Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index

By Jian Yang, A. Bakshi, Zhihong Zhu, Gibran Hemani, A. Vinkhuyzen, Sang Hong Lee, M. Robinson, J. Perry, I. Nolte, Jana V. van Vliet-Ostaptchouk and 14 more. Nature Genetics, 2015

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We propose a method (GREML-LDMS) to estimate heritability for human complex traits in unrelated individuals using whole-genome sequencing data. We demonstrate using simulations based on whole-genome sequencing data that ∼97% and ∼68% of variation at common and rare variants, respectively, can be captured by imputation. Using the GREML-LDMS method, we estimate from 44,126 unrelated individuals that all ∼17 million imputed variants explain 56% (standard error (s.e.) = 2.3%) of variance for height and 27% (s.e. = 2.5%) of variance for body mass index (BMI), and we find evidence that height- and BMI-associated variants have been under natural selection. Considering the imperfect tagging of imputation and potential overestimation of heritability from previous family-based studies, heritability is likely to be 60–70% for height and 30–40% for BMI. Therefore, the missing heritability is small for both traits. For further discovery of genes associated with complex traits, a study design with SNP arrays followed by imputation is more cost-effective than whole-genome sequencing at current prices.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/7725568477be962663fb22be7eb82556c5f5b222

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Citations in the corpus

Genome-wide association studies in ancestrally diverse populations: opportunities, methods, pitfalls, and recommendations. (2019, methodology): “, 2018) or by using a more robust, partitioned GREML approach such as GCTA-LDMS-I (Evans et al., 2018; Yang et al., 2015).”
Meta-analysis of genome-wide association studies for height and body mass index in ∼700,000 individuals of European ancestry (2018, methodology): “We performed GCTA-COJO analyses for height and BMI and analysed SNPs with significance p-value below 10−3, 10−4, 10−5, 10−6, 10−7 and 10−8.”
Using deep learning to model the hierarchical structure and function of a cell (2018, background): “State-of-the-art meth-ods in statistical genetics 32,33 are based on linear regression of phenotype against the independent effects of genetic polymorphisms, without modeling the underlying molecular mechanisms that give rise to nonlinearity and genetic interaction.”
Polygenic prediction of weight and obesity trajectories from birth to adulthood (2019, positive): “However, obesity is known to be heritable, suggesting that inborn DNA variation confers increased susceptibility in some individuals and protection in others (Elks et al., 2012; Maes et al., 1997; Whitaker et al., 1997; Yang et al., 2015).”
An Expanded Genome-Wide Association Study of Type 2 Diabetes in Europeans (2017, neutral): “density sequence-based imputation (13), will continue to represent a powerful strategy for”
10 Years of GWAS Discovery: Biology, Function, and Translation. (2017, background): “Evidence from imputed genotype data for height implies that more additive genetic variation is explained by variants with MAF < 10% than expected under an evolutionary neutral model, consistent with purifying selection of the height-associated loci.(49) In the near future, when additive genetic vari”