ArticleJournal of cardiovascular translational research2024
Polygenic Risk Score Assessment for Coronary Artery Disease in Asian Indians.
Article in Journal of cardiovascular translational research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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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Who cites it
6 citing papers in PubMed, 5 citations in OpenAlex.
- Type 2 Diabetes Causally Reduces Circulating Vitamin D Levels: A Multi-Ancestry Mendelian Randomization Study.Nutrients · 2026Article
- Identification of lipid quantitative trait loci linked with cardiometabolic disease in Asian Indians and Europeans: A genome-wide association study and Mendelian randomization.PLoS medicine · 2026Observational
- Unraveling the genetic blueprint of coronary artery disease: The role of polygenic risk scores in risk prediction.American heart journal plus : cardiology research and practice · 2026Article
- Risk prediction for cardiovascular related diseases using PRS and EHR in the Framingham Heart Study.PloS one · 2026Article
- Excess of rare noncoding variants in several type 2 diabetes candidate genes among Asian Indian families.Communications medicine · 2025Article
- Application of AI in the identification of gastrointestinal stromal tumors: a comprehensive analysis based on pathological, radiological, and genetic variation features.Frontiers in genetics · 2025Review
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
We evaluated the performance of various polygenic risk score (PRS) models derived from European (EU), South Asian (SA), and Punjabi Asian Indians (AI) studies on 13,974 subjects from AI ancestry. While all models successfully predicted Coronary artery disease (CAD) risk, the AI, SA, and EU + AI were superior predictors and more transportable than the EU model; the predictive performance in training and test sets was 18% and 22% higher in AI and EU + AI models, respectively than in EU. Comparing individuals with extreme PRS quartiles, the AI and EU + AI captured individuals with high CAD risk showed 2.6 to 4.6 times higher efficiency than the EU. Interestingly, including the clinical risk score did not significantly change the performance of any genetic model. The enrichment of diversity variants in EU PRS improves risk prediction and transportability. Establishing population-specific normative and risk factors and inclusion into genetic models would refine the risk stratification and improve the clinical utility of CAD PRS.
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Registered trials
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