2023,
Nature Medicine.
DOI:
10.1038/s41591-023-02429-x
A multi-ancestry polygenic risk score improves risk prediction for coronary artery disease
Abstract:
Abstract Identification of individuals at highest risk of coronary artery disease (CAD)—ideally before onset—remains an important public health need. Prior studies have developed genome-wide polygenic scores to enable risk stratification, reflecting the substantial inherited component to CAD risk. Here we develop a new and significantly improved polygenic score for CAD, termed GPSMult , that incorporates genome-wide association data across five ancestries for CAD (>269,000 cases and >1,178,000 controls) and ten CAD risk factors. GPSMult strongly associated with prevalent CAD (odds ratio per standard deviation 2.14, 95% confidence interval 2.10–2.19, P < 0.001) in UK Biobank participants of European ancestry, identifying 20.0% of the population with 3-fold increased risk and conversely 13.9% with 3-fold decreased risk as compared with those in the middle quintile. GPSMult was also associated with incident CAD events (hazard ratio per standard deviation 1.73, 95% confidence interval 1.70–1.76, P < 0.001), identifying 3% of healthy individuals with risk of future CAD events equivalent to those with existing disease and significantly improving risk discrimination and reclassification. Across multiethnic, external validation datasets inclusive of 33,096, 124,467, 16,433 and 16,874 participants of African, European, Hispanic and South Asian ancestry, respectively, GPSMult demonstrated increased strength of associations across all ancestries and outperformed all available previously published CAD polygenic scores. These data contribute a new GPSMult for CAD to the field and provide a generalizable framework for how large-scale integration of genetic association data for CAD and related traits from diverse populations can meaningfully improve polygenic risk prediction.
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2023-07-31 23:45:00
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#paper doi：https://doi.org/10.1038/s41591-023-02429-x A multi-ancestry polygenic risk score improves risk prediction for coronary artery disease
本文使用不同祖先来源人群队列的 冠状动脉疾病（CAD） GWASs分析结果，开发了 CAD 多基因风险评分模型（gps） GPSMult ，用于识别 CAD 风险。其模型分为两层：1.使用LDpred2方法为每个群体分层CAD GWAS构建单独的gps, ，综合 不同群体间gps 构建多祖先来源模型；2.采用步进法选择多祖先gps + 临床性状 的最佳组合，构建逻辑回归模型 GPSMult 。本文验证了GPSMult模型 相对于已发布的CAD风险模型在年轻人群或非欧洲人群风险预测性能的提升，倡导GPSMult辅助指导处于边缘或中度CAD风险个体的他汀类药物治疗决策。