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-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.

Automated summary

The genetic effect-size distribution of a disease, which includes the number of risk variants, their effect sizes, and sample sizes required to discover them, can be accurately estimated using Fourier Mixture Regression (FMR). FMR estimates that 100,000-1,000,000 cases will be needed to find genome-wide significant SNPs explaining 50% of SNP heritability. In large studies, genome-wide significance becomes more conservative and less stringent thresholds can achieve high true positive rates if confounding is controlled.