Many diseases and complex traits exhibit population-specific causal effect sizes with trans-ethnic genetic correlations significantly less than 1, limiting trans-ethnic polygenic risk prediction. We developed a new method, S-LDXR, for stratifying squared trans-ethnic genetic correlation across genomic annotations, and applied S-LDXR to genome-wide association summary statistics for 31 diseases and complex traits in East Asians (EAS) and Europeans (EUR) (average N=90K for EAS, N=267K for EUR) with an average trans-ethnic genetic correlation of 0.85 (s.e. 0.01). We determined that squared trans-ethnic genetic correlation was 0.82× (s.e. 0.01) smaller than the genome-wide average at SNPs in the top quintile of background selection statistic, implying more population-specific causal effect sizes. Accordingly, causal effect sizes were more population-specific in functionally important regions, including conserved and regulatory regions. In analyses of regions surrounding specifically expressed genes, causal effect sizes were most population-specific for skin and immune genes and least population-specific for brain genes. Our results could potentially be explained by stronger gene-environment interaction at loci impacted by selection, particularly positive selection.

中文翻译：

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受选择影响的功能重要区域中特定人群的因果疾病影响大小

许多疾病和复杂性状表现出特定于人群的因果效应，跨种族的遗传相关性显着小于1，从而限制了跨种族的多基因风险预测。我们开发了一种新方法S-LDXR，用于分层跨基因组注释的跨种族遗传相关平方，并将S-LDXR应用于东亚（EAS）和欧洲人（EUR）的31种疾病和复杂特征的全基因组关联汇总统计）（EAS的平均N = 90K，EUR的平均N = 267K），平均跨族裔遗传相关性为0.85（se 0.01）。我们确定，跨种族的平方遗传相关性比背景选择统计数据的前五分之一中SNP的全基因组平均小0.82x（即0.01），这意味着更多的特定人群因果效应大小。因此，因果效应的大小在功能重要的区域（包括保守区和调节区）更具人群特异性。在对特定表达基因周围区域的分析中，因果效应的大小对皮肤和免疫基因而言是大多数人群特异性的，而对于脑基因则是最小人群特异性的。我们的结果可能由选择，尤其是阳性选择影响的基因座上更强的基因-环境相互作用来解释。