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David W Coltman - One of the best experts on this subject based on the ideXlab platform.
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positive Genetic Correlation between parasite resistance and body size in a free living ungulate population
Evolution, 2001Co-Authors: David W Coltman, Jill G Pilkington, Loeske E B Kruuk, Kenneth Wilson, Josephine M PembertonAbstract:Parasite resistance and body size are subject to directional natural selection in a population of feral Soay sheep (Ovis aries) on the island of St. Kilda, Scotland. Classical evolutionary theory predicts that directional selection should erode additive Genetic variation and favor the maintenance of alleles that have negative pleiotropic effects on other traits associated with fitness. Contrary to these predictions, in this study we show that there is considerable additive Genetic variation for both parasite resistance, measured as fecal egg count (FEC), and body size, measured as weight and hindleg length, and that there are positive Genetic Correlations between parasite resistance and body size in both sexes. Body size traits had higher heritabilities than parasite resistance. This was not due to low levels of additive Genetic variation for parasite resistance, but was a consequence of high levels of residual variance in FEC. Measured as coefficients of variation, levels of additive Genetic variation for FEC were actually higher than for weight or hindleg length. High levels of additive Genetic variation for parasite resistance may be maintained by a number of mechanisms including high mutational input, balancing selection, antagonistic pleiotropy, and host-parasite coevolution. The positive Genetic Correlation between parasite resistance and body size, a trait also subject to sexual selection in males, suggests that parasite resistance and growth are not traded off in Soay sheep, but rather that Genetically resistant individuals also experience superior growth.
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positive Genetic Correlation between parasite resistance and body size in a free living ungulate population
Evolution, 2001Co-Authors: David W Coltman, Jill G Pilkington, Loeske E B Kruuk, Kenneth Wilson, Josephine M PembertonAbstract:Parasite resistance and body size are subject to directional natural selection in a population of feral Soay sheep (Ovis aries) on the island of St. Kilda, Scotland. Classical evolutionary theory predicts that directional selection should erode additive Genetic variation and favor the maintenance of alleles that have negative pleiotropic effects on other traits associated with fitness. Contrary to these predictions, in this study we show that there is considerable additive Genetic variation for both parasite resistance, measured as fecal egg count (FEC), and body size, measured as weight and hindleg length, and that there are positive Genetic Correlations between parasite resistance and body size in both sexes. Body size traits had higher heritabilities than parasite resistance. This was not due to low levels of additive Genetic variation for parasite resistance, but was a consequence of high levels of residual variance in FEC. Measured as coefficients of variation, levels of additive Genetic variation for FEC were actually higher than for weight or hindleg length. High levels of additive Genetic variation for parasite resistance may be maintained by a number of mechanisms including high mutational input, balancing selection, antagonistic pleiotropy, and host-parasite coevolution. The positive Genetic Correlation between parasite resistance and body size, a trait also subject to sexual selection in males, suggests that parasite resistance and growth are not traded off in Soay sheep, but rather that Genetically resistant individuals also experience superior growth.
Nicholas G. Martin - One of the best experts on this subject based on the ideXlab platform.
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Genetic overlap between endometriosis and endometrial cancer evidence from cross disease Genetic Correlation and gwas meta analyses
Cancer Medicine, 2018Co-Authors: Jodie N Painter, Tracy A Omara, Andrew P Morris, Timothy H T Cheng, Maggie Gorman, Lynn Martin, Shirley Hodson, Angela Jones, Nicholas G. MartinAbstract:Epidemiological, biological, and molecular data suggest links between endometriosis and endometrial cancer, with recent epidemiological studies providing evidence for an association between a previous diagnosis of endometriosis and risk of endometrial cancer. We used Genetic data as an alternative approach to investigate shared biological etiology of these two diseases. Genetic Correlation analysis of summary level statistics from genomewide association studies (GWAS) using LD Score regression revealed moderate but significant Genetic Correlation (rg = 0.23, P = 9.3 × 10-3 ), and SNP effect concordance analysis provided evidence for significant SNP pleiotropy (P = 6.0 × 10-3 ) and concordance in effect direction (P = 2.0 × 10-3 ) between the two diseases. Cross-disease GWAS meta-analysis highlighted 13 distinct loci associated at P ≤ 10-5 with both endometriosis and endometrial cancer, with one locus (SNP rs2475335) located within PTPRD associated at a genomewide significant level (P = 4.9 × 10-8 , OR = 1.11, 95% CI = 1.07-1.15). PTPRD acts in the STAT3 pathway, which has been implicated in both endometriosis and endometrial cancer. This study demonstrates the value of cross-disease Genetic analysis to support epidemiological observations and to identify biological pathways of relevance to multiple diseases.
