components with good signal to noise ratio. Our control strategy is, in principle, applicable to all multiple actuators and sensors experiments.

Evaluation of an ensemble of genetic models for prediction of a quantitative trait.

PubMed

Milton, Jacqueline N; Steinberg, Martin H; Sebastiani, Paola

2014-01-01

Many genetic markers have been shown to be associated with common quantitative traits in genome-wide association studies. Typically these associated genetic markers have small to modest effect sizes and individually they explain only a small amount of the variability of the phenotype. In order to build a genetic prediction model without fitting a multiple linear regression model with possibly hundreds of genetic markers as predictors, researchers often summarize the joint effect of risk alleles into a genetic score that is used as a covariate in the genetic prediction model. However, the prediction accuracy can be highly variable and selecting the optimal number of markers to be included in the genetic score is challenging. In this manuscript we present a strategy to build an ensemble of genetic prediction models from data and we show that the ensemble-based method makes the challenge of choosing the number of genetic markers more amenable. Using simulated data with varying heritability and number of genetic markers, we compare the predictive accuracy and inclusion of true positive and false positive markers of a single genetic prediction model and our proposed ensemble method. The results show that the ensemble of genetic models tends to include a larger number of genetic variants than a single genetic model and it is more likely to include all of the true genetic markers. This increased sensitivity is obtained at the price of a lower specificity that appears to minimally affect the predictive accuracy of the ensemble.

Multiple-trait structured antedependence model to study the relationship between litter size and birth weight in pigs and rabbits.

PubMed

David, Ingrid; Garreau, Hervé; Balmisse, Elodie; Billon, Yvon; Canario, Laurianne

2017-01-20

Some genetic studies need to take into account correlations between traits that are repeatedly measured over time. Multiple-trait random regression models are commonly used to analyze repeated traits but suffer from several major drawbacks. In the present study, we developed a multiple-trait extension of the structured antedependence model (SAD) to overcome this issue and validated its usefulness by modeling the association between litter size (LS) and average birth weight (ABW) over parities in pigs and rabbits. The single-trait SAD model assumes that a random effect at time [Formula: see text] can be explained by the previous values of the random effect (i.e. at previous times). The proposed multiple-trait extension of the SAD model consists in adding a cross-antedependence parameter to the single-trait SAD model. This model can be easily fitted using ASReml and the OWN Fortran program that we have developed. In comparison with the random regression model, we used our multiple-trait SAD model to analyze the LS and ABW of 4345 litters from 1817 Large White sows and 8706 litters from 2286 L-1777 does over a maximum of five successive parities. For both species, the multiple-trait SAD fitted the data better than the random regression model. The difference between AIC of the two models (AIC_random regression-AIC_SAD) were equal to 7 and 227 for pigs and rabbits, respectively. A similar pattern of heritability and correlation estimates was obtained for both species. Heritabilities were lower for LS (ranging from 0.09 to 0.29) than for ABW (ranging from 0.23 to 0.39). The general trend was a decrease of the genetic correlation for a given trait between more distant parities. Estimates of genetic correlations between LS and ABW were negative and ranged from -0.03 to -0.52 across parities. No correlation was observed between the permanent environmental effects, except between the permanent environmental effects of LS and ABW of the same parity, for which the estimate of the correlation was strongly negative (ranging from -0.57 to -0.67). We demonstrated that application of our multiple-trait SAD model is feasible for studying several traits with repeated measurements and showed that it provided a better fit to the data than the random regression model.

Modeling the influence of genetic and environmental variation on the expression of plant life cycles across landscapes.

PubMed

Burghardt, Liana T; Metcalf, C Jessica E; Wilczek, Amity M; Schmitt, Johanna; Donohue, Kathleen

2015-02-01

Organisms develop through multiple life stages that differ in environmental tolerances. The seasonal timing, or phenology, of life-stage transitions determines the environmental conditions to which each life stage is exposed and the length of time required to complete a generation. Both environmental and genetic factors contribute to phenological variation, yet predicting their combined effect on life cycles across a geographic range remains a challenge. We linked submodels of the plasticity of individual life stages to create an integrated model that predicts life-cycle phenology in complex environments. We parameterized the model for Arabidopsis thaliana and simulated life cycles in four locations. We compared multiple "genotypes" by varying two parameters associated with natural genetic variation in phenology: seed dormancy and floral repression. The model predicted variation in life cycles across locations that qualitatively matches observed natural phenology. Seed dormancy had larger effects on life-cycle length than floral repression, and results suggest that a genetic cline in dormancy maintains a life-cycle length of 1 year across the geographic range of this species. By integrating across life stages, this approach demonstrates how genetic variation in one transition can influence subsequent transitions and the geographic distribution of life cycles more generally.

Integrated Patient-Derived Models Delineate Individualized Therapeutic Vulnerabilities of Pancreatic Cancer.

PubMed

Witkiewicz, Agnieszka K; Balaji, Uthra; Eslinger, Cody; McMillan, Elizabeth; Conway, William; Posner, Bruce; Mills, Gordon B; O'Reilly, Eileen M; Knudsen, Erik S

2016-08-16

Pancreatic ductal adenocarcinoma (PDAC) harbors the worst prognosis of any common solid tumor, and multiple failed clinical trials indicate therapeutic recalcitrance. Here, we use exome sequencing of patient tumors and find multiple conserved genetic alterations. However, the majority of tumors exhibit no clearly defined therapeutic target. High-throughput drug screens using patient-derived cell lines found rare examples of sensitivity to monotherapy, with most models requiring combination therapy. Using PDX models, we confirmed the effectiveness and selectivity of the identified treatment responses. Out of more than 500 single and combination drug regimens tested, no single treatment was effective for the majority of PDAC tumors, and each case had unique sensitivity profiles that could not be predicted using genetic analyses. These data indicate a shortcoming of reliance on genetic analysis to predict efficacy of currently available agents against PDAC and suggest that sensitivity profiling of patient-derived models could inform personalized therapy design for PDAC. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.

Modeling lactation curves and estimation of genetic parameters in Holstein cows using multiple-trait random regression models.

PubMed

Kheirabadi, Khabat; Rashidi, Amir; Alijani, Sadegh; Imumorin, Ikhide

2014-11-01

We compared the goodness of fit of three mathematical functions (including: Legendre polynomials, Lidauer-Mäntysaari function and Wilmink function) for describing the lactation curve of primiparous Iranian Holstein cows by using multiple-trait random regression models (MT-RRM). Lactational submodels provided the largest daily additive genetic (AG) and permanent environmental (PE) variance estimates at the end and at the onset of lactation, respectively, as well as low genetic correlations between peripheral test-day records. For all models, heritability estimates were highest at the end of lactation (245 to 305 days) and ranged from 0.05 to 0.26, 0.03 to 0.12 and 0.04 to 0.24 for milk, fat and protein yields, respectively. Generally, the genetic correlations between traits depend on how far apart they are or whether they are on the same day in any two traits. On average, genetic correlations between milk and fat were the lowest and those between fat and protein were intermediate, while those between milk and protein were the highest. Results from all criteria (Akaike's and Schwarz's Bayesian information criterion, and -2*logarithm of the likelihood function) suggested that a model with 2 and 5 coefficients of Legendre polynomials for AG and PE effects, respectively, was the most adequate for fitting the data. © 2014 Japanese Society of Animal Science.

Model selection with multiple regression on distance matrices leads to incorrect inferences.

PubMed

Franckowiak, Ryan P; Panasci, Michael; Jarvis, Karl J; Acuña-Rodriguez, Ian S; Landguth, Erin L; Fortin, Marie-Josée; Wagner, Helene H

2017-01-01

In landscape genetics, model selection procedures based on Information Theoretic and Bayesian principles have been used with multiple regression on distance matrices (MRM) to test the relationship between multiple vectors of pairwise genetic, geographic, and environmental distance. Using Monte Carlo simulations, we examined the ability of model selection criteria based on Akaike's information criterion (AIC), its small-sample correction (AICc), and the Bayesian information criterion (BIC) to reliably rank candidate models when applied with MRM while varying the sample size. The results showed a serious problem: all three criteria exhibit a systematic bias toward selecting unnecessarily complex models containing spurious random variables and erroneously suggest a high level of support for the incorrectly ranked best model. These problems effectively increased with increasing sample size. The failure of AIC, AICc, and BIC was likely driven by the inflated sample size and different sum-of-squares partitioned by MRM, and the resulting effect on delta values. Based on these findings, we strongly discourage the continued application of AIC, AICc, and BIC for model selection with MRM.

The genetic interacting landscape of 63 candidate genes in Major Depressive Disorder: an explorative study.

PubMed

Lekman, Magnus; Hössjer, Ola; Andrews, Peter; Källberg, Henrik; Uvehag, Daniel; Charney, Dennis; Manji, Husseini; Rush, John A; McMahon, Francis J; Moore, Jason H; Kockum, Ingrid

2014-01-01

Genetic contributions to major depressive disorder (MDD) are thought to result from multiple genes interacting with each other. Different procedures have been proposed to detect such interactions. Which approach is best for explaining the risk of developing disease is unclear. This study sought to elucidate the genetic interaction landscape in candidate genes for MDD by conducting a SNP-SNP interaction analysis using an exhaustive search through 3,704 SNP-markers in 1,732 cases and 1,783 controls provided from the GAIN MDD study. We used three different methods to detect interactions, two logistic regressions models (multiplicative and additive) and one data mining and machine learning (MDR) approach. Although none of the interaction survived correction for multiple comparisons, the results provide important information for future genetic interaction studies in complex disorders. Among the 0.5% most significant observations, none had been reported previously for risk to MDD. Within this group of interactions, less than 0.03% would have been detectable based on main effect approach or an a priori algorithm. We evaluated correlations among the three different models and conclude that all three algorithms detected the same interactions to a low degree. Although the top interactions had a surprisingly large effect size for MDD (e.g. additive dominant model Puncorrected = 9.10E-9 with attributable proportion (AP) value = 0.58 and multiplicative recessive model with Puncorrected = 6.95E-5 with odds ratio (OR estimated from β3) value = 4.99) the area under the curve (AUC) estimates were low (< 0.54). Moreover, the population attributable fraction (PAF) estimates were also low (< 0.15). We conclude that the top interactions on their own did not explain much of the genetic variance of MDD. The different statistical interaction methods we used in the present study did not identify the same pairs of interacting markers. Genetic interaction studies may uncover previously unsuspected effects that could provide novel insights into MDD risk, but much larger sample sizes are needed before this strategy can be powerfully applied.

Genetically distinct populations of northern shrimp, Pandalus borealis, in the North Atlantic: adaptation to different temperatures as an isolation factor.

PubMed

Jorde, Per Erik; Søvik, Guldborg; Westgaard, Jon-Ivar; Albretsen, Jon; André, Carl; Hvingel, Carsten; Johansen, Torild; Sandvik, Anne Dagrun; Kingsley, Michael; Jørstad, Knut Eirik

2015-04-01

The large-scale population genetic structure of northern shrimp, Pandalus borealis, was investigated over the species' range in the North Atlantic, identifying multiple genetically distinct groups. Genetic divergence among sample localities varied among 10 microsatellite loci (range: FST = -0.0002 to 0.0475) with a highly significant average (FST = 0.0149; P < 0.0001). In contrast, little or no genetic differences were observed among temporal replicates from the same localities (FST = 0.0004; P = 0.33). Spatial genetic patterns were compared to geographic distances, patterns of larval drift obtained through oceanographic modelling, and temperature differences, within a multiple linear regression framework. The best-fit model included all three factors and explained approximately 29% of all spatial genetic divergence. However, geographic distance and larval drift alone had only minor effects (2.5-4.7%) on large-scale genetic differentiation patterns, whereas bottom temperature differences explained most (26%). Larval drift was found to promote genetic homogeneity in parts of the study area with strong currents, but appeared ineffective across large temperature gradients. These findings highlight the breakdown of gene flow in a species with a long pelagic larval phase (up to 3 months) and indicate a role for local adaptation to temperature conditions in promoting evolutionary diversification and speciation in the marine environment. © 2015 John Wiley & Sons Ltd.

Modeling the Diagnostic Criteria for Alcohol Dependence with Genetic Animal Models

PubMed Central

Kendler, Kenneth S.; Hitzemann, Robert J.

2012-01-01

A diagnosis of alcohol dependence (AD) using the DSM-IV-R is categorical, based on an individual’s manifestation of three or more symptoms from a list of seven. AD risk can be traced to both genetic and environmental sources. Most genetic studies of AD risk implicitly assume that an AD diagnosis represents a single underlying genetic factor. We recently found that the criteria for an AD diagnosis represent three somewhat distinct genetic paths to individual risk. Specifically, heavy use and tolerance versus withdrawal and continued use despite problems reflected separate genetic factors. However, some data suggest that genetic risk for AD is adequately described with a single underlying genetic risk factor. Rodent animal models for alcohol-related phenotypes typically target discrete aspects of the complex human AD diagnosis. Here, we review the literature derived from genetic animal models in an attempt to determine whether they support a single-factor or multiple-factor genetic structure. We conclude that there is modest support in the animal literature that alcohol tolerance and withdrawal reflect distinct genetic risk factors, in agreement with our human data. We suggest areas where more research could clarify this attempt to align the rodent and human data. PMID:21910077

Genetic analysis of milk production traits of Tunisian Holsteins using random regression test-day model with Legendre polynomials

PubMed Central

2018-01-01

Objective The objective of this study was to estimate genetic parameters of milk, fat, and protein yields within and across lactations in Tunisian Holsteins using a random regression test-day (TD) model. Methods A random regression multiple trait multiple lactation TD model was used to estimate genetic parameters in the Tunisian dairy cattle population. Data were TD yields of milk, fat, and protein from the first three lactations. Random regressions were modeled with third-order Legendre polynomials for the additive genetic, and permanent environment effects. Heritabilities, and genetic correlations were estimated by Bayesian techniques using the Gibbs sampler. Results All variance components tended to be high in the beginning and the end of lactations. Additive genetic variances for milk, fat, and protein yields were the lowest and were the least variable compared to permanent variances. Heritability values tended to increase with parity. Estimates of heritabilities for 305-d yield-traits were low to moderate, 0.14 to 0.2, 0.12 to 0.17, and 0.13 to 0.18 for milk, fat, and protein yields, respectively. Within-parity, genetic correlations among traits were up to 0.74. Genetic correlations among lactations for the yield traits were relatively high and ranged from 0.78±0.01 to 0.82±0.03, between the first and second parities, from 0.73±0.03 to 0.8±0.04 between the first and third parities, and from 0.82±0.02 to 0.84±0.04 between the second and third parities. Conclusion These results are comparable to previously reported estimates on the same population, indicating that the adoption of a random regression TD model as the official genetic evaluation for production traits in Tunisia, as developed by most Interbull countries, is possible in the Tunisian Holsteins. PMID:28823122

Genetic analysis of milk production traits of Tunisian Holsteins using random regression test-day model with Legendre polynomials.

PubMed

Ben Zaabza, Hafedh; Ben Gara, Abderrahmen; Rekik, Boulbaba

2018-05-01

The objective of this study was to estimate genetic parameters of milk, fat, and protein yields within and across lactations in Tunisian Holsteins using a random regression test-day (TD) model. A random regression multiple trait multiple lactation TD model was used to estimate genetic parameters in the Tunisian dairy cattle population. Data were TD yields of milk, fat, and protein from the first three lactations. Random regressions were modeled with third-order Legendre polynomials for the additive genetic, and permanent environment effects. Heritabilities, and genetic correlations were estimated by Bayesian techniques using the Gibbs sampler. All variance components tended to be high in the beginning and the end of lactations. Additive genetic variances for milk, fat, and protein yields were the lowest and were the least variable compared to permanent variances. Heritability values tended to increase with parity. Estimates of heritabilities for 305-d yield-traits were low to moderate, 0.14 to 0.2, 0.12 to 0.17, and 0.13 to 0.18 for milk, fat, and protein yields, respectively. Within-parity, genetic correlations among traits were up to 0.74. Genetic correlations among lactations for the yield traits were relatively high and ranged from 0.78±0.01 to 0.82±0.03, between the first and second parities, from 0.73±0.03 to 0.8±0.04 between the first and third parities, and from 0.82±0.02 to 0.84±0.04 between the second and third parities. These results are comparable to previously reported estimates on the same population, indicating that the adoption of a random regression TD model as the official genetic evaluation for production traits in Tunisia, as developed by most Interbull countries, is possible in the Tunisian Holsteins.

Therapeutic Remyelination Strategies in a Novel Model of Multiple Sclerosis: Japanese Macaque Encephalomyelitis

DTIC Science & Technology

2010-05-01

viruses, especially gama- herpesviruses , may act as a trigger of multiple sclerosis (MS; Ascherio and Munger, 2010). Furthermore, there is growing...than others, suggesting a genetic pre-disposition to JME. Furthermore, we have cloned a gamma- herpesvirus (called Japanese macaque rhadovirus; JMRV...models in non-human primates and rodents. We aim to use this model to better understand how gamma- herpesviruses trigger MS; whether polymorphisms in

Genetic analysis across differential spatial scales reveals multiple dispersal mechanisms for the invasive hydrozoan Cordylophora in the Great Lakes

EPA Science Inventory

Understanding patterns of post-establishment spread by invasive species is critically important for the design of effective management strategies and the development of appropriate theoretical models predicting spatial expansion of introduced populations. Here we explore genetic ...

Generation and comparison of CRISPR-Cas9 and Cre-mediated genetically engineered mouse models of sarcoma

PubMed Central

Huang, Jianguo; Chen, Mark; Whitley, Melodi Javid; Kuo, Hsuan-Cheng; Xu, Eric S.; Walens, Andrea; Mowery, Yvonne M.; Van Mater, David; Eward, William C.; Cardona, Diana M.; Luo, Lixia; Ma, Yan; Lopez, Omar M.; Nelson, Christopher E.; Robinson-Hamm, Jacqueline N.; Reddy, Anupama; Dave, Sandeep S.; Gersbach, Charles A.; Dodd, Rebecca D.; Kirsch, David G.

2017-01-01

Genetically engineered mouse models that employ site-specific recombinase technology are important tools for cancer research but can be costly and time-consuming. The CRISPR-Cas9 system has been adapted to generate autochthonous tumours in mice, but how these tumours compare to tumours generated by conventional recombinase technology remains to be fully explored. Here we use CRISPR-Cas9 to generate multiple subtypes of primary sarcomas efficiently in wild type and genetically engineered mice. These data demonstrate that CRISPR-Cas9 can be used to generate multiple subtypes of soft tissue sarcomas in mice. Primary sarcomas generated with CRISPR-Cas9 and Cre recombinase technology had similar histology, growth kinetics, copy number variation and mutational load as assessed by whole exome sequencing. These results show that sarcomas generated with CRISPR-Cas9 technology are similar to sarcomas generated with conventional modelling techniques and suggest that CRISPR-Cas9 can be used to more rapidly generate genotypically and phenotypically similar cancers. PMID:28691711

Natural genetic variation profoundly regulates gene expression in immune cells and dictates susceptibility to CNS autoimmunity

PubMed Central

Bearoff, Frank; del Rio, Roxana; Case, Laure K.; Dragon, Julie A.; Nguyen-Vu, Trang; Lin, Chin-Yo; Blankenhorn, Elizabeth P.; Teuscher, Cory; Krementsov, Dimitry N.

2016-01-01

Regulation of gene expression in immune cells is known to be under genetic control, and likely contributes to susceptibility to autoimmune diseases, such as multiple sclerosis (MS). How this occurs in concert across multiple immune cell types is poorly understood. Using a mouse model that harnesses the genetic diversity of wild-derived mice, more accurately reflecting genetically diverse human populations, we provide an extensive characterization of the genetic regulation of gene expression in five different naïve immune cell types relevant to MS. The immune cell transcriptome is shown to be under profound genetic control, exhibiting diverse patterns: global, cell-specific, and sex-specific. Bioinformatic analysis of the genetically-controlled transcript networks reveals reduced cell type-specificity and inflammatory activity in wild-derived PWD/PhJ mice, compared with the conventional laboratory strain C57BL/6J. Additionally, candidate MS-GWAS genes were significantly enriched among transcripts overrepresented in C57BL/6J cells compared to PWD. These expression level differences correlate with robust differences in susceptibility to experimental autoimmune encephalomyelitis, the principal model of MS, and skewing of the encephalitogenic T cell responses. Taken together, our results provide functional insights into the genetic regulation of the immune transcriptome, and shed light on how this in turn contributes to susceptibility to autoimmune disease. PMID:27653816

Upper Secondary Students' Understanding of the Use of Multiple Models in Biology Textbooks--The Importance of Conceptual Variation and Incommensurability

ERIC Educational Resources Information Center

Gericke, Niklas; Hagberg, Mariana; Jorde, Doris

2013-01-01

In this study we investigate students' ability to discern conceptual variation and the use of multiple models in genetics when reading content-specific excerpts from biology textbooks. Using the history and philosophy of science as our reference, we were able to develop a research instrument allowing students themselves to investigate the…

Empirical Refinements of a Molecular Genetics Learning Progression: The Molecular Constructs

ERIC Educational Resources Information Center

Todd, Amber; Kenyon, Lisa

2016-01-01

This article describes revisions to four of the eight constructs of the Duncan molecular genetics learning progression [Duncan, Rogat, & Yarden, (2009)]. As learning progressions remain hypothetical models until validated by multiple rounds of empirical studies, these revisions are an important step toward validating the progression. Our…

Genetic Algorithm Phase Retrieval for the Systematic Image-Based Optical Alignment Testbed

NASA Technical Reports Server (NTRS)

Rakoczy, John; Steincamp, James; Taylor, Jaime

2003-01-01

A reduced surrogate, one point crossover genetic algorithm with random rank-based selection was used successfully to estimate the multiple phases of a segmented optical system modeled on the seven-mirror Systematic Image-Based Optical Alignment testbed located at NASA's Marshall Space Flight Center.

Functional linear models for association analysis of quantitative traits.

PubMed

Fan, Ruzong; Wang, Yifan; Mills, James L; Wilson, Alexander F; Bailey-Wilson, Joan E; Xiong, Momiao

2013-11-01

Functional linear models are developed in this paper for testing associations between quantitative traits and genetic variants, which can be rare variants or common variants or the combination of the two. By treating multiple genetic variants of an individual in a human population as a realization of a stochastic process, the genome of an individual in a chromosome region is a continuum of sequence data rather than discrete observations. The genome of an individual is viewed as a stochastic function that contains both linkage and linkage disequilibrium (LD) information of the genetic markers. By using techniques of functional data analysis, both fixed and mixed effect functional linear models are built to test the association between quantitative traits and genetic variants adjusting for covariates. After extensive simulation analysis, it is shown that the F-distributed tests of the proposed fixed effect functional linear models have higher power than that of sequence kernel association test (SKAT) and its optimal unified test (SKAT-O) for three scenarios in most cases: (1) the causal variants are all rare, (2) the causal variants are both rare and common, and (3) the causal variants are common. The superior performance of the fixed effect functional linear models is most likely due to its optimal utilization of both genetic linkage and LD information of multiple genetic variants in a genome and similarity among different individuals, while SKAT and SKAT-O only model the similarities and pairwise LD but do not model linkage and higher order LD information sufficiently. In addition, the proposed fixed effect models generate accurate type I error rates in simulation studies. We also show that the functional kernel score tests of the proposed mixed effect functional linear models are preferable in candidate gene analysis and small sample problems. The methods are applied to analyze three biochemical traits in data from the Trinity Students Study. © 2013 WILEY PERIODICALS, INC.

[Analysis of genetic models and gene effects on main agronomy characters in rapeseed].

PubMed

Li, J; Qiu, J; Tang, Z; Shen, L

1992-01-01

According to four different genetic models, the genetic patterns of 8 agronomy traits were analysed by using the data of 24 generations which included positive and negative cross of 81008 x Tower, both of the varieties are of good quality. The results showed that none of 8 characters could fit in with additive-dominance models. Epistasis was found in all of these characters, and it has significant effect on generation means. Seed weight/plant and some other main yield characters are controlled by duplicate interaction genes. The interaction between triple genes or multiple genes needs to be utilized in yield heterosis.

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.

Testing for multiple invasion routes and source populations for the invasive brown treesnake (Boiga irregularis) on Guam: implications for pest management

USGS Publications Warehouse

Richmond, Jonathan Q.; Wood, Dustin A.; Stanford, James W.; Fisher, Robert N.

2014-01-01

The brown treesnake (Boiga irregularis) population on the Pacific island of Guam has reached iconic status as one of the most destructive invasive species of modern times, yet no published works have used genetic data to identify a source population. We used DNA sequence data from multiple genetic markers and coalescent-based phylogenetic methods to place the Guam population within the broader phylogeographic context of B. irregularis across its native range and tested whether patterns of genetic variation on the island are consistent with one or multiple introductions from different source populations. We also modeled a series of demographic scenarios that differed in the effective size and duration of a population bottleneck immediately following the invasion on Guam, and measured the fit of these simulations to the observed data using approximate Bayesian computation. Our results exclude the possibility of serial introductions from different source populations, and instead verify a single origin from the Admiralty Archipelago off the north coast of Papua New Guinea. This finding is consistent with the hypothesis thatB. irregularis was accidentally transported to Guam during military relocation efforts at the end of World War II. Demographic model comparisons suggest that multiple snakes were transported to Guam from the source locality, but that fewer than 10 individuals could be responsible for establishing the population. Our results also provide evidence that low genetic diversity stemming from the founder event has not been a hindrance to the ecological success of B. irregularis on Guam, and at the same time offers a unique ‘genetic opening’ to manage snake density using classical biological approaches.

A method to estimate the contribution of regional genetic associations to complex traits from summary association statistics.

PubMed

Pare, Guillaume; Mao, Shihong; Deng, Wei Q

2016-06-08

Despite considerable efforts, known genetic associations only explain a small fraction of predicted heritability. Regional associations combine information from multiple contiguous genetic variants and can improve variance explained at established association loci. However, regional associations are not easily amenable to estimation using summary association statistics because of sensitivity to linkage disequilibrium (LD). We now propose a novel method, LD Adjusted Regional Genetic Variance (LARGV), to estimate phenotypic variance explained by regional associations using summary statistics while accounting for LD. Our method is asymptotically equivalent to a multiple linear regression model when no interaction or haplotype effects are present. It has several applications, such as ranking of genetic regions according to variance explained or comparison of variance explained by two or more regions. Using height and BMI data from the Health Retirement Study (N = 7,776), we show that most genetic variance lies in a small proportion of the genome and that previously identified linkage peaks have higher than expected regional variance.

A method to estimate the contribution of regional genetic associations to complex traits from summary association statistics

PubMed Central

Pare, Guillaume; Mao, Shihong; Deng, Wei Q.

2016-01-01

Despite considerable efforts, known genetic associations only explain a small fraction of predicted heritability. Regional associations combine information from multiple contiguous genetic variants and can improve variance explained at established association loci. However, regional associations are not easily amenable to estimation using summary association statistics because of sensitivity to linkage disequilibrium (LD). We now propose a novel method, LD Adjusted Regional Genetic Variance (LARGV), to estimate phenotypic variance explained by regional associations using summary statistics while accounting for LD. Our method is asymptotically equivalent to a multiple linear regression model when no interaction or haplotype effects are present. It has several applications, such as ranking of genetic regions according to variance explained or comparison of variance explained by two or more regions. Using height and BMI data from the Health Retirement Study (N = 7,776), we show that most genetic variance lies in a small proportion of the genome and that previously identified linkage peaks have higher than expected regional variance. PMID:27273519

Distribution path robust optimization of electric vehicle with multiple distribution centers

PubMed Central

Hao, Wei; He, Ruichun; Jia, Xiaoyan; Pan, Fuquan; Fan, Jing; Xiong, Ruiqi

2018-01-01

To identify electrical vehicle (EV) distribution paths with high robustness, insensitivity to uncertainty factors, and detailed road-by-road schemes, optimization of the distribution path problem of EV with multiple distribution centers and considering the charging facilities is necessary. With the minimum transport time as the goal, a robust optimization model of EV distribution path with adjustable robustness is established based on Bertsimas’ theory of robust discrete optimization. An enhanced three-segment genetic algorithm is also developed to solve the model, such that the optimal distribution scheme initially contains all road-by-road path data using the three-segment mixed coding and decoding method. During genetic manipulation, different interlacing and mutation operations are carried out on different chromosomes, while, during population evolution, the infeasible solution is naturally avoided. A part of the road network of Xifeng District in Qingyang City is taken as an example to test the model and the algorithm in this study, and the concrete transportation paths are utilized in the final distribution scheme. Therefore, more robust EV distribution paths with multiple distribution centers can be obtained using the robust optimization model. PMID:29518169

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

PubMed Central

Monir, Md. Mamun; Zhu, Jun

2017-01-01

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

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.

Introductory Biology Students’ Conceptual Models and Explanations of the Origin of Variation

PubMed Central

Shaw, Neil; Momsen, Jennifer; Reinagel, Adam; Le, Paul; Taqieddin, Ranya; Long, Tammy

2014-01-01

Mutation is the key molecular mechanism generating phenotypic variation, which is the basis for evolution. In an introductory biology course, we used a model-based pedagogy that enabled students to integrate their understanding of genetics and evolution within multiple case studies. We used student-generated conceptual models to assess understanding of the origin of variation. By midterm, only a small percentage of students articulated complete and accurate representations of the origin of variation in their models. Targeted feedback was offered through activities requiring students to critically evaluate peers’ models. At semester's end, a substantial proportion of students significantly improved their representation of how variation arises (though one-third still did not include mutation in their models). Students’ written explanations of the origin of variation were mostly consistent with their models, although less effective than models in conveying mechanistic reasoning. This study contributes evidence that articulating the genetic origin of variation is particularly challenging for learners and may require multiple cycles of instruction, assessment, and feedback. To support meaningful learning of the origin of variation, we advocate instruction that explicitly integrates multiple scales of biological organization, assessment that promotes and reveals mechanistic and causal reasoning, and practice with explanatory models with formative feedback. PMID:25185235

Genetic variants in Alzheimer disease – molecular and brain network approaches

PubMed Central

Gaiteri, Chris; Mostafavi, Sara; Honey, Christopher; De Jager, Philip L.; Bennett, David A.

2016-01-01

Genetic studies in late-onset Alzheimer disease (LOAD) are aimed at identifying core disease mechanisms and providing potential biomarkers and drug candidates to improve clinical care for AD. However, due to the complexity of LOAD, including pathological heterogeneity and disease polygenicity, extracting actionable guidance from LOAD genetics has been challenging. Past attempts to summarize the effects of LOAD-associated genetic variants have used pathway analysis and collections of small-scale experiments to hypothesize functional convergence across several variants. In this review, we discuss how the study of molecular, cellular and brain networks provides additional information on the effect of LOAD-associated genetic variants. We then discuss emerging combinations of omic data types in multiscale models, which provide a more comprehensive representation of the effect of LOAD-associated genetic variants at multiple biophysical scales. Further, we highlight the clinical potential of mechanistically coupling genetic variants and disease phenotypes with multiscale brain models. PMID:27282653

Males and Females Contribute Unequally to Offspring Genetic Diversity in the Polygynandrous Mating System of Wild Boar

PubMed Central

Pérez-González, Javier; Costa, Vânia; Santos, Pedro; Slate, Jon; Carranza, Juan; Fernández-Llario, Pedro; Zsolnai, Attila; Monteiro, Nuno M.; Anton, István; Buzgó, József; Varga, Gyula; Beja-Pereira, Albano

2014-01-01

The maintenance of genetic diversity across generations depends on both the number of reproducing males and females. Variance in reproductive success, multiple paternity and litter size can all affect the relative contributions of male and female parents to genetic variation of progeny. The mating system of the wild boar (Sus scrofa) has been described as polygynous, although evidence of multiple paternity in litters has been found. Using 14 microsatellite markers, we evaluated the contribution of males and females to genetic variation in the next generation in independent wild boar populations from the Iberian Peninsula and Hungary. Genetic contributions of males and females were obtained by distinguishing the paternal and maternal genetic component inherited by the progeny. We found that the paternally inherited genetic component of progeny was more diverse than the maternally inherited component. Simulations showed that this finding might be due to a sampling bias. However, after controlling for the bias by fitting both the genetic diversity in the adult population and the number of reproductive individuals in the models, paternally inherited genotypes remained more diverse than those inherited maternally. Our results suggest new insights into how promiscuous mating systems can help maintain genetic variation. PMID:25541986

The genetic basis of female multiple mating in a polyandrous livebearing fish

PubMed Central

Evans, Jonathan P; Gasparini, Clelia

2013-01-01

The widespread occurrence of female multiple mating (FMM) demands evolutionary explanation, particularly in the light of the costs of mating. One explanation encapsulated by “good sperm” and “sexy-sperm” (GS-SS) theoretical models is that FMM facilitates sperm competition, thus ensuring paternity by males that pass on genes for elevated sperm competitiveness to their male offspring. While support for this component of GS-SS theory is accumulating, a second but poorly tested assumption of these models is that there should be corresponding heritable genetic variation in FMM – the proposed mechanism of postcopulatory preferences underlying GS-SS models. Here, we conduct quantitative genetic analyses on paternal half-siblings to test this component of GS-SS theory in the guppy (Poecilia reticulata), a freshwater fish with some of the highest known rates of FMM in vertebrates. As with most previous quantitative genetic analyses of FMM in other species, our results reveal high levels of phenotypic variation in this trait and a correspondingly low narrow-sense heritability (h2 = 0.11). Furthermore, although our analysis of additive genetic variance in FMM was not statistically significant (probably owing to limited statistical power), the ensuing estimate of mean-standardized additive genetic variance (IA = 0.7) was nevertheless relatively low compared with estimates published for life-history traits across a broad range of taxa. Our results therefore add to a growing body of evidence that FMM is characterized by relatively low additive genetic variation, thus apparently contradicting GS-SS theory. However, we qualify this conclusion by drawing attention to potential deficiencies in most designs (including ours) that have tested for genetic variation in FMM, particularly those that fail to account for intersexual interactions that underlie FMM in many systems. PMID:23403856

A Model of Compound Heterozygous, Loss-of-Function Alleles Is Broadly Consistent with Observations from Complex-Disease GWAS Datasets

PubMed Central

Sanjak, Jaleal S.; Long, Anthony D.; Thornton, Kevin R.

2017-01-01

The genetic component of complex disease risk in humans remains largely unexplained. A corollary is that the allelic spectrum of genetic variants contributing to complex disease risk is unknown. Theoretical models that relate population genetic processes to the maintenance of genetic variation for quantitative traits may suggest profitable avenues for future experimental design. Here we use forward simulation to model a genomic region evolving under a balance between recurrent deleterious mutation and Gaussian stabilizing selection. We consider multiple genetic and demographic models, and several different methods for identifying genomic regions harboring variants associated with complex disease risk. We demonstrate that the model of gene action, relating genotype to phenotype, has a qualitative effect on several relevant aspects of the population genetic architecture of a complex trait. In particular, the genetic model impacts genetic variance component partitioning across the allele frequency spectrum and the power of statistical tests. Models with partial recessivity closely match the minor allele frequency distribution of significant hits from empirical genome-wide association studies without requiring homozygous effect sizes to be small. We highlight a particular gene-based model of incomplete recessivity that is appealing from first principles. Under that model, deleterious mutations in a genomic region partially fail to complement one another. This model of gene-based recessivity predicts the empirically observed inconsistency between twin and SNP based estimated of dominance heritability. Furthermore, this model predicts considerable levels of unexplained variance associated with intralocus epistasis. Our results suggest a need for improved statistical tools for region based genetic association and heritability estimation. PMID:28103232

Modeling Human Cancers in Drosophila.

PubMed

Sonoshita, M; Cagan, R L

2017-01-01

Cancer is a complex disease that affects multiple organs. Whole-body animal models provide important insights into oncology that can lead to clinical impact. Here, we review novel concepts that Drosophila studies have established for cancer biology, drug discovery, and patient therapy. Genetic studies using Drosophila have explored the roles of oncogenes and tumor-suppressor genes that when dysregulated promote cancer formation, making Drosophila a useful model to study multiple aspects of transformation. Not limited to mechanism analyses, Drosophila has recently been showing its value in facilitating drug development. Flies offer rapid, efficient platforms by which novel classes of drugs can be identified as candidate anticancer leads. Further, we discuss the use of Drosophila as a platform to develop therapies for individual patients by modeling the tumor's genetic complexity. Drosophila provides both a classical and a novel tool to identify new therapeutics, complementing other more traditional cancer tools. © 2017 Elsevier Inc. All rights reserved.

Dynamic regulation of genetic pathways and targets during aging in Caenorhabditis elegans.

PubMed

He, Kan; Zhou, Tao; Shao, Jiaofang; Ren, Xiaoliang; Zhao, Zhongying; Liu, Dahai

2014-03-01

Numerous genetic targets and some individual pathways associated with aging have been identified using the worm model. However, less is known about the genetic mechanisms of aging in genome wide, particularly at the level of multiple pathways as well as the regulatory networks during aging. Here, we employed the gene expression datasets of three time points during aging in Caenorhabditis elegans (C. elegans) and performed the approach of gene set enrichment analysis (GSEA) on each dataset between adjacent stages. As a result, multiple genetic pathways and targets were identified as significantly down- or up-regulated. Among them, 5 truly aging-dependent signaling pathways including MAPK signaling pathway, mTOR signaling pathway, Wnt signaling pathway, TGF-beta signaling pathway and ErbB signaling pathway as well as 12 significantly associated genes were identified with dynamic expression pattern during aging. On the other hand, the continued declines in the regulation of several metabolic pathways have been demonstrated to display age-related changes. Furthermore, the reconstructed regulatory networks based on three of aging related Chromatin immunoprecipitation experiments followed by sequencing (ChIP-seq) datasets and the expression matrices of 154 involved genes in above signaling pathways provide new insights into aging at the multiple pathways level. The combination of multiple genetic pathways and targets needs to be taken into consideration in future studies of aging, in which the dynamic regulation would be uncovered.

The zebrafish eye—a paradigm for investigating human ocular genetics

PubMed Central

Richardson, R; Tracey-White, D; Webster, A; Moosajee, M

2017-01-01

Although human epidemiological and genetic studies are essential to elucidate the aetiology of normal and aberrant ocular development, animal models have provided us with an understanding of the pathogenesis of multiple developmental ocular malformations. Zebrafish eye development displays in depth molecular complexity and stringent spatiotemporal regulation that incorporates developmental contributions of the surface ectoderm, neuroectoderm and head mesenchyme, similar to that seen in humans. For this reason, and due to its genetic tractability, external fertilisation, and early optical clarity, the zebrafish has become an invaluable vertebrate system to investigate human ocular development and disease. Recently, zebrafish have been at the leading edge of preclinical therapy development, with their amenability to genetic manipulation facilitating the generation of robust ocular disease models required for large-scale genetic and drug screening programmes. This review presents an overview of human and zebrafish ocular development, genetic methodologies employed for zebrafish mutagenesis, relevant models of ocular disease, and finally therapeutic approaches, which may have translational leads in the future. PMID:27612182

Modeling misidentification errors that result from use of genetic tags in capture-recapture studies

USGS Publications Warehouse

Yoshizaki, J.; Brownie, C.; Pollock, K.H.; Link, W.A.

2011-01-01

Misidentification of animals is potentially important when naturally existing features (natural tags) such as DNA fingerprints (genetic tags) are used to identify individual animals. For example, when misidentification leads to multiple identities being assigned to an animal, traditional estimators tend to overestimate population size. Accounting for misidentification in capture-recapture models requires detailed understanding of the mechanism. Using genetic tags as an example, we outline a framework for modeling the effect of misidentification in closed population studies when individual identification is based on natural tags that are consistent over time (non-evolving natural tags). We first assume a single sample is obtained per animal for each capture event, and then generalize to the case where multiple samples (such as hair or scat samples) are collected per animal per capture occasion. We introduce methods for estimating population size and, using a simulation study, we show that our new estimators perform well for cases with moderately high capture probabilities or high misidentification rates. In contrast, conventional estimators can seriously overestimate population size when errors due to misidentification are ignored. ?? 2009 Springer Science+Business Media, LLC.

Optimizing multiple sequence alignments using a genetic algorithm based on three objectives: structural information, non-gaps percentage and totally conserved columns.

PubMed

Ortuño, Francisco M; Valenzuela, Olga; Rojas, Fernando; Pomares, Hector; Florido, Javier P; Urquiza, Jose M; Rojas, Ignacio

2013-09-01

Multiple sequence alignments (MSAs) are widely used approaches in bioinformatics to carry out other tasks such as structure predictions, biological function analyses or phylogenetic modeling. However, current tools usually provide partially optimal alignments, as each one is focused on specific biological features. Thus, the same set of sequences can produce different alignments, above all when sequences are less similar. Consequently, researchers and biologists do not agree about which is the most suitable way to evaluate MSAs. Recent evaluations tend to use more complex scores including further biological features. Among them, 3D structures are increasingly being used to evaluate alignments. Because structures are more conserved in proteins than sequences, scores with structural information are better suited to evaluate more distant relationships between sequences. The proposed multiobjective algorithm, based on the non-dominated sorting genetic algorithm, aims to jointly optimize three objectives: STRIKE score, non-gaps percentage and totally conserved columns. It was significantly assessed on the BAliBASE benchmark according to the Kruskal-Wallis test (P < 0.01). This algorithm also outperforms other aligners, such as ClustalW, Multiple Sequence Alignment Genetic Algorithm (MSA-GA), PRRP, DIALIGN, Hidden Markov Model Training (HMMT), Pattern-Induced Multi-sequence Alignment (PIMA), MULTIALIGN, Sequence Alignment Genetic Algorithm (SAGA), PILEUP, Rubber Band Technique Genetic Algorithm (RBT-GA) and Vertical Decomposition Genetic Algorithm (VDGA), according to the Wilcoxon signed-rank test (P < 0.05), whereas it shows results not significantly different to 3D-COFFEE (P > 0.05) with the advantage of being able to use less structures. Structural information is included within the objective function to evaluate more accurately the obtained alignments. The source code is available at http://www.ugr.es/~fortuno/MOSAStrE/MO-SAStrE.zip.

Age at First Use and Later Substance Use Disorder: Shared Genetic and Environmental Pathways for Nicotine, Alcohol, and Cannabis

PubMed Central

Richmond-Rakerd, Leah S.; Slutske, Wendy S.; Lynskey, Michael T.; Agrawal, Arpana; Madden, Pamela A.F.; Bucholz, Kathleen K.; Heath, Andrew C.; Statham, Dixie J.; Martin, Nicholas G.

2016-01-01

Behavioral genetic studies have provided insights into why early substance use initiation is associated with increased risk for disorder. Few genetically-informative studies, however, have operationalized initiation as the timing of first use and simultaneously modeled the timing of initiation and problematic use of multiple substances. Such research can help capture the risk associated with early initiation and determine the extent to which genetic and environmental risk generalizes across substances. This study utilized a behavior genetic approach to examine the relation between the age of substance use initiation and symptoms of substance use disorder. Participants were 7,285 monozygotic and dizygotic twins (40% male, mean age at interview=30.6) from the Australian Twin Registry who reported on their ages of tobacco, alcohol, and cannabis initiation and symptoms of DSM-IV nicotine dependence, alcohol use disorder, and cannabis use disorder. Biometric modeling was conducted to (a) determine the structure of genetic and environmental influences on initiation and disorder and (b) examine their genetic and environmental overlap. The latent structure of initiation differed across men and women. The familial covariance between initiation and disorder was genetic among men and genetic and environmental among women, suggesting that the relation between first substance use and disorder is partly explained by a shared liability. After accounting for familial overlap, significant unique environmental correlations were observed, indicating that the age of initiation of multiple drugs may directly increase risk for substance-related problems. Results support the utility of conceptualizing initiation in terms of age and of adopting a multivariate approach. PMID:27537477

Multivariate random regression analysis for body weight and main morphological traits in genetically improved farmed tilapia (Oreochromis niloticus).

PubMed

He, Jie; Zhao, Yunfeng; Zhao, Jingli; Gao, Jin; Han, Dandan; Xu, Pao; Yang, Runqing

2017-11-02

Because of their high economic importance, growth traits in fish are under continuous improvement. For growth traits that are recorded at multiple time-points in life, the use of univariate and multivariate animal models is limited because of the variable and irregular timing of these measures. Thus, the univariate random regression model (RRM) was introduced for the genetic analysis of dynamic growth traits in fish breeding. We used a multivariate random regression model (MRRM) to analyze genetic changes in growth traits recorded at multiple time-point of genetically-improved farmed tilapia. Legendre polynomials of different orders were applied to characterize the influences of fixed and random effects on growth trajectories. The final MRRM was determined by optimizing the univariate RRM for the analyzed traits separately via penalizing adaptively the likelihood statistical criterion, which is superior to both the Akaike information criterion and the Bayesian information criterion. In the selected MRRM, the additive genetic effects were modeled by Legendre polynomials of three orders for body weight (BWE) and body length (BL) and of two orders for body depth (BD). By using the covariance functions of the MRRM, estimated heritabilities were between 0.086 and 0.628 for BWE, 0.155 and 0.556 for BL, and 0.056 and 0.607 for BD. Only heritabilities for BD measured from 60 to 140 days of age were consistently higher than those estimated by the univariate RRM. All genetic correlations between growth time-points exceeded 0.5 for either single or pairwise time-points. Moreover, correlations between early and late growth time-points were lower. Thus, for phenotypes that are measured repeatedly in aquaculture, an MRRM can enhance the efficiency of the comprehensive selection for BWE and the main morphological traits.

Defining the role of polyamines in colon carcinogenesis using mouse models

PubMed Central

Ignatenko, Natalia A.; Gerner, Eugene W.; Besselsen, David G.

2011-01-01

Genetics and diet are both considered important risk determinants for colorectal cancer, a leading cause of death in the US and worldwide. Genetically engineered mouse (GEM) models have made a significant contribution to the characterization of colorectal cancer risk factors. Reliable, reproducible, and clinically relevant animal models help in the identification of the molecular events associated with disease progression and in the development of effictive treatment strategies. This review is focused on the use of mouse models for studying the role of polyamines in colon carcinogenesis. We describe how the available mouse models of colon cancer such as the multiple intestinal neoplasia (Min) mice and knockout genetic models facilitate understanding of the role of polyamines in colon carcinogenesis and help in the development of a rational strategy for colon cancer chemoprevention. PMID:21712957

Uncovering Local Trends in Genetic Effects of Multiple Phenotypes via Functional Linear Models.

PubMed

Vsevolozhskaya, Olga A; Zaykin, Dmitri V; Barondess, David A; Tong, Xiaoren; Jadhav, Sneha; Lu, Qing

2016-04-01

Recent technological advances equipped researchers with capabilities that go beyond traditional genotyping of loci known to be polymorphic in a general population. Genetic sequences of study participants can now be assessed directly. This capability removed technology-driven bias toward scoring predominantly common polymorphisms and let researchers reveal a wealth of rare and sample-specific variants. Although the relative contributions of rare and common polymorphisms to trait variation are being debated, researchers are faced with the need for new statistical tools for simultaneous evaluation of all variants within a region. Several research groups demonstrated flexibility and good statistical power of the functional linear model approach. In this work we extend previous developments to allow inclusion of multiple traits and adjustment for additional covariates. Our functional approach is unique in that it provides a nuanced depiction of effects and interactions for the variables in the model by representing them as curves varying over a genetic region. We demonstrate flexibility and competitive power of our approach by contrasting its performance with commonly used statistical tools and illustrate its potential for discovery and characterization of genetic architecture of complex traits using sequencing data from the Dallas Heart Study. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

Predicting type 2 diabetes using genetic and environmental risk factors in a multi-ethnic Malaysian cohort.

PubMed

Abdullah, N; Abdul Murad, N A; Mohd Haniff, E A; Syafruddin, S E; Attia, J; Oldmeadow, C; Kamaruddin, M A; Abd Jalal, N; Ismail, N; Ishak, M; Jamal, R; Scott, R J; Holliday, E G

2017-08-01

Malaysia has a high and rising prevalence of type 2 diabetes (T2D). While environmental (non-genetic) risk factors for the disease are well established, the role of genetic variations and gene-environment interactions remain understudied in this population. This study aimed to estimate the relative contributions of environmental and genetic risk factors to T2D in Malaysia and also to assess evidence for gene-environment interactions that may explain additional risk variation. This was a case-control study including 1604 Malays, 1654 Chinese and 1728 Indians from the Malaysian Cohort Project. The proportion of T2D risk variance explained by known genetic and environmental factors was assessed by fitting multivariable logistic regression models and evaluating McFadden's pseudo R 2 and the area under the receiver-operating characteristic curve (AUC). Models with and without the genetic risk score (GRS) were compared using the log likelihood ratio Chi-squared test and AUCs. Multiplicative interaction between genetic and environmental risk factors was assessed via logistic regression within and across ancestral groups. Interactions were assessed for the GRS and its 62 constituent variants. The models including environmental risk factors only had pseudo R 2 values of 16.5-28.3% and AUC of 0.75-0.83. Incorporating a genetic score aggregating 62 T2D-associated risk variants significantly increased the model fit (likelihood ratio P-value of 2.50 × 10 -4 -4.83 × 10 -12 ) and increased the pseudo R 2 by about 1-2% and AUC by 1-3%. None of the gene-environment interactions reached significance after multiple testing adjustment, either for the GRS or individual variants. For individual variants, 33 out of 310 tested associations showed nominal statistical significance with 0.001 < P < 0.05. This study suggests that known genetic risk variants contribute a significant but small amount to overall T2D risk variation in Malaysian population groups. If gene-environment interactions involving common genetic variants exist, they are likely of small effect, requiring substantially larger samples for detection. Copyright © 2017 The Royal Society for Public Health. All rights reserved.

Feature extraction from multiple data sources using genetic programming

NASA Astrophysics Data System (ADS)

Szymanski, John J.; Brumby, Steven P.; Pope, Paul A.; Eads, Damian R.; Esch-Mosher, Diana M.; Galassi, Mark C.; Harvey, Neal R.; McCulloch, Hersey D.; Perkins, Simon J.; Porter, Reid B.; Theiler, James P.; Young, Aaron C.; Bloch, Jeffrey J.; David, Nancy A.

2002-08-01

Feature extraction from imagery is an important and long-standing problem in remote sensing. In this paper, we report on work using genetic programming to perform feature extraction simultaneously from multispectral and digital elevation model (DEM) data. We use the GENetic Imagery Exploitation (GENIE) software for this purpose, which produces image-processing software that inherently combines spatial and spectral processing. GENIE is particularly useful in exploratory studies of imagery, such as one often does in combining data from multiple sources. The user trains the software by painting the feature of interest with a simple graphical user interface. GENIE then uses genetic programming techniques to produce an image-processing pipeline. Here, we demonstrate evolution of image processing algorithms that extract a range of land cover features including towns, wildfire burnscars, and forest. We use imagery from the DOE/NNSA Multispectral Thermal Imager (MTI) spacecraft, fused with USGS 1:24000 scale DEM data.

Eco-genetic modeling of contemporary life-history evolution.

PubMed

Dunlop, Erin S; Heino, Mikko; Dieckmann, Ulf

2009-10-01

We present eco-genetic modeling as a flexible tool for exploring the course and rates of multi-trait life-history evolution in natural populations. We build on existing modeling approaches by combining features that facilitate studying the ecological and evolutionary dynamics of realistically structured populations. In particular, the joint consideration of age and size structure enables the analysis of phenotypically plastic populations with more than a single growth trajectory, and ecological feedback is readily included in the form of density dependence and frequency dependence. Stochasticity and life-history trade-offs can also be implemented. Critically, eco-genetic models permit the incorporation of salient genetic detail such as a population's genetic variances and covariances and the corresponding heritabilities, as well as the probabilistic inheritance and phenotypic expression of quantitative traits. These inclusions are crucial for predicting rates of evolutionary change on both contemporary and longer timescales. An eco-genetic model can be tightly coupled with empirical data and therefore may have considerable practical relevance, in terms of generating testable predictions and evaluating alternative management measures. To illustrate the utility of these models, we present as an example an eco-genetic model used to study harvest-induced evolution of multiple traits in Atlantic cod. The predictions of our model (most notably that harvesting induces a genetic reduction in age and size at maturation, an increase or decrease in growth capacity depending on the minimum-length limit, and an increase in reproductive investment) are corroborated by patterns observed in wild populations. The predicted genetic changes occur together with plastic changes that could phenotypically mask the former. Importantly, our analysis predicts that evolutionary changes show little signs of reversal following a harvest moratorium. This illustrates how predictions offered by eco-genetic models can enable and guide evolutionarily sustainable resource management.

Radiogenomics to characterize regional genetic heterogeneity in glioblastoma

PubMed Central

Hu, Leland S.; Ning, Shuluo; Eschbacher, Jennifer M.; Baxter, Leslie C.; Gaw, Nathan; Ranjbar, Sara; Plasencia, Jonathan; Dueck, Amylou C.; Peng, Sen; Smith, Kris A.; Nakaji, Peter; Karis, John P.; Quarles, C. Chad; Wu, Teresa; Loftus, Joseph C.; Jenkins, Robert B.; Sicotte, Hugues; Kollmeyer, Thomas M.; O'Neill, Brian P.; Elmquist, William; Hoxworth, Joseph M.; Frakes, David; Sarkaria, Jann; Swanson, Kristin R.; Tran, Nhan L.; Li, Jing; Mitchell, J. Ross

2017-01-01

Background Glioblastoma (GBM) exhibits profound intratumoral genetic heterogeneity. Each tumor comprises multiple genetically distinct clonal populations with different therapeutic sensitivities. This has implications for targeted therapy and genetically informed paradigms. Contrast-enhanced (CE)-MRI and conventional sampling techniques have failed to resolve this heterogeneity, particularly for nonenhancing tumor populations. This study explores the feasibility of using multiparametric MRI and texture analysis to characterize regional genetic heterogeneity throughout MRI-enhancing and nonenhancing tumor segments. Methods We collected multiple image-guided biopsies from primary GBM patients throughout regions of enhancement (ENH) and nonenhancing parenchyma (so called brain-around-tumor, [BAT]). For each biopsy, we analyzed DNA copy number variants for core GBM driver genes reported by The Cancer Genome Atlas. We co-registered biopsy locations with MRI and texture maps to correlate regional genetic status with spatially matched imaging measurements. We also built multivariate predictive decision-tree models for each GBM driver gene and validated accuracies using leave-one-out-cross-validation (LOOCV). Results We collected 48 biopsies (13 tumors) and identified significant imaging correlations (univariate analysis) for 6 driver genes: EGFR, PDGFRA, PTEN, CDKN2A, RB1, and TP53. Predictive model accuracies (on LOOCV) varied by driver gene of interest. Highest accuracies were observed for PDGFRA (77.1%), EGFR (75%), CDKN2A (87.5%), and RB1 (87.5%), while lowest accuracy was observed in TP53 (37.5%). Models for 4 driver genes (EGFR, RB1, CDKN2A, and PTEN) showed higher accuracy in BAT samples (n = 16) compared with those from ENH segments (n = 32). Conclusion MRI and texture analysis can help characterize regional genetic heterogeneity, which offers potential diagnostic value under the paradigm of individualized oncology. PMID:27502248

A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains

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

Khodayari, Ali; Maranas, Costas D.

Kinetic models of metabolism at a genome scale that faithfully recapitulate the effect of multiple genetic interventions would be transformative in our ability to reliably design novel overproducing microbial strains. Here, we introduce k-ecoli457, a genome-scale kinetic model of Escherichia coli metabolism that satisfies fluxomic data for wild-type and 25 mutant strains under different substrates and growth conditions. The k-ecoli457 model contains 457 model reactions, 337 metabolites and 295 substrate-level regulatory interactions. Parameterization is carried out using a genetic algorithm by simultaneously imposing all available fluxomic data (about 30 measured fluxes per mutant). Furthermore, the Pearson correlation coefficient between experimentalmore » data and predicted product yields for 320 engineered strains spanning 24 product metabolites is 0.84. This is substantially higher than that using flux balance analysis, minimization of metabolic adjustment or maximization of product yield exhibiting systematic errors with correlation coefficients of, respectively, 0.18, 0.37 and 0.47.« less

A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains

DOE PAGES

Khodayari, Ali; Maranas, Costas D.

2016-12-20

Kinetic models of metabolism at a genome scale that faithfully recapitulate the effect of multiple genetic interventions would be transformative in our ability to reliably design novel overproducing microbial strains. Here, we introduce k-ecoli457, a genome-scale kinetic model of Escherichia coli metabolism that satisfies fluxomic data for wild-type and 25 mutant strains under different substrates and growth conditions. The k-ecoli457 model contains 457 model reactions, 337 metabolites and 295 substrate-level regulatory interactions. Parameterization is carried out using a genetic algorithm by simultaneously imposing all available fluxomic data (about 30 measured fluxes per mutant). Furthermore, the Pearson correlation coefficient between experimentalmore » data and predicted product yields for 320 engineered strains spanning 24 product metabolites is 0.84. This is substantially higher than that using flux balance analysis, minimization of metabolic adjustment or maximization of product yield exhibiting systematic errors with correlation coefficients of, respectively, 0.18, 0.37 and 0.47.« less

Genetic Programming Transforms in Linear Regression Situations

NASA Astrophysics Data System (ADS)

Castillo, Flor; Kordon, Arthur; Villa, Carlos

The chapter summarizes the use of Genetic Programming (GP) inMultiple Linear Regression (MLR) to address multicollinearity and Lack of Fit (LOF). The basis of the proposed method is applying appropriate input transforms (model respecification) that deal with these issues while preserving the information content of the original variables. The transforms are selected from symbolic regression models with optimal trade-off between accuracy of prediction and expressional complexity, generated by multiobjective Pareto-front GP. The chapter includes a comparative study of the GP-generated transforms with Ridge Regression, a variant of ordinary Multiple Linear Regression, which has been a useful and commonly employed approach for reducing multicollinearity. The advantages of GP-generated model respecification are clearly defined and demonstrated. Some recommendations for transforms selection are given as well. The application benefits of the proposed approach are illustrated with a real industrial application in one of the broadest empirical modeling areas in manufacturing - robust inferential sensors. The chapter contributes to increasing the awareness of the potential of GP in statistical model building by MLR.

Predicting Landscape-Genetic Consequences of Habitat Loss, Fragmentation and Mobility for Multiple Species of Woodland Birds

PubMed Central

Amos, J. Nevil; Bennett, Andrew F.; Mac Nally, Ralph; Newell, Graeme; Pavlova, Alexandra; Radford, James Q.; Thomson, James R.; White, Matt; Sunnucks, Paul

2012-01-01

Inference concerning the impact of habitat fragmentation on dispersal and gene flow is a key theme in landscape genetics. Recently, the ability of established approaches to identify reliably the differential effects of landscape structure (e.g. land-cover composition, remnant vegetation configuration and extent) on the mobility of organisms has been questioned. More explicit methods of predicting and testing for such effects must move beyond post hoc explanations for single landscapes and species. Here, we document a process for making a priori predictions, using existing spatial and ecological data and expert opinion, of the effects of landscape structure on genetic structure of multiple species across replicated landscape blocks. We compare the results of two common methods for estimating the influence of landscape structure on effective distance: least-cost path analysis and isolation-by-resistance. We present a series of alternative models of genetic connectivity in the study area, represented by different landscape resistance surfaces for calculating effective distance, and identify appropriate null models. The process is applied to ten species of sympatric woodland-dependant birds. For each species, we rank a priori the expectation of fit of genetic response to the models according to the expected response of birds to loss of structural connectivity and landscape-scale tree-cover. These rankings (our hypotheses) are presented for testing with empirical genetic data in a subsequent contribution. We propose that this replicated landscape, multi-species approach offers a robust method for identifying the likely effects of landscape fragmentation on dispersal. PMID:22363508

Monogamy in large bee societies: a stingless paradox.

PubMed

Jaffé, Rodolfo; Pioker-Hara, Fabiana C; Dos Santos, Charles F; Santiago, Leandro R; Alves, Denise A; de M P Kleinert, Astrid; Francoy, Tiago M; Arias, Maria C; Imperatriz-Fonseca, Vera L

2014-03-01

High genetic diversity is important for the functioning of large insect societies. Across the social Hymenoptera (ants, bees, and wasps), species with the largest colonies tend to have a high colony-level genetic diversity resulting from multiple queens (polygyny) or queens that mate with multiple males (polyandry). Here we studied the genetic structure of Trigona spinipes, a stingless bee species with colonies an order of magnitude larger than those of polyandrous honeybees. Genotypes of adult workers and pupae from 43 nests distributed across three Brazilian biomes showed that T. spinipes colonies are usually headed by one singly mated queen. Apart from revealing a notable exception from the general incidence of high genetic diversity in large insect societies, our results reinforce previous findings suggesting the absence of polyandry in stingless bees and provide evidence against the sperm limitation hypothesis for the evolution of polyandry. Stingless bee species with large colonies, such as T. spinipes, thus seem promising study models to unravel alternative mechanisms to increase genetic diversity within colonies or understand the adaptive value of low genetic diversity in large insect societies.

Monogamy in large bee societies: a stingless paradox

NASA Astrophysics Data System (ADS)

Jaffé, Rodolfo; Pioker-Hara, Fabiana C.; dos Santos, Charles F.; Santiago, Leandro R.; Alves, Denise A.; de M. P. Kleinert, Astrid; Francoy, Tiago M.; Arias, Maria C.; Imperatriz-Fonseca, Vera L.

2014-03-01

High genetic diversity is important for the functioning of large insect societies. Across the social Hymenoptera (ants, bees, and wasps), species with the largest colonies tend to have a high colony-level genetic diversity resulting from multiple queens (polygyny) or queens that mate with multiple males (polyandry). Here we studied the genetic structure of Trigona spinipes, a stingless bee species with colonies an order of magnitude larger than those of polyandrous honeybees. Genotypes of adult workers and pupae from 43 nests distributed across three Brazilian biomes showed that T. spinipes colonies are usually headed by one singly mated queen. Apart from revealing a notable exception from the general incidence of high genetic diversity in large insect societies, our results reinforce previous findings suggesting the absence of polyandry in stingless bees and provide evidence against the sperm limitation hypothesis for the evolution of polyandry. Stingless bee species with large colonies, such as T. spinipes, thus seem promising study models to unravel alternative mechanisms to increase genetic diversity within colonies or understand the adaptive value of low genetic diversity in large insect societies.

Impact of Gene Patents and Licensing Practices on Access to Genetic Testing for Inherited Susceptibility to Cancer: Comparing Breast and Ovarian Cancers to Colon Cancers

PubMed Central

Cook-Deegan, Robert; DeRienzo, Christopher; Carbone, Julia; Chandrasekharan, Subhashini; Heaney, Christopher; Conover, Christopher

2011-01-01

Genetic testing for inherited susceptibility to breast and ovarian cancer can be compared to similar testing for colorectal cancer as a “natural experiment.” Inherited susceptibility accounts for a similar fraction of both cancers and genetic testing results guide decisions about options for prophylactic surgery in both sets of conditions. One major difference is that in the United States, Myriad Genetics is the sole provider of genetic testing, because it has sole control of relevant patents for BRCA1 and BRCA2 genes whereas genetic testing for familial colorectal cancer is available from multiple laboratories. Colorectal cancer-associated genes are also patented, but they have been nonexclusively licensed. Prices for BRCA1 and 2 testing do not reflect an obvious price premium attributable to exclusive patent rights compared to colorectal cancer testing, and indeed Myriad’s per unit costs are somewhat lower for BRCA1/2 testing than testing for colorectal cancer susceptibility. Myriad has not enforced patents against basic research, and negotiated a Memorandum of Understanding with the National Cancer Institute in 1999 for institutional BRCA testing in clinical research. The main impact of patenting and licensing in BRCA compared to colorectal cancer is the business model of genetic testing, with a sole provider for BRCA and multiple laboratories for colorectal cancer genetic testing. Myriad’s sole provider model has not worked in jurisdictions outside the United States, largely because of differences in breadth of patent protection, responses of government health services, and difficulty in patent enforcement. PMID:20393305

Parallel Markov chain Monte Carlo - bridging the gap to high-performance Bayesian computation in animal breeding and genetics.

PubMed

Wu, Xiao-Lin; Sun, Chuanyu; Beissinger, Timothy M; Rosa, Guilherme Jm; Weigel, Kent A; Gatti, Natalia de Leon; Gianola, Daniel

2012-09-25

Most Bayesian models for the analysis of complex traits are not analytically tractable and inferences are based on computationally intensive techniques. This is true of Bayesian models for genome-enabled selection, which uses whole-genome molecular data to predict the genetic merit of candidate animals for breeding purposes. In this regard, parallel computing can overcome the bottlenecks that can arise from series computing. Hence, a major goal of the present study is to bridge the gap to high-performance Bayesian computation in the context of animal breeding and genetics. Parallel Monte Carlo Markov chain algorithms and strategies are described in the context of animal breeding and genetics. Parallel Monte Carlo algorithms are introduced as a starting point including their applications to computing single-parameter and certain multiple-parameter models. Then, two basic approaches for parallel Markov chain Monte Carlo are described: one aims at parallelization within a single chain; the other is based on running multiple chains, yet some variants are discussed as well. Features and strategies of the parallel Markov chain Monte Carlo are illustrated using real data, including a large beef cattle dataset with 50K SNP genotypes. Parallel Markov chain Monte Carlo algorithms are useful for computing complex Bayesian models, which does not only lead to a dramatic speedup in computing but can also be used to optimize model parameters in complex Bayesian models. Hence, we anticipate that use of parallel Markov chain Monte Carlo will have a profound impact on revolutionizing the computational tools for genomic selection programs.

Parallel Markov chain Monte Carlo - bridging the gap to high-performance Bayesian computation in animal breeding and genetics

PubMed Central

2012-01-01

Background Most Bayesian models for the analysis of complex traits are not analytically tractable and inferences are based on computationally intensive techniques. This is true of Bayesian models for genome-enabled selection, which uses whole-genome molecular data to predict the genetic merit of candidate animals for breeding purposes. In this regard, parallel computing can overcome the bottlenecks that can arise from series computing. Hence, a major goal of the present study is to bridge the gap to high-performance Bayesian computation in the context of animal breeding and genetics. Results Parallel Monte Carlo Markov chain algorithms and strategies are described in the context of animal breeding and genetics. Parallel Monte Carlo algorithms are introduced as a starting point including their applications to computing single-parameter and certain multiple-parameter models. Then, two basic approaches for parallel Markov chain Monte Carlo are described: one aims at parallelization within a single chain; the other is based on running multiple chains, yet some variants are discussed as well. Features and strategies of the parallel Markov chain Monte Carlo are illustrated using real data, including a large beef cattle dataset with 50K SNP genotypes. Conclusions Parallel Markov chain Monte Carlo algorithms are useful for computing complex Bayesian models, which does not only lead to a dramatic speedup in computing but can also be used to optimize model parameters in complex Bayesian models. Hence, we anticipate that use of parallel Markov chain Monte Carlo will have a profound impact on revolutionizing the computational tools for genomic selection programs. PMID:23009363

COST VS. QUALITY IN DEMOGRAPHIC MODELLING: WHEN IS A VITAL RATE GOOD ENOUGH?

EPA Science Inventory

This presentation will focus on the assessment of quality for demographic parameters to be used in population-level risk assessment. Current population models can handle genetic, demographic, and environmental stochasticity, density dependence, and multiple stressors. However, cu...

Genetic Algorithm for Multiple Bus Line Coordination on Urban Arterial

PubMed Central

Yang, Zhen; Wang, Wei; Chen, Shuyan; Ding, Haoyang; Li, Xiaowei

2015-01-01

Bus travel time on road section is defined and analyzed with the effect of multiple bus lines. An analytical model is formulated to calculate the total red time a bus encounters when travelling along the arterial. Genetic algorithm is used to optimize the offset scheme of traffic signals to minimize the total red time that all bus lines encounter in two directions of the arterial. The model and algorithm are applied to the major part of Zhongshan North Street in the city of Nanjing. The results show that the methods in this paper can reduce total red time of all the bus lines by 31.9% on the object arterial and thus improve the traffic efficiency of the whole arterial and promote public transport priority. PMID:25663837

Current and future molecular approaches to investigate the white pine blister rust pathosystem

Treesearch

B. A. Richardson; A. K. M. Ekramoddoulah; J.-J. Liu; M.-S. Kim; N. B. Klopfenstein

2010-01-01

Molecular genetics is proving to be especially useful for addressing a wide variety of research and management questions on the white pine blister rust pathosystem. White pine blister rust, caused by Cronartium ribicola, is an ideal model for studying biogeography, genetics, and evolution because: (1) it involves an introduced pathogen; (2) it includes multiple primary...

Age at first use and later substance use disorder: Shared genetic and environmental pathways for nicotine, alcohol, and cannabis.

PubMed

Richmond-Rakerd, Leah S; Slutske, Wendy S; Lynskey, Michael T; Agrawal, Arpana; Madden, Pamela A F; Bucholz, Kathleen K; Heath, Andrew C; Statham, Dixie J; Martin, Nicholas G

2016-10-01

Behavioral genetic studies have provided insights into why early substance use initiation is associated with increased risk for disorder. Few genetically informative studies, however, have operationalized initiation as the timing of first use and simultaneously modeled the timing of initiation and problematic use of multiple substances. Such research can help capture the risk associated with early initiation and determine the extent to which genetic and environmental risk generalizes across substances. This study utilized a behavior genetic approach to examine the relation between the age of substance use initiation and symptoms of substance use disorder. Participants were 7,285 monozygotic and dizygotic twins (40% male, mean age at interview = 30.6 years) from the Australian Twin Registry who reported on their ages of tobacco, alcohol, and cannabis initiation and symptoms of Diagnostic and Statistical Manual of Mental Disorders (4th ed., DSM-IV) nicotine dependence, alcohol use disorder, and cannabis use disorder. Biometric modeling was conducted to (a) determine the structure of genetic and environmental influences on initiation and disorder and (b) examine their genetic and environmental overlap. The latent structure of initiation differed across men and women. The familial covariance between initiation and disorder was genetic among men and genetic and environmental among women, suggesting that the relation between first substance use and disorder is partly explained by a shared liability. After accounting for familial overlap, significant unique environmental correlations were observed, indicating that the age of initiation of multiple drugs may directly increase risk for substance-related problems. Results support the utility of conceptualizing initiation in terms of age and of adopting a multivariate approach. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Shared additive genetic influences on DSM-IV criteria for alcohol dependence in subjects of European ancestry.

PubMed

Palmer, Rohan H C; McGeary, John E; Heath, Andrew C; Keller, Matthew C; Brick, Leslie A; Knopik, Valerie S

2015-12-01

Genetic studies of alcohol dependence (AD) have identified several candidate loci and genes, but most observed effects are small and difficult to reproduce. A plausible explanation for inconsistent findings may be a violation of the assumption that genetic factors contributing to each of the seven DSM-IV criteria point to a single underlying dimension of risk. Given that recent twin studies suggest that the genetic architecture of AD is complex and probably involves multiple discrete genetic factors, the current study employed common single nucleotide polymorphisms in two multivariate genetic models to examine the assumption that the genetic risk underlying DSM-IV AD is unitary. AD symptoms and genome-wide single nucleotide polymorphism (SNP) data from 2596 individuals of European descent from the Study of Addiction: Genetics and Environment were analyzed using genomic-relatedness-matrix restricted maximum likelihood. DSM-IV AD symptom covariance was described using two multivariate genetic factor models. Common SNPs explained 30% (standard error=0.136, P=0.012) of the variance in AD diagnosis. Additive genetic effects varied across AD symptoms. The common pathway model approach suggested that symptoms could be described by a single latent variable that had a SNP heritability of 31% (0.130, P=0.008). Similarly, the exploratory genetic factor model approach suggested that the genetic variance/covariance across symptoms could be represented by a single genetic factor that accounted for at least 60% of the genetic variance in any one symptom. Additive genetic effects on DSM-IV alcohol dependence criteria overlap. The assumption of common genetic effects across alcohol dependence symptoms appears to be a valid assumption. © 2015 Society for the Study of Addiction.

