The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Orjan Carlborg - One of the best experts on this subject based on the ideXlab platform.
-
the multi allelic Genetic architecture of a Variance heterogeneity locus for molybdenum concentration in leaves acts as a source of unexplained additive Genetic Variance
PLOS Genetics, 2015Co-Authors: Simon K G Forsberg, Matthew Andreatta, Xinyuan Huang, John Danku, David E Salt, Orjan CarlborgAbstract:Genome-wide association (GWA) analyses have generally been used to detect individual loci contributing to the phenotypic diversity in a population by the effects of these loci on the trait mean. More rarely, loci have also been detected based on Variance differences between genotypes. Several hypotheses have been proposed to explain the possible Genetic mechanisms leading to such Variance signals. However, little is known about what causes these signals, or whether this Genetic Variance-heterogeneity reflects mechanisms of importance in natural populations. Previously, we identified a Variance-heterogeneity GWA (vGWA) signal for leaf molybdenum concentrations in Arabidopsis thaliana. Here, fine-mapping of this association reveals that the vGWA emerges from the effects of three independent Genetic polymorphisms that all are in strong LD with the markers displaying the Genetic Variance-heterogeneity. By revealing the Genetic architecture underlying this vGWA signal, we uncovered the molecular source of a significant amount of hidden additive Genetic variation or “missing heritability”. Two of the three polymorphisms underlying the Genetic Variance-heterogeneity are promoter variants for Molybdate transporter 1 (MOT1), and the third a variant located ~25 kb downstream of this gene. A fourth independent association was also detected ~600 kb upstream of MOT1. Use of a T-DNA knockout allele highlights Copper Transporter 6; COPT6 (AT2G26975) as a strong candidate gene for this association. Our results show that an extended LD across a complex locus including multiple functional alleles can lead to a Variance-heterogeneity between genotypes in natural populations. Further, they provide novel insights into the Genetic regulation of ion homeostasis in A. thaliana, and empirically confirm that Variance-heterogeneity based GWA methods are a valuable tool to detect novel associations of biological importance in natural populations.
-
a genome wide association analysis reveals epistatic cancellation of additive Genetic Variance for root length in arabidopsis thaliana
PLOS Genetics, 2015Co-Authors: Jennifer Lachowiec, Xia Shen, Christine Queitsch, Orjan CarlborgAbstract:Efforts to identify loci underlying complex traits generally assume that most Genetic Variance is additive. Here, we examined the Genetics of Arabidopsis thaliana root length and found that the genomic narrow-sense heritability for this trait in the examined population was statistically zero. The low amount of additive Genetic Variance that could be captured by the genome-wide genotypes likely explains why no associations to root length could be found using standard additive-model-based genome-wide association (GWA) approaches. However, as the broad-sense heritability for root length was significantly larger, and primarily due to epistasis, we also performed an epistatic GWA analysis to map loci contributing to the epistatic Genetic Variance. Four interacting pairs of loci were revealed, involving seven chromosomal loci that passed a standard multiple-testing corrected significance threshold. The genotype-phenotype maps for these pairs revealed epistasis that cancelled out the additive Genetic Variance, explaining why these loci were not detected in the additive GWA analysis. Small population sizes, such as in our experiment, increase the risk of identifying false epistatic interactions due to testing for associations with very large numbers of multi-marker genotypes in few phenotyped individuals. Therefore, we estimated the false-positive risk using a new statistical approach that suggested half of the associated pairs to be true positive associations. Our experimental evaluation of candidate genes within the seven associated loci suggests that this estimate is conservative; we identified functional candidate genes that affected root development in four loci that were part of three of the pairs. The statistical epistatic analyses were thus indispensable for confirming known, and identifying new, candidate genes for root length in this population of wild-collected A. thaliana accessions. We also illustrate how epistatic cancellation of the additive Genetic Variance explains the insignificant narrow-sense and significant broad-sense heritability by using a combination of careful statistical epistatic analyses and functional Genetic experiments.