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heritability and Genetic Correlation between the cerebral cortex and associated white matter connections
Human Brain Mapping, 2016Co-Authors: Kaikai Shen, Nicholas G. Martin, Margaret J. Wright, Vincent Dore, Stephen E Rose, Jurgen Fripp, Katie L Mcmahon, Greig I De Zubicaray, Paul M Thompson, Olivier SalvadoAbstract:The aim of this study is to investigate the Genetic influence on the cerebral cortex, based on the analyses of heritability and Genetic Correlation between grey matter (GM) thickness, derived from structural MR images (sMRI), and associated white matter (WM) connections obtained from diffusion MRI (dMRI). We measured on sMRI the cortical thickness (CT) from a large twin imaging cohort using a surface-based approach (N = 308, average age 22.8 ± 2.3 SD). An ACE model was employed to compute the heritability of CT. WM connections were estimated based on probabilistic tractography using fiber orientation distributions (FOD) from dMRI. We then fitted the ACE model to estimate the heritability of CT and FOD peak measures along WM fiber tracts. The WM fiber tracts where Genetic influence was detected were mapped onto the cortical surface. Bivariate Genetic modeling was performed to estimate the cross-trait Genetic Correlation between the CT and the FOD-based connectivity of the tracts associated with the cortical regions. We found some cortical regions displaying heritable and Genetically correlated GM thickness and WM connectivity, forming networks under stronger Genetic influence. Significant heritability and Genetic Correlations between the CT and WM connectivity were found in regions including the right postcentral gyrus, left posterior cingulate gyrus, right middle temporal gyri, suggesting common Genetic factors influencing both GM and WM. Hum Brain Mapp 37:2331-2347, 2016. © 2016 Wiley Periodicals, Inc.
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heritability and Genetic Correlation between the cerebral cortex and associated white matter connections
Faculty of Health; Institute of Health and Biomedical Innovation, 2016Co-Authors: Kaikai Shen, Nicholas G. Martin, Margaret J. Wright, Vincent Dore, Stephen E Rose, Jurgen Fripp, Katie L Mcmahon, Greig I De Zubicaray, Paul M Thompson, Olivier SalvadoAbstract:The aim of this study is to investigate the Genetic influence on the cerebral cortex, based on the analyses of heritability and Genetic Correlation between grey matter (GM) thickness, derived from structural MR images (sMRI), and associated white matter (WM) connections obtained from diffusion MRI (dMRI). We measured on sMRI the cortical thickness (CT) from a large twin imaging cohort using a surface-based approach (N = 308, average age 22.8 ± 2.3 SD). An ACE model was employed to compute the heritability of CT. WM connections were estimated based on probabilistic tractography using fiber orientation distributions (FOD) from dMRI. We then fitted the ACE model to estimate the heritability of CT and FOD peak measures along WM fiber tracts. The WM fiber tracts where Genetic influence was detected were mapped onto the cortical surface. Bivariate Genetic modeling was performed to estimate the cross-trait Genetic Correlation between the CT and the FOD-based connectivity of the tracts associated with the cortical regions. We found some cortical regions displaying heritable and Genetically correlated GM thickness and WM connectivity, forming networks under stronger Genetic influence. Significant heritability and Genetic Correlations between the CT and WM connectivity were found in regions including the right postcentral gyrus, left posterior cingulate gyrus, right middle temporal gyri, suggesting common Genetic factors influencing both GM and WM.
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facial averageness and Genetic quality testing heritability Genetic Correlation with attractiveness and the paternal age effect
Evolution and Human Behavior, 2016Co-Authors: Anthony J Lee, Nicholas G. Martin, Margaret J. Wright, Matthew C Keller, Brendan P Zietsch, Dorian G MitchemAbstract:Popular theory suggests that facial averageness is preferred in a partner for Genetic benefits to offspring. However, whether facial averageness is associated with Genetic quality is yet to be established. Here, we computed an objective measure of facial averageness for a large sample (N = 1,823) of identical and nonidentical twins and their siblings to test two predictions from the theory that facial averageness reflects Genetic quality. First, we use biometrical modelling to estimate the heritability of facial averageness, which is necessary if it reflects Genetic quality. We also test for a Genetic association between facial averageness and facial attractiveness. Second, we assess whether paternal age at conception (a proxy of mutation load) is associated with facial averageness and facial attractiveness. Our findings are mixed with respect to our hypotheses. While we found that facial averageness does have a Genetic component, and a significant phenotypic Correlation exists between facial averageness and attractiveness, we did not find a Genetic Correlation between facial averageness and attractiveness (therefore, we cannot say that the genes that affect facial averageness also affect facial attractiveness) and paternal age at conception was not negatively associated with facial averageness. These findings support some of the previously untested assumptions of the 'Genetic benefits' account of facial averageness, but cast doubt on others.
