Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
Am J Hum Genet. 2023 Jan 5;110(1):13-22. doi: 10.1016/j.ajhg.2022.11.007. Epub 2022 Dec 1.
Polygenic risk score (PRS) has demonstrated its great utility in biomedical research through identifying high-risk individuals for different diseases from their genotypes. However, the broader application of PRS to the general population is hindered by the limited transferability of PRS developed in Europeans to non-European populations. To improve PRS prediction accuracy in non-European populations, we develop a statistical method called SDPRX that can effectively integrate genome wide association study summary statistics from different populations. SDPRX automatically adjusts for linkage disequilibrium differences between populations and characterizes the joint distribution of the effect sizes of a variant in two populations to be both null, population specific, or shared with correlation. Through simulations and applications to real traits, we show that SDPRX improves the prediction performance over existing methods in non-European populations.
多基因风险评分（PRS）通过从个体基因型中识别不同疾病的高危个体，在生物医学研究中显示出了巨大的应用价值。然而，由于在欧洲人群中开发的 PRS 向非欧洲人群的可转移性有限，PRS 的更广泛应用受到了阻碍。为了提高非欧洲人群中 PRS 的预测准确性，我们开发了一种名为 SDPRX 的统计方法，该方法可以有效地整合来自不同人群的全基因组关联研究汇总统计数据。SDPRX 自动调整人群之间的连锁不平衡差异，并描述两个人群中变异效应大小的联合分布，这些分布可以是零、特定于人群的，或者与相关性共享。通过模拟和应用于真实特征，我们表明 SDPRX 提高了非欧洲人群中现有方法的预测性能。
