Enhancing Multi-Population PRS via Genetic Correlation and Data-Fission

Music City Center 

Polygenic risk score (PRS) prediction in non-European populations remains challenging due to limited GWAS sample sizes and individual-level tuning data. While several multi-population PRS methods have been proposed, none consistently achieve optimal performance across diverse data scenarios, particularly when tuning data are unavailable. In this presentation, I will introduce JointPRS, a comprehensive framework that models multiple populations and estimates chromosome-wise cross-population genetic correlation using GWAS summary statistics. JointPRS has robust performance even without individual-level datasets for tuning parameters. When non-European individual-level data are available, we propose a data-adaptive approach combining meta-analysis and tuning strategies and inheriting the merits from both strategies, further enhancing prediction performance and robustness. To address scenarios where no single method dominates (e.g., high causal SNP proportions), I will further discuss MIX, a novel framework that optimally integrates predictions from diverse PRS methods (e.g., JointPRS and SDPRX) using only GWAS summary statistics. MIX employs data fission, a subsampling strategy that partitions GWAS data into pseudo-training and pseudo-testing sets, and incorporates SNP pruning step to mitigate the linkage disequilibrium (LD) mismatch issue. I will present comprehensive evaluations of JointPRS through its application to 26 traits across five populations (European, East Asian, African, South Asian, and Admixed American) in the UK Biobank and All of Us cohorts. Results show that JointPRS outperformed six state-of-the-art PRS methods (SDPRX, XPASS, PRS-CSx, PROSPER, MUSSEL, and BridgePRS) in most scenarios. Furthermore, MIX can effectively integrate different PRS methods, optimizing prediction performance across all settings by leveraging the advantages across methods.