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.

疾病的遗传效应量分布描述了风险变异的数量、它们的效应量范围以及发现它们所需的样本量。准确的估计一直是一个挑战。在这里，我提出傅立叶混合回归 (FMR)，验证它准确地估计了真实和模拟的效应大小分布。应用于十种疾病的汇总统计（平均\(N_{\textrm{eff}} = 169,000\))，FMR 估计需要 100,000–1,000,000 个案例来解释全基因组重要的 SNP 来解释 50% 的 SNP 遗传力。在如此大型的研究中，全基因组意义变得越来越保守，如果控制混杂，不太严格的阈值可实现较高的真阳性率。跨性状，多基因性各不相同，但其影响大小的范围是相似的。与遗传力前 10% 的效应大小（包括迄今为止发现的大多数）相比，那些在最低 10-50% 的效应大小要小几个数量级，而且数量更多，跨越基因组的很大一部分。