De novo establishment of wild-type song culture in the zebra finch.

PubMed

Fehér, Olga; Wang, Haibin; Saar, Sigal; Mitra, Partha P; Tchernichovski, Ofer

2009-05-28

Culture is typically viewed as consisting of traits inherited epigenetically, through social learning. However, cultural diversity has species-typical constraints, presumably of genetic origin. A celebrated, if contentious, example is whether a universal grammar constrains syntactic diversity in human languages. Oscine songbirds exhibit song learning and provide biologically tractable models of culture: members of a species show individual variation in song and geographically separated groups have local song dialects. Different species exhibit distinct song cultures, suggestive of genetic constraints. Without such constraints, innovations and copying errors should cause unbounded variation over multiple generations or geographical distance, contrary to observations. Here we report an experiment designed to determine whether wild-type song culture might emerge over multiple generations in an isolated colony founded by isolates, and, if so, how this might happen and what type of social environment is required. Zebra finch isolates, unexposed to singing males during development, produce song with characteristics that differ from the wild-type song found in laboratory or natural colonies. In tutoring lineages starting from isolate founders, we quantified alterations in song across tutoring generations in two social environments: tutor-pupil pairs in sound-isolated chambers and an isolated semi-natural colony. In both settings, juveniles imitated the isolate tutors but changed certain characteristics of the songs. These alterations accumulated over learning generations. Consequently, songs evolved towards the wild-type in three to four generations. Thus, species-typical song culture can appear de novo. Our study has parallels with language change and evolution. In analogy to models in quantitative genetics, we model song culture as a multigenerational phenotype partly encoded genetically in an isolate founding population, influenced by environmental variables and taking multiple generations to emerge.

Genetic Basis of Atherosclerosis: Insights from Mice and Humans

PubMed Central

Stylianou, Ioannis M.; Bauer, Robert C.; Reilly, Muredach P.; Rader, Daniel J.

2012-01-01

Atherosclerosis is a complex and heritable disease involving multiple cell types and the interactions of many different molecular pathways. The genetic and molecular mechanisms of atherosclerosis have in part been elucidated by mouse models; at least 100 different genes have been shown to influence atherosclerosis in mice. Importantly, unbiased genome-wide association studies have recently identified a number of novel loci robustly associated with atherosclerotic coronary artery disease (CAD). Here we review the genetic data elucidated from mouse models of atherosclerosis, as well as significant associations for human CAD. Furthermore, we discuss in greater detail some of these novel human CAD loci. The combination of mouse and human genetics has the potential to identify and validate novel genes that influence atherosclerosis, some of which may be candidates for new therapeutic approaches. PMID:22267839

lme4qtl: linear mixed models with flexible covariance structure for genetic studies of related individuals.

PubMed

Ziyatdinov, Andrey; Vázquez-Santiago, Miquel; Brunel, Helena; Martinez-Perez, Angel; Aschard, Hugues; Soria, Jose Manuel

2018-02-27

Quantitative trait locus (QTL) mapping in genetic data often involves analysis of correlated observations, which need to be accounted for to avoid false association signals. This is commonly performed by modeling such correlations as random effects in linear mixed models (LMMs). The R package lme4 is a well-established tool that implements major LMM features using sparse matrix methods; however, it is not fully adapted for QTL mapping association and linkage studies. In particular, two LMM features are lacking in the base version of lme4: the definition of random effects by custom covariance matrices; and parameter constraints, which are essential in advanced QTL models. Apart from applications in linkage studies of related individuals, such functionalities are of high interest for association studies in situations where multiple covariance matrices need to be modeled, a scenario not covered by many genome-wide association study (GWAS) software. To address the aforementioned limitations, we developed a new R package lme4qtl as an extension of lme4. First, lme4qtl contributes new models for genetic studies within a single tool integrated with lme4 and its companion packages. Second, lme4qtl offers a flexible framework for scenarios with multiple levels of relatedness and becomes efficient when covariance matrices are sparse. We showed the value of our package using real family-based data in the Genetic Analysis of Idiopathic Thrombophilia 2 (GAIT2) project. Our software lme4qtl enables QTL mapping models with a versatile structure of random effects and efficient computation for sparse covariances. lme4qtl is available at https://github.com/variani/lme4qtl .

Developing a Model of Advanced Training to Promote Career Advancement for Certified Genetic Counselors: An Investigation of Expanded Skills, Advanced Training Paths, and Professional Opportunities.

PubMed

Baty, Bonnie J; Trepanier, Angela; Bennett, Robin L; Davis, Claire; Erby, Lori; Hippman, Catriona; Lerner, Barbara; Matthews, Anne; Myers, Melanie F; Robbins, Carol B; Singletary, Claire N

2016-08-01

There are currently multiple paths through which genetic counselors can acquire advanced knowledge and skills. However, outside of continuing education opportunities, there are few formal training programs designed specifically for the advanced training of genetic counselors. In the genetic counseling profession, there is currently considerable debate about the paths that should be available to attain advanced skills, as well as the skills that might be needed for practice in the future. The Association of Genetic Counseling Program Directors (AGCPD) convened a national committee, the Committee on Advanced Training for Certified Genetic Counselors (CATCGC), to investigate varied paths to post-master's training and career development. The committee began its work by developing three related grids that view career advancement from the viewpoints of the skills needed to advance (skills), ways to obtain these skills (paths), and existing genetic counselor positions that offer career change or advancement (positions). Here we describe previous work related to genetic counselor career advancement, the charge of the CATCGC, our preliminary work in developing a model through which to view genetic counselor advanced training and career advancement opportunities, and our next steps in further developing and disseminating the model.

Evolutionary tabu search strategies for the simultaneous registration of multiple atomic structures in cryo-EM reconstructions.

PubMed

Rusu, Mirabela; Birmanns, Stefan

2010-04-01

A structural characterization of multi-component cellular assemblies is essential to explain the mechanisms governing biological function. Macromolecular architectures may be revealed by integrating information collected from various biophysical sources - for instance, by interpreting low-resolution electron cryomicroscopy reconstructions in relation to the crystal structures of the constituent fragments. A simultaneous registration of multiple components is beneficial when building atomic models as it introduces additional spatial constraints to facilitate the native placement inside the map. The high-dimensional nature of such a search problem prevents the exhaustive exploration of all possible solutions. Here we introduce a novel method based on genetic algorithms, for the efficient exploration of the multi-body registration search space. The classic scheme of a genetic algorithm was enhanced with new genetic operations, tabu search and parallel computing strategies and validated on a benchmark of synthetic and experimental cryo-EM datasets. Even at a low level of detail, for example 35-40 A, the technique successfully registered multiple component biomolecules, measuring accuracies within one order of magnitude of the nominal resolutions of the maps. The algorithm was implemented using the Sculptor molecular modeling framework, which also provides a user-friendly graphical interface and enables an instantaneous, visual exploration of intermediate solutions. (c) 2009 Elsevier Inc. All rights reserved.

Introductory Biology Students' Conceptual Models and Explanations of the Origin of Variation

ERIC Educational Resources Information Center

Bray Speth, Elena; Shaw, Neil; Momsen, Jennifer; Reinagel, Adam; Le, Paul; Taqieddin, Ranya; Long, Tammy

2014-01-01

Mutation is the key molecular mechanism generating phenotypic variation, which is the basis for evolution. In an introductory biology course, we used a model-based pedagogy that enabled students to integrate their understanding of genetics and evolution within multiple case studies. We used student-generated conceptual models to assess…

Adaptive Topographies and Equilibrium Selection in an Evolutionary Game

PubMed Central

Osinga, Hinke M.; Marshall, James A. R.

2015-01-01

It has long been known in the field of population genetics that adaptive topographies, in which population equilibria maximise mean population fitness for a trait regardless of its genetic bases, do not exist. Whether one chooses to model selection acting on a single locus or multiple loci does matter. In evolutionary game theory, analysis of a simple and general game involving distinct roles for the two players has shown that whether strategies are modelled using a single ‘locus’ or one ‘locus’ for each role, the stable population equilibria are unchanged and correspond to the fitness-maximising evolutionary stable strategies of the game. This is curious given the aforementioned population genetical results on the importance of the genetic bases of traits. Here we present a dynamical systems analysis of the game with roles detailing how, while the stable equilibria in this game are unchanged by the number of ‘loci’ modelled, equilibrium selection may differ under the two modelling approaches. PMID:25706762

A synthetic genetic edge detection program.

PubMed

Tabor, Jeffrey J; Salis, Howard M; Simpson, Zachary Booth; Chevalier, Aaron A; Levskaya, Anselm; Marcotte, Edward M; Voigt, Christopher A; Ellington, Andrew D

2009-06-26

Edge detection is a signal processing algorithm common in artificial intelligence and image recognition programs. We have constructed a genetically encoded edge detection algorithm that programs an isogenic community of E. coli to sense an image of light, communicate to identify the light-dark edges, and visually present the result of the computation. The algorithm is implemented using multiple genetic circuits. An engineered light sensor enables cells to distinguish between light and dark regions. In the dark, cells produce a diffusible chemical signal that diffuses into light regions. Genetic logic gates are used so that only cells that sense light and the diffusible signal produce a positive output. A mathematical model constructed from first principles and parameterized with experimental measurements of the component circuits predicts the performance of the complete program. Quantitatively accurate models will facilitate the engineering of more complex biological behaviors and inform bottom-up studies of natural genetic regulatory networks.

An interdisciplinary approach to personalized medicine: case studies from a cardiogenetics clinic.

PubMed

Erskine, Kathleen E; Griffith, Eleanor; Degroat, Nicole; Stolerman, Marina; Silverstein, Louise B; Hidayatallah, Nadia; Wasserman, David; Paljevic, Esma; Cohen, Lilian; Walsh, Christine A; McDonald, Thomas; Marion, Robert W; Dolan, Siobhan M

2013-01-01

In the genomic age, the challenges presented by various inherited conditions present a compelling argument for an interdisciplinary model of care. Cardiac arrhythmias with a genetic basis, such as long QT syndrome, require clinicians with expertise in many specialties to address the complex genetic, psychological, ethical and medical issues involved in treatment. The Montefiore-Einstein Center for CardioGenetics has been established to provide personalized, interdisciplinary care for families with a history of sudden cardiac death or an acute cardiac event. Four vignettes of patient care are presented to illustrate the unique capacity of an interdisciplinary model to address genetic, psychological, ethical and medical issues. Because interdisciplinary clinics facilitate collaboration among multiple specialties, they allow for individualized, comprehensive care to be delivered to families who experience complex inherited medical conditions. As the genetic basis of many complex conditions is discovered, the advantages of an interdisciplinary approach for delivering personalized medicine will become more evident.

A Synthetic Genetic Edge Detection Program

PubMed Central

Tabor, Jeffrey J.; Salis, Howard; Simpson, Zachary B.; Chevalier, Aaron A.; Levskaya, Anselm; Marcotte, Edward M.; Voigt, Christopher A.; Ellington, Andrew D.

2009-01-01

Summary Edge detection is a signal processing algorithm common in artificial intelligence and image recognition programs. We have constructed a genetically encoded edge detection algorithm that programs an isogenic community of E.coli to sense an image of light, communicate to identify the light-dark edges, and visually present the result of the computation. The algorithm is implemented using multiple genetic circuits. An engineered light sensor enables cells to distinguish between light and dark regions. In the dark, cells produce a diffusible chemical signal that diffuses into light regions. Genetic logic gates are used so that only cells that sense light and the diffusible signal produce a positive output. A mathematical model constructed from first principles and parameterized with experimental measurements of the component circuits predicts the performance of the complete program. Quantitatively accurate models will facilitate the engineering of more complex biological behaviors and inform bottom-up studies of natural genetic regulatory networks. PMID:19563759

A review of vulnerability and risks for schizophrenia: Beyond the two hit hypothesis

PubMed Central

Davis, Justin; Eyre, Harris; Jacka, Felice N; Dodd, Seetal; Dean, Olivia; McEwen, Sarah; Debnath, Monojit; McGrath, John; Maes, Michael; Amminger, Paul; McGorry, Patrick D; Pantelis, Christos; Berk, Michael

2016-01-01

Schizophrenia risk has often been conceptualized using a model which requires two hits in order to generate the clinical phenotype—the first as an early priming in a genetically predisposed individual and the second a likely environmental insult. The aim of this paper was to review the literature and reformulate this binary risk-vulnerability model. We sourced the data for this narrative review from the electronic database PUBMED. Our search terms were not limited by language or date of publication. The development of schizophrenia may be driven by genetic vulnerability interacting with multiple vulnerability factors including lowered prenatal vitamin D exposure, viral infections, smoking intelligence quotient, social cognition cannabis use, social defeat, nutrition and childhood trauma. It is likely that these genetic risks, environmental risks and vulnerability factors are cumulative and interactive with each other and with critical periods of neurodevelopmental vulnerability. The development of schizophrenia is likely to be more complex and nuanced than the binary two hit model originally proposed nearly thirty years ago. Risk appears influenced by a more complex process involving genetic risk interfacing with multiple potentially interacting hits and vulnerability factors occurring at key periods of neurodevelopmental activity, which culminate in the expression of disease state. These risks are common across a number of neuropsychiatric and medical disorders, which might inform common preventive and intervention strategies across non-communicable disorders. PMID:27073049

Identifying Loci Under Selection Against Gene Flow in Isolation-with-Migration Models

PubMed Central

Sousa, Vitor C.; Carneiro, Miguel; Ferrand, Nuno; Hey, Jody

2013-01-01

When divergence occurs in the presence of gene flow, there can arise an interesting dynamic in which selection against gene flow, at sites associated with population-specific adaptations or genetic incompatibilities, can cause net gene flow to vary across the genome. Loci linked to sites under selection may experience reduced gene flow and may experience genetic bottlenecks by the action of nearby selective sweeps. Data from histories such as these may be poorly fitted by conventional neutral model approaches to demographic inference, which treat all loci as equally subject to forces of genetic drift and gene flow. To allow for demographic inference in the face of such histories, as well as the identification of loci affected by selection, we developed an isolation-with-migration model that explicitly provides for variation among genomic regions in migration rates and/or rates of genetic drift. The method allows for loci to fall into any of multiple groups, each characterized by a different set of parameters, thus relaxing the assumption that all loci share the same demography. By grouping loci, the method can be applied to data with multiple loci and still have tractable dimensionality and statistical power. We studied the performance of the method using simulated data, and we applied the method to study the divergence of two subspecies of European rabbits (Oryctolagus cuniculus). PMID:23457232

Genomic and cranial phenotype data support multiple modern human dispersals from Africa and a southern route into Asia

PubMed Central

Reyes-Centeno, Hugo; Ghirotto, Silvia; Détroit, Florent; Grimaud-Hervé, Dominique; Barbujani, Guido; Harvati, Katerina

2014-01-01

Despite broad consensus on Africa as the main place of origin for anatomically modern humans, their dispersal pattern out of the continent continues to be intensely debated. In extant human populations, the observation of decreasing genetic and phenotypic diversity at increasing distances from sub-Saharan Africa has been interpreted as evidence for a single dispersal, accompanied by a series of founder effects. In such a scenario, modern human genetic and phenotypic variation was primarily generated through successive population bottlenecks and drift during a rapid worldwide expansion out of Africa in the Late Pleistocene. However, recent genetic studies, as well as accumulating archaeological and paleoanthropological evidence, challenge this parsimonious model. They suggest instead a “southern route” dispersal into Asia as early as the late Middle Pleistocene, followed by a separate dispersal into northern Eurasia. Here we test these competing out-of-Africa scenarios by modeling hypothetical geographical migration routes and assessing their correlation with neutral population differentiation, as measured by genetic polymorphisms and cranial shape variables of modern human populations from Africa and Asia. We show that both lines of evidence support a multiple-dispersals model in which Australo-Melanesian populations are relatively isolated descendants of an early dispersal, whereas other Asian populations are descended from, or highly admixed with, members of a subsequent migration event. PMID:24753576

Broad Bandwidth or High Fidelity? Evidence from the Structure of Genetic and Environmental Effects on the Facets of the Five Factor Model

PubMed Central

Briley, Daniel A.; Tucker-Drob, Elliot M.

2017-01-01

The Five Factor Model (FFM) of personality is well-established at the phenotypic level, but much less is known about the coherence of the genetic and environmental influences within each personality domain. Univariate behavioral genetic analyses have consistently found the influence of additive genes and nonshared environment on multiple personality facets, but the extent to which genetic and environmental influences on specific facets reflect more general influences on higher order factors is less clear. We applied a multivariate quantitative-genetic approach to scores on the CPI-Big Five facets for 490 monozygotic and 317 dizygotic twins who took part in the National Merit Twin Study. Our results revealed a complex genetic structure for facets composing all five factors, with both domain-general and facet-specific genetic and environmental influences. Models that required common genetic and environmental influences on each facet to occur by way of effects on a higher order trait did not fit as well as models allowing for common genetic and environmental effects to act directly on the facets for three of the Big Five domains. These results add to the growing body of literature indicating that important variation in personality occurs at the facet level which may be overshadowed by aggregating to the trait level. Research at the facet level, rather than the factor level, is likely to have pragmatic advantages in future research on the genetics of personality. PMID:22695681

Identification of landscape features influencing gene flow: How useful are habitat selection models?

USGS Publications Warehouse

Roffler, Gretchen H.; Schwartz, Michael K.; Pilgrim, Kristy L.; Talbot, Sandra L.; Sage, Kevin; Adams, Layne G.; Luikart, Gordon

2016-01-01

Understanding how dispersal patterns are influenced by landscape heterogeneity is critical for modeling species connectivity. Resource selection function (RSF) models are increasingly used in landscape genetics approaches. However, because the ecological factors that drive habitat selection may be different from those influencing dispersal and gene flow, it is important to consider explicit assumptions and spatial scales of measurement. We calculated pairwise genetic distance among 301 Dall's sheep (Ovis dalli dalli) in southcentral Alaska using an intensive noninvasive sampling effort and 15 microsatellite loci. We used multiple regression of distance matrices to assess the correlation of pairwise genetic distance and landscape resistance derived from an RSF, and combinations of landscape features hypothesized to influence dispersal. Dall's sheep gene flow was positively correlated with steep slopes, moderate peak normalized difference vegetation indices (NDVI), and open land cover. Whereas RSF covariates were significant in predicting genetic distance, the RSF model itself was not significantly correlated with Dall's sheep gene flow, suggesting that certain habitat features important during summer (rugged terrain, mid-range elevation) were not influential to effective dispersal. This work underscores that consideration of both habitat selection and landscape genetics models may be useful in developing management strategies to both meet the immediate survival of a species and allow for long-term genetic connectivity.

Restricted maximum likelihood estimation of genetic principal components and smoothed covariance matrices

PubMed Central

Meyer, Karin; Kirkpatrick, Mark

2005-01-01

Principal component analysis is a widely used 'dimension reduction' technique, albeit generally at a phenotypic level. It is shown that we can estimate genetic principal components directly through a simple reparameterisation of the usual linear, mixed model. This is applicable to any analysis fitting multiple, correlated genetic effects, whether effects for individual traits or sets of random regression coefficients to model trajectories. Depending on the magnitude of genetic correlation, a subset of the principal component generally suffices to capture the bulk of genetic variation. Corresponding estimates of genetic covariance matrices are more parsimonious, have reduced rank and are smoothed, with the number of parameters required to model the dispersion structure reduced from k(k + 1)/2 to m(2k - m + 1)/2 for k effects and m principal components. Estimation of these parameters, the largest eigenvalues and pertaining eigenvectors of the genetic covariance matrix, via restricted maximum likelihood using derivatives of the likelihood, is described. It is shown that reduced rank estimation can reduce computational requirements of multivariate analyses substantially. An application to the analysis of eight traits recorded via live ultrasound scanning of beef cattle is given. PMID:15588566

Effect of genetic background on the dystrophic phenotype in mdx mice

PubMed Central

Coley, William D.; Bogdanik, Laurent; Vila, Maria Candida; Yu, Qing; Van Der Meulen, Jack H.; Rayavarapu, Sree; Novak, James S.; Nearing, Marie; Quinn, James L.; Saunders, Allison; Dolan, Connor; Andrews, Whitney; Lammert, Catherine; Austin, Andrew; Partridge, Terence A.; Cox, Gregory A.; Lutz, Cathleen; Nagaraju, Kanneboyina

2016-01-01

Genetic background significantly affects phenotype in multiple mouse models of human diseases, including muscular dystrophy. This phenotypic variability is partly attributed to genetic modifiers that regulate the disease process. Studies have demonstrated that introduction of the γ-sarcoglycan-null allele onto the DBA/2J background confers a more severe muscular dystrophy phenotype than the original strain, demonstrating the presence of genetic modifier loci in the DBA/2J background. To characterize the phenotype of dystrophin deficiency on the DBA/2J background, we created and phenotyped DBA/2J-congenic Dmdmdx mice (D2-mdx) and compared them with the original, C57BL/10ScSn-Dmdmdx (B10-mdx) model. These strains were compared with their respective control strains at multiple time points between 6 and 52 weeks of age. Skeletal and cardiac muscle function, inflammation, regeneration, histology and biochemistry were characterized. We found that D2-mdx mice showed significantly reduced skeletal muscle function as early as 7 weeks and reduced cardiac function by 28 weeks, suggesting that the disease phenotype is more severe than in B10-mdx mice. In addition, D2-mdx mice showed fewer central myonuclei and increased calcifications in the skeletal muscle, heart and diaphragm at 7 weeks, suggesting that their pathology is different from the B10-mdx mice. The new D2-mdx model with an earlier onset and more pronounced dystrophy phenotype may be useful for evaluating therapies that target cardiac and skeletal muscle function in dystrophin-deficient mice. Our data align the D2-mdx with Duchenne muscular dystrophy patients with the LTBP4 genetic modifier, making it one of the few instances of cross-species genetic modifiers of monogenic traits. PMID:26566673

Gene genealogies for genetic association mapping, with application to Crohn's disease

PubMed Central

Burkett, Kelly M.; Greenwood, Celia M. T.; McNeney, Brad; Graham, Jinko

2013-01-01

A gene genealogy describes relationships among haplotypes sampled from a population. Knowledge of the gene genealogy for a set of haplotypes is useful for estimation of population genetic parameters and it also has potential application in finding disease-predisposing genetic variants. As the true gene genealogy is unknown, Markov chain Monte Carlo (MCMC) approaches have been used to sample genealogies conditional on data at multiple genetic markers. We previously implemented an MCMC algorithm to sample from an approximation to the distribution of the gene genealogy conditional on haplotype data. Our approach samples ancestral trees, recombination and mutation rates at a genomic focal point. In this work, we describe how our sampler can be used to find disease-predisposing genetic variants in samples of cases and controls. We use a tree-based association statistic that quantifies the degree to which case haplotypes are more closely related to each other around the focal point than control haplotypes, without relying on a disease model. As the ancestral tree is a latent variable, so is the tree-based association statistic. We show how the sampler can be used to estimate the posterior distribution of the latent test statistic and corresponding latent p-values, which together comprise a fuzzy p-value. We illustrate the approach on a publicly-available dataset from a study of Crohn's disease that consists of genotypes at multiple SNP markers in a small genomic region. We estimate the posterior distribution of the tree-based association statistic and the recombination rate at multiple focal points in the region. Reassuringly, the posterior mean recombination rates estimated at the different focal points are consistent with previously published estimates. The tree-based association approach finds multiple sub-regions where the case haplotypes are more genetically related than the control haplotypes, and that there may be one or multiple disease-predisposing loci. PMID:24348515

Signatures of positive selection: from selective sweeps at individual loci to subtle allele frequency changes in polygenic adaptation.

PubMed

Stephan, Wolfgang

2016-01-01

In the past 15 years, numerous methods have been developed to detect selective sweeps underlying adaptations. These methods are based on relatively simple population genetic models, including one or two loci at which positive directional selection occurs, and one or two marker loci at which the impact of selection on linked neutral variation is quantified. Information about the phenotype under selection is not included in these models (except for fitness). In contrast, in the quantitative genetic models of adaptation, selection acts on one or more phenotypic traits, such that a genotype-phenotype map is required to bridge the gap to population genetics theory. Here I describe the range of population genetic models from selective sweeps in a panmictic population of constant size to evolutionary traffic when simultaneous sweeps at multiple loci interfere, and I also consider the case of polygenic selection characterized by subtle allele frequency shifts at many loci. Furthermore, I present an overview of the statistical tests that have been proposed based on these population genetics models to detect evidence for positive selection in the genome. © 2015 John Wiley & Sons Ltd.

Model-Based Linkage Analysis of a Quantitative Trait.

PubMed

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

2017-01-01

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

Using Genetic Mouse Models to Gain Insight into Glaucoma: Past Results and Future Possibilities

PubMed Central

Fernandes, Kimberly A.; Harder, Jeffrey M.; Williams, Pete A.; Rausch, Rebecca L.; Kiernan, Amy E.; Nair, K. Saidas; Anderson, Michael G.; John, Simon W.; Howell, Gareth R.; Libby, Richard T.

2015-01-01

While all forms of glaucoma are characterized by a specific pattern of retinal ganglion cell death, they are clinically divided into several distinct subclasses, including normal tension glaucoma, primary open angle glaucoma, congenital glaucoma, and secondary glaucoma. For each type of glaucoma there are likely numerous molecular pathways that control susceptibility to the disease. Given this complexity, a single animal model will never precisely model all aspects of all the different types of human glaucoma. Therefore, multiple animal models have been utilized to study glaucoma but more are needed. Because of the powerful genetic tools available to use in the laboratory mouse, it has proven to be a highly useful mammalian system for studying the pathophysiology of human disease. The similarity between human and mouse eyes coupled with the ability to use a combination of advanced cell biological and genetic tools in mice have led to a large increase in the number of studies using mice to model specific glaucoma phenotypes. Over the last decade, numerous new mouse models and genetic tools have emerged, providing important insight into the cell biology and genetics of glaucoma. In this review, we describe available mouse genetic models that can be used to study glaucoma-relevant disease/pathobiology. Furthermore, we discuss how these models have been used to gain insights into ocular hypertension (a major risk factor for glaucoma) and glaucomatous retinal ganglion cell death. Finally, the potential for developing new mouse models and using advanced genetic tools and resources for studying glaucoma are discussed. PMID:26116903

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.

Modeling the Volcanic Source at Long Valley, CA, Using a Genetic Algorithm Technique

NASA Technical Reports Server (NTRS)

Tiampo, Kristy F.

1999-01-01

In this project, we attempted to model the deformation pattern due to the magmatic source at Long Valley caldera using a real-value coded genetic algorithm (GA) inversion similar to that found in Michalewicz, 1992. The project has been both successful and rewarding. The genetic algorithm, coded in the C programming language, performs stable inversions over repeated trials, with varying initial and boundary conditions. The original model used a GA in which the geophysical information was coded into the fitness function through the computation of surface displacements for a Mogi point source in an elastic half-space. The program was designed to invert for a spherical magmatic source - its depth, horizontal location and volume - using the known surface deformations. It also included the capability of inverting for multiple sources.

The long-term evolution of multilocus traits under frequency-dependent disruptive selection.

PubMed

van Doorn, G Sander; Dieckmann, Ulf

2006-11-01

Frequency-dependent disruptive selection is widely recognized as an important source of genetic variation. Its evolutionary consequences have been extensively studied using phenotypic evolutionary models, based on quantitative genetics, game theory, or adaptive dynamics. However, the genetic assumptions underlying these approaches are highly idealized and, even worse, predict different consequences of frequency-dependent disruptive selection. Population genetic models, by contrast, enable genotypic evolutionary models, but traditionally assume constant fitness values. Only a minority of these models thus addresses frequency-dependent selection, and only a few of these do so in a multilocus context. An inherent limitation of these remaining studies is that they only investigate the short-term maintenance of genetic variation. Consequently, the long-term evolution of multilocus characters under frequency-dependent disruptive selection remains poorly understood. We aim to bridge this gap between phenotypic and genotypic models by studying a multilocus version of Levene's soft-selection model. Individual-based simulations and deterministic approximations based on adaptive dynamics theory provide insights into the underlying evolutionary dynamics. Our analysis uncovers a general pattern of polymorphism formation and collapse, likely to apply to a wide variety of genetic systems: after convergence to a fitness minimum and the subsequent establishment of genetic polymorphism at multiple loci, genetic variation becomes increasingly concentrated on a few loci, until eventually only a single polymorphic locus remains. This evolutionary process combines features observed in quantitative genetics and adaptive dynamics models, and it can be explained as a consequence of changes in the selection regime that are inherent to frequency-dependent disruptive selection. Our findings demonstrate that the potential of frequency-dependent disruptive selection to maintain polygenic variation is considerably smaller than previously expected.

Breeding for better eye health in Finnish blue fox (Vulpes lagopus).

PubMed

Kempe, R; Strandén, I

2016-02-01

The frequency of eye infections in the Finnish blue fox population has increased during the past decade. Eye infection may incur economic losses to producers due to reduced selection intensity, but ethical aspects need to be considered as well because eye infection can be quite painful and reduce animal well-being. The purpose of this study was to determine the potential for genetic selection against susceptibility to eye infection. The data were collected from 2076 blue foxes at the MTT fur animal research station. Genetic parameters were estimated using single- and multiple-trait animal models. The heritability estimate for eye infection was analysed as a binary trait (EYE) and was moderate (0.24 ± 0.07). EYE had a moderate antagonistic genetic correlation (-0.49 ± 0.20) with grading density (thick underfur). The genetic correlation of EYE with grading size or body condition score was estimated without precision, but all size traits had a low antagonistic phenotypic correlation with EYE. Our results suggest that there is genetic variance in susceptibility to EYE, indicating that eye health can be improved through selection. The current recommendation is that the sick animals should be culled immediately. If more efficient selection is needed, the selection index and multiple-trait animal models can be applied in breeding for better eye health. © 2015 Blackwell Verlag GmbH.

A Semiparametric Approach for Composite Functional Mapping of Dynamic Quantitative Traits

PubMed Central

Yang, Runqing; Gao, Huijiang; Wang, Xin; Zhang, Ji; Zeng, Zhao-Bang; Wu, Rongling

2007-01-01

Functional mapping has emerged as a powerful tool for mapping quantitative trait loci (QTL) that control developmental patterns of complex dynamic traits. Original functional mapping has been constructed within the context of simple interval mapping, without consideration of separate multiple linked QTL for a dynamic trait. In this article, we present a statistical framework for mapping QTL that affect dynamic traits by capitalizing on the strengths of functional mapping and composite interval mapping. Within this so-called composite functional-mapping framework, functional mapping models the time-dependent genetic effects of a QTL tested within a marker interval using a biologically meaningful parametric function, whereas composite interval mapping models the time-dependent genetic effects of the markers outside the test interval to control the genome background using a flexible nonparametric approach based on Legendre polynomials. Such a semiparametric framework was formulated by a maximum-likelihood model and implemented with the EM algorithm, allowing for the estimation and the test of the mathematical parameters that define the QTL effects and the regression coefficients of the Legendre polynomials that describe the marker effects. Simulation studies were performed to investigate the statistical behavior of composite functional mapping and compare its advantage in separating multiple linked QTL as compared to functional mapping. We used the new mapping approach to analyze a genetic mapping example in rice, leading to the identification of multiple QTL, some of which are linked on the same chromosome, that control the developmental trajectory of leaf age. PMID:17947431

A toolkit for incorporating genetics into mainstream medical services: Learning from service development pilots in England

PubMed Central

2010-01-01

Background As advances in genetics are becoming increasingly relevant to mainstream healthcare, a major challenge is to ensure that these are integrated appropriately into mainstream medical services. In 2003, the Department of Health for England announced the availability of start-up funding for ten 'Mainstreaming Genetics' pilot services to develop models to achieve this. Methods Multiple methods were used to explore the pilots' experiences of incorporating genetics which might inform the development of new services in the future. A workshop with project staff, an email questionnaire, interviews and a thematic analysis of pilot final reports were carried out. Results Seven themes relating to the integration of genetics into mainstream medical services were identified: planning services to incorporate genetics; the involvement of genetics departments; the establishment of roles incorporating genetic activities; identifying and involving stakeholders; the challenges of working across specialty boundaries; working with multiple healthcare organisations; and the importance of cultural awareness of genetic conditions. Pilots found that the planning phase often included the need to raise awareness of genetic conditions and services and that early consideration of organisational issues such as clinic location was essential. The formal involvement of genetics departments was crucial to success; benefits included provision of clinical and educational support for staff in new roles. Recruitment and retention for new roles outside usual career pathways sometimes proved difficult. Differences in specialties' working practices and working with multiple healthcare organisations also brought challenges such as the 'genetic approach' of working with families, incompatible record systems and different approaches to health professionals' autonomous practice. 'Practice points' have been collated into a Toolkit which includes resources from the pilots, including job descriptions and clinical tools. These can be customised for reuse by other services. Conclusions Healthcare services need to translate advances in genetics into benefits for patients. Consideration of the issues presented here when incorporating genetics into mainstream medical services will help ensure that new service developments build on the body of experience gained by the pilots, to provide high quality services for patients with or at risk of genetic conditions. PMID:20470377

A toolkit for incorporating genetics into mainstream medical services: Learning from service development pilots in England.

PubMed

Bennett, Catherine L; Burke, Sarah E; Burton, Hilary; Farndon, Peter A

2010-05-14

As advances in genetics are becoming increasingly relevant to mainstream healthcare, a major challenge is to ensure that these are integrated appropriately into mainstream medical services. In 2003, the Department of Health for England announced the availability of start-up funding for ten 'Mainstreaming Genetics' pilot services to develop models to achieve this. Multiple methods were used to explore the pilots' experiences of incorporating genetics which might inform the development of new services in the future. A workshop with project staff, an email questionnaire, interviews and a thematic analysis of pilot final reports were carried out. Seven themes relating to the integration of genetics into mainstream medical services were identified: planning services to incorporate genetics; the involvement of genetics departments; the establishment of roles incorporating genetic activities; identifying and involving stakeholders; the challenges of working across specialty boundaries; working with multiple healthcare organisations; and the importance of cultural awareness of genetic conditions. Pilots found that the planning phase often included the need to raise awareness of genetic conditions and services and that early consideration of organisational issues such as clinic location was essential. The formal involvement of genetics departments was crucial to success; benefits included provision of clinical and educational support for staff in new roles. Recruitment and retention for new roles outside usual career pathways sometimes proved difficult. Differences in specialties' working practices and working with multiple healthcare organisations also brought challenges such as the 'genetic approach' of working with families, incompatible record systems and different approaches to health professionals' autonomous practice. 'Practice points' have been collated into a Toolkit which includes resources from the pilots, including job descriptions and clinical tools. These can be customised for reuse by other services. Healthcare services need to translate advances in genetics into benefits for patients. Consideration of the issues presented here when incorporating genetics into mainstream medical services will help ensure that new service developments build on the body of experience gained by the pilots, to provide high quality services for patients with or at risk of genetic conditions.

Genetic relationships between detailed reproductive traits and performance traits in Holstein-Friesian dairy cattle.

PubMed

Carthy, T R; Ryan, D P; Fitzgerald, A M; Evans, R D; Berry, D P

2016-02-01

The objective of the study was to estimate the genetic relationships between detailed reproductive traits derived from ultrasound examination of the reproductive tract and a range of performance traits in Holstein-Friesian dairy cows. The performance traits investigated included calving performance, milk production, somatic cell score (i.e., logarithm transformation of somatic cell count), carcass traits, and body-related linear type traits. Detailed reproductive traits included (1) resumed cyclicity at the time of examination, (2) multiple ovulations, (3) early ovulation, (4) heat detection, (5) ovarian cystic structures, (6) embryo loss, and (7) uterine score, measured on a 1 (little or no fluid with normal tone) to 4 (large quantity of fluid with a flaccid tone) scale, based on the tone of the uterine wall and the quantity of fluid present in the uterus. (Co)variance components were estimated using a repeatability animal linear mixed model. Genetic merit for greater milk, fat, and protein yield was associated with a reduced ability to resume cyclicity postpartum (genetic correlations ranged from -0.25 to -0.15). Higher genetic merit for milk yield was also associated with a greater genetic susceptibility to multiple ovulations. Genetic predisposition to elevated somatic cell score was associated with a decreased likelihood of cyclicity postpartum (genetic correlation of -0.32) and a greater risk of both multiple ovulations (genetic correlation of 0.25) and embryo loss (genetic correlation of 0.32). Greater body condition score was genetically associated with an increased likelihood of resumption of cyclicity postpartum (genetic correlation of 0.52). Genetically heavier, fatter carcasses with better conformation were also associated with an increased likelihood of resumed cyclicity by the time of examination (genetic correlations ranged from 0.24 to 0.41). Genetically heavier carcasses were associated with an inferior uterine score as well as a greater predisposition to embryo loss. Despite the overall antagonistic relationship between reproductive performance and both milk and carcass traits, not all detailed aspects of reproduction performance exhibited an antagonistic relationship. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Lessons learned from the dog genome.

PubMed

Wayne, Robert K; Ostrander, Elaine A

2007-11-01

Extensive genetic resources and a high-quality genome sequence position the dog as an important model species for understanding genome evolution, population genetics and genes underlying complex phenotypic traits. Newly developed genomic resources have expanded our understanding of canine evolutionary history and dog origins. Domestication involved genetic contributions from multiple populations of gray wolves probably through backcrossing. More recently, the advent of controlled breeding practices has segregated genetic variability into distinct dog breeds that possess specific phenotypic traits. Consequently, genome-wide association and selective sweep scans now allow the discovery of genes underlying breed-specific characteristics. The dog is finally emerging as a novel resource for studying the genetic basis of complex traits, including behavior.

Using Workflow Modeling to Identify Areas to Improve Genetic Test Processes in the University of Maryland Translational Pharmacogenomics Project.

PubMed

Cutting, Elizabeth M; Overby, Casey L; Banchero, Meghan; Pollin, Toni; Kelemen, Mark; Shuldiner, Alan R; Beitelshees, Amber L

Delivering genetic test results to clinicians is a complex process. It involves many actors and multiple steps, requiring all of these to work together in order to create an optimal course of treatment for the patient. We used information gained from focus groups in order to illustrate the current process of delivering genetic test results to clinicians. We propose a business process model and notation (BPMN) representation of this process for a Translational Pharmacogenomics Project being implemented at the University of Maryland Medical Center, so that personalized medicine program implementers can identify areas to improve genetic testing processes. We found that the current process could be improved to reduce input errors, better inform and notify clinicians about the implications of certain genetic tests, and make results more easily understood. We demonstrate our use of BPMN to improve this important clinical process for CYP2C19 genetic testing in patients undergoing invasive treatment of coronary heart disease.

Using Workflow Modeling to Identify Areas to Improve Genetic Test Processes in the University of Maryland Translational Pharmacogenomics Project

PubMed Central

Cutting, Elizabeth M.; Overby, Casey L.; Banchero, Meghan; Pollin, Toni; Kelemen, Mark; Shuldiner, Alan R.; Beitelshees, Amber L.

2015-01-01

Delivering genetic test results to clinicians is a complex process. It involves many actors and multiple steps, requiring all of these to work together in order to create an optimal course of treatment for the patient. We used information gained from focus groups in order to illustrate the current process of delivering genetic test results to clinicians. We propose a business process model and notation (BPMN) representation of this process for a Translational Pharmacogenomics Project being implemented at the University of Maryland Medical Center, so that personalized medicine program implementers can identify areas to improve genetic testing processes. We found that the current process could be improved to reduce input errors, better inform and notify clinicians about the implications of certain genetic tests, and make results more easily understood. We demonstrate our use of BPMN to improve this important clinical process for CYP2C19 genetic testing in patients undergoing invasive treatment of coronary heart disease. PMID:26958179

CDMetaPOP: An individual-based, eco-evolutionary model for spatially explicit simulation of landscape demogenetics

USGS Publications Warehouse

Landguth, Erin L; Bearlin, Andrew; Day, Casey; Dunham, Jason B.

2016-01-01

1. Combining landscape demographic and genetics models offers powerful methods for addressing questions for eco-evolutionary applications.2. Using two illustrative examples, we present Cost–Distance Meta-POPulation, a program to simulate changes in neutral and/or selection-driven genotypes through time as a function of individual-based movement, complex spatial population dynamics, and multiple and changing landscape drivers.3. Cost–Distance Meta-POPulation provides a novel tool for questions in landscape genetics by incorporating population viability analysis, while linking directly to conservation applications.

Genetic Introgression and the Survival of Florida Panther Kittens

PubMed Central

Hostetler, Jeffrey A.; Onorato, David P.; Nichols, James D.; Johnson, Warren E.; Roelke, Melody E.; O’Brien, Stephen J.; Jansen, Deborah; Oli, Madan K.

2010-01-01

Estimates of survival for the young of a species are critical for population models. These models can often be improved by determining the effects of management actions and population abundance on this demographic parameter. We used multiple sources of data collected during 1982-2008 and a live recapture-dead recovery modeling framework to estimate and model survival of Florida panther (Puma concolor coryi) kittens (age 0 – 1 year). Overall, annual survival of Florida panther kittens was 0.323 ± 0.071 (SE), which was lower than estimates used in previous population models. In 1995, female pumas from Texas (P. c. stanleyana) were released into occupied panther range as part of an intentional introgression program to restore genetic variability. We found that kitten survival generally increased with degree of admixture: F1 admixed and backcrossed to Texas kittens survived better than canonical Florida panther and backcrossed to canonical kittens. Average heterozygosity positively influenced kitten and older panther survival, whereas index of panther abundance negatively influenced kitten survival. Our results provide strong evidence for the positive population-level impact of genetic introgression on Florida panthers. Our approach to integrate data from multiple sources was effective at improving robustness as well as precision of estimates of Florida panther kitten survival, and can be useful in estimating vital rates for other elusive species with sparse data. PMID:21113436

Genetic introgression and the survival of Florida panther kittens

USGS Publications Warehouse

Hostetler, Jeffrey A.; Onorato, David P.; Nichols, James D.; Johnson, Warren E.; Roelke, Melody E.; O'Brien, Stephen J.; Jansen, Deborah; Oli, Madan K.

2010-01-01

Estimates of survival for the young of a species are critical for population models. These models can often be improved by determining the effects of management actions and population abundance on this demographic parameter. We used multiple sources of data collected during 1982–2008 and a live-recapture dead-recovery modeling framework to estimate and model survival of Florida panther (Puma concolor coryi) kittens (age 0–1 year). Overall, annual survival of Florida panther kittens was 0.323 ± 0.071 (SE), which was lower than estimates used in previous population models. In 1995, female pumas from Texas (P. c. stanleyana) were released into occupied panther range as part of an intentional introgression program to restore genetic variability. We found that kitten survival generally increased with degree of admixture: F1 admixed and backcrossed to Texas kittens survived better than canonical Florida panther and backcrossed to canonical kittens. Average heterozygosity positively influenced kitten and older panther survival, whereas index of panther abundance negatively influenced kitten survival. Our results provide strong evidence for the positive population-level impact of genetic introgression on Florida panthers. Our approach to integrate data from multiple sources was effective at improving robustness as well as precision of estimates of Florida panther kitten survival, and can be useful in estimating vital rates for other elusive species with sparse data.

Transgressive Hybrids as Hopeful Monsters.

PubMed

Dittrich-Reed, Dylan R; Fitzpatrick, Benjamin M

2013-06-01

The origin of novelty is a critical subject for evolutionary biologists. Early geneticists speculated about the sudden appearance of new species via special macromutations, epitomized by Goldschmidt's infamous "hopeful monster". Although these ideas were easily dismissed by the insights of the Modern Synthesis, a lingering fascination with the possibility of sudden, dramatic change has persisted. Recent work on hybridization and gene exchange suggests an underappreciated mechanism for the sudden appearance of evolutionary novelty that is entirely consistent with the principles of modern population genetics. Genetic recombination in hybrids can produce transgressive phenotypes, "monstrous" phenotypes beyond the range of parental populations. Transgressive phenotypes can be products of epistatic interactions or additive effects of multiple recombined loci. We compare several epistatic and additive models of transgressive segregation in hybrids and find that they are special cases of a general, classic quantitative genetic model. The Dobzhansky-Muller model predicts "hopeless" monsters, sterile and inviable transgressive phenotypes. The Bateson model predicts "hopeful" monsters with fitness greater than either parental population. The complementation model predicts both. Transgressive segregation after hybridization can rapidly produce novel phenotypes by recombining multiple loci simultaneously. Admixed populations will also produce many similar recombinant phenotypes at the same time, increasing the probability that recombinant "hopeful monsters" will establish true-breeding evolutionary lineages. Recombination is not the only (or even most common) process generating evolutionary novelty, but might be the most credible mechanism for sudden appearance of new forms.

Grains of connectivity: analysis at multiple spatial scales in landscape genetics.

PubMed

Galpern, Paul; Manseau, Micheline; Wilson, Paul

2012-08-01

Landscape genetic analyses are typically conducted at one spatial scale. Considering multiple scales may be essential for identifying landscape features influencing gene flow. We examined landscape connectivity for woodland caribou (Rangifer tarandus caribou) at multiple spatial scales using a new approach based on landscape graphs that creates a Voronoi tessellation of the landscape. To illustrate the potential of the method, we generated five resistance surfaces to explain how landscape pattern may influence gene flow across the range of this population. We tested each resistance surface using a raster at the spatial grain of available landscape data (200 m grid squares). We then used our method to produce up to 127 additional grains for each resistance surface. We applied a causal modelling framework with partial Mantel tests, where evidence of landscape resistance is tested against an alternative hypothesis of isolation-by-distance, and found statistically significant support for landscape resistance to gene flow in 89 of the 507 spatial grains examined. We found evidence that major roads as well as the cumulative effects of natural and anthropogenic disturbance may be contributing to the genetic structure. Using only the original grid surface yielded no evidence for landscape resistance to gene flow. Our results show that using multiple spatial grains can reveal landscape influences on genetic structure that may be overlooked with a single grain, and suggest that coarsening the grain of landcover data may be appropriate for highly mobile species. We discuss how grains of connectivity and related analyses have potential landscape genetic applications in a broad range of systems. © 2012 Blackwell Publishing Ltd.

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

Genomic profiles of low-grade murine gliomas evolve during progression to glioblastoma. | Office of Cancer Genomics

Cancer.gov

Background: Gliomas are diverse neoplasms with multiple molecular subtypes. How tumor-initiating mutations relate to molecular subtypes as these tumors evolve during malignant progression remains unclear.Methods: We used genetically engineered mouse models, histopathology, genetic lineage tracing, expression profiling, and copy number analyses to examine how genomic tumor diversity evolves during the course of malignant progression from low- to high-grade disease.

Complement pathway biomarkers and age-related macular degeneration

PubMed Central

Gemenetzi, M; Lotery, A J

2016-01-01

In the age-related macular degeneration (AMD) ‘inflammation model', local inflammation plus complement activation contributes to the pathogenesis and progression of the disease. Multiple genetic associations have now been established correlating the risk of development or progression of AMD. Stratifying patients by their AMD genetic profile may facilitate future AMD therapeutic trials resulting in meaningful clinical trial end points with smaller sample sizes and study duration. PMID:26493033

Multiple Query Evaluation Based on an Enhanced Genetic Algorithm.

ERIC Educational Resources Information Center

Tamine, Lynda; Chrisment, Claude; Boughanem, Mohand

2003-01-01

Explains the use of genetic algorithms to combine results from multiple query evaluations to improve relevance in information retrieval. Discusses niching techniques, relevance feedback techniques, and evolution heuristics, and compares retrieval results obtained by both genetic multiple query evaluation and classical single query evaluation…

Radiogenomics to characterize regional genetic heterogeneity in glioblastoma.

PubMed

Hu, Leland S; Ning, Shuluo; Eschbacher, Jennifer M; Baxter, Leslie C; Gaw, Nathan; Ranjbar, Sara; Plasencia, Jonathan; Dueck, Amylou C; Peng, Sen; Smith, Kris A; Nakaji, Peter; Karis, John P; Quarles, C Chad; Wu, Teresa; Loftus, Joseph C; Jenkins, Robert B; Sicotte, Hugues; Kollmeyer, Thomas M; O'Neill, Brian P; Elmquist, William; Hoxworth, Joseph M; Frakes, David; Sarkaria, Jann; Swanson, Kristin R; Tran, Nhan L; Li, Jing; Mitchell, J Ross

2017-01-01

Glioblastoma (GBM) exhibits profound intratumoral genetic heterogeneity. Each tumor comprises multiple genetically distinct clonal populations with different therapeutic sensitivities. This has implications for targeted therapy and genetically informed paradigms. Contrast-enhanced (CE)-MRI and conventional sampling techniques have failed to resolve this heterogeneity, particularly for nonenhancing tumor populations. This study explores the feasibility of using multiparametric MRI and texture analysis to characterize regional genetic heterogeneity throughout MRI-enhancing and nonenhancing tumor segments. We collected multiple image-guided biopsies from primary GBM patients throughout regions of enhancement (ENH) and nonenhancing parenchyma (so called brain-around-tumor, [BAT]). For each biopsy, we analyzed DNA copy number variants for core GBM driver genes reported by The Cancer Genome Atlas. We co-registered biopsy locations with MRI and texture maps to correlate regional genetic status with spatially matched imaging measurements. We also built multivariate predictive decision-tree models for each GBM driver gene and validated accuracies using leave-one-out-cross-validation (LOOCV). We collected 48 biopsies (13 tumors) and identified significant imaging correlations (univariate analysis) for 6 driver genes: EGFR, PDGFRA, PTEN, CDKN2A, RB1, and TP53. Predictive model accuracies (on LOOCV) varied by driver gene of interest. Highest accuracies were observed for PDGFRA (77.1%), EGFR (75%), CDKN2A (87.5%), and RB1 (87.5%), while lowest accuracy was observed in TP53 (37.5%). Models for 4 driver genes (EGFR, RB1, CDKN2A, and PTEN) showed higher accuracy in BAT samples (n = 16) compared with those from ENH segments (n = 32). MRI and texture analysis can help characterize regional genetic heterogeneity, which offers potential diagnostic value under the paradigm of individualized oncology. © The Author(s) 2016. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Process-time Optimization of Vacuum Degassing Using a Genetic Alloy Design Approach

PubMed Central

Dilner, David; Lu, Qi; Mao, Huahai; Xu, Wei; van der Zwaag, Sybrand; Selleby, Malin

2014-01-01

This paper demonstrates the use of a new model consisting of a genetic algorithm in combination with thermodynamic calculations and analytical process models to minimize the processing time during a vacuum degassing treatment of liquid steel. The model sets multiple simultaneous targets for final S, N, O, Si and Al levels and uses the total slag mass, the slag composition, the steel composition and the start temperature as optimization variables. The predicted optimal conditions agree well with industrial practice. For those conditions leading to the shortest process time the target compositions for S, N and O are reached almost simultaneously. PMID:28788286

Enhanced entrainability of genetic oscillators by period mismatch

PubMed Central

Hasegawa, Yoshihiko; Arita, Masanori

2013-01-01

Biological oscillators coordinate individual cellular components so that they function coherently and collectively. They are typically composed of multiple feedback loops, and period mismatch is unavoidable in biological implementations. We investigated the advantageous effect of this period mismatch in terms of a synchronization response to external stimuli. Specifically, we considered two fundamental models of genetic circuits: smooth and relaxation oscillators. Using phase reduction and Floquet multipliers, we numerically analysed their entrainability under different coupling strengths and period ratios. We found that a period mismatch induces better entrainment in both types of oscillator; the enhancement occurs in the vicinity of the bifurcation on their limit cycles. In the smooth oscillator, the optimal period ratio for the enhancement coincides with the experimentally observed ratio, which suggests biological exploitation of the period mismatch. Although the origin of multiple feedback loops is often explained as a passive mechanism to ensure robustness against perturbation, we study the active benefits of the period mismatch, which include increasing the efficiency of the genetic oscillators. Our findings show a qualitatively different perspective for both the inherent advantages of multiple loops and their essentiality. PMID:23389900

Modeling non-syndromic autism and the impact of TRPC6 disruption in human neurons.

PubMed

Griesi-Oliveira, K; Acab, A; Gupta, A R; Sunaga, D Y; Chailangkarn, T; Nicol, X; Nunez, Y; Walker, M F; Murdoch, J D; Sanders, S J; Fernandez, T V; Ji, W; Lifton, R P; Vadasz, E; Dietrich, A; Pradhan, D; Song, H; Ming, G-L; Gu, X; Haddad, G; Marchetto, M C N; Spitzer, N; Passos-Bueno, M R; State, M W; Muotri, A R

2015-11-01

An increasing number of genetic variants have been implicated in autism spectrum disorders (ASDs), and the functional study of such variants will be critical for the elucidation of autism pathophysiology. Here, we report a de novo balanced translocation disruption of TRPC6, a cation channel, in a non-syndromic autistic individual. Using multiple models, such as dental pulp cells, induced pluripotent stem cell (iPSC)-derived neuronal cells and mouse models, we demonstrate that TRPC6 reduction or haploinsufficiency leads to altered neuronal development, morphology and function. The observed neuronal phenotypes could then be rescued by TRPC6 complementation and by treatment with insulin-like growth factor-1 or hyperforin, a TRPC6-specific agonist, suggesting that ASD individuals with alterations in this pathway may benefit from these drugs. We also demonstrate that methyl CpG binding protein-2 (MeCP2) levels affect TRPC6 expression. Mutations in MeCP2 cause Rett syndrome, revealing common pathways among ASDs. Genetic sequencing of TRPC6 in 1041 ASD individuals and 2872 controls revealed significantly more nonsynonymous mutations in the ASD population, and identified loss-of-function mutations with incomplete penetrance in two patients. Taken together, these findings suggest that TRPC6 is a novel predisposing gene for ASD that may act in a multiple-hit model. This is the first study to use iPSC-derived human neurons to model non-syndromic ASD and illustrate the potential of modeling genetically complex sporadic diseases using such cells.

Modeling non-syndromic autism and the impact of TRPC6 disruption in human neurons

PubMed Central

Griesi-Oliveira, Karina; Acab, Allan; Gupta, Abha R.; Sunaga, Daniele Yumi; Chailangkarn, Thanathom; Nicol, Xavier; Nunez, Yanelli; Walker, Michael F.; Murdoch, John D.; Sanders, Stephan J.; Fernandez, Thomas V.; Ji, Weizhen; Lifton, Richard P.; Vadasz, Estevão; Dietrich, Alexander; Pradhan, Dennis; Song, Hongjun; Ming, Guo-li; Guoe, Xiang; Haddad, Gabriel; Marchetto, Maria C. N.; Spitzer, Nicholas; Passos-Bueno, Maria Rita; State, Matthew W.; Muotri, Alysson R.

2014-01-01

An increasing number of genetic variants have been implicated in autism spectrum disorders (ASD), and the functional study of such variants will be critical for the elucidation of autism pathophysiology. Here, we report a de novo balanced translocation disruption of TRPC6, a cation channel, in a non-syndromic autistic individual. Using multiple models, such as dental pulp cells, iPSC-derived neuronal cells and mouse models, we demonstrate that TRPC6 reduction or haploinsufficiency leads to altered neuronal development, morphology, and function. The observed neuronal phenotypes could then be rescued by TRPC6 complementation and by treatment with IGF1 or hyperforin, a TRPC6-specific agonist, suggesting that ASD individuals with alterations in this pathway might benefit from these drugs. We also demonstrate that MeCP2 levels affect TRPC6 expression. Mutations in MeCP2 cause Rett syndrome, revealing common pathways among ASDs. Genetic sequencing of TRPC6 in 1041 ASD individuals and 2872 controls revealed significantly more nonsynonymous mutations in the ASD population, and identified loss-of-function mutations with incomplete penetrance in two patients. Taken together, these findings suggest that TRPC6 is a novel predisposing gene for ASD that may act in a multiple-hit model. This is the first study to use iPSC-derived human neurons to model non-syndromic ASD and illustrate the potential of modeling genetically complex sporadic diseases using such cells. PMID:25385366

Using genetic markers to orient the edges in quantitative trait networks: the NEO software.

PubMed

Aten, Jason E; Fuller, Tova F; Lusis, Aldons J; Horvath, Steve

2008-04-15

Systems genetic studies have been used to identify genetic loci that affect transcript abundances and clinical traits such as body weight. The pairwise correlations between gene expression traits and/or clinical traits can be used to define undirected trait networks. Several authors have argued that genetic markers (e.g expression quantitative trait loci, eQTLs) can serve as causal anchors for orienting the edges of a trait network. The availability of hundreds of thousands of genetic markers poses new challenges: how to relate (anchor) traits to multiple genetic markers, how to score the genetic evidence in favor of an edge orientation, and how to weigh the information from multiple markers. We develop and implement Network Edge Orienting (NEO) methods and software that address the challenges of inferring unconfounded and directed gene networks from microarray-derived gene expression data by integrating mRNA levels with genetic marker data and Structural Equation Model (SEM) comparisons. The NEO software implements several manual and automatic methods for incorporating genetic information to anchor traits. The networks are oriented by considering each edge separately, thus reducing error propagation. To summarize the genetic evidence in favor of a given edge orientation, we propose Local SEM-based Edge Orienting (LEO) scores that compare the fit of several competing causal graphs. SEM fitting indices allow the user to assess local and overall model fit. The NEO software allows the user to carry out a robustness analysis with regard to genetic marker selection. We demonstrate the utility of NEO by recovering known causal relationships in the sterol homeostasis pathway using liver gene expression data from an F2 mouse cross. Further, we use NEO to study the relationship between a disease gene and a biologically important gene co-expression module in liver tissue. The NEO software can be used to orient the edges of gene co-expression networks or quantitative trait networks if the edges can be anchored to genetic marker data. R software tutorials, data, and supplementary material can be downloaded from: http://www.genetics.ucla.edu/labs/horvath/aten/NEO.

Dissection of the complex genetic basis of craniofacial anomalies using haploid genetics and interspecies hybrids in Nasonia wasps

PubMed Central

Werren, John H.; Cohen, Lorna B.; Gadau, Juergen; Ponce, Rita; Baudry, Emmanuelle; Lynch, Jeremy A.

2016-01-01

The animal head is a complex structure where numerous sensory, structural and alimentary structures are concentrated and integrated, and its ontogeny requires precise and delicate interactions among genes, cells, and tissues. Thus, it is perhaps unsurprising that craniofacial abnormalities are among the most common birth defects in people, or that these defects have a complex genetic basis involving interactions among multiple loci. Developmental processes that depend on such epistatic interactions become exponentially more difficult to study in diploid organisms as the number of genes involved increases. Here, we present hybrid haploid males of the wasp species pair Nasonia vitripennis and Nasonia giraulti, which have distinct male head morphologies, as a genetic model of craniofacial development that possesses the genetic advantages of haploidy, along with many powerful genomic tools. Viable, fertile hybrids can be made between the species, and quantitative trail loci related to shape differences have been identified. In addition, a subset of hybrid males show head abnormalities, including clefting at the midline and asymmetries. Crucially, epistatic interactions among multiple loci underlie several developmental differences and defects observed in the F2 hybrid males. Furthermore, we demonstrate an introgression of a chromosomal region from N. giraulti into N. vitripennis that shows an abnormality in relative eye size, which maps to a region containing a major QTL for this trait. Therefore, the genetic sources of head morphology can, in principle, be identified by positional cloning. Thus, Nasonia is well positioned to be a uniquely powerful model invertebrate system with which to probe both development and complex genetics of craniofacial patterning and defects. PMID:26721604

Partitioning the Genetic Diversity of a Virus Family: Approach and Evaluation through a Case Study of Picornaviruses

PubMed Central

Lauber, Chris

2012-01-01

The recent advent of genome sequences as the only source available to classify many newly discovered viruses challenges the development of virus taxonomy by expert virologists who traditionally rely on extensive virus characterization. In this proof-of-principle study, we address this issue by presenting a computational approach (DEmARC) to classify viruses of a family into groups at hierarchical levels using a sole criterion—intervirus genetic divergence. To quantify genetic divergence, we used pairwise evolutionary distances (PEDs) estimated by maximum likelihood inference on a multiple alignment of family-wide conserved proteins. PEDs were calculated for all virus pairs, and the resulting distribution was modeled via a mixture of probability density functions. The model enables the quantitative inference of regions of distance discontinuity in the family-wide PED distribution, which define the levels of hierarchy. For each level, a limit on genetic divergence, below which two viruses join the same group, was objectively selected among a set of candidates by minimizing violations of intragroup PEDs to the limit. In a case study, we applied the procedure to hundreds of genome sequences of picornaviruses and extensively evaluated it by modulating four key parameters. It was found that the genetics-based classification largely tolerates variations in virus sampling and multiple alignment construction but is affected by the choice of protein and the measure of genetic divergence. In an accompanying paper (C. Lauber and A. E. Gorbalenya, J. Virol. 86:3905–3915, 2012), we analyze the substantial insight gained with the genetics-based classification approach by comparing it with the expert-based picornavirus taxonomy. PMID:22278230

Random regression analysis for body weights and main morphological traits in genetically improved farmed tilapia (Oreochromis niloticus).

PubMed

He, Jie; Zhao, Yunfeng; Zhao, Jingli; Gao, Jin; Xu, Pao; Yang, Runqing

2018-02-01

To genetically analyse growth traits in genetically improved farmed tilapia (GIFT), the body weight (BWE) and main morphological traits, including body length (BL), body depth (BD), body width (BWI), head length (HL) and length of the caudal peduncle (CPL), were measured six times in growth duration on 1451 fish from 45 mixed families of full and half sibs. A random regression model (RRM) was used to model genetic changes of the growth traits with days of age and estimate the heritability for any growth point and genetic correlations between pairwise growth points. Using the covariance function based on optimal RRMs, the heritabilities were estimated to be from 0.102 to 0.662 for BWE, 0.157 to 0.591 for BL, 0.047 to 0.621 for BD, 0.018 to 0.577 for BWI, 0.075 to 0.597 for HL and 0.032 to 0.610 for CPL between 60 and 140 days of age. All genetic correlations exceeded 0.5 between pairwise growth points. Moreover, the traits at initial days of age showed less correlation with those at later days of age. With phenotypes observed repeatedly, the model choice showed that the optimal RRMs could more precisely predict breeding values at a specific growth time than repeatability models or multiple trait animal models, which enhanced the efficiency of selection for the BWE and main morphological traits.

A Population Genetics Model of Marker-Assisted Selection

PubMed Central

Luo, Z. W.; Thompson, R.; Woolliams, J. A.

1997-01-01

A deterministic two-loci model was developed to predict genetic response to marker-assisted selection (MAS) in one generation and in multiple generations. Formulas were derived to relate linkage disequilibrium in a population to the proportion of additive genetic variance used by MAS, and in turn to an extra improvement in genetic response over phenotypic selection. Predictions of the response were compared to those predicted by using an infinite-loci model and the factors affecting efficiency of MAS were examined. Theoretical analyses of the present study revealed the nonlinearity between the selection intensity and genetic response in MAS. In addition to the heritability of the trait and the proportion of the marker-associated genetic variance, the frequencies of the selectively favorable alleles at the two loci, one marker and one quantitative trait locus, were found to play an important role in determining both the short- and long-term efficiencies of MAS. The evolution of linkage disequilibrium and thus the genetic response over several generations were predicted theoretically and examined by simulation. MAS dissipated the disequilibrium more quickly than drift alone. In some cases studied, the rate of dissipation was as large as that to be expected in the circumstance where the true recombination fraction was increased by three times and selection was absent. PMID:9215918

Scientific reporting is suboptimal for aspects that characterize genetic risk prediction studies: a review of published articles based on the Genetic RIsk Prediction Studies statement.

PubMed

Iglesias, Adriana I; Mihaescu, Raluca; Ioannidis, John P A; Khoury, Muin J; Little, Julian; van Duijn, Cornelia M; Janssens, A Cecile J W

2014-05-01

Our main objective was to raise awareness of the areas that need improvements in the reporting of genetic risk prediction articles for future publications, based on the Genetic RIsk Prediction Studies (GRIPS) statement. We evaluated studies that developed or validated a prediction model based on multiple DNA variants, using empirical data, and were published in 2010. A data extraction form based on the 25 items of the GRIPS statement was created and piloted. Forty-two studies met our inclusion criteria. Overall, more than half of the evaluated items (34 of 62) were reported in at least 85% of included articles. Seventy-seven percentage of the articles were identified as genetic risk prediction studies through title assessment, but only 31% used the keywords recommended by GRIPS in the title or abstract. Seventy-four percentage mentioned which allele was the risk variant. Overall, only 10% of the articles reported all essential items needed to perform external validation of the risk model. Completeness of reporting in genetic risk prediction studies is adequate for general elements of study design but is suboptimal for several aspects that characterize genetic risk prediction studies such as description of the model construction. Improvements in the transparency of reporting of these aspects would facilitate the identification, replication, and application of genetic risk prediction models. Copyright © 2014 Elsevier Inc. All rights reserved.

Genome-wide prediction models that incorporate de novo GWAS are a powerful new tool for tropical rice improvement

PubMed Central

Spindel, J E; Begum, H; Akdemir, D; Collard, B; Redoña, E; Jannink, J-L; McCouch, S

2016-01-01

To address the multiple challenges to food security posed by global climate change, population growth and rising incomes, plant breeders are developing new crop varieties that can enhance both agricultural productivity and environmental sustainability. Current breeding practices, however, are unable to keep pace with demand. Genomic selection (GS) is a new technique that helps accelerate the rate of genetic gain in breeding by using whole-genome data to predict the breeding value of offspring. Here, we describe a new GS model that combines RR-BLUP with markers fit as fixed effects selected from the results of a genome-wide-association study (GWAS) on the RR-BLUP training data. We term this model GS + de novo GWAS. In a breeding population of tropical rice, GS + de novo GWAS outperformed six other models for a variety of traits and in multiple environments. On the basis of these results, we propose an extended, two-part breeding design that can be used to efficiently integrate novel variation into elite breeding populations, thus expanding genetic diversity and enhancing the potential for sustainable productivity gains. PMID:26860200

A Study of Two Instructional Sequences Informed by Alternative Learning Progressions in Genetics

NASA Astrophysics Data System (ADS)

Duncan, Ravit Golan; Choi, Jinnie; Castro-Faix, Moraima; Cavera, Veronica L.

2017-12-01

Learning progressions (LPs) are hypothetical models of how learning in a domain develops over time with appropriate instruction. In the domain of genetics, there are two independently developed alternative LPs. The main difference between the two progressions hinges on their assumptions regarding the accessibility of classical (Mendelian) versus molecular genetics and the order in which they should be taught. In order to determine the relative difficulty of the different genetic ideas included in the two progressions, and to test which one is a better fit with students' actual learning, we developed two modules in classical and molecular genetics and alternated their sequence in an implementation study with 11th grade students studying biology. We developed a set of 56 ordered multiple-choice items that collectively assessed both molecular and classical genetic ideas. We found significant gains in students' learning in both molecular and classical genetics, with the largest gain relating to understanding the informational content of genes and the smallest gain in understanding modes of inheritance. Using multidimensional item response modeling, we found no statistically significant differences between the two instructional sequences. However, there was a trend of slightly higher gains for the molecular-first sequence for all genetic ideas.

Applications of population genetics to animal breeding, from wright, fisher and lush to genomic prediction.

PubMed

Hill, William G

2014-01-01

Although animal breeding was practiced long before the science of genetics and the relevant disciplines of population and quantitative genetics were known, breeding programs have mainly relied on simply selecting and mating the best individuals on their own or relatives' performance. This is based on sound quantitative genetic principles, developed and expounded by Lush, who attributed much of his understanding to Wright, and formalized in Fisher's infinitesimal model. Analysis at the level of individual loci and gene frequency distributions has had relatively little impact. Now with access to genomic data, a revolution in which molecular information is being used to enhance response with "genomic selection" is occurring. The predictions of breeding value still utilize multiple loci throughout the genome and, indeed, are largely compatible with additive and specifically infinitesimal model assumptions. I discuss some of the history and genetic issues as applied to the science of livestock improvement, which has had and continues to have major spin-offs into ideas and applications in other areas.

Merits and complexities of modeling multiple sclerosis in non-human primates: implications for drug discovery.

PubMed

't Hart, Bert A; Laman, Jon D; Kap, Yolanda S

2018-05-01

The translation of scientific discoveries made in animal models into effective treatments for patients often fails, indicating that currently used disease models in preclinical research are insufficiently predictive for clinical success. An often-used model in the preclinical research of autoimmune neurological diseases, multiple sclerosis in particular, is experimental autoimmune encephalomyelitis (EAE). Most EAE models are based on genetically susceptible inbred/SPF mouse strains used at adolescent age (10-12 weeks), which lack exposure to genetic and microbial factors which shape the human immune system. Areas covered: Herein, the authors ask whether an EAE model in adult non-human primates from an outbred conventionally-housed colony could help bridge the translational gap between rodent EAE models and MS patients. Particularly, the authors discuss a novel and translationally relevant EAE model in common marmosets (Callithrix jacchus) that shares remarkable pathological similarity with MS. Expert opinion: The MS-like pathology in this model is caused by the interaction of effector memory T cells with B cells infected with the γ1-herpesvirus (CalHV3), both present in the pathogen-educated marmoset immune repertoire. The authors postulate that depletion of only the small subset (<0.05%) of CalHV3-infected B cells may be sufficient to limit chronic inflammatory demyelination.

Use of qualitative environmental and phenotypic variables in the context of allele distribution models: detecting signatures of selection in the genome of Lake Victoria cichlids.

PubMed

Joost, Stéphane; Kalbermatten, Michael; Bezault, Etienne; Seehausen, Ole

2012-01-01

When searching for loci possibly under selection in the genome, an alternative to population genetics theoretical models is to establish allele distribution models (ADM) for each locus to directly correlate allelic frequencies and environmental variables such as precipitation, temperature, or sun radiation. Such an approach implementing multiple logistic regression models in parallel was implemented within a computing program named MATSAM: . Recently, this application was improved in order to support qualitative environmental predictors as well as to permit the identification of associations between genomic variation and individual phenotypes, allowing the detection of loci involved in the genetic architecture of polymorphic characters. Here, we present the corresponding methodological developments and compare the results produced by software implementing population genetics theoretical models (DFDIST: and BAYESCAN: ) and ADM (MATSAM: ) in an empirical context to detect signatures of genomic divergence associated with speciation in Lake Victoria cichlid fishes.

Genomic Selection in Multi-environment Crop Trials.

PubMed

Oakey, Helena; Cullis, Brian; Thompson, Robin; Comadran, Jordi; Halpin, Claire; Waugh, Robbie

2016-05-03

Genomic selection in crop breeding introduces modeling challenges not found in animal studies. These include the need to accommodate replicate plants for each line, consider spatial variation in field trials, address line by environment interactions, and capture nonadditive effects. Here, we propose a flexible single-stage genomic selection approach that resolves these issues. Our linear mixed model incorporates spatial variation through environment-specific terms, and also randomization-based design terms. It considers marker, and marker by environment interactions using ridge regression best linear unbiased prediction to extend genomic selection to multiple environments. Since the approach uses the raw data from line replicates, the line genetic variation is partitioned into marker and nonmarker residual genetic variation (i.e., additive and nonadditive effects). This results in a more precise estimate of marker genetic effects. Using barley height data from trials, in 2 different years, of up to 477 cultivars, we demonstrate that our new genomic selection model improves predictions compared to current models. Analyzing single trials revealed improvements in predictive ability of up to 5.7%. For the multiple environment trial (MET) model, combining both year trials improved predictive ability up to 11.4% compared to a single environment analysis. Benefits were significant even when fewer markers were used. Compared to a single-year standard model run with 3490 markers, our partitioned MET model achieved the same predictive ability using between 500 and 1000 markers depending on the trial. Our approach can be used to increase accuracy and confidence in the selection of the best lines for breeding and/or, to reduce costs by using fewer markers. Copyright © 2016 Oakey et al.

The power to detect linkage in complex disease by means of simple LOD-score analyses.

