美国布罗德研究所Luke J. O’Connor揭示常见变异效应大小的分布。2021年7月29日，《自然—遗传学》杂志在线发表了这项成果。

研究人员提出了傅里叶混合回归（FMR），验证了它对真实和模拟的效应大小分布的准确估计。应用于十种疾病（平均N_{textrm{eff}}=169,000）的汇总统计，FMR估计，全基因组显著SNP需要100,000-1,000,000个病例才能解释50%的SNP遗传性。在这样的大型研究中，全基因组显著性变得越来越保守，如果混杂因素得到控制，不那么严格的阈值也能获得高的真阳性率。

在不同的性状中，多基因性是不同的，但其效应大小的范围是相似的。与遗传率前10%的效应大小相比，包括迄今为止发现的大多数效应大小，那些处于底部10-50%的效应大小要小几个数量级，而且数量更多，并且横跨了基因组的很大一部分。

据悉，一种疾病的遗传效应大小分布描述了风险变体的数量、其效应大小的范围和发现它们所需的样本量。准确的估计一直是一个挑战。

附：英文原文

Title: The distribution of common-variant effect sizes

Author: OConnor, Luke J.

Issue&Volume: 2021-07-29

Abstract: 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 Neff=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.

DOI: 10.1038/s41588-021-00901-3

Source: https://www.nature.com/articles/s41588-021-00901-3