The landscape of polygenic score prediction accuracy and transportability across the phenome in ancestrally diverse populations

Polygenic scores (PGS) are increasingly common in human genetics research, yet their performance and portability vary substantially across traits and populations. We systematically evaluate prediction accuracy and cross-ancestry portability for 3,994 published PGSs across the phenome in the ancestrally diverse CCPM biobank. We identify widespread trait-specific and cross-trait associations, demonstrating both the utility and breadth of existing polygenic predictors. However, predictive effects frequently differ across ancestry groups, highlighting persistent challenges for portability that threaten equity in downstream applications. We further find that accuracy and portability depend strongly on whether the PGS is continuous or thresholded. Finally, through hierarchical mixed modeling, we identify score-development, genome features, and cohort characteristics associated with improved PGS accuracy and portability. These results highlight key factors supporting future development of robust and portable scores for research and clinical translation.