PubMed Central

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

1998-01-01

Maximum-likelihood analysis (via LOD score) provides the most powerful method for finding linkage when the mode of inheritance (MOI) is known. However, because one must assume an MOI, the application of LOD-score analysis to complex disease has been questioned. Although it is known that one can legitimately maximize the maximum LOD score with respect to genetic parameters, this approach raises three concerns: (1) multiple testing, (2) effect on power to detect linkage, and (3) adequacy of the approximate MOI for the true MOI. We evaluated the power of LOD scores to detect linkage when the true MOI was complex but a LOD score analysis assumed simple models. We simulated data from 14 different genetic models, including dominant and recessive at high (80%) and low (20%) penetrances, intermediate models, and several additive two-locus models. We calculated LOD scores by assuming two simple models, dominant and recessive, each with 50% penetrance, then took the higher of the two LOD scores as the raw test statistic and corrected for multiple tests. We call this test statistic "MMLS-C." We found that the ELODs for MMLS-C are >=80% of the ELOD under the true model when the ELOD for the true model is >=3. Similarly, the power to reach a given LOD score was usually >=80% that of the true model, when the power under the true model was >=60%. These results underscore that a critical factor in LOD-score analysis is the MOI at the linked locus, not that of the disease or trait per se. Thus, a limited set of simple genetic models in LOD-score analysis can work well in testing for linkage. PMID:9718328

The power to detect linkage in complex disease by means of simple LOD-score analyses.

PubMed

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

1998-09-01

Maximum-likelihood analysis (via LOD score) provides the most powerful method for finding linkage when the mode of inheritance (MOI) is known. However, because one must assume an MOI, the application of LOD-score analysis to complex disease has been questioned. Although it is known that one can legitimately maximize the maximum LOD score with respect to genetic parameters, this approach raises three concerns: (1) multiple testing, (2) effect on power to detect linkage, and (3) adequacy of the approximate MOI for the true MOI. We evaluated the power of LOD scores to detect linkage when the true MOI was complex but a LOD score analysis assumed simple models. We simulated data from 14 different genetic models, including dominant and recessive at high (80%) and low (20%) penetrances, intermediate models, and several additive two-locus models. We calculated LOD scores by assuming two simple models, dominant and recessive, each with 50% penetrance, then took the higher of the two LOD scores as the raw test statistic and corrected for multiple tests. We call this test statistic "MMLS-C." We found that the ELODs for MMLS-C are >=80% of the ELOD under the true model when the ELOD for the true model is >=3. Similarly, the power to reach a given LOD score was usually >=80% that of the true model, when the power under the true model was >=60%. These results underscore that a critical factor in LOD-score analysis is the MOI at the linked locus, not that of the disease or trait per se. Thus, a limited set of simple genetic models in LOD-score analysis can work well in testing for linkage.

Detrimental effects of an autosomal selfish genetic element on sperm competitiveness in house mice

PubMed Central

Sutter, Andreas; Lindholm, Anna K.

2015-01-01

Female multiple mating (polyandry) is widespread across many animal taxa and indirect genetic benefits are a major evolutionary force favouring polyandry. An incentive for polyandry arises when multiple mating leads to sperm competition that disadvantages sperm from genetically inferior mates. A reduction in genetic quality is associated with costly selfish genetic elements (SGEs), and studies in invertebrates have shown that males bearing sex ratio distorting SGEs are worse sperm competitors than wild-type males. We used a vertebrate model species to test whether females can avoid an autosomal SGE, the t haplotype, through polyandry. The t haplotype in house mice exhibits strong drive in t heterozygous males by affecting spermatogenesis and is associated with homozygous in utero lethality. We used controlled matings to test the effect of the t haplotype on sperm competitiveness. Regardless of mating order, t heterozygous males sired only 11% of zygotes when competing against wild-type males, suggesting a very strong effect of the t haplotype on sperm quality. We provide, to our knowledge, the first substantial evidence that polyandry ameliorates the harmful effects of an autosomal SGE arising through genetic incompatibility. We discuss potential mechanisms in our study species and the broader implications for the benefits of polyandry. PMID:26136452

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

Genetic analysis across different spatial scales reveals multiple dispersal mechanisms for the invasive hydrozoan Cordylophora in the Great Lakes.

PubMed

Darling, John A; Folino-Rorem, Nadine C

2009-12-01

Discerning patterns of post-establishment spread by invasive species is critically important for the design of effective management strategies and the development of appropriate theoretical models predicting spatial expansion of introduced populations. The globally invasive colonial hydrozoan Cordylophora produces propagules both sexually and vegetatively and is associated with multiple potential dispersal mechanisms, making it a promising system to investigate complex patterns of population structure generated throughout the course of rapid range expansion. Here, we explore genetic patterns associated with the spread of this taxon within the North American Great Lakes basin. We collected intensively from eight harbours in the Chicago area in order to conduct detailed investigation of local population expansion. In addition, we collected from Lakes Michigan, Erie, and Ontario, as well as Lake Cayuga in the Finger Lakes of upstate New York in order to assess genetic structure on a regional scale. Based on data from eight highly polymorphic microsatellite loci we examined the spatial extent of clonal genotypes, assessed levels of neutral genetic diversity, and explored patterns of migration and dispersal at multiple spatial scales through assessment of population level genetic differentiation (pairwise F(ST) and factorial correspondence analysis), Bayesian inference of population structure, and assignment tests on individual genotypes. Results of these analyses indicate that Cordylophora populations in this region spread predominantly through sexually produced propagules, and that while limited natural larval dispersal can drive expansion locally, regional expansion likely relies on anthropogenic dispersal vectors.

Systems-Level Synthetic Biology for Advanced Biofuel Production

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

Ruffing, Anne; Jensen, Travis J.; Strickland, Lucas Marshall

2015-03-01

Cyanobacteria have been shown to be capable of producing a variety of advanced biofuels; however, product yields remain well below those necessary for large scale production. New genetic tools and high throughput metabolic engineering techniques are needed to optimize cyanobacterial metabolisms for enhanced biofuel production. Towards this goal, this project advances the development of a multiple promoter replacement technique for systems-level optimization of gene expression in a model cyanobacterial host: Synechococcus sp. PCC 7002. To realize this multiple-target approach, key capabilities were developed, including a high throughput detection method for advanced biofuels, enhanced transformation efficiency, and genetic tools for Synechococcusmore » sp. PCC 7002. Moreover, several additional obstacles were identified for realization of this multiple promoter replacement technique. The techniques and tools developed in this project will help to enable future efforts in the advancement of cyanobacterial biofuels.« less

Inferring human population size and separation history from multiple genome sequences.

PubMed

Schiffels, Stephan; Durbin, Richard

2014-08-01

The availability of complete human genome sequences from populations across the world has given rise to new population genetic inference methods that explicitly model ancestral relationships under recombination and mutation. So far, application of these methods to evolutionary history more recent than 20,000-30,000 years ago and to population separations has been limited. Here we present a new method that overcomes these shortcomings. The multiple sequentially Markovian coalescent (MSMC) analyzes the observed pattern of mutations in multiple individuals, focusing on the first coalescence between any two individuals. Results from applying MSMC to genome sequences from nine populations across the world suggest that the genetic separation of non-African ancestors from African Yoruban ancestors started long before 50,000 years ago and give information about human population history as recent as 2,000 years ago, including the bottleneck in the peopling of the Americas and separations within Africa, East Asia and Europe.

Population diversity and multiplicity of infection in Theileria annulata

PubMed Central

Weir, William; Karagenç, Tülin; Gharbi, Mohamed; Simuunza, Martin; Aypak, Suleyman; Aysul, Nuran; Darghouth, Mohamed Aziz; Shiels, Brian; Tait, Andrew

2011-01-01

The tick-borne apicomplexan parasite Theileria annulata is endemic in many sub-tropical countries and causes the bovine disease tropical theileriosis. Although the parasite is known to be highly diverse, detailed information is lacking on the genetic structure of natural populations and levels of multiplicity of infection in the cattle host. With the widespread deployment of live attenuated vaccines and the emergence of drug-resistant parasites in the field, it is vital to appreciate the factors which shape genetic diversity of the parasite both within individual hosts and in the wider population. This study addresses these issues and represents an extensive genetic analysis of T. annulata populations in two endemic countries utilising a high-throughput adaptation of a micro- and mini-satellite genotyping system. Parasite material was collected from infected cattle in defined regions of Turkey and Tunisia to allow a variety of analyses to be conducted. All animals (n = 305) were found to harbour multiple parasite genotypes and only two isolates shared an identical predominant multi-locus profile. A modelling approach was used to demonstrate that host age, location and vaccination status play a measurable role in determining multiplicity of infection in an individual animal. Age was shown to positively correlate with multiplicity of infection and while positive vaccination status exerted a similar effect, it was shown to be due not simply to the presence of the immunising genotype. Importantly, no direct evidence was found for the immunising genotype spreading or recombining within the local parasite community. Genetic analysis confirmed the tentative conclusion of a previous study that the parasite population appears to be, in general, panmictic. Nevertheless, evidence supporting linkage disequilibrium and a departure from panmixia was uncovered in some localities and a number of explanations for these findings are advanced. PMID:20833170

The relative influence of habitat amount and configuration on genetic structure across multiple spatial scales

PubMed Central

Millette, Katie L; Keyghobadi, Nusha

2015-01-01

Despite strong interest in understanding how habitat spatial structure shapes the genetics of populations, the relative importance of habitat amount and configuration for patterns of genetic differentiation remains largely unexplored in empirical systems. In this study, we evaluate the relative influence of, and interactions among, the amount of habitat and aspects of its spatial configuration on genetic differentiation in the pitcher plant midge, Metriocnemus knabi. Larvae of this species are found exclusively within the water-filled leaves of pitcher plants (Sarracenia purpurea) in a system that is naturally patchy at multiple spatial scales (i.e., leaf, plant, cluster, peatland). Using generalized linear mixed models and multimodel inference, we estimated effects of the amount of habitat, patch size, interpatch distance, and patch isolation, measured at different spatial scales, on genetic differentiation (FST) among larval samples from leaves within plants, plants within clusters, and clusters within peatlands. Among leaves and plants, genetic differentiation appears to be driven by female oviposition behaviors and is influenced by habitat isolation at a broad (peatland) scale. Among clusters, gene flow is spatially restricted and aspects of both the amount of habitat and configuration at the focal scale are important, as is their interaction. Our results suggest that both habitat amount and configuration can be important determinants of genetic structure and that their relative influence is scale dependent. PMID:25628865

The relative influence of habitat amount and configuration on genetic structure across multiple spatial scales.

PubMed

Millette, Katie L; Keyghobadi, Nusha

2015-01-01

Despite strong interest in understanding how habitat spatial structure shapes the genetics of populations, the relative importance of habitat amount and configuration for patterns of genetic differentiation remains largely unexplored in empirical systems. In this study, we evaluate the relative influence of, and interactions among, the amount of habitat and aspects of its spatial configuration on genetic differentiation in the pitcher plant midge, Metriocnemus knabi. Larvae of this species are found exclusively within the water-filled leaves of pitcher plants (Sarracenia purpurea) in a system that is naturally patchy at multiple spatial scales (i.e., leaf, plant, cluster, peatland). Using generalized linear mixed models and multimodel inference, we estimated effects of the amount of habitat, patch size, interpatch distance, and patch isolation, measured at different spatial scales, on genetic differentiation (F ST) among larval samples from leaves within plants, plants within clusters, and clusters within peatlands. Among leaves and plants, genetic differentiation appears to be driven by female oviposition behaviors and is influenced by habitat isolation at a broad (peatland) scale. Among clusters, gene flow is spatially restricted and aspects of both the amount of habitat and configuration at the focal scale are important, as is their interaction. Our results suggest that both habitat amount and configuration can be important determinants of genetic structure and that their relative influence is scale dependent.

Multifactorial genetic divergence processes drive the onset of speciation in an Amazonian fish

PubMed Central

Torrente-Vilara, Gislene; Quilodran, Claudio; Rodrigues da Costa Doria, Carolina; Montoya-Burgos, Juan I.

2017-01-01

Understanding the processes that drive population genetic divergence in the Amazon is challenging because of the vast scale, the environmental richness and the outstanding biodiversity of the region. We addressed this issue by determining the genetic structure of the widespread Amazonian common sardine fish Triportheus albus (Characidae). We then examined the influence, on this species, of all previously proposed population-structuring factors, including isolation-by-distance, isolation-by-barrier (the Teotônio Falls) and isolation-by-environment using variables that describe floodplain and water characteristics. The population genetics analyses revealed an unusually strong structure with three geographical groups: Negro/Tapajós rivers, Lower Madeira/Central Amazon, and Upper Madeira. Distance-based redundancy analyses showed that the optimal model for explaining the extreme genetic structure contains all proposed structuring factors and accounts for up to 70% of the genetic structure. We further quantified the contribution of each factor via a variance-partitioning analysis. Our results demonstrate that multiple factors, often proposed as individual drivers of population divergence, have acted in conjunction to divide T. albus into three genetic lineages. Because the conjunction of multiple long-standing population-structuring processes may lead to population reproductive isolation, that is, the onset of speciation, we suggest that the multifactorial population-structuring processes highlighted in this study could account for the high speciation rate characterising the Amazon Basin. PMID:29261722

Fine-mapping additive and dominant SNP effects using group-LASSO and Fractional Resample Model Averaging

PubMed Central

Sabourin, Jeremy; Nobel, Andrew B.; Valdar, William

2014-01-01

Genomewide association studies sometimes identify loci at which both the number and identities of the underlying causal variants are ambiguous. In such cases, statistical methods that model effects of multiple SNPs simultaneously can help disentangle the observed patterns of association and provide information about how those SNPs could be prioritized for follow-up studies. Current multi-SNP methods, however, tend to assume that SNP effects are well captured by additive genetics; yet when genetic dominance is present, this assumption translates to reduced power and faulty prioritizations. We describe a statistical procedure for prioritizing SNPs at GWAS loci that efficiently models both additive and dominance effects. Our method, LLARRMA-dawg, combines a group LASSO procedure for sparse modeling of multiple SNP effects with a resampling procedure based on fractional observation weights; it estimates for each SNP the robustness of association with the phenotype both to sampling variation and to competing explanations from other SNPs. In producing a SNP prioritization that best identifies underlying true signals, we show that: our method easily outperforms a single marker analysis; when additive-only signals are present, our joint model for additive and dominance is equivalent to or only slightly less powerful than modeling additive-only effects; and, when dominance signals are present, even in combination with substantial additive effects, our joint model is unequivocally more powerful than a model assuming additivity. We also describe how performance can be improved through calibrated randomized penalization, and discuss how dominance in ungenotyped SNPs can be incorporated through either heterozygote dosage or multiple imputation. PMID:25417853

ON MODEL SELECTION STRATEGIES TO IDENTIFY GENES UNDERLYING BINARY TRAITS USING GENOME-WIDE ASSOCIATION DATA.

PubMed

Wu, Zheyang; Zhao, Hongyu

2012-01-01

For more fruitful discoveries of genetic variants associated with diseases in genome-wide association studies, it is important to know whether joint analysis of multiple markers is more powerful than the commonly used single-marker analysis, especially in the presence of gene-gene interactions. This article provides a statistical framework to rigorously address this question through analytical power calculations for common model search strategies to detect binary trait loci: marginal search, exhaustive search, forward search, and two-stage screening search. Our approach incorporates linkage disequilibrium, random genotypes, and correlations among score test statistics of logistic regressions. We derive analytical results under two power definitions: the power of finding all the associated markers and the power of finding at least one associated marker. We also consider two types of error controls: the discovery number control and the Bonferroni type I error rate control. After demonstrating the accuracy of our analytical results by simulations, we apply them to consider a broad genetic model space to investigate the relative performances of different model search strategies. Our analytical study provides rapid computation as well as insights into the statistical mechanism of capturing genetic signals under different genetic models including gene-gene interactions. Even though we focus on genetic association analysis, our results on the power of model selection procedures are clearly very general and applicable to other studies.

ON MODEL SELECTION STRATEGIES TO IDENTIFY GENES UNDERLYING BINARY TRAITS USING GENOME-WIDE ASSOCIATION DATA

PubMed Central

Wu, Zheyang; Zhao, Hongyu

2013-01-01

For more fruitful discoveries of genetic variants associated with diseases in genome-wide association studies, it is important to know whether joint analysis of multiple markers is more powerful than the commonly used single-marker analysis, especially in the presence of gene-gene interactions. This article provides a statistical framework to rigorously address this question through analytical power calculations for common model search strategies to detect binary trait loci: marginal search, exhaustive search, forward search, and two-stage screening search. Our approach incorporates linkage disequilibrium, random genotypes, and correlations among score test statistics of logistic regressions. We derive analytical results under two power definitions: the power of finding all the associated markers and the power of finding at least one associated marker. We also consider two types of error controls: the discovery number control and the Bonferroni type I error rate control. After demonstrating the accuracy of our analytical results by simulations, we apply them to consider a broad genetic model space to investigate the relative performances of different model search strategies. Our analytical study provides rapid computation as well as insights into the statistical mechanism of capturing genetic signals under different genetic models including gene-gene interactions. Even though we focus on genetic association analysis, our results on the power of model selection procedures are clearly very general and applicable to other studies. PMID:23956610

Heritabilities and genetic correlations in the same traits across different strata of herds created according to continuous genomic, genetic, and phenotypic descriptors.

PubMed

Yin, Tong; König, Sven

2018-03-01

The most common approach in dairy cattle to prove genotype by environment interactions is a multiple-trait model application, and considering the same traits in different environments as different traits. We enhanced such concepts by defining continuous phenotypic, genetic, and genomic herd descriptors, and applying random regression sire models. Traits of interest were test-day traits for milk yield, fat percentage, protein percentage, and somatic cell score, considering 267,393 records from 32,707 first-lactation Holstein cows. Cows were born in the years 2010 to 2013, and kept in 52 large-scale herds from 2 federal states of north-east Germany. The average number of genotyped cows per herd (45,613 single nucleotide polymorphism markers per cow) was 133.5 (range: 45 to 415 genotyped cows). Genomic herd descriptors were (1) the level of linkage disequilibrium (r 2 ) within specific chromosome segments, and (2) the average allele frequency for single nucleotide polymorphisms in close distance to a functional mutation. Genetic herd descriptors were the (1) intra-herd inbreeding coefficient, and (2) the percentage of daughters from foreign sires. Phenotypic herd descriptors were (1) herd size, and (2) the herd mean for nonreturn rate. Most correlations among herd descriptors were close to 0, indicating independence of genomic, genetic, and phenotypic characteristics. Heritabilities for milk yield increased with increasing intra-herd linkage disequilibrium, inbreeding, and herd size. Genetic correlations in same traits between adjacent levels of herd descriptors were close to 1, but declined for descriptor levels in greater distance. Genetic correlation declines were more obvious for somatic cell score, compared with test-day traits with larger heritabilities (fat percentage and protein percentage). Also, for milk yield, alterations of herd descriptor levels had an obvious effect on heritabilities and genetic correlations. By trend, multiple trait model results (based on created discrete herd classes) confirmed the random regression estimates. Identified alterations of breeding values in dependency of herd descriptors suggest utilization of specific sires for specific herd structures, offering new possibilities to improve sire selection strategies. Regarding genomic selection designs and genetic gain transfer into commercial herds, cow herds for the utilization in cow training sets should reflect the genomic, genetic, and phenotypic pattern of the broad population. Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

De novo establishment of wild-type song culture in the zebra finch

PubMed Central

Feher, Olga; Wang, Haibin; Saar, Sigal; Mitra, Partha P.; Tchernichovski, Ofer

2009-01-01

What sort of culture would evolve in an island colony of naive founders? This question cannot be studied experimentally in humans. We performed the analogous experiment using socially learned birdsong. Culture is typically viewed as consisting of traits inherited epigenetically, via social learning. However, cultural diversity has species-typical constraints1, presumably of genetic origin. A celebrated, if contentious, example is whether a universal grammar constrains syntactic diversity in human languages2. Oscine songbirds exhibit song learning and provide biologically tractable models of culture: members of a species show individual variation in song3 and geographically separated groups have local song dialects 4,5. Different species exhibit distinct song cultures6,7, suggestive of genetic constraints8,9. Absent such constraints, innovations and copying errors should cause unbounded variation over multiple generations or geographical distance, contrary to observations9. We asked if wild-type song culture might emerge over multiple generations in an isolated colony founded by isolates, and if so, how this might happen and what type of social environment is required10. Zebra finch isolates, unexposed to singing males during development, produce song with characteristics that differ from the wild-type song found in laboratory11 or natural colonies. In tutoring lineages starting from isolate founders, we quantified alterations in song across tutoring generations in two social environments: tutor-pupil pairs in sound-isolated chambers and an isolated semi-natural colony. In both settings, juveniles imitated the isolate tutors, but changed certain characteristics of the songs. These alterations accumulated over learning generations. Consequently, songs evolved toward the wild-type in 3–4 generations. Thus, species-typical song culture can appear de novo. Our study has parallels with language change and evolution12,13. In analogy to models in quantitative genetics14,15, we model song culture as a multi-generational phenotype, partly encoded genetically in an isolate founding population, influenced by environmental variables, and taking multiple generations to emerge. PMID:19412161

Multiple-trait estimates of genetic parameters for metabolic disease traits, fertility disorders, and their predictors in Canadian Holsteins.

PubMed

Jamrozik, J; Koeck, A; Kistemaker, G J; Miglior, F

2016-03-01

Producer-recorded health data for metabolic disease traits and fertility disorders on 35,575 Canadian Holstein cows were jointly analyzed with selected indicator traits. Metabolic diseases included clinical ketosis (KET) and displaced abomasum (DA); fertility disorders were metritis (MET) and retained placenta (RP); and disease indicators were fat-to-protein ratio, milk β-hydroxybutyrate, and body condition score (BCS) in the first lactation. Traits in first and later (up to fifth) lactations were treated as correlated in the multiple-trait (13 traits in total) animal linear model. Bayesian methods with Gibbs sampling were implemented for the analysis. Estimates of heritability for disease incidence were low, up to 0.06 for DA in first lactation. Among disease traits, the environmental herd-year variance constituted 4% of the total variance for KET and less for other traits. First- and later-lactation disease traits were genetically correlated (from 0.66 to 0.72) across all traits, indicating different genetic backgrounds for first and later lactations. Genetic correlations between KET and DA were relatively strong and positive (up to 0.79) in both first- and later-lactation cows. Genetic correlations between fertility disorders were slightly lower. Metritis was strongly genetically correlated with both metabolic disease traits in the first lactation only. All other genetic correlations between metabolic and fertility diseases were statistically nonsignificant. First-lactation KET and MET were strongly positively correlated with later-lactation performance for these traits due to the environmental herd-year effect. Indicator traits were moderately genetically correlated (from 0.30 to 0.63 in absolute values) with both metabolic disease traits in the first lactation. Smaller and mostly nonsignificant genetic correlations were among indicators and metabolic diseases in later lactations. The only significant genetic correlations between indicators and fertility disorders were those between BCS and MET in both first and later lactations. Results indicated a limited value of a joint genetic evaluation model for metabolic disease traits and fertility disorders in Canadian Holsteins. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

A Unified Framework for Association Analysis with Multiple Related Phenotypes

PubMed Central

Stephens, Matthew

2013-01-01

We consider the problem of assessing associations between multiple related outcome variables, and a single explanatory variable of interest. This problem arises in many settings, including genetic association studies, where the explanatory variable is genotype at a genetic variant. We outline a framework for conducting this type of analysis, based on Bayesian model comparison and model averaging for multivariate regressions. This framework unifies several common approaches to this problem, and includes both standard univariate and standard multivariate association tests as special cases. The framework also unifies the problems of testing for associations and explaining associations – that is, identifying which outcome variables are associated with genotype. This provides an alternative to the usual, but conceptually unsatisfying, approach of resorting to univariate tests when explaining and interpreting significant multivariate findings. The method is computationally tractable genome-wide for modest numbers of phenotypes (e.g. 5–10), and can be applied to summary data, without access to raw genotype and phenotype data. We illustrate the methods on both simulated examples, and to a genome-wide association study of blood lipid traits where we identify 18 potential novel genetic associations that were not identified by univariate analyses of the same data. PMID:23861737

Conceptual Variation or Incoherence? Textbook Discourse on Genes in Six Countries

NASA Astrophysics Data System (ADS)

Gericke, Niklas M.; Hagberg, Mariana; dos Santos, Vanessa Carvalho; Joaquim, Leyla Mariane; El-Hani, Charbel N.

2014-02-01

The aim of this paper is to investigate in a systematic and comparative way previous results of independent studies on the treatment of genes and gene function in high school textbooks from six different countries. We analyze how the conceptual variation within the scientific domain of Genetics regarding gene function models and gene concepts is transformed via the didactic transposition into school science textbooks. The results indicate that a common textbook discourse on genes and their function exist in textbooks from the different countries. The structure of science as represented by conceptual variation and the use of multiple models was present in all the textbooks. However, the existence of conceptual variation and multiple models is implicit in these textbooks, i.e., the phenomenon of conceptual variation and multiple models are not addressed explicitly, nor its consequences and, thus, it ends up introducing conceptual incoherence about the gene concept and its function within the textbooks. We conclude that within the found textbook-discourse ontological aspects of the academic disciplines of genetics and molecular biology were retained, but without their epistemological underpinnings; these are lost in the didactic transposition. These results are of interest since students might have problems reconstructing the correct scientific understanding from the transformed school science knowledge as depicted within the high school textbooks. Implications for textbook writing as well as teaching are discussed in the paper.

A versatile modular vector system for rapid combinatorial mammalian genetics.

PubMed

Albers, Joachim; Danzer, Claudia; Rechsteiner, Markus; Lehmann, Holger; Brandt, Laura P; Hejhal, Tomas; Catalano, Antonella; Busenhart, Philipp; Gonçalves, Ana Filipa; Brandt, Simone; Bode, Peter K; Bode-Lesniewska, Beata; Wild, Peter J; Frew, Ian J

2015-04-01

Here, we describe the multiple lentiviral expression (MuLE) system that allows multiple genetic alterations to be introduced simultaneously into mammalian cells. We created a toolbox of MuLE vectors that constitute a flexible, modular system for the rapid engineering of complex polycistronic lentiviruses, allowing combinatorial gene overexpression, gene knockdown, Cre-mediated gene deletion, or CRISPR/Cas9-mediated (where CRISPR indicates clustered regularly interspaced short palindromic repeats) gene mutation, together with expression of fluorescent or enzymatic reporters for cellular assays and animal imaging. Examples of tumor engineering were used to illustrate the speed and versatility of performing combinatorial genetics using the MuLE system. By transducing cultured primary mouse cells with single MuLE lentiviruses, we engineered tumors containing up to 5 different genetic alterations, identified genetic dependencies of molecularly defined tumors, conducted genetic interaction screens, and induced the simultaneous CRISPR/Cas9-mediated knockout of 3 tumor-suppressor genes. Intramuscular injection of MuLE viruses expressing oncogenic H-RasG12V together with combinations of knockdowns of the tumor suppressors cyclin-dependent kinase inhibitor 2A (Cdkn2a), transformation-related protein 53 (Trp53), and phosphatase and tensin homolog (Pten) allowed the generation of 3 murine sarcoma models, demonstrating that genetically defined autochthonous tumors can be rapidly generated and quantitatively monitored via direct injection of polycistronic MuLE lentiviruses into mouse tissues. Together, our results demonstrate that the MuLE system provides genetic power for the systematic investigation of the molecular mechanisms that underlie human diseases.

Multiple Memory Stores and Operant Conditioning: A Rationale for Memory's Complexity

ERIC Educational Resources Information Center

Meeter, Martijn; Veldkamp, Rob; Jin, Yaochu

2009-01-01

Why does the brain contain more than one memory system? Genetic algorithms can play a role in elucidating this question. Here, model animals were constructed containing a dorsal striatal layer that controlled actions, and a ventral striatal layer that controlled a dopaminergic learning signal. Both layers could gain access to three modeled memory…

Self-Tuning of Design Variables for Generalized Predictive Control

NASA Technical Reports Server (NTRS)

Lin, Chaung; Juang, Jer-Nan

2000-01-01

Three techniques are introduced to determine the order and control weighting for the design of a generalized predictive controller. These techniques are based on the application of fuzzy logic, genetic algorithms, and simulated annealing to conduct an optimal search on specific performance indexes or objective functions. Fuzzy logic is found to be feasible for real-time and on-line implementation due to its smooth and quick convergence. On the other hand, genetic algorithms and simulated annealing are applicable for initial estimation of the model order and control weighting, and final fine-tuning within a small region of the solution space, Several numerical simulations for a multiple-input and multiple-output system are given to illustrate the techniques developed in this paper.

Contributions of dopamine-related genes and environmental factors to highly sensitive personality: a multi-step neuronal system-level approach.

PubMed

Chen, Chunhui; Chen, Chuansheng; Moyzis, Robert; Stern, Hal; He, Qinghua; Li, He; Li, Jin; Zhu, Bi; Dong, Qi

2011-01-01

Traditional behavioral genetic studies (e.g., twin, adoption studies) have shown that human personality has moderate to high heritability, but recent molecular behavioral genetic studies have failed to identify quantitative trait loci (QTL) with consistent effects. The current study adopted a multi-step approach (ANOVA followed by multiple regression and permutation) to assess the cumulative effects of multiple QTLs. Using a system-level (dopamine system) genetic approach, we investigated a personality trait deeply rooted in the nervous system (the Highly Sensitive Personality, HSP). 480 healthy Chinese college students were given the HSP scale and genotyped for 98 representative polymorphisms in all major dopamine neurotransmitter genes. In addition, two environment factors (stressful life events and parental warmth) that have been implicated for their contributions to personality development were included to investigate their relative contributions as compared to genetic factors. In Step 1, using ANOVA, we identified 10 polymorphisms that made statistically significant contributions to HSP. In Step 2, these polymorphism's main effects and interactions were assessed using multiple regression. This model accounted for 15% of the variance of HSP (p<0.001). Recent stressful life events accounted for an additional 2% of the variance. Finally, permutation analyses ascertained the probability of obtaining these findings by chance to be very low, p ranging from 0.001 to 0.006. Dividing these loci by the subsystems of dopamine synthesis, degradation/transport, receptor and modulation, we found that the modulation and receptor subsystems made the most significant contribution to HSP. The results of this study demonstrate the utility of a multi-step neuronal system-level approach in assessing genetic contributions to individual differences in human behavior. It can potentially bridge the gap between the high heritability estimates based on traditional behavioral genetics and the lack of reproducible genetic effects observed currently from molecular genetic studies.

Contributions of Dopamine-Related Genes and Environmental Factors to Highly Sensitive Personality: A Multi-Step Neuronal System-Level Approach

PubMed Central

Chen, Chunhui; Chen, Chuansheng; Moyzis, Robert; Stern, Hal; He, Qinghua; Li, He; Li, Jin; Zhu, Bi; Dong, Qi

2011-01-01

Traditional behavioral genetic studies (e.g., twin, adoption studies) have shown that human personality has moderate to high heritability, but recent molecular behavioral genetic studies have failed to identify quantitative trait loci (QTL) with consistent effects. The current study adopted a multi-step approach (ANOVA followed by multiple regression and permutation) to assess the cumulative effects of multiple QTLs. Using a system-level (dopamine system) genetic approach, we investigated a personality trait deeply rooted in the nervous system (the Highly Sensitive Personality, HSP). 480 healthy Chinese college students were given the HSP scale and genotyped for 98 representative polymorphisms in all major dopamine neurotransmitter genes. In addition, two environment factors (stressful life events and parental warmth) that have been implicated for their contributions to personality development were included to investigate their relative contributions as compared to genetic factors. In Step 1, using ANOVA, we identified 10 polymorphisms that made statistically significant contributions to HSP. In Step 2, these polymorphism's main effects and interactions were assessed using multiple regression. This model accounted for 15% of the variance of HSP (p<0.001). Recent stressful life events accounted for an additional 2% of the variance. Finally, permutation analyses ascertained the probability of obtaining these findings by chance to be very low, p ranging from 0.001 to 0.006. Dividing these loci by the subsystems of dopamine synthesis, degradation/transport, receptor and modulation, we found that the modulation and receptor subsystems made the most significant contribution to HSP. The results of this study demonstrate the utility of a multi-step neuronal system-level approach in assessing genetic contributions to individual differences in human behavior. It can potentially bridge the gap between the high heritability estimates based on traditional behavioral genetics and the lack of reproducible genetic effects observed currently from molecular genetic studies. PMID:21765900

Efficient inference for genetic association studies with multiple outcomes.

PubMed

Ruffieux, Helene; Davison, Anthony C; Hager, Jorg; Irincheeva, Irina

2017-10-01

Combined inference for heterogeneous high-dimensional data is critical in modern biology, where clinical and various kinds of molecular data may be available from a single study. Classical genetic association studies regress a single clinical outcome on many genetic variants one by one, but there is an increasing demand for joint analysis of many molecular outcomes and genetic variants in order to unravel functional interactions. Unfortunately, most existing approaches to joint modeling are either too simplistic to be powerful or are impracticable for computational reasons. Inspired by Richardson and others (2010, Bayesian Statistics 9), we consider a sparse multivariate regression model that allows simultaneous selection of predictors and associated responses. As Markov chain Monte Carlo (MCMC) inference on such models can be prohibitively slow when the number of genetic variants exceeds a few thousand, we propose a variational inference approach which produces posterior information very close to that of MCMC inference, at a much reduced computational cost. Extensive numerical experiments show that our approach outperforms popular variable selection methods and tailored Bayesian procedures, dealing within hours with problems involving hundreds of thousands of genetic variants and tens to hundreds of clinical or molecular outcomes. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Short communication: Genetic correlation and heritability of milk coagulation traits within and across lactations in Holstein cows using multiple-lactation random regression animal models.

PubMed

Pretto, D; Vallas, M; Pärna, E; Tänavots, A; Kiiman, H; Kaart, T

2014-12-01

Genetic parameters of milk rennet coagulation time (RCT) and curd firmness (a30) among the first 3 lactations in Holstein cows were estimated. The data set included 39,960 test-day records from 5,216 Estonian Holstein cows (the progeny of 306 sires), which were recorded from April 2005 to May 2010 in 98 herds across the country. A multiple-lactation random regression animal model was used. Individual milk samples from each cow were collected during routine milk recording. These samples were analyzed for milk composition and coagulation traits with intervals of 2 to 3 mo in each lactation (7 to 305 DIM) and from first to third lactation. Mean heritabilities were 0.36, 0.32, and 0.28 for log-transformed RCT [ln(RCT)] and 0.47, 0.40, and 0.62 for a30 for parities 1, 2, and 3, respectively. Mean repeatabilities for ln(RCT) were 0.53, 0.55, and 0.56, but 0.59, 0.61, and 0.68 for a30 for parities 1, 2 and 3, respectively. Mean genetic correlations between ln(RCT) and a30 were -0.19, -0.14, and 0.02 for parities 1, 2, and 3, respectively. Mean genetic correlations were 0.91, 0.79, and 0.99 for ln(RCT), and 0.95, 0.94, and 0.94 for a30 between parities 1 and 2, 1 and 3, and 2 and 3, respectively. Due to these high genetic correlations, we concluded that for a proper genetic evaluation of milk coagulation properties it is sufficient to record RCT and a30 only in the first lactation. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Gene-Environment Interactions in Cardiovascular Disease

PubMed Central

Flowers, Elena; Froelicher, Erika Sivarajan; Aouizerat, Bradley E.

2011-01-01

Background Historically, models to describe disease were exclusively nature-based or nurture-based. Current theoretical models for complex conditions such as cardiovascular disease acknowledge the importance of both biologic and non-biologic contributors to disease. A critical feature is the occurrence of interactions between numerous risk factors for disease. The interaction between genetic (i.e. biologic, nature) and environmental (i.e. non-biologic, nurture) causes of disease is an important mechanism for understanding both the etiology and public health impact of cardiovascular disease. Objectives The purpose of this paper is to describe theoretical underpinnings of gene-environment interactions, models of interaction, methods for studying gene-environment interactions, and the related concept of interactions between epigenetic mechanisms and the environment. Discussion Advances in methods for measurement of genetic predictors of disease have enabled an increasingly comprehensive understanding of the causes of disease. In order to fully describe the effects of genetic predictors of disease, it is necessary to place genetic predictors within the context of known environmental risk factors. The additive or multiplicative effect of the interaction between genetic and environmental risk factors is often greater than the contribution of either risk factor alone. PMID:21684212

Selection of higher order regression models in the analysis of multi-factorial transcription data.

PubMed

Prazeres da Costa, Olivia; Hoffman, Arthur; Rey, Johannes W; Mansmann, Ulrich; Buch, Thorsten; Tresch, Achim

2014-01-01

Many studies examine gene expression data that has been obtained under the influence of multiple factors, such as genetic background, environmental conditions, or exposure to diseases. The interplay of multiple factors may lead to effect modification and confounding. Higher order linear regression models can account for these effects. We present a new methodology for linear model selection and apply it to microarray data of bone marrow-derived macrophages. This experiment investigates the influence of three variable factors: the genetic background of the mice from which the macrophages were obtained, Yersinia enterocolitica infection (two strains, and a mock control), and treatment/non-treatment with interferon-γ. We set up four different linear regression models in a hierarchical order. We introduce the eruption plot as a new practical tool for model selection complementary to global testing. It visually compares the size and significance of effect estimates between two nested models. Using this methodology we were able to select the most appropriate model by keeping only relevant factors showing additional explanatory power. Application to experimental data allowed us to qualify the interaction of factors as either neutral (no interaction), alleviating (co-occurring effects are weaker than expected from the single effects), or aggravating (stronger than expected). We find a biologically meaningful gene cluster of putative C2TA target genes that appear to be co-regulated with MHC class II genes. We introduced the eruption plot as a tool for visual model comparison to identify relevant higher order interactions in the analysis of expression data obtained under the influence of multiple factors. We conclude that model selection in higher order linear regression models should generally be performed for the analysis of multi-factorial microarray data.

Debunking Occam's razor: Diagnosing multiple genetic diseases in families by whole-exome sequencing.

PubMed

Balci, T B; Hartley, T; Xi, Y; Dyment, D A; Beaulieu, C L; Bernier, F P; Dupuis, L; Horvath, G A; Mendoza-Londono, R; Prasad, C; Richer, J; Yang, X-R; Armour, C M; Bareke, E; Fernandez, B A; McMillan, H J; Lamont, R E; Majewski, J; Parboosingh, J S; Prasad, A N; Rupar, C A; Schwartzentruber, J; Smith, A C; Tétreault, M; Innes, A M; Boycott, K M

2017-09-01

Recent clinical whole exome sequencing (WES) cohorts have identified unanticipated multiple genetic diagnoses in single patients. However, the frequency of multiple genetic diagnoses in families is largely unknown. We set out to identify the rate of multiple genetic diagnoses in probands and their families referred for analysis in two national research programs in Canada. We retrospectively analyzed WES results for 802 undiagnosed probands referred over the past 5 years in either the FORGE or Care4Rare Canada WES initiatives. Of the 802 probands, 226 (28.2%) were diagnosed based on mutations in known disease genes. Eight (3.5%) had two or more genetic diagnoses explaining their clinical phenotype, a rate in keeping with the large published studies (average 4.3%; 1.4 - 7.2%). Seven of the 8 probands had family members with one or more of the molecularly diagnosed diseases. Consanguinity and multisystem disease appeared to increase the likelihood of multiple genetic diagnoses in a family. Our findings highlight the importance of comprehensive clinical phenotyping of family members to ultimately provide accurate genetic counseling. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Island-Model Genomic Selection for Long-Term Genetic Improvement of Autogamous Crops.

PubMed

Yabe, Shiori; Yamasaki, Masanori; Ebana, Kaworu; Hayashi, Takeshi; Iwata, Hiroyoshi

2016-01-01

Acceleration of genetic improvement of autogamous crops such as wheat and rice is necessary to increase cereal production in response to the global food crisis. Population and pedigree methods of breeding, which are based on inbred line selection, are used commonly in the genetic improvement of autogamous crops. These methods, however, produce a few novel combinations of genes in a breeding population. Recurrent selection promotes recombination among genes and produces novel combinations of genes in a breeding population, but it requires inaccurate single-plant evaluation for selection. Genomic selection (GS), which can predict genetic potential of individuals based on their marker genotype, might have high reliability of single-plant evaluation and might be effective in recurrent selection. To evaluate the efficiency of recurrent selection with GS, we conducted simulations using real marker genotype data of rice cultivars. Additionally, we introduced the concept of an "island model" inspired by evolutionary algorithms that might be useful to maintain genetic variation through the breeding process. We conducted GS simulations using real marker genotype data of rice cultivars to evaluate the efficiency of recurrent selection and the island model in an autogamous species. Results demonstrated the importance of producing novel combinations of genes through recurrent selection. An initial population derived from admixture of multiple bi-parental crosses showed larger genetic gains than a population derived from a single bi-parental cross in whole cycles, suggesting the importance of genetic variation in an initial population. The island-model GS better maintained genetic improvement in later generations than the other GS methods, suggesting that the island-model GS can utilize genetic variation in breeding and can retain alleles with small effects in the breeding population. The island-model GS will become a new breeding method that enhances the potential of genomic selection in autogamous crops, especially bringing long-term improvement.

[Application of SAS macro to evaluated multiplicative and additive interaction in logistic and Cox regression in clinical practices].

PubMed

Nie, Z Q; Ou, Y Q; Zhuang, J; Qu, Y J; Mai, J Z; Chen, J M; Liu, X Q

2016-05-01

Conditional logistic regression analysis and unconditional logistic regression analysis are commonly used in case control study, but Cox proportional hazard model is often used in survival data analysis. Most literature only refer to main effect model, however, generalized linear model differs from general linear model, and the interaction was composed of multiplicative interaction and additive interaction. The former is only statistical significant, but the latter has biological significance. In this paper, macros was written by using SAS 9.4 and the contrast ratio, attributable proportion due to interaction and synergy index were calculated while calculating the items of logistic and Cox regression interactions, and the confidence intervals of Wald, delta and profile likelihood were used to evaluate additive interaction for the reference in big data analysis in clinical epidemiology and in analysis of genetic multiplicative and additive interactions.

Genetic analysis of fat-to-protein ratio, milk yield and somatic cell score of Holstein cows in Japan in the first three lactations by using a random regression model.

PubMed

Nishiura, Akiko; Sasaki, Osamu; Aihara, Mitsuo; Takeda, Hisato; Satoh, Masahiro

2015-12-01

We estimated the genetic parameters of fat-to-protein ratio (FPR) and the genetic correlations between FPR and milk yield or somatic cell score in the first three lactations in dairy cows. Data included 3,079,517 test-day records of 201,138 Holstein cows in Japan from 2006 to 2011. Genetic parameters were estimated with a multiple-trait random regression model in which the records within and between parities were treated as separate traits. The phenotypic values of FPR increased soon after parturition and peaked at 10 to 20 days in milk, then decreased slowly in mid- and late lactation. Heritability estimates for FPR yielded moderate values. Genetic correlations of FPR among parities were low in early lactation. Genetic correlations between FPR and milk yield were positive and low in early lactation, but only in the first lactation. Genetic correlations between FPR and somatic cell score were positive in early lactation and decreased to become negative in mid- to late lactation. By using these results for genetic evaluation it should be possible to improve energy balance in dairy cows. © 2015 Japanese Society of Animal Science.

How's the Flu Getting Through? Landscape genetics suggests both humans and birds spread H5N1 in Egypt.

PubMed

Young, Sean G; Carrel, Margaret; Kitchen, Andrew; Malanson, George P; Tamerius, James; Ali, Mohamad; Kayali, Ghazi

2017-04-01

First introduced to Egypt in 2006, H5N1 highly pathogenic avian influenza has resulted in the death of millions of birds and caused over 350 infections and at least 117 deaths in humans. After a decade of viral circulation, outbreaks continue to occur and diffusion mechanisms between poultry farms remain unclear. Using landscape genetics techniques, we identify the distance models most strongly correlated with the genetic relatedness of the viruses, suggesting the most likely methods of viral diffusion within Egyptian poultry. Using 73 viral genetic sequences obtained from infected birds throughout northern Egypt between 2009 and 2015, we calculated the genetic dissimilarity between H5N1 viruses for all eight gene segments. Spatial correlation was evaluated using Mantel tests and correlograms and multiple regression of distance matrices within causal modeling and relative support frameworks. These tests examine spatial patterns of genetic relatedness, and compare different models of distance. Four models were evaluated: Euclidean distance, road network distance, road network distance via intervening markets, and a least-cost path model designed to approximate wild waterbird travel using niche modeling and circuit theory. Samples from backyard farms were most strongly correlated with least cost path distances. Samples from commercial farms were most strongly correlated with road network distances. Results were largely consistent across gene segments. Results suggest wild birds play an important role in viral diffusion between backyard farms, while commercial farms experience human-mediated diffusion. These results can inform avian influenza surveillance and intervention strategies in Egypt. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

Receptor tyrosine kinase alterations in AML - biology and therapy.

PubMed

Stirewalt, Derek L; Meshinchi, Soheil

2010-01-01

Acute myeloid leukemia (AML) is the most common form of leukemia in adults, and despite some recent progress in understanding the biology of the disease, AML remains the leading cause of leukemia-related deaths in adults and children. AML is a complex and heterogeneous disease, often involving multiple genetic defects that promote leukemic transformation and drug resistance. The cooperativity model suggests that an initial genetic event leads to maturational arrest in a myeloid progenitor cell, and subsequent genetic events induce proliferation and block apoptosis. Together, these genetic abnormalities lead to clonal expansion and frank leukemia. The purpose of this chapter is to review the biology of receptor tyrosine kinases (RTKs) in AML, exploring how RTKs are being used as novel prognostic factors and potential therapeutic targets.

The use of fish models to study human neurological disorders.

PubMed

Matsui, Hideaki

2017-07-01

Small teleost fish including zebrafish and medaka have been used as animal models in basic science research due to the relative ease of handling and transparency during embryogenesis. Current advances in genetic engineering and progress in disease genetics allowed utilization of these fish to study neurological diseases and psychiatric disorders. This review summarizes the advantages and disadvantages of using fish for neuropsychiatric research using primarily our own studies as examples. We discuss how fish belong to a class of vertebrates, are feasible for imaging, and include diverse species with multiple research possibilities yet to be discovered. Copyright © 2017 Elsevier Ireland Ltd and Japan Neuroscience Society. All rights reserved.

Mouse forward genetics in the study of the peripheral nervous system and human peripheral neuropathy

PubMed Central

Douglas, Darlene S.; Popko, Brian

2009-01-01

Forward genetics, the phenotype-driven approach to investigating gene identity and function, has a long history in mouse genetics. Random mutations in the mouse transcend bias about gene function and provide avenues towards unique discoveries. The study of the peripheral nervous system is no exception; from historical strains such as the trembler mouse, which led to the identification of PMP22 as a human disease gene causing multiple forms of peripheral neuropathy, to the more recent identification of the claw paw and sprawling mutations, forward genetics has long been a tool for probing the physiology, pathogenesis, and genetics of the PNS. Even as spontaneous and mutagenized mice continue to enable the identification of novel genes, provide allelic series for detailed functional studies, and generate models useful for clinical research, new methods, such as the piggyBac transposon, are being developed to further harness the power of forward genetics. PMID:18481175

Precise Network Modeling of Systems Genetics Data Using the Bayesian Network Webserver.

PubMed

Ziebarth, Jesse D; Cui, Yan

2017-01-01

The Bayesian Network Webserver (BNW, http://compbio.uthsc.edu/BNW ) is an integrated platform for Bayesian network modeling of biological datasets. It provides a web-based network modeling environment that seamlessly integrates advanced algorithms for probabilistic causal modeling and reasoning with Bayesian networks. BNW is designed for precise modeling of relatively small networks that contain less than 20 nodes. The structure learning algorithms used by BNW guarantee the discovery of the best (most probable) network structure given the data. To facilitate network modeling across multiple biological levels, BNW provides a very flexible interface that allows users to assign network nodes into different tiers and define the relationships between and within the tiers. This function is particularly useful for modeling systems genetics datasets that often consist of multiscalar heterogeneous genotype-to-phenotype data. BNW enables users to, within seconds or minutes, go from having a simply formatted input file containing a dataset to using a network model to make predictions about the interactions between variables and the potential effects of experimental interventions. In this chapter, we will introduce the functions of BNW and show how to model systems genetics datasets with BNW.

Transforming growth factor‐β in liver cancer stem cells and regeneration

PubMed Central

Rao, Shuyun; Zaidi, Sobia; Banerjee, Jaideep; Jogunoori, Wilma; Sebastian, Raul; Mishra, Bibhuti; Nguyen, Bao‐Ngoc; Wu, Ray‐Chang; White, Jon; Deng, Chuxia; Amdur, Richard; Li, Shulin

2017-01-01

Cancer stem cells have established mechanisms that contribute to tumor heterogeneity as well as resistance to therapy. Over 40% of hepatocellular carcinomas (HCCs) are considered to be clonal and arise from a stem‐like/cancer stem cell. Moreover, HCC is the second leading cause of cancer death worldwide, and an improved understanding of cancer stem cells and targeting these in this cancer are urgently needed. Multiple studies have revealed etiological patterns and multiple genes/pathways signifying initiation and progression of HCC; however, unlike the transforming growth factor β (TGF‐β) pathway, loss of p53 and/or activation of β‐catenin do not spontaneously drive HCC in animal models. Despite many advances in cancer genetics that include identifying the dominant role of TGF‐β signaling in gastrointestinal cancers, we have not reached an integrated view of genetic mutations, copy number changes, driver pathways, and animal models that support effective targeted therapies for these common and lethal cancers. Moreover, pathways involved in stem cell transformation into gastrointestinal cancers remain largely undefined. Identifying the key mechanisms and developing models that reflect the human disease can lead to effective new treatment strategies. In this review, we dissect the evidence obtained from mouse and human liver regeneration, and mouse genetics, to provide insight into the role of TGF‐β in regulating the cancer stem cell niche. (Hepatology Communications 2017;1:477–493) PMID:29404474

Are Australasian Genetic Counselors Interested in Private Practice at the Primary Care Level of Health Service?

PubMed

Sane, Vrunda; Humphreys, Linda; Peterson, Madelyn

2015-10-01

This study explored the perceived interest in development of private genetic counseling services in collaboration with primary care physicians in the Australasian setting by online survey of members of the Australasian Society of Genetic Counselors. Four hypothetical private practice models of professional collaboration between genetic counselors and primary care physicians or clinical geneticists were proposed to gauge interest and enthusiasm of ASGC members for this type of professional development. Perceived barriers and facilitators were also evaluated. 78 completed responses were included for analysis. The majority of participants (84.6 %) showed a positive degree of interest and enthusiasm towards potential for clinical work in private practice. All proposed practice models yielded a positive degree of interest from participants. Model 4 (the only model of collaboration with a clinical geneticist rather than primary care physician) was the clearly preferred option (mean = 4.26/5), followed by Model 2 (collaboration with a single primary care practice) (mean = 4.09/5), Model 3 (collaboration with multiple primary care clinics, multidisciplinary clinic or specialty clinic) (mean = 3.77/5) and finally, Model 1 (mean = 3.61/5), which was the most independent model of practice. When participants ranked the options in the order of preference, Model 4 remained the most popular first preference (44.6 %), followed by model 2 (21.6 %), model 3 (18.9 %) and model 1 was again least popular (10.8 %). There was no significant statistical correlation between demographic characteristics (age bracket, years of work experience, current level of work autonomy) and participants' preference for private practice models. Support from clinical genetics colleagues and the professional society was highly rated as a facilitator and, conversely, lack of such support as a significant barrier.

An integrative model of evolutionary covariance: a symposium on body shape in fishes.

PubMed

Walker, Jeffrey A

2010-12-01

A major direction of current and future biological research is to understand how multiple, interacting functional systems coordinate in producing a body that works. This understanding is complicated by the fact that organisms need to work well in multiple environments, with both predictable and unpredictable environmental perturbations. Furthermore, organismal design reflects a history of past environments and not a plan for future environments. How complex, interacting functional systems evolve, then, is a truly grand challenge. In accepting the challenge, an integrative model of evolutionary covariance is developed. The model combines quantitative genetics, functional morphology/physiology, and functional ecology. The model is used to convene scientists ranging from geneticists, to physiologists, to ecologists, to engineers to facilitate the emergence of body shape in fishes as a model system for understanding how complex, interacting functional systems develop and evolve. Body shape of fish is a complex morphology that (1) results from many developmental paths and (2) functions in many different behaviors. Understanding the coordination and evolution of the many paths from genes to body shape, body shape to function, and function to a working fish body in a dynamic environment is now possible given new technologies from genetics to engineering and new theoretical models that integrate the different levels of biological organization (from genes to ecology).

Modifiers and mechanisms of multi-system polyglutamine neurodegenerative disorders: lessons from fly models.

PubMed

Mallik, Moushami; Lakhotia, Subhash C

2010-12-01

Polyglutamine (polyQ) diseases, resulting from a dynamic expansion of glutamine repeats in a polypeptide, are a class of genetically inherited late onset neurodegenerative disorders which, despite expression of the mutated gene widely in brain and other tissues, affect defined subpopulations of neurons in a disease-specific manner. We briefly review the different polyQ-expansion-induced neurodegenerative disorders and the advantages of modelling them in Drosophila. Studies using the fly models have successfully identified a variety of genetic modifiers and have helped in understanding some of the molecular events that follow expression of the abnormal polyQ proteins. Expression of the mutant polyQ proteins causes, as a consequence of intra-cellular and inter-cellular networking, mis-regulation at multiple steps like transcriptional and posttranscriptional regulations, cell signalling, protein quality control systems (protein folding and degradation networks), axonal transport machinery etc., in the sensitive neurons, resulting ultimately in their death. The diversity of genetic modifiers of polyQ toxicity identified through extensive genetic screens in fly and other models clearly reflects a complex network effect of the presence of the mutated protein. Such network effects pose a major challenge for therapeutic applications.

Modifications to the Patient Rule-Induction Method that utilize non-additive combinations of genetic and environmental effects to define partitions that predict ischemic heart disease.

PubMed

Dyson, Greg; Frikke-Schmidt, Ruth; Nordestgaard, Børge G; Tybjaerg-Hansen, Anne; Sing, Charles F

2009-05-01

This article extends the Patient Rule-Induction Method (PRIM) for modeling cumulative incidence of disease developed by Dyson et al. (Genet Epidemiol 31:515-527) to include the simultaneous consideration of non-additive combinations of predictor variables, a significance test of each combination, an adjustment for multiple testing and a confidence interval for the estimate of the cumulative incidence of disease in each partition. We employ the partitioning algorithm component of the Combinatorial Partitioning Method to construct combinations of predictors, permutation testing to assess the significance of each combination, theoretical arguments for incorporating a multiple testing adjustment and bootstrap resampling to produce the confidence intervals. An illustration of this revised PRIM utilizing a sample of 2,258 European male participants from the Copenhagen City Heart Study is presented that assesses the utility of genetic variants in predicting the presence of ischemic heart disease beyond the established risk factors.

The BTBR mouse model of idiopathic autism – current view on mechanisms

PubMed Central

Meyza, K. Z.; Blanchard, D. C.

2017-01-01

Autism spectrum disorder (ASD) is the most commonly diagnosed neurodevelopmental disorder, with current estimates of more than 1% of affected children across nations. The patients form a highly heterogeneous group with only the behavioral phenotype in common. The genetic heterogeneity is reflected in a plethora of animal models representing multiple mutations found in families of affected children. Despite many years of scientific effort, for the majority of cases the genetic cause remains elusive. It is therefore crucial to include well-validated models of idiopathic autism in studies searching for potential therapeutic agents. One of these models is the BTBR T+Itpr3tf/J mouse. The current review summarizes data gathered in recent research on potential molecular mechanisms responsible for the autism-like behavioral phenotype of this strain. PMID:28167097

SUMMIT (Serially Unified Multicenter Multiple Sclerosis Investigation): creating a repository of deeply phenotyped contemporary multiple sclerosis cohorts.

PubMed

Bove, Riley; Chitnis, Tanuja; Cree, Bruce Ac; Tintoré, Mar; Naegelin, Yvonne; Uitdehaag, Bernard Mj; Kappos, Ludwig; Khoury, Samia J; Montalban, Xavier; Hauser, Stephen L; Weiner, Howard L

2017-08-01

There is a pressing need for robust longitudinal cohort studies in the modern treatment era of multiple sclerosis. Build a multiple sclerosis (MS) cohort repository to capture the variability of disability accumulation, as well as provide the depth of characterization (clinical, radiologic, genetic, biospecimens) required to adequately model and ultimately predict a patient's course. Serially Unified Multicenter Multiple Sclerosis Investigation (SUMMIT) is an international multi-center, prospectively enrolled cohort with over a decade of comprehensive follow-up on more than 1000 patients from two large North American academic MS Centers (Brigham and Women's Hospital (Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Women's Hospital (CLIMB; BWH)) and University of California, San Francisco (Expression/genomics, Proteomics, Imaging, and Clinical (EPIC))). It is bringing online more than 2500 patients from additional international MS Centers (Basel (Universitätsspital Basel (UHB)), VU University Medical Center MS Center Amsterdam (MSCA), Multiple Sclerosis Center of Catalonia-Vall d'Hebron Hospital (Barcelona clinically isolated syndrome (CIS) cohort), and American University of Beirut Medical Center (AUBMC-Multiple Sclerosis Interdisciplinary Research (AMIR)). We provide evidence for harmonization of two of the initial cohorts in terms of the characterization of demographics, disease, and treatment-related variables; demonstrate several proof-of-principle analyses examining genetic and radiologic predictors of disease progression; and discuss the steps involved in expanding SUMMIT into a repository accessible to the broader scientific community.

Clumpak: a program for identifying clustering modes and packaging population structure inferences across K.

PubMed

Kopelman, Naama M; Mayzel, Jonathan; Jakobsson, Mattias; Rosenberg, Noah A; Mayrose, Itay

2015-09-01

The identification of the genetic structure of populations from multilocus genotype data has become a central component of modern population-genetic data analysis. Application of model-based clustering programs often entails a number of steps, in which the user considers different modelling assumptions, compares results across different predetermined values of the number of assumed clusters (a parameter typically denoted K), examines multiple independent runs for each fixed value of K, and distinguishes among runs belonging to substantially distinct clustering solutions. Here, we present Clumpak (Cluster Markov Packager Across K), a method that automates the postprocessing of results of model-based population structure analyses. For analysing multiple independent runs at a single K value, Clumpak identifies sets of highly similar runs, separating distinct groups of runs that represent distinct modes in the space of possible solutions. This procedure, which generates a consensus solution for each distinct mode, is performed by the use of a Markov clustering algorithm that relies on a similarity matrix between replicate runs, as computed by the software Clumpp. Next, Clumpak identifies an optimal alignment of inferred clusters across different values of K, extending a similar approach implemented for a fixed K in Clumpp and simplifying the comparison of clustering results across different K values. Clumpak incorporates additional features, such as implementations of methods for choosing K and comparing solutions obtained by different programs, models, or data subsets. Clumpak, available at http://clumpak.tau.ac.il, simplifies the use of model-based analyses of population structure in population genetics and molecular ecology. © 2015 John Wiley & Sons Ltd.

Evolved differences in larval social behavior mediated by novel pheromones

USDA-ARS?s Scientific Manuscript database

Pheromones, chemical signals that convey social information, mediate many insect social behaviors in both adult and immature stages. Multiple pheromones and neural pathways that underlie adult social behavior have been described in the genetic model organism, Drosophila melanogaster, but there is no...

Human genetics of infectious diseases: a unified theory

PubMed Central

Casanova, Jean-Laurent; Abel, Laurent

2007-01-01

Since the early 1950s, the dominant paradigm in the human genetics of infectious diseases postulates that rare monogenic immunodeficiencies confer vulnerability to multiple infectious diseases (one gene, multiple infections), whereas common infections are associated with the polygenic inheritance of multiple susceptibility genes (one infection, multiple genes). Recent studies, since 1996 in particular, have challenged this view. A newly recognised group of primary immunodeficiencies predisposing the individual to a principal or single type of infection is emerging. In parallel, several common infections have been shown to reflect the inheritance of one major susceptibility gene, at least in some populations. This novel causal relationship (one gene, one infection) blurs the distinction between patient-based Mendelian genetics and population-based complex genetics, and provides a unified conceptual frame for exploring the molecular genetic basis of infectious diseases in humans. PMID:17255931

SNPassoc: an R package to perform whole genome association studies.

PubMed

González, Juan R; Armengol, Lluís; Solé, Xavier; Guinó, Elisabet; Mercader, Josep M; Estivill, Xavier; Moreno, Víctor

2007-03-01

The popularization of large-scale genotyping projects has led to the widespread adoption of genetic association studies as the tool of choice in the search for single nucleotide polymorphisms (SNPs) underlying susceptibility to complex diseases. Although the analysis of individual SNPs is a relatively trivial task, when the number is large and multiple genetic models need to be explored it becomes necessary a tool to automate the analyses. In order to address this issue, we developed SNPassoc, an R package to carry out most common analyses in whole genome association studies. These analyses include descriptive statistics and exploratory analysis of missing values, calculation of Hardy-Weinberg equilibrium, analysis of association based on generalized linear models (either for quantitative or binary traits), and analysis of multiple SNPs (haplotype and epistasis analysis). Package SNPassoc is available at CRAN from http://cran.r-project.org. A tutorial is available on Bioinformatics online and in http://davinci.crg.es/estivill_lab/snpassoc.

Whole transcriptome analysis using next-generation sequencing of model species Setaria viridis to support C4 photosynthesis research.

PubMed

Xu, Jiajia; Li, Yuanyuan; Ma, Xiuling; Ding, Jianfeng; Wang, Kai; Wang, Sisi; Tian, Ye; Zhang, Hui; Zhu, Xin-Guang

2013-09-01

Setaria viridis is an emerging model species for genetic studies of C4 photosynthesis. Many basic molecular resources need to be developed to support for this species. In this paper, we performed a comprehensive transcriptome analysis from multiple developmental stages and tissues of S. viridis using next-generation sequencing technologies. Sequencing of the transcriptome from multiple tissues across three developmental stages (seed germination, vegetative growth, and reproduction) yielded a total of 71 million single end 100 bp long reads. Reference-based assembly using Setaria italica genome as a reference generated 42,754 transcripts. De novo assembly generated 60,751 transcripts. In addition, 9,576 and 7,056 potential simple sequence repeats (SSRs) covering S. viridis genome were identified when using the reference based assembled transcripts and the de novo assembled transcripts, respectively. This identified transcripts and SSR provided by this study can be used for both reverse and forward genetic studies based on S. viridis.

Applications of Population Genetics to Animal Breeding, from Wright, Fisher and Lush to Genomic Prediction

PubMed Central

Hill, William G.

2014-01-01

Although animal breeding was practiced long before the science of genetics and the relevant disciplines of population and quantitative genetics were known, breeding programs have mainly relied on simply selecting and mating the best individuals on their own or relatives’ performance. This is based on sound quantitative genetic principles, developed and expounded by Lush, who attributed much of his understanding to Wright, and formalized in Fisher’s infinitesimal model. Analysis at the level of individual loci and gene frequency distributions has had relatively little impact. Now with access to genomic data, a revolution in which molecular information is being used to enhance response with “genomic selection” is occurring. The predictions of breeding value still utilize multiple loci throughout the genome and, indeed, are largely compatible with additive and specifically infinitesimal model assumptions. I discuss some of the history and genetic issues as applied to the science of livestock improvement, which has had and continues to have major spin-offs into ideas and applications in other areas. PMID:24395822

Genomic-based multiple-trait evaluation in Eucalyptus grandis using dominant DArT markers.

PubMed

Cappa, Eduardo P; El-Kassaby, Yousry A; Muñoz, Facundo; Garcia, Martín N; Villalba, Pamela V; Klápště, Jaroslav; Marcucci Poltri, Susana N

2018-06-01

We investigated the impact of combining the pedigree- and genomic-based relationship matrices in a multiple-trait individual-tree mixed model (a.k.a., multiple-trait combined approach) on the estimates of heritability and on the genomic correlations between growth and stem straightness in an open-pollinated Eucalyptus grandis population. Additionally, the added advantage of incorporating genomic information on the theoretical accuracies of parents and offspring breeding values was evaluated. Our results suggested that the use of the combined approach for estimating heritabilities and additive genetic correlations in multiple-trait evaluations is advantageous and including genomic information increases the expected accuracy of breeding values. Furthermore, the multiple-trait combined approach was proven to be superior to the single-trait combined approach in predicting breeding values, in particular for low-heritability traits. Finally, our results advocate the use of the combined approach in forest tree progeny testing trials, specifically when a multiple-trait individual-tree mixed model is considered. Copyright © 2018 Elsevier B.V. All rights reserved.

Inherited behavioral susceptibility to adiposity in infancy: a multivariate genetic analysis of appetite and weight in the Gemini birth cohort.

PubMed

Llewellyn, Clare H; van Jaarsveld, Cornelia H M; Plomin, Robert; Fisher, Abigail; Wardle, Jane

2012-03-01

The behavioral susceptibility model proposes that inherited differences in traits such as appetite confer differential risk of weight gain and contribute to the heritability of weight. Evidence that the FTO gene may influence weight partly through its effects on appetite supports this model, but testing the behavioral pathways for multiple genes with very small effects is not feasible. Twin analyses make it possible to get a broad-based estimate of the extent of shared genetic influence between appetite and weight. The objective was to use multivariate twin analyses to test the hypothesis that associations between appetite and weight are underpinned by shared genetic effects. Data were from Gemini, a population-based birth cohort of twins (n = 4804) born in 2007. Infant weights at 3 mo were taken from the records of health professionals. Appetite was assessed at 3 mo for the milk-feeding period by using the Baby Eating Behaviour Questionnaire (BEBQ), a parent-reported measure of appetite [enjoyment of food, food responsiveness, slowness in eating (SE), satiety responsiveness (SR), and appetite size (AS)]. Multivariate quantitative genetic modeling was used to test for shared genetic influences. Significant correlations were found between all BEBQ traits and weight. Significant shared genetic influence was identified for weight with SE, SR, and AS; genetic correlations were between 0.22 and 0.37. Shared genetic effects explained 41-45% of these phenotypic associations. Differences in weight in infancy may be due partly to genetically determined differences in appetitive traits that confer differential susceptibility to obesogenic environments.

INVOLVEMENT OF MULTIPLE MOLECULAR PATHWAYS IN THE GENETICS OF OCULAR REFRACTION AND MYOPIA.

PubMed

Wojciechowski, Robert; Cheng, Ching-Yu

2018-01-01

The prevalence of myopia has increased dramatically worldwide within the last three decades. Recent studies have shown that refractive development is influenced by environmental, behavioral, and inherited factors. This review aims to analyze recent progress in the genetics of refractive error and myopia. A comprehensive literature search of PubMed and OMIM was conducted to identify relevant articles in the genetics of refractive error. Genome-wide association and sequencing studies have increased our understanding of the genetics involved in refractive error. These studies have identified interesting candidate genes. All genetic loci discovered to date indicate that refractive development is a heterogeneous process mediated by a number of overlapping biological processes. The exact mechanisms by which these biological networks regulate eye growth are poorly understood. Although several individual genes and/or molecular pathways have been investigated in animal models, a systematic network-based approach in modeling human refractive development is necessary to understand the complex interplay between genes and environment in refractive error. New biomedical technologies and better-designed studies will continue to refine our understanding of the genetics and molecular pathways of refractive error, and may lead to preventative and therapeutic measures to combat the myopia epidemic.

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

Multiple trait genetic evaluation of clinical mastitis in three dairy cattle breeds.

PubMed

Govignon-Gion, A; Dassonneville, R; Baloche, G; Ducrocq, V

2016-04-01

In 2010, a routine genetic evaluation on occurrence of clinical mastitis in three main dairy cattle breeds-- Montbéliarde (MO), Normande (NO) and Holstein (HO)--was implemented in France. Records were clinical mastitis events reported by farmers to milk recording technicians and the analyzed trait was the binary variable describing the occurrence of a mastitis case within the first 150 days of the first three lactations. Genetic parameters of clinical mastitis were estimated for the three breeds. Low heritability estimates were found: between 2% and 4% depending on the breed. Despite its low heritability, the trait exhibits genetic variation so efficient genetic improvement is possible. Genetic correlations with other traits were estimated, showing large correlations (often>0.50, in absolute value) between clinical mastitis and somatic cell score (SCS), longevity and some udder traits. Correlation with milk yield was moderate and unfavorable (ρ=0.26 to 0.30). High milking speed was genetically associated with less mastitis in MO (ρ=-0.14) but with more mastitis in HO (ρ=0.18). A two-step approach was implemented for routine evaluation: first, a univariate evaluation based on a linear animal model with permanent environment effect led to pre-adjusted records (defined as records corrected for all non-genetic effects) and associated weights. These data were then combined with similar pre-adjusted records for others traits in a multiple trait BLUP animal model. The combined breeding values for clinical mastitis obtained are the official (published) ones. Mastitis estimated breeding values (EBV) were then combined with SCSs EBV into an udder health index, which receives a weight of 14.5% to 18.5% in the French total merit index (ISU) of the three breeds. Interbull genetic correlations for mastitis occurrence were very high (ρ=0.94) with Nordic countries, where much stricter recording systems exist reflecting a satisfactory quality of phenotypes as reported by the farmers. They were lower (around 0.80) with countries supplying SCS as a proxy for the international evaluation on clinical mastitis.

Island-Model Genomic Selection for Long-Term Genetic Improvement of Autogamous Crops

PubMed Central

Yabe, Shiori; Yamasaki, Masanori; Ebana, Kaworu; Hayashi, Takeshi; Iwata, Hiroyoshi

2016-01-01

Acceleration of genetic improvement of autogamous crops such as wheat and rice is necessary to increase cereal production in response to the global food crisis. Population and pedigree methods of breeding, which are based on inbred line selection, are used commonly in the genetic improvement of autogamous crops. These methods, however, produce a few novel combinations of genes in a breeding population. Recurrent selection promotes recombination among genes and produces novel combinations of genes in a breeding population, but it requires inaccurate single-plant evaluation for selection. Genomic selection (GS), which can predict genetic potential of individuals based on their marker genotype, might have high reliability of single-plant evaluation and might be effective in recurrent selection. To evaluate the efficiency of recurrent selection with GS, we conducted simulations using real marker genotype data of rice cultivars. Additionally, we introduced the concept of an “island model” inspired by evolutionary algorithms that might be useful to maintain genetic variation through the breeding process. We conducted GS simulations using real marker genotype data of rice cultivars to evaluate the efficiency of recurrent selection and the island model in an autogamous species. Results demonstrated the importance of producing novel combinations of genes through recurrent selection. An initial population derived from admixture of multiple bi-parental crosses showed larger genetic gains than a population derived from a single bi-parental cross in whole cycles, suggesting the importance of genetic variation in an initial population. The island-model GS better maintained genetic improvement in later generations than the other GS methods, suggesting that the island-model GS can utilize genetic variation in breeding and can retain alleles with small effects in the breeding population. The island-model GS will become a new breeding method that enhances the potential of genomic selection in autogamous crops, especially bringing long-term improvement. PMID:27115872

Environment dominates over host genetics in shaping human gut microbiota.

