The distribution of common-variant effect sizes

The genetic effect-size distribution of a disease describes the number of risk variants, the range of their effect sizes and sample sizes that will be required to discover them. Accurate estimation has been a challenge. Here I propose Fourier Mixture Regression (FMR), validating that it accurately estimates real and simulated effect-size distributions. Applied to summary statistics for ten diseases (average $$N_{\textrm{eff}} = 169,000$$ ), FMR estimates that 100,000–1,000,000 cases will be required for genome-wide significant SNPs to explain 50% of SNP heritability. In such large studies, genome-wide significance becomes increasingly conservative, and less stringent thresholds achieve high true positive rates if confounding is controlled. Across traits, polygenicity varies, but the range of their effect sizes is similar. Compared with effect sizes in the top 10% of heritability, including most discovered thus far, those in the bottom 10–50% are orders of magnitude smaller and more numerous, spanning a large fraction of the genome. Fourier Mixture Regression (FMR) is a method for estimating common-variant effect-size distributions. Applying FMR to summary statistics for complex traits from the UK Biobank shows that heritability is spread across a wide range of effect sizes.