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Is There a Genetic Correlation Between General Factors of Intelligence and Personality
Twin research and human genetics : the official journal of the International Society for Twin Studies, 2015Co-Authors: John C. Loehlin, Nicholas G. Martin, Meike Bartels, Dorret I. Boomsma, Denis Bratko, Robert C. Nichols, Margaret J. WrightAbstract:We tested a hypothesis that there is no Genetic Correlation between general factors of intelligence and personality, despite both having been selected for in human evolution. This was done using twin samples from Australia, the United States, the Netherlands, Great Britain, and Croatia, comprising altogether 1,748 monozygotic and 1,329 same-sex dizygotic twin pairs. Although parameters in the model-fitting differed among the twin samples, the Genetic Correlation between the two general factors could be set to zero, with a better fit if the U.S. sample was excepted.
Yunpeng Wang - One of the best experts on this subject based on the ideXlab platform.
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bivariate causal mixture model quantifies polygenic overlap between complex traits beyond Genetic Correlation
Nature Communications, 2019Co-Authors: Oleksandr Frei, Dominic Holland, Olav B Smeland, Alexey A Shadrin, Chun Chieh Fan, Steffen Maeland, Kevin Oconnell, Yunpeng WangAbstract:Accumulating evidence from genome wide association studies (GWAS) suggests an abundance of shared Genetic influences among complex human traits and disorders, such as mental disorders. Here we introduce a statistical tool, MiXeR, which quantifies polygenic overlap irrespective of Genetic Correlation, using GWAS summary statistics. MiXeR results are presented as a Venn diagram of unique and shared polygenic components across traits. At 90% of SNP-heritability explained for each phenotype, MiXeR estimates that 8.3 K variants causally influence schizophrenia and 6.4 K influence bipolar disorder. Among these variants, 6.2 K are shared between the disorders, which have a high Genetic Correlation. Further, MiXeR uncovers polygenic overlap between schizophrenia and educational attainment. Despite a Genetic Correlation close to zero, the phenotypes share 8.3 K causal variants, while 2.5 K additional variants influence only educational attainment. By considering the polygenicity, discoverability and heritability of complex phenotypes, MiXeR analysis may improve our understanding of cross-trait Genetic architectures.
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bivariate gaussian mixture model of gwas bgmg quantifies polygenic overlap between complex traits beyond Genetic Correlation
bioRxiv, 2017Co-Authors: Oleksandr Frei, Dominic Holland, Olav B Smeland, Alexey A Shadrin, Chun Chieh Fan, Yunpeng Wang, Aree Witoelar, Srdjan Djurovic, Wesley K Thompson, Ole A AndreassenAbstract:Accumulating evidence from genome wide association studies (GWAS) suggests abundant presence of shared Genetic influences among complex human traits and disorders. A major challenge that often limits our ability to detect and quantify shared Genetic variation is that current methods of cross-trait analysis are not designed to work in scenarios with low or absent Genetic Correlation. Here we introduce a statistical tool BGMG (Bivariate Gaussian Mixture Model of GWAS) which can uncover various scenarios of Genetic overlap regardless of Genetic Correlation, using GWAS summary statistics from studies with potentially shared participants. We perform extensive simulation on synthetic GWAS data to ensure that BGMG provides accurate estimates of model parameters in the presence of realistic linkage disequilibrium (LD) structure.
Alkes L Price - One of the best experts on this subject based on the ideXlab platform.