PubMed

Rothschild, Daphna; Weissbrod, Omer; Barkan, Elad; Kurilshikov, Alexander; Korem, Tal; Zeevi, David; Costea, Paul I; Godneva, Anastasia; Kalka, Iris N; Bar, Noam; Shilo, Smadar; Lador, Dar; Vila, Arnau Vich; Zmora, Niv; Pevsner-Fischer, Meirav; Israeli, David; Kosower, Noa; Malka, Gal; Wolf, Bat Chen; Avnit-Sagi, Tali; Lotan-Pompan, Maya; Weinberger, Adina; Halpern, Zamir; Carmi, Shai; Fu, Jingyuan; Wijmenga, Cisca; Zhernakova, Alexandra; Elinav, Eran; Segal, Eran

2018-03-08

Human gut microbiome composition is shaped by multiple factors but the relative contribution of host genetics remains elusive. Here we examine genotype and microbiome data from 1,046 healthy individuals with several distinct ancestral origins who share a relatively common environment, and demonstrate that the gut microbiome is not significantly associated with genetic ancestry, and that host genetics have a minor role in determining microbiome composition. We show that, by contrast, there are significant similarities in the compositions of the microbiomes of genetically unrelated individuals who share a household, and that over 20% of the inter-person microbiome variability is associated with factors related to diet, drugs and anthropometric measurements. We further demonstrate that microbiome data significantly improve the prediction accuracy for many human traits, such as glucose and obesity measures, compared to models that use only host genetic and environmental data. These results suggest that microbiome alterations aimed at improving clinical outcomes may be carried out across diverse genetic backgrounds.

Integrated Analysis of Genetic and Proteomic Data Identifies Biomarkers Associated with Adverse Events Following Smallpox Vaccination

EPA Science Inventory

Complex clinical outcomes, such as adverse reaction to vaccination, arise from the concerted interactions among the myriad components of a biological system. Therefore, comprehensive etiological models can be developed only through the integrated study of multiple types of experi...

A spatially and temporally explicit, individual-based, life-history and productivity modeling approach for aquatic species

EPA Science Inventory

Realized life history expression and productivity in aquatic species, and salmonid fishes in particular, is the result of multiple interacting factors including genetics, habitat, growth potential and condition, and the thermal regime individuals experience, both at critical stag...

Modeling relief demands in an emergency supply chain system under large-scale disasters based on a queuing network.

PubMed

He, Xinhua; Hu, Wenfa

2014-01-01

This paper presents a multiple-rescue model for an emergency supply chain system under uncertainties in large-scale affected area of disasters. The proposed methodology takes into consideration that the rescue demands caused by a large-scale disaster are scattered in several locations; the servers are arranged in multiple echelons (resource depots, distribution centers, and rescue center sites) located in different places but are coordinated within one emergency supply chain system; depending on the types of rescue demands, one or more distinct servers dispatch emergency resources in different vehicle routes, and emergency rescue services queue in multiple rescue-demand locations. This emergency system is modeled as a minimal queuing response time model of location and allocation. A solution to this complex mathematical problem is developed based on genetic algorithm. Finally, a case study of an emergency supply chain system operating in Shanghai is discussed. The results demonstrate the robustness and applicability of the proposed model.

Modeling Relief Demands in an Emergency Supply Chain System under Large-Scale Disasters Based on a Queuing Network

PubMed Central

He, Xinhua

2014-01-01

This paper presents a multiple-rescue model for an emergency supply chain system under uncertainties in large-scale affected area of disasters. The proposed methodology takes into consideration that the rescue demands caused by a large-scale disaster are scattered in several locations; the servers are arranged in multiple echelons (resource depots, distribution centers, and rescue center sites) located in different places but are coordinated within one emergency supply chain system; depending on the types of rescue demands, one or more distinct servers dispatch emergency resources in different vehicle routes, and emergency rescue services queue in multiple rescue-demand locations. This emergency system is modeled as a minimal queuing response time model of location and allocation. A solution to this complex mathematical problem is developed based on genetic algorithm. Finally, a case study of an emergency supply chain system operating in Shanghai is discussed. The results demonstrate the robustness and applicability of the proposed model. PMID:24688367

A Comparison Study of Multivariate Fixed Models and Gene Association with Multiple Traits (GAMuT) for Next-Generation Sequencing

PubMed Central

Chiu, Chi-yang; Jung, Jeesun; Wang, Yifan; Weeks, Daniel E.; Wilson, Alexander F.; Bailey-Wilson, Joan E.; Amos, Christopher I.; Mills, James L.; Boehnke, Michael; Xiong, Momiao; Fan, Ruzong

2016-01-01

In this paper, extensive simulations are performed to compare two statistical methods to analyze multiple correlated quantitative phenotypes: (1) approximate F-distributed tests of multivariate functional linear models (MFLM) and additive models of multivariate analysis of variance (MANOVA), and (2) Gene Association with Multiple Traits (GAMuT) for association testing of high-dimensional genotype data. It is shown that approximate F-distributed tests of MFLM and MANOVA have higher power and are more appropriate for major gene association analysis (i.e., scenarios in which some genetic variants have relatively large effects on the phenotypes); GAMuT has higher power and is more appropriate for analyzing polygenic effects (i.e., effects from a large number of genetic variants each of which contributes a small amount to the phenotypes). MFLM and MANOVA are very flexible and can be used to perform association analysis for: (i) rare variants, (ii) common variants, and (iii) a combination of rare and common variants. Although GAMuT was designed to analyze rare variants, it can be applied to analyze a combination of rare and common variants and it performs well when (1) the number of genetic variants is large and (2) each variant contributes a small amount to the phenotypes (i.e., polygenes). MFLM and MANOVA are fixed effect models which perform well for major gene association analysis. GAMuT can be viewed as an extension of sequence kernel association tests (SKAT). Both GAMuT and SKAT are more appropriate for analyzing polygenic effects and they perform well not only in the rare variant case, but also in the case of a combination of rare and common variants. Data analyses of European cohorts and the Trinity Students Study are presented to compare the performance of the two methods. PMID:27917525

[Application of Multiple Genetic Markers in a Case of Determination of Half Sibling].

PubMed

Yang, Xue; Shi, Mei-sen; Yuan, Li; Lu, Di

2016-02-01

A case of half sibling was determined with multiple genetic markers, which could be potentially applied for determination of half sibling relationship from same father. Half sibling relationship was detected by 39 autosomal STR genetic markers, 23 Y-chromosomal STR genetic markers and 12 X -chromosomal STR genetic markers among ZHAO -1, ZHAO -2, ZHAO -3, ZHAO -4, and ZHAO-5. According to autosomal STR, Y-STR and X-STR genotyping results, it was determined that ZHAO-4 (alleged half sibling) was unrelated with ZHAO-1 and ZHAO-2; however, ZHAO-3 (alleged half sibling), ZHAO-5 (alleged half sibling) shared same genetic profile with ZHAO-1, and ZHAO-2 from same father. It is reliable to use multiple genetic markers and family gene reconstruction to determine half sibling relationship from same father, but it is difficult to determination by calculating half sibling index with ITO and discriminant functions.

Genomic and Coexpression Analyses Predict Multiple Genes Involved in Triterpene Saponin Biosynthesis in Medicago truncatula[C][W

PubMed Central

Naoumkina, Marina A.; Modolo, Luzia V.; Huhman, David V.; Urbanczyk-Wochniak, Ewa; Tang, Yuhong; Sumner, Lloyd W.; Dixon, Richard A.

2010-01-01

Saponins, an important group of bioactive plant natural products, are glycosides of triterpenoid or steroidal aglycones (sapogenins). Saponins possess many biological activities, including conferring potential health benefits for humans. However, most of the steps specific for the biosynthesis of triterpene saponins remain uncharacterized at the molecular level. Here, we use comprehensive gene expression clustering analysis to identify candidate genes involved in the elaboration, hydroxylation, and glycosylation of the triterpene skeleton in the model legume Medicago truncatula. Four candidate uridine diphosphate glycosyltransferases were expressed in Escherichia coli, one of which (UGT73F3) showed specificity for multiple sapogenins and was confirmed to glucosylate hederagenin at the C28 position. Genetic loss-of-function studies in M. truncatula confirmed the in vivo function of UGT73F3 in saponin biosynthesis. This report provides a basis for future studies to define genetically the roles of multiple cytochromes P450 and glycosyltransferases in triterpene saponin biosynthesis in Medicago. PMID:20348429

Introgression of a Block of Genome Under Infinitesimal Selection.

PubMed

Sachdeva, Himani; Barton, Nicholas H

2018-06-12

Adaptive introgression is common in nature and can be driven by selection acting on multiple, linked genes. We explore the effects of polygenic selection on introgression under the infinitesimal model with linkage. This model assumes that the introgressing block has an effectively infinite number of loci, each with an infinitesimal effect on the trait under selection. The block is assumed to introgress under directional selection within a native population that is genetically homogeneous. We use individual-based simulations and a branching process approximation to compute various statistics of the introgressing block, and explore how these depend on parameters such as the map length and initial trait value associated with the introgressing block, the genetic variability along the block, and the strength of selection. Our results show that the introgression dynamics of a block under infinitesimal selection are qualitatively different from the dynamics of neutral introgression. We also find that in the long run, surviving descendant blocks are likely to have intermediate lengths, and clarify how their length is shaped by the interplay between linkage and infinitesimal selection. Our results suggest that it may be difficult to distinguish the long-term introgression of a block of genome with a single strongly selected locus from the introgression of a block with multiple, tightly linked and weakly selected loci. Copyright © 2018, Genetics.

Genetically distinct leukemic stem cells in human CD34− acute myeloid leukemia are arrested at a hemopoietic precursor-like stage

PubMed Central

Quek, Lynn; Garnett, Catherine; Karamitros, Dimitris; Stoilova, Bilyana; Doondeea, Jessica; Kennedy, Alison; Metzner, Marlen; Ivey, Adam; Sternberg, Alexander; Hunter, Hannah; Price, Andrew; Virgo, Paul; Grimwade, David; Freeman, Sylvie; Russell, Nigel; Mead, Adam

2016-01-01

Our understanding of the perturbation of normal cellular differentiation hierarchies to create tumor-propagating stem cell populations is incomplete. In human acute myeloid leukemia (AML), current models suggest transformation creates leukemic stem cell (LSC) populations arrested at a progenitor-like stage expressing cell surface CD34. We show that in ∼25% of AML, with a distinct genetic mutation pattern where >98% of cells are CD34−, there are multiple, nonhierarchically arranged CD34+ and CD34− LSC populations. Within CD34− and CD34+ LSC–containing populations, LSC frequencies are similar; there are shared clonal structures and near-identical transcriptional signatures. CD34− LSCs have disordered global transcription profiles, but these profiles are enriched for transcriptional signatures of normal CD34− mature granulocyte–macrophage precursors, downstream of progenitors. But unlike mature precursors, LSCs express multiple normal stem cell transcriptional regulators previously implicated in LSC function. This suggests a new refined model of the relationship between LSCs and normal hemopoiesis in which the nature of genetic/epigenetic changes determines the disordered transcriptional program, resulting in LSC differentiation arrest at stages that are most like either progenitor or precursor stages of hemopoiesis. PMID:27377587

Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies

PubMed Central

Manitz, Juliane; Burger, Patricia; Amos, Christopher I.; Chang-Claude, Jenny; Wichmann, Heinz-Erich; Kneib, Thomas; Bickeböller, Heike

2017-01-01

The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm. A model to explain case-control status is created iteratively by selecting pathways that improve its prediction ability. We evaluated our method in simulation studies adopting 50 pathways for different sample sizes and genetic effect strengths. Additionally, we included an exemplary application of kernel boosting to a rheumatoid arthritis and a lung cancer dataset. Simulations indicate that kernel boosting outperforms the LKMT in certain genetic scenarios. Applications to GWAS data on rheumatoid arthritis and lung cancer resulted in sparse models which were based on pathways interpretable in a clinical sense. Kernel boosting is highly flexible in terms of considered variables and overcomes the problem of multiple testing. Additionally, it enables the prediction of clinical outcomes. Thus, kernel boosting constitutes a new, powerful tool in the analysis of GWAS data and towards the understanding of biological processes involved in disease susceptibility. PMID:28785300

Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies.

PubMed

Friedrichs, Stefanie; Manitz, Juliane; Burger, Patricia; Amos, Christopher I; Risch, Angela; Chang-Claude, Jenny; Wichmann, Heinz-Erich; Kneib, Thomas; Bickeböller, Heike; Hofner, Benjamin

2017-01-01

The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm. A model to explain case-control status is created iteratively by selecting pathways that improve its prediction ability. We evaluated our method in simulation studies adopting 50 pathways for different sample sizes and genetic effect strengths. Additionally, we included an exemplary application of kernel boosting to a rheumatoid arthritis and a lung cancer dataset. Simulations indicate that kernel boosting outperforms the LKMT in certain genetic scenarios. Applications to GWAS data on rheumatoid arthritis and lung cancer resulted in sparse models which were based on pathways interpretable in a clinical sense. Kernel boosting is highly flexible in terms of considered variables and overcomes the problem of multiple testing. Additionally, it enables the prediction of clinical outcomes. Thus, kernel boosting constitutes a new, powerful tool in the analysis of GWAS data and towards the understanding of biological processes involved in disease susceptibility.

Multiple mechanisms influencing the relationship between alcohol consumption and peer alcohol use.

PubMed

Edwards, Alexis C; Maes, Hermine H; Prescott, Carol A; Kendler, Kenneth S

2015-02-01

Alcohol consumption is typically correlated with the alcohol use behaviors of one's peers. Previous research has suggested that this positive relationship could be due to social selection, social influence, or a combination of both processes. However, few studies have considered the role of shared genetic and environmental influences in conjunction with causal processes. This study uses data from a sample of male twins (N = 1,790) who provided retrospective reports of their own alcohol consumption and their peers' alcohol-related behaviors, from adolescence into young adulthood (ages 12 to 25). Structural equation modeling was employed to compare 3 plausible models of genetic and environmental influences on the relationship between phenotypes over time. Model fitting indicated that one's own alcohol consumption and the alcohol use of one's peers are related through both genetic and shared environmental factors and through unique environmental causal influences. The relative magnitude of these factors, and their contribution to covariation, changed over time, with genetic factors becoming more meaningful later in development. Peers' alcohol use behaviors and one's own alcohol consumption are related through a complex combination of genetic and environmental factors that act via correlated factors and the complementary causal mechanisms of social selection and influence. Understanding these processes can inform risk assessment as well as improve our ability to model the development of alcohol use. Copyright © 2015 by the Research Society on Alcoholism.

Multiple mechanisms influencing the relationship between alcohol consumption and peer alcohol use

PubMed Central

Edwards, Alexis C.; Maesr, Hermine H.; Prescott, Carol A.; Kendler, Kenneth S.

2014-01-01

Background Alcohol consumption is typically correlated with the alcohol use behaviors of one’s peers. Previous research has suggested that this positive relationship could be due to social selection, social influence, or a combination of both processes. However, few studies have considered the role of shared genetic and environmental influences in conjunction with causal processes. Methods The current study uses data from a sample of male twins (N=1790) who provided retrospective reports of their own alcohol consumption and their peers’ alcohol related behaviors, from adolescence into young adulthood (ages 12–25). Structural equation modeling was employed to compare three plausible models of genetic and environmental influences on the relationship between phenotypes over time. Results Model fitting indicated that one’s own alcohol consumption and the alcohol use of one’s peers are related through both genetic and shared environmental factors and through unique environmental causal influences. The relative magnitude of these factors, and their contribution to covariation, changed over time, with genetic factors becoming more meaningful later in development. Conclusions Peers’ alcohol use behaviors and one’s own alcohol consumption are related through a complex combination of genetic and environmental factors that act via correlated factors and the complementary causal mechanisms of social selection and influence. Understanding these processes can inform risk assessment as well as improve our ability to model the development of alcohol use. PMID:25597346

Genetic polymorphisms and the risk of stroke after cardiac surgery.

PubMed

Grocott, Hilary P; White, William D; Morris, Richard W; Podgoreanu, Mihai V; Mathew, Joseph P; Nielsen, Dahlia M; Schwinn, Debra A; Newman, Mark F

2005-09-01

Stroke represents a significant cause of morbidity and mortality after cardiac surgery. Although the risk of stroke varies according to both patient and procedural factors, the impact of genetic variants on stroke risk is not well understood. Therefore, we tested the hypothesis that specific genetic polymorphisms are associated with an increased risk of stroke after cardiac surgery. Patients undergoing cardiac surgery utilizing cardiopulmonary bypass surgery were studied. DNA was isolated from preoperative blood and analyzed for 26 different single-nucleotide polymorphisms. Multivariable logistic regression modeling was used to determine the association of clinical and genetic characteristics with stroke. Permutation analysis was used to adjust for multiple comparisons inherent in genetic association studies. A total of 1635 patients experiencing 28 strokes (1.7%) were included in the final genetic model. The combination of the 2 minor alleles of C-reactive protein (CRP; 3'UTR 1846C/T) and interleukin-6 (IL-6; -174G/C) polymorphisms, occurring in 583 (35.7%) patients, was significantly associated with stroke (odds ratio, 3.3; 95% CI, 1.4 to 8.1; P=0.0023). In a multivariable logistic model adjusting for age, the CRP and IL-6 single-nucleotide polymorphism combination remained significantly associated with stroke (P=0.0020). We demonstrate that common genetic variants of CRP (3'UTR 1846C/T) and IL-6 (-174G/C) are significantly associated with the risk of stroke after cardiac surgery, suggesting a pivotal role of inflammation in post-cardiac surgery stroke.

Contribution of Large Region Joint Associations to Complex Traits Genetics

PubMed Central

Paré, Guillaume; Asma, Senay; Deng, Wei Q.

2015-01-01

A polygenic model of inheritance, whereby hundreds or thousands of weakly associated variants contribute to a trait’s heritability, has been proposed to underlie the genetic architecture of complex traits. However, relatively few genetic variants have been positively identified so far and they collectively explain only a small fraction of the predicted heritability. We hypothesized that joint association of multiple weakly associated variants over large chromosomal regions contributes to complex traits variance. Confirmation of such regional associations can help identify new loci and lead to a better understanding of known ones. To test this hypothesis, we first characterized the ability of commonly used genetic association models to identify large region joint associations. Through theoretical derivation and simulation, we showed that multivariate linear models where multiple SNPs are included as independent predictors have the most favorable association profile. Based on these results, we tested for large region association with height in 3,740 European participants from the Health and Retirement Study (HRS) study. Adjusting for SNPs with known association with height, we demonstrated clustering of weak associations (p = 2x10-4) in regions extending up to 433.0 Kb from known height loci. The contribution of regional associations to phenotypic variance was estimated at 0.172 (95% CI 0.063-0.279; p < 0.001), which compared favorably to 0.129 explained by known height variants. Conversely, we showed that suggestively associated regions are enriched for known height loci. To extend our findings to other traits, we also tested BMI, HDLc and CRP for large region associations, with consistent results for CRP. Our results demonstrate the presence of large region joint associations and suggest these can be used to pinpoint weakly associated SNPs. PMID:25856144

Limited common origins of multiple adult health-related behaviors: Evidence from U.S. twins.

PubMed

Sudharsanan, Nikkil; Behrman, Jere R; Kohler, Hans-Peter

2016-12-01

Health-related behaviors are significant contributors to morbidity and mortality in the United States, yet evidence on the underlying causes of the vast within-population variation in behaviors is mixed. While many potential causes of health-related behaviors have been identified-such as schooling, genetics, and environments-little is known on how much of the variation across multiple behaviors is due to a common set of causes. We use three separate datasets on U.S. twins to investigate the degree to which multiple health-related behaviors correlate and can be explained by a common set of factors. We find that aside from smoking and drinking, most behaviors are not strongly correlated among individuals. Based on the results of both within-identical-twins regressions and multivariate behavioral genetics models, we find some evidence that schooling may be related to smoking but not to the covariation between multiple behaviors. Similarly, we find that a large fraction of the variance in each of the behaviors is consistent with genetic factors; however, we do not find strong evidence that a single common set of genes explains variation in multiple behaviors. We find, however, that a large portion of the correlation between smoking and heavy drinking is consistent with common, mostly childhood, environments. This suggests that the initiation and patterns of these two behaviors might arise from a common childhood origin. Research and policy to identify and modify this source may provide a strong way to reduce the population health burden of smoking and heavy drinking. Copyright © 2016 Elsevier Ltd. All rights reserved.

Genetic consequences of sequential founder events by an island-colonizing bird.

PubMed

Clegg, Sonya M; Degnan, Sandie M; Kikkawa, Jiro; Moritz, Craig; Estoup, Arnaud; Owens, Ian P F

2002-06-11

The importance of founder events in promoting evolutionary changes on islands has been a subject of long-running controversy. Resolution of this debate has been hindered by a lack of empirical evidence from naturally founded island populations. Here we undertake a genetic analysis of a series of historically documented, natural colonization events by the silvereye species-complex (Zosterops lateralis), a group used to illustrate the process of island colonization in the original founder effect model. Our results indicate that single founder events do not affect levels of heterozygosity or allelic diversity, nor do they result in immediate genetic differentiation between populations. Instead, four to five successive founder events are required before indices of diversity and divergence approach that seen in evolutionarily old forms. A Bayesian analysis based on computer simulation allows inferences to be made on the number of effective founders and indicates that founder effects are weak because island populations are established from relatively large flocks. Indeed, statistical support for a founder event model was not significantly higher than for a gradual-drift model for all recently colonized islands. Taken together, these results suggest that single colonization events in this species complex are rarely accompanied by severe founder effects, and multiple founder events and/or long-term genetic drift have been of greater consequence for neutral genetic diversity.

Plasticity of genetic interactions in metabolic networks of yeast.

PubMed

Harrison, Richard; Papp, Balázs; Pál, Csaba; Oliver, Stephen G; Delneri, Daniela

2007-02-13

Why are most genes dispensable? The impact of gene deletions may depend on the environment (plasticity), the presence of compensatory mechanisms (mutational robustness), or both. Here, we analyze the interaction between these two forces by exploring the condition-dependence of synthetic genetic interactions that define redundant functions and alternative pathways. We performed systems-level flux balance analysis of the yeast (Saccharomyces cerevisiae) metabolic network to identify genetic interactions and then tested the model's predictions with in vivo gene-deletion studies. We found that the majority of synthetic genetic interactions are restricted to certain environmental conditions, partly because of the lack of compensation under some (but not all) nutrient conditions. Moreover, the phylogenetic cooccurrence of synthetically interacting pairs is not significantly different from random expectation. These findings suggest that these gene pairs have at least partially independent functions, and, hence, compensation is only a byproduct of their evolutionary history. Experimental analyses that used multiple gene deletion strains not only confirmed predictions of the model but also showed that investigation of false predictions may both improve functional annotation within the model and also lead to the discovery of higher-order genetic interactions. Our work supports the view that functional redundancy may be more apparent than real, and it offers a unified framework for the evolution of environmental adaptation and mutational robustness.

Robust discovery of genetic associations incorporating gene-environment interaction and independence.

PubMed

Tchetgen Tchetgen, Eric

2011-03-01

This article considers the detection and evaluation of genetic effects incorporating gene-environment interaction and independence. Whereas ordinary logistic regression cannot exploit the assumption of gene-environment independence, the proposed approach makes explicit use of the independence assumption to improve estimation efficiency. This method, which uses both cases and controls, fits a constrained retrospective regression in which the genetic variant plays the role of the response variable, and the disease indicator and the environmental exposure are the independent variables. The regression model constrains the association of the environmental exposure with the genetic variant among the controls to be null, thus explicitly encoding the gene-environment independence assumption, which yields substantial gain in accuracy in the evaluation of genetic effects. The proposed retrospective regression approach has several advantages. It is easy to implement with standard software, and it readily accounts for multiple environmental exposures of a polytomous or of a continuous nature, while easily incorporating extraneous covariates. Unlike the profile likelihood approach of Chatterjee and Carroll (Biometrika. 2005;92:399-418), the proposed method does not require a model for the association of a polytomous or continuous exposure with the disease outcome, and, therefore, it is agnostic to the functional form of such a model and completely robust to its possible misspecification.

Genome-Wide Association Studies of Drug-Resistance Determinants.

PubMed

Volkman, Sarah K; Herman, Jonathan; Lukens, Amanda K; Hartl, Daniel L

2017-03-01

Population genetic strategies that leverage association, selection, and linkage have identified drug-resistant loci. However, challenges and limitations persist in identifying drug-resistance loci in malaria. In this review we discuss the genetic basis of drug resistance and the use of genome-wide association studies, complemented by selection and linkage studies, to identify and understand mechanisms of drug resistance and response. We also discuss the implications of nongenetic mechanisms of drug resistance recently reported in the literature, and present models of the interplay between nongenetic and genetic processes that contribute to the emergence of drug resistance. Throughout, we examine artemisinin resistance as an example to emphasize challenges in identifying phenotypes suitable for population genetic studies as well as complications due to multiple-factor drug resistance. Copyright © 2016. Published by Elsevier Ltd.

A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2001-01-01

In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and estimation. This hybrid approach takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms. The hybrid diagnostic technique also has the ability to rank multiple potential solutions for a given set of anomalous sensor measurements in order to reduce false alarms and missed detections. The performance of the hybrid diagnostic technique is evaluated through some case studies derived from a turbofan engine simulation. The results show this approach is promising for reliable diagnostics of aircraft engines.

Effective utilization of genetic information for athletes and coaches: focus on ACTN3 R577X polymorphism

PubMed Central

Kikuchi, Naoki; Nakazato, Koichi

2015-01-01

Training variants (type, intensity, and duration of exercise) can be selected according to individual aims and fitness assessment. Recently, various methods of resistance and endurance training have been used for muscle hypertrophy and VO2max improvement. Although several genetic variants are associated with elite athletic performance and muscle phenotypes, genetic background has not been used as variant for physical training. ACTN3 R577X is a well-studied genetic polymorphism. It is the only genotype associated with elite athletic performance in multiple cohorts. This association is strongly supported by mechanistic data from an Actn3-knockout mouse model. In this review, possible guidelines are discussed for effective utilization of ACTN3 R577X polymorphism for physical training. PMID:26526670

Genetics Reasoning with Multiple External Representations.

ERIC Educational Resources Information Center

Tsui, Chi-Yan; Treagust, David F.

2003-01-01

Explores a case study of a class of 10th grade students whose learning of genetics involved activities using BioLogica, a computer program that features multiple external representations (MERs). Findings indicate that the MERs in BioLogica contributed to students' development of genetics reasoning by engendering their motivation and interest but…

Patterns of gene flow and selection across multiple species of Acrocephalus warblers: footprints of parallel selection on the Z chromosome.

PubMed

Reifová, Radka; Majerová, Veronika; Reif, Jiří; Ahola, Markus; Lindholm, Antero; Procházka, Petr

2016-06-16

Understanding the mechanisms and selective forces leading to adaptive radiations and origin of biodiversity is a major goal of evolutionary biology. Acrocephalus warblers are small passerines that underwent an adaptive radiation in the last approximately 10 million years that gave rise to 37 extant species, many of which still hybridize in nature. Acrocephalus warblers have served as model organisms for a wide variety of ecological and behavioral studies, yet our knowledge of mechanisms and selective forces driving their radiation is limited. Here we studied patterns of interspecific gene flow and selection across three European Acrocephalus warblers to get a first insight into mechanisms of radiation of this avian group. We analyzed nucleotide variation at eight nuclear loci in three hybridizing Acrocephalus species with overlapping breeding ranges in Europe. Using an isolation-with-migration model for multiple populations, we found evidence for unidirectional gene flow from A. scirpaceus to A. palustris and from A. palustris to A. dumetorum. Gene flow was higher between genetically more closely related A. scirpaceus and A. palustris than between ecologically more similar A. palustris and A. dumetorum, suggesting that gradual accumulation of intrinsic barriers rather than divergent ecological selection are more efficient in restricting interspecific gene flow in Acrocephalus warblers. Although levels of genetic differentiation between different species pairs were in general not correlated, we found signatures of apparently independent instances of positive selection at the same two Z-linked loci in multiple species. Our study brings the first evidence that gene flow occurred during Acrocephalus radiation and not only between sister species. Interspecific gene flow could thus be an important source of genetic variation in individual Acrocephalus species and could have accelerated adaptive evolution and speciation rate in this avian group by creating novel genetic combinations and new phenotypes. Independent instances of positive selection at the same loci in multiple species indicate an interesting possibility that the same loci might have contributed to reproductive isolation in several speciation events.

Genetic parameters for different growth scales in GIFT strain of Nile tilapia (Oreochromis niloticus).

PubMed

He, J; Gao, H; Xu, P; Yang, R

2015-12-01

Body weight, length, width and depth at two growth stages were observed for a total of 5015 individuals of GIFT strain, along with a pedigree including 5588 individuals from 104 sires and 162 dams was collected. Multivariate animal models and a random regression model were used to genetically analyse absolute and relative growth scales of these growth traits. In absolute growth scale, the observed growth traits had moderate heritabilities ranging from 0.321 to 0.576, while pairwise ratios between body length, width and depth were lowly inherited and maximum heritability was only 0.146 for length/depth. All genetic correlations were above 0.5 between pairwise growth traits and genetic correlation between length/width and length/depth varied between both growth stages. Based on those estimates, selection index of multiple traits of interest can be formulated in future breeding program to improve genetically body weight and morphology of the GIFT strain. In relative growth scale, heritabilities in relative growths of body length, width and depth to body weight were 0.257, 0.412 and 0.066, respectively, while genetic correlations among these allometry scalings were above 0.8. Genetic analysis for joint allometries of body weight to body length, width and depth will contribute to genetically regulate the growth rate between body shape and body weight. © 2015 Blackwell Verlag GmbH.

Integrating Nonadditive Genomic Relationship Matrices into the Study of Genetic Architecture of Complex Traits.

PubMed

Nazarian, Alireza; Gezan, Salvador A

2016-03-01

The study of genetic architecture of complex traits has been dramatically influenced by implementing genome-wide analytical approaches during recent years. Of particular interest are genomic prediction strategies which make use of genomic information for predicting phenotypic responses instead of detecting trait-associated loci. In this work, we present the results of a simulation study to improve our understanding of the statistical properties of estimation of genetic variance components of complex traits, and of additive, dominance, and genetic effects through best linear unbiased prediction methodology. Simulated dense marker information was used to construct genomic additive and dominance matrices, and multiple alternative pedigree- and marker-based models were compared to determine if including a dominance term into the analysis may improve the genetic analysis of complex traits. Our results showed that a model containing a pedigree- or marker-based additive relationship matrix along with a pedigree-based dominance matrix provided the best partitioning of genetic variance into its components, especially when some degree of true dominance effects was expected to exist. Also, we noted that the use of a marker-based additive relationship matrix along with a pedigree-based dominance matrix had the best performance in terms of accuracy of correlations between true and estimated additive, dominance, and genetic effects. © The American Genetic Association 2015. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Rat Models of Cardiovascular Disease Demonstrate Distinctive Pulmonary Gene Expressions for Vascular Response Genes: Impact of Ozone Exposure

EPA Science Inventory

Comparative gene expression profiling of multiple tissues from rat strains with genetic predisposition to diverse cardiovascular diseases (CVD) can help decode the transcriptional program that governs organ-specific functions. We examined expressions of CVD genes in the lungs of ...

The Clinical Delivery of Pharmacogenetic Testing Services: A Proposed Partnership between Genetic Counselors and Pharmacists

PubMed Central

Mills, Rachel; Haga, Susanne B.

2013-01-01

One of the basic questions in the early uses of pharmacogenetic (PGx) testing revolves around the clinical delivery of testing. Because multiple health professionals may play a role in the delivery of PGx testing, various clinical delivery models have begun to be studied. We propose that a partnership between genetic counselors and pharmacists can assist clinicians in the delivery of comprehensive PGx services. Based on their expert knowledge of pharmacokinetics and pharmacodynamics, pharmacists can facilitate the appropriate application of PGx test results to adjust medication use as warranted and act as a liaison to the healthcare team recommending changes in medication based on test results and patient input. Genetic counselors are well-trained in genetics as well as risk communication and counseling methodology, but have limited knowledge of pharmaceuticals. The complementary knowledge and skill set supports the partnership between genetic counselors and pharmacists to provide effective PGx testing services. PMID:23746189

Of mice and men: molecular genetics of congenital heart disease.

PubMed

Andersen, Troels Askhøj; Troelsen, Karin de Linde Lind; Larsen, Lars Allan

2014-04-01

Congenital heart disease (CHD) affects nearly 1 % of the population. It is a complex disease, which may be caused by multiple genetic and environmental factors. Studies in human genetics have led to the identification of more than 50 human genes, involved in isolated CHD or genetic syndromes, where CHD is part of the phenotype. Furthermore, mapping of genomic copy number variants and exome sequencing of CHD patients have led to the identification of a large number of candidate disease genes. Experiments in animal models, particularly in mice, have been used to verify human disease genes and to gain further insight into the molecular pathology behind CHD. The picture emerging from these studies suggest that genetic lesions associated with CHD affect a broad range of cellular signaling components, from ligands and receptors, across down-stream effector molecules to transcription factors and co-factors, including chromatin modifiers.

The genetics of human obesity.

PubMed

Xia, Qianghua; Grant, Struan F A

2013-04-01

It has long been known that there is a genetic component to obesity, and that characterizing this underlying factor would likely offer the possibility of better intervention in the future. Monogenic obesity has proved to be relatively straightforward, with a combination of linkage analysis and mouse models facilitating the identification of multiple genes. In contrast, genome-wide association studies have successfully revealed a variety of genetic loci associated with the more common form of obesity, allowing for very strong consensus on the underlying genetic architecture of the phenotype for the first time. Although a number of significant findings have been made, it appears that very little of the apparent heritability of body mass index has actually been explained to date. New approaches for data analyses and advances in technology will be required to uncover the elusive missing heritability, and to aid in the identification of the key causative genetic underpinnings of obesity. © 2013 New York Academy of Sciences.

Genetic variation and gene expression across multiple tissues and developmental stages in a nonhuman primate.

PubMed

Jasinska, Anna J; Zelaya, Ivette; Service, Susan K; Peterson, Christine B; Cantor, Rita M; Choi, Oi-Wa; DeYoung, Joseph; Eskin, Eleazar; Fairbanks, Lynn A; Fears, Scott; Furterer, Allison E; Huang, Yu S; Ramensky, Vasily; Schmitt, Christopher A; Svardal, Hannes; Jorgensen, Matthew J; Kaplan, Jay R; Villar, Diego; Aken, Bronwen L; Flicek, Paul; Nag, Rishi; Wong, Emily S; Blangero, John; Dyer, Thomas D; Bogomolov, Marina; Benjamini, Yoav; Weinstock, George M; Dewar, Ken; Sabatti, Chiara; Wilson, Richard K; Jentsch, J David; Warren, Wesley; Coppola, Giovanni; Woods, Roger P; Freimer, Nelson B

2017-12-01

By analyzing multitissue gene expression and genome-wide genetic variation data in samples from a vervet monkey pedigree, we generated a transcriptome resource and produced the first catalog of expression quantitative trait loci (eQTLs) in a nonhuman primate model. This catalog contains more genome-wide significant eQTLs per sample than comparable human resources and identifies sex- and age-related expression patterns. Findings include a master regulatory locus that likely has a role in immune function and a locus regulating hippocampal long noncoding RNAs (lncRNAs), whose expression correlates with hippocampal volume. This resource will facilitate genetic investigation of quantitative traits, including brain and behavioral phenotypes relevant to neuropsychiatric disorders.

A Lens for Evaluating Genetic Information Governance Models: Balancing Equity, Efficiency and Sustainability.

PubMed

Skorve, Espen; Vassilakopoulou, Polyxeni; Aanestad, Margunn; Grünfeld, Thomas

2017-01-01

This paper draws from the literature on collective action and the governance of the commons to address the governance of genetic data on variants of specific genes. Specifically, the data arrangements under study relate to the BRCA genes (BRCA1 and BRCA2) which are linked to breast and ovarian cancer. These data are stored in global genetic data repositories and accessed by researchers and clinicians, from both public and private institutions. The current BRCA data arrangements are fragmented and politicized as there are multiple tensions around data ownership and sharing. Three key principles are proposed for forming and evaluating data governance arrangements in the field. These principles are: equity, efficiency and sustainability.

Root Systems Biology: Integrative Modeling across Scales, from Gene Regulatory Networks to the Rhizosphere1

PubMed Central

Hill, Kristine; Porco, Silvana; Lobet, Guillaume; Zappala, Susan; Mooney, Sacha; Draye, Xavier; Bennett, Malcolm J.

2013-01-01

Genetic and genomic approaches in model organisms have advanced our understanding of root biology over the last decade. Recently, however, systems biology and modeling have emerged as important approaches, as our understanding of root regulatory pathways has become more complex and interpreting pathway outputs has become less intuitive. To relate root genotype to phenotype, we must move beyond the examination of interactions at the genetic network scale and employ multiscale modeling approaches to predict emergent properties at the tissue, organ, organism, and rhizosphere scales. Understanding the underlying biological mechanisms and the complex interplay between systems at these different scales requires an integrative approach. Here, we describe examples of such approaches and discuss the merits of developing models to span multiple scales, from network to population levels, and to address dynamic interactions between plants and their environment. PMID:24143806

Overview of Genetically Engineered Mouse Models of Distinct Breast Cancer Subtypes.

PubMed

Usary, Jerry; Darr, David Brian; Pfefferle, Adam D; Perou, Charles M

2016-03-18

Advances in the screening of new therapeutic options have significantly reduced the breast cancer death rate over the last decade. Despite these advances, breast cancer remains the second leading cause of cancer death among women. This is due in part to the complexity of the disease, which is characterized by multiple subtypes that are driven by different genetic mechanisms and that likely arise from different cell types of origin. Because these differences often drive treatment options and outcomes, it is important to select relevant preclinical model systems to study new therapeutic interventions and tumor biology. Described in this unit are the characteristics and applications of validated genetically engineered mouse models (GEMMs) of basal-like, luminal, and claudin-low human subtypes of breast cancer. These different subtypes have different clinical outcomes and require different treatment strategies. These GEMMs can be considered faithful surrogates of their human disease counterparts. They represent alternative preclinical tumor models to cell line and patient-derived xenografts for preclinical drug discovery and tumor biology studies. Copyright © 2016 John Wiley & Sons, Inc.

The rare and undiagnosed diseases diagnostic service - application of massively parallel sequencing in a state-wide clinical service.

PubMed

Baynam, Gareth; Pachter, Nicholas; McKenzie, Fiona; Townshend, Sharon; Slee, Jennie; Kiraly-Borri, Cathy; Vasudevan, Anand; Hawkins, Anne; Broley, Stephanie; Schofield, Lyn; Verhoef, Hedwig; Walker, Caroline E; Molster, Caron; Blackwell, Jenefer M; Jamieson, Sarra; Tang, Dave; Lassmann, Timo; Mina, Kym; Beilby, John; Davis, Mark; Laing, Nigel; Murphy, Lesley; Weeramanthri, Tarun; Dawkins, Hugh; Goldblatt, Jack

2016-06-11

The Rare and Undiagnosed Diseases Diagnostic Service (RUDDS) refers to a genomic diagnostic platform operating within the Western Australian Government clinical services delivered through Genetic Services of Western Australia (GSWA). GSWA has provided a state-wide service for clinical genetic care for 28 years and it serves a population of 2.5 million people across a geographical area of 2.5milion Km(2). Within this context, GSWA has established a clinically integrated genomic diagnostic platform in partnership with other public health system managers and service providers, including but not limited to the Office of Population Health Genomics, Diagnostic Genomics (PathWest Laboratories) and with executive level support from the Department of Health. Herein we describe report presents the components of this service that are most relevant to the heterogeneity of paediatric clinical genetic care. Briefly the platform : i) offers multiple options including non-genetic testing; monogenic and genomic (targeted in silico filtered and whole exome) analysis; and matchmaking; ii) is delivered in a patient-centric manner that is resonant with the patient journey, it has multiple points for entry, exit and re-entry to allow people access to information they can use, when they want to receive it; iii) is synchronous with precision phenotyping methods; iv) captures new knowledge, including multiple expert review; v) is integrated with current translational genomic research activities and best practice; and vi) is designed for flexibility for interactive generation of, and integration with, clinical research for diagnostics, community engagement, policy and models of care. The RUDDS has been established as part of routine clinical genetic services and is thus sustainable, equitably managed and seeks to translate new knowledge into efficient diagnostics and improved health for the whole community.

Comparing ESC and iPSC-Based Models for Human Genetic Disorders.

PubMed

Halevy, Tomer; Urbach, Achia

2014-10-24

Traditionally, human disorders were studied using animal models or somatic cells taken from patients. Such studies enabled the analysis of the molecular mechanisms of numerous disorders, and led to the discovery of new treatments. Yet, these systems are limited or even irrelevant in modeling multiple genetic diseases. The isolation of human embryonic stem cells (ESCs) from diseased blastocysts, the derivation of induced pluripotent stem cells (iPSCs) from patients' somatic cells, and the new technologies for genome editing of pluripotent stem cells have opened a new window of opportunities in the field of disease modeling, and enabled studying diseases that couldn't be modeled in the past. Importantly, despite the high similarity between ESCs and iPSCs, there are several fundamental differences between these cells, which have important implications regarding disease modeling. In this review we compare ESC-based models to iPSC-based models, and highlight the advantages and disadvantages of each system. We further suggest a roadmap for how to choose the optimal strategy to model each specific disorder.

Stochastic models for inferring genetic regulation from microarray gene expression data.

PubMed

Tian, Tianhai

2010-03-01

Microarray expression profiles are inherently noisy and many different sources of variation exist in microarray experiments. It is still a significant challenge to develop stochastic models to realize noise in microarray expression profiles, which has profound influence on the reverse engineering of genetic regulation. Using the target genes of the tumour suppressor gene p53 as the test problem, we developed stochastic differential equation models and established the relationship between the noise strength of stochastic models and parameters of an error model for describing the distribution of the microarray measurements. Numerical results indicate that the simulated variance from stochastic models with a stochastic degradation process can be represented by a monomial in terms of the hybridization intensity and the order of the monomial depends on the type of stochastic process. The developed stochastic models with multiple stochastic processes generated simulations whose variance is consistent with the prediction of the error model. This work also established a general method to develop stochastic models from experimental information. 2009 Elsevier Ireland Ltd. All rights reserved.

The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis

PubMed Central

Malosetti, Marcos; Ribaut, Jean-Marcel; van Eeuwijk, Fred A.

2013-01-01

Genotype-by-environment interaction (GEI) is an important phenomenon in plant breeding. This paper presents a series of models for describing, exploring, understanding, and predicting GEI. All models depart from a two-way table of genotype by environment means. First, a series of descriptive and explorative models/approaches are presented: Finlay–Wilkinson model, AMMI model, GGE biplot. All of these approaches have in common that they merely try to group genotypes and environments and do not use other information than the two-way table of means. Next, factorial regression is introduced as an approach to explicitly introduce genotypic and environmental covariates for describing and explaining GEI. Finally, QTL modeling is presented as a natural extension of factorial regression, where marker information is translated into genetic predictors. Tests for regression coefficients corresponding to these genetic predictors are tests for main effect QTL expression and QTL by environment interaction (QEI). QTL models for which QEI depends on environmental covariables form an interesting model class for predicting GEI for new genotypes and new environments. For realistic modeling of genotypic differences across multiple environments, sophisticated mixed models are necessary to allow for heterogeneity of genetic variances and correlations across environments. The use and interpretation of all models is illustrated by an example data set from the CIMMYT maize breeding program, containing environments differing in drought and nitrogen stress. To help readers to carry out the statistical analyses, GenStat® programs, 15th Edition and Discovery® version, are presented as “Appendix.” PMID:23487515

The zebrafish as a model for complex tissue regeneration

PubMed Central

Gemberling, Matthew; Bailey, Travis J.; Hyde, David R.; Poss, Kenneth D.

2013-01-01

For centuries, philosophers and scientists have been fascinated by the principles and implications of regeneration in lower vertebrate species. Two features have made zebrafish an informative model system for determining mechanisms of regenerative events. First, they are highly regenerative, able to regrow amputated fins, as well as a lesioned brain, retina, spinal cord, heart, and other tissues. Second, they are amenable to both forward and reverse genetic approaches, with a research toolset regularly updated by an expanding community of zebrafish researchers. Zebrafish studies have helped identify new mechanistic underpinnings of regeneration in multiple tissues, and in some cases have served as a guide for contemplating regenerative strategies in mammals. Here, we review the recent history of zebrafish as a genetic model system for understanding how and why tissue regeneration occurs. PMID:23927865

IBSEM: An Individual-Based Atlantic Salmon Population Model.

PubMed

Castellani, Marco; Heino, Mikko; Gilbey, John; Araki, Hitoshi; Svåsand, Terje; Glover, Kevin A

2015-01-01

Ecology and genetics can influence the fate of individuals and populations in multiple ways. However, to date, few studies consider them when modelling the evolutionary trajectory of populations faced with admixture with non-local populations. For the Atlantic salmon, a model incorporating these elements is urgently needed because many populations are challenged with gene-flow from non-local and domesticated conspecifics. We developed an Individual-Based Salmon Eco-genetic Model (IBSEM) to simulate the demographic and population genetic change of an Atlantic salmon population through its entire life-cycle. Processes such as growth, mortality, and maturation are simulated through stochastic procedures, which take into account environmental variables as well as the genotype of the individuals. IBSEM is based upon detailed empirical data from salmon biology, and parameterized to reproduce the environmental conditions and the characteristics of a wild population inhabiting a Norwegian river. Simulations demonstrated that the model consistently and reliably reproduces the characteristics of the population. Moreover, in absence of farmed escapees, the modelled populations reach an evolutionary equilibrium that is similar to our definition of a 'wild' genotype. We assessed the sensitivity of the model in the face of assumptions made on the fitness differences between farm and wild salmon, and evaluated the role of straying as a buffering mechanism against the intrusion of farm genes into wild populations. These results demonstrate that IBSEM is able to capture the evolutionary forces shaping the life history of wild salmon and is therefore able to model the response of populations under environmental and genetic stressors.

Landscape genetics of the nonnative red fox of California.

PubMed

Sacks, Benjamin N; Brazeal, Jennifer L; Lewis, Jeffrey C

2016-07-01

Invasive mammalian carnivores contribute disproportionately to declines in global biodiversity. In California, nonnative red foxes (Vulpes vulpes) have significantly impacted endangered ground-nesting birds and native canids. These foxes derive primarily from captive-reared animals associated with the fur-farming industry. Over the past five decades, the cumulative area occupied by nonnative red fox increased to cover much of central and southern California. We used a landscape-genetic approach involving mitochondrial DNA (mtDNA) sequences and 13 microsatellites of 402 nonnative red foxes removed in predator control programs to investigate source populations, contemporary connectivity, and metapopulation dynamics. Both markers indicated high population structuring consistent with origins from multiple introductions and low subsequent gene flow. Landscape-genetic modeling indicated that population connectivity was especially low among coastal sampling sites surrounded by mountainous wildlands but somewhat higher through topographically flat, urban and agricultural landscapes. The genetic composition of populations tended to be stable for multiple generations, indicating a degree of demographic resilience to predator removal programs. However, in two sites where intensive predator control reduced fox abundance, we observed increases in immigration, suggesting potential for recolonization to counter eradication attempts. These findings, along with continued genetic monitoring, can help guide localized management of foxes by identifying points of introductions and routes of spread and evaluating the relative importance of reproduction and immigration in maintaining populations. More generally, the study illustrates the utility of a landscape-genetic approach for understanding invasion dynamics and metapopulation structure of one of the world's most destructive invasive mammals, the red fox.

Genetic variation and biological activity of isolates of lymantria dispar multiple nucleopolyhedrovirus from north america, europe, and asia

USDA-ARS?s Scientific Manuscript database

Little is known about genetic variation of Lymantria dispar multiple nucleopolyhedrovirus (LdMNPV; Baculoviridae: Alphabaculovirus) at the nucleotide sequence level. To obtain a more comprehensive view of genetic diversity among isolates of LdMNPV, partial sequences of the lef-8 gene were generated...

ADME evaluation in drug discovery. 1. Applications of genetic algorithms to the prediction of blood-brain partitioning of a large set of drugs.

PubMed

Hou, Tingjun; Xu, Xiaojie

2002-12-01

In this study, the relationships between the brain-blood concentration ratio of 96 structurally diverse compounds with a large number of structurally derived descriptors were investigated. The linear models were based on molecular descriptors that can be calculated for any compound simply from a knowledge of its molecular structure. The linear correlation coefficients of the models were optimized by genetic algorithms (GAs), and the descriptors used in the linear models were automatically selected from 27 structurally derived descriptors. The GA optimizations resulted in a group of linear models with three or four molecular descriptors with good statistical significance. The change of descriptor use as the evolution proceeds demonstrates that the octane/water partition coefficient and the partial negative solvent-accessible surface area multiplied by the negative charge are crucial to brain-blood barrier permeability. Moreover, we found that the predictions using multiple QSPR models from GA optimization gave quite good results in spite of the diversity of structures, which was better than the predictions using the best single model. The predictions for the two external sets with 37 diverse compounds using multiple QSPR models indicate that the best linear models with four descriptors are sufficiently effective for predictive use. Considering the ease of computation of the descriptors, the linear models may be used as general utilities to screen the blood-brain barrier partitioning of drugs in a high-throughput fashion.

Therapeutic Remyelination Strategies in a Novel Model of Multiple Sclerosis: Japanese Macaque Encephalomyelitis

DTIC Science & Technology

2013-05-01

Although the etiology of MS is still poorly understood, particular viruses, especially gama- herpesviruses , may act as triggers of MS (Levin et al., 2010...than others, suggesting a genetic pre-disposition to JME. Furthermore, we have cloned a gamma- herpesvirus (called Japanese macaque rhadovirus; JMRV...than other EAE and viral models in non-human primates and rodents. Our major aim was to use this model to better understand how gamma- herpesviruses

Striatal cholinergic dysfunction as a unifying theme in the pathophysiology of dystonia

PubMed Central

Jaunarajs, K.L. Eskow; Bonsi, P.; Chesselet, M.F.; Standaert, D.G.; Pisani, A.

2015-01-01

Dystonia is a movement disorder of both genetic and non-genetic causes, which typically results in twisted posturing due to abnormal muscle contraction. Evidence from dystonia patients and animal models of dystonia indicate a crucial role for the striatal cholinergic system in the pathophysiology of dystonia. In this review, we focus on striatal circuitry and the centrality of the acetylcholine system in the function of the basal ganglia in the control of voluntary movement and ultimately clinical manifestion of movement disorders. We consider the impact of cholinergic interneurons (ChIs) on dopamine-acetylcholine interactions and examine new evidence for impairment of ChIs in dysfunction of the motor systems producing dystonic movements, particularly in animal models. We have observed paradoxical excitation of ChIs in the presence of dopamine D2 receptor agonists and impairment of striatal synaptic plasticity in a mouse model of DYT1 dystonia, which are improved by administration of recently developed M1 receptor antagonists. These findings have been confirmed across multiple animal models of DYT1 dystonia and may represent a common endophenotype by which to investigate dystonia induced by other types of genetic and non-genetic causes and to investigate the potential effectiveness of pharmacotherapeutics and other strategies to improve dystonia. PMID:25697043

Parametric optimization of multiple quality characteristics in laser cutting of Inconel-718 by using hybrid approach of multiple regression analysis and genetic algorithm

NASA Astrophysics Data System (ADS)

Shrivastava, Prashant Kumar; Pandey, Arun Kumar

2018-06-01

Inconel-718 has found high demand in different industries due to their superior mechanical properties. The traditional cutting methods are facing difficulties for cutting these alloys due to their low thermal potential, lower elasticity and high chemical compatibility at inflated temperature. The challenges of machining and/or finishing of unusual shapes and/or sizes in these materials have also faced by traditional machining. Laser beam cutting may be applied for the miniaturization and ultra-precision cutting and/or finishing by appropriate control of different process parameter. This paper present multi-objective optimization the kerf deviation, kerf width and kerf taper in the laser cutting of Incone-718 sheet. The second order regression models have been developed for different quality characteristics by using the experimental data obtained through experimentation. The regression models have been used as objective function for multi-objective optimization based on the hybrid approach of multiple regression analysis and genetic algorithm. The comparison of optimization results to experimental results shows an improvement of 88%, 10.63% and 42.15% in kerf deviation, kerf width and kerf taper, respectively. Finally, the effects of different process parameters on quality characteristics have also been discussed.

Cooperation in the dark: signalling and collective action in quorum-sensing bacteria.

PubMed

Brown, S P; Johnstone, R A

2001-05-07

The study of quorum-sensing bacteria has revealed a widespread mechanism of coordinating bacterial gene expression with cell density. By monitoring a constitutively produced signal molecule, individual bacteria can limit their expression of group-beneficial phenotypes to cell densities that guarantee an effective group outcome. In this paper, we attempt to move away from a commonly expressed view that these impressive feats of coordination are examples of multicellularity in prokaryotic populations. Here, we look more closely at the individual conflict underlying this cooperation, illustrating that, even under significant levels of genetic conflict, signalling and resultant cooperative behaviour can stably exist. A predictive two-trait model of signal strength and of the extent of cooperation is developed as a function of relatedness (reflecting multiplicity of infection) and basic population demographic parameters. The model predicts that the strength of quorum signalling will increase as conflict (multiplicity of infecting strains) increases, as individuals attempt to coax more cooperative contributions from their competitors, leading to a devaluation of the signal as an indicator of density. Conversely, as genetic conflict increases, the model predicts that the threshold density for cooperation will increase and the subsequent strength of group cooperation will be depressed.

Genealogies of rapidly adapting populations

PubMed Central

Neher, Richard A.; Hallatschek, Oskar

2013-01-01

The genetic diversity of a species is shaped by its recent evolutionary history and can be used to infer demographic events or selective sweeps. Most inference methods are based on the null hypothesis that natural selection is a weak or infrequent evolutionary force. However, many species, particularly pathogens, are under continuous pressure to adapt in response to changing environments. A statistical framework for inference from diversity data of such populations is currently lacking. Towards this goal, we explore the properties of genealogies in a model of continual adaptation in asexual populations. We show that lineages trace back to a small pool of highly fit ancestors, in which almost simultaneous coalescence of more than two lineages frequently occurs. Whereas such multiple mergers are unlikely under the neutral coalescent, they create a unique genetic footprint in adapting populations. The site frequency spectrum of derived neutral alleles, for example, is nonmonotonic and has a peak at high frequencies, whereas Tajima’s D becomes more and more negative with increasing sample size. Because multiple merger coalescents emerge in many models of rapid adaptation, we argue that they should be considered as a null model for adapting populations. PMID:23269838

Genetic testing in congenital heart disease: A clinical approach

PubMed Central

Chaix, Marie A; Andelfinger, Gregor; Khairy, Paul

2016-01-01

Congenital heart disease (CHD) is the most common type of birth defect. Traditionally, a polygenic model defined by the interaction of multiple genes and environmental factors was hypothesized to account for different forms of CHD. It is now understood that the contribution of genetics to CHD extends beyond a single unified paradigm. For example, monogenic models and chromosomal abnormalities have been associated with various syndromic and non-syndromic forms of CHD. In such instances, genetic investigation and testing may potentially play an important role in clinical care. A family tree with a detailed phenotypic description serves as the initial screening tool to identify potentially inherited defects and to guide further genetic investigation. The selection of a genetic test is contingent upon the particular diagnostic hypothesis generated by clinical examination. Genetic investigation in CHD may carry the potential to improve prognosis by yielding valuable information with regards to personalized medical care, confidence in the clinical diagnosis, and/or targeted patient follow-up. Moreover, genetic assessment may serve as a tool to predict recurrence risk, define the pattern of inheritance within a family, and evaluate the need for further family screening. In some circumstances, prenatal or preimplantation genetic screening could identify fetuses or embryos at high risk for CHD. Although genetics may appear to constitute a highly specialized sector of cardiology, basic knowledge regarding inheritance patterns, recurrence risks, and available screening and diagnostic tools, including their strengths and limitations, could assist the treating physician in providing sound counsel. PMID:26981213

An Interval Type-2 Fuzzy Multiple Echelon Supply Chain Model

NASA Astrophysics Data System (ADS)

Miller, Simon; John, Robert

Planning resources for a supply chain is a major factor determining its success or failure. In this paper we build on previous work introducing an Interval Type-2 Fuzzy Logic model of a multiple echelon supply chain. It is believed that the additional degree of uncertainty provided by Interval Type-2 Fuzzy Logic will allow for better representation of the uncertainty and vagueness present in resource planning models. First, the subject of Supply Chain Management is introduced, then some background is given on related work using Type-1 Fuzzy Logic. A description of the Interval Type-2 Fuzzy model is given, and a test scenario detailed. A Genetic Algorithm uses the model to search for a near-optimal plan for the scenario. A discussion of the results follows, along with conclusions and details of intended further work.

Nonlinear inversion of potential-field data using a hybrid-encoding genetic algorithm

USGS Publications Warehouse

Chen, C.; Xia, J.; Liu, J.; Feng, G.

2006-01-01

Using a genetic algorithm to solve an inverse problem of complex nonlinear geophysical equations is advantageous because it does not require computer gradients of models or "good" initial models. The multi-point search of a genetic algorithm makes it easier to find the globally optimal solution while avoiding falling into a local extremum. As is the case in other optimization approaches, the search efficiency for a genetic algorithm is vital in finding desired solutions successfully in a multi-dimensional model space. A binary-encoding genetic algorithm is hardly ever used to resolve an optimization problem such as a simple geophysical inversion with only three unknowns. The encoding mechanism, genetic operators, and population size of the genetic algorithm greatly affect search processes in the evolution. It is clear that improved operators and proper population size promote the convergence. Nevertheless, not all genetic operations perform perfectly while searching under either a uniform binary or a decimal encoding system. With the binary encoding mechanism, the crossover scheme may produce more new individuals than with the decimal encoding. On the other hand, the mutation scheme in a decimal encoding system will create new genes larger in scope than those in the binary encoding. This paper discusses approaches of exploiting the search potential of genetic operations in the two encoding systems and presents an approach with a hybrid-encoding mechanism, multi-point crossover, and dynamic population size for geophysical inversion. We present a method that is based on the routine in which the mutation operation is conducted in the decimal code and multi-point crossover operation in the binary code. The mix-encoding algorithm is called the hybrid-encoding genetic algorithm (HEGA). HEGA provides better genes with a higher probability by a mutation operator and improves genetic algorithms in resolving complicated geophysical inverse problems. Another significant result is that final solution is determined by the average model derived from multiple trials instead of one computation due to the randomness in a genetic algorithm procedure. These advantages were demonstrated by synthetic and real-world examples of inversion of potential-field data. ?? 2005 Elsevier Ltd. All rights reserved.

Model-Based Teaching and Learning with BioLogica[TM]: What Do They Learn? How Do They Learn? How Do We Know?

ERIC Educational Resources Information Center

Buckley, Barbara C.; Gobert, Janice D.; Kindfield, Ann C. H.; Horwitz, Paul; Tinker, Robert F.; Gerlits, Bobbi; Wilensky, Uri; Dede, Chris; Willett, John

2004-01-01

This paper describes part of a project called Modeling Across the Curriculum which is a large-scale research study in 15 schools across the United States. The specific data presented and discussed here in this paper is based on BioLogica, a hypermodel, interactive environment for learning genetics, which was implemented in multiple classes in…

[The genetics of thrombosis in cancer].

PubMed

Soria, José Manuel; López, Sonia

2015-01-01

Venous thromboembolism (VTE) is a multifactorial and complex disease in which the interaction of genetic factors (estimated at 60%) and environmental factors (e.g., the use of oral contraceptives, pregnancy, immobility and cancer) determine the risk of thrombosis for each individual. In particular, the association between thrombosis and cancer is well established. Approximately 20% of patients with cancer develop a thromboembolic event over the course of the natural history of the tumor process, with thrombosis being the second leading cause of death for these patients. One of the greatest challenges currently facing the field of oncology is the identification of patients at high risk of VTE who can benefit from thromboprophylaxis. Currently, there is a VTE risk prediction model for patients with cancer (the Khorana risk score); however, its ability to identify patients at high risk is very low. It is important to note that this score, which is based on five clinical parameters, ignores the genetic variability associated with VTE risk. In this article, we present the preliminary results of the Oncothromb study, whose objective is to develop an individual VTE risk prediction model for patients with cancer who are treated with outpatient chemotherapy. Our model includes the clinical and genetic data on each patient (Thrombo inCode(®) genetic profile). Only by integrating multiple layers of biological information (clinical, plasmatic and genetic) we could obtain models that provide accurate information as to which patients are at high risk of developing a thromboembolic event associated with cancer so as to take appropriate prophylactic measures. Copyright © 2015 Elsevier España, S.L.U. All rights reserved.

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

Dampened hippocampal oscillations and enhanced spindle activity in an asymptomatic model of developmental cortical malformations

PubMed Central

Cid, Elena; Gomez-Dominguez, Daniel; Martin-Lopez, David; Gal, Beatriz; Laurent, François; Ibarz, Jose M.; Francis, Fiona; Menendez de la Prida, Liset

2014-01-01

Developmental cortical malformations comprise a large spectrum of histopathological brain abnormalities and syndromes. Their genetic, developmental and clinical complexity suggests they should be better understood in terms of the complementary action of independently timed perturbations (i.e., the multiple-hit hypothesis). However, understanding the underlying biological processes remains puzzling. Here we induced developmental cortical malformations in offspring, after intraventricular injection of methylazoxymethanol (MAM) in utero in mice. We combined extensive histological and electrophysiological studies to characterize the model. We found that MAM injections at E14 and E15 induced a range of cortical and hippocampal malformations resembling histological alterations of specific genetic mutations and transplacental mitotoxic agent injections. However, in contrast to most of these models, intraventricularly MAM-injected mice remained asymptomatic and showed no clear epilepsy-related phenotype as tested in long-term chronic recordings and with pharmacological manipulations. Instead, they exhibited a non-specific reduction of hippocampal-related brain oscillations (mostly in CA1); including theta, gamma and HFOs; and enhanced thalamocortical spindle activity during non-REM sleep. These data suggest that developmental cortical malformations do not necessarily correlate with epileptiform activity. We propose that the intraventricular in utero MAM approach exhibiting a range of rhythmopathies is a suitable model for multiple-hit studies of associated neurological disorders. PMID:24782720

Dampened hippocampal oscillations and enhanced spindle activity in an asymptomatic model of developmental cortical malformations.

PubMed

Cid, Elena; Gomez-Dominguez, Daniel; Martin-Lopez, David; Gal, Beatriz; Laurent, François; Ibarz, Jose M; Francis, Fiona; Menendez de la Prida, Liset

2014-01-01

Developmental cortical malformations comprise a large spectrum of histopathological brain abnormalities and syndromes. Their genetic, developmental and clinical complexity suggests they should be better understood in terms of the complementary action of independently timed perturbations (i.e., the multiple-hit hypothesis). However, understanding the underlying biological processes remains puzzling. Here we induced developmental cortical malformations in offspring, after intraventricular injection of methylazoxymethanol (MAM) in utero in mice. We combined extensive histological and electrophysiological studies to characterize the model. We found that MAM injections at E14 and E15 induced a range of cortical and hippocampal malformations resembling histological alterations of specific genetic mutations and transplacental mitotoxic agent injections. However, in contrast to most of these models, intraventricularly MAM-injected mice remained asymptomatic and showed no clear epilepsy-related phenotype as tested in long-term chronic recordings and with pharmacological manipulations. Instead, they exhibited a non-specific reduction of hippocampal-related brain oscillations (mostly in CA1); including theta, gamma and HFOs; and enhanced thalamocortical spindle activity during non-REM sleep. These data suggest that developmental cortical malformations do not necessarily correlate with epileptiform activity. We propose that the intraventricular in utero MAM approach exhibiting a range of rhythmopathies is a suitable model for multiple-hit studies of associated neurological disorders.

Determining the drivers of population structure in a highly urbanized landscape to inform conservation planning.

PubMed

Thomassen, Henri A; Harrigan, Ryan J; Semple Delaney, Kathleen; Riley, Seth P D; Serieys, Laurel E K; Pease, Katherine; Wayne, Robert K; Smith, Thomas B

2018-02-01

Understanding the environmental contributors to population structure is of paramount importance for conservation in urbanized environments. We used spatially explicit models to determine genetic population structure under current and future environmental conditions across a highly fragmented, human-dominated environment in Southern California to assess the effects of natural ecological variation and urbanization. We focused on 7 common species with diverse habitat requirements, home-range sizes, and dispersal abilities. We quantified the relative roles of potential barriers, including natural environmental characteristics and an anthropogenic barrier created by a major highway, in shaping genetic variation. The ability to predict genetic variation in our models differed among species: 11-81% of intraspecific genetic variation was explained by environmental variables. Although an anthropogenically induced barrier (a major highway) severely restricted gene flow and movement at broad scales for some species, genetic variation seemed to be primarily driven by natural environmental heterogeneity at a local level. Our results show how assessing environmentally associated variation for multiple species under current and future climate conditions can help identify priority regions for maximizing population persistence under environmental change in urbanized regions. © 2017 Society for Conservation Biology.

Phylogeography of the Rickett's big-footed bat, Myotis pilosus (Chiroptera: Vespertilionidae): a novel pattern of genetic structure of bats in China.

PubMed

Lu, Guanjun; Lin, Aiqing; Luo, Jinhong; Blondel, Dimitri V; Meiklejohn, Kelly A; Sun, Keping; Feng, Jiang

2013-11-05

China is characterized by complex topographic structure and dramatic palaeoclimatic changes, making species biogeography studies particularly interesting. Previous researchers have also demonstrated multiple species experienced complex population histories, meanwhile multiple shelters existed in Chinese mainland. Despite this, species phylogeography is still largely unexplored. In the present study, we used a combination of microsatellites and mitochondrial DNA (mtDNA) to investigate the phylogeography of the east Asian fish-eating bat (Myotis pilosus). Phylogenetic analyses showed that M. pilosus comprised three main lineages: A, B and C, which corresponded to distinct geographic populations of the Yangtze Plain (YTP), Sichuan Basin (SCB) and North and South of China (NSC), respectively. The most recent common ancestor of M. pilosus was dated as 0.25 million years before present (BP). Population expansion events were inferred for populations of Clade C, North China Plain region, Clade B and YunGui Plateau region at 38,700, 15,900, 4,520 and 4,520 years BP, respectively. Conflicting results were obtained from mtDNA and microsatellite analyses; strong population genetic structure was obtained from mtDNA data but not microsatellite data. The microsatellite data indicated that genetic subdivision fits an isolation-by-distance (IBD) model, but the mtDNA data failed to support this model. Our results suggested that Pleistocene climatic oscillations might have had a profound influence on the demographic history of M. pilosus. Spatial genetic structures of maternal lineages that are different from those observed in other sympatric bats species may be as a result of interactions among special population history and local environmental factors. There are at least three possible refugia for M. pilosus during glacial episodes. Apparently contradictory genetic structure patterns of mtDNA and microsatellite could be explained by male-mediated gene flow among populations. This study also provides insights on the necessity of conservation of M. pilosus populations to conserve this genetic biodiversity, especially in the areas of YTP, SCB and NSC regions.

Genomic introgression mapping of field-derived multiple-anthelmintic resistance in Teladorsagia circumcincta

PubMed Central

Hallsworth-Pepin, Kymberlie; Martin, John; Mitreva, Makedonka

2017-01-01

Preventive chemotherapy has long been practiced against nematode parasites of livestock, leading to widespread drug resistance, and is increasingly being adopted for eradication of human parasitic nematodes even though it is similarly likely to lead to drug resistance. Given that the genetic architecture of resistance is poorly understood for any nematode, we have analyzed multidrug resistant Teladorsagia circumcincta, a major parasite of sheep, as a model for analysis of resistance selection. We introgressed a field-derived multiresistant genotype into a partially inbred susceptible genetic background (through repeated backcrossing and drug selection) and performed genome-wide scans in the backcross progeny and drug-selected F2 populations to identify the major genes responsible for the multidrug resistance. We identified variation linking candidate resistance genes to each drug class. Putative mechanisms included target site polymorphism, changes in likely regulatory regions and copy number variation in efflux transporters. This work elucidates the genetic architecture of multiple anthelmintic resistance in a parasitic nematode for the first time and establishes a framework for future studies of anthelmintic resistance in nematode parasites of humans. PMID:28644839

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.

A predictive assessment of genetic correlations between traits in chickens using markers.

PubMed

Momen, Mehdi; Mehrgardi, Ahmad Ayatollahi; Sheikhy, Ayoub; Esmailizadeh, Ali; Fozi, Masood Asadi; Kranis, Andreas; Valente, Bruno D; Rosa, Guilherme J M; Gianola, Daniel

2017-02-01

Genomic selection has been successfully implemented in plant and animal breeding programs to shorten generation intervals and accelerate genetic progress per unit of time. In practice, genomic selection can be used to improve several correlated traits simultaneously via multiple-trait prediction, which exploits correlations between traits. However, few studies have explored multiple-trait genomic selection. Our aim was to infer genetic correlations between three traits measured in broiler chickens by exploring kinship matrices based on a linear combination of measures of pedigree and marker-based relatedness. A predictive assessment was used to gauge genetic correlations. A multivariate genomic best linear unbiased prediction model was designed to combine information from pedigree and genome-wide markers in order to assess genetic correlations between three complex traits in chickens, i.e. body weight at 35 days of age (BW), ultrasound area of breast meat (BM) and hen-house egg production (HHP). A dataset with 1351 birds that were genotyped with the 600 K Affymetrix platform was used. A kinship kernel (K) was constructed as K = λ G + (1 - λ)A, where A is the numerator relationship matrix, measuring pedigree-based relatedness, and G is a genomic relationship matrix. The weight (λ) assigned to each source of information varied over the grid λ = (0, 0.2, 0.4, 0.6, 0.8, 1). Maximum likelihood estimates of heritability and genetic correlations were obtained at each λ, and the "optimum" λ was determined using cross-validation. Estimates of genetic correlations were affected by the weight placed on the source of information used to build K. For example, the genetic correlation between BW-HHP and BM-HHP changed markedly when λ varied from 0 (only A used for measuring relatedness) to 1 (only genomic information used). As λ increased, predictive correlations (correlation between observed phenotypes and predicted breeding values) increased and mean-squared predictive error decreased. However, the improvement in predictive ability was not monotonic, with an optimum found at some 0 < λ < 1, i.e., when both sources of information were used together. Our findings indicate that multiple-trait prediction may benefit from combining pedigree and marker information. Also, it appeared that expected correlated responses to selection computed from standard theory may differ from realized responses. The predictive assessment provided a metric for performance evaluation as well as a means for expressing uncertainty of outcomes of multiple-trait selection.

The five-factor model of personality and borderline personality disorder: a genetic analysis of comorbidity.

PubMed

Distel, Marijn A; Trull, Timothy J; Willemsen, Gonneke; Vink, Jacqueline M; Derom, Catherine A; Lynskey, Michael; Martin, Nicholas G; Boomsma, Dorret I

2009-12-15

Recently, the nature of personality disorders and their relationship with normal personality traits has received extensive attention. The five-factor model (FFM) of personality, consisting of the personality traits neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness, is one of the proposed models to conceptualize personality disorders as maladaptive variants of continuously distributed personality traits. The present study examined the phenotypic and genetic association between borderline personality and FFM personality traits. Data were available for 4403 monozygotic twins, 4425 dizygotic twins, and 1661 siblings from 6140 Dutch, Belgian, and Australian families. Broad-sense heritability estimates for neuroticism, agreeableness, conscientiousness, extraversion, openness to experience, and borderline personality were 43%, 36%, 43%, 47%, 54%, and 45%, respectively. Phenotypic correlations between borderline personality and the FFM personality traits ranged from .06 for openness to experience to .68 for neuroticism. Multiple regression analyses showed that a combination of high neuroticism and low agreeableness best predicted borderline personality. Multivariate genetic analyses showed the genetic factors that influence individual differences in neuroticism, agreeableness, conscientiousness, and extraversion account for all genetic liability to borderline personality. Unique environmental effects on borderline personality, however, were not completely shared with those for the FFM traits (33% is unique to borderline personality). Borderline personality shares all genetic variation with neuroticism, agreeableness, conscientiousness, and extraversion. The unique environmental influences specific to borderline personality may cause individuals with a specific pattern of personality traits to cross a threshold and develop borderline personality.

