A multi-ancestry polygenic risk score improves risk prediction for coronary artery disease

• Type of Material: Article
• Authors: Aniruddh P. Patel, Massachusetts General Hospital; Minxian Wang, Chinese Academy of Sciences; Yunfeng Ruan, Massachusetts General Hospital; Satoshi Koyama, Broad Institute of MIT and Harvard; Shoa L. Clarke, Stanford University; Xiong Yang, Chinese Academy of Sciences; Catherine Tcheandjieu, Gladstone Institutes; Saaket Agrawal, Massachusetts General Hospital; Akl C. Fahed, Massachusetts General Hospital; Patrick T. Ellinor, Massachusetts General Hospital; Philip S. Tsao, Stanford University; Yan Sun, Emory University; Kelly Cho, Veteran Affairs Boston Healthcare System; Peter Wilson, Emory University; Themistocles L. Assimes, Veteran Affairs Palo Alto Healthcare System; David A. van Heel, Queen Mary University London; Adam S. Butterworth, University of Cambridge; Krishna G. Aragam, Massachusetts General Hospital; Pradeep Natarajan, Massachusetts General Hospital; Amit V. Khera, Massachusetts General Hospital
• Language: English
• Date: 2023-07-01
• Publisher: NATURE PORTFOLIO
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2023
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1038/s41591-023-02429-x
• Title of Journal or Parent Work: NATURE MEDICINE
• Volume: 29
• Issue: 7
• Start Page: 1793
• End Page: +
• Grant/Funding Information: This work was supported by the KL2/Catalyst Medical Research Investigator Training award from Harvard Catalyst (to A.P.P. and K.G.A.); the Sarnoff Cardiovascular Research Foundation Fellowship (to S.A.); grants 1K08HL153937 (to K.G.A.), 1K08HL161448 (to A.C.F.), R01HL1427 (to P.N.), R01HL148565 (to P.N.), R01HL148050 (to P.N.), 1RO1HL092577 (to P.T.E.), 1R01HL157635 (to P.T.E.) and 1R01HL157635 (to P.T.E.) from the National Heart, Lung, and Blood Institute; grants RG/18/13/33946 and CH/12/2/29428 from the British Heart Foundation (to A.S.B.) grants BRC-1215-20014 and NIHR203312 from the NIHR Cambridge Biomedical Research Centre (to A.S.B.) grant RE/18/1/34212 from the Cambridge British Heart Foundation Centre of Research Excellence (to A.S.B.); grants 862032 (to K.G.A.) 18SFRN34110082 (to P.T.E.), 17IFUNP3384001 (to K.G.A.) from the American Heart Association; grant MAESTRIA 965286 from the European Union (to P.T.E.); grants 1K08HG010155 (to A.V.K.) and 1U01HG011719 from the National Human Genome Research Institute (to A.P.P., P.N. and A.V.K.); a Hassenfeld Scholar Award from Massachusetts General Hospital (to P.N. and A.V.K.); a Merkin Institute Fellowship from the Broad Institute of MIT and Harvard (to A.V.K.).
• Keywords: CARDIOVASCULAR-DISEASE; VALIDATION; Medicine, Research & Experimental; Biochemistry & Molecular Biology; HEALTH; VARIANTS; HEART-DISEASE; Life Sciences & Biomedicine; ACCURACY; GENETIC RISK; Science & Technology; GENOMIC RISK; ASSOCIATION; Research & Experimental Medicine; Cell Biology; BIOBANK
• Research Categories: Health Sciences, Medicine and Surgery; Biology, Genetics; Biology, Cell

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.