ArticleFrontiers in bioinformatics2026
Evaluation of functional annotation-informed and ancestry-specific polygenic risk scores for ischemic stroke.
Article in Frontiers in bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Abstract
Introduction: Ischemic stroke (IS) is a leading cause of morbidity and mortality, and predicting events remains challenging. Polygenic risk scores (PRSs) aggregate genetic variants, but performance varies across ancestries and remains modest, particularly in non-European populations. Methods: We compared Bayesian PRS methods incorporating functional annotations and expanded linkage disequilibrium reference panels for IS prediction. PRSs were generated using PRS-CS with HapMap3 and TagIt panels, and SBayesRC integrating functional priors. Scores were optimized in 1,403 European ancestry (EA) and 6,342 African ancestry (AA) participants from the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study and independently validated in All of Us participants (78,155 EA and 29,594 AA participants), a REGARDS holdout cohort (11,459 EA participants), and the Genetics of Hypertension Associated Treatments (GenHAT) study (6,908 AA participants). Predictive performance was evaluated using logistic regression adjusted for age, sex, the top 10 genetic principal components, and antihypertensive treatment assignment where applicable. Performance was assessed using odds ratios per standard deviation (OR/SD), liability-scale R Results: Among EA participants, SBayesRC demonstrated the largest effect size and liability R Conclusion: Functional annotation-informed PRSs and expanded reference panels modestly improved prediction, particularly among EA participants. Limited performance among AA participants underscores the need for larger, more diverse genomic datasets and integrative models incorporating genetic, clinical, lifestyle, and social determinants of health.
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