On the reconciliation of missing heritability for genome-wide association studies

Chen, Guo-Bo

doi:10.1038/ejhg.2016.89

Cited by 12 publications

(10 citation statements)

References 29 publications

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“…If average per-SNP heritability declines in higher-LD regions, say, then the estimated heritability must fall short of the true herit- ability. This sensitivity to LD is a feature shared with the heritability-estimation method GREML (Chen, 2016; Lee & Chow, 2014; Speed, Hemani, Johnson, & Balding, 2012; Yang et al, 2015).…”

Section: Resultsmentioning

confidence: 99%

The accuracy of LD Score regression as an estimator of confounding and genetic correlations in genome‐wide association studies

Lee

McGue

Iacono

et al. 2018

Genetic Epidemiology

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To infer that a single-nucleotide polymorphism (SNP) either affects a phenotype or is linkage disequilibrium with a causal site, we must have some assurance that any SNP-phenotype correlation is not the result of confounding with environmental variables that also affect the trait. In this study, we study the properties of linkage disequilibrium (LD) Score regression, a recently developed method for using summary statistics from genome-wide association studies to ensure that confounding does not inflate the number of false positives. We do not treat the effects of genetic variation as a random variable and thus are able to obtain results about the unbiasedness of this method. We demonstrate that LD Score regression can produce estimates of confounding at null SNPs that are unbiased or conservative under fairly general conditions. This robustness holds in the case of the parent genotype affecting the offspring phenotype through some environmental mechanism, despite the resulting correlation over SNPs between LD Scores and the degree of confounding. Additionally, we demonstrate that LD Score regression can produce reasonably robust estimates of the genetic correlation, even when its estimates of the genetic covariance and the two univariate heritabilities are substantially biased.

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Section: Resultsmentioning

confidence: 99%

The accuracy of LD Score regression as an estimator of confounding and genetic correlations in genome‐wide association studies

Lee

McGue

Iacono

et al. 2018

Genetic Epidemiology

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“…"# $ = 0.383 ± 0.005 under HE and 0.304 ± 0.004 under REML). If REML and HE estimates are expected to converge under a classical polygenic model, the genetic architecture of a trait can bias the estimates of the two methods in different ways [33][34][35] . Previous studies 36,37 have suggested that urate concentration is a trait on the low spectrum of polygenicity and controlled by three main genes and two large effect QTLs on underlying the consistency of HE and REML estimators.…”

Section: Discussionmentioning

confidence: 99%

Estimation of non-additive genetic variance in human complex traits from a large sample of unrelated individuals

Hivert

Sidorenko

Rohart

et al. 2020

Preprint

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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 , dominance and additive-by-additive 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 . In contrast, the average estimate of across traits was 0.001, implying negligible dominance variance at causal variants tagged by common SNPs. The average epistatic variance 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.

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“…Although it is not informative about genetic architecture and has its own problem when the genetic architecture does not fit the assumptions [15], we are not suggesting that the classical method of partitioning variance components be abandoned. The classical model, and particularly the concepts of V A and h 2 , have been and will continue to be the foundation of quantitative genetics, predicting resemblance between relatives and response to selection: they inform us about the proportion of phenotypic variation that is “breedable” [3].…”

Section: Discussionmentioning

confidence: 99%

The Genetic Architecture of Quantitative Traits Cannot Be Inferred from Variance Component Analysis

2016

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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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On the reconciliation of missing heritability for genome-wide association studies

Cited by 12 publications

References 29 publications

The accuracy of LD Score regression as an estimator of confounding and genetic correlations in genome‐wide association studies

The accuracy of LD Score regression as an estimator of confounding and genetic correlations in genome‐wide association studies

Estimation of non-additive genetic variance in human complex traits from a large sample of unrelated individuals

The Genetic Architecture of Quantitative Traits Cannot Be Inferred from Variance Component Analysis

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