The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
Jordan W Smoller - One of the best experts on this subject based on the ideXlab platform.
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Heritability analysis with repeat measurements and its application to resting state functional connectivity
Proceedings of the National Academy of Sciences of the United States of America, 2017Co-Authors: Tian Ge, Mert R Sabuncu, Jordan W Smoller, Avram J Holmes, Randy L BucknerAbstract:Heritability, defined as the proportion of phenotypic variation attributable to genetic variation, provides important information about the genetic basis of a trait. Existing Heritability analysis methods do not discriminate between stable effects (e.g., due to the subject’s unique environment) and transient effects, such as measurement error. This can lead to misleading assessments, particularly when comparing the Heritability of traits that exhibit different levels of reliability. Here, we present a linear mixed effects model to conduct Heritability analyses that explicitly accounts for intrasubject fluctuations (e.g., due to measurement noise or biological transients) using repeat measurements. We apply the proposed strategy to the analysis of resting-state fMRI measurements—a prototypic data modality that exhibits variable levels of test–retest reliability across space. Our results reveal that the stable components of functional connectivity within and across well-established large-scale brain networks can be considerably heritable. Furthermore, we demonstrate that dissociating intra- and intersubject variation can reveal genetic influence on a phenotype that is not fully captured by conventional Heritability analyses.
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phenome wide Heritability analysis of the uk biobank
PLOS Genetics, 2017Co-Authors: Benjamin M. Neale, Tian Ge, Chiayen Chen, Mert R Sabuncu, Jordan W SmollerAbstract:Heritability estimation provides important information about the relative contribution of genetic and environmental factors to phenotypic variation, and provides an upper bound for the utility of genetic risk prediction models. Recent technological and statistical advances have enabled the estimation of additive Heritability attributable to common genetic variants (SNP Heritability) across a broad phenotypic spectrum. Here, we present a computationally and memory efficient Heritability estimation method that can handle large sample sizes, and report the SNP Heritability for 551 complex traits derived from the interim data release (152,736 subjects) of the large-scale, population-based UK Biobank, comprising both quantitative phenotypes and disease codes. We demonstrate that common genetic variation contributes to a broad array of quantitative traits and human diseases in the UK population, and identify phenotypes whose Heritability is moderated by age (e.g., a majority of physical measures including height and body mass index), sex (e.g., blood pressure related traits) and socioeconomic status (education). Our study represents the first comprehensive phenome-wide Heritability analysis in the UK Biobank, and underscores the importance of considering population characteristics in interpreting Heritability.
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phenome wide Heritability analysis of the uk biobank
bioRxiv, 2016Co-Authors: Tian Ge, Benjamin M. Neale, Chiayen Chen, Mert R Sabuncu, Jordan W SmollerAbstract:Heritability estimation provides important information about the relative contribution of genetic and environmental factors to phenotypic variation, and provides an upper bound for the utility of genetic risk prediction models. Recent technological and statistical advances have enabled the estimation of additive Heritability attributable to common genetic variants (SNP Heritability) across a broad phenotypic spectrum. However, assessing the comparative Heritability of multiple traits estimated in different cohorts may be misleading due to the population-specific nature of Heritability. Here we report the SNP Heritability for 551 complex traits derived from the large-scale, population-based UK Biobank, comprising both quantitative phenotypes and disease codes, and examine the moderating effect of three major demographic variables (age, sex and socioeconomic status) on the Heritability estimates. Our study represents the first comprehensive phenome-wide Heritability analysis in the UK Biobank, and underscores the importance of considering population characteristics in comparing and interpreting Heritability.
