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Trudy F. C. Mackay - One of the best experts on this subject based on the ideXlab platform.
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the genetic architecture of Quantitative Traits cannot be inferred from variance component analysis
PLOS Genetics, 2016Co-Authors: W. Huang, Trudy F. C. MackayAbstract:Classical Quantitative genetic analyses estimate additive and non-additive genetic and environmental components of variance from phenotypes of related individuals without knowing the identities of Quantitative trait loci (QTLs). Many studies have found a large proportion of Quantitative trait variation can be attributed to the additive genetic variance (VA), providing the basis for claims that non-additive gene actions are unimportant. In this study, we show that arbitrarily defined parameterizations of genetic effects seemingly consistent with non-additive gene actions can also capture the majority of genetic variation. This reveals a logical flaw in using the relative magnitudes of variance components to indicate the relative importance of additive and non-additive gene actions. We discuss the implications and propose that variance component analyses should not be used to infer the genetic architecture of Quantitative Traits.
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the genetic architecture of Quantitative Traits cannot be inferred from variance component analysis
bioRxiv, 2016Co-Authors: W. Huang, Trudy F. C. MackayAbstract:Classical Quantitative genetic analyses estimate additive and non-additive genetic and environmental components of variance from phenotypes of related individuals. The genetic variance components are defined in terms of genotypic values reflecting underlying genetic architecture (additive, dominance and epistatic genotypic effects) and allele frequencies. However, the dependency of the definition of genetic variance components on the underlying genetic models is not often appreciated. Here, we show how the partitioning of additive and non-additive genetic variation is affected by the genetic models and parameterization of allelic effects. We show that arbitrarily defined variance components often capture a substantial fraction of total genetic variation regardless of the underlying genetic architecture in simulated and real data. Therefore, variance component analysis cannot be used to infer genetic architecture of Quantitative Traits. The genetic basis of Quantitative trait variation in a natural population can only be defined empirically using high resolution mapping methods followed by detailed characterization of QTL effects.
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epistasis and Quantitative Traits using model organisms to study gene gene interactions
Nature Reviews Genetics, 2014Co-Authors: Trudy F. C. MackayAbstract:The role of epistasis in the genetic architecture of Quantitative Traits is controversial, despite the biological plausibility that nonlinear molecular interactions underpin the genotype-phenotype map. This controversy arises because most genetic variation for Quantitative Traits is additive. However, additive variance is consistent with pervasive epistasis. In this Review, I discuss experimental designs to detect the contribution of epistasis to Quantitative trait phenotypes in model organisms. These studies indicate that epistasis is common, and that additivity can be an emergent property of underlying genetic interaction networks. Epistasis causes hidden Quantitative genetic variation in natural populations and could be responsible for the small additive effects, missing heritability and the lack of replication that are typically observed for human complex Traits.
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The genetics of Quantitative Traits: Challenges and prospects
Nature Reviews Genetics, 2009Co-Authors: Trudy F. C. Mackay, Eric A Stone, J. F. AyrolesAbstract:A major challenge in current biology is to understand the genetic basis of variation for Quantitative Traits. We review the principles of Quantitative trait locus mapping and summarize insights about the genetic architecture of Quantitative Traits that have been obtained over the past decades. We are currently in the midst of a genomic revolution, which enables us to incorporate genetic variation in transcript abundance and other intermediate molecular phenotypes into a Quantitative trait locus mapping framework. This systems genetics approach enables us to understand the biology inside the 'black box' that lies between genotype and phenotype in terms of causal networks of interacting genes.
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the genetic architecture of Quantitative Traits lessons from drosophila
Current Opinion in Genetics & Development, 2004Co-Authors: Trudy F. C. MackayAbstract:Understanding the genetic architecture of Quantitative Traits begins with identifying the genes regulating these Traits, mapping the subset of genetically varying Quantitative trait loci (QTLs) in natural populations, and pinpointing the molecular polymorphisms defining QTL alleles. Studies in Drosophila have revealed large numbers of pleiotropic genes that interact epistatically to regulate Quantitative Traits, and large numbers of QTLs with sex-, environment- and genotype-specific effects. Multiple molecular polymorphisms in regulatory regions of candidate genes are often associated with variation for complex Traits. These observations offer valuable lessons for understanding the genetic basis of variation for complex Traits in other organisms, including humans.
Momiao Xiong - One of the best experts on this subject based on the ideXlab platform.
