Key result
Multi-ancestry PRSs improve prediction over single-ancestry models across diverse populations despite clinical calibration limits.
Multi-ancestry frameworks and ancestry-aware models are essential for improving the predictive performance and clinical utility of polygenic risk scores across diverse populations.
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Ancestry-aware PRSs may enhance CAD prediction across groups; leaves open prospective validation before clinical use.
Polygenic risk scores (PRSs) are increasingly being considered as tools to refine risk stratification in cardiovascular and cardiometabolic disease, but their clinical translation remains constrained by a central limitation: most currently available PRSs were derived in predominantly European-ancestry datasets and perform less well in admixed and underrepresented populations. This limitation reflects differences in allele frequencies, linkage disequilibrium structure, imputation performance, ancestry-specific effect sizes, and environmental context, and is especially consequential in recently admixed populations, in whom local ancestry and internal heterogeneity further complicate prediction. In this review, we examine recent methodological and translational advances in PRS development across diverse populations, with emphasis on coronary artery disease (CAD), blood pressure and hypertension, type 2 diabetes, obesity, and atrial fibrillation. We highlight the transition from single-ancestry prediction to multi-ancestry frameworks, as well as emerging approaches tailored to admixed genomes, including ancestry deconvolution-based and local-ancestry-aware models. Across traits, broader discovery resources and ancestry-aware methods have improved predictive performance beyond naive European transfer, but progress remains uneven. CAD currently represents the most mature phenotype, with the strongest evidence for clinically relevant gains from multi-ancestry PRS development and validation. Blood pressure and hypertension, as well as type 2 diabetes, show substantial methodological progress but remain limited by calibration, context dependence, and incomplete evidence for implementation. Obesity and atrial fibrillation are advancing rapidly, but their translational readiness remains less developed. We argue that admixed and underrepresented populations should not be viewed only as settings in which PRSs underperform, but as essential contexts for building more robust and clinically generalizable models. The next phase of precision cardiovascular medicine will depend not simply on improving prediction, but on demonstrating that PRS-informed risk assessment can be calibrated, interpretable, and clinically useful across the diverse populations in whom it is intended to guide care.
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Jose E. Krieger (2026) conducted a review in Cardiovascular and cardiometabolic diseases. Multi-ancestry polygenic risk scores vs. Single-ancestry European models was evaluated. Multi-ancestry and admixture-aware polygenic risk scores improve predictive performance across diverse populations compared to single-ancestry European models, though clinical implementation remains limited by calibration and context dependence.
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