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massively expedited genome wide Heritability analysis megha
Proceedings of the National Academy of Sciences of the United States of America, 2015Co-Authors: Tian Ge, Mert R Sabuncu, Jordan W Smoller, Thomas E Nichols, Avram J Holmes, Joshua L Roffman, Randy L BucknerAbstract:The discovery and prioritization of heritable phenotypes is a computational challenge in a variety of settings, including neuroimaging genetics and analyses of the vast phenotypic repositories in electronic health record systems and population-based biobanks. Classical estimates of Heritability require twin or pedigree data, which can be costly and difficult to acquire. Genome-wide complex trait analysis is an alternative tool to compute Heritability estimates from unrelated individuals, using genome-wide data that are increasingly ubiquitous, but is computationally demanding and becomes difficult to apply in evaluating very large numbers of phenotypes. Here we present a fast and accurate statistical method for high-dimensional Heritability analysis using genome-wide SNP data from unrelated individuals, termed massively expedited genome-wide Heritability analysis (MEGHA) and accompanying nonparametric sampling techniques that enable flexible inferences for arbitrary statistics of interest. MEGHA produces estimates and significance measures of Heritability with several orders of magnitude less computational time than existing methods, making Heritability-based prioritization of millions of phenotypes based on data from unrelated individuals tractable for the first time to our knowledge. As a demonstration of application, we conducted Heritability analyses on global and local morphometric measurements derived from brain structural MRI scans, using genome-wide SNP data from 1,320 unrelated young healthy adults of non-Hispanic European ancestry. We also computed surface maps of Heritability for cortical thickness measures and empirically localized cortical regions where thickness measures were significantly heritable. Our analyses demonstrate the unique capability of MEGHA for large-scale Heritability-based screening and high-dimensional Heritability profile construction.
Tian Ge - One of the best experts on this subject based on the ideXlab platform.
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Heritability analysis with repeat measurements and its application to resting state functional connectivity
Proceedings of the National Academy of Sciences of the United States of America, 2017Co-Authors: Tian Ge, Mert R Sabuncu, Jordan W Smoller, Avram J Holmes, Randy L BucknerAbstract:Heritability, defined as the proportion of phenotypic variation attributable to genetic variation, provides important information about the genetic basis of a trait. Existing Heritability analysis methods do not discriminate between stable effects (e.g., due to the subject’s unique environment) and transient effects, such as measurement error. This can lead to misleading assessments, particularly when comparing the Heritability of traits that exhibit different levels of reliability. Here, we present a linear mixed effects model to conduct Heritability analyses that explicitly accounts for intrasubject fluctuations (e.g., due to measurement noise or biological transients) using repeat measurements. We apply the proposed strategy to the analysis of resting-state fMRI measurements—a prototypic data modality that exhibits variable levels of test–retest reliability across space. Our results reveal that the stable components of functional connectivity within and across well-established large-scale brain networks can be considerably heritable. Furthermore, we demonstrate that dissociating intra- and intersubject variation can reveal genetic influence on a phenotype that is not fully captured by conventional Heritability analyses.
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phenome wide Heritability analysis of the uk biobank
PLOS Genetics, 2017Co-Authors: Benjamin M. Neale, Tian Ge, Chiayen Chen, Mert R Sabuncu, Jordan W SmollerAbstract:Heritability estimation provides important information about the relative contribution of genetic and environmental factors to phenotypic variation, and provides an upper bound for the utility of genetic risk prediction models. Recent technological and statistical advances have enabled the estimation of additive Heritability attributable to common genetic variants (SNP Heritability) across a broad phenotypic spectrum. Here, we present a computationally and memory efficient Heritability estimation method that can handle large sample sizes, and report the SNP Heritability for 551 complex traits derived from the interim data release (152,736 subjects) of the large-scale, population-based UK Biobank, comprising both quantitative phenotypes and disease codes. We demonstrate that common genetic variation contributes to a broad array of quantitative traits and human diseases in the UK population, and identify phenotypes whose Heritability is moderated by age (e.g., a majority of physical measures including height and body mass index), sex (e.g., blood pressure related traits) and socioeconomic status (education). Our study represents the first comprehensive phenome-wide Heritability analysis in the UK Biobank, and underscores the importance of considering population characteristics in interpreting Heritability.