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pleiotropy analysis of Quantitative Traits at gene level by multivariate functional linear models
Genetic Epidemiology, 2015Co-Authors: Yifan Wang, Aiyi Liu, James L Mills, Michael Boehnke, Alexander F Wilson, Joan E Baileywilson, Momiao Xiong, Ruzong FanAbstract:In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic Traits. A common approach is to analyze the phenotypic Traits separately using univariate analyses and combine the test results through multiple comparisons. This approach may lead to low power. Multivariate functional linear models are developed to connect genetic variant data to multiple Quantitative Traits adjusting for covariates for a unified analysis. Three types of approximate F-distribution tests based on Pillai–Bartlett trace, Hotelling–Lawley trace, and Wilks’s Lambda are introduced to test for association between multiple Quantitative Traits and multiple genetic variants in one genetic region. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and optimal sequence kernel association test (SKAT-O). Extensive simulations were performed to evaluate the false positive rates and power performance of the proposed models and tests. We show that the approximate F-distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple Traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid Traits in eight European cohorts, and (2) three biochemical Traits in the Trinity Students Study. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and SKAT-O for the three biochemical Traits. The approximate F-distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT-O in the univariate case. The analysis of the four lipid Traits and the three biochemical Traits detects more association than SKAT-O in the univariate case.
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gene level meta analysis of Quantitative Traits by functional linear models
Genetics, 2015Co-Authors: Ruzong Fan, Yifan Wang, Michael Boehnke, Wei Chen, Haobo Ren, Iryna Lobach, Momiao XiongAbstract: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.
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functional linear models for association analysis of Quantitative Traits
Genetic Epidemiology, 2013Co-Authors: Ruzong Fan, Yifan Wang, James L Mills, Alexander F Wilson, Joan E Baileywilson, Momiao XiongAbstract: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.
Ruzong Fan - One of the best experts on this subject based on the ideXlab platform.
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pleiotropy analysis of Quantitative Traits at gene level by multivariate functional linear models
Genetic Epidemiology, 2015Co-Authors: Yifan Wang, Aiyi Liu, James L Mills, Michael Boehnke, Alexander F Wilson, Joan E Baileywilson, Momiao Xiong, Ruzong FanAbstract:In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic Traits. A common approach is to analyze the phenotypic Traits separately using univariate analyses and combine the test results through multiple comparisons. This approach may lead to low power. Multivariate functional linear models are developed to connect genetic variant data to multiple Quantitative Traits adjusting for covariates for a unified analysis. Three types of approximate F-distribution tests based on Pillai–Bartlett trace, Hotelling–Lawley trace, and Wilks’s Lambda are introduced to test for association between multiple Quantitative Traits and multiple genetic variants in one genetic region. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and optimal sequence kernel association test (SKAT-O). Extensive simulations were performed to evaluate the false positive rates and power performance of the proposed models and tests. We show that the approximate F-distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple Traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid Traits in eight European cohorts, and (2) three biochemical Traits in the Trinity Students Study. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and SKAT-O for the three biochemical Traits. The approximate F-distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT-O in the univariate case. The analysis of the four lipid Traits and the three biochemical Traits detects more association than SKAT-O in the univariate case.
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gene level meta analysis of Quantitative Traits by functional linear models
Genetics, 2015Co-Authors: Ruzong Fan, Yifan Wang, Michael Boehnke, Wei Chen, Haobo Ren, Iryna Lobach, Momiao XiongAbstract: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.
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functional linear models for association analysis of Quantitative Traits
Genetic Epidemiology, 2013Co-Authors: Ruzong Fan, Yifan Wang, James L Mills, Alexander F Wilson, Joan E Baileywilson, Momiao XiongAbstract: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.
David B Allison - One of the best experts on this subject based on the ideXlab platform.
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are we there yet deciding when one has demonstrated specific genetic causation in complex diseases and Quantitative Traits
American Journal of Human Genetics, 2003Co-Authors: Grier P Page, Varghese George, Patricia Z Page, David B AllisonAbstract:Although mathematical relationships can be proven by deductive logic, biological relationships can only be inferred from empirical observations. This is a distinct disadvantage for those of us who strive to identify the genes involved in complex diseases and Quantitative Traits. If causation cannot be proven, however, what does constitute sufficient evidence for causation? The philosopher Karl Popper said, "Our belief in a hypothesis can have no stronger basis than our repeated unsuccessful critical attempts to refute it." We believe that to establish causation, as scientists, we must make a serious attempt to refute our own hypotheses and to eliminate all known sources of bias before association becomes causation. In addition, we suggest that investigators must provide sufficient data and evidence of their unsuccessful efforts to find any confounding biases. In this editorial, we discuss what "causation" means in the context of complex diseases and Quantitative Traits, and we suggest guidelines for steps that may be taken to address possible confounders of association before polymorphisms may be called "causative."
