Genome-wide association studies (GWAS) have become increasingly popular recently and contributed to the discovery of many susceptibility variants. However, a large proportion of the heritability still remained unexplained. This observation raises queries regarding the ability of GWAS to uncover the genetic basis of complex diseases. In this study, we propose a simple and fast statistical framework to estimate the total heritability explained by all true susceptibility variants in a GWAS. It is expected that many true risk variants will not be detected in a GWAS due to limited power. The proposed framework aims at recovering the "hidden" heritability. Importantly, only the summary z-statistics are required as input and no raw genotype data are needed. The strategy is to recover the true effect sizes from the observed z-statistics. The methodology does not rely on any distributional assumptions of the effect sizes of variants. Both binary and quantitative traits can be handled and covariates may be included. Population-based or family-based designs are allowed as long as the summary statistics are available. Simulations were conducted and showed satisfactory performance of the proposed approach. Application to real data (Crohn's disease, HDL, LDL, and triglycerides) reveals that at least around 10-20% of variance in liability or phenotype can be explained by GWAS panels. This translates to around 10-40% of the total heritability for the studied traits.
© 2011 Wiley-Liss, Inc.