Genetics

Polygenic risk scores (PRSs) have the potential to improve disease prediction and enable personalized treatment, but their accuracy is often limited when applied to individuals from diverse genetic backgrounds. Several factors contribute to this challenge, including differences in heritability, allele frequencies, linkage disequilibrium (LD) patterns, and social determinants of health (SDOH) across populations. To address these limitations, researchers are developing methods to integrate data from diverse populations, such as combining population-specific summary statistics, joint modeling of multiple populations, and incorporating additional functional information. Evaluating the clinical utility of PRSs in diverse populations requires careful consideration of performance metrics beyond the area under the receiver operating characteristic curve (AUC), such as absolute risk and net reclassification indices. The future of PRS research depends on increasing diversity in genomic studies, improving data collection and harmonization, addressing SDOH, and developing universal PRSs that can be applied across diverse populations. Ultimately, contextualizing PRS performance in healthcare settings and integrating them with existing clinical predictors will be crucial for realizing the potential of precision medicine for all populations.