Widespread and Biologically Driven Sex Disparities in Polygenic Risk Prediction Across Complex Traits

Polygenic risk scores (PRS) are increasingly used for disease prediction, yet their performance equity across sexes remains unclear. We evaluated sex differences in PRS performance using 3,263 scores across 145 traits from the PGS Catalog in 409,440 UK Biobank participants. Sex-differential prediction was widespread and trait-specific, affecting 15 of 64 (23%) of diseases and 43 of 81 (53%) of quantitative traits. Female-favoring performance was enriched in autoimmune and endocrine traits, whereas cardiometabolic traits more often favored males. Discovery GWAS sex imbalance partially explained disease-level disparities (R² = 0.36), whereas quantitative traits showed minimal association. Notably, sex differences in predictive performance strongly correlated with differences in SNP-based heritability from sex-stratified GWAS (R² = 0.81 for diseases; R² = 0.58 for quantitative traits). In contrast, PRS estimates were highly consistent across seven construction methods (intraclass correlation coefficient = 0.93), indicating limited methodological influence. These findings demonstrate that sex disparities in PRS performance are common and largely reflect underlying genetic architecture rather than analytic artifacts, highlighting the need for sex-aware GWAS design and PRS modeling to ensure equitable clinical implementation.