-
a genome wide association analysis reveals epistatic cancellation of additive Genetic Variance for root length in arabidopsis thaliana
bioRxiv, 2015Co-Authors: Jennifer Lachowiec, Xia Shen, Christine Queitsch, Orjan CarlborgAbstract:Efforts to identify loci underlying complex traits generally assume that most Genetic Variance is additive. Here, we examined the Genetics of Arabidopsis thaliana root length and found that the narrow-sense heritability for this trait was statistically zero. This low additive Genetic Variance likely explains why no associations to root length could be found using standard additive-model-based genome-wide association (GWA) approaches. However, the broad-sense heritability for root length was significantly larger, and we therefore also performed an epistatic GWA analysis to map loci contributing to the epistatic Genetic Variance. This analysis revealed four interacting pairs involving seven chromosomal loci that passed a standard multiple-testing corrected significance threshold. Explorations of the genotype-phenotype maps for these pairs revealed that the detected epistasis cancelled out the additive Genetic Variance, explaining why these loci were not detected in the additive GWA analysis. Small population sizes, such as in our experiment, increase the risk of identifying false epistatic interactions due to testing for associations with very large numbers of multi-marker genotypes in few phenotyped individuals. Therefore, we estimated the false-positive risk using a new statistical approach that suggested half of the associated pairs to be true positive associations. Our experimental evaluation of candidate genes within the seven associated loci suggests that this estimate is conservative; we identified functional candidate genes that affected root development in four loci that were part of three of the pairs. In summary, statistical epistatic analyses were found to be indispensable for confirming known, and identifying several new, functional candidate genes for root length using a population of wild-collected A. thaliana accessions. We also illustrated how epistatic cancellation of the additive Genetic Variance resulted in an insignificant narrow-sense, but significant broad-sense heritability that could be dissected into the contributions of several individual loci using a combination of careful statistical epistatic analyses and functional Genetic experiments.
-
the multi allelic Genetic architecture of a Variance heterogeneity locus for molybdenum accumulation acts as a source of unexplained additive Genetic Variance
bioRxiv, 2015Co-Authors: Forsberg Skg, Matthew Andreatta, Xinyuan Huang, John Danku, David E Salt, Orjan CarlborgAbstract:Most biological traits are regulated by both Genetic and environmental factors. Individual loci contributing to the phenotypic diversity in a population are generally identified by their contributions to the trait mean. Genome-wide association (GWA) analyses can also detect loci based on Variance differences between genotypes and several hypotheses have been proposed regarding the possible Genetic mechanisms leading to such signals. Little is, however, known about what causes them and whether this Genetic Variance-heterogeneity reflects mechanisms of importance in natural populations. Previously, we identified a Variance-heterogeneity GWA (vGWA) signal for leaf molybdenum concentrations in Arabidopsis thaliana. Here, fine-mapping of this association to a ~78 kb Linkage Disequilibrium (LD)-block reveals that it emerges from the independent effects of three Genetic polymorphisms on the high-Variance associated version of this LD-block. By revealing the Genetic architecture underlying this vGWA signal, we uncovered the molecular source of a significant amount of hidden additive Genetic variation (“missing heritability”). Two of the three polymorphisms on the high-Variance LD-block are promoter variants for Molybdate transporter 1 (MOT1), and the third a variant located ~25 kb downstream of this gene. A fourth independent association was also detected ~600 kb upstream of the LD-block. Testing of T-DNA knockout alleles for genes in the associated regions suggest AT2G25660 (unknown function) and AT2G26975 (Copper Transporter 6; COPT6) as the strongest candidates for the associations outside MOT1. Our results show that multi-allelic Genetic architectures within a single LD-block can lead to a Variance-heterogeneity between genotypes in natural populations. Further they provide novel insights into the Genetic regulation of ion homeostasis in A. thaliana, and empirically confirm that Variance-heterogeneity based GWA methods are a valuable tool to detect novel associations of biological importance in natural populations.
Patrick C. Phillips - One of the best experts on this subject based on the ideXlab platform.