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transmission disequilibrium tests for Quantitative Traits
American Journal of Human Genetics, 1997Co-Authors: David B AllisonAbstract:Abstract The transmission-disequilibrium test (TDT) of Spielman et al. is a family-based linkage-disequilibrium test that offers a powerful way to test for linkage between alleles and phenotypes that is either causal (i.e., the marker locus is the disease/trait allele) or due to linkage disequilibrium. The TDT is equivalent to a randomized experiment and, therefore, is resistant to confounding. When the marker is extremely close to the disease locus or is the disease locus itself, tests such as the TDT can be far more powerful than conventional linkage tests. To date, the TDT and most other family-based association tests have been applied only to dichotomous Traits. This paper develops five TDT-type tests for use with Quantitative Traits. These tests accommodate either unselected sampling or sampling based on selection of phenotypically extreme offspring. Power calculations are provided and show that, when a candidate gene is available (1) these TDT-type tests are at least an order of magnitude more efficient than two common sib-pair tests of linkage; (2) extreme sampling results in substantial increases in power; and (3) if the most extreme 20% of the phenotypic distribution is selectively sampled, across a wide variety of plausible genetic models, Quantitative-trait loci explaining as little as 5% of the phenotypic variation can be detected at the .0001 alpha level with <300 observations.
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transmission disequilibrium tests for Quantitative Traits
American Journal of Human Genetics, 1997Co-Authors: David B AllisonAbstract:The transmission-disequilibrium test (TDT) of Spielman et al. is a family-based linkage-disequilibrium test that offers a powerful way to test for linkage between alleles and phenotypes that is either causal (i.e., the marker locus is the disease/trait allele) or due to linkage disequilibrium. The TDT is equivalent to a randomized experiment and, therefore, is resistant to confounding. When the marker is extremely close to the disease locus or is the disease locus itself, tests such as the TDT can be far more powerful than conventional linkage tests. To date, the TDT and most other family-based association tests have been applied only to dichotomous Traits. This paper develops five TDT-type tests for use with Quantitative Traits. These tests accommodate either unselected sampling or sampling based on selection of phenotypically extreme offspring. Power calculations are provided and show that, when a candidate gene is available (1) these TDT-type tests are at least an order of magnitude more efficient than two common sib-pair tests of linkage; (2) extreme sampling results in substantial increases in power; and (3) if the most extreme 20% of the phenotypic distribution is selectively sampled, across a wide variety of plausible genetic models, Quantitative-trait loci explaining as little asmore » 5% of the phenotypic variation can be detected at the .0001 a level with <300 observations. 57 refs., 2 figs., 5 tabs.« less
Carole Ober - One of the best experts on this subject based on the ideXlab platform.
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the sex specific genetic architecture of Quantitative Traits in humans
Nature Genetics, 2006Co-Authors: Lauren A Weiss, Lin Pan, Mark Abney, Carole OberAbstract:Mapping genetically complex Traits remains one of the greatest challenges in human genetics today. In particular, gene-environment and gene-gene interactions, genetic heterogeneity and incomplete penetrance make thorough genetic dissection of complex Traits difficult, if not impossible. Sex could be considered an environmental factor that can modify both penetrance and expressivity of a wide variety of Traits. Sex is easily determined and has measurable effects on recognizable morphology; neurobiological circuits; susceptibility to autoimmune disease, diabetes, asthma, cardiovascular and psychiatric disease; and Quantitative Traits like blood pressure, obesity and lipid levels, among others. In this study, we evaluated sex-specific heritability and genome-wide linkages for 17 Quantitative Traits in the Hutterites. The results of this study could have important implications for mapping complex trait genes.
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estimation of variance components of Quantitative Traits in inbred populations
American Journal of Human Genetics, 2000Co-Authors: Mark Abney, Mary Sara Mcpeek, Carole OberAbstract:Use of variance-component estimation for mapping of Quantitative-trait loci in humans is a subject of great current interest. When only trait values, not genotypic information, are considered, variance-component estimation can also be used to estimate heritability of a Quantitative trait. Inbred pedigrees present special challenges for variance-component estimation. First, there are more variance components to be estimated in the inbred case, even for a relatively simple model including additive, dominance, and environmental effects. Second, more identity coefficients need to be calculated from an inbred pedigree in order to perform the estimation, and these are computationally more difficult to obtain in the inbred than in the outbred case. As a result, inbreeding effects have generally been ignored in practice. We describe here the calculation of identity coefficients and estimation of variance components of Quantitative Traits in large inbred pedigrees, using the example of HDL in the Hutterites. We use a multivariate normal model for the genetic effects, extending the central-limit theorem of Lange to allow for both inbreeding and dominance under the assumptions of our variance-component model. We use simulated examples to give an indication of under what conditions one has the power to detect the additional variance components and to examine their impact on variance-component estimation. We discuss the implications for mapping and heritability estimation by use of variance components in inbred populations.