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phenome wide Heritability analysis of the uk biobank
bioRxiv, 2016Co-Authors: Tian Ge, Benjamin M. Neale, Chiayen Chen, Mert R Sabuncu, Jordan W SmollerAbstract:Heritability estimation provides important information about the relative contribution of genetic and environmental factors to phenotypic variation, and provides an upper bound for the utility of genetic risk prediction models. Recent technological and statistical advances have enabled the estimation of additive Heritability attributable to common genetic variants (SNP Heritability) across a broad phenotypic spectrum. However, assessing the comparative Heritability of multiple traits estimated in different cohorts may be misleading due to the population-specific nature of Heritability. Here we report the SNP Heritability for 551 complex traits derived from the large-scale, population-based UK Biobank, comprising both quantitative phenotypes and disease codes, and examine the moderating effect of three major demographic variables (age, sex and socioeconomic status) on the Heritability estimates. Our study represents the first comprehensive phenome-wide Heritability analysis in the UK Biobank, and underscores the importance of considering population characteristics in comparing and interpreting Heritability.
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massively expedited genome wide Heritability analysis megha
Proceedings of the National Academy of Sciences of the United States of America, 2015Co-Authors: Tian Ge, Mert R Sabuncu, Jordan W Smoller, Thomas E Nichols, Avram J Holmes, Joshua L Roffman, Randy L BucknerAbstract:The discovery and prioritization of heritable phenotypes is a computational challenge in a variety of settings, including neuroimaging genetics and analyses of the vast phenotypic repositories in electronic health record systems and population-based biobanks. Classical estimates of Heritability require twin or pedigree data, which can be costly and difficult to acquire. Genome-wide complex trait analysis is an alternative tool to compute Heritability estimates from unrelated individuals, using genome-wide data that are increasingly ubiquitous, but is computationally demanding and becomes difficult to apply in evaluating very large numbers of phenotypes. Here we present a fast and accurate statistical method for high-dimensional Heritability analysis using genome-wide SNP data from unrelated individuals, termed massively expedited genome-wide Heritability analysis (MEGHA) and accompanying nonparametric sampling techniques that enable flexible inferences for arbitrary statistics of interest. MEGHA produces estimates and significance measures of Heritability with several orders of magnitude less computational time than existing methods, making Heritability-based prioritization of millions of phenotypes based on data from unrelated individuals tractable for the first time to our knowledge. As a demonstration of application, we conducted Heritability analyses on global and local morphometric measurements derived from brain structural MRI scans, using genome-wide SNP data from 1,320 unrelated young healthy adults of non-Hispanic European ancestry. We also computed surface maps of Heritability for cortical thickness measures and empirically localized cortical regions where thickness measures were significantly heritable. Our analyses demonstrate the unique capability of MEGHA for large-scale Heritability-based screening and high-dimensional Heritability profile construction.
Mert R Sabuncu - One of the best experts on this subject based on the ideXlab platform.
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Heritability analysis with repeat measurements and its application to resting state functional connectivity
Proceedings of the National Academy of Sciences of the United States of America, 2017Co-Authors: Tian Ge, Mert R Sabuncu, Jordan W Smoller, Avram J Holmes, Randy L BucknerAbstract:Heritability, defined as the proportion of phenotypic variation attributable to genetic variation, provides important information about the genetic basis of a trait. Existing Heritability analysis methods do not discriminate between stable effects (e.g., due to the subject’s unique environment) and transient effects, such as measurement error. This can lead to misleading assessments, particularly when comparing the Heritability of traits that exhibit different levels of reliability. Here, we present a linear mixed effects model to conduct Heritability analyses that explicitly accounts for intrasubject fluctuations (e.g., due to measurement noise or biological transients) using repeat measurements. We apply the proposed strategy to the analysis of resting-state fMRI measurements—a prototypic data modality that exhibits variable levels of test–retest reliability across space. Our results reveal that the stable components of functional connectivity within and across well-established large-scale brain networks can be considerably heritable. Furthermore, we demonstrate that dissociating intra- and intersubject variation can reveal genetic influence on a phenotype that is not fully captured by conventional Heritability analyses.