-
hierarchical comparison of Genetic Variance coVariance matrices i using the flury hierarchy
Evolution, 1999Co-Authors: Patrick C. Phillips, Stevan J. ArnoldAbstract:The comparison of additive Genetic Variance-coVariance matrices (G-matrices) is an increasingly popular exercise in evolutionary biology because the evolution of the G-matrix is central to the issue of persistence of Genetic constraints and to the use of dynamic models in an evolutionary time frame. The comparison of G-matrices is a nontrivial statistical problem because family structure induces nonindependence among the elements in each matrix. Past solutions to the problem of G-matrix comparison have dealt with this problem, with varying success, but have tested a single null hypothesis (matrix equality or matrix dissimilarity). Because matrices can differ in many ways, several hypotheses are of interest in matrix comparisons. Flury (1988) has provided an approach to matrix comparison in which a variety of hypotheses are tested, including the two extreme hypotheses prevalent in the evolutionary literature. The hypotheses are arranged in a hierarchy and involve comparisons of both the principal components (eigenvectors) and eigenvalues of the matrix. We adapt Flury's hierarchy of tests to the problem of comparing G- matrices by using randomization testing to account for nonindependence induced by family structure. Software has been developed for carrying out this analysis for both Genetic and phenotypic data. The method is illustrated with a garter snake test case.
-
HIERARCHICAL COMPARISON OF Genetic Variance-COVariance MATRICES. II COASTAL-INLAND DIVERGENCE IN THE GARTER SNAKE, THAMNOPHIS ELEGANS.
Evolution; international journal of organic evolution, 1999Co-Authors: Stevan J. Arnold, Patrick C. PhillipsAbstract:The time-scale for the evolution of additive Genetic Variance-coVariance matrices (G-matrices) is a crucial issue in evolutionary biology. If the evolution of G-matrices is slow enough, we can use standard multivariate equations to model drift and selection response on evolutionary time scales. We compared the G-matrices for meristic traits in two populations of garter snakes (Thamnophis elegans) with an apparent separation time of 2 million years. Despite considerable divergence in the meristic traits, foraging habits, and diet, these populations show conservation of structure in their G-matrices. Using Flury's hierarchial approach to matrix comparisons, we found that the populations have retained the principal components (eigenvectors) of their G-matrices, but their eigenvalues have diverged. In contrast, we were unable to reject the hypothesis of equal environmental matrices (E-matrices) for these populations. We propose that a conserved pattern of multivariate stabilizing selection may have contributed to conservation of G- and E-matrix structure during the divergence of these populations.
-
gene interaction affects the additive Genetic Variance in subdivided populations with migration and extinction
Evolution, 1993Co-Authors: Michael C Whitlock, Patrick C. Phillips, Michael J WadeAbstract:We investigated the effect of nonadditive Genetic Variance on the amount of additive Genetic Variance within local populations in an infinite-allele, infinite-island model with migration, extinction, and recolonization, using two-locus descent measures. For an island model with extinction, one- and two-locus descent measures are expressed in a matrix form that allows equilibrium solutions to be calculated similar to previous work on Wright's F-statistics. In a subdivided population, the additive Genetic variation within a local deme depends on the dominance and epistatic Genetic variation in the species. Moreover, to a good approximation, the amount of additive Variance within a deme is a simple function of Fst , which is twice the demic fraction of genic Variance. At equilibrium, it is equal to (1 - Fst ) VA plus 4 Fst (1 - Fst ) VA×A , where VA and VA×A are the additive and additive × additive epistatic Variances at the level of the species, respectively, plus a contribution from the dominance Variance and other terms including dominance. Paradoxically, with nonadditive Genetic effects, drift on average increases the amount of additive Genetic Variance within populations, whereas migration decreases the equilibrium amount. In the presence of nonadditive Genetic effects, measurements of additive Genetic Variance in natural populations must be taken at the proper spatial scale with respect to natural selection, or they will provide an inaccurate description of evolutionary potential both within local populations and within the species as a whole.
Peter M Visscher - One of the best experts on this subject based on the ideXlab platform.
