ArticleCommunications biology2025
Assessment of polygenic risk score performance in East Asian populations for ten common diseases.
Article in Communications biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Genome-wide meta-analysis across East Asian and European populations provides insights into the molecular basis of lung cancer.Genome medicine · 2026Pooled it
- Improving Polygenic Risk Prediction for Atherosclerotic Cardiovascular Disease in East Asian Populations.JACC. Asia · 2026Review
- Diversity and Representation in Cardiovascular Research: Evidence Gaps, Emerging Models, and Policy Implications.International journal of environmental research and public health · 2026Review
- Bridging the Genomic Equity Gap with Context-Enhanced Risk Stratification in American Indians: the Strong Heart Study.medRxiv : the preprint server for health sciences · 2026Article
- Toward personalized medicine in AD/ADRD through genetic-exposome dementia risk assessments.NPJ dementia · 2026Review
- Machine learning prediction of hypertension integrating polygenic risk scores in inner Eurasian populations.Frontiers in cardiovascular medicine · 2026Article
- Transferability of polygenic risk scores for metabolic and cardiovascular traits in an underrepresented population.NPJ genomic medicine · 2025Article
- Pathway insights and predictive modeling for type 2 diabetes using polygenic risk scores.Scientific reports · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
Polygenic risk score (PRS) uses genetic variants to assess disease susceptibility. While PRS performance is well-studied in Europeans, its accuracy in East Asians is less explored. This study evaluated PRSs for ten diseases in the Health Examinees (HEXA) cohort (n = 55,870) in Korea. Single-population PRSs were constructed using PRS-CS, LDpred2, and Lassosum based on East Asian GWAS summary statistics (sample sizes: 51,442-341,204), while cross-population PRSs were developed using PRS-CSx and CT-SLEB by integrating European and East Asian GWAS data. PRS-CS consistently outperformed other single-population methods across key metrics, including the likelihood ratio test (LRT), odds ratio per standard deviation (perSD OR), net reclassification improvement (NRI), and area under the curve (AUC). Cross-population PRSs further improved predictive performance, with average increases of 1.08-fold (LRT), 1.07-fold (perSD OR), and 1.15-fold (NRI) across seven diseases with statistical significance, and a 1.01-fold improvement in AUC. Differences in R² between single- and cross-population PRSs were statistically significant for five diseases, showing an average increase of 1.13%. Cross-population PRSs achieved 87.8% of the predictive performance observed in European PRSs. These findings highlight the benefits of integrating European GWAS data while underscoring the need for larger East Asian datasets to improve prediction accuracy.
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