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phenome wide Heritability analysis of the uk biobank
PLOS Genetics, 2017Co-Authors: Benjamin M. Neale, Tian Ge, Chiayen Chen, Mert R Sabuncu, Jordan W SmollerAbstract:Heritability estimation provides important information about the relative contribution of genetic and environmental factors to phenotypic variation, and provides an upper bound for the utility of genetic risk prediction models. Recent technological and statistical advances have enabled the estimation of additive Heritability attributable to common genetic variants (SNP Heritability) across a broad phenotypic spectrum. Here, we present a computationally and memory efficient Heritability estimation method that can handle large sample sizes, and report the SNP Heritability for 551 complex traits derived from the interim data release (152,736 subjects) of the large-scale, population-based UK Biobank, comprising both quantitative phenotypes and disease codes. We demonstrate that common genetic variation contributes to a broad array of quantitative traits and human diseases in the UK population, and identify phenotypes whose Heritability is moderated by age (e.g., a majority of physical measures including height and body mass index), sex (e.g., blood pressure related traits) and socioeconomic status (education). Our study represents the first comprehensive phenome-wide Heritability analysis in the UK Biobank, and underscores the importance of considering population characteristics in interpreting Heritability.
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phenome wide Heritability analysis of the uk biobank
bioRxiv, 2016Co-Authors: Tian Ge, Benjamin M. Neale, Chiayen Chen, Mert R Sabuncu, Jordan W SmollerAbstract:Heritability estimation provides important information about the relative contribution of genetic and environmental factors to phenotypic variation, and provides an upper bound for the utility of genetic risk prediction models. Recent technological and statistical advances have enabled the estimation of additive Heritability attributable to common genetic variants (SNP Heritability) across a broad phenotypic spectrum. However, assessing the comparative Heritability of multiple traits estimated in different cohorts may be misleading due to the population-specific nature of Heritability. Here we report the SNP Heritability for 551 complex traits derived from the large-scale, population-based UK Biobank, comprising both quantitative phenotypes and disease codes, and examine the moderating effect of three major demographic variables (age, sex and socioeconomic status) on the Heritability estimates. Our study represents the first comprehensive phenome-wide Heritability analysis in the UK Biobank, and underscores the importance of considering population characteristics in comparing and interpreting Heritability.
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massively expedited genome wide Heritability analysis megha
Proceedings of the National Academy of Sciences of the United States of America, 2015Co-Authors: Tian Ge, Mert R Sabuncu, Jordan W Smoller, Thomas E Nichols, Avram J Holmes, Joshua L Roffman, Randy L BucknerAbstract:The discovery and prioritization of heritable phenotypes is a computational challenge in a variety of settings, including neuroimaging genetics and analyses of the vast phenotypic repositories in electronic health record systems and population-based biobanks. Classical estimates of Heritability require twin or pedigree data, which can be costly and difficult to acquire. Genome-wide complex trait analysis is an alternative tool to compute Heritability estimates from unrelated individuals, using genome-wide data that are increasingly ubiquitous, but is computationally demanding and becomes difficult to apply in evaluating very large numbers of phenotypes. Here we present a fast and accurate statistical method for high-dimensional Heritability analysis using genome-wide SNP data from unrelated individuals, termed massively expedited genome-wide Heritability analysis (MEGHA) and accompanying nonparametric sampling techniques that enable flexible inferences for arbitrary statistics of interest. MEGHA produces estimates and significance measures of Heritability with several orders of magnitude less computational time than existing methods, making Heritability-based prioritization of millions of phenotypes based on data from unrelated individuals tractable for the first time to our knowledge. As a demonstration of application, we conducted Heritability analyses on global and local morphometric measurements derived from brain structural MRI scans, using genome-wide SNP data from 1,320 unrelated young healthy adults of non-Hispanic European ancestry. We also computed surface maps of Heritability for cortical thickness measures and empirically localized cortical regions where thickness measures were significantly heritable. Our analyses demonstrate the unique capability of MEGHA for large-scale Heritability-based screening and high-dimensional Heritability profile construction.