-
Estimation of non-additive Genetic Variance in human complex traits from a large sample of unrelated individuals
2020Co-Authors: Valentin Hivert, Michael E. Goddard, Naomi R Wray, Julia Sidorenko, Florian Rohart, Jian Yang, Loic Yengo, Peter M VisscherAbstract:Non-additive Genetic Variance for complex traits is traditionally estimated from data on relatives. It is notoriously difficult to estimate without bias in non-laboratory species, including humans, because of possible confounding with environmental coVariance among relatives. In principle, non-additive Variance attributable to common DNA variants can be estimated from a random sample of unrelated individuals with genome-wide SNP data. Here, we jointly estimate the proportion of Variance explained by additive [Formula], dominance [Formula] and additive-by-additive [Formula] Genetic Variance in a single analysis model. We first show by simulations that our model leads to unbiased estimates and provide new theory to predict standard errors estimated using either least squares or maximum likelihood. We then apply the model to 70 complex traits using 254,679 unrelated individuals from the UK Biobank and 1.1M genotyped and imputed SNPs. We found strong evidence for additive Variance (average across traits [Formula]. In contrast, the average estimate of [Formula] across traits was 0.001, implying negligible dominance Variance at causal variants tagged by common SNPs. The average epistatic Variance [Formula] across the traits was 0.058, not significantly different from zero because of the large sampling Variance. Our results provide new evidence that Genetic Variance for complex traits is predominantly additive, and that sample sizes of many millions of unrelated individuals are needed to estimate epistatic Variance with sufficient precision.
-
using the realized relationship matrix to disentangle confounding factors for the estimation of Genetic Variance components of complex traits
Genetics Selection Evolution, 2010Co-Authors: Sang Hong Lee, Peter M Visscher, Michael E. Goddard, Julius H J Van Der WerfAbstract:Background In the analysis of complex traits, Genetic effects can be confounded with non-Genetic effects, especially when using full-sib families. Dominance and epistatic effects are typically confounded with additive Genetic and non-Genetic effects. This confounding may cause the estimated Genetic Variance components to be inaccurate and biased.
-
using the realized relationship matrix to disentangle confounding factors for the estimation of Genetic Variance components of complex traits
Genetics Selection Evolution, 2010Co-Authors: Sang Hong Lee, Peter M Visscher, Michael E. Goddard, Julius H J Van Der WerfAbstract:In the analysis of complex traits, Genetic effects can be confounded with non-Genetic effects, especially when using full-sib families. Dominance and epistatic effects are typically confounded with additive Genetic and non-Genetic effects. This confounding may cause the estimated Genetic Variance components to be inaccurate and biased. In this study, we constructed Genetic coVariance structures from whole-genome marker data, and thus used realized relationship matrices to estimate Variance components in a heterogenous population of ~ 2200 mice for which four complex traits were investigated. These mice were genotyped for more than 10,000 single nucleotide polymorphisms (SNP) and the Variances due to family, cage and Genetic effects were estimated by models based on pedigree information only, aggregate SNP information, and model selection for specific SNP effects. We show that the use of genome-wide SNP information can disentangle confounding factors to estimate Genetic Variances by separating Genetic and non-Genetic effects. The estimated Variance components using realized relationship were more accurate and less biased, compared to those based on pedigree information only. Models that allow the selection of individual SNP in addition to fitting a relationship matrix are more efficient for traits with a significant dominance Variance.
-
Data and theory point to mainly additive Genetic Variance for complex traits
PLoS Genetics, 2008Co-Authors: William G. Hill, Michael E. Goddard, Peter M VisscherAbstract:The relative proportion of additive and non-additive variation for complex traits is important in evolutionary biology, medicine, and agriculture. We address a long-standing controversy and paradox about the contribution of non-additive Genetic variation, namely that knowledge about biological pathways and gene networks imply that epistasis is important. Yet empirical data across a range of traits and species imply that most Genetic Variance is additive. We evaluate the evidence from empirical studies of Genetic Variance components and find that additive Variance typically accounts for over half, and often close to 100%, of the total Genetic Variance. We present new theoretical results, based upon the distribution of allele frequencies under neutral and other population Genetic models, that show why this is the case even if there are non-additive effects at the level of gene action. We conclude that interactions at the level of genes are not likely to generate much interaction at the level of Variance.
