We propose an extended Gaussian mixture model for the distribution of causal effects of common single nucleotide polymorphisms (SNPs) for human complex phenotypes that depends on linkage disequilibrium (LD) and heterozygosity (H), while also allowing for independent components for small and large effects. Using a precise methodology showing how genome-wide association studies (GWAS) summary statistics (z-scores) arise through LD with underlying causal SNPs, we applied the model to GWAS of multiple human phenotypes. Our findings indicated that causal effects are distributed with dependence on total LD and H, whereby SNPs with lower total LD and H are more likely to be causal with larger effects; this dependence is consistent with models of the influence of negative pressure from natural selection. Compared with the basic Gaussian mixture model it is built on, the extended model -- primarily through quantification of selection pressure -- reproduces with greater accuracy the empirical distributions of z-scores, thus providing better estimates of genetic quantities, such as polygenicity and heritability, that arise from the distribution of causal effects.

中文翻译：

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连锁不平衡和杂合性调节人类复杂表型的遗传结构。

我们提出了一个扩展的高斯混合模型，用于人类复杂表型的常见单核苷酸多态性（SNP）的因果效应分布，该因果关系取决于连锁不平衡（LD）和杂合性（H），同时也允许独立的成分产生较小和较大的影响。使用一种精确的方法来显示全基因组关联研究（GWAS）摘要统计数据（z得分）是如何通过LD与潜在因果SNP产生的，我们将该模型应用于多种人类表型的GWAS。我们的发现表明，因果效应的分布与总LD和H有关，因此总LD和H较低的SNP更有可能因果而具有更大的效应。这种依赖性与自然选择产生的负压影响模型一致。