Department of Pediatrics, Section of Genetics, College of Medicine, University of Oklahoma Health Sciences Center, 940 Stanton L. Young Blvd., Rm 317 BMSB, Oklahoma City, OK, 73104, USA.
Guru Nanak Dev University, Amritsar, Punjab, India.
J Cardiovasc Transl Res. 2024 Oct;17(5):1086-1096. doi: 10.1007/s12265-024-10511-z. Epub 2024 Apr 24.
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.
我们评估了来自欧洲（EU）、南亚（SA）和旁遮普印度裔（AI）研究的各种多基因风险评分（PRS）模型在 13974 名 AI 血统个体中的表现。虽然所有模型都成功地预测了冠心病（CAD）风险，但 AI、SA 和 EU+AI 是更好的预测因子，比 EU 模型更具可转移性；在训练和测试集中，AI 和 EU+AI 模型的预测性能分别比 EU 模型高 18%和 22%。比较具有极端 PRS 四分位数的个体，AI 和 EU+AI 模型捕捉到的具有高 CAD 风险的个体比 EU 模型的效率高 2.6 至 4.6 倍。有趣的是，包括临床风险评分并没有显著改变任何遗传模型的性能。在 EU PRS 中丰富多样性变异可以提高风险预测和可转移性。建立特定人群的规范和风险因素，并将其纳入遗传模型中，将细化风险分层并提高 CAD PRS 的临床实用性。
