Polygenic risk scores (PRS) have attenuated cross-population predictive performance. As existing genome-wide association studies (GWAS) were predominantly conducted in individuals of European descent, the limited transferability of PRS reduces its clinical value in non-European populations and may exacerbate healthcare disparities. Recent efforts to level ancestry imbalance in genomic research have expanded the scale of non-European GWAS, although most of them remain under-powered. Here we present a novel PRS construction method, PRS-CSx, which improves cross-population polygenic prediction by integrating GWAS summary statistics from multiple populations. PRS-CSx couples genetic effects across populations via a shared continuous shrinkage prior, enabling more accurate effect size estimation by sharing information between summary statistics and leveraging linkage disequilibrium (LD) diversity across discovery samples, while inheriting computational efficiency and robustness from PRS-CS. We show that PRS-CSx outperforms alternative methods across traits with a wide range of genetic architectures, cross-population genetic overlaps and discovery GWAS sample sizes in simulations, and improves the prediction of quantitative traits and schizophrenia risk in non-European populations.

中文翻译：

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改进祖先多样化人群的多基因预测

多基因风险评分 (PRS) 减弱了跨群体预测性能。由于现有的全基因组关联研究 (GWAS) 主要在欧洲血统的个体中进行，PRS 有限的可转移性降低了其在非欧洲人群中的临床价值，并可能加剧医疗保健差异。最近为平衡基因组研究中的祖先失衡所做的努力扩大了非欧洲 GWAS 的规模，尽管其中大多数仍然动力不足。在这里，我们提出了一种新的 PRS 构建方法 PRS-CSx，它通过整合来自多个种群的 GWAS 汇总统计数据来改进跨种群多基因预测。PRS-CSx 通过共享的连续收缩在种群间耦合遗传效应，通过在汇总统计数据之间共享信息并利用发现样本之间的连锁不平衡（LD）多样性，实现更准确的效应大小估计，同时继承 PRS-CS 的计算效率和稳健性。我们表明，PRS-CSx 在模拟中具有广泛的遗传结构、跨群体遗传重叠和发现 GWAS 样本大小的性状优于替代方法，并改善了对非欧洲人群数量性状和精神分裂症风险的预测。