Polygenic risk scores (PRS) calculated from genome-wide association studies (GWAS) of Europeans are known to have substantially reduced predictive accuracy in non-European populations, limiting their clinical utility and raising concerns about health disparities across ancestral populations. Here, we introduce a statistical framework named X-Wing to improve predictive performance in ancestrally diverse populations. X-Wing quantifies local genetic correlations for complex traits between populations, employs an annotation-dependent estimation procedure to amplify correlated genetic effects between populations, and combines multiple population-specific PRS into a unified score with GWAS summary statistics alone as input. Through extensive benchmarking, we demonstrate that X-Wing pinpoints portable genetic effects and substantially improves PRS performance in non-European populations, showing 14.1%–119.1% relative gain in predictive R² compared to state-of-the-art methods based on GWAS summary statistics. Overall, X-Wing addresses critical limitations in existing approaches and may have broad applications in cross-population polygenic risk prediction.

据了解，根据欧洲人全基因组关联研究 (GWAS) 计算得出的多基因风险评分 (PRS) 在非欧洲人群中的预测准确性大大降低，限制了其临床效用，并引发了对祖先人群健康差异的担忧。在这里，我们引入了一个名为 X-Wing 的统计框架，以提高祖先多样化人群的预测性能。 X-Wing 量化种群之间复杂性状的局部遗传相关性，采用依赖于注释的估计程序来放大种群之间的相关遗传效应，并将多个种群特定的 PRS 组合成一个统一的分数，仅使用 GWAS 汇总统计数据作为输入。通过广泛的基准测试，我们证明 X-Wing 精确定位了可移植遗传效应，并显着提高了非欧洲人群中的 PRS 性能，与基于 GWAS 的最先进方法相比，预测 R ²的相对增益为 14.1%–119.1%汇总统计。总体而言，X-Wing 解决了现有方法的关键局限性，并且可能在跨群体多基因风险预测方面具有广泛的应用。