Benjamin M. Neale - One of the best experts on this subject based on the ideXlab platform.
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Comparison of methods that use whole genome data to estimate the Heritability and genetic architecture of complex traits
Nature Genetics, 2018Co-Authors: Luke M. Evans, Sayantan Das, Douglas W. Bjelland, Teresa R. Candia, Rasool Tahmasbi, Scott I. Vrieze, Steven Gazal, Michael E. Goddard, Goncalo R Abecasis, Benjamin M. NealeAbstract:Multiple methods have been developed to estimate narrow-sense Heritability, h ^2, using single nucleotide polymorphisms (SNPs) in unrelated individuals. However, a comprehensive evaluation of these methods has not yet been performed, leading to confusion and discrepancy in the literature. We present the most thorough and realistic comparison of these methods to date. We used thousands of real whole-genome sequences to simulate phenotypes under varying genetic architectures and confounding variables, and we used array, imputed, or whole genome sequence SNPs to obtain ‘SNP-Heritability’ estimates. We show that SNP-Heritability can be highly sensitive to assumptions about the frequencies, effect sizes, and levels of linkage disequilibrium of underlying causal variants, but that methods that bin SNPs according to minor allele frequency and linkage disequilibrium are less sensitive to these assumptions across a wide range of genetic architectures and possible confounding factors. These findings provide guidance for best practices and proper interpretation of published estimates.This analysis compares methods for estimating the Heritability and genetic architecture of complex traits using whole-genome data. The results provide guidance for best practices and proper interpretation of published Heritability estimates.
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phenome wide Heritability analysis of the uk biobank
PLOS Genetics, 2017Co-Authors: Benjamin M. Neale, Tian Ge, Chiayen Chen, Mert R Sabuncu, Jordan W SmollerAbstract:Heritability estimation provides important information about the relative contribution of genetic and environmental factors to phenotypic variation, and provides an upper bound for the utility of genetic risk prediction models. Recent technological and statistical advances have enabled the estimation of additive Heritability attributable to common genetic variants (SNP Heritability) across a broad phenotypic spectrum. Here, we present a computationally and memory efficient Heritability estimation method that can handle large sample sizes, and report the SNP Heritability for 551 complex traits derived from the interim data release (152,736 subjects) of the large-scale, population-based UK Biobank, comprising both quantitative phenotypes and disease codes. We demonstrate that common genetic variation contributes to a broad array of quantitative traits and human diseases in the UK population, and identify phenotypes whose Heritability is moderated by age (e.g., a majority of physical measures including height and body mass index), sex (e.g., blood pressure related traits) and socioeconomic status (education). Our study represents the first comprehensive phenome-wide Heritability analysis in the UK Biobank, and underscores the importance of considering population characteristics in interpreting Heritability.
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phenome wide Heritability analysis of the uk biobank
bioRxiv, 2016Co-Authors: Tian Ge, Benjamin M. Neale, Chiayen Chen, Mert R Sabuncu, Jordan W SmollerAbstract:Heritability estimation provides important information about the relative contribution of genetic and environmental factors to phenotypic variation, and provides an upper bound for the utility of genetic risk prediction models. Recent technological and statistical advances have enabled the estimation of additive Heritability attributable to common genetic variants (SNP Heritability) across a broad phenotypic spectrum. However, assessing the comparative Heritability of multiple traits estimated in different cohorts may be misleading due to the population-specific nature of Heritability. Here we report the SNP Heritability for 551 complex traits derived from the large-scale, population-based UK Biobank, comprising both quantitative phenotypes and disease codes, and examine the moderating effect of three major demographic variables (age, sex and socioeconomic status) on the Heritability estimates. Our study represents the first comprehensive phenome-wide Heritability analysis in the UK Biobank, and underscores the importance of considering population characteristics in comparing and interpreting Heritability.
