An Uncertainty-aware Optimal Transport Framework for Cross-Population Polygenic Risk Prediction

Polygenic risk scores (PRS) often lose predictive accuracy across ancestry groups because differences in allele frequencies, linkage disequilibrium (LD), and genetic architecture create distribution shifts between source and target populations. Existing cross-population PRS methods improve portability through Bayesian shrinkage and multi-ancestry modeling, but generally do not explicitly model structured correspondence between ancestry-specific genomic contexts. To address this limitation, we propose OT-PRS, an uncertainty-aware optimal transport framework for cross-population polygenic risk prediction. OT-PRS uses block-wise fused Gromov–Wasserstein transport to align source and target variants by jointly modeling SNP-level similarity and ancestry-specific LD geometry. The transported source effects define a target-aligned prior that is refined using target-population summary statistics, while transport entropy quantifies transfer uncertainty and enables uncertainty-aware score construction and individual-level reliability assessment. Experiments across multiple diseases and ancestry groups in UK Biobank demonstrate competitive performance against established PRS methods, and external validation in the All of Us Research Program further supports the portability of OT-PRS across cohorts. Our results highlight optimal transport as a flexible framework for improving the portability and reliability of polygenic risk prediction.