Modeling the effects of mineral nutrition for improving growth and development of micropropagated red raspberries

USDA-ARS?s Scientific Manuscript database

In vitro propagation is important for rapid multiplication of a wide range of nursery crops, including red raspberries. The genetic variation of the many red raspberry cultivars makes it difficult to use one growth medium for all. Although some cultivars grow well on Murashige and Skoog (1962) mediu...

Gene flow in complex landscapes: Testing multiple hypotheses with causal modeling

Treesearch

Samuel A. Cushman; Kevin S. McKelvey; Jim Hayden; Michael K. Schwartz

2006-01-01

Predicting population-level effects of landscape change depends on identifying factors that influence population connectivity in complex landscapes. However, most putative movement corridors and barriers have not been based on empirical data. In this study, we identify factors that influence connectivity by comparing patterns of genetic similarity among 146 black bears...

Adaptive variation in Pinus ponderosa from Intermountain regions. II. Middle Columbia River system

Treesearch

Gerald Rehfeldt

1986-01-01

Seedling populations were grown and compared in common environments. Statistical analyses detected genetic differences between populations for numerous traits reflecting growth potential and periodicity of shoot elongation. Multiple regression models described an adaptive landscape in which populations from low elevations have a high growth potential while those from...

Makeup of the genetic correlation between milk production traits using genome-wide single nucleotide polymorphism information.

PubMed

van Binsbergen, R; Veerkamp, R F; Calus, M P L

2012-04-01

The correlated responses between traits may differ depending on the makeup of genetic covariances, and may differ from the predictions of polygenic covariances. Therefore, the objective of the present study was to investigate the makeup of the genetic covariances between the well-studied traits: milk yield, fat yield, protein yield, and their percentages in more detail. Phenotypic records of 1,737 heifers of research farms in 4 different countries were used after homogenizing and adjusting for management effects. All cows had a genotype for 37,590 single nucleotide polymorphisms (SNP). A bayesian stochastic search variable selection model was used to estimate the SNP effects for each trait. About 0.5 to 1.0% of the SNP had a significant effect on 1 or more traits; however, the SNP without a significant effect explained most of the genetic variances and covariances of the traits. Single nucleotide polymorphism correlations differed from the polygenic correlations, but only 10 regions were found with an effect on multiple traits; in 1 of these regions the DGAT1 gene was previously reported with an effect on multiple traits. This region explained up to 41% of the variances of 4 traits and explained a major part of the correlation between fat yield and fat percentage and contributes to asymmetry in correlated response between fat yield and fat percentage. Overall, for the traits in this study, the infinitesimal model is expected to be sufficient for the estimation of the variances and covariances. Copyright © 2012 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Molecular Genetics of Supernumerary Tooth Formation

PubMed Central

Wang, Xiu-Ping; Fan, Jiabing

2011-01-01

Summary Despite advances in the knowledge of tooth morphogenesis and differentiation, relatively little is known about the aetiology and molecular mechanisms underlying supernumerary tooth formation. A small number of supernumerary teeth may be a common developmental dental anomaly, while multiple supernumerary teeth usually have a genetic component and they are sometimes thought to represent a partial third dentition in humans. Mice, which are commonly used for studying tooth development, only exhibit one dentition, with very few mouse models exhibiting supernumerary teeth similar to those in humans. Inactivation of Apc or forced activation of Wnt/β(catenin signalling results in multiple supernumerary tooth formation in both humans and in mice, but the key genes in these pathways are not very clear. Analysis of other model systems with continuous tooth replacement or secondary tooth formation, such as fish, snake, lizard, and ferret, is providing insights into the molecular and cellular mechanisms underlying succesional tooth development, and will assist in the studies on supernumerary tooth formation in humans. This information, together with the advances in stem cell biology and tissue engineering, will pave ways for the tooth regeneration and tooth bioengineering. PMID:21309064

Factors that cause genotype by environment interaction and use of a multiple-trait herd-cluster model for milk yield of Holstein cattle from Brazil and Colombia.

PubMed

Cerón-Muñoz, M F; Tonhati, H; Costa, C N; Rojas-Sarmiento, D; Echeverri Echeverri, D M

2004-08-01

Descriptive herd variables (DVHE) were used to explain genotype by environment interactions (G x E) for milk yield (MY) in Brazilian and Colombian production environments and to develop a herd-cluster model to estimate covariance components and genetic parameters for each herd environment group. Data consisted of 180,522 lactation records of 94,558 Holstein cows from 937 Brazilian and 400 Colombian herds. Herds in both countries were jointly grouped in thirds according to 8 DVHE: production level, phenotypic variability, age at first calving, calving interval, percentage of imported semen, lactation length, and herd size. For each DVHE, REML bivariate animal model analyses were used to estimate genetic correlations for MY between upper and lower thirds of the data. Based on estimates of genetic correlations, weights were assigned to each DVHE to group herds in a cluster analysis using the FASTCLUS procedure in SAS. Three clusters were defined, and genetic and residual variance components were heterogeneous among herd clusters. Estimates of heritability in clusters 1 and 3 were 0.28 and 0.29, respectively, but the estimate was larger (0.39) in Cluster 2. The genetic correlations of MY from different clusters ranged from 0.89 to 0.97. The herd-cluster model based on DVHE properly takes into account G x E by grouping similar environments accordingly and seems to be an alternative to simply considering country borders to distinguish between environments.

Developmental mechanisms underlying variation in craniofacial disease and evolution.

PubMed

Fish, Jennifer L

2016-07-15

Craniofacial disease phenotypes exhibit significant variation in penetrance and severity. Although many genetic contributions to phenotypic variation have been identified, genotype-phenotype correlations remain imprecise. Recent work in evolutionary developmental biology has exposed intriguing developmental mechanisms that potentially explain incongruities in genotype-phenotype relationships. This review focuses on two observations from work in comparative and experimental animal model systems that highlight how development structures variation. First, multiple genetic inputs converge on relatively few developmental processes. Investigation of when and how variation in developmental processes occurs may therefore help predict potential genetic interactions and phenotypic outcomes. Second, genetic mutation is typically associated with an increase in phenotypic variance. Several models outlining developmental mechanisms underlying mutational increases in phenotypic variance are discussed using Satb2-mediated variation in jaw size as an example. These data highlight development as a critical mediator of genotype-phenotype correlations. Future research in evolutionary developmental biology focusing on tissue-level processes may help elucidate the "black box" between genotype and phenotype, potentially leading to novel treatment, earlier diagnoses, and better clinical consultations for individuals affected by craniofacial anomalies. Copyright © 2015 Elsevier Inc. All rights reserved.

Health effects models for nuclear power plant accident consequence analysis: Low LET radiation: Part 2, Scientific bases for health effects models

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

Abrahamson, S.; Bender, M.; Book, S.

1989-05-01

This report provides dose-response models intended to be used in estimating the radiological health effects of nuclear power plant accidents. Models of early and continuing effects, cancers and thyroid nodules, and genetic effects are provided. Two-parameter Weibull hazard functions are recommended for estimating the risks of early and continuing health effects. Three potentially lethal early effects -- the hematopoietic, pulmonary and gastrointestinal syndromes -- are considered. Linear and linear-quadratic models are recommended for estimating cancer risks. Parameters are given for analyzing the risks of seven types of cancer in adults -- leukemia, bone, lung, breast, gastrointestinal, thyroid and ''other''. Themore » category, ''other'' cancers, is intended to reflect the combined risks of multiple myeloma, lymphoma, and cancers of the bladder, kidney, brain, ovary, uterus and cervix. Models of childhood cancers due to in utero exposure are also provided. For most cancers, both incidence and mortality are addressed. Linear and linear-quadratic models are also recommended for assessing genetic risks. Five classes of genetic disease -- dominant, x-linked, aneuploidy, unbalanced translocation and multifactorial diseases --are considered. In addition, the impact of radiation-induced genetic damage on the incidence of peri-implantation embryo losses is discussed. The uncertainty in modeling radiological health risks is addressed by providing central, upper, and lower estimates of all model parameters. Data are provided which should enable analysts to consider the timing and severity of each type of health risk. 22 refs., 14 figs., 51 tabs.« less

Environmental Sensing of Expert Knowledge in a Computational Evolution System for Complex Problem Solving in Human Genetics

NASA Astrophysics Data System (ADS)

Greene, Casey S.; Hill, Douglas P.; Moore, Jason H.

The relationship between interindividual variation in our genomes and variation in our susceptibility to common diseases is expected to be complex with multiple interacting genetic factors. A central goal of human genetics is to identify which DNA sequence variations predict disease risk in human populations. Our success in this endeavour will depend critically on the development and implementation of computational intelligence methods that are able to embrace, rather than ignore, the complexity of the genotype to phenotype relationship. To this end, we have developed a computational evolution system (CES) to discover genetic models of disease susceptibility involving complex relationships between DNA sequence variations. The CES approach is hierarchically organized and is capable of evolving operators of any arbitrary complexity. The ability to evolve operators distinguishes this approach from artificial evolution approaches using fixed operators such as mutation and recombination. Our previous studies have shown that a CES that can utilize expert knowledge about the problem in evolved operators significantly outperforms a CES unable to use this knowledge. This environmental sensing of external sources of biological or statistical knowledge is important when the search space is both rugged and large as in the genetic analysis of complex diseases. We show here that the CES is also capable of evolving operators which exploit one of several sources of expert knowledge to solve the problem. This is important for both the discovery of highly fit genetic models and because the particular source of expert knowledge used by evolved operators may provide additional information about the problem itself. This study brings us a step closer to a CES that can solve complex problems in human genetics in addition to discovering genetic models of disease.

Genetics Home Reference: multiple epiphyseal dysplasia

MedlinePlus

... MedlinePlus (5 links) Diagnostic Tests Drug Therapy Genetic Counseling Palliative Care Surgery and Rehabilitation Related Information How ... manifestations of multiple epiphyseal dysplasia caused by MATN3 versus COMP mutations: a case control study. BMC Musculoskelet ...

Multiple losses of flight and recent speciation in steamer ducks

PubMed Central

Fulton, Tara L.; Letts, Brandon; Shapiro, Beth

2012-01-01

Steamer ducks (Tachyeres) comprise four species, three of which are flightless. The flightless species are believed to have diverged from a flying common ancestor during the Late Pleistocene; however, their taxonomy remains contentious. Of particular interest is the previously unstudied population of flying steamer ducks in the Falkland Islands. We present the first genetic data from this insular population, and illustrate that the flying and flightless steamer ducks on the Falkland Islands are genetically indistinguishable, in contrast to their traditional classification as separate species. The three species that reside in continental South America form a genetically distinct lineage from the Falkland Island ducks. The Falkland steamer ducks diverged from their continental relatives 2.2–0.6 million years ago, coincident with a probable land bridge connecting the Falkland Islands to the mainland. The three continental species share a common ancestor approximately 15 000 years ago, possibly owing to isolation during a recent glacial advance. The continental steamer duck species are not reciprocally monophyletic, but show some amount of genetic differentiation between them. Each lineage of Tachyeres represents a different stage between flight and flightlessness. Their phylogenetic relationships suggest multiple losses of flight and/or long-term persistence of mixed-flight capability. As such, steamer ducks may provide a model system to study the evolution of flightlessness. PMID:22319122

Reverse genetics in high throughput: rapid generation of complete negative strand RNA virus cDNA clones and recombinant viruses thereof.

PubMed

Nolden, T; Pfaff, F; Nemitz, S; Freuling, C M; Höper, D; Müller, T; Finke, Stefan

2016-04-05

Reverse genetics approaches are indispensable tools for proof of concepts in virus replication and pathogenesis. For negative strand RNA viruses (NSVs) the limited number of infectious cDNA clones represents a bottleneck as clones are often generated from cell culture adapted or attenuated viruses, with limited potential for pathogenesis research. We developed a system in which cDNA copies of complete NSV genomes were directly cloned into reverse genetics vectors by linear-to-linear RedE/T recombination. Rapid cloning of multiple rabies virus (RABV) full length genomes and identification of clones identical to field virus consensus sequence confirmed the approache's reliability. Recombinant viruses were recovered from field virus cDNA clones. Similar growth kinetics of parental and recombinant viruses, preservation of field virus characters in cell type specific replication and virulence in the mouse model were confirmed. Reduced titers after reporter gene insertion indicated that the low level of field virus replication is affected by gene insertions. The flexibility of the strategy was demonstrated by cloning multiple copies of an orthobunyavirus L genome segment. This important step in reverse genetics technology development opens novel avenues for the analysis of virus variability combined with phenotypical characterization of recombinant viruses at a clonal level.

Gene–environment interactions: key to unraveling the mystery of Parkinson’s disease

PubMed Central

Gao, Hui-Ming; Hong, Jau-shyong

2011-01-01

Parkinson’s disease (PD) is the second most common neurodegenerative disease. The gradual, irreversible loss of dopamine neurons in the substantia nigra isthe signature lesion of PD. Clinical symptoms of PD become apparent when 50–60% of nigral dopamine neurons are lost. PD progresses insidiously for 5–7 years (preclinical period) and then continues to worsen even under the symptomatic treatment. To determine what triggers the disease onset and what drives the chronic, self-propelling neurodegenerative process becomes critical and urgent, since lack of such knowledge impedes the discovery of effective treatments to retard PD progression. At present, available therapeutics only temporarily relieve PD symptoms. While the identification of causative gene defects in familial PD uncovers important genetic influences in this disease, the majority of PD cases are sporadic and idiopathic. The current consensus suggests that PD develops from multiple risk factors including aging, genetic predisposition, and environmental exposure. Here, we briefly review research on the genetic and environmental causes of PD. We also summarize very recent genome-wide association studies on risk gene polymorphisms in the emergence of PD. We highlight the new converging evidence on gene-environment interplay in the development of PD with an emphasis on newly developed multiple-hit PD models involving both genetic lesions and environmental triggers. PMID:21439347

Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants.

PubMed

Pierce, Brandon L; Ahsan, Habibul; Vanderweele, Tyler J

2011-06-01

Mendelian Randomization (MR) studies assess the causality of an exposure-disease association using genetic determinants [i.e. instrumental variables (IVs)] of the exposure. Power and IV strength requirements for MR studies using multiple genetic variants have not been explored. We simulated cohort data sets consisting of a normally distributed disease trait, a normally distributed exposure, which affects this trait and a biallelic genetic variant that affects the exposure. We estimated power to detect an effect of exposure on disease for varying allele frequencies, effect sizes and samples sizes (using two-stage least squares regression on 10,000 data sets-Stage 1 is a regression of exposure on the variant. Stage 2 is a regression of disease on the fitted exposure). Similar analyses were conducted using multiple genetic variants (5, 10, 20) as independent or combined IVs. We assessed IV strength using the first-stage F statistic. Simulations of realistic scenarios indicate that MR studies will require large (n > 1000), often very large (n > 10,000), sample sizes. In many cases, so-called 'weak IV' problems arise when using multiple variants as independent IVs (even with as few as five), resulting in biased effect estimates. Combining genetic factors into fewer IVs results in modest power decreases, but alleviates weak IV problems. Ideal methods for combining genetic factors depend upon knowledge of the genetic architecture underlying the exposure. The feasibility of well-powered, unbiased MR studies will depend upon the amount of variance in the exposure that can be explained by known genetic factors and the 'strength' of the IV set derived from these genetic factors.

A population based twin study of DSM-5 maladaptive personality domains.

PubMed

South, Susan C; Krueger, Robert F; Knudsen, Gun Peggy; Ystrom, Eivind; Czajkowski, Nikolai; Aggen, Steven H; Neale, Michael C; Gillespie, Nathan A; Kendler, Kenneth S; Reichborn-Kjennerud, Ted

2017-10-01

Personality disorders (PDs) can be partly captured by dimensional traits, a viewpoint reflected in the most recent Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition (DSM-5) Alternative (Section III) Model for PD classification. The current study adds to the literature on the Alternative Model by examining the magnitude of genetic and environmental influences on 6 domains of maladaptive personality: negative emotionality, detachment, antagonism, disinhibition, compulsivity, and psychoticism. In a large, population-based sample (N = 2,293) of Norwegian male and female twin pairs, we investigated (a) if the domains demonstrated measurement invariance across gender at the phenotypic level, meaning that the relationships between the items and the latent factor were equivalent in men and women; and (b) if genetic and environmental influences on variation in these domains were equivalent across gender. Multiple group confirmatory factor modeling provided evidence that all 6 domain scale measurement models were gender-invariant. The best fitting biometric model for 4 of the 6 domains (negative emotionality, detachment, disinhibition, and compulsivity) was one in which genetic and environmental influences could be set invariant across gender. Evidence for sex differences in psychoticism was mixed, but the only clear evidence for quantitative sex differences was for the antagonism scale, with greater genetic influences found for men than women. Genetic influences across domains were moderate overall (19-37%), in line with previous research using symptom-based measures of PDs. This study adds to the very limited knowledge currently existing on the etiology of maladaptive personality traits. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Influence of genetic, biological and pharmacological factors on levodopa dose in Parkinson's disease.

PubMed

Altmann, Vivian; Schumacher-Schuh, Artur F; Rieck, Mariana; Callegari-Jacques, Sidia M; Rieder, Carlos R M; Hutz, Mara H

2016-04-01

Levodopa is first-line treatment of Parkinson's disease motor symptoms but, dose response is highly variable. Therefore, the aim of this study was to determine how much levodopa dose could be explained by biological, pharmacological and genetic factors. A total of 224 Parkinson's disease patients were genotyped for SV2C and SLC6A3 polymorphisms by allelic discrimination assays. Comedication, demographic and clinical data were also assessed. All variables with p < 0.20 were included in a multiple regression analysis for dose prediction. The final model explained 23% of dose variation (F = 11.54; p < 0.000001). Although a good prediction model was obtained, it still needs to be tested in an independent sample to be validated.

Evolving models of the immunopathogenesis of T-cell mediated drug allergy: the role of host, pathogens, and drug response

PubMed Central

White, Katie D.; Chung, Wen-Hung; Hung, Shuen-Iu; Mallal, Simon; Phillips, Elizabeth J.

2015-01-01

Immune-mediated adverse drug reactions (IM-ADRs) are an underrecognized source of preventable morbidity, mortality, and cost. Increasingly, genetic variation in the HLA loci is associated with risk of severe reactions, highlighting the importance of T-cell immune responses in the mechanisms of both B-cell mediated and primary T-cell mediated IM-ADRs. In this review, we summarize the role of host genetics, microbes and drugs in the development of IM-ADRs, expand upon the existing models of IM-ADR pathogenesis to address multiple unexplained observations, discuss the implications of this work in clinical practice today, and describe future applications for pre-clinical drug toxicity screening, drug design, and development. PMID:26254049

LIGO detector characterization with genetic programming

NASA Astrophysics Data System (ADS)

Cavaglia, Marco; Staats, Kai; Errico, Luciano; Mogushi, Kentaro; Gabbard, Hunter

2017-01-01

Genetic Programming (GP) is a supervised approach to Machine Learning. GP has for two decades been applied to a diversity of problems, from predictive and financial modelling to data mining, from code repair to optical character recognition and product design. GP uses a stochastic search, tournament, and fitness function to explore a solution space. GP evolves a population of individual programs, through multiple generations, following the principals of biological evolution (mutation and reproduction) to discover a model that best fits or categorizes features in a given data set. We apply GP to categorization of LIGO noise and show that it can effectively be used to characterize the detector non-astrophysical noise both in low latency and offline searches. National Science Foundation award PHY-1404139.

Anti-diabetic activity of a mineraloid isolate, in vitro and in genetically diabetic mice.

PubMed

Deneau, Joel; Ahmed, Taufeeq; Blotsky, Roger; Bojanowski, Krzysztof

2011-01-01

Type II diabetes is a metabolic disease mediated through multiple molecular pathways. Here, we report anti-diabetic effect of a standardized isolate from a fossil material - a mineraloid leonardite - in in vitro tests and in genetically diabetic mice. The mineraloid isolate stimulated mitochondrial metabolism in human fibroblasts and this stimulation correlated with enhanced expression of genes coding for mitochondrial proteins such as ATP synthases and ribosomal protein precursors, as measured by DNA microarrays. In the diabetic animal model, consumption of the Totala isolate resulted in decreased weight gain, blood glucose, and glycated hemoglobin. To our best knowledge, this is the first description ever of a fossil material having anti-diabetic activity in pre-clinical models.

The fabulous destiny of the Drosophila heart.

PubMed

Medioni, Caroline; Sénatore, Sébastien; Salmand, Pierre-Adrien; Lalevée, Nathalie; Perrin, Laurent; Sémériva, Michel

2009-10-01

For the last 15 years the fly cardiovascular system has attracted developmental geneticists for its potential as a model system of organogenesis. Heart development in Drosophila indeed provides a remarkable system for elucidating the basic molecular and cellular mechanisms of morphogenesis and, more recently, for understanding the genetic control of cardiac physiology. The success of these studies can in part be attributed to multidisciplinary approaches, the multiplicity of existing genetic tools, and a detailed knowledge of the system. Striking similarities with vertebrate cardiogenesis have long been stressed, in particular concerning the conservation of key molecular regulators of cardiogenesis and the new data presented here confirm Drosophila cardiogenesis as a model not only for organogenesis but also for the study of molecular mechanisms of human cardiac disease.

The Human CHRNA7 and CHRFAM7A Genes: A Review of the Genetics, Regulation, and Function

PubMed Central

Sinkus, Melissa L.; Graw, Sharon; Freedman, Robert; Ross, Randal G.; Lester, Henry A.; Leonard, Sherry

2015-01-01

The human α7 neuronal nicotinic acetylcholine receptor gene (CHRNA7) is ubiquitously expressed in both the central nervous system and in the periphery. CHRNA7 is genetically linked to multiple disorders with cognitive deficits, including schizophrenia, bipolar disorder, ADHD, epilepsy, Alzheimer’s disease, and Rett syndrome. The regulation of CHRNA7 is complex; more than a dozen mechanisms are known, one of which is a partial duplication of the parent gene. Exons 5-10 of CHRNA7 on chromosome 15 were duplicated and inserted 1.6 Mb upstream of CHRNA7, interrupting an earlier partial duplication of two other genes. The chimeric CHRFAM7A gene product, dupα7, assembles with α7 subunits, resulting in a dominant negative regulation of function. The duplication is human specific, occurring neither in primates nor in rodents. The duplicated α7 sequence in exons 5-10 of CHRFAM7A is almost identical to CHRNA7, and thus is not completely queried in high throughput genetic studies (GWAS). Further, pre-clinical animal models of the α7nAChR utilized in drug development research do not have CHRFAM7A (dupα7) and cannot fully model human drug responses. The wide expression of CHRNA7, its multiple functions and modes of regulation present challenges for study of this gene in disease. PMID:25701707

Genetic risk factors for ovarian cancer and their role for endometriosis risk.

PubMed

Burghaus, Stefanie; Fasching, Peter A; Häberle, Lothar; Rübner, Matthias; Büchner, Kathrin; Blum, Simon; Engel, Anne; Ekici, Arif B; Hartmann, Arndt; Hein, Alexander; Beckmann, Matthias W; Renner, Stefan P

2017-04-01

Several genetic variants have been validated as risk factors for ovarian cancer. Endometriosis has also been described as a risk factor for ovarian cancer. Identifying genetic risk factors that are common to the two diseases might help improve our understanding of the molecular pathogenesis potentially linking the two conditions. In a hospital-based case-control analysis, 12 single nucleotide polymorphisms (SNPs), validated by the Ovarian Cancer Association Consortium (OCAC) and the Collaborative Oncological Gene-environment Study (COGS) project, were genotyped using TaqMan® OpenArray™ analysis. The cases consisted of patients with endometriosis, and the controls were healthy individuals without endometriosis. A total of 385 cases and 484 controls were analyzed. Odds ratios and P values were obtained using simple logistic regression models, as well as from multiple logistic regression models with adjustment for clinical predictors. rs11651755 in HNF1B was found to be associated with endometriosis in this case-control study. The OR was 0.66 (95% CI, 0.51 to 0.84) and the P value after correction for multiple testing was 0.01. None of the other genotypes was associated with a risk for endometriosis. As rs11651755 in HNF1B modified both the ovarian cancer risk and also the risk for endometriosis, HNF1B may be causally involved in the pathogenetic pathway leading from endometriosis to ovarian cancer. Copyright © 2017 Elsevier Inc. All rights reserved.

Genetics and Genomics of Social Behavior in a Chicken Model.

PubMed

Johnsson, Martin; Henriksen, Rie; Fogelholm, Jesper; Höglund, Andrey; Jensen, Per; Wright, Dominic

2018-05-01

The identification of genes affecting sociality can give insights into the maintenance and development of sociality and personality. In this study, we used the combination of an advanced intercross between wild and domestic chickens with a combined QTL and eQTL genetical genomics approach to identify genes for social reinstatement, a social and anxiety-related behavior. A total of 24 social reinstatement QTL were identified and overlaid with over 600 eQTL obtained from the same birds using hypothalamic tissue. Correlations between overlapping QTL and eQTL indicated five strong candidate genes, with the gene TTRAP being strongly significantly correlated with multiple aspects of social reinstatement behavior, as well as possessing a highly significant eQTL. Copyright © 2018 by the Genetics Society of America.

Chemical genetics and regeneration.

PubMed

Sengupta, Sumitra; Zhang, Liyun; Mumm, Jeff S

2015-01-01

Regeneration involves interactions between multiple signaling pathways acting in a spatially and temporally complex manner. As signaling pathways are highly conserved, understanding how regeneration is controlled in animal models exhibiting robust regenerative capacities should aid efforts to stimulate repair in humans. One way to discover molecular regulators of regeneration is to alter gene/protein function and quantify effect(s) on the regenerative process: dedifferentiation/reprograming, stem/progenitor proliferation, migration/remodeling, progenitor cell differentiation and resolution. A powerful approach for applying this strategy to regenerative biology is chemical genetics, the use of small-molecule modulators of specific targets or signaling pathways. Here, we review advances that have been made using chemical genetics for hypothesis-focused and discovery-driven studies aimed at furthering understanding of how regeneration is controlled.

Genetic variation and gene expression across multiple tissues and developmental stages in a non-human primate

PubMed Central

Jasinska, Anna J.; Zelaya, Ivette; Service, Susan K.; Peterson, Christine B.; Cantor, Rita M.; Choi, Oi-Wa; DeYoung, Joseph; Eskin, Eleazar; Fairbanks, Lynn A.; Fears, Scott; Furterer, Allison E.; Huang, Yu S.; Ramensky, Vasily; Schmitt, Christopher A.; Svardal, Hannes; Jorgensen, Matthew J.; Kaplan, Jay R.; Villar, Diego; Aken, Bronwen L.; Flicek, Paul; Nag, Rishi; Wong, Emily S.; Blangero, John; Dyer, Thomas D.; Bogomolov, Marina; Benjamini, Yoav; Weinstock, George M.; Dewar, Ken; Sabatti, Chiara; Wilson, Richard K.; Jentsch, J. David; Warren, Wesley; Coppola, Giovanni; Woods, Roger P.; Freimer, Nelson B.

2017-01-01

By analyzing multi-tissue gene expression and genome-wide genetic variation data in samples from a vervet monkey pedigree, we generated a transcriptome resource and produced the first catalogue of expression quantitative trait loci (eQTLs) in a non-human primate model. This catalogue contains more genome-wide significant eQTLs, per sample, than comparable human resources, and reveals sex and age-related expression patterns. Findings include a master regulatory locus that likely plays a role in immune function, and a locus regulating hippocampal long non-coding RNAs (lncRNAs), whose expression correlates with hippocampal volume. This resource will facilitate genetic investigation of quantitative traits, including brain and behavioral phenotypes relevant to neuropsychiatric disorders. PMID:29083405

Teaching and Learning Activity Sequencing System using Distributed Genetic Algorithms

NASA Astrophysics Data System (ADS)

Matsui, Tatsunori; Ishikawa, Tomotake; Okamoto, Toshio

The purpose of this study is development of a supporting system for teacher's design of lesson plan. Especially design of lesson plan which relates to the new subject "Information Study" is supported. In this study, we developed a system which generates teaching and learning activity sequences by interlinking lesson's activities corresponding to the various conditions according to the user's input. Because user's input is multiple information, there will be caused contradiction which the system should solve. This multiobjective optimization problem is resolved by Distributed Genetic Algorithms, in which some fitness functions are defined with reference models on lesson, thinking and teaching style. From results of various experiments, effectivity and validity of the proposed methods and reference models were verified; on the other hand, some future works on reference models and evaluation functions were also pointed out.

Identification of Mom12 and Mom13, two novel modifier loci of Apc (Min) -mediated intestinal tumorigenesis.

PubMed

Crist, Richard C; Roth, Jacquelyn J; Lisanti, Michael P; Siracusa, Linda D; Buchberg, Arthur M

2011-04-01

Colorectal cancer is a heterogeneous disease resulting from a combination of genetic and environmental factors. The C57BL/6J (B6) Apc (Min/+) mouse develops polyps throughout the gastrointestinal tract and has been a valuable model for understanding the genetic basis of intestinal tumorigenesis. Apc (Min/+) mice have been used to study known oncogenes and tumor suppressor genes on a controlled genetic background. These studies often utilize congenic knockout alleles, which can carry an unknown amount of residual donor DNA. The Apc (Min) model has also been used to identify modifer loci, known as Modifier of Min (Mom) loci, which alter Apc (Min) -mediated intestinal tumorigenesis. B6 mice carrying a knockout allele generated in WW6 embryonic stem cells were crossed to B6 Apc (Min/+) mice to determine the effect on polyp multiplicity. The newly generated colony developed significantly more intestinal polyps than Apc (Min/+) controls. Polyp multiplicity did not correlate with inheritance of the knockout allele, suggesting the presence of one or more modifier loci segregating in the colony. Genotyping of simple sequence length polymorphism (SSLP) markers revealed residual 129X1/SvJ genomic DNA within the congenic region of the parental knockout line. An analysis of polyp multiplicity data and SSLP genotyping indicated the presence of two Mom loci in the colony: 1) Mom12, a dominant modifier linked to the congenic region on chromosome 6, and 2) Mom13, which is unlinked to the congenic region and whose effect is masked by Mom12. The identification of Mom12 and Mom13 demonstrates the potential problems resulting from residual heterozygosity present in congenic lines.

CFH Variants Affect Structural and Functional Brain Changes and Genetic Risk of Alzheimer's Disease.

PubMed

Zhang, Deng-Feng; Li, Jin; Wu, Huan; Cui, Yue; Bi, Rui; Zhou, He-Jiang; Wang, Hui-Zhen; Zhang, Chen; Wang, Dong; Kong, Qing-Peng; Li, Tao; Fang, Yiru; Jiang, Tianzi; Yao, Yong-Gang

2016-03-01

The immune response is highly active in Alzheimer's disease (AD). Identification of genetic risk contributed by immune genes to AD may provide essential insight for the prognosis, diagnosis, and treatment of this neurodegenerative disease. In this study, we performed a genetic screening for AD-related top immune genes identified in Europeans in a Chinese cohort, followed by a multiple-stage study focusing on Complement Factor H (CFH) gene. Effects of the risk SNPs on AD-related neuroimaging endophenotypes were evaluated through magnetic resonance imaging scan, and the effects on AD cerebrospinal fluid biomarkers (CSF) and CFH expression changes were measured in aged and AD brain tissues and AD cellular models. Our results showed that the AD-associated top immune genes reported in Europeans (CR1, CD33, CLU, and TREML2) have weak effects in Chinese, whereas CFH showed strong effects. In particular, rs1061170 (P(meta)=5.0 × 10(-4)) and rs800292 (P(meta)=1.3 × 10(-5)) showed robust associations with AD, which were confirmed in multiple world-wide sample sets (4317 cases and 16 795 controls). Rs1061170 (P=2.5 × 10(-3)) and rs800292 (P=4.7 × 10(-4)) risk-allele carriers have an increased entorhinal thickness in their young age and a higher atrophy rate as the disease progresses. Rs800292 risk-allele carriers have higher CSF tau and Aβ levels and severe cognitive decline. CFH expression level, which was affected by the risk-alleles, was increased in AD brains and cellular models. These comprehensive analyses suggested that CFH is an important immune factor in AD and affects multiple pathological changes in early life and during disease progress.

The Genetic Architecture of Seed Composition in Soybean Is Refined by Genome-Wide Association Scans Across Multiple Populations

PubMed Central

Vaughn, Justin N.; Nelson, Randall L.; Song, Qijian; Cregan, Perry B.; Li, Zenglu

2014-01-01

Soybean oil and meal are major contributors to world-wide food production. Consequently, the genetic basis for soybean seed composition has been intensely studied using family-based mapping. Population-based mapping approaches, in the form of genome-wide association (GWA) scans, have been able to resolve loci controlling moderately complex quantitative traits (QTL) in numerous crop species. Yet, it is still unclear how soybean’s unique population history will affect GWA scans. Using one of the populations in this study, we simulated phenotypes resulting from a range of genetic architectures. We found that with a heritability of 0.5, ∼100% and ∼33% of the 4 and 20 simulated QTL can be recovered, respectively, with a false-positive rate of less than ∼6×10−5 per marker tested. Additionally, we demonstrated that combining information from multi-locus mixed models and compressed linear-mixed models improves QTL identification and interpretation. We applied these insights to exploring seed composition in soybean, refining the linkage group I (chromosome 20) protein QTL and identifying additional oil QTL that may allow some decoupling of highly correlated oil and protein phenotypes. Because the value of protein meal is closely related to its essential amino acid profile, we attempted to identify QTL underlying methionine, threonine, cysteine, and lysine content. Multiple QTL were found that have not been observed in family-based mapping studies, and each trait exhibited associations across multiple populations. Chromosomes 1 and 8 contain strong candidate alleles for essential amino acid increases. Overall, we present these and additional data that will be useful in determining breeding strategies for the continued improvement of soybean’s nutrient portfolio. PMID:25246241

Helicobacter pylori genetic diversification in the Mongolian gerbil model.

PubMed

Beckett, Amber C; Loh, John T; Chopra, Abha; Leary, Shay; Lin, Aung Soe; McDonnell, Wyatt J; Dixon, Beverly R E A; Noto, Jennifer M; Israel, Dawn A; Peek, Richard M; Mallal, Simon; Algood, Holly M Scott; Cover, Timothy L

2018-01-01

Helicobacter pylori requires genetic agility to infect new hosts and establish long-term colonization of changing gastric environments. In this study, we analyzed H. pylori genetic adaptation in the Mongolian gerbil model. This model is of particular interest because H. pylori -infected gerbils develop a high level of gastric inflammation and often develop gastric adenocarcinoma or gastric ulceration. We analyzed the whole genome sequences of H. pylori strains cultured from experimentally infected gerbils, in comparison to the genome sequence of the input strain. The mean annualized single nucleotide polymorphism (SNP) rate per site was 1.5e -5 , which is similar to the rates detected previously in H. pylori- infected humans. Many of the mutations occurred within or upstream of genes associated with iron-related functions ( fur , tonB1 , fecA2 , fecA3 , and frpB3 ) or encoding outer membrane proteins ( alpA, oipA, fecA2, fecA3, frpB3 and cagY ). Most of the SNPs within coding regions (86%) were non-synonymous mutations. Several deletion or insertion mutations led to disruption of open reading frames, suggesting that the corresponding gene products are not required or are deleterious during chronic H. pylori colonization of the gerbil stomach. Five variants (three SNPs and two deletions) were detected in isolates from multiple animals, which suggests that these mutations conferred a selective advantage. One of the mutations (FurR88H) detected in isolates from multiple animals was previously shown to confer increased resistance to oxidative stress, and we now show that this SNP also confers a survival advantage when H. pylori is co-cultured with neutrophils. Collectively, these analyses allow the identification of mutations that are positively selected during H. pylori colonization of the gerbil model.

Genetic and environmental (physical fitness and sedentary activity) interaction effects on cardiometabolic risk factors in Mexican American children and adolescents.

PubMed

Arya, Rector; Farook, Vidya S; Fowler, Sharon P; Puppala, Sobha; Chittoor, Geetha; Resendez, Roy G; Mummidi, Srinivas; Vanamala, Jairam; Almasy, Laura; Curran, Joanne E; Comuzzie, Anthony G; Lehman, Donna M; Jenkinson, Christopher P; Lynch, Jane L; DeFronzo, Ralph A; Blangero, John; Hale, Daniel E; Duggirala, Ravindranath; Diego, Vincent P

2018-06-01

Knowledge on genetic and environmental (G × E) interaction effects on cardiometabolic risk factors (CMRFs) in children is limited.  The purpose of this study was to examine the impact of G × E interaction effects on CMRFs in Mexican American (MA) children (n = 617, ages 6-17 years). The environments examined were sedentary activity (SA), assessed by recalls from "yesterday" (SAy) and "usually" (SAu) and physical fitness (PF) assessed by Harvard PF scores (HPFS). CMRF data included body mass index (BMI), waist circumference (WC), fat mass (FM), fasting insulin (FI), homeostasis model of assessment-insulin resistance (HOMA-IR), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), systolic (SBP) and diastolic (DBP) blood pressure, and number of metabolic syndrome components (MSC). We examined potential G × E interaction in the phenotypic expression of CMRFs using variance component models and likelihood-based statistical inference. Significant G × SA interactions were identified for six CMRFs: BMI, WC, FI, HOMA-IR, MSC, and HDL, and significant G × HPFS interactions were observed for four CMRFs: BMI, WC, FM, and HOMA-IR. However, after correcting for multiple hypothesis testing, only WC × SAy, FM × SAy, and FI × SAu interactions became marginally significant. After correcting for multiple testing, most of CMRFs exhibited significant G × E interactions (Reduced G × E model vs. Constrained model). These findings provide evidence that genetic factors interact with SA and PF to influence variation in CMRFs, and underscore the need for better understanding of these relationships to develop strategies and interventions to effectively reduce or prevent cardiometabolic risk in children. © 2018 WILEY PERIODICALS, INC.

IBSEM: An Individual-Based Atlantic Salmon Population Model

PubMed Central

Castellani, Marco; Heino, Mikko; Gilbey, John; Araki, Hitoshi; Svåsand, Terje; Glover, Kevin A.

2015-01-01

Ecology and genetics can influence the fate of individuals and populations in multiple ways. However, to date, few studies consider them when modelling the evolutionary trajectory of populations faced with admixture with non-local populations. For the Atlantic salmon, a model incorporating these elements is urgently needed because many populations are challenged with gene-flow from non-local and domesticated conspecifics. We developed an Individual-Based Salmon Eco-genetic Model (IBSEM) to simulate the demographic and population genetic change of an Atlantic salmon population through its entire life-cycle. Processes such as growth, mortality, and maturation are simulated through stochastic procedures, which take into account environmental variables as well as the genotype of the individuals. IBSEM is based upon detailed empirical data from salmon biology, and parameterized to reproduce the environmental conditions and the characteristics of a wild population inhabiting a Norwegian river. Simulations demonstrated that the model consistently and reliably reproduces the characteristics of the population. Moreover, in absence of farmed escapees, the modelled populations reach an evolutionary equilibrium that is similar to our definition of a ‘wild’ genotype. We assessed the sensitivity of the model in the face of assumptions made on the fitness differences between farm and wild salmon, and evaluated the role of straying as a buffering mechanism against the intrusion of farm genes into wild populations. These results demonstrate that IBSEM is able to capture the evolutionary forces shaping the life history of wild salmon and is therefore able to model the response of populations under environmental and genetic stressors. PMID:26383256

Selective sweep on human amylase genes postdates the split with Neanderthals

PubMed Central

Inchley, Charlotte E.; Larbey, Cynthia D. A.; Shwan, Nzar A. A.; Pagani, Luca; Saag, Lauri; Antão, Tiago; Jacobs, Guy; Hudjashov, Georgi; Metspalu, Ene; Mitt, Mario; Eichstaedt, Christina A.; Malyarchuk, Boris; Derenko, Miroslava; Wee, Joseph; Abdullah, Syafiq; Ricaut, François-Xavier; Mormina, Maru; Mägi, Reedik; Villems, Richard; Metspalu, Mait; Jones, Martin K.; Armour, John A. L.; Kivisild, Toomas

2016-01-01

Humans have more copies of amylase genes than other primates. It is still poorly understood, however, when the copy number expansion occurred and whether its spread was enhanced by selection. Here we assess amylase copy numbers in a global sample of 480 high coverage genomes and find that regions flanking the amylase locus show notable depression of genetic diversity both in African and non-African populations. Analysis of genetic variation in these regions supports the model of an early selective sweep in the human lineage after the split of humans from Neanderthals which led to the fixation of multiple copies of AMY1 in place of a single copy. We find evidence of multiple secondary losses of copy number with the highest frequency (52%) of a deletion of AMY2A and associated low copy number of AMY1 in Northeast Siberian populations whose diet has been low in starch content. PMID:27853181

Selective sweep on human amylase genes postdates the split with Neanderthals.

PubMed

Inchley, Charlotte E; Larbey, Cynthia D A; Shwan, Nzar A A; Pagani, Luca; Saag, Lauri; Antão, Tiago; Jacobs, Guy; Hudjashov, Georgi; Metspalu, Ene; Mitt, Mario; Eichstaedt, Christina A; Malyarchuk, Boris; Derenko, Miroslava; Wee, Joseph; Abdullah, Syafiq; Ricaut, François-Xavier; Mormina, Maru; Mägi, Reedik; Villems, Richard; Metspalu, Mait; Jones, Martin K; Armour, John A L; Kivisild, Toomas

2016-11-17

Humans have more copies of amylase genes than other primates. It is still poorly understood, however, when the copy number expansion occurred and whether its spread was enhanced by selection. Here we assess amylase copy numbers in a global sample of 480 high coverage genomes and find that regions flanking the amylase locus show notable depression of genetic diversity both in African and non-African populations. Analysis of genetic variation in these regions supports the model of an early selective sweep in the human lineage after the split of humans from Neanderthals which led to the fixation of multiple copies of AMY1 in place of a single copy. We find evidence of multiple secondary losses of copy number with the highest frequency (52%) of a deletion of AMY2A and associated low copy number of AMY1 in Northeast Siberian populations whose diet has been low in starch content.

Efficient exploration of pan-cancer networks by generalized covariance selection and interactive web content

PubMed Central

Kling, Teresia; Johansson, Patrik; Sanchez, José; Marinescu, Voichita D.; Jörnsten, Rebecka; Nelander, Sven

2015-01-01

Statistical network modeling techniques are increasingly important tools to analyze cancer genomics data. However, current tools and resources are not designed to work across multiple diagnoses and technical platforms, thus limiting their applicability to comprehensive pan-cancer datasets such as The Cancer Genome Atlas (TCGA). To address this, we describe a new data driven modeling method, based on generalized Sparse Inverse Covariance Selection (SICS). The method integrates genetic, epigenetic and transcriptional data from multiple cancers, to define links that are present in multiple cancers, a subset of cancers, or a single cancer. It is shown to be statistically robust and effective at detecting direct pathway links in data from TCGA. To facilitate interpretation of the results, we introduce a publicly accessible tool (cancerlandscapes.org), in which the derived networks are explored as interactive web content, linked to several pathway and pharmacological databases. To evaluate the performance of the method, we constructed a model for eight TCGA cancers, using data from 3900 patients. The model rediscovered known mechanisms and contained interesting predictions. Possible applications include prediction of regulatory relationships, comparison of network modules across multiple forms of cancer and identification of drug targets. PMID:25953855

Genetic diversity promotes homeostasis in insect colonies.

PubMed

Oldroyd, Benjamin P; Fewell, Jennifer H

2007-08-01

Although most insect colonies are headed by a singly mated queen, some ant, wasp and bee taxa have evolved high levels of multiple mating or 'polyandry'. We argue here that a contributing factor towards the evolution of polyandry is that the resulting genetic diversity within colonies provides them with a system of genetically based task specialization, enabling them to respond resiliently to environmental perturbation. An alternate view is that genetic contributions to task specialization are a side effect of multiple mating, which evolved through other causes, and that genetically based task specialization now makes little or no contribution to colony fitness.

MARRVEL: Integration of Human and Model Organism Genetic Resources to Facilitate Functional Annotation of the Human Genome.

PubMed

Wang, Julia; Al-Ouran, Rami; Hu, Yanhui; Kim, Seon-Young; Wan, Ying-Wooi; Wangler, Michael F; Yamamoto, Shinya; Chao, Hsiao-Tuan; Comjean, Aram; Mohr, Stephanie E; Perrimon, Norbert; Liu, Zhandong; Bellen, Hugo J

2017-06-01

One major challenge encountered with interpreting human genetic variants is the limited understanding of the functional impact of genetic alterations on biological processes. Furthermore, there remains an unmet demand for an efficient survey of the wealth of information on human homologs in model organisms across numerous databases. To efficiently assess the large volume of publically available information, it is important to provide a concise summary of the most relevant information in a rapid user-friendly format. To this end, we created MARRVEL (model organism aggregated resources for rare variant exploration). MARRVEL is a publicly available website that integrates information from six human genetic databases and seven model organism databases. For any given variant or gene, MARRVEL displays information from OMIM, ExAC, ClinVar, Geno2MP, DGV, and DECIPHER. Importantly, it curates model organism-specific databases to concurrently display a concise summary regarding the human gene homologs in budding and fission yeast, worm, fly, fish, mouse, and rat on a single webpage. Experiment-based information on tissue expression, protein subcellular localization, biological process, and molecular function for the human gene and homologs in the seven model organisms are arranged into a concise output. Hence, rather than visiting multiple separate databases for variant and gene analysis, users can obtain important information by searching once through MARRVEL. Altogether, MARRVEL dramatically improves efficiency and accessibility to data collection and facilitates analysis of human genes and variants by cross-disciplinary integration of 18 million records available in public databases to facilitate clinical diagnosis and basic research. Copyright © 2017 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

Estimates of genetic parameters, genetic trends, and inbreeding in a crossbred dairy sheep research flock in the United States.

PubMed

Murphy, T W; Berger, Y M; Holman, P W; Baldin, M; Burgett, R L; Thomas, D L

2017-10-01

For the past 2 decades, the Spooner Agriculture Research Station (ARS) of the University of Wisconsin-Madison operated the only dairy sheep research flock in North America. The objectives of the present study were to 1) obtain estimates of genetic parameters for lactation and reproductive traits in dairy ewes, 2) estimate the amount of genetic change in these traits over time, and 3) quantify the level of inbreeding in this flock over the last 20 yr. Multiple-trait repeatability models (MTRM) were used to analyze ewe traits through their first 6 parities. The first MTRM jointly analyzed milk (180-d-adjusted milk yield [180d MY]), fat (180-d-adjusted fat yield [180d FY]), and protein (180-d-adjusted protein yield [180d PY]) yields adjusted to 180 d of lactation; number of lambs born per ewe lambing (NLB); and lactation average test-day somatic cell score (LSCS). A second MTRM analyzed 180d MY, NLB, LSCS, and percentage milk fat (%F) and percentage milk protein (%P). The 3 yield traits were moderately heritable (0.26 to 0.32) and strongly genetically correlated (0.91 to 0.96). Percentage milk fat and %P were highly heritable (0.53 and 0.61, respectively) and moderately genetically correlated (0.61). Milk yield adjusted to 180 d was negatively genetically correlated with %F and %P (-0.31 and -0.34, respectively). Ewe prolificacy was not significantly ( > 0.67) genetically correlated with yield traits, %P, or LSCS but lowly negatively correlated with %F (-0.26). Lactation somatic cell score was unfavorably genetically correlated with yield traits (0.28 to 0.39) but not significantly ( > 0.09) correlated with %F, %P, and NLB. Within-trait multiple-trait models through the first 4 parities revealed that 180d MY, 180d FY, 180d PY, %F, and %P were strongly genetically correlated across parity (0.67 to 1.00). However, the genetic correlations across parity for NLB and LSCS were somewhat lower (0.51 to 0.96). Regressing predicted breeding values for 180d MY, without and with the addition of breed effects, on ewe year of birth revealed a positive genetic gain of 2.30 and 6.24 kg/yr, respectively, over the past 20 yr in this flock. Inbreeding coefficients of ewes with an extended pedigree ranged from 0.0 to 0.29, with an average of 0.07. To optimize genetic gains and avoid excessive inbreeding, the development of a national genetic improvement program should be a top priority for the growing dairy sheep industry.

Disrupting the male germ line to find infertility and contraception targets.

PubMed

Archambeault, Denise R; Matzuk, Martin M

2014-05-01

Genetically-manipulated mouse models have become indispensible for broadening our understanding of genes and pathways related to male germ cell development. Until suitable in vitro systems for studying spermatogenesis are perfected, in vivo models will remain the gold standard for inquiry into testicular function. Here, we discuss exciting advances that are allowing researchers faster, easier, and more customizable access to their mouse models of interest. Specifically, the trans-NIH Knockout Mouse Project (KOMP) is working to generate knockout mouse models of every gene in the mouse genome. The related Knockout Mouse Phenotyping Program (KOMP2) is performing systematic phenotypic analysis of this genome-wide collection of knockout mice, including fertility screening. Together, these programs will not only uncover new genes involved in male germ cell development but also provide the research community with the mouse models necessary for further investigations. In addition to KOMP/KOMP2, another promising development in the field of mouse models is the advent of CRISPR (clustered regularly interspaced short palindromic repeat)-Cas technology. Utilizing 20 nucleotide guide sequences, CRISPR/Cas has the potential to introduce sequence-specific insertions, deletions, and point mutations to produce null, conditional, activated, or reporter-tagged alleles. CRISPR/Cas can also successfully target multiple genes in a single experimental step, forgoing the multiple generations of breeding traditionally required to produce mouse models with deletions, insertions, or mutations in multiple genes. In addition, CRISPR/Cas can be used to create mouse models carrying variants identical to those identified in infertile human patients, providing the opportunity to explore the effects of such mutations in an in vivo system. Both the KOMP/KOMP2 projects and the CRISPR/Cas system provide powerful, accessible genetic approaches to the study of male germ cell development in the mouse. A more complete understanding of male germ cell biology is critical for the identification of novel targets for potential non-hormonal contraceptive intervention. Copyright © 2014. Published by Elsevier Masson SAS.

Study on validation method for femur finite element model under multiple loading conditions

NASA Astrophysics Data System (ADS)

Guan, Fengjiao; Zhang, Guanjun; Liu, Jie; Wang, Shujing; Luo, Xu

2018-03-01

Acquisition of accurate and reliable constitutive parameters related to bio-tissue materials was beneficial to improve biological fidelity of a Finite Element (FE) model and predict impact damages more effectively. In this paper, a femur FE model was established under multiple loading conditions with diverse impact positions. Then, based on sequential response surface method and genetic algorithms, the material parameters identification was transformed to a multi-response optimization problem. Finally, the simulation results successfully coincided with force-displacement curves obtained by numerous experiments. Thus, computational accuracy and efficiency of the entire inverse calculation process were enhanced. This method was able to effectively reduce the computation time in the inverse process of material parameters. Meanwhile, the material parameters obtained by the proposed method achieved higher accuracy.

Multiple quay cranes scheduling for double cycling in container terminals

PubMed Central

Chu, Yanling; Zhang, Xiaoju; Yang, Zhongzhen

2017-01-01

Double cycling is an efficient tool to increase the efficiency of quay crane (QC) in container terminals. In this paper, an optimization model for double cycling is developed to optimize the operation sequence of multiple QCs. The objective is to minimize the makespan of the ship handling operation considering the ship balance constraint. To solve the model, an algorithm based on Lagrangian relaxation is designed. Finally, we compare the efficiency of the Lagrangian relaxation based heuristic with the branch-and-bound method and a genetic algorithm using instances of different sizes. The results of numerical experiments indicate that the proposed model can effectively reduce the unloading and loading times of QCs. The effects of the ship balance constraint are more notable when the number of QCs is high. PMID:28692699

Multiple quay cranes scheduling for double cycling in container terminals.

PubMed

Chu, Yanling; Zhang, Xiaoju; Yang, Zhongzhen

2017-01-01

Double cycling is an efficient tool to increase the efficiency of quay crane (QC) in container terminals. In this paper, an optimization model for double cycling is developed to optimize the operation sequence of multiple QCs. The objective is to minimize the makespan of the ship handling operation considering the ship balance constraint. To solve the model, an algorithm based on Lagrangian relaxation is designed. Finally, we compare the efficiency of the Lagrangian relaxation based heuristic with the branch-and-bound method and a genetic algorithm using instances of different sizes. The results of numerical experiments indicate that the proposed model can effectively reduce the unloading and loading times of QCs. The effects of the ship balance constraint are more notable when the number of QCs is high.

Fitness change in relation to mutation number in spontaneous mutation accumulation lines of Chlamydomonas reinhardtii

PubMed Central

Kraemer, Susanne A.; Böndel, Katharina B.; Ness, Robert W.; Keightley, Peter D.; Colegrave, Nick

2017-01-01

Abstract Although all genetic variation ultimately stems from mutations, their properties are difficult to study directly. Here, we used multiple mutation accumulation (MA) lines derived from five genetic backgrounds of the green algae Chlamydomonas reinhardtii that have been previously subjected to whole genome sequencing to investigate the relationship between the number of spontaneous mutations and change in fitness from a nonevolved ancestor. MA lines were on average less fit than their ancestors and we detected a significantly negative correlation between the change in fitness and the total number of accumulated mutations in the genome. Likewise, the number of mutations located within coding regions significantly and negatively impacted MA line fitness. We used the fitness data to parameterize a maximum likelihood model to estimate discrete categories of mutational effects, and found that models containing one to two mutational effect categories (one neutral and one deleterious category) fitted the data best. However, the best‐fitting mutational effects models were highly dependent on the genetic background of the ancestral strain. PMID:28884790

Implications of genetic research on the role of the serotonin in depression: emphasis on the serotonin type 1A receptor and the serotonin transporter.

PubMed

Neumeister, Alexander; Young, Theresa; Stastny, Juergen

2004-08-01

Serotonin systems appear to play a key role in the pathophysiology of major depressive disorder. Consequently, ongoing research determines whether serotonin related genes account for the very robust differential behavioral and neural mechanisms that discriminate patients with depression from healthy controls. Serotonin type 1(A) receptors and the serotonin transporters are reduced in depression, and recent genetic research in animals and humans has implicated both in depression. Preclinical studies have utilized a variety of animal models that have been used to explain pathophysiological mechanisms in humans, although it is not clear at all whether these models constitute relevant models for depression in humans. However, data from preclinical studies can generate hypotheses that are tested in humans by combining genetic data with behavioral and physiological challenge paradigms and neuroimaging. These studies will enhance our understanding about combined influences from multiple interacting genes, as well as from environmental factors on brain circuits and their function, and about how these mechanisms may contribute to the pathophysiology of neuropsychiatric disorders.

Unified framework to evaluate panmixia and migration direction among multiple sampling locations.

PubMed

Beerli, Peter; Palczewski, Michal

2010-05-01

For many biological investigations, groups of individuals are genetically sampled from several geographic locations. These sampling locations often do not reflect the genetic population structure. We describe a framework using marginal likelihoods to compare and order structured population models, such as testing whether the sampling locations belong to the same randomly mating population or comparing unidirectional and multidirectional gene flow models. In the context of inferences employing Markov chain Monte Carlo methods, the accuracy of the marginal likelihoods depends heavily on the approximation method used to calculate the marginal likelihood. Two methods, modified thermodynamic integration and a stabilized harmonic mean estimator, are compared. With finite Markov chain Monte Carlo run lengths, the harmonic mean estimator may not be consistent. Thermodynamic integration, in contrast, delivers considerably better estimates of the marginal likelihood. The choice of prior distributions does not influence the order and choice of the better models when the marginal likelihood is estimated using thermodynamic integration, whereas with the harmonic mean estimator the influence of the prior is pronounced and the order of the models changes. The approximation of marginal likelihood using thermodynamic integration in MIGRATE allows the evaluation of complex population genetic models, not only of whether sampling locations belong to a single panmictic population, but also of competing complex structured population models.

Multiple myeloma and family history of lymphohaematopoietic cancers: Results from the International Multiple Myeloma Consortium.

PubMed

Schinasi, Leah H; Brown, Elizabeth E; Camp, Nicola J; Wang, Sophia S; Hofmann, Jonathan N; Chiu, Brian C; Miligi, Lucia; Beane Freeman, Laura E; de Sanjose, Silvia; Bernstein, Leslie; Monnereau, Alain; Clavel, Jacqueline; Tricot, Guido J; Atanackovic, Djordje; Cocco, Pierluigi; Orsi, Laurent; Dosman, James A; McLaughlin, John R; Purdue, Mark P; Cozen, Wendy; Spinelli, John J; de Roos, Anneclaire J

2016-10-01

Family clusters of multiple myeloma (MM) suggest disease heritability. Nevertheless, patterns of inheritance and the importance of genetic versus environmental risk factors in MM aetiology remain unclear. We pooled data from eleven case-control studies from the International Multiple Myeloma Consortium to characterize the association of MM risk with having a first-degree relative with a history of a lympho-haematapoietic cancer. Unconditional logistic regression models, adjusted for study, sex, age and education level, were used to estimate associations between MM risk and having a first-degree relative with a history of non-Hodgkin lymphoma, Hodgkin lymphoma, leukaemia or MM. Sex, African American race/ethnicity and age were explored as effect modifiers. A total of 2843 cases and 11 470 controls were included. MM risk was elevated in association with having a first-degree relative with any lympho-haematapoietic cancer (Odds Ratio (OR) = 1·29, 95% Confidence Interval (CI): 1·08-1·55). The association was particularly strong for having a first-degree relative with MM (OR = 1·90, 95% CI: 1·26-2·87), especially among men (OR = 4·13, 95% CI: 2·17-7·85) and African Americans (OR = 5·52, 95% CI: 1·87-16·27).These results support the hypothesis that genetic inheritance plays a role in MM aetiology. Future studies are warranted to characterize interactions of genetic markers with environmental exposures. © 2016 John Wiley & Sons Ltd.

Single-Nucleotide Polymorphisms Associated with Skin Naphthyl–Keratin Adduct Levels in Workers Exposed to Naphthalene

PubMed Central

Jiang, Rong; French, John E.; Stober, Vandy P.; Kang-Sickel, Juei-Chuan C.; Zou, Fei

2012-01-01

Background: Individual genetic variation that results in differences in systemic response to xenobiotic exposure is not accounted for as a predictor of outcome in current exposure assessment models. Objective: We developed a strategy to investigate individual differences in single-nucleotide polymorphisms (SNPs) as genetic markers associated with naphthyl–keratin adduct (NKA) levels measured in the skin of workers exposed to naphthalene. Methods: The SNP-association analysis was conducted in PLINK using candidate-gene analysis and genome-wide analysis. We identified significant SNP–NKA associations and investigated the potential impact of these SNPs along with personal and workplace factors on NKA levels using a multiple linear regression model and the Pratt index. Results: In candidate-gene analysis, a SNP (rs4852279) located near the CYP26B1 gene contributed to the 2-naphthyl–keratin adduct (2NKA) level. In the multiple linear regression model, the SNP rs4852279, dermal exposure, exposure time, task replacing foam, age, and ethnicity all were significant predictors of 2NKA level. In genome-wide analysis, no single SNP reached genome-wide significance for NKA levels (all p ≥ 1.05 × 10–5). Pathway and network analyses of SNPs associated with NKA levels were predicted to be involved in the regulation of cellular processes and homeostasis. Conclusions: These results provide evidence that a quantitative biomarker can be used as an intermediate phenotype when investigating the association between genetic markers and exposure–dose relationship in a small, well-characterized exposed worker population. PMID:22391508

Multiple introductions boosted genetic diversity in the invasive range of black cherry (Prunus serotina; Rosaceae)

PubMed Central

Pairon, Marie; Petitpierre, Blaise; Campbell, Michael; Guisan, Antoine; Broennimann, Olivier; Baret, Philippe V.; Jacquemart, Anne-Laure; Besnard, Guillaume

2010-01-01

Background and Aims Black cherry (Prunus serotina) is a North American tree that is rapidly invading European forests. This species was introduced first as an ornamental plant then it was massively planted by foresters in many countries but its origins and the process of invasion remain poorly documented. Based on a genetic survey of both native and invasive ranges, the invasion history of black cherry was investigated by identifying putative source populations and then assessing the importance of multiple introductions on the maintenance of gene diversity. Methods Genetic variability and structure of 23 populations from the invasive range and 22 populations from the native range were analysed using eight nuclear microsatellite loci and five chloroplast DNA regions. Key Results Chloroplast DNA diversity suggests there were multiple introductions from a single geographic region (the north-eastern United States). A low reduction of genetic diversity was observed in the invasive range for both nuclear and plastid genomes. High propagule pressure including both the size and number of introductions shaped the genetic structure in Europe and boosted genetic diversity. Populations from Denmark, The Netherlands, Belgium and Germany showed high genetic diversity and low differentiation among populations, supporting the hypothesis that numerous introduction events, including multiple individuals and exchanges between sites, have taken place during two centuries of plantation. Conclusions This study postulates that the invasive black cherry has originated from east of the Appalachian Mountains (mainly the Allegheny plateau) and its invasiveness in north-western Europe is mainly due to multiple introductions containing high numbers of individuals. PMID:20400456

Achondroplasia and multiple-suture craniosynostosis.

PubMed

Albino, Frank P; Wood, Benjamin C; Oluigbo, Chima O; Lee, Angela C; Oh, Albert K; Rogers, Gary F

2015-01-01

Genetic mutations in the fibroblast growth factor receptor 3 gene may lead to achondroplasia or syndromic forms of craniosynostosis. Despite sharing a common genetic basis, craniosynostosis has rarely been described in cases of confirmed achondroplasia. We report an infant with achondroplasia who developed progressive multiple-suture craniosynostosis to discuss the genetic link between these clinical entities and to describe the technical challenges associated with the operative management.

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

PubMed

Feltus, F Alex

2014-06-01

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

Validation of variants in SLC28A3 and UGT1A6 as genetic markers predictive of anthracycline-induced cardiotoxicity in children.

PubMed

Visscher, H; Ross, C J D; Rassekh, S R; Sandor, G S S; Caron, H N; van Dalen, E C; Kremer, L C; van der Pal, H J; Rogers, P C; Rieder, M J; Carleton, B C; Hayden, M R

2013-08-01

The use of anthracyclines as effective antineoplastic drugs is limited by the occurrence of cardiotoxicity. Multiple genetic variants predictive of anthracycline-induced cardiotoxicity (ACT) in children were recently identified. The current study was aimed to assess replication of these findings in an independent cohort of children. . Twenty-three variants were tested for association with ACT in an independent cohort of 218 patients. Predictive models including genetic and clinical risk factors were constructed in the original cohort and assessed in the current replication cohort. . We confirmed the association of rs17863783 in UGT1A6 and ACT in the replication cohort (P = 0.0062, odds ratio (OR) 7.98). Additional evidence for association of rs7853758 (P = 0.058, OR 0.46) and rs885004 (P = 0.058, OR 0.42) in SLC28A3 was found (combined P = 1.6 × 10(-5) and P = 3.0 × 10(-5), respectively). A previously constructed prediction model did not significantly improve risk prediction in the replication cohort over clinical factors alone. However, an improved prediction model constructed using replicated genetic variants as well as clinical factors discriminated significantly better between cases and controls than clinical factors alone in both original (AUC 0.77 vs. 0.68, P = 0.0031) and replication cohort (AUC 0.77 vs. 0.69, P = 0.060). . We validated genetic variants in two genes predictive of ACT in an independent cohort. A prediction model combining replicated genetic variants as well as clinical risk factors might be able to identify high- and low-risk patients who could benefit from alternative treatment options. Copyright © 2013 Wiley Periodicals, Inc.

Genetics of dispersal.

PubMed

Saastamoinen, Marjo; Bocedi, Greta; Cote, Julien; Legrand, Delphine; Guillaume, Frédéric; Wheat, Christopher W; Fronhofer, Emanuel A; Garcia, Cristina; Henry, Roslyn; Husby, Arild; Baguette, Michel; Bonte, Dries; Coulon, Aurélie; Kokko, Hanna; Matthysen, Erik; Niitepõld, Kristjan; Nonaka, Etsuko; Stevens, Virginie M; Travis, Justin M J; Donohue, Kathleen; Bullock, James M; Del Mar Delgado, Maria

2018-02-01

Dispersal is a process of central importance for the ecological and evolutionary dynamics of populations and communities, because of its diverse consequences for gene flow and demography. It is subject to evolutionary change, which begs the question, what is the genetic basis of this potentially complex trait? To address this question, we (i) review the empirical literature on the genetic basis of dispersal, (ii) explore how theoretical investigations of the evolution of dispersal have represented the genetics of dispersal, and (iii) discuss how the genetic basis of dispersal influences theoretical predictions of the evolution of dispersal and potential consequences. Dispersal has a detectable genetic basis in many organisms, from bacteria to plants and animals. Generally, there is evidence for significant genetic variation for dispersal or dispersal-related phenotypes or evidence for the micro-evolution of dispersal in natural populations. Dispersal is typically the outcome of several interacting traits, and this complexity is reflected in its genetic architecture: while some genes of moderate to large effect can influence certain aspects of dispersal, dispersal traits are typically polygenic. Correlations among dispersal traits as well as between dispersal traits and other traits under selection are common, and the genetic basis of dispersal can be highly environment-dependent. By contrast, models have historically considered a highly simplified genetic architecture of dispersal. It is only recently that models have started to consider multiple loci influencing dispersal, as well as non-additive effects such as dominance and epistasis, showing that the genetic basis of dispersal can influence evolutionary rates and outcomes, especially under non-equilibrium conditions. For example, the number of loci controlling dispersal can influence projected rates of dispersal evolution during range shifts and corresponding demographic impacts. Incorporating more realism in the genetic architecture of dispersal is thus necessary to enable models to move beyond the purely theoretical towards making more useful predictions of evolutionary and ecological dynamics under current and future environmental conditions. To inform these advances, empirical studies need to answer outstanding questions concerning whether specific genes underlie dispersal variation, the genetic architecture of context-dependent dispersal phenotypes and behaviours, and correlations among dispersal and other traits. © 2017 The Authors. Biological Reviews published by John Wiley & Sons Ltd on behalf of Cambridge Philosophical Society.

Genetics of dispersal

PubMed Central

Bocedi, Greta; Cote, Julien; Legrand, Delphine; Guillaume, Frédéric; Wheat, Christopher W.; Fronhofer, Emanuel A.; Garcia, Cristina; Henry, Roslyn; Husby, Arild; Baguette, Michel; Bonte, Dries; Coulon, Aurélie; Kokko, Hanna; Matthysen, Erik; Niitepõld, Kristjan; Nonaka, Etsuko; Stevens, Virginie M.; Travis, Justin M. J.; Donohue, Kathleen; Bullock, James M.; del Mar Delgado, Maria

2017-01-01

ABSTRACT Dispersal is a process of central importance for the ecological and evolutionary dynamics of populations and communities, because of its diverse consequences for gene flow and demography. It is subject to evolutionary change, which begs the question, what is the genetic basis of this potentially complex trait? To address this question, we (i) review the empirical literature on the genetic basis of dispersal, (ii) explore how theoretical investigations of the evolution of dispersal have represented the genetics of dispersal, and (iii) discuss how the genetic basis of dispersal influences theoretical predictions of the evolution of dispersal and potential consequences. Dispersal has a detectable genetic basis in many organisms, from bacteria to plants and animals. Generally, there is evidence for significant genetic variation for dispersal or dispersal‐related phenotypes or evidence for the micro‐evolution of dispersal in natural populations. Dispersal is typically the outcome of several interacting traits, and this complexity is reflected in its genetic architecture: while some genes of moderate to large effect can influence certain aspects of dispersal, dispersal traits are typically polygenic. Correlations among dispersal traits as well as between dispersal traits and other traits under selection are common, and the genetic basis of dispersal can be highly environment‐dependent. By contrast, models have historically considered a highly simplified genetic architecture of dispersal. It is only recently that models have started to consider multiple loci influencing dispersal, as well as non‐additive effects such as dominance and epistasis, showing that the genetic basis of dispersal can influence evolutionary rates and outcomes, especially under non‐equilibrium conditions. For example, the number of loci controlling dispersal can influence projected rates of dispersal evolution during range shifts and corresponding demographic impacts. Incorporating more realism in the genetic architecture of dispersal is thus necessary to enable models to move beyond the purely theoretical towards making more useful predictions of evolutionary and ecological dynamics under current and future environmental conditions. To inform these advances, empirical studies need to answer outstanding questions concerning whether specific genes underlie dispersal variation, the genetic architecture of context‐dependent dispersal phenotypes and behaviours, and correlations among dispersal and other traits. PMID:28776950

Joint effect of unlinked genotypes: application to type 2 diabetes in the EPIC-Potsdam case-cohort study.

PubMed

Knüppel, Sven; Meidtner, Karina; Arregui, Maria; Holzhütter, Hermann-Georg; Boeing, Heiner

2015-07-01

Analyzing multiple single nucleotide polymorphisms (SNPs) is a promising approach to finding genetic effects beyond single-locus associations. We proposed the use of multilocus stepwise regression (MSR) to screen for allele combinations as a method to model joint effects, and compared the results with the often used genetic risk score (GRS), conventional stepwise selection, and the shrinkage method LASSO. In contrast to MSR, the GRS, conventional stepwise selection, and LASSO model each genotype by the risk allele doses. We reanalyzed 20 unlinked SNPs related to type 2 diabetes (T2D) in the EPIC-Potsdam case-cohort study (760 cases, 2193 noncases). No SNP-SNP interactions and no nonlinear effects were found. Two SNP combinations selected by MSR (Nagelkerke's R² = 0.050 and 0.048) included eight SNPs with mean allele combination frequency of 2%. GRS and stepwise selection selected nearly the same SNP combinations consisting of 12 and 13 SNPs (Nagelkerke's R² ranged from 0.020 to 0.029). LASSO showed similar results. The MSR method showed the best model fit measured by Nagelkerke's R² suggesting that further improvement may render this method a useful tool in genetic research. However, our comparison suggests that the GRS is a simple way to model genetic effects since it does not consider linkage, SNP-SNP interactions, and no non-linear effects. © 2015 John Wiley & Sons Ltd/University College London.

Strengthening the reporting of genetic risk prediction studies (GRIPS): explanation and elaboration

PubMed Central

Janssens, A Cecile JW; Ioannidis, John PA; Bedrosian, Sara; Boffetta, Paolo; Dolan, Siobhan M; Dowling, Nicole; Fortier, Isabel; Freedman, Andrew N; Grimshaw, Jeremy M; Gulcher, Jeffrey; Gwinn, Marta; Hlatky, Mark A; Janes, Holly; Kraft, Peter; Melillo, Stephanie; O'Donnell, Christopher J; Pencina, Michael J; Ransohoff, David; Schully, Sheri D; Seminara, Daniela; Winn, Deborah M; Wright, Caroline F; van Duijn, Cornelia M; Little, Julian; Khoury, Muin J

2011-01-01

The rapid and continuing progress in gene discovery for complex diseases is fueling interest in the potential application of genetic risk models for clinical and public health practice. The number of studies assessing the predictive ability is steadily increasing, but they vary widely in completeness of reporting and apparent quality. Transparent reporting of the strengths and weaknesses of these studies is important to facilitate the accumulation of evidence on genetic risk prediction. A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of Genetic RIsk Prediction Studies (GRIPS), building on the principles established by previous reporting guidelines. These recommendations aim to enhance the transparency, quality and completeness of study reporting, and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct or analysis. PMID:21407270

GENETIC AND ENVIRONMENTAL CONTRIBUTIONS TO THE CO-OCCURRENCE OF DEPRESSIVE PERSONALITY DISORDER AND DSM-IV PERSONALITY DISORDERS

PubMed Central

Ørstavik, Ragnhild E.; Kendler, Kenneth S.; Røysamb, Espen; Czajkowski, Nikolai; Tambs, Kristian; Reichborn-Kjennerud, Ted

2012-01-01

One of the main controversies with regard to depressive personality disorder (DPD) concerns the co-occurrence with the established DSM-IV personality disorders (PDs). The main aim of this study was to examine to what extent DPD and the DSM-IV PDs share genetic and environmental risk factors, using multivariate twin modeling. The DSM-IV Structured Interview for Personality was applied to 2,794 young adult twins. Paranoid PD from Cluster A, borderline PD from Cluster B, and all three PDs from Cluster C were independently and significantly associated with DPD in multiple regression analysis. The genetic correlations between DPD and the other PDs were strong (.53–.83), while the environmental correlations were moderate (.36–.40). Close to 50% of the total variance in DPD was disorder specific. However, only 5% was due to disorder-specific genetic factors, indicating that a substantial part of the genetic vulnerability to DPD also increases the vulnerability to other PDs. PMID:22686231

Genetic and environmental contributions to the co-occurrence of depressive personality disorder and DSM-IV personality disorders.

