Differential performance of polygenic risk scores for heart disease in Hispanic/Latino subgroups: Findings of the Hispanic Community Health Study/Study of Latinos.
Christina G Hutten, Frederick J Boehm, Jennifer A Smith, Brian W Spitzer, Sylvia Wassertheil-Smoller, Carmen R Isasi, Jianwen Cai, Jonathan T Unkart, Jiehuan Sun, Victoria Persky, Martha L Daviglus, Tamar Sofer, Maria Argos
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Abstract
Coronary heart disease (CHD) is a leading cause of death among Hispanics/Latinos in the United States (US) whose underrepresentation in genomic research may worsen health disparities. We evaluated predictive performance of polygenic risk scores (PRSs) for myocardial infarction (MI) using data from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), a cohort of 16,415 participants from 4 US centers. Standardized CHD-PRSs were derived (LDpred, AnnoPred, stacked clumping and thresholding, and LDPred2-GPSmult) and evaluated in survey-weighted Cox models for time to adjudicated MI, adjusted for age, sex, and first 5 principal components. Analyses were stratified by Caribbean (Puerto Rican, Dominican, or Cuban) and Mainland (Mexican, Central American, or South American) heritage. Concordance statistic (C-index), integrated discrimination improvement (IDI), and net reclassification improvement (NRI) were used to compare PRS performances with traditional risk factors (TRFs). Over 13 years (2008-2021), MI incidence was 1.9% (n = 140/7,248), mean age 48.7 years, 61% female. PRSs showed stronger associations with MI among Mainland participants; LDPred2-GPSmult+TRFs performed best (hazard ratio = 2.09; 95% confidence interval 1.59-2.75; C-index = 0.884; IDI p < 0.001; NRI p < 0.001; and improved C-index over TRFs by 0.008). Among Caribbean participants, AnnoPred+TRFs performed best (C-index = 0.739) and LDPred2-GPSmults discriminated best (IDI p = 0.02), but neither were significantly associated with MI risk. PRS performance remains limited among Caribbean individuals with substantial African ancestry. AnnoPred and LDPred2-GPSmult showed potential that leveraging functional annotations and multi-trait approaches may enhance risk prediction in diverse populations. These findings emphasize the need to optimize genetic risk prediction of CHD in underrepresented Hispanic/Latino populations.
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