Using partitioned heritability methods to explore genetic architecture

Evans, Luke M.

doi:10.1038/nrg.2018.6

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…Notably, though, the LDSC-derived heritability was 0.17 ( SE = 0.005) in the full sample of nonadopted individuals ( N = 375,343) and 0.14 ( SE = 0.073) for adoptees, corroborating the pattern of results found using GREML. LDSC estimates are typically lower than GCTA and GREML-derived estimates ( Evans & Keller, 2018 ).…”

Section: Resultsmentioning

confidence: 99%

Comparison of Adopted and Nonadopted Individuals Reveals Gene–Environment Interplay for Education in the UK Biobank

et al. 2020

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Polygenic scores now explain approximately 10% of the variation in educational attainment. However, they capture not only genetic propensity but also information about the family environment. This is because of passive gene–environment correlation, whereby the correlation between offspring and parent genotypes results in an association between offspring genotypes and the rearing environment. We measured passive gene–environment correlation using information on 6,311 adoptees in the UK Biobank. Adoptees’ genotypes were less correlated with their rearing environments because they did not share genes with their adoptive parents. We found that polygenic scores were twice as predictive of years of education in nonadopted individuals compared with adoptees ( R2s = .074 vs. .037, p = 8.23 × 10−24). Individuals in the lowest decile of polygenic scores for education attained significantly more education if they were adopted, possibly because of educationally supportive adoptive environments. Overall, these results suggest that genetic influences on education are mediated via the home environment.

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Section: Resultsmentioning

confidence: 99%

Comparison of Adopted and Nonadopted Individuals Reveals Gene–Environment Interplay for Education in the UK Biobank

et al. 2020

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“…In addition, estimates of familial heritability for some traits, such as human height, can also be overestimated if assortative mating is not properly modeled (Lynch and Walsh 1998;Yang et al 2015). Finally, any source of genotype-covariate interaction is likely to have an effect on the estimates of SNP-heritability (Evans and Keller 2018;Ni et al 2018). For example, genotype-environment interactions have also been proposed to explain part of the genetic variance of complex traits (Zheng et al 2013;Robinson et al 2017) and, thus, their heritability.…”

Section: Gap Between the Expected Gwas And Familial Heritabilitiesmentioning

confidence: 99%

Inferring the Nature of Missing Heritability in Human Traits Using Data from the GWAS Catalog

López‐Cortegano

Caballero²

2019

Genetics

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Thousands of genes responsible for many diseases and other common traits in humans have been detected by Genome Wide Association Studies (GWAS) in the last decade. However, candidate causal variants found so far usually explain only a small fraction of the heritability estimated by family data. The most common explanation for this observation is that the missing heritability corresponds to variants, either rare or common, with very small effect, which pass undetected due to a lack of statistical power. We carried out a meta-analysis using data from the NHGRI-EBI GWAS Catalog in order to explore the observed distribution of locus effects for a set of 42 complex traits and to quantify their contribution to narrow-sense heritability. With the data at hand, we were able to predict the expected distribution of locus effects for 16 traits and diseases, their expected contribution to heritability, and the missing number of loci yet to be discovered to fully explain the familial heritability estimates. Our results indicate that, for 6 out of the 16 traits, the additive contribution of a great number of loci is unable to explain the familial (broad-sense) heritability, suggesting that the gap between GWAS and familial estimates of heritability may not ever be closed for these traits. In contrast, for the other 10 traits, the additive contribution of hundreds or thousands of loci yet to be found could potentially explain the familial heritability estimates, if this were the case. Computer simulations are used to illustrate the possible contribution from nonadditive genetic effects to the gap between GWAS and familial estimates of heritability.

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“…A comprehensive evaluation of the frequency spectrum, the influence of dominance genetic variance and the contributions of functional annotations is needed to provide a more complete picture of the genetic architecture underlying complex smoking behaviors. Here, we use recently developed methods [18,20,21,25,[27][28][29] to evaluate these heritable contributions and characterize the genetic architecture of four smoking behaviors: smoking initiation (whether an individual has ever been a regular smoker), age of initiation of regular smoking, cigarettes per day (CPD, evaluated with different data encodings) and smoking cessation. These four behaviors represent a cross-section of the full spectrum from experimentation to dependence [30][31][32], and have been evaluated in recent GWAS [11,16,17].…”

Section: Introductionmentioning

confidence: 99%

Genetic architecture of four smoking behaviors using partitioned SNP heritability

et al. 2021

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Background and Aims Although genome-wide association studies have identified many loci that influence smoking behaviors, much of the genetic variance remains unexplained. We characterized the genetic architecture of four smoking behaviors using single nucleotide polymorphism (SNP) heritability (h 2 SNP ). This is an estimate of narrow-sense heritability specifically estimating the proportion of phenotypic variation due to causal variants (CVs) tagged by SNPs. Design Partitioned h 2 SNP analysis of smoking behavior traits. Setting UK Biobank. Participants UK Biobank participants of European ancestry. The number of participants varied depending on the trait, from 54 792 to 323 068. Measurements Smoking initiation, age of initiation, cigarettes per day (CPD; count, log-transformed, binned and dichotomized into heavy versus light) and smoking cessation with imputed genome-wide SNPs. Findings We estimated that, in aggregate, approximately 18% of the phenotypic variance in smoking initiation was captured by imputed SNPs [h 2 SNP = 0.18, standard error (SE) = 0.01] and 12% [SE = 0.02] for smoking cessation, both of which were more than twice the previously reported estimates. Estimated age of initiation (h 2 SNP = 0.05, SE = 0.01) and binned CPD (h 2 SNP = 0.1, SE = 0.01) were substantially below published twin-based h 2 of 50%. CPD encoding influenced estimates, with dichotomized CPD h 2 SNP = 0.28. There was no evidence of dominance genetic variance for any trait. Conclusion A biobank study of smoking behavior traits suggested that the phenotypic variance explained by SNPs of smoking initiation, age of initiation, cigarettes per day and smoking cessation is modest overall.

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Using partitioned heritability methods to explore genetic architecture

Cited by 8 publications

References 19 publications

Comparison of Adopted and Nonadopted Individuals Reveals Gene–Environment Interplay for Education in the UK Biobank

Comparison of Adopted and Nonadopted Individuals Reveals Gene–Environment Interplay for Education in the UK Biobank

Inferring the Nature of Missing Heritability in Human Traits Using Data from the GWAS Catalog

Genetic architecture of four smoking behaviors using partitioned SNP heritability

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