Seema N Sheth - One of the best experts on this subject based on the ideXlab platform.
-
additive Genetic Variance for lifetime fitness and the capacity for adaptation in an annual plant
Evolution, 2019Co-Authors: Mason W Kulbaba, Seema N Sheth, Rachel E Pain, Vincent M Eckhart, Ruth G ShawAbstract:The immediate capacity for adaptation under current environmental conditions is directly proportional to the additive Genetic Variance for fitness, VA (W). Mean absolute fitness, W¯ , is predicted to change at the rate VA(W)W¯ , according to Fisher's Fundamental Theorem of Natural Selection. Despite ample research evaluating degree of local adaptation, direct assessment of VA (W) and the capacity for ongoing adaptation is exceedingly rare. We estimated VA (W) and W¯ in three pedigreed populations of annual Chamaecrista fasciculata, over three years in the wild. Contrasting with common expectations, we found significant VA (W) in all populations and years, predicting increased mean fitness in subsequent generations (0.83 to 6.12 seeds per individual). Further, we detected two cases predicting "evolutionary rescue," where selection on standing VA (W) was expected to increase fitness of declining populations ( W¯ < 1.0) to levels consistent with population sustainability and growth. Within populations, inter-annual differences in Genetic expression of fitness were striking. Significant genotype-by-year interactions reflected modest correlations between breeding values across years, indicating temporally variable selection at the genotypic level that could contribute to maintaining VA (W). By directly estimating VA (W) and total lifetime W¯ , our study presents an experimental approach for studies of adaptive capacity in the wild.
-
additive Genetic Variance for lifetime fitness and the capacity for adaptation in an annual plant
bioRxiv, 2019Co-Authors: Mason W Kulbaba, Seema N Sheth, Rachel E Pain, Vincent M Eckhart, Ruth G ShawAbstract:The immediate capacity for adaptation under current environmental conditions is directly proportional to the additive Genetic Variance for fitness, V A (W). Mean absolute fitness, W , is predicted to change at the rate (V A (W))/W , according to Fisher`s Fundamental Theorem of Natural Selection. Despite ample research evaluating degree of local adaptation, direct assessment of V A (W) and the capacity for ongoing adaptation is exceedingly rare. We estimated V A (W) and W in three pedigreed populations of annual Chamaecrista fasciculata, over three years in the wild. Contrasting with common expectations, we found significant V A (W) in all populations and years, predicting increased mean fitness in subsequent generations (0.83 to 6.12 seeds per individual). Further, we detected two cases predicting `evolutionary rescue`, where selection on standing V A (W) was expected to increase fitness of declining populations ((W ) A (W). By directly estimating V A (W) and total lifetime W , our study presents an experimental approach for studies of adaptive capacity in the wild.
-
expression of additive Genetic Variance for fitness in a population of partridge pea in two field sites
Evolution, 2018Co-Authors: Mason W Kulbaba, Seema N Sheth, Rachel E Pain, Ruth G ShawAbstract:: Despite the importance of adaptation in shaping biological diversity over many generations, little is known about populations' capacities to adapt at any particular time. Theory predicts that a population's rate of ongoing adaptation is the ratio of its additive Genetic Variance for fitness, VA(W) , to its mean absolute fitness, W¯ . We conducted a transplant study to quantify W¯ and standing VA(W) for a population of the annual legume Chamaecrista fasciculata in one field site from which we initially sampled it and another site where it does not currently occur naturally. We also examined genotype-by-environment interactions, G × E, as well as its components, differences between sites in VA(W) and in rank of breeding values for fitness. The mean fitness indicated population persistence in both sites, and there was substantial VA(W) for ongoing adaptation at both sites. Statistically significant G × E indicated that the adaptive process would differ between sites. We found a positive correlation between fitness of genotypes in the "home" and "away" environments, and G × E was more pronounced as the life-cycle proceeds. This study exemplifies an approach to assessing whether there is sufficient VA(W) to support evolutionary rescue in populations that are declining.
