An efficient LASSO framework for admixture-aware polygenic scores

Polygenic scores (PGSs) have promising clinical applications for risk stratification, disease screening, and personalized medicine. However, most PGSs are trained on predominantly European ancestry cohorts and have limited portability to external populations. While cross-population PGSs have demonstrated greater generalizability than single-ancestry PGSs, they fail to properly account for individuals with recent admixture between continental ancestry groups. GAUDI, a recently proposed PGS method, overcomes this gap by leveraging local ancestry to estimate ancestry-specific effects, penalizing but allowing ancestry-differential effects. However, the modified fused LASSO approach used by GAUDI is computationally expensive and does not readily accommodate more than two-way admixture. To address these limitations, we introduce HAUDI, an efficient LASSO framework for admixed PGS construction. HAUDI reparameterizes the GAUDI model as a standard LASSO problem, allowing for extension to multiway admixture settings and far superior computational speed than GAUDI. In extensive simulations, HAUDI compares favorably to GAUDI while dramatically reducing computation time. In real data applications, HAUDI uniformly outperforms GAUDI across 18 clinical phenotypes, including total triglycerides, C-reactive protein, and mean corpuscular hemoglobin concentration, and shows substantial benefits over ancestry-agnostic PGSs for white blood cell count and chronic kidney disease. It is also substantially faster and more accurate than the recently proposed SDPR_admix method.