“Estimating the Total Number of Susceptibility Variants Underlying Complex Diseases from Genome-Wide Association Studies”, 2010-10-19 (; similar):
Recently genome-wide association studies (GWAS) have identified numerous susceptibility variants for complex diseases. In this study, we proposed several approaches to estimate the total number of variants underlying these diseases. We assume that the variance explained by genetic markers (Vg) follow an exponential distribution, which is justified by previous studies on theories of adaptation. Our aim is to fit the observed distribution of Vg from GWAS to its theoretical distribution.
The number of variants is obtained by the heritability divided by the estimated mean of the exponential distribution. In practice, due to limited sample sizes, there is insufficient power to detect variants with small effects. Therefore, the power was taken into account in fitting. Besides considering the most variants, we also tried to relax the statistical-significance threshold, allowing more markers to be fitted. The effects of false-positive variants were removed by considering the local false discovery rates. In addition, we developed an alternative approach by directly fitting the z-statistics from GWAS to its theoretical distribution. In all cases, the “winner’s curse” effect was corrected analytically.
Confidence intervals were also derived. Simulations were performed to compare and verify the performance of different estimators (which incorporates various means of winner’s curse correction) and the coverage of the proposed analytic confidence intervals. Our methodology only requires summary statistics and is able to handle both binary and continuous traits.
Finally, we applied the methods to a few real disease examples (lipid traits, type 2 diabetes, and Crohn’s disease) and estimated that hundreds to nearly a thousand variants underlie these traits.