PubMed

Ørstavik, Ragnhild E; Kendler, Kenneth S; Røysamb, Espen; Czajkowski, Nikolai; Tambs, Kristian; Reichborn-Kjennerud, Ted

2012-06-01

One of the main controversies with regard to depressive personality disorder (DPD) concerns the co-occurrence with the established DSM-IV personality disorders (PDs). The main aim of this study was to examine to what extent DPD and the DSM-IV PDs share genetic and environmental risk factors, using multivariate twin modeling. The DSM-IV Structured Interview for Personality was applied to 2,794 young adult twins. Paranoid PD from Cluster A, borderline PD from Cluster B, and all three PDs from Cluster C were independently and significantly associated with DPD in multiple regression analysis. The genetic correlations between DPD and the other PDs were strong (.53-.83), while the environmental correlations were moderate (.36-.40). Close to 50% of the total variance in DPD was disorder specific. However, only 5% was due to disorder-specific genetic factors, indicating that a substantial part of the genetic vulnerability to DPD also increases the vulnerability to other PDs.

Genetic data simulators and their applications: an overview

PubMed Central

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

2016-01-01

Computer simulations have played an indispensable role in the development and application of statistical models and methods for genetic studies across multiple disciplines. The need to simulate complex evolutionary scenarios and pseudo-datasets for various studies has fueled the development of dozens of computer programs with varying reliability, performance, and application areas. To help researchers compare and choose the most appropriate simulators for their studies, we have created the Genetic Simulation Resources (GSR) website, which allows authors of simulation software to register their applications and describe them with more than 160 defined attributes. This article summarizes the properties of 93 simulators currently registered at GSR and provides an overview of the development and applications of genetic simulators. Unlike other review articles that address technical issues or compare simulators for particular application areas, we focus on software development, maintenance, and features of simulators, often from a historical perspective. Publications that cite these simulators are used to summarize both the applications of genetic simulations and the utilization of simulators. PMID:25504286

Modelling the dispersal of the two main hosts of the raccoon rabies variant in heterogeneous environments with landscape genetics.

PubMed

Rioux Paquette, Sébastien; Talbot, Benoit; Garant, Dany; Mainguy, Julien; Pelletier, Fanie

2014-08-01

Predicting the geographic spread of wildlife epidemics requires knowledge about the movement patterns of disease hosts or vectors. The field of landscape genetics provides valuable approaches to study dispersal indirectly, which in turn may be used to understand patterns of disease spread. Here, we applied landscape genetic analyses and spatially explicit models to identify the potential path of raccoon rabies spread in a mesocarnivore community. We used relatedness estimates derived from microsatellite genotypes of raccoons and striped skunks to investigate their dispersal patterns in a heterogeneous landscape composed predominantly of agricultural, forested and residential areas. Samples were collected in an area covering 22 000 km(2) in southern Québec, where the raccoon rabies variant (RRV) was first detected in 2006. Multiple regressions on distance matrices revealed that genetic distance among male raccoons was strictly a function of geographic distance, while dispersal in female raccoons was significantly reduced by the presence of agricultural fields. In skunks, our results suggested that dispersal is increased in edge habitats between fields and forest fragments in both males and females. Resistance modelling allowed us to identify likely dispersal corridors used by these two rabies hosts, which may prove especially helpful for surveillance and control (e.g. oral vaccination) activities.

Slow Perceptual Processing at the Core of Developmental Dyslexia: A Parameter-Based Assessment of Visual Attention

ERIC Educational Resources Information Center

Stenneken, Prisca; Egetemeir, Johanna; Schulte-Korne, Gerd; Muller, Hermann J.; Schneider, Werner X.; Finke, Kathrin

2011-01-01

The cognitive causes as well as the neurological and genetic basis of developmental dyslexia, a complex disorder of written language acquisition, are intensely discussed with regard to multiple-deficit models. Accumulating evidence has revealed dyslexics' impairments in a variety of tasks requiring visual attention. The heterogeneity of these…

Harnessing Genetic Variation in Leaf Angle to Increase Productivity of Sorghum bicolor

PubMed Central

Truong, Sandra K.; McCormick, Ryan F.; Rooney, William L.; Mullet, John E.

2015-01-01

The efficiency with which a plant intercepts solar radiation is determined primarily by its architecture. Understanding the genetic regulation of plant architecture and how changes in architecture affect performance can be used to improve plant productivity. Leaf inclination angle, the angle at which a leaf emerges with respect to the stem, is a feature of plant architecture that influences how a plant canopy intercepts solar radiation. Here we identify extensive genetic variation for leaf inclination angle in the crop plant Sorghum bicolor, a C4 grass species used for the production of grain, forage, and bioenergy. Multiple genetic loci that regulate leaf inclination angle were identified in recombinant inbred line populations of grain and bioenergy sorghum. Alleles of sorghum dwarf-3, a gene encoding a P-glycoprotein involved in polar auxin transport, are shown to change leaf inclination angle by up to 34° (0.59 rad). The impact of heritable variation in leaf inclination angle on light interception in sorghum canopies was assessed using functional-structural plant models and field experiments. Smaller leaf inclination angles caused solar radiation to penetrate deeper into the canopy, and the resulting redistribution of light is predicted to increase the biomass yield potential of bioenergy sorghum by at least 3%. These results show that sorghum leaf angle is a heritable trait regulated by multiple loci and that genetic variation in leaf angle can be used to modify plant architecture to improve sorghum crop performance. PMID:26323882

Inbreeding Ratio and Genetic Relationships among Strains of the Western Clawed Frog, Xenopus tropicalis

PubMed Central

Igawa, Takeshi; Watanabe, Ai; Suzuki, Atsushi; Kashiwagi, Akihiko; Kashiwagi, Keiko; Noble, Anna; Guille, Matt; Simpson, David E.; Horb, Marko E.; Fujii, Tamotsu; Sumida, Masayuki

2015-01-01

The Western clawed frog, Xenopus tropicalis, is a highly promising model amphibian, especially in developmental and physiological research, and as a tool for understanding disease. It was originally found in the West African rainforest belt, and was introduced to the research community in the 1990s. The major strains thus far known include the Nigerian and Ivory Coast strains. However, due to its short history as an experimental animal, the genetic relationship among the various strains has not yet been clarified, and establishment of inbred strains has not yet been achieved. Since 2003 the Institute for Amphibian Biology (IAB), Hiroshima University has maintained stocks of multiple X. tropicalis strains and conducted consecutive breeding as part of the National BioResource Project. In the present study we investigated the inbreeding ratio and genetic relationship of four inbred strains at IAB, as well as stocks from other institutions, using highly polymorphic microsatellite markers and mitochondrial haplotypes. Our results show successive reduction of heterozygosity in the genome of the IAB inbred strains. The Ivory Coast strains clearly differed from the Nigerian strains genetically, and three subgroups were identified within both the Nigerian and Ivory Coast strains. It is noteworthy that the Ivory Coast strains have an evolutionary divergent genetic background. Our results serve as a guide for the most effective use of X. tropicalis strains, and the long-term maintenance of multiple strains will contribute to further research efforts. PMID:26222540

Multiple-trait random regression models for the estimation of genetic parameters for milk, fat, and protein yield in buffaloes.

PubMed

Borquis, Rusbel Raul Aspilcueta; Neto, Francisco Ribeiro de Araujo; Baldi, Fernando; Hurtado-Lugo, Naudin; de Camargo, Gregório M F; Muñoz-Berrocal, Milthon; Tonhati, Humberto

2013-09-01

In this study, genetic parameters for test-day milk, fat, and protein yield were estimated for the first lactation. The data analyzed consisted of 1,433 first lactations of Murrah buffaloes, daughters of 113 sires from 12 herds in the state of São Paulo, Brazil, with calvings from 1985 to 2007. Ten-month classes of lactation days were considered for the test-day yields. The (co)variance components for the 3 traits were estimated using the regression analyses by Bayesian inference applying an animal model by Gibbs sampling. The contemporary groups were defined as herd-year-month of the test day. In the model, the random effects were additive genetic, permanent environment, and residual. The fixed effects were contemporary group and number of milkings (1 or 2), the linear and quadratic effects of the covariable age of the buffalo at calving, as well as the mean lactation curve of the population, which was modeled by orthogonal Legendre polynomials of fourth order. The random effects for the traits studied were modeled by Legendre polynomials of third and fourth order for additive genetic and permanent environment, respectively, the residual variances were modeled considering 4 residual classes. The heritability estimates for the traits were moderate (from 0.21-0.38), with higher estimates in the intermediate lactation phase. The genetic correlation estimates within and among the traits varied from 0.05 to 0.99. The results indicate that the selection for any trait test day will result in an indirect genetic gain for milk, fat, and protein yield in all periods of the lactation curve. The accuracy associated with estimated breeding values obtained using multi-trait random regression was slightly higher (around 8%) compared with single-trait random regression. This difference may be because to the greater amount of information available per animal. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

The BioGRID Interaction Database: 2011 update

PubMed Central

Stark, Chris; Breitkreutz, Bobby-Joe; Chatr-aryamontri, Andrew; Boucher, Lorrie; Oughtred, Rose; Livstone, Michael S.; Nixon, Julie; Van Auken, Kimberly; Wang, Xiaodong; Shi, Xiaoqi; Reguly, Teresa; Rust, Jennifer M.; Winter, Andrew; Dolinski, Kara; Tyers, Mike

2011-01-01

The Biological General Repository for Interaction Datasets (BioGRID) is a public database that archives and disseminates genetic and protein interaction data from model organisms and humans (http://www.thebiogrid.org). BioGRID currently holds 347 966 interactions (170 162 genetic, 177 804 protein) curated from both high-throughput data sets and individual focused studies, as derived from over 23 000 publications in the primary literature. Complete coverage of the entire literature is maintained for budding yeast (Saccharomyces cerevisiae), fission yeast (Schizosaccharomyces pombe) and thale cress (Arabidopsis thaliana), and efforts to expand curation across multiple metazoan species are underway. The BioGRID houses 48 831 human protein interactions that have been curated from 10 247 publications. Current curation drives are focused on particular areas of biology to enable insights into conserved networks and pathways that are relevant to human health. The BioGRID 3.0 web interface contains new search and display features that enable rapid queries across multiple data types and sources. An automated Interaction Management System (IMS) is used to prioritize, coordinate and track curation across international sites and projects. BioGRID provides interaction data to several model organism databases, resources such as Entrez-Gene and other interaction meta-databases. The entire BioGRID 3.0 data collection may be downloaded in multiple file formats, including PSI MI XML. Source code for BioGRID 3.0 is freely available without any restrictions. PMID:21071413

A subset of skin tumor modifier loci determines survival time of tumor-bearing mice

PubMed Central

Nagase, Hiroki; Mao, Jian-Hua; Balmain, Allan

1999-01-01

Studies of mouse models of human cancer have established the existence of multiple tumor modifiers that influence parameters of cancer susceptibility such as tumor multiplicity, tumor size, or the probability of malignant progression. We have carried out an analysis of skin tumor susceptibility in interspecific Mus musculus/Mus spretus hybrid mice and have identified another seven loci showing either significant (six loci) or suggestive (one locus) linkage to tumor susceptibility or resistance. A specific search was carried out for skin tumor modifier loci associated with time of survival after development of a malignant tumor. A combination of resistance alleles at three markers [D6Mit15 (Skts12), D7Mit12 (Skts2), and D17Mit7 (Skts10)], all of which are close to or the same as loci associated with carcinoma incidence and/or papilloma multiplicity, is significantly associated with increased survival of mice with carcinomas, whereas the reverse combination of susceptibility alleles is significantly linked to early mortality caused by rapid carcinoma growth (χ2 = 25.22; P = 5.1 × 10−8). These data indicate that host genetic factors may be used to predict carcinoma growth rate and/or survival of individual backcross mice exposed to the same carcinogenic stimulus and suggest that mouse models may provide an approach to the identification of genetic modifiers of cancer survival in humans. PMID:10611333

Regional Management Units for Marine Turtles: A Novel Framework for Prioritizing Conservation and Research across Multiple Scales

PubMed Central

Wallace, Bryan P.; DiMatteo, Andrew D.; Hurley, Brendan J.; Finkbeiner, Elena M.; Bolten, Alan B.; Chaloupka, Milani Y.; Hutchinson, Brian J.; Abreu-Grobois, F. Alberto; Amorocho, Diego; Bjorndal, Karen A.; Bourjea, Jerome; Bowen, Brian W.; Dueñas, Raquel Briseño; Casale, Paolo; Choudhury, B. C.; Costa, Alice; Dutton, Peter H.; Fallabrino, Alejandro; Girard, Alexandre; Girondot, Marc; Godfrey, Matthew H.; Hamann, Mark; López-Mendilaharsu, Milagros; Marcovaldi, Maria Angela; Mortimer, Jeanne A.; Musick, John A.; Nel, Ronel; Pilcher, Nicolas J.; Seminoff, Jeffrey A.; Troëng, Sebastian; Witherington, Blair; Mast, Roderic B.

2010-01-01

Background Resolving threats to widely distributed marine megafauna requires definition of the geographic distributions of both the threats as well as the population unit(s) of interest. In turn, because individual threats can operate on varying spatial scales, their impacts can affect different segments of a population of the same species. Therefore, integration of multiple tools and techniques — including site-based monitoring, genetic analyses, mark-recapture studies and telemetry — can facilitate robust definitions of population segments at multiple biological and spatial scales to address different management and research challenges. Methodology/Principal Findings To address these issues for marine turtles, we collated all available studies on marine turtle biogeography, including nesting sites, population abundances and trends, population genetics, and satellite telemetry. We georeferenced this information to generate separate layers for nesting sites, genetic stocks, and core distributions of population segments of all marine turtle species. We then spatially integrated this information from fine- to coarse-spatial scales to develop nested envelope models, or Regional Management Units (RMUs), for marine turtles globally. Conclusions/Significance The RMU framework is a solution to the challenge of how to organize marine turtles into units of protection above the level of nesting populations, but below the level of species, within regional entities that might be on independent evolutionary trajectories. Among many potential applications, RMUs provide a framework for identifying data gaps, assessing high diversity areas for multiple species and genetic stocks, and evaluating conservation status of marine turtles. Furthermore, RMUs allow for identification of geographic barriers to gene flow, and can provide valuable guidance to marine spatial planning initiatives that integrate spatial distributions of protected species and human activities. In addition, the RMU framework — including maps and supporting metadata — will be an iterative, user-driven tool made publicly available in an online application for comments, improvements, download and analysis. PMID:21253007

Polygenic sex determination in the cichlid fish Astatotilapia burtoni.

PubMed

Roberts, Natalie B; Juntti, Scott A; Coyle, Kaitlin P; Dumont, Bethany L; Stanley, M Kaitlyn; Ryan, Allyson Q; Fernald, Russell D; Roberts, Reade B

2016-10-26

The East African riverine cichlid species Astatotilapia burtoni serves as an important laboratory model for sexually dimorphic physiology and behavior, and also serves as an outgroup species for the explosive adaptive radiations of cichlid species in Lake Malawi and Lake Victoria. An astounding diversity of genetic sex determination systems have been revealed within the adaptive radiation of East African cichlids thus far, including polygenic sex determination systems involving the epistatic interaction of multiple, independently segregating sex determination alleles. However, sex determination has remained unmapped in A. burtoni. Here we present mapping results supporting the presence of multiple, novel sex determination alleles, and thus the presence of polygenic sex determination in A. burtoni. Using mapping in small families in conjunction with restriction-site associated DNA sequencing strategies, we identify associations with sex at loci on linkage group 13 and linkage group 5-14. Inheritance patterns support an XY sex determination system on linkage group 5-14 (a chromosome fusion relative to other cichlids studied), and an XYW system on linkage group 13, and these associations are replicated in multiple families. Additionally, combining our genetic data with comparative genomic analysis identifies another fusion that is unassociated with sex, with linkage group 8-24 and linkage group 16-21 fused in A. burtoni relative to other East African cichlid species. We identify genetic signals supporting the presence of three previously unidentified sex determination alleles at two loci in the species A. burtoni, strongly supporting the presence of polygenic sex determination system in the species. These results provide a foundation for future mapping of multiple sex determination genes and their interactions. A better understanding of sex determination in A. burtoni provides important context for their use in behavioral studies, as well as studies of the evolution of genetic sex determination and sexual conflicts in East African cichlids.

Transient SNAIL1 expression is necessary for metastatic competence in breast cancer.

PubMed

Tran, Hung D; Luitel, Krishna; Kim, Michael; Zhang, Kun; Longmore, Gregory D; Tran, David D

2014-11-01

SNAIL1 has been suggested to regulate breast cancer metastasis based on analyses of human breast tumor transcriptomes and experiments using cancer cell lines and xenografts. However, in vivo genetic experimental support for a role for SNAIL1 in breast cancer metastasis that develops in an immunocompetent tumor microenvironment has not been determined. To address this question, we created a genetic SNAIL1 model by coupling an endogenous SNAIL1 reporter with an inducible SNAIL1 transgene. Using multiple genetic models of breast cancer, we demonstrated that endogenous SNAIL1 expression was restricted to primary tumors that ultimately disseminate. SNAIL1 gene deletion either during the premalignant phase or after primary tumors have reached a palpable size blunted metastasis, indicating that late metastasis was the main driver of metastasis and that this was dependent on SNAIL1. Importantly, SNAIL1 expression during breast cancer metastasis was transient and forced transient, but not continuous. SNAIL1 expression in breast tumors was sufficient to increase metastasis. ©2014 American Association for Cancer Research.

Generation of intervention strategy for a genetic regulatory network represented by a family of Markov Chains.

PubMed

Berlow, Noah; Pal, Ranadip

2011-01-01

Genetic Regulatory Networks (GRNs) are frequently modeled as Markov Chains providing the transition probabilities of moving from one state of the network to another. The inverse problem of inference of the Markov Chain from noisy and limited experimental data is an ill posed problem and often generates multiple model possibilities instead of a unique one. In this article, we address the issue of intervention in a genetic regulatory network represented by a family of Markov Chains. The purpose of intervention is to alter the steady state probability distribution of the GRN as the steady states are considered to be representative of the phenotypes. We consider robust stationary control policies with best expected behavior. The extreme computational complexity involved in search of robust stationary control policies is mitigated by using a sequential approach to control policy generation and utilizing computationally efficient techniques for updating the stationary probability distribution of a Markov chain following a rank one perturbation.

A standard-enabled workflow for synthetic biology.

PubMed

Myers, Chris J; Beal, Jacob; Gorochowski, Thomas E; Kuwahara, Hiroyuki; Madsen, Curtis; McLaughlin, James Alastair; Mısırlı, Göksel; Nguyen, Tramy; Oberortner, Ernst; Samineni, Meher; Wipat, Anil; Zhang, Michael; Zundel, Zach

2017-06-15

A synthetic biology workflow is composed of data repositories that provide information about genetic parts, sequence-level design tools to compose these parts into circuits, visualization tools to depict these designs, genetic design tools to select parts to create systems, and modeling and simulation tools to evaluate alternative design choices. Data standards enable the ready exchange of information within such a workflow, allowing repositories and tools to be connected from a diversity of sources. The present paper describes one such workflow that utilizes, among others, the Synthetic Biology Open Language (SBOL) to describe genetic designs, the Systems Biology Markup Language to model these designs, and SBOL Visual to visualize these designs. We describe how a standard-enabled workflow can be used to produce types of design information, including multiple repositories and software tools exchanging information using a variety of data standards. Recently, the ACS Synthetic Biology journal has recommended the use of SBOL in their publications. © 2017 The Author(s); published by Portland Press Limited on behalf of the Biochemical Society.

Powerful multilocus tests of genetic association in the presence of gene-gene and gene-environment interactions.

PubMed

Chatterjee, Nilanjan; Kalaylioglu, Zeynep; Moslehi, Roxana; Peters, Ulrike; Wacholder, Sholom

2006-12-01

In modern genetic epidemiology studies, the association between the disease and a genomic region, such as a candidate gene, is often investigated using multiple SNPs. We propose a multilocus test of genetic association that can account for genetic effects that might be modified by variants in other genes or by environmental factors. We consider use of the venerable and parsimonious Tukey's 1-degree-of-freedom model of interaction, which is natural when individual SNPs within a gene are associated with disease through a common biological mechanism; in contrast, many standard regression models are designed as if each SNP has unique functional significance. On the basis of Tukey's model, we propose a novel but computationally simple generalized test of association that can simultaneously capture both the main effects of the variants within a genomic region and their interactions with the variants in another region or with an environmental exposure. We compared performance of our method with that of two standard tests of association, one ignoring gene-gene/gene-environment interactions and the other based on a saturated model of interactions. We demonstrate major power advantages of our method both in analysis of data from a case-control study of the association between colorectal adenoma and DNA variants in the NAT2 genomic region, which are well known to be related to a common biological phenotype, and under different models of gene-gene interactions with use of simulated data.

Genetic relationships between feral cattle from Chirikof Island, Alaska and other breeds.

PubMed

MacNeil, M D; Cronin, M A; Blackburn, H D; Richards, C M; Lockwood, D R; Alexander, L J

2007-06-01

The origin of cattle on Chirikof Island, off the coast of Alaska, is not well documented. We assessed genetic differentiation of cattle isolated on Chirikof Island from several breeds commonly used for commercial production in North America including breeds popularly believed to have contributed to the Chirikof Island population. A set of 34 microsatellite loci was used to genotype Angus, Charolais, Hereford, Highland, Limousin, Red Angus, Salers, Shorthorn, Simmental, Tarentaise and Texas Longhorn cattle sampled from North America and the Chirikof Island population. Resulting F(ST) statistics for these loci ranged from 0.06 to 0.22 and on average, 14% of total genetic variation was between breeds. Whether population structure was modelled as a bifurcating tree or genetic network, Chirikof Island cattle appeared to be unique and strongly differentiated relative to the other breeds that were sampled. Bayesian clustering for multiple-locus assignment to genetic groups indicated low levels of admixture in the Chirikof Island population. Thus, the Chirikof Island population may be a novel genetic resource of some importance for conservation and industry.

Isolation with Migration Models for More Than Two Populations

PubMed Central

Hey, Jody

2010-01-01

A method for studying the divergence of multiple closely related populations is described and assessed. The approach of Hey and Nielsen (2007, Integration within the Felsenstein equation for improved Markov chain Monte Carlo methods in population genetics. Proc Natl Acad Sci USA. 104:2785–2790) for fitting an isolation-with-migration model was extended to the case of multiple populations with a known phylogeny. Analysis of simulated data sets reveals the kinds of history that are accessible with a multipopulation analysis. Necessarily, processes associated with older time periods in a phylogeny are more difficult to estimate; and histories with high levels of gene flow are particularly difficult with more than two populations. However, for histories with modest levels of gene flow, or for very large data sets, it is possible to study large complex divergence problems that involve multiple closely related populations or species. PMID:19955477

Isolation with migration models for more than two populations.

PubMed

Hey, Jody

2010-04-01

A method for studying the divergence of multiple closely related populations is described and assessed. The approach of Hey and Nielsen (2007, Integration within the Felsenstein equation for improved Markov chain Monte Carlo methods in population genetics. Proc Natl Acad Sci USA. 104:2785-2790) for fitting an isolation-with-migration model was extended to the case of multiple populations with a known phylogeny. Analysis of simulated data sets reveals the kinds of history that are accessible with a multipopulation analysis. Necessarily, processes associated with older time periods in a phylogeny are more difficult to estimate; and histories with high levels of gene flow are particularly difficult with more than two populations. However, for histories with modest levels of gene flow, or for very large data sets, it is possible to study large complex divergence problems that involve multiple closely related populations or species.

Genetics and the investigation of developmental delay/intellectual disability.

PubMed

Srour, Myriam; Shevell, Michael

2014-04-01

Global developmental delay and intellectual disabilities are common reasons for diagnostic assessment by paediatricians. There are a multiplicity of possible causes many of which have genetic, management and treatment implications for the child and family. Genetic causes are estimated to be responsible for approximately a quarter to one-half of identified cases. The multiplicity of individually rare genetic causes challenges the practitioner with respect to the selection of diagnostic tests and accurate diagnosis. To assist the practitioner practice guidelines have been formulated and these are reviewed and summarised in this particular article.

Estimating black bear population density and genetic diversity at Tensas River, Louisiana using microsatellite DNA markers

USGS Publications Warehouse

Boersen, Mark R.; Clark, Joseph D.; King, Tim L.

2003-01-01

The Recovery Plan for the federally threatened Louisiana black bear (Ursus americanus luteolus) mandates that remnant populations be estimated and monitored. In 1999 we obtained genetic material with barbed-wire hair traps to estimate bear population size and genetic diversity at the 329-km2 Tensas River Tract, Louisiana. We constructed and monitored 122 hair traps, which produced 1,939 hair samples. Of those, we randomly selected 116 subsamples for genetic analysis and used up to 12 microsatellite DNA markers to obtain multilocus genotypes for 58 individuals. We used Program CAPTURE to compute estimates of population size using multiple mark-recapture models. The area of study was almost entirely circumscribed by agricultural land, thus the population was geographically closed. Also, study-area boundaries were biologically discreet, enabling us to accurately estimate population density. Using model Chao Mh to account for possible effects of individual heterogeneity in capture probabilities, we estimated the population size to be 119 (SE=29.4) bears, or 0.36 bears/km2. We were forced to examine a substantial number of loci to differentiate between some individuals because of low genetic variation. Despite the probable introduction of genes from Minnesota bears in the 1960s, the isolated population at Tensas exhibited characteristics consistent with inbreeding and genetic drift. Consequently, the effective population size at Tensas may be as few as 32, which warrants continued monitoring or possibly genetic augmentation.

Modeling human diseases: an education in interactions and interdisciplinary approaches.

PubMed

Zon, Leonard

2016-06-01

Traditionally, most investigators in the biomedical arena exploit one model system in the course of their careers. Occasionally, an investigator will switch models. The selection of a suitable model system is a crucial step in research design. Factors to consider include the accuracy of the model as a reflection of the human disease under investigation, the numbers of animals needed and ease of husbandry, its physiology and developmental biology, and the ability to apply genetics and harness the model for drug discovery. In my lab, we have primarily used the zebrafish but combined it with other animal models and provided a framework for others to consider the application of developmental biology for therapeutic discovery. Our interdisciplinary approach has led to many insights into human diseases and to the advancement of candidate drugs to clinical trials. Here, I draw on my experiences to highlight the importance of combining multiple models, establishing infrastructure and genetic tools, forming collaborations, and interfacing with the medical community for successful translation of basic findings to the clinic. © 2016. Published by The Company of Biologists Ltd.

Habitat continuity and stepping-stone oceanographic distances explain population genetic connectivity of the brown alga Cystoseira amentacea.

PubMed

Buonomo, Roberto; Assis, Jorge; Fernandes, Francisco; Engelen, Aschwin H; Airoldi, Laura; Serrão, Ester A

2017-02-01

Effective predictive and management approaches for species occurring in a metapopulation structure require good understanding of interpopulation connectivity. In this study, we ask whether population genetic structure of marine species with fragmented distributions can be predicted by stepping-stone oceanographic transport and habitat continuity, using as model an ecosystem-structuring brown alga, Cystoseira amentacea var. stricta. To answer this question, we analysed the genetic structure and estimated the connectivity of populations along discontinuous rocky habitat patches in southern Italy, using microsatellite markers at multiple scales. In addition, we modelled the effect of rocky habitat continuity and ocean circulation on gene flow by simulating Lagrangian particle dispersal based on ocean surface currents allowing multigenerational stepping-stone dynamics. Populations were highly differentiated, at scales from few metres up to thousands of kilometres. The best possible model fit to explain the genetic results combined current direction, rocky habitat extension and distance along the coast among rocky sites. We conclude that a combination of variable suitable habitat and oceanographic transport is a useful predictor of genetic structure. This relationship provides insight into the mechanisms of dispersal and the role of life-history traits. Our results highlight the importance of spatially explicit modelling of stepping-stone dynamics and oceanographic directional transport coupled with habitat suitability, to better describe and predict marine population structure and differentiation. This study also suggests the appropriate spatial scales for the conservation, restoration and management of species that are increasingly affected by habitat modifications. © 2016 John Wiley & Sons Ltd.

Modeling Structure-Function Relationships in Synthetic DNA Sequences using Attribute Grammars

PubMed Central

Cai, Yizhi; Lux, Matthew W.; Adam, Laura; Peccoud, Jean

2009-01-01

Recognizing that certain biological functions can be associated with specific DNA sequences has led various fields of biology to adopt the notion of the genetic part. This concept provides a finer level of granularity than the traditional notion of the gene. However, a method of formally relating how a set of parts relates to a function has not yet emerged. Synthetic biology both demands such a formalism and provides an ideal setting for testing hypotheses about relationships between DNA sequences and phenotypes beyond the gene-centric methods used in genetics. Attribute grammars are used in computer science to translate the text of a program source code into the computational operations it represents. By associating attributes with parts, modifying the value of these attributes using rules that describe the structure of DNA sequences, and using a multi-pass compilation process, it is possible to translate DNA sequences into molecular interaction network models. These capabilities are illustrated by simple example grammars expressing how gene expression rates are dependent upon single or multiple parts. The translation process is validated by systematically generating, translating, and simulating the phenotype of all the sequences in the design space generated by a small library of genetic parts. Attribute grammars represent a flexible framework connecting parts with models of biological function. They will be instrumental for building mathematical models of libraries of genetic constructs synthesized to characterize the function of genetic parts. This formalism is also expected to provide a solid foundation for the development of computer assisted design applications for synthetic biology. PMID:19816554

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

Generalization and dilution of association results from European GWAS in populations of non-European ancestry: the PAGE study.

PubMed

Carlson, Christopher S; Matise, Tara C; North, Kari E; Haiman, Christopher A; Fesinmeyer, Megan D; Buyske, Steven; Schumacher, Fredrick R; Peters, Ulrike; Franceschini, Nora; Ritchie, Marylyn D; Duggan, David J; Spencer, Kylee L; Dumitrescu, Logan; Eaton, Charles B; Thomas, Fridtjof; Young, Alicia; Carty, Cara; Heiss, Gerardo; Le Marchand, Loic; Crawford, Dana C; Hindorff, Lucia A; Kooperberg, Charles L

2013-09-01

The vast majority of genome-wide association study (GWAS) findings reported to date are from populations with European Ancestry (EA), and it is not yet clear how broadly the genetic associations described will generalize to populations of diverse ancestry. The Population Architecture Using Genomics and Epidemiology (PAGE) study is a consortium of multi-ancestry, population-based studies formed with the objective of refining our understanding of the genetic architecture of common traits emerging from GWAS. In the present analysis of five common diseases and traits, including body mass index, type 2 diabetes, and lipid levels, we compare direction and magnitude of effects for GWAS-identified variants in multiple non-EA populations against EA findings. We demonstrate that, in all populations analyzed, a significant majority of GWAS-identified variants have allelic associations in the same direction as in EA, with none showing a statistically significant effect in the opposite direction, after adjustment for multiple testing. However, 25% of tagSNPs identified in EA GWAS have significantly different effect sizes in at least one non-EA population, and these differential effects were most frequent in African Americans where all differential effects were diluted toward the null. We demonstrate that differential LD between tagSNPs and functional variants within populations contributes significantly to dilute effect sizes in this population. Although most variants identified from GWAS in EA populations generalize to all non-EA populations assessed, genetic models derived from GWAS findings in EA may generate spurious results in non-EA populations due to differential effect sizes. Regardless of the origin of the differential effects, caution should be exercised in applying any genetic risk prediction model based on tagSNPs outside of the ancestry group in which it was derived. Models based directly on functional variation may generalize more robustly, but the identification of functional variants remains challenging.

Generalization and Dilution of Association Results from European GWAS in Populations of Non-European Ancestry: The PAGE Study

PubMed Central

Carlson, Christopher S.; Matise, Tara C.; North, Kari E.; Haiman, Christopher A.; Fesinmeyer, Megan D.; Buyske, Steven; Schumacher, Fredrick R.; Peters, Ulrike; Franceschini, Nora; Ritchie, Marylyn D.; Duggan, David J.; Spencer, Kylee L.; Dumitrescu, Logan; Eaton, Charles B.; Thomas, Fridtjof; Young, Alicia; Carty, Cara; Heiss, Gerardo; Le Marchand, Loic; Crawford, Dana C.; Hindorff, Lucia A.; Kooperberg, Charles L.

2013-01-01

The vast majority of genome-wide association study (GWAS) findings reported to date are from populations with European Ancestry (EA), and it is not yet clear how broadly the genetic associations described will generalize to populations of diverse ancestry. The Population Architecture Using Genomics and Epidemiology (PAGE) study is a consortium of multi-ancestry, population-based studies formed with the objective of refining our understanding of the genetic architecture of common traits emerging from GWAS. In the present analysis of five common diseases and traits, including body mass index, type 2 diabetes, and lipid levels, we compare direction and magnitude of effects for GWAS-identified variants in multiple non-EA populations against EA findings. We demonstrate that, in all populations analyzed, a significant majority of GWAS-identified variants have allelic associations in the same direction as in EA, with none showing a statistically significant effect in the opposite direction, after adjustment for multiple testing. However, 25% of tagSNPs identified in EA GWAS have significantly different effect sizes in at least one non-EA population, and these differential effects were most frequent in African Americans where all differential effects were diluted toward the null. We demonstrate that differential LD between tagSNPs and functional variants within populations contributes significantly to dilute effect sizes in this population. Although most variants identified from GWAS in EA populations generalize to all non-EA populations assessed, genetic models derived from GWAS findings in EA may generate spurious results in non-EA populations due to differential effect sizes. Regardless of the origin of the differential effects, caution should be exercised in applying any genetic risk prediction model based on tagSNPs outside of the ancestry group in which it was derived. Models based directly on functional variation may generalize more robustly, but the identification of functional variants remains challenging. PMID:24068893

Genetic improvement of total milk yield and total lactation persistency of the first three lactations in dairy cattle.

PubMed

Togashi, K; Lin, C Y

2008-07-01

The objective of this study was to compare 6 selection criteria in terms of 3-parity total milk yield and 9 selection criteria in terms of total net merit (H) comprising 3-parity total milk yield and total lactation persistency. The 6 selection criteria compared were as follows: first-parity milk estimated breeding value (EBV; M1), first 2-parity milk EBV (M2), first 3-parity milk EBV (M3), first-parity eigen index (EI(1)), first 2-parity eigen index (EI(2)), and first 3-parity eigen index (EI(3)). The 9 selection criteria compared in terms of H were M1, M2, M3, EI(1), EI(2), EI(3), and first-parity, first 2-parity, and first 3-parity selection indices (I(1), I(2), and I(3), respectively). In terms of total milk yield, selection on M3 or EI(3) achieved the greatest genetic response, whereas selection on EI(1) produced the largest genetic progress per day. In terms of total net merit, selection on I(3) brought the largest response, whereas selection EI(1) yielded the greatest genetic progress per day. A multiple-lactation random regression test-day model simultaneously yields the EBV of the 3 lactations for all animals included in the analysis even though the younger animals do not have the opportunity to complete the first 3 lactations. It is important to use the first 3 lactation EBV for selection decision rather than only the first lactation EBV in spite of the fact that the first-parity selection criteria achieved a faster genetic progress per day than the 3-parity selection criteria. Under a multiple-lactation random regression animal model analysis, the use of the first 3 lactation EBV for selection decision does not prolong the generation interval as compared with the use of only the first lactation EBV. Thus, it is justified to compare genetic response on a lifetime basis rather than on a per-day basis. The results suggest the use of M3 or EI(3) for genetic improvement of total milk yield and the use of I(3) for genetic improvement of total net merit H. Although this study deals with selection for 3-parity milk production, the same principle applies to selection for lifetime milk production.

Effective search for stable segregation configurations at grain boundaries with data-mining techniques

NASA Astrophysics Data System (ADS)

Kiyohara, Shin; Mizoguchi, Teruyasu

2018-03-01

Grain boundary segregation of dopants plays a crucial role in materials properties. To investigate the dopant segregation behavior at the grain boundary, an enormous number of combinations have to be considered in the segregation of multiple dopants at the complex grain boundary structures. Here, two data mining techniques, the random-forests regression and the genetic algorithm, were applied to determine stable segregation sites at grain boundaries efficiently. Using the random-forests method, a predictive model was constructed from 2% of the segregation configurations and it has been shown that this model could determine the stable segregation configurations. Furthermore, the genetic algorithm also successfully determined the most stable segregation configuration with great efficiency. We demonstrate that these approaches are quite effective to investigate the dopant segregation behaviors at grain boundaries.

Symbiont diversity may help coral reefs survive moderate climate change.

PubMed

Baskett, Marissa L; Gaines, Steven D; Nisbet, Roger M

2009-01-01

Given climate change, thermal stress-related mass coral-bleaching events present one of the greatest anthropogenic threats to coral reefs. While corals and their symbiotic algae may respond to future temperatures through genetic adaptation and shifts in community compositions, the climate may change too rapidly for coral response. To test this potential for response, here we develop a model of coral and symbiont ecological dynamics and symbiont evolutionary dynamics. Model results without variation in symbiont thermal tolerance predict coral reef collapse within decades under multiple future climate scenarios, consistent with previous threshold-based predictions. However, model results with genetic or community-level variation in symbiont thermal tolerance can predict coral reef persistence into the next century, provided low enough greenhouse gas emissions occur. Therefore, the level of greenhouse gas emissions will have a significant effect on the future of coral reefs, and accounting for biodiversity and biological dynamics is vital to estimating the size of this effect.

Kernel Machine SNP-set Testing under Multiple Candidate Kernels

PubMed Central

Wu, Michael C.; Maity, Arnab; Lee, Seunggeun; Simmons, Elizabeth M.; Harmon, Quaker E.; Lin, Xinyi; Engel, Stephanie M.; Molldrem, Jeffrey J.; Armistead, Paul M.

2013-01-01

Joint testing for the cumulative effect of multiple single nucleotide polymorphisms grouped on the basis of prior biological knowledge has become a popular and powerful strategy for the analysis of large scale genetic association studies. The kernel machine (KM) testing framework is a useful approach that has been proposed for testing associations between multiple genetic variants and many different types of complex traits by comparing pairwise similarity in phenotype between subjects to pairwise similarity in genotype, with similarity in genotype defined via a kernel function. An advantage of the KM framework is its flexibility: choosing different kernel functions allows for different assumptions concerning the underlying model and can allow for improved power. In practice, it is difficult to know which kernel to use a priori since this depends on the unknown underlying trait architecture and selecting the kernel which gives the lowest p-value can lead to inflated type I error. Therefore, we propose practical strategies for KM testing when multiple candidate kernels are present based on constructing composite kernels and based on efficient perturbation procedures. We demonstrate through simulations and real data applications that the procedures protect the type I error rate and can lead to substantially improved power over poor choices of kernels and only modest differences in power versus using the best candidate kernel. PMID:23471868

Evolution of female multiple mating: A quantitative model of the “sexually selected sperm” hypothesis

PubMed Central

Bocedi, Greta; Reid, Jane M

2015-01-01

Explaining the evolution and maintenance of polyandry remains a key challenge in evolutionary ecology. One appealing explanation is the sexually selected sperm (SSS) hypothesis, which proposes that polyandry evolves due to indirect selection stemming from positive genetic covariance with male fertilization efficiency, and hence with a male's success in postcopulatory competition for paternity. However, the SSS hypothesis relies on verbal analogy with “sexy-son” models explaining coevolution of female preferences for male displays, and explicit models that validate the basic SSS principle are surprisingly lacking. We developed analogous genetically explicit individual-based models describing the SSS and “sexy-son” processes. We show that the analogy between the two is only partly valid, such that the genetic correlation arising between polyandry and fertilization efficiency is generally smaller than that arising between preference and display, resulting in less reliable coevolution. Importantly, indirect selection was too weak to cause polyandry to evolve in the presence of negative direct selection. Negatively biased mutations on fertilization efficiency did not generally rescue runaway evolution of polyandry unless realized fertilization was highly skewed toward a single male, and coevolution was even weaker given random mating order effects on fertilization. Our models suggest that the SSS process is, on its own, unlikely to generally explain the evolution of polyandry. PMID:25330405

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

PubMed

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

2017-12-07

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

Convergent functional genomics in addiction research - a translational approach to study candidate genes and gene networks.

PubMed

Spanagel, Rainer

2013-01-01

Convergent functional genomics (CFG) is a translational methodology that integrates in a Bayesian fashion multiple lines of evidence from studies in human and animal models to get a better understanding of the genetics of a disease or pathological behavior. Here the integration of data sets that derive from forward genetics in animals and genetic association studies including genome wide association studies (GWAS) in humans is described for addictive behavior. The aim of forward genetics in animals and association studies in humans is to identify mutations (e.g. SNPs) that produce a certain phenotype; i.e. "from phenotype to genotype". Most powerful in terms of forward genetics is combined quantitative trait loci (QTL) analysis and gene expression profiling in recombinant inbreed rodent lines or genetically selected animals for a specific phenotype, e.g. high vs. low drug consumption. By Bayesian scoring genomic information from forward genetics in animals is then combined with human GWAS data on a similar addiction-relevant phenotype. This integrative approach generates a robust candidate gene list that has to be functionally validated by means of reverse genetics in animals; i.e. "from genotype to phenotype". It is proposed that studying addiction relevant phenotypes and endophenotypes by this CFG approach will allow a better determination of the genetics of addictive behavior.

Geographic Differences in Genetic Susceptibility to IgA Nephropathy: GWAS Replication Study and Geospatial Risk Analysis

PubMed Central

Kiryluk, Krzysztof; Li, Yifu; Sanna-Cherchi, Simone; Rohanizadegan, Mersedeh; Suzuki, Hitoshi; Eitner, Frank; Snyder, Holly J.; Choi, Murim; Hou, Ping; Scolari, Francesco; Izzi, Claudia; Gigante, Maddalena; Gesualdo, Loreto; Savoldi, Silvana; Amoroso, Antonio; Cusi, Daniele; Zamboli, Pasquale; Julian, Bruce A.; Novak, Jan; Wyatt, Robert J.; Mucha, Krzysztof; Perola, Markus; Kristiansson, Kati; Viktorin, Alexander; Magnusson, Patrik K.; Thorleifsson, Gudmar; Thorsteinsdottir, Unnur; Stefansson, Kari; Boland, Anne; Metzger, Marie; Thibaudin, Lise; Wanner, Christoph; Jager, Kitty J.; Goto, Shin; Maixnerova, Dita; Karnib, Hussein H.; Nagy, Judit; Panzer, Ulf; Xie, Jingyuan; Chen, Nan; Tesar, Vladimir; Narita, Ichiei; Berthoux, Francois; Floege, Jürgen; Stengel, Benedicte; Zhang, Hong; Lifton, Richard P.; Gharavi, Ali G.

2012-01-01

IgA nephropathy (IgAN), major cause of kidney failure worldwide, is common in Asians, moderately prevalent in Europeans, and rare in Africans. It is not known if these differences represent variation in genes, environment, or ascertainment. In a recent GWAS, we localized five IgAN susceptibility loci on Chr.6p21 (HLA-DQB1/DRB1, PSMB9/TAP1, and DPA1/DPB2 loci), Chr.1q32 (CFHR3/R1 locus), and Chr.22q12 (HORMAD2 locus). These IgAN loci are associated with risk of other immune-mediated disorders such as type I diabetes, multiple sclerosis, or inflammatory bowel disease. We tested association of these loci in eight new independent cohorts of Asian, European, and African-American ancestry (N = 4,789), followed by meta-analysis with risk-score modeling in 12 cohorts (N = 10,755) and geospatial analysis in 85 world populations. Four susceptibility loci robustly replicated and all five loci were genome-wide significant in the combined cohort (P = 5×10−32–3×10−10), with heterogeneity detected only at the PSMB9/TAP1 locus (I2 = 0.60). Conditional analyses identified two new independent risk alleles within the HLA-DQB1/DRB1 locus, defining multiple risk and protective haplotypes within this interval. We also detected a significant genetic interaction, whereby the odds ratio for the HORMAD2 protective allele was reversed in homozygotes for a CFHR3/R1 deletion (P = 2.5×10−4). A seven–SNP genetic risk score, which explained 4.7% of overall IgAN risk, increased sharply with Eastward and Northward distance from Africa (r = 0.30, P = 3×10−128). This model paralleled the known East–West gradient in disease risk. Moreover, the prediction of a South–North axis was confirmed by registry data showing that the prevalence of IgAN–attributable kidney failure is increased in Northern Europe, similar to multiple sclerosis and type I diabetes. Variation at IgAN susceptibility loci correlates with differences in disease prevalence among world populations. These findings inform genetic, biological, and epidemiological investigations of IgAN and permit cross-comparison with other complex traits that share genetic risk loci and geographic patterns with IgAN. PMID:22737082

Method for evaluating multiple mediators: mediating effects of smoking and COPD on the association between the CHRNA5-A3 variant and lung cancer risk.

PubMed

Wang, Jian; Spitz, Margaret R; Amos, Christopher I; Wu, Xifeng; Wetter, David W; Cinciripini, Paul M; Shete, Sanjay

2012-01-01

A mediation model explores the direct and indirect effects between an independent variable and a dependent variable by including other variables (or mediators). Mediation analysis has recently been used to dissect the direct and indirect effects of genetic variants on complex diseases using case-control studies. However, bias could arise in the estimations of the genetic variant-mediator association because the presence or absence of the mediator in the study samples is not sampled following the principles of case-control study design. In this case, the mediation analysis using data from case-control studies might lead to biased estimates of coefficients and indirect effects. In this article, we investigated a multiple-mediation model involving a three-path mediating effect through two mediators using case-control study data. We propose an approach to correct bias in coefficients and provide accurate estimates of the specific indirect effects. Our approach can also be used when the original case-control study is frequency matched on one of the mediators. We employed bootstrapping to assess the significance of indirect effects. We conducted simulation studies to investigate the performance of the proposed approach, and showed that it provides more accurate estimates of the indirect effects as well as the percent mediated than standard regressions. We then applied this approach to study the mediating effects of both smoking and chronic obstructive pulmonary disease (COPD) on the association between the CHRNA5-A3 gene locus and lung cancer risk using data from a lung cancer case-control study. The results showed that the genetic variant influences lung cancer risk indirectly through all three different pathways. The percent of genetic association mediated was 18.3% through smoking alone, 30.2% through COPD alone, and 20.6% through the path including both smoking and COPD, and the total genetic variant-lung cancer association explained by the two mediators was 69.1%.

Moral enhancement requires multiple virtues.

PubMed

Hughes, James J

2015-01-01

Some of the debates around the concept of moral enhancement have focused on whether the improvement of a single trait, such as empathy or intelligence, would be a good in general, or in all circumstances. All virtue theories, however, both secular and religious, have articulated multiple virtues that temper and inform one another in the development of a mature moral character. The project of moral enhancement requires a reengagement with virtue ethics and contemporary moral psychology to develop an empirically grounded model of the virtues and a fuller model of character development. Each of these virtues may be manipulable with electronic, psychopharmaceutical, and genetic interventions. A set of interdependent virtues is proposed, along with some of the research pointing to ways such virtues could be enhanced.

The heritability of mental health and wellbeing defined using COMPAS-W, a new composite measure of wellbeing.

PubMed

Gatt, Justine M; Burton, Karen L O; Schofield, Peter R; Bryant, Richard A; Williams, Leanne M

2014-09-30

Mental health is not simply the absence of mental illness; rather it is a distinct entity representing wellness. Models of wellbeing have been proposed that emphasize components of subjective wellbeing, psychological wellbeing, or a combination of both. A new 26-item scale of wellbeing (COMPAS-W) was developed in a cohort of 1669 healthy adult twins (18-61 years). The scale was derived using factor analysis of multiple scales of complementary constructs and confirmed using tests of reliability and convergent validity. Bivariate genetic modeling confirmed its heritability. From an original 89 items we identified six independent subcomponents that contributed to wellbeing. The COMPAS-W scale and its subcomponents showed construct validity against psychological and physical health behaviors, high internal consistency (average r=0.71, Wellbeing r=0.84), and 12-month test-retest reliability (average r=0.62, Wellbeing r=0.82). There was a moderate contribution of genetics to total Wellbeing (heritability h(2)=48%) and its subcomponents: Composure (h(2)=24%), Own-worth (h(2)=42%), Mastery (h(2)=40%), Positivity (h(2)=42%), Achievement (h(2)=32%) and Satisfaction (h(2)=43%). Multivariate genetic modeling indicated genetic variance was correlated across the scales, suggesting common genetic factors contributed to Wellbeing and its subcomponents. The COMPAS-W scale provides a validated indicator of wellbeing and offers a new tool to quantify mental health. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Leber congenital amaurosis/early-onset severe retinal dystrophy: clinical features, molecular genetics and therapeutic interventions.

PubMed

Kumaran, Neruban; Moore, Anthony T; Weleber, Richard G; Michaelides, Michel

2017-09-01

Leber congenital amaurosis (LCA) and early-onset severe retinal dystrophy (EOSRD) are both genetically and phenotypically heterogeneous, and characterised clinically by severe congenital/early infancy visual loss, nystagmus, amaurotic pupils and markedly reduced/absent full-field electroretinograms. The vast genetic heterogeneity of inherited retinal disease has been established over the last 10 - 20 years, with disease-causing variants identified in 25 genes to date associated with LCA/EOSRD, accounting for 70-80% of cases, with thereby more genes yet to be identified. There is now far greater understanding of the structural and functional associations seen in the various LCA/EOSRD genotypes. Subsequent development/characterisation of LCA/EOSRD animal models has shed light on the underlying pathogenesis and allowed the demonstration of successful rescue with gene replacement therapy and pharmacological intervention in multiple models. These advancements have culminated in more than 12 completed, ongoing and anticipated phase I/II and phase III gene therapy and pharmacological human clinical trials. This review describes the clinical and genetic characteristics of LCA/EOSRD and the differential diagnoses to be considered. We discuss in further detail the diagnostic clinical features, pathophysiology, animal models and human treatment studies and trials, in the more common genetic subtypes and/or those closest to intervention. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Genetic architecture of resistance in Daphnia hosts against two species of host-specific parasites.

PubMed

Routtu, J; Ebert, D

2015-02-01

Understanding the genetic architecture of host resistance is key for understanding the evolution of host-parasite interactions. Evolutionary models often assume simple genetics based on few loci and strong epistasis. It is unknown, however, whether these assumptions apply to natural populations. Using a quantitative trait loci (QTL) approach, we explore the genetic architecture of resistance in the crustacean Daphnia magna to two of its natural parasites: the horizontally transmitted bacterium Pasteuria ramosa and the horizontally and vertically transmitted microsporidium Hamiltosporidium tvaerminnensis. These two systems have become models for studies on the evolution of host-parasite interactions. In the QTL panel used here, Daphnia's resistance to P. ramosa is controlled by a single major QTL (which explains 50% of the observed variation). Resistance to H. tvaerminnensis horizontal infections shows a signature of a quantitative trait based in multiple loci with weak epistatic interactions (together explaining 38% variation). Resistance to H. tvaerminnensis vertical infections, however, shows only one QTL (explaining 13.5% variance) that colocalizes with one of the QTLs for horizontal infections. QTLs for resistance to Pasteuria and Hamiltosporidium do not colocalize. We conclude that the genetics of resistance in D. magna are drastically different for these two parasites. Furthermore, we infer that based on these and earlier results, the mechanisms of coevolution differ strongly for the two host-parasite systems. Only the Pasteuria-Daphnia system is expected to follow the negative frequency-dependent selection (Red Queen) model. How coevolution works in the Hamiltosporidium-Daphnia system remains unclear.

Genetic architecture of resistance in Daphnia hosts against two species of host-specific parasites

PubMed Central

Routtu, J; Ebert, D

2015-01-01

Understanding the genetic architecture of host resistance is key for understanding the evolution of host–parasite interactions. Evolutionary models often assume simple genetics based on few loci and strong epistasis. It is unknown, however, whether these assumptions apply to natural populations. Using a quantitative trait loci (QTL) approach, we explore the genetic architecture of resistance in the crustacean Daphnia magna to two of its natural parasites: the horizontally transmitted bacterium Pasteuria ramosa and the horizontally and vertically transmitted microsporidium Hamiltosporidium tvaerminnensis. These two systems have become models for studies on the evolution of host–parasite interactions. In the QTL panel used here, Daphnia's resistance to P. ramosa is controlled by a single major QTL (which explains 50% of the observed variation). Resistance to H. tvaerminnensis horizontal infections shows a signature of a quantitative trait based in multiple loci with weak epistatic interactions (together explaining 38% variation). Resistance to H. tvaerminnensis vertical infections, however, shows only one QTL (explaining 13.5% variance) that colocalizes with one of the QTLs for horizontal infections. QTLs for resistance to Pasteuria and Hamiltosporidium do not colocalize. We conclude that the genetics of resistance in D. magna are drastically different for these two parasites. Furthermore, we infer that based on these and earlier results, the mechanisms of coevolution differ strongly for the two host–parasite systems. Only the Pasteuria–Daphnia system is expected to follow the negative frequency-dependent selection (Red Queen) model. How coevolution works in the Hamiltosporidium–Daphnia system remains unclear. PMID:25335558

Genomic Prediction for Quantitative Traits Is Improved by Mapping Variants to Gene Ontology Categories in Drosophila melanogaster

PubMed Central

Edwards, Stefan M.; Sørensen, Izel F.; Sarup, Pernille; Mackay, Trudy F. C.; Sørensen, Peter

2016-01-01

Predicting individual quantitative trait phenotypes from high-resolution genomic polymorphism data is important for personalized medicine in humans, plant and animal breeding, and adaptive evolution. However, this is difficult for populations of unrelated individuals when the number of causal variants is low relative to the total number of polymorphisms and causal variants individually have small effects on the traits. We hypothesized that mapping molecular polymorphisms to genomic features such as genes and their gene ontology categories could increase the accuracy of genomic prediction models. We developed a genomic feature best linear unbiased prediction (GFBLUP) model that implements this strategy and applied it to three quantitative traits (startle response, starvation resistance, and chill coma recovery) in the unrelated, sequenced inbred lines of the Drosophila melanogaster Genetic Reference Panel. Our results indicate that subsetting markers based on genomic features increases the predictive ability relative to the standard genomic best linear unbiased prediction (GBLUP) model. Both models use all markers, but GFBLUP allows differential weighting of the individual genetic marker relationships, whereas GBLUP weighs the genetic marker relationships equally. Simulation studies show that it is possible to further increase the accuracy of genomic prediction for complex traits using this model, provided the genomic features are enriched for causal variants. Our GFBLUP model using prior information on genomic features enriched for causal variants can increase the accuracy of genomic predictions in populations of unrelated individuals and provides a formal statistical framework for leveraging and evaluating information across multiple experimental studies to provide novel insights into the genetic architecture of complex traits. PMID:27235308

Estimating parametric phenotypes that determine anthesis date in Zea mays: Challenges in combining ecophysiological models with genetics

PubMed Central

Welch, Stephen M.; White, Jeffrey W.; Thorp, Kelly R.; Bello, Nora M.

2018-01-01

Ecophysiological crop models encode intra-species behaviors using parameters that are presumed to summarize genotypic properties of individual lines or cultivars. These genotype-specific parameters (GSP’s) can be interpreted as quantitative traits that can be mapped or otherwise analyzed, as are more conventional traits. The goal of this study was to investigate the estimation of parameters controlling maize anthesis date with the CERES-Maize model, based on 5,266 maize lines from 11 plantings at locations across the eastern United States. High performance computing was used to develop a database of 356 million simulated anthesis dates in response to four CERES-Maize model parameters. Although the resulting estimates showed high predictive value (R2 = 0.94), three issues presented serious challenges for use of GSP’s as traits. First (expressivity), the model was unable to express the observed data for 168 to 3,339 lines (depending on the combination of site-years), many of which ended up sharing the same parameter value irrespective of genetics. Second, for 2,254 lines, the model reproduced the data, but multiple parameter sets were equally effective (equifinality). Third, parameter values were highly dependent (p<10−6919) on the sets of environments used to estimate them (instability), calling in to question the assumption that they represent fundamental genetic traits. The issues of expressivity, equifinality and instability must be addressed before the genetic mapping of GSP’s becomes a robust means to help solve the genotype-to-phenotype problem in crops. PMID:29672629

An Investigational RNAi Therapeutic Targeting Glycolate Oxidase Reduces Oxalate Production in Models of Primary Hyperoxaluria

PubMed Central

Li, Xingsheng; Racie, Timothy; Hettinger, Julia; Bettencourt, Brian R.; Najafian, Nader; Haslett, Patrick; Fitzgerald, Kevin; Holmes, Ross P.; Erbe, David; Querbes, William; Knight, John

2017-01-01

Primary hyperoxaluria type 1 (PH1), an inherited rare disease of glyoxylate metabolism, arises from mutations in the enzyme alanine-glyoxylate aminotransferase. The resulting deficiency in this enzyme leads to abnormally high oxalate production resulting in calcium oxalate crystal formation and deposition in the kidney and many other tissues, with systemic oxalosis and ESRD being a common outcome. Although a small subset of patients manages the disease with vitamin B6 treatments, the only effective treatment for most is a combined liver-kidney transplant, which requires life-long immune suppression and carries significant mortality risk. In this report, we discuss the development of ALN-GO1, an investigational RNA interference (RNAi) therapeutic targeting glycolate oxidase, to deplete the substrate for oxalate synthesis. Subcutaneous administration of ALN-GO1 resulted in potent, dose-dependent, and durable silencing of the mRNA encoding glycolate oxidase and increased serum glycolate concentrations in wild-type mice, rats, and nonhuman primates. ALN-GO1 also increased urinary glycolate concentrations in normal nonhuman primates and in a genetic mouse model of PH1. Notably, ALN-GO1 reduced urinary oxalate concentration up to 50% after a single dose in the genetic mouse model of PH1, and up to 98% after multiple doses in a rat model of hyperoxaluria. These data demonstrate the ability of ALN-GO1 to reduce oxalate production in preclinical models of PH1 across multiple species and provide a clear rationale for clinical trials with this compound. PMID:27432743

Genotype-Based Association Mapping of Complex Diseases: Gene-Environment Interactions with Multiple Genetic Markers and Measurement Error in Environmental Exposures

PubMed Central

Lobach, Irvna; Fan, Ruzone; Carroll, Raymond T.

2011-01-01

With the advent of dense single nucleotide polymorphism genotyping, population-based association studies have become the major tools for identifying human disease genes and for fine gene mapping of complex traits. We develop a genotype-based approach for association analysis of case-control studies of gene-environment interactions in the case when environmental factors are measured with error and genotype data are available on multiple genetic markers. To directly use the observed genotype data, we propose two genotype-based models: genotype effect and additive effect models. Our approach offers several advantages. First, the proposed risk functions can directly incorporate the observed genotype data while modeling the linkage disequihbrium information in the regression coefficients, thus eliminating the need to infer haplotype phase. Compared with the haplotype-based approach, an estimating procedure based on the proposed methods can be much simpler and significantly faster. In addition, there is no potential risk due to haplotype phase estimation. Further, by fitting the proposed models, it is possible to analyze the risk alleles/variants of complex diseases, including their dominant or additive effects. To model measurement error, we adopt the pseudo-likelihood method by Lobach et al. [2008]. Performance of the proposed method is examined using simulation experiments. An application of our method is illustrated using a population-based case-control study of association between calcium intake with the risk of colorectal adenoma development. PMID:21031455

Ecological correlates of population genetic structure: a comparative approach using a vertebrate metacommunity.

PubMed

Manier, Mollie K; Arnold, Stevan J

2006-12-07

Identifying ecological factors associated with population genetic differentiation is important for understanding microevolutionary processes and guiding the management of threatened populations. We identified ecological correlates of several population genetic parameters for three interacting species (two garter snakes and an anuran) that occupy a common landscape. Using multiple regression analysis, we found that species interactions were more important in explaining variation in population genetic parameters than habitat and nearest-neighbour characteristics. Effective population size was best explained by census size, while migration was associated with differences in species abundance. In contrast, genetic distance was poorly explained by the ecological correlates that we tested, but geographical distance was prominent in models for all species. We found substantially different population dynamics for the prey species relative to the two predators, characterized by larger effective sizes, lower gene flow and a state of migration-drift equilibrium. We also identified an escarpment formed by a series of block faults that serves as a barrier to dispersal for the predators. Our results suggest that successful landscape-level management should incorporate genetic and ecological data for all relevant species, because even closely associated species can exhibit very different population genetic dynamics on the same landscape.

Genetics Home Reference: adiposis dolorosa

MedlinePlus

... are some genetic conditions more common in particular ethnic groups? Genetic Changes The cause of adiposis dolorosa is unknown. The condition is thought to have a genetic component because a few families with multiple affected family members have been reported. ...

Gene flow in environmental Legionella pneumophila leads to genetic and pathogenic heterogeneity within a Legionnaires' disease outbreak.

PubMed

McAdam, Paul R; Vander Broek, Charles W; Lindsay, Diane S J; Ward, Melissa J; Hanson, Mary F; Gillies, Michael; Watson, Mick; Stevens, Joanne M; Edwards, Giles F; Fitzgerald, J Ross

2014-01-01

Legionnaires' disease is a severe form of pneumonia caused by the environmental bacterium Legionella pneumophila. Outbreaks commonly affect people with known risk factors, but the genetic and pathogenic complexity of L. pneumophila within an outbreak is not well understood. Here, we investigate the etiology of the major Legionnaires' disease outbreak that occurred in Edinburgh, UK, in 2012, by examining the evolutionary history, genome content, and virulence of L. pneumophila clinical isolates. Our high resolution genomic approach reveals that the outbreak was caused by multiple genetic subtypes of L. pneumophila, the majority of which had diversified from a single progenitor through mutation, recombination, and horizontal gene transfer within an environmental reservoir prior to release. In addition, we discover that some patients were infected with multiple L. pneumophila subtypes, a finding which can affect the certainty of source attribution. Importantly, variation in the complement of type IV secretion systems encoded by different genetic subtypes correlates with virulence in a Galleria mellonella model of infection, revealing variation in pathogenic potential among the outbreak source population of L. pneumophila. Taken together, our study indicates previously cryptic levels of pathogen heterogeneity within a Legionnaires' disease outbreak, a discovery that impacts on source attribution for future outbreak investigations. Furthermore, our data suggest that in addition to host immune status, pathogen diversity may be an important influence on the clinical outcome of individual outbreak infections.

Evolving Non-Dominated Parameter Sets for Computational Models from Multiple Experiments

NASA Astrophysics Data System (ADS)

Lane, Peter C. R.; Gobet, Fernand

2013-03-01

Creating robust, reproducible and optimal computational models is a key challenge for theorists in many sciences. Psychology and cognitive science face particular challenges as large amounts of data are collected and many models are not amenable to analytical techniques for calculating parameter sets. Particular problems are to locate the full range of acceptable model parameters for a given dataset, and to confirm the consistency of model parameters across different datasets. Resolving these problems will provide a better understanding of the behaviour of computational models, and so support the development of general and robust models. In this article, we address these problems using evolutionary algorithms to develop parameters for computational models against multiple sets of experimental data; in particular, we propose the `speciated non-dominated sorting genetic algorithm' for evolving models in several theories. We discuss the problem of developing a model of categorisation using twenty-nine sets of data and models drawn from four different theories. We find that the evolutionary algorithms generate high quality models, adapted to provide a good fit to all available data.

Inferring genetic interactions via a nonlinear model and an optimization algorithm.

PubMed

Chen, Chung-Ming; Lee, Chih; Chuang, Cheng-Long; Wang, Chia-Chang; Shieh, Grace S

2010-02-26

Biochemical pathways are gradually becoming recognized as central to complex human diseases and recently genetic/transcriptional interactions have been shown to be able to predict partial pathways. With the abundant information made available by microarray gene expression data (MGED), nonlinear modeling of these interactions is now feasible. Two of the latest advances in nonlinear modeling used sigmoid models to depict transcriptional interaction of a transcription factor (TF) for a target gene, but do not model cooperative or competitive interactions of several TFs for a target. An S-shape model and an optimization algorithm (GASA) were developed to infer genetic interactions/transcriptional regulation of several genes simultaneously using MGED. GASA consists of a genetic algorithm (GA) and a simulated annealing (SA) algorithm, which is enhanced by a steepest gradient descent algorithm to avoid being trapped in local minimum. Using simulated data with various degrees of noise, we studied how GASA with two model selection criteria and two search spaces performed. Furthermore, GASA was shown to outperform network component analysis, the time series network inference algorithm (TSNI), GA with regular GA (GAGA) and GA with regular SA. Two applications are demonstrated. First, GASA is applied to infer a subnetwork of human T-cell apoptosis. Several of the predicted interactions are supported by the literature. Second, GASA was applied to infer the transcriptional factors of 34 cell cycle regulated targets in S. cerevisiae, and GASA performed better than one of the latest advances in nonlinear modeling, GAGA and TSNI. Moreover, GASA is able to predict multiple transcription factors for certain targets, and these results coincide with experiments confirmed data in YEASTRACT. GASA is shown to infer both genetic interactions and transcriptional regulatory interactions well. In particular, GASA seems able to characterize the nonlinear mechanism of transcriptional regulatory interactions (TIs) in yeast, and may be applied to infer TIs in other organisms. The predicted genetic interactions of a subnetwork of human T-cell apoptosis coincide with existing partial pathways, suggesting the potential of GASA on inferring biochemical pathways.

In defence of model-based inference in phylogeography

PubMed Central

Beaumont, Mark A.; Nielsen, Rasmus; Robert, Christian; Hey, Jody; Gaggiotti, Oscar; Knowles, Lacey; Estoup, Arnaud; Panchal, Mahesh; Corander, Jukka; Hickerson, Mike; Sisson, Scott A.; Fagundes, Nelson; Chikhi, Lounès; Beerli, Peter; Vitalis, Renaud; Cornuet, Jean-Marie; Huelsenbeck, John; Foll, Matthieu; Yang, Ziheng; Rousset, Francois; Balding, David; Excoffier, Laurent

2017-01-01

Recent papers have promoted the view that model-based methods in general, and those based on Approximate Bayesian Computation (ABC) in particular, are flawed in a number of ways, and are therefore inappropriate for the analysis of phylogeographic data. These papers further argue that Nested Clade Phylogeographic Analysis (NCPA) offers the best approach in statistical phylogeography. In order to remove the confusion and misconceptions introduced by these papers, we justify and explain the reasoning behind model-based inference. We argue that ABC is a statistically valid approach, alongside other computational statistical techniques that have been successfully used to infer parameters and compare models in population genetics. We also examine the NCPA method and highlight numerous deficiencies, either when used with single or multiple loci. We further show that the ages of clades are carelessly used to infer ages of demographic events, that these ages are estimated under a simple model of panmixia and population stationarity but are then used under different and unspecified models to test hypotheses, a usage the invalidates these testing procedures. We conclude by encouraging researchers to study and use model-based inference in population genetics. PMID:29284924

Multiple capacitors for natural genetic variation in Drosophila melanogaster.

PubMed

Takahashi, Kazuo H

2013-03-01

Cryptic genetic variation (CGV) or a standing genetic variation that is not ordinarily expressed as a phenotype is released when the robustness of organisms is impaired under environmental or genetic perturbations. Evolutionary capacitors modulate the amount of genetic variation exposed to natural selection and hidden cryptically; they have a fundamental effect on the evolvability of traits on evolutionary timescales. In this study, I have demonstrated the effects of multiple genomic regions of Drosophila melanogaster on CGV in wing shape. I examined the effects of 61 genomic deficiencies on quantitative and qualitative natural genetic variation in the wing shape of D. melanogaster. I have identified 10 genomic deficiencies that do not encompass a known candidate evolutionary capacitor, Hsp90, exposing natural CGV differently depending on the location of the deficiencies in the genome. Furthermore, five genomic deficiencies uncovered qualitative CGV in wing morphology. These findings suggest that CGV in wing shape of wild-type D. melanogaster is regulated by multiple capacitors with divergent functions. Future analysis of genes encompassed by these genomic regions would help elucidate novel capacitor genes and better understand the general features of capacitors regarding natural genetic variation. © 2012 Blackwell Publishing Ltd.

Tricholoma matsutake in a natural Pinus densiflora forest: correspondence between above- and below-ground genets, association with multiple host trees and alteration of existing ectomycorrhizal communities.

PubMed

Lian, Chunlan; Narimatsu, Maki; Nara, Kazuhide; Hogetsu, Taizo

2006-01-01

Tricholoma matsutake (matsutake) is an ectomycorrhizal (ECM) fungus that produces economically important mushrooms in Japan. Here, we use microsatellite markers to identify genets of matsutake sporocarps and below-ground ECM tips, as well as associated host genotypes of Pinus densiflora. We also studied ECM fungal community structure inside, beneath and outside the matsutake fairy rings, using morphological and internal transcribed spacer (ITS) polymorphism analysis. Based on sporocarp samples, one to four genets were found within each fairy ring, and no genetic differentiation among six sites was detected. Matsutake ECM tips were only found beneath fairy rings and corresponded with the genotypes of the above-ground sporocarps. We detected nine below-ground matsutake genets, all of which colonized multiple pine trees (three to seven trees per genet). The ECM fungal community beneath fairy rings was species-poor and significantly differed from those inside and outside the fairy rings. We conclude that matsutake genets occasionally establish from basidiospores and expand on the root systems of multiple host trees. Although matsutake mycelia suppress other ECM fungi during expansion, most of them may recover after the passage of the fairy rings.

Efficient computation of the joint probability of multiple inherited risk alleles from pedigree data.

PubMed

Madsen, Thomas; Braun, Danielle; Peng, Gang; Parmigiani, Giovanni; Trippa, Lorenzo

2018-06-25

The Elston-Stewart peeling algorithm enables estimation of an individual's probability of harboring germline risk alleles based on pedigree data, and serves as the computational backbone of important genetic counseling tools. However, it remains limited to the analysis of risk alleles at a small number of genetic loci because its computing time grows exponentially with the number of loci considered. We propose a novel, approximate version of this algorithm, dubbed the peeling and paring algorithm, which scales polynomially in the number of loci. This allows extending peeling-based models to include many genetic loci. The algorithm creates a trade-off between accuracy and speed, and allows the user to control this trade-off. We provide exact bounds on the approximation error and evaluate it in realistic simulations. Results show that the loss of accuracy due to the approximation is negligible in important applications. This algorithm will improve genetic counseling tools by increasing the number of pathogenic risk alleles that can be addressed. To illustrate we create an extended five genes version of BRCAPRO, a widely used model for estimating the carrier probabilities of BRCA1 and BRCA2 risk alleles and assess its computational properties. © 2018 WILEY PERIODICALS, INC.

Genetic and physiological bases for phenological responses to current and predicted climates

PubMed Central

Wilczek, A. M.; Burghardt, L. T.; Cobb, A. R.; Cooper, M. D.; Welch, S. M.; Schmitt, J.