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subtle stratification confounds estimates of Heritability from rare variants
bioRxiv, 2016Co-Authors: Gaurav Bhatia, Teresa R. Candia, Alexander Gusev, Bjarni J Vilhjalmsson, Eli A Stahl, Hilary K Finucane, Stephan Ripke, Shaun Purcell, Mark J Daly, Benjamin M. NealeAbstract:Genome-wide significant associations generally explain only a small proportion of the narrow-sense Heritability of complex disease (h2). While considerably more Heritability is explained by all genotyped SNPs (hg2), for most traits, much Heritability remains missing (hg2 < h2). Rare variants, poorly tagged by genotyped SNPs, are a major potential source of the gap between hg2 and h2. Recent efforts to assess the contribution of both sequenced and imputed rare variants to phenotypes suggest that substantial Heritability may lie in these variants. Here we analyze sequenced SNPs, imputed SNPs and haploSNPs (haplotype variants constructed from within a sample, without using a reference panel) and show that studies of Heritability from these variants may be strongly confounded by subtle population stratification. For example, when meta-analyzing Heritability estimates from 22 randomly ascertained case-control traits from the GERA cohort, we observe a statistically significant increase in Heritability explained by imputed SNPs even after correcting for principal components (PCs) from genotyped (or imputed) SNPs. However, this increase is eliminated when correcting for stratification using PCs from a larger number of haploSNPs. We note that subtle stratification may also impact estimates of Heritability from array SNPs, although we find that this is generally a less severe problem. Overall, our results suggest that estimating the Heritability explained by rare variants for case-control traits requires exquisite control for population stratification, but current methods may not provide this level of control.
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partitioning the Heritability of tourette syndrome and obsessive compulsive disorder reveals differences in genetic architecture
PLOS Genetics, 2013Co-Authors: Lea K Davis, Benjamin M. Neale, Dongmei Yu, Clare L Keenan, Eric R Gamazon, Anuar Konkashbaev, Eske M Derks, Jian Yang, Patrick Evans, Cathy L BarrAbstract:The direct estimation of Heritability from genome-wide common variant data as implemented in the program Genome-wide Complex Trait Analysis (GCTA) has provided a means to quantify Heritability attributable to all interrogated variants. We have quantified the variance in liability to disease explained by all SNPs for two phenotypically-related neurobehavioral disorders, obsessive-compulsive disorder (OCD) and Tourette Syndrome (TS), using GCTA. Our analysis yielded a Heritability point estimate of 0.58 (se = 0.09, p = 5.64e-12) for TS, and 0.37 (se = 0.07, p = 1.5e-07) for OCD. In addition, we conducted multiple genomic partitioning analyses to identify genomic elements that concentrate this Heritability. We examined genomic architectures of TS and OCD by chromosome, MAF bin, and functional annotations. In addition, we assessed Heritability for early onset and adult onset OCD. Among other notable results, we found that SNPs with a minor allele frequency of less than 5% accounted for 21% of the TS Heritability and 0% of the OCD Heritability. Additionally, we identified a significant contribution to TS and OCD Heritability by variants significantly associated with gene expression in two regions of the brain (parietal cortex and cerebellum) for which we had available expression quantitative trait loci (eQTLs). Finally we analyzed the genetic correlation between TS and OCD, revealing a genetic correlation of 0.41 (se = 0.15, p = 0.002). These results are very close to previous Heritability estimates for TS and OCD based on twin and family studies, suggesting that very little, if any, Heritability is truly missing (i.e., unassayed) from TS and OCD GWAS studies of common variation. The results also indicate that there is some genetic overlap between these two phenotypically-related neuropsychiatric disorders, but suggest that the two disorders have distinct genetic architectures.
Bogdan Pasaniuc - One of the best experts on this subject based on the ideXlab platform.
