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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.
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Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with hundreds of human complex traits and diseases1. However, genome-wide significant SNPs often explain only a small proportion of the heritability estimated from family-based studies, in the so-called 'missing heritability' problem2. Recent studies have shown that the total variance explained by all common SNPs is a large proportion of the heritability for complex traits and diseases3,4. This implies that much of the missing heritability is due to variants whose effects are too small to reach the level of genome-wide significance. This conclusion is supported by recent findings that complex traits and diseases such as height, BMI, age at menarche, inflammatory bowel diseases and schizophrenia are influenced by hundreds or even thousands of genetic variants of small effect5-9. Nevertheless, the genetic variance accounted for by all common SNPs is still less than that expected from family-based studies, and there has not been a consensus explanation for the missing heritability problem2. There are three major hypotheses. The first hypothesis is that missing heritability is largely due to rare variants of large effect, which are neither on the currently available commercial SNP arrays nor well tagged by the SNPs on the arrays. Here we define rare variants as variants with...