2010-01-01

We are now reaching the stage at which specific genetic factors with known physiological effects can be tied directly and quantitatively to variation in phenology. With such a mechanistic understanding, scientists can better predict phenological responses to novel seasonal climates. Using the widespread model species Arabidopsis thaliana, we explore how variation in different genetic pathways can be linked to phenology and life-history variation across geographical regions and seasons. We show that the expression of phenological traits including flowering depends critically on the growth season, and we outline an integrated life-history approach to phenology in which the timing of later life-history events can be contingent on the environmental cues regulating earlier life stages. As flowering time in many plants is determined by the integration of multiple environmentally sensitive gene pathways, the novel combinations of important seasonal cues in projected future climates will alter how phenology responds to variation in the flowering time gene network with important consequences for plant life history. We discuss how phenology models in other systems—both natural and agricultural—could employ a similar framework to explore the potential contribution of genetic variation to the physiological integration of cues determining phenology. PMID:20819808

Multiple dispersal vectors drive range expansion in an invasive marine species.

PubMed

Richardson, Mark F; Sherman, Craig D H; Lee, Randall S; Bott, Nathan J; Hirst, Alastair J

2016-10-01

The establishment and subsequent spread of invasive species is widely recognized as one of the most threatening processes contributing to global biodiversity loss. This is especially true for marine and estuarine ecosystems, which have experienced significant increases in the number of invasive species with the increase in global maritime trade. Understanding the rate and mechanisms of range expansion is therefore of significant interest to ecologists and conservation managers alike. Using a combination of population genetic surveys, environmental DNA (eDNA) plankton sampling and hydrodynamic modelling, we examined the patterns of introduction of the predatory Northern Pacific seastar (Asterias amurensis) and pathways of secondary spread within southeast Australia. Genetic surveys across the invasive range reveal some genetic divergence between the two main invasive regions and no evidence of ongoing gene flow, a pattern that is consistent with the establishment of the second invasive region via a human-mediated translocation event. In contrast, hydrodynamic modelling combined with eDNA plankton sampling demonstrated that the establishment of range expansion populations within a region is consistent with natural larval dispersal and recruitment. Our results suggest that both anthropogenic and natural dispersal vectors have played an important role in the range expansion of this species in Australia. The multiple modes of spread combined with high levels of fecundity and a long larval duration in A. amurensis suggests it is likely to continue its range expansion and significantly impact Australian marine ecosystems. © 2016 John Wiley & Sons Ltd.

Axon Regeneration in C. elegans

PubMed Central

Hammarlund, Marc; Jin, Yishi

2014-01-01

Single axon transection by laser surgery has made C. elegans a new model for axon regeneration. Multiple conserved molecular signaling modules have been discovered through powerful genetic screening. in vivo imaging with single cell and axon resolution has revealed unprecedented cellular dynamics in regenerating axons. Information from C. elegans has greatly expanded our knowledge of the molecular and cellular mechanisms of axon regeneration. PMID:24794753

Generating Enhanced Natural Environments and Terrain for Interactive Combat Simulations (GENETICS)

DTIC Science & Technology

2005-09-01

split to avoid T-junctions ........................................................................52 Figure 2-23 Longest edge bisection...database. This feature allows trainers the flexibility to use the same terrain repeatedly or use a new one each time, forcing trainees to avoid ...model are favored to create a good surface approximation. Cracks are avoided by projecting primitives and their respective textures onto multiple

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

Landscape genetics of leaf-toed geckos in the tropical dry forest of northern Mexico.

PubMed

Blair, Christopher; Jiménez Arcos, Victor H; Mendez de la Cruz, Fausto R; Murphy, Robert W

2013-01-01

Habitat fragmentation due to both natural and anthropogenic forces continues to threaten the evolution and maintenance of biological diversity. This is of particular concern in tropical regions that are experiencing elevated rates of habitat loss. Although less well-studied than tropical rain forests, tropical dry forests (TDF) contain an enormous diversity of species and continue to be threatened by anthropogenic activities including grazing and agriculture. However, little is known about the processes that shape genetic connectivity in species inhabiting TDF ecosystems. We adopt a landscape genetic approach to understanding functional connectivity for leaf-toed geckos (Phyllodactylus tuberculosus) at multiple sites near the northernmost limit of this ecosystem at Alamos, Sonora, Mexico. Traditional analyses of population genetics are combined with multivariate GIS-based landscape analyses to test hypotheses on the potential drivers of spatial genetic variation. Moderate levels of within-population diversity and substantial levels of population differentiation are revealed by FST and Dest. Analyses using structure suggest the occurrence of from 2 to 9 genetic clusters depending on the model used. Landscape genetic analysis suggests that forest cover, stream connectivity, undisturbed habitat, slope, and minimum temperature of the coldest period explain more genetic variation than do simple Euclidean distances. Additional landscape genetic studies throughout TDF habitat are required to understand species-specific responses to landscape and climate change and to identify common drivers. We urge researchers interested in using multivariate distance methods to test for, and report, significant correlations among predictor matrices that can impact results, particularly when adopting least-cost path approaches. Further investigation into the use of information theoretic approaches for model selection is also warranted.

Conditional Creation and Rescue of Nipbl-Deficiency in Mice Reveals Multiple Determinants of Risk for Congenital Heart Defects

PubMed Central

Jacobs, Russell E.; Lopez-Burks, Martha E.; Choi, Hojae; Wikenheiser, Jamie; Hallgrimsson, Benedikt; Jamniczky, Heather A.; Fraser, Scott E.; Lander, Arthur D.; Calof, Anne L.

2016-01-01

Elucidating the causes of congenital heart defects is made difficult by the complex morphogenesis of the mammalian heart, which takes place early in development, involves contributions from multiple germ layers, and is controlled by many genes. Here, we use a conditional/invertible genetic strategy to identify the cell lineage(s) responsible for the development of heart defects in a Nipbl-deficient mouse model of Cornelia de Lange Syndrome, in which global yet subtle transcriptional dysregulation leads to development of atrial septal defects (ASDs) at high frequency. Using an approach that allows for recombinase-mediated creation or rescue of Nipbl deficiency in different lineages, we uncover complex interactions between the cardiac mesoderm, endoderm, and the rest of the embryo, whereby the risk conferred by genetic abnormality in any one lineage is modified, in a surprisingly non-additive way, by the status of others. We argue that these results are best understood in the context of a model in which the risk of heart defects is associated with the adequacy of early progenitor cell populations relative to the sizes of the structures they must eventually form. PMID:27606604

Environmental Interactions and Epistasis Are Revealed in the Proteomic Responses to Complex Stimuli

PubMed Central

Samir, Parimal; Rahul; Slaughter, James C.; Link, Andrew J.

2015-01-01

Ultimately, the genotype of a cell and its interaction with the environment determine the cell’s biochemical state. While the cell’s response to a single stimulus has been studied extensively, a conceptual framework to model the effect of multiple environmental stimuli applied concurrently is not as well developed. In this study, we developed the concepts of environmental interactions and epistasis to explain the responses of the S. cerevisiae proteome to simultaneous environmental stimuli. We hypothesize that, as an abstraction, environmental stimuli can be treated as analogous to genetic elements. This would allow modeling of the effects of multiple stimuli using the concepts and tools developed for studying gene interactions. Mirroring gene interactions, our results show that environmental interactions play a critical role in determining the state of the proteome. We show that individual and complex environmental stimuli behave similarly to genetic elements in regulating the cellular responses to stimuli, including the phenomena of dominance and suppression. Interestingly, we observed that the effect of a stimulus on a protein is dominant over other stimuli if the response to the stimulus involves the protein. Using publicly available transcriptomic data, we find that environmental interactions and epistasis regulate transcriptomic responses as well. PMID:26247773

Homozygous parent affected sib pair method for detecting disease predisposing variants: application to insulin dependent diabetes mellitus.

PubMed

Robinson, W P; Barbosa, J; Rich, S S; Thomson, G

1993-01-01

For complex genetic diseases involving incomplete penetrance, genetic heterogeneity, and multiple disease genes, it is often difficult to determine the molecular variant(s) responsible for the disease pathogenesis. Linkage and association studies may help identify genetic regions and molecular variants suspected of being directly responsible for disease predisposition or protection, but, especially for complex diseases, they are less useful for determining when a predisposing molecular variant has been identified. In this paper, we expand upon the simple concept that if a genetic factor predisposing to disease has been fully identified, then a parent homozygous for this factor should transmit either of his/her copies at random to any affected children. Closely linked markers are used to determine identity by descent values in affected sib pairs from a parent homozygous for a putative disease predisposing factor. The expected deviation of haplotype sharing from 50%, when not all haplotypes carrying this factor are in fact equally predisposing, has been algebraically determined for a single locus general disease model. Equations to determine expected sharing for multiple disease alleles or multiple disease locus models have been formulated. The recessive case is in practice limiting and therefore can be used to estimate the maximum proportion of putative susceptibility haplotypes which are in fact predisposing to disease when the mode of inheritance of a disease is unknown. This method has been applied to 27 DR3/DR3 parents and 50 DR4/DR4 parents who have at least 2 children affected with insulin dependent diabetes mellitus (IDDM). The transmission of both DR3 and DR4 haplotypes is statistically different from 50% (P < 0.05 and P < 0.001, respectively). An upper estimate for the proportion of DR3 haplotypes associated with a high IDDM susceptibility is 49%, and for DR4 haplotypes 38%. Our results show that the joint presence of non-Asp at DQ beta position 57 and Arg at DQ alpha position 52, which has been proposed as a strong IDDM predisposing factor, is insufficient to explain the HLA component of IDDM predisposition.

Assessing Species Boundaries Using Multilocus Species Delimitation in a Morphologically Conserved Group of Neotropical Freshwater Fishes, the Poecilia sphenops Species Complex (Poeciliidae)

PubMed Central

Bagley, Justin C.; Alda, Fernando; Breitman, M. Florencia; Bermingham, Eldredge; van den Berghe, Eric P.; Johnson, Jerald B.

2015-01-01

Accurately delimiting species is fundamentally important for understanding species diversity and distributions and devising effective strategies to conserve biodiversity. However, species delimitation is problematic in many taxa, including ‘non-adaptive radiations’ containing morphologically cryptic lineages. Fortunately, coalescent-based species delimitation methods hold promise for objectively estimating species limits in such radiations, using multilocus genetic data. Using coalescent-based approaches, we delimit species and infer evolutionary relationships in a morphologically conserved group of Central American freshwater fishes, the Poecilia sphenops species complex. Phylogenetic analyses of multiple genetic markers (sequences of two mitochondrial DNA genes and five nuclear loci) from 10/15 species and genetic lineages recognized in the group support the P. sphenops species complex as monophyletic with respect to outgroups, with eight mitochondrial ‘major-lineages’ diverged by ≥2% pairwise genetic distances. From general mixed Yule-coalescent models, we discovered (conservatively) 10 species within our concatenated mitochondrial DNA dataset, 9 of which were strongly supported by subsequent multilocus Bayesian species delimitation and species tree analyses. Results suggested species-level diversity is underestimated or overestimated by at least ~15% in different lineages in the complex. Nonparametric statistics and coalescent simulations indicate genealogical discordance among our gene tree results has mainly derived from interspecific hybridization in the nuclear genome. However, mitochondrial DNA show little evidence for introgression, and our species delimitation results appear robust to effects of this process. Overall, our findings support the utility of combining multiple lines of genetic evidence and broad phylogeographical sampling to discover and validate species using coalescent-based methods. Our study also highlights the importance of testing for hybridization versus incomplete lineage sorting, which aids inference of not only species limits but also evolutionary processes influencing genetic diversity. PMID:25849959

Transatlantic invasion routes and adaptive potential in North American populations of the invasive glossy buckthorn, Frangula alnus

PubMed Central

De Kort, Hanne; Mergeay, Joachim; Jacquemyn, Hans; Honnay, Olivier

2016-01-01

Background and Aims Many invasive species severely threaten native biodiversity and ecosystem functioning. One of the most prominent questions in invasion genetics is how invasive populations can overcome genetic founder effects to establish stable populations after colonization of new habitats. High native genetic diversity and multiple introductions are expected to increase genetic diversity and adaptive potential in the invasive range. Our aim was to identify the European source populations of Frangula alnus (glossy buckthorn), an ornamental and highly invasive woody species that was deliberately introduced into North America at the end of the 18th century. A second aim of this study was to assess the adaptive potential as an explanation for the invasion success of this species. Methods Using a set of annotated single-nucleotide polymorphisms (SNPs) that were assigned a putative function based on sequence comparison with model species, a total of 38 native European and 21 invasive North American populations were subjected to distance-based structure and assignment analyses combined with population genomic tools. Genetic diversity at SNPs with ecologically relevant functions was considered as a proxy for adaptive potential. Key Results Patterns of invasion coincided with early modern transatlantic trading routes. Multiple introductions through transatlantic trade from a limited number of European port regions to American urban areas led to the establishment of bridgehead populations with high allelic richness and expected heterozygosity, allowing continuous secondary migration to natural areas. Conclusions Targeted eradication of the urban populations, where the highest genetic diversity and adaptive potential were observed, offers a promising strategy to arrest further invasion of native American prairies and forests. PMID:27539599

Spatial patterns of AFLP diversity in Bulbophyllum occultum (Orchidaceae) indicate long-term refugial isolation in Madagascar and long-distance colonization effects in La Réunion.

PubMed

Jaros, U; Fischer, G A; Pailler, T; Comes, H P

2016-05-01

Bulbophyllum occultum, an epiphytic orchid mainly distributed in the rainforests of (north)eastern Madagascar and La Réunion, represents an interesting model case for testing the effects of anthropogenic vs historical (e.g., climate induced) habitat isolation and long-distance colonization on the genetic structure of plant species with disjunct distributions in the Madagascan region. To this aim, we surveyed amplified fragment length polymorphisms (AFLPs) across 13 populations in Madagascar and nine in La Réunion (206 individuals in total). We found overall high levels of population subdivision (Φ(PT)=0.387) and low within-population diversity (H(E), range: 0.026-0.124), indicating non-equilibrium conditions in a mainly selfing species. There was no impact of recent deforestation (Madagascar) or habitat disturbance (La Réunion) detectable on AFLP diversity. K-means clustering and BARRIER analyses identified multiple gene pools and several genetic breaks, both within and among islands. Inter-island levels of population genetic diversity and subdivision were similar, whereby inter-individual divergence in flower colour explained a significant part of gene pool divergence in La Réunion. Our results suggest that (i) B. occultum persisted across multiple isolated ('refugial') regions along the eastern rainforest corridor of Madagascar over recent climatic cycles and (ii) populations in La Réunion arose from either single or few independent introductions from Madagascar. High selfing rates and sufficient time for genetic drift likely promoted unexpectedly high population genetic and phenotypic (flower colour) differentiation in La Réunion. Overall, this study highlights a strong imprint of history on the genetic structure of a low-gene-dispersing epiphytic orchid from the Madagascan region.

Assessing species boundaries using multilocus species delimitation in a morphologically conserved group of neotropical freshwater fishes, the Poecilia sphenops species complex (Poeciliidae).

PubMed

Bagley, Justin C; Alda, Fernando; Breitman, M Florencia; Bermingham, Eldredge; van den Berghe, Eric P; Johnson, Jerald B

2015-01-01

Accurately delimiting species is fundamentally important for understanding species diversity and distributions and devising effective strategies to conserve biodiversity. However, species delimitation is problematic in many taxa, including 'non-adaptive radiations' containing morphologically cryptic lineages. Fortunately, coalescent-based species delimitation methods hold promise for objectively estimating species limits in such radiations, using multilocus genetic data. Using coalescent-based approaches, we delimit species and infer evolutionary relationships in a morphologically conserved group of Central American freshwater fishes, the Poecilia sphenops species complex. Phylogenetic analyses of multiple genetic markers (sequences of two mitochondrial DNA genes and five nuclear loci) from 10/15 species and genetic lineages recognized in the group support the P. sphenops species complex as monophyletic with respect to outgroups, with eight mitochondrial 'major-lineages' diverged by ≥2% pairwise genetic distances. From general mixed Yule-coalescent models, we discovered (conservatively) 10 species within our concatenated mitochondrial DNA dataset, 9 of which were strongly supported by subsequent multilocus Bayesian species delimitation and species tree analyses. Results suggested species-level diversity is underestimated or overestimated by at least ~15% in different lineages in the complex. Nonparametric statistics and coalescent simulations indicate genealogical discordance among our gene tree results has mainly derived from interspecific hybridization in the nuclear genome. However, mitochondrial DNA show little evidence for introgression, and our species delimitation results appear robust to effects of this process. Overall, our findings support the utility of combining multiple lines of genetic evidence and broad phylogeographical sampling to discover and validate species using coalescent-based methods. Our study also highlights the importance of testing for hybridization versus incomplete lineage sorting, which aids inference of not only species limits but also evolutionary processes influencing genetic diversity.

Monoamine Oxidase A (MAOA) Gene and Personality Traits from Late Adolescence through Early Adulthood: A Latent Variable Investigation

PubMed Central

Xu, Man K.; Gaysina, Darya; Tsonaka, Roula; Morin, Alexandre J. S.; Croudace, Tim J.; Barnett, Jennifer H.; Houwing-Duistermaat, Jeanine; Richards, Marcus; Jones, Peter B.

2017-01-01

Very few molecular genetic studies of personality traits have used longitudinal phenotypic data, therefore molecular basis for developmental change and stability of personality remains to be explored. We examined the role of the monoamine oxidase A gene (MAOA) on extraversion and neuroticism from adolescence to adulthood, using modern latent variable methods. A sample of 1,160 male and 1,180 female participants with complete genotyping data was drawn from a British national birth cohort, the MRC National Survey of Health and Development (NSHD). The predictor variable was based on a latent variable representing genetic variations of the MAOA gene measured by three SNPs (rs3788862, rs5906957, and rs979606). Latent phenotype variables were constructed using psychometric methods to represent cross-sectional and longitudinal phenotypes of extraversion and neuroticism measured at ages 16 and 26. In males, the MAOA genetic latent variable (AAG) was associated with lower extraversion score at age 16 (β = −0.167; CI: −0.289, −0.045; p = 0.007, FDRp = 0.042), as well as greater increase in extraversion score from 16 to 26 years (β = 0.197; CI: 0.067, 0.328; p = 0.003, FDRp = 0.036). No genetic association was found for neuroticism after adjustment for multiple testing. Although, we did not find statistically significant associations after multiple testing correction in females, this result needs to be interpreted with caution due to issues related to x-inactivation in females. The latent variable method is an effective way of modeling phenotype- and genetic-based variances and may therefore improve the methodology of molecular genetic studies of complex psychological traits. PMID:29075213

Monoamine Oxidase A (MAOA) Gene and Personality Traits from Late Adolescence through Early Adulthood: A Latent Variable Investigation.

PubMed

Xu, Man K; Gaysina, Darya; Tsonaka, Roula; Morin, Alexandre J S; Croudace, Tim J; Barnett, Jennifer H; Houwing-Duistermaat, Jeanine; Richards, Marcus; Jones, Peter B

2017-01-01

Very few molecular genetic studies of personality traits have used longitudinal phenotypic data, therefore molecular basis for developmental change and stability of personality remains to be explored. We examined the role of the monoamine oxidase A gene ( MAOA ) on extraversion and neuroticism from adolescence to adulthood, using modern latent variable methods. A sample of 1,160 male and 1,180 female participants with complete genotyping data was drawn from a British national birth cohort, the MRC National Survey of Health and Development (NSHD). The predictor variable was based on a latent variable representing genetic variations of the MAOA gene measured by three SNPs (rs3788862, rs5906957, and rs979606). Latent phenotype variables were constructed using psychometric methods to represent cross-sectional and longitudinal phenotypes of extraversion and neuroticism measured at ages 16 and 26. In males, the MAOA genetic latent variable (AAG) was associated with lower extraversion score at age 16 (β = -0.167; CI: -0.289, -0.045; p = 0.007, FDRp = 0.042), as well as greater increase in extraversion score from 16 to 26 years (β = 0.197; CI: 0.067, 0.328; p = 0.003, FDRp = 0.036). No genetic association was found for neuroticism after adjustment for multiple testing. Although, we did not find statistically significant associations after multiple testing correction in females, this result needs to be interpreted with caution due to issues related to x-inactivation in females. The latent variable method is an effective way of modeling phenotype- and genetic-based variances and may therefore improve the methodology of molecular genetic studies of complex psychological traits.

Genetic variation and virulence of Autographa californica multiple nucleopolyhedrovirus and Trichoplusia ni single nucleopolyhedrovirus isolates

USDA-ARS?s Scientific Manuscript database

To determine the genetic diversity within the baculovirus species Autographa calfornica multiple nucleopolyhedrovirus (AcMNPV; Baculoviridae: Alphabaculovirus), a PCR-based method was used to identify and classify baculoviruses found in virus samples from the lepidopteran host species A. californi...

Sex-specific effect of CPB2 Ala147Thr but not Thr325Ile variants on the risk of venous thrombosis: A comprehensive meta-analysis

PubMed Central

Zwingerman, Nora; Medina-Rivera, Alejandra; Kassam, Irfahan; Wilson, Michael D.; Morange, Pierre-Emmanuel; Trégouët, David-Alexandre; Gagnon, France

2017-01-01

Background Thrombin activatable fibrinolysis inhibitor (TAFI), encoded by the Carboxypeptidase B2 gene (CPB2), is an inhibitor of fibrinolysis and plays a role in the pathogenesis of venous thrombosis. Experimental findings support a functional role of genetic variants in CPB2, while epidemiological studies have been unable to confirm associations with risk of venous thrombosis. Sex-specific effects could underlie the observed inconsistent associations between CPB2 genetic variants and venous thrombosis. Methods A comprehensive literature search was conducted for associations between Ala147Thr and Thr325Ile variants with venous thrombosis. Authors were contacted to provide sex-specific genotype counts from their studies. Combined and sex-specific random effects meta-analyses were used to estimate a pooled effect estimate for primary and secondary genetic models. Results A total of 17 studies met the inclusion criteria. A sex-specific meta-analysis applying a dominant model supported a protective effect of Ala147Thr on venous thrombosis in females (OR = 0.81, 95%CI: 0.68,0.97; p = 0.018), but not in males (OR = 1.06, 95%CI:0.96–1.16; p = 0.263). The Thr325Ile did not show a sex-specific effect but showed variation in allele frequencies by geographic region. A subgroup analysis of studies in European countries showed decreased risk, with a recessive model (OR = 0.83, 95%CI:0.71–0.97, p = 0.021) for venous thrombosis. Conclusions A comprehensive literature review, including unpublished data, provided greater statistical power for the analyses and decreased the likelihood of publication bias influencing the results. Sex-specific analyses explained apparent discrepancies across genetic studies of Ala147Thr and venous thrombosis. While, careful selection of genetic models based on population genetics, evolutionary and biological knowledge can increase power by decreasing the need to adjust for testing multiple models. PMID:28552956

Sex-specific effect of CPB2 Ala147Thr but not Thr325Ile variants on the risk of venous thrombosis: A comprehensive meta-analysis.

PubMed

Zwingerman, Nora; Medina-Rivera, Alejandra; Kassam, Irfahan; Wilson, Michael D; Morange, Pierre-Emmanuel; Trégouët, David-Alexandre; Gagnon, France

2017-01-01

Thrombin activatable fibrinolysis inhibitor (TAFI), encoded by the Carboxypeptidase B2 gene (CPB2), is an inhibitor of fibrinolysis and plays a role in the pathogenesis of venous thrombosis. Experimental findings support a functional role of genetic variants in CPB2, while epidemiological studies have been unable to confirm associations with risk of venous thrombosis. Sex-specific effects could underlie the observed inconsistent associations between CPB2 genetic variants and venous thrombosis. A comprehensive literature search was conducted for associations between Ala147Thr and Thr325Ile variants with venous thrombosis. Authors were contacted to provide sex-specific genotype counts from their studies. Combined and sex-specific random effects meta-analyses were used to estimate a pooled effect estimate for primary and secondary genetic models. A total of 17 studies met the inclusion criteria. A sex-specific meta-analysis applying a dominant model supported a protective effect of Ala147Thr on venous thrombosis in females (OR = 0.81, 95%CI: 0.68,0.97; p = 0.018), but not in males (OR = 1.06, 95%CI:0.96-1.16; p = 0.263). The Thr325Ile did not show a sex-specific effect but showed variation in allele frequencies by geographic region. A subgroup analysis of studies in European countries showed decreased risk, with a recessive model (OR = 0.83, 95%CI:0.71-0.97, p = 0.021) for venous thrombosis. A comprehensive literature review, including unpublished data, provided greater statistical power for the analyses and decreased the likelihood of publication bias influencing the results. Sex-specific analyses explained apparent discrepancies across genetic studies of Ala147Thr and venous thrombosis. While, careful selection of genetic models based on population genetics, evolutionary and biological knowledge can increase power by decreasing the need to adjust for testing multiple models.

Integrating multiple evidences in taxonomy: species diversity and phylogeny of mustached bats (Mormoopidae: Pteronotus).

PubMed

Pavan, Ana Carolina; Marroig, Gabriel

2016-10-01

A phylogenetic systematic perspective is instrumental in recovering new species and their evolutionary relationships. The advent of new technologies for molecular and morphological data acquisition and analysis, allied to the integration of knowledge from different areas, such as ecology and population genetics, allows for the emergence of more rigorous, accurate and complete scientific hypothesis on species diversity. Mustached bats (genus Pteronotus) are a good model for the application of this integrative approach. They are a widely distributed and a morphologically homogeneous group, but comprising species with remarkable differences in their echolocation strategy and feeding behavior. The latest systematic review suggested six species with 17 subspecies in Pteronotus. Subsequent studies using discrete morphological characters supported the same arrangement. However, recent papers reported high levels of genetic divergence among conspecific taxa followed by bioacoustic and geographic agreement, suggesting an underestimated diversity in the genus. To date, no study merging genetic evidences and morphometric variation along the entire geographic range of this group has been attempted. Based on a comprehensive sampling including representatives of all current taxonomic units, we attempt to delimit species in Pteronotus through the application of multiple methodologies and hierarchically distinct datasets. The molecular approach includes six molecular markers from three genetic transmission systems; morphological investigations used 41 euclidean distances estimated through three-dimensional landmarks collected from 1628 skulls. The phylogenetic analysis reveals a greater diversity than previously reported, with a high correspondence among the genetic lineages and the currently recognized subspecies in the genus. Discriminant analysis of variables describing size and shape of cranial bones support the rising of the genetic groups to the specific status. Based on multiples evidences, we present an updated taxonomic arrangement composed by 16 extant species and a new and more robust phylogenetic hypothesis for the species included in the genus Pteronotus. Studies developed under such integrative taxonomic approach are timely for a deeper and wider comprehension of Neotropical diversity, representing the first step for answering broader questions on evolutionary and ecological aspects of Neotropical life history. Copyright © 2016 Elsevier Inc. All rights reserved.

Etiology in psychiatry: embracing the reality of poly‐gene‐environmental causation of mental illness

PubMed Central

Uher, Rudolf; Zwicker, Alyson

2017-01-01

Intriguing findings on genetic and environmental causation suggest a need to reframe the etiology of mental disorders. Molecular genetics shows that thousands of common and rare genetic variants contribute to mental illness. Epidemiological studies have identified dozens of environmental exposures that are associated with psychopathology. The effect of environment is likely conditional on genetic factors, resulting in gene‐environment interactions. The impact of environmental factors also depends on previous exposures, resulting in environment‐environment interactions. Most known genetic and environmental factors are shared across multiple mental disorders. Schizophrenia, bipolar disorder and major depressive disorder, in particular, are closely causally linked. Synthesis of findings from twin studies, molecular genetics and epidemiological research suggests that joint consideration of multiple genetic and environmental factors has much greater explanatory power than separate studies of genetic or environmental causation. Multi‐factorial gene‐environment interactions are likely to be a generic mechanism involved in the majority of cases of mental illness, which is only partially tapped by existing gene‐environment studies. Future research may cut across psychiatric disorders and address poly‐causation by considering multiple genetic and environmental measures across the life course with a specific focus on the first two decades of life. Integrative analyses of poly‐causation including gene‐environment and environment‐environment interactions can realize the potential for discovering causal types and mechanisms that are likely to generate new preventive and therapeutic tools. PMID:28498595

Who's your daddy? Using RADseq to explore survival and paternity in the clownfish, Amphiprion clarkii.

NASA Astrophysics Data System (ADS)

Stuart, M. R.; Pinsky, M. L.

2016-02-01

The ability to use DNA to identify individuals and their offspring has begun to revolutionize marine ecology. However, genetic mark-recapture and parentage studies typically require large numbers of individuals and associated high genotyping costs. Here, we describe a rapid and relatively low-cost protocol for genotyping non-model organisms at thousands of Single Nucleotide Polymorphisms (SNPs) using massively parallel sequencing. We apply the approach to a population of yellowtail clownfish, Amphiprion clarkii, to detect genetic mark-recaptures and parent-offspring relationships. We test multiple bioinformatic approaches and describe how this method could be applied to a wide variety of marine organisms.

Using Movies to Analyse Gene Circuit Dynamics in Single Cells

PubMed Central

Locke, James CW; Elowitz, Michael B

2010-01-01

Preface Many bacterial systems rely on dynamic genetic circuits to control critical processes. A major goal of systems biology is to understand these behaviours in terms of individual genes and their interactions. However, traditional techniques based on population averages wash out critical dynamics that are either unsynchronized between cells or driven by fluctuations, or ‘noise,’ in cellular components. Recently, the combination of time-lapse microscopy, quantitative image analysis, and fluorescent protein reporters has enabled direct observation of multiple cellular components over time in individual cells. In conjunction with mathematical modelling, these techniques are now providing powerful insights into genetic circuit behaviour in diverse microbial systems. PMID:19369953

Genotype-phenotype association study via new multi-task learning model

PubMed Central

Huo, Zhouyuan; Shen, Dinggang

2018-01-01

Research on the associations between genetic variations and imaging phenotypes is developing with the advance in high-throughput genotype and brain image techniques. Regression analysis of single nucleotide polymorphisms (SNPs) and imaging measures as quantitative traits (QTs) has been proposed to identify the quantitative trait loci (QTL) via multi-task learning models. Recent studies consider the interlinked structures within SNPs and imaging QTs through group lasso, e.g. ℓ2,1-norm, leading to better predictive results and insights of SNPs. However, group sparsity is not enough for representing the correlation between multiple tasks and ℓ2,1-norm regularization is not robust either. In this paper, we propose a new multi-task learning model to analyze the associations between SNPs and QTs. We suppose that low-rank structure is also beneficial to uncover the correlation between genetic variations and imaging phenotypes. Finally, we conduct regression analysis of SNPs and QTs. Experimental results show that our model is more accurate in prediction than compared methods and presents new insights of SNPs. PMID:29218896

Methods for cost estimation in software project management

NASA Astrophysics Data System (ADS)

Briciu, C. V.; Filip, I.; Indries, I. I.

2016-02-01

The speed in which the processes used in software development field have changed makes it very difficult the task of forecasting the overall costs for a software project. By many researchers, this task has been considered unachievable, but there is a group of scientist for which this task can be solved using the already known mathematical methods (e.g. multiple linear regressions) and the new techniques as genetic programming and neural networks. The paper presents a solution for building a model for the cost estimation models in the software project management using genetic algorithms starting from the PROMISE datasets related COCOMO 81 model. In the first part of the paper, a summary of the major achievements in the research area of finding a model for estimating the overall project costs is presented together with the description of the existing software development process models. In the last part, a basic proposal of a mathematical model of a genetic programming is proposed including here the description of the chosen fitness function and chromosome representation. The perspective of model described it linked with the current reality of the software development considering as basis the software product life cycle and the current challenges and innovations in the software development area. Based on the author's experiences and the analysis of the existing models and product lifecycle it was concluded that estimation models should be adapted with the new technologies and emerging systems and they depend largely by the chosen software development method.

Two methods for parameter estimation using multiple-trait models and beef cattle field data.

PubMed

Bertrand, J K; Kriese, L A

1990-08-01

Two methods are presented for estimating variances and covariances from beef cattle field data using multiple-trait sire models. Both methods require that the first trait have no missing records and that the contemporary groups for the second trait be subsets of the contemporary groups for the first trait; however, the second trait may have missing records. One method uses pseudo expectations involving quadratics composed of the solutions and the right-hand sides of the mixed model equations. The other method is an extension of Henderson's Simple Method to the multiple trait case. Neither of these methods requires any inversions of large matrices in the computation of the parameters; therefore, both methods can handle very large sets of data. Four simulated data sets were generated to evaluate the methods. In general, both methods estimated genetic correlations and heritabilities that were close to the Restricted Maximum Likelihood estimates and the true data set values, even when selection within contemporary groups was practiced. The estimates of residual correlations by both methods, however, were biased by selection. These two methods can be useful in estimating variances and covariances from multiple-trait models in large populations that have undergone a minimal amount of selection within contemporary groups.

Obesity during childhood and adolescence increases susceptibility to multiple sclerosis after accounting for established genetic and environmental risk factors.

PubMed

Gianfrancesco, Milena A; Acuna, Brigid; Shen, Ling; Briggs, Farren B S; Quach, Hong; Bellesis, Kalliope H; Bernstein, Allan; Hedstrom, Anna K; Kockum, Ingrid; Alfredsson, Lars; Olsson, Tomas; Schaefer, Catherine; Barcellos, Lisa F

2014-01-01

To investigate the association between obesity and multiple sclerosis (MS) while accounting for established genetic and environmental risk factors. Participants included members of Kaiser Permanente Medical Care Plan, Northern California Region (KPNC) (1235 MS cases and 697 controls). Logistic regression models were used to estimate odds ratios (ORs) with 95% confidence intervals (95% CI). Body mass index (BMI) or body size was the primary predictor of each model. Both incident and prevalent MS cases were studied. In analyses stratified by gender, being overweight at ages 10 and 20 were associated with MS in females (p<0.01). Estimates trended in the same direction for males, but were not significant. BMI in 20s demonstrated a linear relationship with MS (p-trend=9.60×10(-4)), and a twofold risk of MS for females with a BMI≥30kg/m(2) was observed (OR=2.15, 95% CI 1.18, 3.92). Significant associations between BMI in 20s and MS in males were not observed. Multivariate modelling demonstrated that significant associations between BMI or body size with MS in females persisted after adjusting for history of infectious mononucleosis and genetic risk factors, including HLA-DRB1*15:01 and established non-HLA risk alleles. Results show that childhood and adolescence obesity confer increased risk of MS in females beyond established heritable and environmental risk factors. Strong evidence for a dose-effect of BMI in 20s and MS was observed. The magnitude of BMI association with MS is as large as other known MS risk factors. Copyright © 2014 Asian Oceanian Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.

Obesity during childhood and adolescence increases susceptibility to multiple sclerosis after accounting for established genetic and environmental risk factors

PubMed Central

Gianfrancesco, Milena A.; Acuna, Brigid; Shen, Ling; Briggs, Farren B.S.; Quach, Hong; Bellesis, Kalliope H.; Bernstein, Allan; Hedstrom, Anna K.; Kockum, Ingrid; Alfredsson, Lars; Olsson, Tomas; Schaefer, Catherine; Barcellos, Lisa F.

2014-01-01

Objective To investigate the association between obesity and multiple sclerosis (MS) while accounting for established genetic and environmental risk factors. Methods Participants included members of Kaiser Permanente Medical Care Plan, Northern California Region (KPNC) (1,235 MS cases and 697 controls). Logistic regression models were used to estimate odds ratios (ORs) with 95% confidence intervals (95% CI). Body mass index (BMI) or body size was the primary predictor of each model. Both incident and prevalent MS cases were studied. Results In analyses stratified by gender, being overweight at age 10 and 20 were associated with MS in females (p<0.01). Estimates trended in the same direction for males, but were not significant. BMI in 20’s demonstrated a linear relationship with MS (p-trend=9.60 × 10−4), and a twofold risk of MS for females with a BMI ≥ 30 kg/m2 was observed (OR = 2.15, 95% CI 1.18, 3.92). Significant associations between BMI in 20’s and MS in males were not observed. Multivariate modeling demonstrated that significant associations between BMI or body size with MS in females persisted after adjusting for history of infectious mononucleosis and genetic risk factors, including HLA-DRB1*15:01 and established non-HLA risk alleles. Interpretation Results show that childhood and adolescence obesity confer increased risk of MS in females beyond established heritable and environmental risk factors. Strong evidence for a dose-effect of BMI in 20’s and MS was observed. The magnitude of BMI association with MS is as large as other known MS risk factors. PMID:25263833

GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures.

PubMed

Urbanowicz, Ryan J; Kiralis, Jeff; Sinnott-Armstrong, Nicholas A; Heberling, Tamra; Fisher, Jonathan M; Moore, Jason H

2012-10-01

Geneticists who look beyond single locus disease associations require additional strategies for the detection of complex multi-locus effects. Epistasis, a multi-locus masking effect, presents a particular challenge, and has been the target of bioinformatic development. Thorough evaluation of new algorithms calls for simulation studies in which known disease models are sought. To date, the best methods for generating simulated multi-locus epistatic models rely on genetic algorithms. However, such methods are computationally expensive, difficult to adapt to multiple objectives, and unlikely to yield models with a precise form of epistasis which we refer to as pure and strict. Purely and strictly epistatic models constitute the worst-case in terms of detecting disease associations, since such associations may only be observed if all n-loci are included in the disease model. This makes them an attractive gold standard for simulation studies considering complex multi-locus effects. We introduce GAMETES, a user-friendly software package and algorithm which generates complex biallelic single nucleotide polymorphism (SNP) disease models for simulation studies. GAMETES rapidly and precisely generates random, pure, strict n-locus models with specified genetic constraints. These constraints include heritability, minor allele frequencies of the SNPs, and population prevalence. GAMETES also includes a simple dataset simulation strategy which may be utilized to rapidly generate an archive of simulated datasets for given genetic models. We highlight the utility and limitations of GAMETES with an example simulation study using MDR, an algorithm designed to detect epistasis. GAMETES is a fast, flexible, and precise tool for generating complex n-locus models with random architectures. While GAMETES has a limited ability to generate models with higher heritabilities, it is proficient at generating the lower heritability models typically used in simulation studies evaluating new algorithms. In addition, the GAMETES modeling strategy may be flexibly combined with any dataset simulation strategy. Beyond dataset simulation, GAMETES could be employed to pursue theoretical characterization of genetic models and epistasis.

Genetic diversity among isolates of Autographa californica multiple nucleopolyhedrovirus

USDA-ARS?s Scientific Manuscript database

Our knowledge of genetic variation at the nucleotide sequence level of Autographa californica multiple nucleopolyhedrovirus (AcMNPV; Baculoviridae: Alphabaculovirus) derives from complete genome sequences of the C6 clonal isolate of AcMNPV and the R1 and CL3 clonal isolates of AcMNPV variants Rachip...

An analysis of indirect genetic effects on adult body weight of the Pacific white shrimp Litopenaeus vannamei at low rearing density.

PubMed

Luan, Sheng; Luo, Kun; Chai, Zhan; Cao, Baoxiang; Meng, Xianhong; Lu, Xia; Liu, Ning; Xu, Shengyu; Kong, Jie

2015-12-14

Our aim was to estimate the genetic parameters for the direct genetic effect (DGE) and indirect genetic effects (IGE) on adult body weight in the Pacific white shrimp. IGE is the heritable effect of an individual on the trait values of its group mates. To examine IGE on body weight, 4725 shrimp from 105 tagged families were tested in multiple small test groups (MSTG). Each family was separated into three groups (15 shrimp per group) that were randomly assigned to 105 concrete tanks with shrimp from two other families. To estimate breeding values, one large test group (OLTG) in a 300 m(2) circular concrete tank was used for the communal rearing of 8398 individuals from 105 families. Body weight was measured after a growth-test period of more than 200 days. Variance components for body weight in the MSTG programs were estimated using an animal model excluding or including IGE whereas variance components in the OLTG programs were estimated using a conventional animal model that included only DGE. The correlation of DGE between MSTG and OLTG programs was estimated by a two-trait animal model that included or excluded IGE. Heritability estimates for body weight from the conventional animal model in MSTG and OLTG programs were 0.26 ± 0.13 and 0.40 ± 0.06, respectively. The log likelihood ratio test revealed significant IGE on body weight. Total heritable variance was the sum of direct genetic variance (43.5%), direct-indirect genetic covariance (2.1%), and indirect genetic variance (54.4%). It represented 73% of the phenotypic variance and was more than two-fold greater than that (32%) obtained by using a classical heritability model for body weight. Correlations of DGE on body weight between MSTG and OLTG programs were intermediate regardless of whether IGE were included or not in the model. Our results suggest that social interactions contributed to a large part of the heritable variation in body weight. Small and non-significant direct-indirect genetic correlations implied that neutral or slightly cooperative heritable interactions, rather than competition, were dominant in this population but this may be due to the low rearing density.

Host–parasite genotypic interactions in the honey bee: the dynamics of diversity

PubMed Central

Evison, Sophie E F; Fazio, Geraldine; Chappell, Paula; Foley, Kirsten; Jensen, Annette B; Hughes, William O H

2013-01-01

Parasites are thought to be a major driving force shaping genetic variation in their host, and are suggested to be a significant reason for the maintenance of sexual reproduction. A leading hypothesis for the occurrence of multiple mating (polyandry) in social insects is that the genetic diversity generated within-colonies through this behavior promotes disease resistance. This benefit is likely to be particularly significant when colonies are exposed to multiple species and strains of parasites, but host–parasite genotypic interactions in social insects are little known. We investigated this using honey bees, which are naturally polyandrous and consequently produce genetically diverse colonies containing multiple genotypes (patrilines), and which are also known to host multiple strains of various parasite species. We found that host genotypes differed significantly in their resistance to different strains of the obligate fungal parasite that causes chalkbrood disease, while genotypic variation in resistance to the facultative fungal parasite that causes stonebrood disease was less pronounced. Our results show that genetic variation in disease resistance depends in part on the parasite genotype, as well as species, with the latter most likely relating to differences in parasite life history and host–parasite coevolution. Our results suggest that the selection pressure from genetically diverse parasites might be an important driving force in the evolution of polyandry, a mechanism that generates significant genetic diversity in social insects. PMID:23919163

Host-parasite genotypic interactions in the honey bee: the dynamics of diversity.

PubMed

Evison, Sophie E F; Fazio, Geraldine; Chappell, Paula; Foley, Kirsten; Jensen, Annette B; Hughes, William O H

2013-07-01

Parasites are thought to be a major driving force shaping genetic variation in their host, and are suggested to be a significant reason for the maintenance of sexual reproduction. A leading hypothesis for the occurrence of multiple mating (polyandry) in social insects is that the genetic diversity generated within-colonies through this behavior promotes disease resistance. This benefit is likely to be particularly significant when colonies are exposed to multiple species and strains of parasites, but host-parasite genotypic interactions in social insects are little known. We investigated this using honey bees, which are naturally polyandrous and consequently produce genetically diverse colonies containing multiple genotypes (patrilines), and which are also known to host multiple strains of various parasite species. We found that host genotypes differed significantly in their resistance to different strains of the obligate fungal parasite that causes chalkbrood disease, while genotypic variation in resistance to the facultative fungal parasite that causes stonebrood disease was less pronounced. Our results show that genetic variation in disease resistance depends in part on the parasite genotype, as well as species, with the latter most likely relating to differences in parasite life history and host-parasite coevolution. Our results suggest that the selection pressure from genetically diverse parasites might be an important driving force in the evolution of polyandry, a mechanism that generates significant genetic diversity in social insects.

[Future challenges in multiple sclerosis].

PubMed

Fernández, Óscar

2014-12-01

Multiple sclerosis occurs in genetically susceptible individuals, in whom an unknown environmental factor triggers an immune response, giving rise to a chronic and disabling autoimmune disease. Currently, significant progress is being made in our knowledge of the frequency and distribution of multiple sclerosis and its risk factors, genetics, pathology, pathogenesis, diagnostic and prognostic markers, and treatment. This has radically changed patients' and clinicians' expectations of multiple sclerosis and has raised hope that there will soon be a way to control the disease. Copyright © 2014 Elsevier España, S.L.U. All rights reserved.

Value of genetic profiling for the prediction of coronary heart disease.

PubMed

van der Net, Jeroen B; Janssens, A Cecile J W; Sijbrands, Eric J G; Steyerberg, Ewout W

2009-07-01

Advances in high-throughput genomics facilitate the identification of novel genetic susceptibility variants for coronary heart disease (CHD). This may improve CHD risk prediction. The aim of the present simulation study was to investigate to what degree CHD risk can be predicted by testing multiple genetic variants (genetic profiling). We simulated genetic profiles for a population of 100,000 individuals with a 10-year CHD incidence of 10%. For each combination of model parameters (number of variants, genotype frequency and odds ratio [OR]), we calculated the area under the receiver operating characteristic curve (AUC) to indicate the discrimination between individuals who will and will not develop CHD. The AUC of genetic profiles could rise to 0.90 when 100 hypothetical variants with ORs of 1.5 and genotype frequencies of 50% were simulated. The AUC of a genetic profile consisting of 10 established variants, with ORs ranging from 1.13 to 1.42, was 0.59. When 2, 5, and 10 times as many identical variants would be identified, the AUCs were 0.63, 0.69, and 0.76. To obtain AUCs similar to those of conventional CHD risk predictors, a considerable number of additional common genetic variants need to be identified with preferably strong effects.

Quantitative Genetic Modeling of the Parental Care Hypothesis for the Evolution of Endothermy

PubMed Central

Bacigalupe, Leonardo D.; Moore, Allen J.; Nespolo, Roberto F.; Rezende, Enrico L.; Bozinovic, Francisco

2017-01-01

There are two heuristic explanations proposed for the evolution of endothermy in vertebrates: a correlated response to selection for stable body temperatures, or as a correlated response to increased activity. Parental care has been suggested as a major driving force in this context given its impact on the parents' activity levels and energy budgets, and in the offspring's growth rates due to food provisioning and controlled incubation temperature. This results in a complex scenario involving multiple traits and transgenerational fitness benefits that can be hard to disentangle, quantify and ultimately test. Here we demonstrate how standard quantitative genetic models of maternal effects can be applied to study the evolution of endothermy, focusing on the interplay between daily energy expenditure (DEE) of the mother and growth rates of the offspring. Our model shows that maternal effects can dramatically exacerbate evolutionary responses to selection in comparison to regular univariate models (breeder's equation). This effect would emerge from indirect selection mediated by maternal effects concomitantly with a positive genetic covariance between DEE and growth rates. The multivariate nature of selection, which could favor a higher DEE, higher growth rates or both, might partly explain how high turnover rates were continuously favored in a self-reinforcing process. Overall, our quantitative genetic analysis provides support for the parental care hypothesis for the evolution of endothermy. We contend that much has to be gained from quantifying maternal and developmental effects on metabolic and thermoregulatory variation during adulthood. PMID:29311952

3D Protein structure prediction with genetic tabu search algorithm

PubMed Central

2010-01-01

Background Protein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task. Results In order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods. Conclusions The hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively. PMID:20522256

Korean, Japanese, and Chinese populations featured similar genes encoding drug-metabolizing enzymes and transporters: a DMET Plus microarray assessment.

PubMed

Yi, SoJeong; An, Hyungmi; Lee, Howard; Lee, Sangin; Ieiri, Ichiro; Lee, Youngjo; Cho, Joo-Youn; Hirota, Takeshi; Fukae, Masato; Yoshida, Kenji; Nagatsuka, Shinichiro; Kimura, Miyuki; Irie, Shin; Sugiyama, Yuichi; Shin, Dong Wan; Lim, Kyoung Soo; Chung, Jae-Yong; Yu, Kyung-Sang; Jang, In-Jin

2014-10-01

Interethnic differences in genetic polymorphism in genes encoding drug-metabolizing enzymes and transporters are one of the major factors that cause ethnic differences in drug response. This study aimed to investigate genetic polymorphisms in genes involved in drug metabolism, transport, and excretion among Korean, Japanese, and Chinese populations, the three major East Asian ethnic groups. The frequencies of 1936 variants representing 225 genes encoding drug-metabolizing enzymes and transporters were determined from 786 healthy participants (448 Korean, 208 Japanese, and 130 Chinese) using the Affymetrix Drug-Metabolizing Enzymes and Transporters Plus microarray. To compare allele or genotype frequencies in the high-dimensional data among the three East Asian ethnic groups, multiple testing, principal component analysis (PCA), and regularized multinomial logit model through least absolute shrinkage and selection operator were used. On microarray analysis, 1071 of 1936 variants (>50% of markers) were found to be monomorphic. In a large number of genetic variants, the fixation index and Pearson's correlation coefficient of minor allele frequencies were less than 0.034 and greater than 0.95, respectively, among the three ethnic groups. PCA identified 47 genetic variants with multiple testing, but was unable to discriminate ethnic groups by the first three components. Multinomial least absolute shrinkage and selection operator analysis identified 269 genetic variants that showed different frequencies among the three ethnic groups. However, none of those variants distinguished between the three ethnic groups during subsequent PCA. Korean, Japanese, and Chinese populations are not pharmacogenetically distant from one another, at least with regard to drug disposition, metabolism, and elimination.

LEOPARD syndrome

MedlinePlus

Multiple lentigines syndrome; Noonan syndrome with multiple lentigines ... Genetics Home Reference -- ghr.nlm.nih.gov/condition/noonan-syndrome-with-multiple-lentigines National Organization for Rare Disorders -- ...

Latent variable models for gene-environment interactions in longitudinal studies with multiple correlated exposures.

PubMed

Tao, Yebin; Sánchez, Brisa N; Mukherjee, Bhramar

2015-03-30

Many existing cohort studies designed to investigate health effects of environmental exposures also collect data on genetic markers. The Early Life Exposures in Mexico to Environmental Toxicants project, for instance, has been genotyping single nucleotide polymorphisms on candidate genes involved in mental and nutrient metabolism and also in potentially shared metabolic pathways with the environmental exposures. Given the longitudinal nature of these cohort studies, rich exposure and outcome data are available to address novel questions regarding gene-environment interaction (G × E). Latent variable (LV) models have been effectively used for dimension reduction, helping with multiple testing and multicollinearity issues in the presence of correlated multivariate exposures and outcomes. In this paper, we first propose a modeling strategy, based on LV models, to examine the association between repeated outcome measures (e.g., child weight) and a set of correlated exposure biomarkers (e.g., prenatal lead exposure). We then construct novel tests for G × E effects within the LV framework to examine effect modification of outcome-exposure association by genetic factors (e.g., the hemochromatosis gene). We consider two scenarios: one allowing dependence of the LV models on genes and the other assuming independence between the LV models and genes. We combine the two sets of estimates by shrinkage estimation to trade off bias and efficiency in a data-adaptive way. Using simulations, we evaluate the properties of the shrinkage estimates, and in particular, we demonstrate the need for this data-adaptive shrinkage given repeated outcome measures, exposure measures possibly repeated and time-varying gene-environment association. Copyright © 2014 John Wiley & Sons, Ltd.

S4MPLE--Sampler for Multiple Protein-Ligand Entities: Methodology and Rigid-Site Docking Benchmarking.

PubMed

Hoffer, Laurent; Chira, Camelia; Marcou, Gilles; Varnek, Alexandre; Horvath, Dragos

2015-05-19

This paper describes the development of the unified conformational sampling and docking tool called Sampler for Multiple Protein-Ligand Entities (S4MPLE). The main novelty in S4MPLE is the unified dealing with intra- and intermolecular degrees of freedom (DoF). While classically programs are either designed for folding or docking, S4MPLE transcends this artificial specialization. It supports folding, docking of a flexible ligand into a flexible site and simultaneous docking of several ligands. The trick behind it is the formal assimilation of inter-molecular to intra-molecular DoF associated to putative inter-molecular contact axes. This is implemented within the genetic operators powering a Lamarckian Genetic Algorithm (GA). Further novelty includes differentiable interaction fingerprints to control population diversity, and fitting a simple continuum solvent model and favorable contact bonus terms to the AMBER/GAFF force field. Novel applications-docking of fragment-like compounds, simultaneous docking of multiple ligands, including free crystallographic waters-were published elsewhere. This paper discusses: (a) methodology, (b) set-up of the force field energy functions and (c) their validation in classical redocking tests. More than 80% success in redocking was achieved (RMSD of top-ranked pose < 2.0 Å).

Moderate Multiple Parentage and Low Genetic Variation Reduces the Potential for Genetic Incompatibility Avoidance Despite High Risk of Inbreeding

PubMed Central

Tuni, Cristina; Goodacre, Sara; Bechsgaard, Jesper; Bilde, Trine

2012-01-01

Background Polyandry is widespread throughout the animal kingdom. In the absence of direct benefits of mating with different males, the underlying basis for polyandry is enigmatic because it can carry considerable costs such as elevated exposure to sexual diseases, physical injury or other direct fitness costs. Such costs may be balanced by indirect genetic benefits to the offspring of polyandrous females. We investigated polyandry and patterns of parentage in the spider Stegodyphus lineatus. This species experiences relatively high levels of inbreeding as a result of its spatial population structure, philopatry and limited male mating dispersal. Polyandry may provide an opportunity for post mating inbreeding avoidance that reduces the risk of genetic incompatibilities arising from incestuous matings. However, multiple mating carries direct fitness costs to females suggesting that genetic benefits must be substantial to counter direct costs. Methodology/Principal Findings Genetic parentage analyses in two populations from Israel and a Greek island, showed mixed-brood parentage in approximately 50% of the broods. The number of fathers ranged from 1–2 indicating low levels of multiple parentage and there was no evidence for paternity bias in mixed-broods from both populations. Microsatellite loci variation suggested limited genetic variation within populations, especially in the Greek island population. Relatedness estimates among females in the maternal generation and potentially interacting individuals were substantial indicating full-sib and half-sib relationships. Conclusions/Significance Three lines of evidence indicate limited potential to obtain substantial genetic benefits in the form of reduced inbreeding. The relatively low frequency of multiple parentage together with low genetic variation among potential mates and the elevated risk of mating among related individuals as corroborated by our genetic data suggest that there are limited actual outbreeding opportunities for polyandrous females. Polyandry in S. lineatus is thus unlikely to be maintained through adaptive female choice. PMID:22235316

Dynamics of Biomarkers in Relation to Aging and Mortality

PubMed Central

Arbeev, Konstantin G.; Ukraintseva, Svetlana V.; Yashin, Anatoliy I.

2016-01-01

Contemporary longitudinal studies collect repeated measurements of biomarkers allowing one to analyze their dynamics in relation to mortality, morbidity, or other health-related outcomes. Rich and diverse data collected in such studies provide opportunities to investigate how various socioeconomic, demographic, behavioral and other variables can interact with biological and genetic factors to produce differential rates of aging in individuals. In this paper, we review some recent publications investigating dynamics of biomarkers in relation to mortality, which use single biomarkers as well as cumulative measures combining information from multiple biomarkers. We also discuss the analytical approach, the stochastic process models, which conceptualizes several aging-related mechanisms in the structure of the model and allows evaluating “hidden” characteristics of aging-related changes indirectly from available longitudinal data on biomarkers and follow-up on mortality or onset of diseases taking into account other relevant factors (both genetic and non-genetic). We also discuss an extension of the approach, which considers ranges of “optimal values” of biomarkers rather than a single optimal value as in the original model. We discuss practical applications of the approach to single biomarkers and cumulative measures highlighting that the potential of applications to cumulative measures is still largely underused. PMID:27138087

The Genetic and Environmental Etiologies of the Relations between Cognitive Skills and Components of Reading Ability

PubMed Central

Christopher, Micaela E.; Keenan, Janice M.; Hulslander, Jacqueline; DeFries, John C.; Miyake, Akira; Wadsworth, Sally J.; Willcutt, Erik; Pennington, Bruce; Olson, Richard K.

2016-01-01

While previous research has shown cognitive skills to be important predictors of reading ability in children, the respective roles for genetic and environmental influences on these relations is an open question. The present study explored the genetic and environmental etiologies underlying the relations between selected executive functions and cognitive abilities (working memory, inhibition, processing speed, and naming speed) with three components of reading ability (word reading, reading comprehension, and listening comprehension). Twin pairs drawn from the Colorado Front Range (n = 676; 224 monozygotic pairs; 452 dizygotic pairs) between the ages of eight and 16 (M = 11.11) were assessed on multiple measures of each cognitive and reading-related skill. Each cognitive and reading-related skill was modeled as a latent variable, and behavioral genetic analyses estimated the portions of phenotypic variance on each latent variable due to genetic, shared environmental, and nonshared environmental influences. The covariance between the cognitive skills and reading-related skills was driven primarily by genetic influences. The cognitive skills also shared large amounts of genetic variance, as did the reading-related skills. The common cognitive genetic variance was highly correlated with the common reading genetic variance, suggesting that genetic influences involved in general cognitive processing are also important for reading ability. Skill-specific genetic variance in working memory and processing speed also predicted components of reading ability. Taken together, the present study supports a genetic association between children’s cognitive ability and reading ability. PMID:26974208

Genetic architecture of adiposity and organ weight using combined generation QTL analysis.

PubMed

Fawcett, Gloria L; Roseman, Charles C; Jarvis, Joseph P; Wang, Bing; Wolf, Jason B; Cheverud, James M

2008-08-01

We present here a detailed study of the genetic contributions to adult body size and adiposity in the LG,SM advanced intercross line (AIL), an obesity model. This study represents a first step in fine-mapping obesity quantitative trait loci (QTLs) in an AIL. QTLs for adiposity in this model were previously isolated to chromosomes 1, 6, 7, 8, 9, 12, 13, and 18. This study focuses on heritable contributions and the genetic architecture of fatpad and organ weights. We analyzed both the F(2) and F(3) generations of the LG,SM AIL population single-nucleotide polymorphism (SNP) genotyped with a marker density of approximately 4 cM. We replicate 88% of the previously identified obesity QTLs and identify 13 new obesity QTLs. Nearly half of the single-trait QTLs were sex-specific. Several broad QTL regions were resolved into multiple, narrower peaks. The 113 single-trait QTLs for organs and body weight clustered into 27 pleiotropic loci. A large number of epistatic interactions are described which begin to elucidate potential interacting molecular networks. We present a relatively rapid means to obtain fine-mapping details from AILs using dense marker maps and consecutive generations. Analysis of the complex genetic architecture underlying fatpad and organ weights in this model may eventually help to elucidate not only heritable contributions to obesity but also common gene sets for obesity and its comorbidities.

The genetic links between the big five personality traits and general interest domains.

PubMed

Kandler, Christian; Bleidorn, Wiebke; Riemann, Rainer; Angleitner, Alois; Spinath, Frank M

2011-12-01

This is the first genetically informative study in which multiple informants were used to quantify the genetic and environmental sources of individual differences in general interests as well as the phenotypic and genetic links between general interests and Big Five personality traits. Self-reports and two peer ratings from 844 individuals, including 225 monozygotic and 113 dizygotic complete twin pairs, were collected. Multiple-rater scores (composites) revealed that the averaged levels of genetic and environmental effects on seven broad interest domains were similar to those on personality traits. Multivariate analyses showed that about 35% of the genetic and 9% of the environmental variance in interests were explained by personality domains, in particular by Openness. The findings suggest that interests cannot easily be considered as a byproduct of the interactions between personality genotypes and the environmental influences but rather as an internal regulation of behavior with an own genetic basis.

Interactions within the MHC contribute to the genetic architecture of celiac disease.

PubMed

Goudey, Benjamin; Abraham, Gad; Kikianty, Eder; Wang, Qiao; Rawlinson, Dave; Shi, Fan; Haviv, Izhak; Stern, Linda; Kowalczyk, Adam; Inouye, Michael

2017-01-01

Interaction analysis of GWAS can detect signal that would be ignored by single variant analysis, yet few robust interactions in humans have been detected. Recent work has highlighted interactions in the MHC region between known HLA risk haplotypes for various autoimmune diseases. To better understand the genetic interactions underlying celiac disease (CD), we have conducted exhaustive genome-wide scans for pairwise interactions in five independent CD case-control studies, using a rapid model-free approach to examine over 500 billion SNP pairs in total. We found 14 independent interaction signals within the MHC region that achieved stringent replication criteria across multiple studies and were independent of known CD risk HLA haplotypes. The strongest independent CD interaction signal corresponded to genes in the HLA class III region, in particular PRRC2A and GPANK1/C6orf47, which are known to contain variants for non-Hodgkin's lymphoma and early menopause, co-morbidities of celiac disease. Replicable evidence for statistical interaction outside the MHC was not observed. Both within and between European populations, we observed striking consistency of two-locus models and model distribution. Within the UK population, models of CD based on both interactions and additive single-SNP effects increased explained CD variance by approximately 1% over those of single SNPs. The interactions signal detected across the five cohorts indicates the presence of novel associations in the MHC region that cannot be detected using additive models. Our findings have implications for the determination of genetic architecture and, by extension, the use of human genetics for validation of therapeutic targets.

Genetic and environmental influences on last-year major depression in adulthood: a highly heritable stable liability but strong environmental effects on 1-year prevalence.

PubMed

Kendler, K S; Gardner, C O

2017-07-01

This study seeks to clarify the contribution of temporally stable and occasion-specific genetic and environmental influences on risk for major depression (MD). Our sample was 2153 members of female-female twin pairs from the Virginia Twin Registry. We examined four personal interview waves conducted over an 8-year period with MD in the last year defined by DSM-IV criteria. We fitted a structural equation model to the data using classic Mx. The model included genetic and environmental risk factors for a latent, stable vulnerability to MD and for episodes in each of the four waves. The best-fit model was simple and included genetic and unique environmental influences on the latent liability to MD and unique wave-specific environmental effects. The path from latent liability to MD in the last year was constant over time, moderate in magnitude (+0.65) and weaker than the impact of occasion-specific environmental effects (+0.76). Heritability of the latent stable liability to MD was much higher (78%) than that estimated for last-year MD (32%). Of the total unique environmental influences on MD, 13% reflected enduring consequences of earlier environmental insults, 17% diagnostic error and 70% wave-specific short-lived environmental stressors. Both genetic influences on MD and MD heritability are stable over middle adulthood. However, the largest influence on last-year MD is short-lived environmental effects. As predicted by genetic theory, the heritability of MD is increased substantially by measurement at multiple time points largely through the reduction of the effects of measurement error and short-term environmental risk factors.

Bayesian state space models for dynamic genetic network construction across multiple tissues.

PubMed

Liang, Yulan; Kelemen, Arpad

2016-08-01

Construction of gene-gene interaction networks and potential pathways is a challenging and important problem in genomic research for complex diseases while estimating the dynamic changes of the temporal correlations and non-stationarity are the keys in this process. In this paper, we develop dynamic state space models with hierarchical Bayesian settings to tackle this challenge for inferring the dynamic profiles and genetic networks associated with disease treatments. We treat both the stochastic transition matrix and the observation matrix time-variant and include temporal correlation structures in the covariance matrix estimations in the multivariate Bayesian state space models. The unevenly spaced short time courses with unseen time points are treated as hidden state variables. Hierarchical Bayesian approaches with various prior and hyper-prior models with Monte Carlo Markov Chain and Gibbs sampling algorithms are used to estimate the model parameters and the hidden state variables. We apply the proposed Hierarchical Bayesian state space models to multiple tissues (liver, skeletal muscle, and kidney) Affymetrix time course data sets following corticosteroid (CS) drug administration. Both simulation and real data analysis results show that the genomic changes over time and gene-gene interaction in response to CS treatment can be well captured by the proposed models. The proposed dynamic Hierarchical Bayesian state space modeling approaches could be expanded and applied to other large scale genomic data, such as next generation sequence (NGS) combined with real time and time varying electronic health record (EHR) for more comprehensive and robust systematic and network based analysis in order to transform big biomedical data into predictions and diagnostics for precision medicine and personalized healthcare with better decision making and patient outcomes.

Genetic Complexity and Quantitative Trait Loci Mapping of Yeast Morphological Traits

PubMed Central

Nogami, Satoru; Ohya, Yoshikazu; Yvert, Gaël

2007-01-01

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

Applications of genetic algorithms on the structure-activity relationship analysis of some cinnamamides.

PubMed

Hou, T J; Wang, J M; Liao, N; Xu, X J

1999-01-01

Quantitative structure-activity relationships (QSARs) for 35 cinnamamides were studied. By using a genetic algorithm (GA), a group of multiple regression models with high fitness scores was generated. From the statistical analyses of the descriptors used in the evolution procedure, the principal features affecting the anticonvulsant activity were found. The significant descriptors include the partition coefficient, the molar refraction, the Hammet sigma constant of the substituents on the benzene ring, and the formation energy of the molecules. It could be found that the steric complementarity and the hydrophobic interaction between the inhibitors and the receptor were very important to the biological activity, while the contribution of the electronic effect was not so obvious. Moreover, by construction of the spline models for these four principal descriptors, the effective range for each descriptor was identified.

A genetic stochastic process model for genome-wide joint analysis of biomarker dynamics and disease susceptibility with longitudinal data.

PubMed

He, Liang; Zhbannikov, Ilya; Arbeev, Konstantin G; Yashin, Anatoliy I; Kulminski, Alexander M

2017-11-01

Unraveling the underlying biological mechanisms or pathways behind the effects of genetic variations on complex diseases remains one of the major challenges in the post-GWAS (where GWAS is genome-wide association study) era. To further explore the relationship between genetic variations, biomarkers, and diseases for elucidating underlying pathological mechanism, a huge effort has been placed on examining pleiotropic and gene-environmental interaction effects. We propose a novel genetic stochastic process model (GSPM) that can be applied to GWAS and jointly investigate the genetic effects on longitudinally measured biomarkers and risks of diseases. This model is characterized by more profound biological interpretation and takes into account the dynamics of biomarkers during follow-up when investigating the hazards of a disease. We illustrate the rationale and evaluate the performance of the proposed model through two GWAS. One is to detect single nucleotide polymorphisms (SNPs) having interaction effects on type 2 diabetes (T2D) with body mass index (BMI) and the other is to detect SNPs affecting the optimal BMI level for protecting from T2D. We identified multiple SNPs that showed interaction effects with BMI on T2D, including a novel SNP rs11757677 in the CDKAL1 gene (P = 5.77 × 10 -7 ). We also found a SNP rs1551133 located on 2q14.2 that reversed the effect of BMI on T2D (P = 6.70 × 10 -7 ). In conclusion, the proposed GSPM provides a promising and useful tool in GWAS of longitudinal data for interrogating pleiotropic and interaction effects to gain more insights into the relationship between genes, quantitative biomarkers, and risks of complex diseases. © 2017 WILEY PERIODICALS, INC.

Modifications to the Patient Rule-Induction Method that utilize non-additive combinations of genetic and environmental effects to define partitions that predict ischemic heart disease

PubMed Central

Dyson, Greg; Frikke-Schmidt, Ruth; Nordestgaard, Børge G.; Tybjærg-Hansen, Anne; Sing, Charles F.

2009-01-01

This paper extends the Patient Rule-Induction Method (PRIM) for modeling cumulative incidence of disease developed by Dyson et al. (2007) to include the simultaneous consideration of non-additive combinations of predictor variables, a significance test of each combination, an adjustment for multiple testing and a confidence interval for the estimate of the cumulative incidence of disease in each partition. We employ the partitioning algorithm component of the Combinatorial Partitioning Method (CPM) to construct combinations of predictors, permutation testing to assess the significance of each combination, theoretical arguments for incorporating a multiple testing adjustment and bootstrap resampling to produce the confidence intervals. An illustration of this revised PRIM utilizing a sample of 2258 European male participants from the Copenhagen City Heart Study is presented that assesses the utility of genetic variants in predicting the presence of ischemic heart disease beyond the established risk factors. PMID:19025787

Serotonin transporter gene and childhood trauma--a G × E effect on anxiety sensitivity.

PubMed

Klauke, Benedikt; Deckert, Jürgen; Reif, Andreas; Pauli, Paul; Zwanzger, Peter; Baumann, Christian; Arolt, Volker; Glöckner-Rist, Angelika; Domschke, Katharina

2011-12-21

Genetic factors and environmental factors are assumed to interactively influence the pathogenesis of anxiety disorders. Thus, a gene-environment interaction (G × E) study was conducted with respect to anxiety sensitivity (AS) as a promising intermediate phenotype of anxiety disorders. Healthy subjects (N = 363) were assessed for AS, childhood maltreatment (Childhood Trauma Questionnaire), and genotyped for functional serotonin transporter gene variants (5-HTTLPR/5-HTT rs25531). The influence of genetic and environmental variables on AS and its subdimensions was determined by a step-wise hierarchical regression and a multiple indicator multiple cause (MIMIC) model. A significant G × E effect of the more active 5-HTT genotypes and childhood maltreatment on AS was observed. Furthermore, genotype (LL)-childhood trauma interaction particularly influenced somatic AS subdimensions, whereas cognitive subdimensions were affected by childhood maltreatment only. Results indicate a G × E effect of the more active 5-HTT genotypes and childhood maltreatment on AS, with particular impact on its somatic subcomponent. © 2011 Wiley Periodicals, Inc.

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/.

Placental genome and maternal-placental genetic interactions: a genome-wide and candidate gene association study of placental abruption.

PubMed

Denis, Marie; Enquobahrie, Daniel A; Tadesse, Mahlet G; Gelaye, Bizu; Sanchez, Sixto E; Salazar, Manuel; Ananth, Cande V; Williams, Michelle A

2014-01-01

While available evidence supports the role of genetics in the pathogenesis of placental abruption (PA), PA-related placental genome variations and maternal-placental genetic interactions have not been investigated. Maternal blood and placental samples collected from participants in the Peruvian Abruptio Placentae Epidemiology study were genotyped using Illumina's Cardio-Metabochip platform. We examined 118,782 genome-wide SNPs and 333 SNPs in 32 candidate genes from mitochondrial biogenesis and oxidative phosphorylation pathways in placental DNA from 280 PA cases and 244 controls. We assessed maternal-placental interactions in the candidate gene SNPS and two imprinted regions (IGF2/H19 and C19MC). Univariate and penalized logistic regression models were fit to estimate odds ratios. We examined the combined effect of multiple SNPs on PA risk using weighted genetic risk scores (WGRS) with repeated ten-fold cross-validations. A multinomial model was used to investigate maternal-placental genetic interactions. In placental genome-wide and candidate gene analyses, no SNP was significant after false discovery rate correction. The top genome-wide association study (GWAS) hits were rs544201, rs1484464 (CTNNA2), rs4149570 (TNFRSF1A) and rs13055470 (ZNRF3) (p-values: 1.11e-05 to 3.54e-05). The top 200 SNPs of the GWAS overrepresented genes involved in cell cycle, growth and proliferation. The top candidate gene hits were rs16949118 (COX10) and rs7609948 (THRB) (p-values: 6.00e-03 and 8.19e-03). Participants in the highest quartile of WGRS based on cross-validations using SNPs selected from the GWAS and candidate gene analyses had a 8.40-fold (95% CI: 5.8-12.56) and a 4.46-fold (95% CI: 2.94-6.72) higher odds of PA compared to participants in the lowest quartile. We found maternal-placental genetic interactions on PA risk for two SNPs in PPARG (chr3:12313450 and chr3:12412978) and maternal imprinting effects for multiple SNPs in the C19MC and IGF2/H19 regions. Variations in the placental genome and interactions between maternal-placental genetic variations may contribute to PA risk. Larger studies may help advance our understanding of PA pathogenesis.

Prenatal Genetic Counseling (For Parents)

MedlinePlus

... Before you meet with a genetic counselor in person, you may be asked to gather information about your family history. The counselor will want to know of any relatives with genetic disorders, multiple miscarriages, ...

The complexity of personality: advantages of a genetically sensitive multi-group design.

PubMed

Hahn, Elisabeth; Spinath, Frank M; Siedler, Thomas; Wagner, Gert G; Schupp, Jürgen; Kandler, Christian

2012-03-01

Findings from many behavioral genetic studies utilizing the classical twin design suggest that genetic and non-shared environmental effects play a significant role in human personality traits. This study focuses on the methodological advantages of extending the sampling frame to include multiple dyads of relatives. We investigated the sensitivity of heritability estimates to the inclusion of sibling pairs, mother-child pairs and grandparent-grandchild pairs from the German Socio-Economic Panel Study in addition to a classical German twin sample consisting of monozygotic- and dizygotic twins. The resulting dataset contained 1.308 pairs, including 202 monozygotic and 147 dizygotic twin pairs, along with 419 sibling pairs, 438 mother-child dyads, and 102 grandparent-child dyads. This genetically sensitive multi-group design allowed the simultaneous testing of additive and non-additive genetic, common and specific environmental effects, including cultural transmission and twin-specific environmental influences. Using manifest and latent modeling of phenotypes (i.e., controlling for measurement error), we compare results from the extended sample with those from the twin sample alone and discuss implications for future research.