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distinguishing Genetic Correlation from causation across 52 diseases and complex traits
Nature Genetics, 2018Co-Authors: Luke J Oconnor, Alkes L PriceAbstract:Mendelian randomization, a method to infer causal relationships, is confounded by Genetic Correlations reflecting shared etiology. We developed a model in which a latent causal variable mediates the Genetic Correlation; trait 1 is partially Genetically causal for trait 2 if it is strongly Genetically correlated with the latent causal variable, quantified using the Genetic causality proportion. We fit this model using mixed fourth moments $${\it{E}}({\it{\alpha }}_1^2{\it{\alpha }}_1{\it{\alpha }}_2)$$ E ( α 1 2 α 1 α 2 ) and $${\it{E}}\left( {{\it{\alpha }}_2^2{\it{\alpha }}_1{\it{\alpha }}_2} \right)$$ E α 2 2 α 1 α 2 of marginal effect sizes for each trait; if trait 1 is causal for trait 2, then SNPs affecting trait 1 (large $${\it{\alpha }}_1^2$$ α 1 2 ) will have correlated effects on trait 2 (large α1α2), but not vice versa. In simulations, our method avoided false positives due to Genetic Correlations, unlike Mendelian randomization. Across 52 traits (average n = 331,000), we identified 30 causal relationships with high Genetic causality proportion estimates. Novel findings included a causal effect of low-density lipoprotein on bone mineral density, consistent with clinical trials of statins in osteoporosis.
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distinguishing Genetic Correlation from causation across 52 diseases and complex traits
bioRxiv, 2017Co-Authors: Luke J Oconnor, Alkes L PriceAbstract:Mendelian randomization (MR) is widely used to identify causal relationships among heritable traits, but can be confounded by Genetic Correlations reflecting shared etiology. We propose a model in which a latent causal variable mediates the Genetic Correlation between two traits. Under the latent causal variable (LCV) model, trait 1 is fully Genetically causal for trait 2 if it is perfectly Genetically correlated with the latent variable, implying that the entire Genetic component of trait 1 is causal for trait 2; it is partially Genetically causal for trait 2 if the latent variable has a high Genetic Correlation with the latent variable, implying that part of the Genetic component of trait 1 is causal for trait 2. To quantify the degree of partial Genetic causality, we define the Genetic causality proportion (gcp). We fit this model using mixed fourth moments E(α 2 1 α 1 α 2 ) and E(α 2 2 α 1 α 2 ) of marginal effect sizes for each trait, exploiting the fact that if trait 1 is causal for trait 2 then SNPs with large effects on trait 1 (large E(α 2 1 )) will have correlated effects on trait 2 (large E(α 1 α 2 )), but not vice versa. We performed simulations under a wide range of Genetic architectures and determined that LCV, unlike state-of-the-art MR methods, produced well-calibrated false positive rates and reliable gcp estimates in the presence of Genetic Correlations and asymmetric Genetic architectures; we also determined that LCV is well-powered to detect a causal effect. We applied LCV to GWAS summary statistics for 52 traits (average N =331k), identifying partially or fully Genetically causal effects (1% FDR) for 59 pairs of traits, including 30 pairs of traits with high gcp estimates (gcp>0.6). Results consistent with the published literature included causal effects on myocardial infarction (MI) for LDL, triglycerides and BMI. Novel findings included an effect of LDL on bone mineral density, consistent with clinical trials of statins in osteoporosis. These results demonstrate that it is possible to distinguish between Correlation and causation using Genetic data.
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transethnic Genetic Correlation estimates from summary statistics
American Journal of Human Genetics, 2016Co-Authors: Brielin C Brown, Alkes L Price, Noah ZaitlenAbstract:The increasing number of Genetic association studies conducted in multiple populations provides an unprecedented opportunity to study how the Genetic architecture of complex phenotypes varies between populations, a problem important for both medical and population Genetics. Here, we have developed a method for estimating the transethnic Genetic Correlation: the Correlation of causal-variant effect sizes at SNPs common in populations. This methods takes advantage of the entire spectrum of SNP associations and uses only summary-level data from genome-wide association studies. This avoids the computational costs and privacy concerns associated with genotype-level information while remaining scalable to hundreds of thousands of individuals and millions of SNPs. We applied our method to data on gene expression, rheumatoid arthritis, and type 2 diabetes and overwhelmingly found that the Genetic Correlation was significantly less than 1. Our method is implemented in a Python package called Popcorn.
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transethnic Genetic Correlation estimates from summary statistics
bioRxiv, 2016Co-Authors: Brielin C Brown, Alkes L Price, Noah ZaitlenAbstract:The increasing number of Genetic association studies conducted in multiple populations provides unprecedented opportunity to study how the Genetic architecture of complex phenotypes varies between populations, a problem important for both medical and population Genetics. Here we develop a method for estimating the transethnic Genetic Correlation: the Correlation of causal variant effect sizes at SNPs common in populations. We take advantage of the entire spectrum of SNP associations and use only summary-level GWAS data. This avoids the computational costs and privacy concerns associated with genotype-level information while remaining scalable to hundreds of thousands of individuals and millions of SNPs. We apply our method to gene expression, rheumatoid arthritis, and type-two diabetes data and overwhelmingly find that the Genetic Correlation is significantly less than 1. Our method is implemented in a python package called popcorn.
