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

Abstract

We propose a method (GREML-LDMS) to estimate heritability for human complex traits in unrelated individuals using whole-genome sequencing (WGS) data. We demonstrate using simulations based on WGS 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 ~17M imputed variants explain 56% (s.e. = 2.3%) of variance for height and 27% (s.e. = 2.5%) for body mass index (BMI), and find evidence that height- and BMI-associated variants have been under natural selection. Considering 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, missing heritability is small for both traits. For further gene discovery of complex traits, a design with SNP arrays followed by imputation is more cost-effective than WGS at current prices.

The impact of genome‐wide association studies on the pathophysiology and therapy of cardiovascular disease

“…Additionally, the field of epistasis, that is the interaction of different genes, is rapidly evolving. Finally, it has been shown for other complex disorders that the combined small effects of hundreds of thousands of SNPs may jointly explain the currently poorly understood inheritance patterns (Yang et al , 2015). The results achieved thus far do, however, provide valuable insight into the pathophysiology of CAD and MI and are starting points for individualized treatment strategies.…”

10 Years of GWAS Discovery: Biology, Function, and Translation

“…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 variance will be estimated from WGS data in large samples, the contribution of observed rare and low-frequency variants will be estimated explicitly. Estimates from data available to date provide the first evidence for different genetic architectures between diseases, 50 for example, there is more signal from rare variants for amyotrophic lateral sclerosis (motor neuron disease [MIM: 105400]) than for schizophrenia 51 and more predicted loci for schizophrenia than for immune disorders 52 and hypertension.…”

Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture

“…Yang et al [26] found that imputed genome sequence explained 50% of the phenotypic variance for height. Sequence variants with MAF , 0.1 explained more variance than other MAF classes despite the fact that they were not accurately imputed.…”

“…Their allele frequencies are biased towards low MAF compared with a neutral model, but only slightly. That is, most of the variance is due to common variants [26].…”