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accurate estimation of snp Heritability from biobank scale data irrespective of genetic architecture
Nature Genetics, 2019Co-Authors: Kathryn S Burch, Arunabha Majumdar, Nicholas Mancuso, Yue Wu, Sriram Sankararaman, Bogdan PasaniucAbstract:SNP-Heritability is a fundamental quantity in the study of complex traits. Recent studies have shown that existing methods to estimate genome-wide SNP-Heritability can yield biases when their assumptions are violated. While various approaches have been proposed to account for frequency- and linkage disequilibrium (LD)-dependent genetic architectures, it remains unclear which estimates reported in the literature are reliable. Here we show that genome-wide SNP-Heritability can be accurately estimated from biobank-scale data irrespective of genetic architecture, without specifying a Heritability model or partitioning SNPs by allele frequency and/or LD. We show analytically and through extensive simulations starting from real genotypes (UK Biobank, N = 337 K) that, unlike existing methods, our closed-form estimator is robust across a wide range of architectures. We provide estimates of SNP-Heritability for 22 complex traits in the UK Biobank and show that, consistent with our results in simulations, existing biobank-scale methods yield estimates up to 30% different from our theoretically-justified approach.
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accurate estimation of snp Heritability from biobank scale data irrespective of genetic architecture
bioRxiv, 2019Co-Authors: Kathryn S Burch, Arunabha Majumdar, Nicholas Mancuso, Yue Wu, Sriram Sankararaman, Bogdan PasaniucAbstract:The proportion of phenotypic variance attributable to the additive effects of a given set of genotyped SNPs (i.e. SNP-Heritability) is a fundamental quantity in the study of complex traits. Recent works have shown that existing methods to estimate genome-wide SNP-Heritability often yield biases when their assumptions are violated. While various approaches have been proposed to account for frequency- and LD-dependent genetic architectures, it remains unclear which estimates of SNP-Heritability reported in the literature are reliable. Here we show that genome-wide SNP-Heritability can be accurately estimated from biobank-scale data irrespective of the underlying genetic architecture of the trait, without specifying a Heritability model or partitioning SNPs by minor allele frequency and/or LD. We use theoretical justifications coupled with extensive simulations starting from real genotypes from the UK Biobank (N=337K) to show that, unlike existing methods, our closed-form estimator for SNP-Heritability is highly accurate across a wide range of architectures. We provide estimates of SNP-Heritability for 22 complex traits and diseases in the UK Biobank and show that, consistent with our results in simulations, existing biobank-scale methods yield estimates up to 30% different from our theoretically-justified approach.
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using extended genealogy to estimate components of Heritability for 23 quantitative and dichotomous traits
PLOS Genetics, 2013Co-Authors: Noah Zaitlen, Gaurav Bhatia, P Kraft, Nick Patterson, Bogdan Pasaniuc, Samuela Pollack, Alkes L PriceAbstract:Important knowledge about the determinants of complex human phenotypes can be obtained from the estimation of Heritability, the fraction of phenotypic variation in a population that is determined by genetic factors. Here, we make use of extensive phenotype data in Iceland, long-range phased genotypes, and a population-wide genealogical database to examine the Heritability of 11 quantitative and 12 dichotomous phenotypes in a sample of 38,167 individuals. Most previous estimates of Heritability are derived from family-based approaches such as twin studies, which may be biased upwards by epistatic interactions or shared environment. Our estimates of Heritability, based on both closely and distantly related pairs of individuals, are significantly lower than those from previous studies. We examine phenotypic correlations across a range of relationships, from siblings to first cousins, and find that the excess phenotypic correlation in these related individuals is predominantly due to shared environment as opposed to dominance or epistasis. We also develop a new method to jointly estimate narrow-sense Heritability and the Heritability explained by genotyped SNPs. Unlike existing methods, this approach permits the use of information from both closely and distantly related pairs of individuals, thereby reducing the variance of estimates of Heritability explained by genotyped SNPs while preventing upward bias. Our results show that common SNPs explain a larger proportion of the Heritability than previously thought, with SNPs present on Illumina 300K genotyping arrays explaining more than half of the Heritability for the 23 phenotypes examined in this study. Much of the remaining Heritability is likely to be due to rare alleles that are not captured by standard genotyping arrays.