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transethnic Genetic Correlation estimates from summary statistics support widespread non additive effects
bioRxiv, 2016Co-Authors: Brielin C Brown, Alkes L Price, Noah ZaitlenAbstract:The increasing number of Genetic association studies conducted in multiple populations provides unprecedented opportunity to study how the Genetic architecture of complex phenotypes varies between populations, a problem important for both medical and population Genetics. Here we develop a method for estimating the transethnic Genetic Correlation; the Correlation of causal variant effect sizes at SNPs common in populations. Unlike some prior approaches, we take advantage of the entire spectrum of SNP associations and utilize only summary-level GWAS data, thereby avoiding the computational costs and privacy concerns associated with genotype-level information while remaining scalable to hundreds of thousands of individuals and millions of SNPs. We apply our method to gene expression, rheumatoid arthritis, and type-two diabetes data and overwhelmingly find that the Genetic Correlation is significantly less than 1. We argue that this is evidence for the presence of non-additive or differential tagging effects that modify the marginal effect sizes at SNPs common in both populations. Our method is implemented in a python package called popcorn.
Josephine M Pemberton - One of the best experts on this subject based on the ideXlab platform.
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positive Genetic Correlation between parasite resistance and body size in a free living ungulate population
Evolution, 2001Co-Authors: David W Coltman, Jill G Pilkington, Loeske E B Kruuk, Kenneth Wilson, Josephine M PembertonAbstract:Parasite resistance and body size are subject to directional natural selection in a population of feral Soay sheep (Ovis aries) on the island of St. Kilda, Scotland. Classical evolutionary theory predicts that directional selection should erode additive Genetic variation and favor the maintenance of alleles that have negative pleiotropic effects on other traits associated with fitness. Contrary to these predictions, in this study we show that there is considerable additive Genetic variation for both parasite resistance, measured as fecal egg count (FEC), and body size, measured as weight and hindleg length, and that there are positive Genetic Correlations between parasite resistance and body size in both sexes. Body size traits had higher heritabilities than parasite resistance. This was not due to low levels of additive Genetic variation for parasite resistance, but was a consequence of high levels of residual variance in FEC. Measured as coefficients of variation, levels of additive Genetic variation for FEC were actually higher than for weight or hindleg length. High levels of additive Genetic variation for parasite resistance may be maintained by a number of mechanisms including high mutational input, balancing selection, antagonistic pleiotropy, and host-parasite coevolution. The positive Genetic Correlation between parasite resistance and body size, a trait also subject to sexual selection in males, suggests that parasite resistance and growth are not traded off in Soay sheep, but rather that Genetically resistant individuals also experience superior growth.
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positive Genetic Correlation between parasite resistance and body size in a free living ungulate population
Evolution, 2001Co-Authors: David W Coltman, Jill G Pilkington, Loeske E B Kruuk, Kenneth Wilson, Josephine M PembertonAbstract:Parasite resistance and body size are subject to directional natural selection in a population of feral Soay sheep (Ovis aries) on the island of St. Kilda, Scotland. Classical evolutionary theory predicts that directional selection should erode additive Genetic variation and favor the maintenance of alleles that have negative pleiotropic effects on other traits associated with fitness. Contrary to these predictions, in this study we show that there is considerable additive Genetic variation for both parasite resistance, measured as fecal egg count (FEC), and body size, measured as weight and hindleg length, and that there are positive Genetic Correlations between parasite resistance and body size in both sexes. Body size traits had higher heritabilities than parasite resistance. This was not due to low levels of additive Genetic variation for parasite resistance, but was a consequence of high levels of residual variance in FEC. Measured as coefficients of variation, levels of additive Genetic variation for FEC were actually higher than for weight or hindleg length. High levels of additive Genetic variation for parasite resistance may be maintained by a number of mechanisms including high mutational input, balancing selection, antagonistic pleiotropy, and host-parasite coevolution. The positive Genetic Correlation between parasite resistance and body size, a trait also subject to sexual selection in males, suggests that parasite resistance and growth are not traded off in Soay sheep, but rather that Genetically resistant individuals also experience superior growth.