Experimental Performance of a Genetic Algorithm for Airborne Strategic Conflict Resolution

NASA Technical Reports Server (NTRS)

Karr, David A.; Vivona, Robert A.; Roscoe, David A.; DePascale, Stephen M.; Consiglio, Maria

2009-01-01

The Autonomous Operations Planner, a research prototype flight-deck decision support tool to enable airborne self-separation, uses a pattern-based genetic algorithm to resolve predicted conflicts between the ownship and traffic aircraft. Conflicts are resolved by modifying the active route within the ownship s flight management system according to a predefined set of maneuver pattern templates. The performance of this pattern-based genetic algorithm was evaluated in the context of batch-mode Monte Carlo simulations running over 3600 flight hours of autonomous aircraft in en-route airspace under conditions ranging from typical current traffic densities to several times that level. Encountering over 8900 conflicts during two simulation experiments, the genetic algorithm was able to resolve all but three conflicts, while maintaining a required time of arrival constraint for most aircraft. Actual elapsed running time for the algorithm was consistent with conflict resolution in real time. The paper presents details of the genetic algorithm s design, along with mathematical models of the algorithm s performance and observations regarding the effectiveness of using complimentary maneuver patterns when multiple resolutions by the same aircraft were required.

Experimental Performance of a Genetic Algorithm for Airborne Strategic Conflict Resolution

NASA Technical Reports Server (NTRS)

Karr, David A.; Vivona, Robert A.; Roscoe, David A.; DePascale, Stephen M.; Consiglio, Maria

2009-01-01

The Autonomous Operations Planner, a research prototype flight-deck decision support tool to enable airborne self-separation, uses a pattern-based genetic algorithm to resolve predicted conflicts between the ownship and traffic aircraft. Conflicts are resolved by modifying the active route within the ownship's flight management system according to a predefined set of maneuver pattern templates. The performance of this pattern-based genetic algorithm was evaluated in the context of batch-mode Monte Carlo simulations running over 3600 flight hours of autonomous aircraft in en-route airspace under conditions ranging from typical current traffic densities to several times that level. Encountering over 8900 conflicts during two simulation experiments, the genetic algorithm was able to resolve all but three conflicts, while maintaining a required time of arrival constraint for most aircraft. Actual elapsed running time for the algorithm was consistent with conflict resolution in real time. The paper presents details of the genetic algorithm's design, along with mathematical models of the algorithm's performance and observations regarding the effectiveness of using complimentary maneuver patterns when multiple resolutions by the same aircraft were required.

The genetic basis of traits regulating sperm competition and polyandry: can selection favour the evolution of good- and sexy-sperm?

PubMed

Evans, Jonathan P; Simmons, Leigh W

2008-09-01

The good-sperm and sexy-sperm (GS-SS) hypotheses predict that female multiple mating (polyandry) can fuel sexual selection for heritable male traits that promote success in sperm competition. A major prediction generated by these models, therefore, is that polyandry will benefit females indirectly via their sons' enhanced fertilization success. Furthermore, like classic 'good genes' and 'sexy son' models for the evolution of female preferences, GS-SS processes predict a genetic correlation between genes for female mating frequency (analogous to the female preference) and those for traits influencing fertilization success (the sexually selected traits). We examine the premise for these predictions by exploring the genetic basis of traits thought to influence fertilization success and female mating frequency. We also highlight recent debates that stress the possible genetic constraints to evolution of traits influencing fertilization success via GS-SS processes, including sex-linked inheritance, nonadditive effects, interacting parental genotypes, and trade-offs between integrated ejaculate components. Despite these possible constraints, the available data suggest that male traits involved in sperm competition typically exhibit substantial additive genetic variance and rapid evolutionary responses to selection. Nevertheless, the limited data on the genetic variation in female mating frequency implicate strong genetic maternal effects, including X-linkage, which is inconsistent with GS-SS processes. Although the relative paucity of studies on the genetic basis of polyandry does not allow us to draw firm conclusions about the evolutionary origins of this trait, the emerging pattern of sex linkage in genes for polyandry is more consistent with an evolutionary history of antagonistic selection over mating frequency. We advocate further development of GS-SS theory to take account of the complex evolutionary dynamics imposed by sexual conflict over mating frequency.

History, geography and host use shape genomewide patterns of genetic variation in the redheaded pine sawfly (Neodiprion lecontei).

PubMed

Bagley, Robin K; Sousa, Vitor C; Niemiller, Matthew L; Linnen, Catherine R

2017-02-01

Divergent host use has long been suspected to drive population differentiation and speciation in plant-feeding insects. Evaluating the contribution of divergent host use to genetic differentiation can be difficult, however, as dispersal limitation and population structure may also influence patterns of genetic variation. In this study, we use double-digest restriction-associated DNA (ddRAD) sequencing to test the hypothesis that divergent host use contributes to genetic differentiation among populations of the redheaded pine sawfly (Neodiprion lecontei), a widespread pest that uses multiple Pinus hosts throughout its range in eastern North America. Because this species has a broad range and specializes on host plants known to have migrated extensively during the Pleistocene, we first assess overall genetic structure using model-based and model-free clustering methods and identify three geographically distinct genetic clusters. Next, using a composite-likelihood approach based on the site frequency spectrum and a novel strategy for maximizing the utility of linked RAD markers, we infer the population topology and date divergence to the Pleistocene. Based on existing knowledge of Pinus refugia, estimated demographic parameters and patterns of diversity among sawfly populations, we propose a Pleistocene divergence scenario for N. lecontei. Finally, using Mantel and partial Mantel tests, we identify a significant relationship between genetic distance and geography in all clusters, and between genetic distance and host use in two of three clusters. Overall, our results indicate that Pleistocene isolation, dispersal limitation and ecological divergence all contribute to genomewide differentiation in this species and support the hypothesis that host use is a common driver of population divergence in host-specialized insects. © 2016 John Wiley & Sons Ltd.

Effect of genetic background on the contribution of New Zealand Black loci to autoimmune lupus nephritis

PubMed Central

Rozzo, Stephen J.; Vyse, Timothy J.; Drake, Charles G.; Kotzin, Brian L.

1996-01-01

Autoimmune diseases such as systemic lupus erythematosus are complex genetic traits with contributions from major histocompatibility complex (MHC) genes and multiple unknown non-MHC genes. Studies of animal models of lupus have provided important insight into the immunopathogenesis of disease, and genetic analyses of these models overcome certain obstacles encountered when studying human patients. Genome-wide scans of different genetic crosses have been used to map several disease-linked loci in New Zealand hybrid mice. Although some consensus exists among studies mapping the New Zealand Black (NZB) and New Zealand White (NZW) loci that contribute to lupus-like disease, considerable variability is also apparent. A variable in these studies is the genetic background of the non-autoimmune strain, which could influence genetic contributions from the affected strain. A direct examination of this question was undertaken in the present study by mapping NZB nephritis-linked loci in backcrosses involving different non-autoimmune backgrounds. In a backcross with MHC-congenic C57BL/6J mice, H2z appeared to be the strongest genetic determinant of severe lupus nephritis, whereas in a backcross with congenic BALB/cJ mice, H2z showed no influence on disease expression. NZB loci on chromosomes 1, 4, 11, and 14 appeared to segregate with disease in the BALB/cJ cross, but only the influence of the chromosome 1 locus spanned both crosses and showed linkage with disease when all mice were considered. Thus, the results indicate that contributions from disease-susceptibility loci, including MHC, may vary markedly depending on the non-autoimmune strain used in a backcross analysis. These studies provide insight into variables that affect genetic heterogeneity and add an important dimension of complexity for linkage analyses of human autoimmune disease. PMID:8986781

Operationalizing the Reciprocal Engagement Model of Genetic Counseling Practice: a Framework for the Scalable Delivery of Genomic Counseling and Testing.

PubMed

Schmidlen, Tara; Sturm, Amy C; Hovick, Shelly; Scheinfeldt, Laura; Scott Roberts, J; Morr, Lindsey; McElroy, Joseph; Toland, Amanda E; Christman, Michael; O'Daniel, Julianne M; Gordon, Erynn S; Bernhardt, Barbara A; Ormond, Kelly E; Sweet, Kevin

2018-02-19

With the advent of widespread genomic testing for diagnostic indications and disease risk assessment, there is increased need to optimize genetic counseling services to support the scalable delivery of precision medicine. Here, we describe how we operationalized the reciprocal engagement model of genetic counseling practice to develop a framework of counseling components and strategies for the delivery of genomic results. This framework was constructed based upon qualitative research with patients receiving genomic counseling following online receipt of potentially actionable complex disease and pharmacogenomics reports. Consultation with a transdisciplinary group of investigators, including practicing genetic counselors, was sought to ensure broad scope and applicability of these strategies for use with any large-scale genomic testing effort. We preserve the provision of pre-test education and informed consent as established in Mendelian/single-gene disease genetic counseling practice. Following receipt of genomic results, patients are afforded the opportunity to tailor the counseling agenda by selecting the specific test results they wish to discuss, specifying questions for discussion, and indicating their preference for counseling modality. The genetic counselor uses these patient preferences to set the genomic counseling session and to personalize result communication and risk reduction recommendations. Tailored visual aids and result summary reports divide areas of risk (genetic variant, family history, lifestyle) for each disease to facilitate discussion of multiple disease risks. Post-counseling, session summary reports are actively routed to both the patient and their physician team to encourage review and follow-up. Given the breadth of genomic information potentially resulting from genomic testing, this framework is put forth as a starting point to meet the need for scalable genetic counseling services in the delivery of precision medicine.

Adaptive testing for multiple traits in a proportional odds model with applications to detect SNP-brain network associations.

PubMed

Kim, Junghi; Pan, Wei

2017-04-01

There has been increasing interest in developing more powerful and flexible statistical tests to detect genetic associations with multiple traits, as arising from neuroimaging genetic studies. Most of existing methods treat a single trait or multiple traits as response while treating an SNP as a predictor coded under an additive inheritance mode. In this paper, we follow an earlier approach in treating an SNP as an ordinal response while treating traits as predictors in a proportional odds model (POM). In this way, it is not only easier to handle mixed types of traits, e.g., some quantitative and some binary, but it is also potentially more robust to the commonly adopted additive inheritance mode. More importantly, we develop an adaptive test in a POM so that it can maintain high power across many possible situations. Compared to the existing methods treating multiple traits as responses, e.g., in a generalized estimating equation (GEE) approach, the proposed method can be applied to a high dimensional setting where the number of phenotypes (p) can be larger than the sample size (n), in addition to a usual small P setting. The promising performance of the proposed method was demonstrated with applications to the Alzheimer's Disease Neuroimaging Initiative (ADNI) data, in which either structural MRI driven phenotypes or resting-state functional MRI (rs-fMRI) derived brain functional connectivity measures were used as phenotypes. The applications led to the identification of several top SNPs of biological interest. Furthermore, simulation studies showed competitive performance of the new method, especially for p>n. © 2017 WILEY PERIODICALS, INC.

Integrating Genetic, Psychopharmacological and Neuroimaging Studies: A Converging Methods Approach to Understanding the Neurobiology of ADHD

ERIC Educational Resources Information Center

Durston, Sarah; Konrad, Kerstin

2007-01-01

This paper aims to illustrate how combining multiple approaches can inform us about the neurobiology of ADHD. Converging evidence from genetic, psychopharmacological and functional neuroimaging studies has implicated dopaminergic fronto-striatal circuitry in ADHD. However, while the observation of converging evidence from multiple vantage points…

Genomic evidence of demographic fluctuations and lack of genetic structure across flyways in a long distance migrant, the European turtle dove.

PubMed

Calderón, Luciano; Campagna, Leonardo; Wilke, Thomas; Lormee, Hervé; Eraud, Cyril; Dunn, Jenny C; Rocha, Gregorio; Zehtindjiev, Pavel; Bakaloudis, Dimitrios E; Metzger, Benjamin; Cecere, Jacopo G; Marx, Melanie; Quillfeldt, Petra

2016-11-07

Understanding how past climatic oscillations have affected organismic evolution will help predict the impact that current climate change has on living organisms. The European turtle dove, Streptopelia turtur, is a warm-temperature adapted species and a long distance migrant that uses multiple flyways to move between Europe and Africa. Despite being abundant, it is categorized as vulnerable because of a long-term demographic decline. We studied the demographic history and population genetic structure of the European turtle dove using genomic data and mitochondrial DNA sequences from individuals sampled across Europe, and performing paleoclimatic niche modelling simulations. Overall our data suggest that this species is panmictic across Europe, and is not genetically structured across flyways. We found the genetic signatures of demographic fluctuations, inferring an effective population size (Ne) expansion that occurred between the late Pleistocene and early Holocene, followed by a decrease in the Ne that started between the mid Holocene and the present. Our niche modelling analyses suggest that the variations in the Ne are coincident with recent changes in the availability of suitable habitat. We argue that the European turtle dove is prone to undergo demographic fluctuations, a trait that makes it sensitive to anthropogenic impacts, especially when its numbers are decreasing. Also, considering the lack of genetic structure, we suggest all populations across Europe are equally relevant for conservation.

Individual crypt genetic heterogeneity and the origin of metaplastic glandular epithelium in human Barrett’s oesophagus

PubMed Central

Leedham, S J; Preston, S L; McDonald, S A C; Elia, G; Bhandari, P; Poller, D; Harrison, R; Novelli, M R; Jankowski, J A; Wright, N A

2008-01-01

Objectives: Current models of clonal expansion in human Barrett’s oesophagus are based upon heterogenous, flow-purified biopsy analysis taken at multiple segment levels. Detection of identical mutation fingerprints from these biopsy samples led to the proposal that a mutated clone with a selective advantage can clonally expand to fill an entire Barrett’s segment at the expense of competing clones (selective sweep to fixation model). We aimed to assess clonality at a much higher resolution by microdissecting and genetically analysing individual crypts. The histogenesis of Barrett’s metaplasia and neo-squamous islands has never been demonstrated. We investigated the oesophageal gland squamous ducts as the source of both epithelial sub-types. Methods: Individual crypts across Barrett’s biopsy and oesophagectomy blocks were dissected. Determination of tumour suppressor gene loss of heterozygosity patterns, p16 and p53 point mutations were carried out on a crypt-by-crypt basis. Cases of contiguous neo-squamous islands and columnar metaplasia with oesophageal squamous ducts were identified. Tissues were isolated by laser capture microdissection and genetically analysed. Results: Individual crypt dissection revealed mutation patterns that were masked in whole biopsy analysis. Dissection across oesophagectomy specimens demonstrated marked clonal heterogeneity, with multiple independent clones present. We identified a p16 point mutation arising in the squamous epithelium of the oesophageal gland duct, which was also present in a contiguous metaplastic crypt, whereas neo-squamous islands arising from squamous ducts were wild-type with respect to surrounding Barrett’s dysplasia. Conclusions: By studying clonality at the crypt level we demonstrate that Barrett’s heterogeneity arises from multiple independent clones, in contrast to the selective sweep to fixation model of clonal expansion previously described. We suggest that the squamous gland ducts situated throughout the oesophagus are the source of a progenitor cell that may be susceptible to gene mutation resulting in conversion to Barrett’s metaplastic epithelium. Additionally, these data suggest that wild-type ducts may be the source of neo-squamous islands. PMID:18305067

Variance-based selection may explain general mating patterns in social insects.

PubMed

Rueppell, Olav; Johnson, Nels; Rychtár, Jan

2008-06-23

Female mating frequency is one of the key parameters of social insect evolution. Several hypotheses have been suggested to explain multiple mating and considerable empirical research has led to conflicting results. Building on several earlier analyses, we present a simple general model that links the number of queen matings to variance in colony performance and this variance to average colony fitness. The model predicts selection for multiple mating if the average colony succeeds in a focal task, and selection for single mating if the average colony fails, irrespective of the proximate mechanism that links genetic diversity to colony fitness. Empirical support comes from interspecific comparisons, e.g. between the bee genera Apis and Bombus, and from data on several ant species, but more comprehensive empirical tests are needed.

Genetic identification and molecular modeling characterization reveal a novel PROM1 mutation in Stargardt4-like macular dystrophy

PubMed Central

Imani, Saber; Cheng, Jingliang; Shasaltaneh, Marzieh Dehghan; Wei, Chunli; Yang, Lisha; Fu, Shangyi; Zou, Hui; Khan, Md. Asaduzzaman; Zhang, Xianqin; Chen, Hanchun; Zhang, Dianzheng; Duan, Chengxia; Lv, Hongbin; Li, Yumei; Chen, Rui; Fu, Junjiang

2018-01-01

Stargardt disease-4 (STGD4) is an autosomal dominant complex, genetically heterogeneous macular degeneration/dystrophy (MD) disorder. In this paper, we used targeted next generation sequencing and multiple molecular dynamics analyses to identify and characterize a disease-causing genetic variant in four generations of a Chinese family with STGD4-like MD. We found a novel heterozygous missense mutation, c.734T>C (p.L245P) in the PROM1 gene. Structurally, this mutation most likely impairs PROM1 protein stability, flexibility, and amino acid interaction network after changing the amino acid residue Leucine into Proline in the basic helix-loop-helix leucine zipper domain. Molecular dynamic simulation and principal component analysis provide compelling evidence that this PROM1 mutation contributes to disease causativeness or susceptibility variants in patients with STGD4-like MD. Thus, this finding defines new approaches in genetic characterization, accurate diagnosis, and prevention of STGD4-like MD. PMID:29416601

Writing DNA with GenoCAD.

PubMed

Czar, Michael J; Cai, Yizhi; Peccoud, Jean

2009-07-01

Chemical synthesis of custom DNA made to order calls for software streamlining the design of synthetic DNA sequences. GenoCAD (www.genocad.org) is a free web-based application to design protein expression vectors, artificial gene networks and other genetic constructs composed of multiple functional blocks called genetic parts. By capturing design strategies in grammatical models of DNA sequences, GenoCAD guides the user through the design process. By successively clicking on icons representing structural features or actual genetic parts, complex constructs composed of dozens of functional blocks can be designed in a matter of minutes. GenoCAD automatically derives the construct sequence from its comprehensive libraries of genetic parts. Upon completion of the design process, users can download the sequence for synthesis or further analysis. Users who elect to create a personal account on the system can customize their workspace by creating their own parts libraries, adding new parts to the libraries, or reusing designs to quickly generate sets of related constructs.

The transcription factor titration effect dictates level of gene expression.

PubMed

Brewster, Robert C; Weinert, Franz M; Garcia, Hernan G; Song, Dan; Rydenfelt, Mattias; Phillips, Rob

2014-03-13

Models of transcription are often built around a picture of RNA polymerase and transcription factors (TFs) acting on a single copy of a promoter. However, most TFs are shared between multiple genes with varying binding affinities. Beyond that, genes often exist at high copy number-in multiple identical copies on the chromosome or on plasmids or viral vectors with copy numbers in the hundreds. Using a thermodynamic model, we characterize the interplay between TF copy number and the demand for that TF. We demonstrate the parameter-free predictive power of this model as a function of the copy number of the TF and the number and affinities of the available specific binding sites; such predictive control is important for the understanding of transcription and the desire to quantitatively design the output of genetic circuits. Finally, we use these experiments to dynamically measure plasmid copy number through the cell cycle. Copyright © 2014 Elsevier Inc. All rights reserved.

The Genetics of Infertility: Current Status of the Field

PubMed Central

Zorrilla, Michelle; Yatsenko, Alexander N

2013-01-01

Infertility is a relatively common health condition, affecting nearly 7% of all couples. Clinically, it is a highly heterogeneous pathology with a complex etiology that includes environmental and genetic factors. It has been estimated that nearly 50% of infertility cases are due to genetic defects. Hundreds of studies with animal knockout models convincingly showed infertility to be caused by gene defects, single or multiple. However, despite enormous efforts, progress in translating basic research findings into clinical studies has been challenging. The genetic causes remain unexplained for the vast majority of male or female infertility patients. A particular difficulty is the huge number of candidate genes to be studied; there are more than 2,300 genes expressed in the testis alone, and hundreds of those genes influence reproductive function in humans and could contribute to male infertility. At present, there are only a handful of genes or genetic defects that have been shown to cause, or to be strongly associated with, primary infertility. Yet, with completion of the human genome and progress in personalized medicine, the situation is rapidly changing. Indeed, there are 10-15 new gene tests, on average, being added to the clinical genetic testing list annually. PMID:24416713

Improving treatment of neurodevelopmental disorders: recommendations based on preclinical studies.

PubMed

Homberg, Judith R; Kyzar, Evan J; Stewart, Adam Michael; Nguyen, Michael; Poudel, Manoj K; Echevarria, David J; Collier, Adam D; Gaikwad, Siddharth; Klimenko, Viktor M; Norton, William; Pittman, Julian; Nakamura, Shun; Koshiba, Mamiko; Yamanouchi, Hideo; Apryatin, Sergey A; Scattoni, Maria Luisa; Diamond, David M; Ullmann, Jeremy F P; Parker, Matthew O; Brown, Richard E; Song, Cai; Kalueff, Allan V

2016-01-01

Neurodevelopmental disorders (NDDs) are common and severely debilitating. Their chronic nature and reliance on both genetic and environmental factors makes studying NDDs and their treatment a challenging task. Herein, the authors discuss the neurobiological mechanisms of NDDs, and present recommendations on their translational research and therapy, outlined by the International Stress and Behavior Society. Various drugs currently prescribed to treat NDDs also represent a highly diverse group. Acting on various neurotransmitter and physiological systems, these drugs often lack specificity of action, and are commonly used to treat multiple other psychiatric conditions. There has also been relatively little progress in the development of novel medications to treat NDDs. Based on clinical, preclinical and translational models of NDDs, our recommendations cover a wide range of methodological approaches and conceptual strategies. To improve pharmacotherapy and drug discovery for NDDs, we need a stronger emphasis on targeting multiple endophenotypes, a better dissection of genetic/epigenetic factors or "hidden heritability," and a careful consideration of potential developmental/trophic roles of brain neurotransmitters. The validity of animal NDD models can be improved through discovery of novel (behavioral, physiological and neuroimaging) biomarkers, applying proper environmental enrichment, widening the spectrum of model organisms, targeting developmental trajectories of NDD-related behaviors and comorbid conditions beyond traditional NDDs. While these recommendations cannot be addressed all in once, our increased understanding of NDD pathobiology may trigger innovative cross-disciplinary research expanding beyond traditional methods and concepts.

Genetics Home Reference: nonsyndromic aplasia cutis congenita

MedlinePlus

... are some genetic conditions more common in particular ethnic groups? Genetic Changes Nonsyndromic aplasia cutis congenita can have different causes, and often the cause is unknown. Because the condition is sometimes found in multiple members of a family, it is thought to have a genetic component; ...

The genetics of celiac disease: A comprehensive review of clinical implications.

PubMed

Dieli-Crimi, Romina; Cénit, M Carmen; Núñez, Concepción

2015-11-01

Celiac disease (CD) is a complex immune-related disease with a very strong genetic component. Multiple genetic findings over the last decade have added to the already known MHC influence numerous genetic variants associated to CD susceptibility. Currently, it is well-established that 6 MHC and 39 non-MHC loci, including a higher number of independent genetic variants, are associated to disease risk. Moreover, additional regions have been recently implicated in the disease, which would increase the number of involved loci. Together, the firmly described genetic variants account for roughly 31% of CD heritability, being 25% explained by the MHC influence. These new variants represent markers of disease risk and turn the identification of the causal genes and the causal variants inside the associated loci, as well as their precise biological role on the disease, into a major challenge in CD research. Numerous studies have been developed with this aim showing the high impact of risk variants on gene expression. These studies also indicate a central role of CD4(+) T cells in CD pathogenesis and point to B cells as important players, which is in accordance with the key steps highlighted by the immunological models of pathogenesis. We comprehensively summarize the current knowledge about the genetic architecture of CD, characterized by multiple low-risk variants located within diverse loci which are most likely affecting genes with immune-related functions. These findings are leading to a better understanding of CD pathogenesis and helping in the design of new treatments. The repertoire of potential drug targets for CD has largely broadened last years, bringing us closer to get alternative or complementary treatments to the life-long gluten-free diet, the only effective treatment so far. Epigenetics and microbiota are emerging as potent factors modulating disease risk and putatively affecting disease manifestation, which are also being explored as therapeutic targets. Copyright © 2015 Elsevier Ltd. All rights reserved.

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 variation, multiple paternity, and measures of reproductive success in the critically endangered hawksbill turtle (Eretmochelys imbricata).

PubMed

González-Garza, Blanca Idalia; Stow, Adam; Sánchez-Teyer, Lorenzo Felipe; Zapata-Pérez, Omar

2015-12-01

The Yucatán Peninsula in Mexico contains some of the largest breeding groups of the globally distributed and critically endangered hawksbill turtle (Eretmochelys imbricata). An improved understanding of the breeding system of this species and how its genetic variation is structured among nesting areas is required before the threats to its survival can be properly evaluated. Here, we genotype 1195 hatchlings and 41 nesting females at 12 microsatellite loci to assess levels of multiple paternity, genetic variation and whether individual levels of homozygosity are associated with reproductive success. Of the 50 clutches analyzed, only 6% have multiple paternity. The distribution of pairwise relatedness among nesting localities (rookeries) was not random with elevated within-rookery relatedness, and declining relatedness with geographic distance indicating some natal philopatry. Although there was no strong evidence that particular rookeries had lost allelic variation via drift, younger turtles had significantly lower levels of genetic variation than older turtles, suggesting some loss of genetic variation. At present there is no indication that levels of genetic variation are associated with measures of reproductive success such as clutch size, hatching success, and frequency of infertile eggs.

A statistical simulation model for field testing of non-target organisms in environmental risk assessment of genetically modified plants.

PubMed

Goedhart, Paul W; van der Voet, Hilko; Baldacchino, Ferdinando; Arpaia, Salvatore

2014-04-01

Genetic modification of plants may result in unintended effects causing potentially adverse effects on the environment. A comparative safety assessment is therefore required by authorities, such as the European Food Safety Authority, in which the genetically modified plant is compared with its conventional counterpart. Part of the environmental risk assessment is a comparative field experiment in which the effect on non-target organisms is compared. Statistical analysis of such trials come in two flavors: difference testing and equivalence testing. It is important to know the statistical properties of these, for example, the power to detect environmental change of a given magnitude, before the start of an experiment. Such prospective power analysis can best be studied by means of a statistical simulation model. This paper describes a general framework for simulating data typically encountered in environmental risk assessment of genetically modified plants. The simulation model, available as Supplementary Material, can be used to generate count data having different statistical distributions possibly with excess-zeros. In addition the model employs completely randomized or randomized block experiments, can be used to simulate single or multiple trials across environments, enables genotype by environment interaction by adding random variety effects, and finally includes repeated measures in time following a constant, linear or quadratic pattern in time possibly with some form of autocorrelation. The model also allows to add a set of reference varieties to the GM plants and its comparator to assess the natural variation which can then be used to set limits of concern for equivalence testing. The different count distributions are described in some detail and some examples of how to use the simulation model to study various aspects, including a prospective power analysis, are provided.

A statistical simulation model for field testing of non-target organisms in environmental risk assessment of genetically modified plants

PubMed Central

Goedhart, Paul W; van der Voet, Hilko; Baldacchino, Ferdinando; Arpaia, Salvatore

2014-01-01

Genetic modification of plants may result in unintended effects causing potentially adverse effects on the environment. A comparative safety assessment is therefore required by authorities, such as the European Food Safety Authority, in which the genetically modified plant is compared with its conventional counterpart. Part of the environmental risk assessment is a comparative field experiment in which the effect on non-target organisms is compared. Statistical analysis of such trials come in two flavors: difference testing and equivalence testing. It is important to know the statistical properties of these, for example, the power to detect environmental change of a given magnitude, before the start of an experiment. Such prospective power analysis can best be studied by means of a statistical simulation model. This paper describes a general framework for simulating data typically encountered in environmental risk assessment of genetically modified plants. The simulation model, available as Supplementary Material, can be used to generate count data having different statistical distributions possibly with excess-zeros. In addition the model employs completely randomized or randomized block experiments, can be used to simulate single or multiple trials across environments, enables genotype by environment interaction by adding random variety effects, and finally includes repeated measures in time following a constant, linear or quadratic pattern in time possibly with some form of autocorrelation. The model also allows to add a set of reference varieties to the GM plants and its comparator to assess the natural variation which can then be used to set limits of concern for equivalence testing. The different count distributions are described in some detail and some examples of how to use the simulation model to study various aspects, including a prospective power analysis, are provided. PMID:24834325

Identification of SNPs associated with variola virus virulence.

PubMed

Hoen, Anne Gatewood; Gardner, Shea N; Moore, Jason H

2013-02-14

Decades after the eradication of smallpox, its etiological agent, variola virus (VARV), remains a threat as a potential bioweapon. Outbreaks of smallpox around the time of the global eradication effort exhibited variable case fatality rates (CFRs), likely attributable in part to complex viral genetic determinants of smallpox virulence. We aimed to identify genome-wide single nucleotide polymorphisms associated with CFR. We evaluated unadjusted and outbreak geographic location-adjusted models of single SNPs and two- and three-way interactions between SNPs. Using the data mining approach multifactor dimensionality reduction (MDR), we identified five VARV SNPs in models significantly associated with CFR. The top performing unadjusted model and adjusted models both revealed the same two-way gene-gene interaction. We discuss the biological plausibility of the influence of the SNPs identified these and other significant models on the strain-specific virulence of VARV. We have identified genetic loci in the VARV genome that are statistically associated with VARV virulence as measured by CFR. While our ability to infer a causal relationship between the specific SNPs identified in our analysis and VARV virulence is limited, our results suggest that smallpox severity is in part associated with VARV strain variation and that VARV virulence may be determined by multiple genetic loci. This study represents the first application of MDR to the identification of pathogen gene-gene interactions for predicting infectious disease outbreak severity.

Identification of SNPs associated with variola virus virulence

PubMed Central

2013-01-01

Background Decades after the eradication of smallpox, its etiological agent, variola virus (VARV), remains a threat as a potential bioweapon. Outbreaks of smallpox around the time of the global eradication effort exhibited variable case fatality rates (CFRs), likely attributable in part to complex viral genetic determinants of smallpox virulence. We aimed to identify genome-wide single nucleotide polymorphisms associated with CFR. We evaluated unadjusted and outbreak geographic location-adjusted models of single SNPs and two- and three-way interactions between SNPs. Findings Using the data mining approach multifactor dimensionality reduction (MDR), we identified five VARV SNPs in models significantly associated with CFR. The top performing unadjusted model and adjusted models both revealed the same two-way gene-gene interaction. We discuss the biological plausibility of the influence of the SNPs identified these and other significant models on the strain-specific virulence of VARV. Conclusions We have identified genetic loci in the VARV genome that are statistically associated with VARV virulence as measured by CFR. While our ability to infer a causal relationship between the specific SNPs identified in our analysis and VARV virulence is limited, our results suggest that smallpox severity is in part associated with VARV strain variation and that VARV virulence may be determined by multiple genetic loci. This study represents the first application of MDR to the identification of pathogen gene-gene interactions for predicting infectious disease outbreak severity. PMID:23410064

Multidimensional structure-function relationships in human β-cardiac myosin from population-scale genetic variation

PubMed Central

Homburger, Julian R.; Green, Eric M.; Caleshu, Colleen; Sunitha, Margaret S.; Taylor, Rebecca E.; Ruppel, Kathleen M.; Metpally, Raghu Prasad Rao; Colan, Steven D.; Michels, Michelle; Day, Sharlene M.; Olivotto, Iacopo; Bustamante, Carlos D.; Dewey, Frederick E.; Ho, Carolyn Y.; Spudich, James A.; Ashley, Euan A.

2016-01-01

Myosin motors are the fundamental force-generating elements of muscle contraction. Variation in the human β-cardiac myosin heavy chain gene (MYH7) can lead to hypertrophic cardiomyopathy (HCM), a heritable disease characterized by cardiac hypertrophy, heart failure, and sudden cardiac death. How specific myosin variants alter motor function or clinical expression of disease remains incompletely understood. Here, we combine structural models of myosin from multiple stages of its chemomechanical cycle, exome sequencing data from two population cohorts of 60,706 and 42,930 individuals, and genetic and phenotypic data from 2,913 patients with HCM to identify regions of disease enrichment within β-cardiac myosin. We first developed computational models of the human β-cardiac myosin protein before and after the myosin power stroke. Then, using a spatial scan statistic modified to analyze genetic variation in protein 3D space, we found significant enrichment of disease-associated variants in the converter, a kinetic domain that transduces force from the catalytic domain to the lever arm to accomplish the power stroke. Focusing our analysis on surface-exposed residues, we identified a larger region significantly enriched for disease-associated variants that contains both the converter domain and residues on a single flat surface on the myosin head described as the myosin mesa. Notably, patients with HCM with variants in the enriched regions have earlier disease onset than patients who have HCM with variants elsewhere. Our study provides a model for integrating protein structure, large-scale genetic sequencing, and detailed phenotypic data to reveal insight into time-shifted protein structures and genetic disease. PMID:27247418

Qualitatively modelling and analysing genetic regulatory networks: a Petri net approach.

PubMed

Steggles, L Jason; Banks, Richard; Shaw, Oliver; Wipat, Anil

2007-02-01

New developments in post-genomic technology now provide researchers with the data necessary to study regulatory processes in a holistic fashion at multiple levels of biological organization. One of the major challenges for the biologist is to integrate and interpret these vast data resources to gain a greater understanding of the structure and function of the molecular processes that mediate adaptive and cell cycle driven changes in gene expression. In order to achieve this biologists require new tools and techniques to allow pathway related data to be modelled and analysed as network structures, providing valuable insights which can then be validated and investigated in the laboratory. We propose a new technique for constructing and analysing qualitative models of genetic regulatory networks based on the Petri net formalism. We take as our starting point the Boolean network approach of treating genes as binary switches and develop a new Petri net model which uses logic minimization to automate the construction of compact qualitative models. Our approach addresses the shortcomings of Boolean networks by providing access to the wide range of existing Petri net analysis techniques and by using non-determinism to cope with incomplete and inconsistent data. The ideas we present are illustrated by a case study in which the genetic regulatory network controlling sporulation in the bacterium Bacillus subtilis is modelled and analysed. The Petri net model construction tool and the data files for the B. subtilis sporulation case study are available at http://bioinf.ncl.ac.uk/gnapn.

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.

Genome-wide association mapping identifies multiple loci for a canine SLE-related disease complex.

PubMed

Wilbe, Maria; Jokinen, Päivi; Truvé, Katarina; Seppala, Eija H; Karlsson, Elinor K; Biagi, Tara; Hughes, Angela; Bannasch, Danika; Andersson, Göran; Hansson-Hamlin, Helene; Lohi, Hannes; Lindblad-Toh, Kerstin

2010-03-01

The unique canine breed structure makes dogs an excellent model for studying genetic diseases. Within a dog breed, linkage disequilibrium is extensive, enabling genome-wide association (GWA) with only around 15,000 SNPs and fewer individuals than in human studies. Incidences of specific diseases are elevated in different breeds, indicating that a few genetic risk factors might have accumulated through drift or selective breeding. In this study, a GWA study with 81 affected dogs (cases) and 57 controls from the Nova Scotia duck tolling retriever breed identified five loci associated with a canine systemic lupus erythematosus (SLE)-related disease complex that includes both antinuclear antibody (ANA)-positive immune-mediated rheumatic disease (IMRD) and steroid-responsive meningitis-arteritis (SRMA). Fine mapping with twice as many dogs validated these loci. Our results indicate that the homogeneity of strong genetic risk factors within dog breeds allows multigenic disorders to be mapped with fewer than 100 cases and 100 controls, making dogs an excellent model in which to identify pathways involved in human complex diseases.

ACCELERATED FAILURE TIME MODELS PROVIDE A USEFUL STATISTICAL FRAMEWORK FOR AGING RESEARCH

PubMed Central

Swindell, William R.

2009-01-01

Survivorship experiments play a central role in aging research and are performed to evaluate whether interventions alter the rate of aging and increase lifespan. The accelerated failure time (AFT) model is seldom used to analyze survivorship data, but offers a potentially useful statistical approach that is based upon the survival curve rather than the hazard function. In this study, AFT models were used to analyze data from 16 survivorship experiments that evaluated the effects of one or more genetic manipulations on mouse lifespan. Most genetic manipulations were found to have a multiplicative effect on survivorship that is independent of age and well-characterized by the AFT model “deceleration factor”. AFT model deceleration factors also provided a more intuitive measure of treatment effect than the hazard ratio, and were robust to departures from modeling assumptions. Age-dependent treatment effects, when present, were investigated using quantile regression modeling. These results provide an informative and quantitative summary of survivorship data associated with currently known long-lived mouse models. In addition, from the standpoint of aging research, these statistical approaches have appealing properties and provide valuable tools for the analysis of survivorship data. PMID:19007875

Accelerated failure time models provide a useful statistical framework for aging research.

PubMed

Swindell, William R

2009-03-01

Survivorship experiments play a central role in aging research and are performed to evaluate whether interventions alter the rate of aging and increase lifespan. The accelerated failure time (AFT) model is seldom used to analyze survivorship data, but offers a potentially useful statistical approach that is based upon the survival curve rather than the hazard function. In this study, AFT models were used to analyze data from 16 survivorship experiments that evaluated the effects of one or more genetic manipulations on mouse lifespan. Most genetic manipulations were found to have a multiplicative effect on survivorship that is independent of age and well-characterized by the AFT model "deceleration factor". AFT model deceleration factors also provided a more intuitive measure of treatment effect than the hazard ratio, and were robust to departures from modeling assumptions. Age-dependent treatment effects, when present, were investigated using quantile regression modeling. These results provide an informative and quantitative summary of survivorship data associated with currently known long-lived mouse models. In addition, from the standpoint of aging research, these statistical approaches have appealing properties and provide valuable tools for the analysis of survivorship data.

Multispecies genetic objectives in spatial conservation planning.

PubMed

Nielsen, Erica S; Beger, Maria; Henriques, Romina; Selkoe, Kimberly A; von der Heyden, Sophie

2017-08-01

Growing threats to biodiversity and global alteration of habitats and species distributions make it increasingly necessary to consider evolutionary patterns in conservation decision making. Yet, there is no clear-cut guidance on how genetic features can be incorporated into conservation-planning processes, despite multiple molecular markers and several genetic metrics for each marker type to choose from. Genetic patterns differ between species, but the potential tradeoffs among genetic objectives for multiple species in conservation planning are currently understudied. We compared spatial conservation prioritizations derived from 2 metrics of genetic diversity (nucleotide and haplotype diversity) and 2 metrics of genetic isolation (private haplotypes and local genetic differentiation) in mitochondrial DNA of 5 marine species. We compared outcomes of conservation plans based only on habitat representation with plans based on genetic data and habitat representation. Fewer priority areas were selected for conservation plans based solely on habitat representation than on plans that included habitat and genetic data. All 4 genetic metrics selected approximately similar conservation-priority areas, which is likely a result of prioritizing genetic patterns across a genetically diverse array of species. Largely, our results suggest that multispecies genetic conservation objectives are vital to creating protected-area networks that appropriately preserve community-level evolutionary patterns. © 2016 Society for Conservation Biology.

Tracking of multiple targets using online learning for reference model adaptation.

PubMed

Pernkopf, Franz

2008-12-01

Recently, much work has been done in multiple object tracking on the one hand and on reference model adaptation for a single-object tracker on the other side. In this paper, we do both tracking of multiple objects (faces of people) in a meeting scenario and online learning to incrementally update the models of the tracked objects to account for appearance changes during tracking. Additionally, we automatically initialize and terminate tracking of individual objects based on low-level features, i.e., face color, face size, and object movement. Many methods unlike our approach assume that the target region has been initialized by hand in the first frame. For tracking, a particle filter is incorporated to propagate sample distributions over time. We discuss the close relationship between our implemented tracker based on particle filters and genetic algorithms. Numerous experiments on meeting data demonstrate the capabilities of our tracking approach. Additionally, we provide an empirical verification of the reference model learning during tracking of indoor and outdoor scenes which supports a more robust tracking. Therefore, we report the average of the standard deviation of the trajectories over numerous tracking runs depending on the learning rate.

Pleistocene glacial refugia across the Appalachian Mountains and coastal plain in the millipede genus Narceus: Evidence from population genetic, phylogeographic, and paleoclimatic data

PubMed Central

Walker, Matt J; Stockman, Amy K; Marek, Paul E; Bond, Jason E

2009-01-01

Background Species that are widespread throughout historically glaciated and currently non-glaciated areas provide excellent opportunities to investigate the role of Pleistocene climatic change on the distribution of North American biodiversity. Many studies indicate that northern animal populations exhibit low levels of genetic diversity over geographically widespread areas whereas southern populations exhibit relatively high levels. Recently, paleoclimatic data have been combined with niche-based distribution modeling to locate possible refugia during the Last Glacial Maximum. Using phylogeographic, population, and paleoclimatic data, we show that the distribution and mitochondrial data for the millipede genus Narceus are consistent with classical examples of Pleistocene refugia and subsequent post-glacial population expansion seen in other organismal groups. Results The phylogeographic structure of Narceus reveals a complex evolutionary history with signatures of multiple refugia in southeastern North America followed by two major northern expansions. Evidence for refugial populations were found in the southern Appalachian Mountains and in the coastal plain. The northern expansions appear to have radiated from two separate refugia, one from the Gulf Coastal Plain area and the other from the mid-Atlantic coastal region. Distributional models of Narceus during the Last Glacial Maximum show a dramatic reduction from the current distribution, with suitable ecological zones concentrated along the Gulf and Atlantic coastal plain. We found a strong correlation between these zones of ecological suitability inferred from our paleo-model with levels of genetic diversity derived from phylogenetic and population estimates of genetic structuring. Conclusion The signature of climatic change, during and after the Pleistocene, on the distribution of the millipede genus Narceus is evident in the genetic data presented. Niche-based historical distribution modeling strengthens the conclusions drawn from the genetic data and proves useful in identifying probable refugia. Such interdisciplinary biogeographic studies provide a comprehensive approach to understanding these processes that generate and maintain biodiversity as well as the framework necessary to explore questions regarding evolutionary diversification of taxa. PMID:19183468

Genetics Home Reference: esophageal atresia/tracheoesophageal fistula

MedlinePlus

... are some genetic conditions more common in particular ethnic groups? Genetic Changes Isolated EA/TEF is considered to be a multifactorial condition, which means that multiple gene variations and environmental factors likely contribute to its occurrence. ...

Studying circadian rhythm and sleep using genetic screens in Drosophila.

PubMed

Axelrod, Sofia; Saez, Lino; Young, Michael W

2015-01-01

The power of Drosophila melanogaster as a model organism lies in its ability to be used for large-scale genetic screens with the capacity to uncover the genetic basis of biological processes. In particular, genetic screens for circadian behavior, which have been performed since 1971, allowed researchers to make groundbreaking discoveries on multiple levels: they discovered that there is a genetic basis for circadian behavior, they identified the so-called core clock genes that govern this process, and they started to paint a detailed picture of the molecular functions of these clock genes and their encoded proteins. Since the discovery that fruit flies sleep in 2000, researchers have successfully been using genetic screening to elucidate the many questions surrounding this basic animal behavior. In this chapter, we briefly recall the history of circadian rhythm and sleep screens and then move on to describe techniques currently employed for mutagenesis and genetic screening in the field. The emphasis lies on comparing the newer approaches of transgenic RNA interference (RNAi) to classical forms of mutagenesis, in particular in their application to circadian behavior and sleep. We discuss the different screening approaches in light of the literature and published and unpublished sleep and rhythm screens utilizing ethyl methanesulfonate mutagenesis and transgenic RNAi from our lab. © 2015 Elsevier Inc. All rights reserved.

Genetic consequences of trumpeter swan (Cygnus buccinator) reintroductions

USGS Publications Warehouse

Ransler, F.A.; Quinn, T.W.; Oyler-McCance, S.J.

2011-01-01

Relocation programs are often initiated to restore threatened species to previously occupied portions of their range. A primary challenge of restoration efforts is to translocate individuals in a way that prevents loss of genetic diversity and decreases differentiation relative to source populations-a challenge that becomes increasingly difficult when remnant populations of the species are already genetically depauperate. Trumpeter swans were previously extirpated in the entire eastern half of their range. Physical translocations of birds over the last 70 years have restored the species to portions of its historical range. Despite the long history of management, there has been little monitoring of the genetic outcomes of these restoration attempts. We assessed the consequences of this reintroduction program by comparing patterns of genetic variation at 17 microsatellite loci across four restoration flocks (three wild-released, one captive) and their source populations. We found that a wild-released population established from a single source displayed a trend toward reduced genetic diversity relative to and significant genetic differentiation from its source population, though small founder population effects may also explain this pattern. Wild-released flocks restored from multiple populations maintained source levels of genetic variation and lacked significant differentiation from at least one of their sources. Further, the flock originating from a single source revealed significantly lower levels of genetic variation than those established from multiple sources. The distribution of genetic variation in the captive flock was similar to its source. While the case of trumpeter swans provides evidence that restorations from multiple versus single source populations may better preserve natural levels of genetic diversity, more studies are needed to understand the general applicability of this management strategy. ?? 2010 Springer Science+Business Media B.V. (outside the USA).

Volatile terpenoids: multiple functions, biosynthesis, modulation and manipulation by genetic engineering.

PubMed

Abbas, Farhat; Ke, Yanguo; Yu, Rangcai; Yue, Yuechong; Amanullah, Sikandar; Jahangir, Muhammad Muzammil; Fan, Yanping

2017-11-01

Terpenoids play several physiological and ecological functions in plant life through direct and indirect plant defenses and also in human society because of their enormous applications in the pharmaceutical, food and cosmetics industries. Through the aid of genetic engineering its role can by magnified to broad spectrum by improving genetic ability of crop plants, enhancing the aroma quality of fruits and flowers and the production of pharmaceutical terpenoids contents in medicinal plants. Terpenoids are structurally diverse and the most abundant plant secondary metabolites, playing an important role in plant life through direct and indirect plant defenses, by attracting pollinators and through different interactions between the plants and their environment. Terpenoids are also significant because of their enormous applications in the pharmaceutical, food and cosmetics industries. Due to their broad distribution and functional versatility, efforts are being made to decode the biosynthetic pathways and comprehend the regulatory mechanisms of terpenoids. This review summarizes the recent advances in biosynthetic pathways, including the spatiotemporal, transcriptional and post-transcriptional regulatory mechanisms. Moreover, we discuss the multiple functions of the terpene synthase genes (TPS), their interaction with the surrounding environment and the use of genetic engineering for terpenoid production in model plants. Here, we also provide an overview of the significance of terpenoid metabolic engineering in crop protection, plant reproduction and plant metabolic engineering approaches for pharmaceutical terpenoids production and future scenarios in agriculture, which call for sustainable production platforms by improving different plant traits.

Adaptation to seasonality and the winter freeze

PubMed Central

Preston, Jill C.; Sandve, Simen R.

2013-01-01

Flowering plants initially diversified during the Mesozoic era at least 140 million years ago in regions of the world where temperate seasonal environments were not encountered. Since then several cooling events resulted in the contraction of warm and wet environments and the establishment of novel temperate zones in both hemispheres. In response, less than half of modern angiosperm families have members that evolved specific adaptations to cold seasonal climates, including cold acclimation, freezing tolerance, endodormancy, and vernalization responsiveness. Despite compelling evidence for multiple independent origins, the level of genetic constraint on the evolution of adaptations to seasonal cold is not well understood. However, the recent increase in molecular genetic studies examining the response of model and crop species to seasonal cold offers new insight into the evolutionary lability of these traits. This insight has major implications for our understanding of complex trait evolution, and the potential role of local adaptation in response to past and future climate change. In this review, we discuss the biochemical, morphological, and developmental basis of adaptations to seasonal cold, and synthesize recent literature on the genetic basis of these traits in a phylogenomic context. We find evidence for multiple genetic links between distinct physiological responses to cold, possibly reinforcing the coordinated expression of these traits. Furthermore, repeated recruitment of the same or similar ancestral pathways suggests that land plants might be somewhat pre-adapted to dealing with temperature stress, perhaps making inducible cold traits relatively easy to evolve. PMID:23761798

Deficiency for endoglin in tumor vasculature weakens the endothelial barrier to metastatic dissemination

PubMed Central

Anderberg, Charlotte; Cunha, Sara I.; Zhai, Zhenhua; Cortez, Eliane; Pardali, Evangelia; Johnson, Jill R.; Franco, Marcela; Páez-Ribes, Marta; Cordiner, Ross; Fuxe, Jonas; Johansson, Bengt R.; Goumans, Marie-José; Casanovas, Oriol; ten Dijke, Peter; Arthur, Helen M.

2013-01-01

Therapy-induced resistance remains a significant hurdle to achieve long-lasting responses and cures in cancer patients. We investigated the long-term consequences of genetically impaired angiogenesis by engineering multiple tumor models deprived of endoglin, a co-receptor for TGF-β in endothelial cells actively engaged in angiogenesis. Tumors from endoglin-deficient mice adapted to the weakened angiogenic response, and refractoriness to diminished endoglin signaling was accompanied by increased metastatic capability. Mechanistic studies in multiple mouse models of cancer revealed that deficiency for endoglin resulted in a tumor vasculature that displayed hallmarks of endothelial-to-mesenchymal transition, a process of previously unknown significance in cancer biology, but shown by us to be associated with a reduced capacity of the vasculature to avert tumor cell intra- and extravasation. Nevertheless, tumors deprived of endoglin exhibited a delayed onset of resistance to anti-VEGF (vascular endothelial growth factor) agents, illustrating the therapeutic utility of combinatorial targeting of multiple angiogenic pathways for the treatment of cancer. PMID:23401487

Genetic evolutionary taboo search for optimal marker placement in infrared patient setup

NASA Astrophysics Data System (ADS)

Riboldi, M.; Baroni, G.; Spadea, M. F.; Tagaste, B.; Garibaldi, C.; Cambria, R.; Orecchia, R.; Pedotti, A.

2007-09-01

In infrared patient setup adequate selection of the external fiducial configuration is required for compensating inner target displacements (target registration error, TRE). Genetic algorithms (GA) and taboo search (TS) were applied in a newly designed approach to optimal marker placement: the genetic evolutionary taboo search (GETS) algorithm. In the GETS paradigm, multiple solutions are simultaneously tested in a stochastic evolutionary scheme, where taboo-based decision making and adaptive memory guide the optimization process. The GETS algorithm was tested on a group of ten prostate patients, to be compared to standard optimization and to randomly selected configurations. The changes in the optimal marker configuration, when TRE is minimized for OARs, were specifically examined. Optimal GETS configurations ensured a 26.5% mean decrease in the TRE value, versus 19.4% for conventional quasi-Newton optimization. Common features in GETS marker configurations were highlighted in the dataset of ten patients, even when multiple runs of the stochastic algorithm were performed. Including OARs in TRE minimization did not considerably affect the spatial distribution of GETS marker configurations. In conclusion, the GETS algorithm proved to be highly effective in solving the optimal marker placement problem. Further work is needed to embed site-specific deformation models in the optimization process.

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

Does environmental confounding mask pleiotropic effects of a multiple sclerosis susceptibility variant on vitamin D in psychosis?

PubMed

Iyegbe, Conrad O; Acharya, Anita; Lally, John; Gardner-Sood, Poonam; Smith, Louise S; Smith, Shubulade; Murray, Robin; Howes, Oliver; Gaughran, Fiona

2015-01-01

This work addresses the existing and emerging evidence of overlap within the environmental and genetic profiles of multiple sclerosis (MS) and schizophrenia. To investigate whether a genetic risk factor for MS (rs703842), whose variation is indicative of vitamin D status in the disorder, could also be a determinant of vitamin D status in chronic psychosis patients. A cohort of 224 chronic psychosis cases was phenotyped and biologically profiled. The relationship between rs703842 and physiological vitamin D status in the blood plasma was assessed by logistic regression. Deficiency was defined as a blood plasma concentration below 10 ng/µl. Potential environmental confounders of the vitamin D status were considered as part of the analysis. We report suggestive evidence of an association with vitamin D status in established psychosis (ß standardized=0.51, P=0.04). The logistic model fit significantly benefited from controlling for body mass index, depression and ethnicity (χ (2)=91.7; 2 degrees of freedom (df); P=1.2×10(20)). The results suggest that, in addition to lifestyle changes that accompany the onset of illness, vitamin D dysregulation in psychosis has a genetic component that links into MS. Further, comprehensive studies are needed to evaluate this prospect.

A perspective on interaction effects in genetic association studies

PubMed Central

2016-01-01

ABSTRACT The identification of gene–gene and gene–environment interaction in human traits and diseases is an active area of research that generates high expectation, and most often lead to high disappointment. This is partly explained by a misunderstanding of the inherent characteristics of standard regression‐based interaction analyses. Here, I revisit and untangle major theoretical aspects of interaction tests in the special case of linear regression; in particular, I discuss variables coding scheme, interpretation of effect estimate, statistical power, and estimation of variance explained in regard of various hypothetical interaction patterns. Linking this components it appears first that the simplest biological interaction models—in which the magnitude of a genetic effect depends on a common exposure—are among the most difficult to identify. Second, I highlight the demerit of the current strategy to evaluate the contribution of interaction effects to the variance of quantitative outcomes and argue for the use of new approaches to overcome this issue. Finally, I explore the advantages and limitations of multivariate interaction models, when testing for interaction between multiple SNPs and/or multiple exposures, over univariate approaches. Together, these new insights can be leveraged for future method development and to improve our understanding of the genetic architecture of multifactorial traits. PMID:27390122

Dosage changes of a segment at 17p13.1 lead to intellectual disability and microcephaly as a result of complex genetic interaction of multiple genes.

PubMed

Carvalho, Claudia M B; Vasanth, Shivakumar; Shinawi, Marwan; Russell, Chad; Ramocki, Melissa B; Brown, Chester W; Graakjaer, Jesper; Skytte, Anne-Bine; Vianna-Morgante, Angela M; Krepischi, Ana C V; Patel, Gayle S; Immken, LaDonna; Aleck, Kyrieckos; Lim, Cynthia; Cheung, Sau Wai; Rosenberg, Carla; Katsanis, Nicholas; Lupski, James R

2014-11-06

The 17p13.1 microdeletion syndrome is a recently described genomic disorder with a core clinical phenotype of intellectual disability, poor to absent speech, dysmorphic features, and a constellation of more variable clinical features, most prominently microcephaly. We identified five subjects with copy-number variants (CNVs) on 17p13.1 for whom we performed detailed clinical and molecular studies. Breakpoint mapping and retrospective analysis of published cases refined the smallest region of overlap (SRO) for microcephaly to a genomic interval containing nine genes. Dissection of this phenotype in zebrafish embryos revealed a complex genetic architecture: dosage perturbation of four genes (ASGR1, ACADVL, DVL2, and GABARAP) impeded neurodevelopment and decreased dosage of the same loci caused a reduced mitotic index in vitro. Moreover, epistatic analyses in vivo showed that dosage perturbations of discrete gene pairings induce microcephaly. Taken together, these studies support a model in which concomitant dosage perturbation of multiple genes within the CNV drive the microcephaly and possibly other neurodevelopmental phenotypes associated with rearrangements in the 17p13.1 SRO. Copyright © 2014 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

Individual and shared effects of social environment and polygenic risk scores on adolescent body mass index.

PubMed

Coleman, Jonathan R I; Krapohl, Eva; Eley, Thalia C; Breen, Gerome

2018-04-20

Juvenile obesity is associated with adverse health outcomes. Understanding genetic and environmental influences on body mass index (BMI) during adolescence could inform interventions. We investigated independent and interactive effects of parenting, socioeconomic status (SES) and polygenic risk on BMI pre-adolescence, and on the rate of change in BMI across adolescence. Genome-wide genotype data, BMI and child perceptions of parental warmth and punitive discipline were available at 11 years old, and parental SES was available from birth on 3,414 unrelated participants. Linear models were used to test the effects of social environment and polygenic risk on pre-adolescent BMI. Change in BMI across adolescence was assessed in a subset (N = 1943). Sex-specific effects were assessed. Higher genetic risk was associated with increased BMI pre-adolescence and across adolescence (p < 0.00417, corrected for multiple tests). Negative parenting was not significantly associated with either phenotype, but lower SES was associated with increased BMI pre-adolescence. No interactions passed correction for multiple testing. Polygenic risk scores from adult GWAS meta-analyses are associated with BMI in juveniles, suggesting a stable genetic component. Pre-adolescent BMI was associated with social environment, but parental style has, at most, a small effect.

Detecting epistasis with the marginal epistasis test in genetic mapping studies of quantitative traits

PubMed Central

Zeng, Ping; Mukherjee, Sayan; Zhou, Xiang

2017-01-01

Epistasis, commonly defined as the interaction between multiple genes, is an important genetic component underlying phenotypic variation. Many statistical methods have been developed to model and identify epistatic interactions between genetic variants. However, because of the large combinatorial search space of interactions, most epistasis mapping methods face enormous computational challenges and often suffer from low statistical power due to multiple test correction. Here, we present a novel, alternative strategy for mapping epistasis: instead of directly identifying individual pairwise or higher-order interactions, we focus on mapping variants that have non-zero marginal epistatic effects—the combined pairwise interaction effects between a given variant and all other variants. By testing marginal epistatic effects, we can identify candidate variants that are involved in epistasis without the need to identify the exact partners with which the variants interact, thus potentially alleviating much of the statistical and computational burden associated with standard epistatic mapping procedures. Our method is based on a variance component model, and relies on a recently developed variance component estimation method for efficient parameter inference and p-value computation. We refer to our method as the “MArginal ePIstasis Test”, or MAPIT. With simulations, we show how MAPIT can be used to estimate and test marginal epistatic effects, produce calibrated test statistics under the null, and facilitate the detection of pairwise epistatic interactions. We further illustrate the benefits of MAPIT in a QTL mapping study by analyzing the gene expression data of over 400 individuals from the GEUVADIS consortium. PMID:28746338

Lessons to be learned: how a comprehensive neurobiological framework of atypical reading development can inform educational practice

PubMed Central

Ozernov-Palchik, Ola; Yu, Xi; Wang, Yingying; Gaab, Nadine

2016-01-01

Dyslexia is a heritable reading disorder with an estimated prevalence of 5–17%. A multiple deficit model has been proposed that illustrates dyslexia as an outcome of multiple risks and protective factors interacting at the genetic, neural, cognitive, and environmental levels. Here we review the evidence on each of these levels and discuss possible underlying mechanisms and their reciprocal interactions along a developmental timeline. Current and potential implications of neuroscientific findings for contemporary challenges in the field of dyslexia, as well as for reading development and education in general, are then discussed. PMID:27766284

'Food addiction' and its association with a dopaminergic multilocus genetic profile.

PubMed

Davis, Caroline; Loxton, Natalie J; Levitan, Robert D; Kaplan, Allan S; Carter, Jacqueline C; Kennedy, James L

2013-06-13

Our objective was to employ a novel genetic methodology - whereby functional variants of the dopamine pathway were aggregated to reflect a polygenic liability - in the study of food addiction. We anticipated that the composite index of elevated dopamine signaling (a multilocus genetic profile score [MLGP]) would distinguish those with a designation of food addiction (according to the Yale Food Addiction Scale [YFAS] criteria), and age and weight equivalent controls. Our second aim was to assess whether this index was positively associated with eating-related sub-phenotypes of food addiction (e.g. binge eating and food cravings). Adults (n=120) recruited from the community were solicited for an overeating/overweight study. Eating-behavior questionnaires were completed and a blood sample was taken for genotyping. The YFAS identified 21 participants with food addiction. As predicted, the MLGP score was higher in those with YFAS-diagnosed food addiction, and it correlated positively with binge eating, food cravings, and emotional overeating. We then tested a multiple-mediation model proposing that reward-driven overeating facilitates the relationship between the MLGP score and food addiction. The model was statistically significant, supporting the view that the relationship between a composite genetic index of dopamine signaling and food addiction is mediated by certain aspects of reward-responsive overeating. Copyright © 2013 Elsevier Inc. All rights reserved.

Environmental Variables Explain Genetic Structure in a Beetle-Associated Nematode

PubMed Central

McGaughran, Angela; Morgan, Katy; Sommer, Ralf J.

2014-01-01

The distribution of a species is a complex expression of its ecological and evolutionary history and integrating population genetic, environmental, and ecological data can provide new insights into the effects of the environment on the population structure of species. Previous work demonstrated strong patterns of genetic differentiation in natural populations of the hermaphroditic nematode Pristionchus pacificus in its La Réunion Island habitat, but gave no clear understanding of the role of the environment in structuring this variation. Here, we present what is to our knowledge the first study to statistically evaluate the role of the environment in shaping the structure and distribution of nematode populations. We test the hypothesis that genetic structure in P. pacificus is influenced by environmental variables, by combining population genetic analyses of microsatellite data from 18 populations and 370 strains, with multivariate statistics on environmental data, and species distribution modelling. We assess and quantify the relative importance of environmental factors (geographic distance, altitude, temperature, precipitation, and beetle host) on genetic variation among populations. Despite the fact that geographic populations of P. pacificus comprise vast genetic diversity sourced from multiple ancestral lineages, we find strong evidence for local associations between environment and genetic variation. Further, we show that significantly more genetic variation in P. pacificus populations is explained by environmental variation than by geographic distances. This supports a strong role for environmental heterogeneity vs. genetic drift in the divergence of populations, which we suggest may be influenced by adaptive forces. PMID:24498073

Multiple Myeloma Genomics: A Systematic Review.

PubMed

Weaver, Casey J; Tariman, Joseph D

2017-08-01

This integrative review describes the genomic variants that have been found to be associated with poor prognosis in patients diagnosed with multiple myeloma (MM). Second, it identifies MM genetic and genomic changes using next-generation sequencing, specifically whole-genome sequencing or exome sequencing. A search for peer-reviewed articles through PubMed, EBSCOhost, and DePaul WorldCat Libraries Worldwide yielded 33 articles that were included in the final analysis. The most commonly reported genetic changes were KRAS, NRAS, TP53, FAM46C, BRAF, DIS3, ATM, and CCND1. These genetic changes play a role in the pathogenesis of MM, prognostication, and therapeutic targets for novel therapies. MM genetics and genomics are expanding rapidly; oncology nurse clinicians must have basic competencies in genetics and genomics to help patients understand the complexities of genetic and genomic alterations and be able to refer patients to appropriate genomic professionals if needed. Copyright © 2017 Elsevier Inc. All rights reserved.

Genetic susceptibility to lung cancer—light at the end of the tunnel?

PubMed Central

Christiani, David C.

2013-01-01

Lung cancer is one of the most common and deadliest cancers in the world. The major socio-environmental risk factor involved in the development of lung cancer is cigarette smoking. Additionally, there are multiple genetic factors, which may also play a role in lung cancer risk. Early work focused on the presence of relatively prevalent but low-penetrance alterations in candidate genes leading to increased risk of lung cancer. Development of new technologies such as genomic profiling and genome-wide association studies has been helpful in the detection of new genetic variants likely involved in lung cancer risk. In this review, we discuss the role of multiple genetic variants and review their putative role in the risk of lung cancer. Identifying genetic biomarkers and patterns of genetic risk may be useful in the earlier detection and treatment of lung cancer patients. PMID:23349013

Utterance selection model of language change

NASA Astrophysics Data System (ADS)

Baxter, G. J.; Blythe, R. A.; Croft, W.; McKane, A. J.

2006-04-01

We present a mathematical formulation of a theory of language change. The theory is evolutionary in nature and has close analogies with theories of population genetics. The mathematical structure we construct similarly has correspondences with the Fisher-Wright model of population genetics, but there are significant differences. The continuous time formulation of the model is expressed in terms of a Fokker-Planck equation. This equation is exactly soluble in the case of a single speaker and can be investigated analytically in the case of multiple speakers who communicate equally with all other speakers and give their utterances equal weight. Whilst the stationary properties of this system have much in common with the single-speaker case, time-dependent properties are richer. In the particular case where linguistic forms can become extinct, we find that the presence of many speakers causes a two-stage relaxation, the first being a common marginal distribution that persists for a long time as a consequence of ultimate extinction being due to rare fluctuations.

Genetic Network Inference: From Co-Expression Clustering to Reverse Engineering

NASA Technical Reports Server (NTRS)

Dhaeseleer, Patrik; Liang, Shoudan; Somogyi, Roland

2000-01-01

Advances in molecular biological, analytical, and computational technologies are enabling us to systematically investigate the complex molecular processes underlying biological systems. In particular, using high-throughput gene expression assays, we are able to measure the output of the gene regulatory network. We aim here to review datamining and modeling approaches for conceptualizing and unraveling the functional relationships implicit in these datasets. Clustering of co-expression profiles allows us to infer shared regulatory inputs and functional pathways. We discuss various aspects of clustering, ranging from distance measures to clustering algorithms and multiple-duster memberships. More advanced analysis aims to infer causal connections between genes directly, i.e., who is regulating whom and how. We discuss several approaches to the problem of reverse engineering of genetic networks, from discrete Boolean networks, to continuous linear and non-linear models. We conclude that the combination of predictive modeling with systematic experimental verification will be required to gain a deeper insight into living organisms, therapeutic targeting, and bioengineering.

Neural networks with multiple general neuron models: a hybrid computational intelligence approach using Genetic Programming.

PubMed

Barton, Alan J; Valdés, Julio J; Orchard, Robert

2009-01-01

Classical neural networks are composed of neurons whose nature is determined by a certain function (the neuron model), usually pre-specified. In this paper, a type of neural network (NN-GP) is presented in which: (i) each neuron may have its own neuron model in the form of a general function, (ii) any layout (i.e network interconnection) is possible, and (iii) no bias nodes or weights are associated to the connections, neurons or layers. The general functions associated to a neuron are learned by searching a function space. They are not provided a priori, but are rather built as part of an Evolutionary Computation process based on Genetic Programming. The resulting network solutions are evaluated based on a fitness measure, which may, for example, be based on classification or regression errors. Two real-world examples are presented to illustrate the promising behaviour on classification problems via construction of a low-dimensional representation of a high-dimensional parameter space associated to the set of all network solutions.

Genetic variation affecting exon skipping contributes to brain structural atrophy in Alzheimer's disease.

PubMed

Lee, Younghee; Han, Seonggyun; Kim, Dongwook; Kim, Dokyoon; Horgousluoglu, Emrin; Risacher, Shannon L; Saykin, Andrew J; Nho, Kwangsik

2018-01-01

Genetic variation in cis-regulatory elements related to splicing machinery and splicing regulatory elements (SREs) results in exon skipping and undesired protein products. We developed a splicing decision model to identify actionable loci among common SNPs for gene regulation. The splicing decision model identified SNPs affecting exon skipping by analyzing sequence-driven alternative splicing (AS) models and by scanning the genome for the regions with putative SRE motifs. We used non-Hispanic Caucasians with neuroimaging, and fluid biomarkers for Alzheimer's disease (AD) and identified 17,088 common exonic SNPs affecting exon skipping. GWAS identified one SNP (rs1140317) in HLA-DQB1 as significantly associated with entorhinal cortical thickness, AD neuroimaging biomarker, after controlling for multiple testing. Further analysis revealed that rs1140317 was significantly associated with brain amyloid-f deposition (PET and CSF). HLA-DQB1 is an essential immune gene and may regulate AS, thereby contributing to AD pathology. SRE may hold potential as novel therapeutic targets for AD.

Drug response in a genetically engineered mouse model of multiple myeloma is predictive of clinical efficacy

PubMed Central

Chesi, Marta; Matthews, Geoffrey M.; Garbitt, Victoria M.; Palmer, Stephen E.; Shortt, Jake; Lefebure, Marcus; Stewart, A. Keith; Johnstone, Ricky W.

2012-01-01

The attrition rate for anticancer drugs entering clinical trials is unacceptably high. For multiple myeloma (MM), we postulate that this is because of preclinical models that overemphasize the antiproliferative activity of drugs, and clinical trials performed in refractory end-stage patients. We validate the Vk*MYC transgenic mouse as a faithful model to predict single-agent drug activity in MM with a positive predictive value of 67% (4 of 6) for clinical activity, and a negative predictive value of 86% (6 of 7) for clinical inactivity. We identify 4 novel agents that should be prioritized for evaluation in clinical trials. Transplantation of Vk*MYC tumor cells into congenic mice selected for a more aggressive disease that models end-stage drug-resistant MM and responds only to combinations of drugs with single-agent activity in untreated Vk*MYC MM. We predict that combinations of standard agents, histone deacetylase inhibitors, bromodomain inhibitors, and hypoxia-activated prodrugs will demonstrate efficacy in the treatment of relapsed MM. PMID:22451422

Widespread genetic heterogeneity in multiple myeloma: implications for targeted therapy

PubMed Central

Lohr, Jens G.; Stojanov, Petar; Carter, Scott L.; Cruz-Gordillo, Peter; Lawrence, Michael S.; Auclair, Daniel; Sougnez, Carrie; Knoechel, Birgit; Gould, Joshua; Saksena, Gordon; Cibulskis, Kristian; McKenna, Aaron; Chapman, Michael A.; Straussman, Ravid; Levy, Joan; Perkins, Louise M.; Keats, Jonathan J.; Schumacher, Steven E.; Rosenberg, Mara; Getz, Gad

2014-01-01

SUMMARY We performed massively parallel sequencing of paired tumor/normal samples from 203 multiple myeloma (MM) patients and identified significantly mutated genes and copy number alterations, and discovered putative tumor suppressor genes by determining homozygous deletions and loss-of-heterozygosity. We observed frequent mutations in KRAS (particularly in previously treated patients), NRAS, BRAF, FAM46C, TP53 and DIS3 (particularly in non-hyperdiploid MM). Mutations were often present in subclonal populations, and multiple mutations within the same pathway (e.g. KRAS, NRAS and BRAF) were observed in the same patient. In vitro modeling predicts only partial treatment efficacy of targeting subclonal mutations, and even growth promotion of non-mutated subclones in some cases. These results emphasize the importance of heterogeneity analysis for treatment decisions. PMID:24434212

Widespread genetic heterogeneity in multiple myeloma: implications for targeted therapy.

PubMed

Lohr, Jens G; Stojanov, Petar; Carter, Scott L; Cruz-Gordillo, Peter; Lawrence, Michael S; Auclair, Daniel; Sougnez, Carrie; Knoechel, Birgit; Gould, Joshua; Saksena, Gordon; Cibulskis, Kristian; McKenna, Aaron; Chapman, Michael A; Straussman, Ravid; Levy, Joan; Perkins, Louise M; Keats, Jonathan J; Schumacher, Steven E; Rosenberg, Mara; Getz, Gad; Golub, Todd R

2014-01-13

We performed massively parallel sequencing of paired tumor/normal samples from 203 multiple myeloma (MM) patients and identified significantly mutated genes and copy number alterations and discovered putative tumor suppressor genes by determining homozygous deletions and loss of heterozygosity. We observed frequent mutations in KRAS (particularly in previously treated patients), NRAS, BRAF, FAM46C, TP53, and DIS3 (particularly in nonhyperdiploid MM). Mutations were often present in subclonal populations, and multiple mutations within the same pathway (e.g., KRAS, NRAS, and BRAF) were observed in the same patient. In vitro modeling predicts only partial treatment efficacy of targeting subclonal mutations, and even growth promotion of nonmutated subclones in some cases. These results emphasize the importance of heterogeneity analysis for treatment decisions. Copyright © 2014 Elsevier Inc. All rights reserved.