Polygenic scores (PGSs) offer moderate to high prediction accuracy for complex traits, but most are developed in European ancestry cohorts, reducing their performance in populations of other ancestries. This study aimed to improve standing height prediction, a heritable and ancestry-influenced trait, in an admixed Latino cohort, the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), by modeling ancestry using principal components (PCs) alongside PGSs. SNPs were selected from a large European ancestry genome-wide association study (GWAS) using various p value thresholds, and weights were trained using traditional and penalized regression in the UK Biobank (UKB). PGSs with PCs were trained separately in the HCHS/SOL and UKB. Compared to PGSs alone, modeling PGSs with PCs moderately improved height prediction in the HCHS/SOL (squared correlation [R 2] increase of ∼0.05), while mild improvements were observed in the UKB (R 2 increase of ∼0.01). These results underscore the importance of incorporating genetic ancestry into predictive models for admixed populations, particularly when the trait exhibits ancestry-specific associations.

Polygenic scores (PGSs) are predominantly derived from European cohorts and often fail to generalize to admixed populations. Using standing height, we show that incorporating genetic ancestry alongside PGSs improves prediction accuracy in an admixed Latino cohort, underscoring the need for ancestry-informed modeling to enhance performance in diverse populations.