-
expression of additive Genetic Variance for fitness in a population of partridge pea in two field sites
Evolution, 2018Co-Authors: Mason W Kulbaba, Seema N Sheth, Rachel E Pain, Ruth G ShawAbstract:Despite the importance of adaptation in shaping biological diversity over many generations, little is known about populations' capacities to adapt at any particular time. Theory predicts that a population's rate of ongoing adaptation is the ratio of its additive Genetic Variance for fitness, V A ( W ) , to its mean absolute fitness, W ¯ . We conducted a transplant study to quantify W ¯ and standing V A ( W ) for a population of the annual legume Chamaecrista fasciculata in one field site from which we initially sampled it and another site where it does not currently occur naturally. We also examined genotype-by-environment interactions, G × E, as well as its components, differences between sites in V A ( W ) and in rank of breeding values for fitness. The mean fitness indicated population persistence in both sites, and there was substantial V A ( W ) for ongoing adaptation at both sites. Statistically significant G × E indicated that the adaptive process would differ between sites. We found a positive correlation between fitness of genotypes in the "home" and "away" environments, and G × E was more pronounced as the life-cycle proceeds. This study exemplifies an approach to assessing whether there is sufficient V A ( W ) to support evolutionary rescue in populations that are declining.
Stevan J. Arnold - One of the best experts on this subject based on the ideXlab platform.
-
hierarchical comparison of Genetic Variance coVariance matrices i using the flury hierarchy
Evolution, 1999Co-Authors: Patrick C. Phillips, Stevan J. ArnoldAbstract:The comparison of additive Genetic Variance-coVariance matrices (G-matrices) is an increasingly popular exercise in evolutionary biology because the evolution of the G-matrix is central to the issue of persistence of Genetic constraints and to the use of dynamic models in an evolutionary time frame. The comparison of G-matrices is a nontrivial statistical problem because family structure induces nonindependence among the elements in each matrix. Past solutions to the problem of G-matrix comparison have dealt with this problem, with varying success, but have tested a single null hypothesis (matrix equality or matrix dissimilarity). Because matrices can differ in many ways, several hypotheses are of interest in matrix comparisons. Flury (1988) has provided an approach to matrix comparison in which a variety of hypotheses are tested, including the two extreme hypotheses prevalent in the evolutionary literature. The hypotheses are arranged in a hierarchy and involve comparisons of both the principal components (eigenvectors) and eigenvalues of the matrix. We adapt Flury's hierarchy of tests to the problem of comparing G- matrices by using randomization testing to account for nonindependence induced by family structure. Software has been developed for carrying out this analysis for both Genetic and phenotypic data. The method is illustrated with a garter snake test case.
-
HIERARCHICAL COMPARISON OF Genetic Variance-COVariance MATRICES. II COASTAL-INLAND DIVERGENCE IN THE GARTER SNAKE, THAMNOPHIS ELEGANS.
Evolution; international journal of organic evolution, 1999Co-Authors: Stevan J. Arnold, Patrick C. PhillipsAbstract:The time-scale for the evolution of additive Genetic Variance-coVariance matrices (G-matrices) is a crucial issue in evolutionary biology. If the evolution of G-matrices is slow enough, we can use standard multivariate equations to model drift and selection response on evolutionary time scales. We compared the G-matrices for meristic traits in two populations of garter snakes (Thamnophis elegans) with an apparent separation time of 2 million years. Despite considerable divergence in the meristic traits, foraging habits, and diet, these populations show conservation of structure in their G-matrices. Using Flury's hierarchial approach to matrix comparisons, we found that the populations have retained the principal components (eigenvectors) of their G-matrices, but their eigenvalues have diverged. In contrast, we were unable to reject the hypothesis of equal environmental matrices (E-matrices) for these populations. We propose that a conserved pattern of multivariate stabilizing selection may have contributed to conservation of G- and E-matrix structure during the divergence of these populations.