Minxian Wang1, Ramesh Menon2, Sanghamitra Mishra2, Aniruddh P Patel3, Mark Chaffin1, Deepak Tanneeru2, Manjari Deshmukh2, Oshin Mathew2, Sanika Apte2, Christina S Devanboo2, Sumathi Sundaram2, Praveena Lakshmipathy2, Sakthivel Murugan2, Krishna Kumar Sharma4, Karthikeyan Rajendran5, Sam Santhosh2, Rajesh Thachathodiyl6, Hisham Ahamed6, Aniketh Vijay Balegadde6, Thomas Alexander7, Krishnan Swaminathan7, Rajeev Gupta4, Ajit S Mullasari8, Alben Sigamani5, Muralidhar Kanchi5, Andrew S Peterson9, Adam S Butterworth10, John Danesh11, Emanuele Di Angelantonio10, Aliya Naheed12, Michael Inouye13, Rajiv Chowdhury14, Ramprasad L Vedam2, Sekar Kathiresan15, Ravi Gupta2, Amit V Khera16. 1. Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts. 2. MedGenome Labs Ltd., Bengaluru, India. 3. Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts; Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts; Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts. 4. Eternal Heart Care Centre, Jaipur, India. 5. Narayana Health, Bengaluru, India. 6. Amrita Institute Medical Sciences, Kochi, India. 7. Kovai Medical Center and Hospital Research Foundation, Coimbatore, India. 8. Madras Medical Mission, Chennai, India. 9. MedGenome Inc., Foster City, California. 10. British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom; National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, United Kingdom. 11. British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom; National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, United Kingdom; British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, United Kingdom; National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, United Kingdom; Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, United Kingdom; Department of Human Genetics, Wellcome Sanger Institute, Hinxton, United Kingdom. 12. International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh. 13. Cambridge Baker Systems Genomics Initiative, Melbourne, Victoria, Australia, and Cambridge, United Kingdom; Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom; Department of Clinical Pathology and School of BioSciences, University of Melbourne, Parkville, Victoria, Australia; The Alan Turing Institute, London, United Kingdom. 14. British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom; Centre for Non-Communicable Disease Research, Dhaka, Bangladesh. 15. Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts; Verve Therapeutics, Cambridge, Massachusetts. 16. Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts; Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts; Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts. Electronic address: avkhera@mgh.harvard.edu.
Abstract
BACKGROUND: Genome-wide polygenic scores (GPS) integrate information from many common DNA variants into a single number. Because rates of coronary artery disease (CAD) are substantially higher among South Asians, a GPS to identify high-risk individuals may be particularly useful in this population. OBJECTIVES: This analysis used summary statistics from a prior genome-wide association study to derive a new GPSCAD for South Asians. METHODS: This GPSCAD was validated in 7,244 South Asian UK Biobank participants and tested in 491 individuals from a case-control study in Bangladesh. Next, a static ancestry and GPSCAD reference distribution was built using whole-genome sequencing from 1,522 Indian individuals, and a framework was tested for projecting individuals onto this static ancestry and GPSCAD reference distribution using 1,800 CAD cases and 1,163 control subjects newly recruited in India. RESULTS: The GPSCAD, containing 6,630,150 common DNA variants, had an odds ratio (OR) per SD of 1.58 in South Asian UK Biobank participants and 1.60 in the Bangladeshi study (p < 0.001 for each). Next, individuals of the Indian case-control study were projected onto static reference distributions, observing an OR/SD of 1.66 (p < 0.001). Compared with the middle quintile, risk for CAD was most pronounced for those in the top 5% of the GPSCAD distribution-ORs of 4.16, 2.46, and 3.22 in the South Asian UK Biobank, Bangladeshi, and Indian studies, respectively (p < 0.05 for each). CONCLUSIONS: The new GPSCAD has been developed and tested using 3 distinct South Asian studies, and provides a generalizable framework for ancestry-specific GPS assessment.
BACKGROUND: Genome-wide polygenic scores (GPS) integrate information from many common DNA variants into a single number. Because rates of coronary artery disease (CAD) are substantially higher among South Asians, a GPS to identify high-risk individuals may be particularly useful in this population. OBJECTIVES: This analysis used summary statistics from a prior genome-wide association study to derive a new GPSCAD for South Asians. METHODS: This GPSCAD was validated in 7,244 South Asian UK Biobank participants and tested in 491 individuals from a case-control study in Bangladesh. Next, a static ancestry and GPSCAD reference distribution was built using whole-genome sequencing from 1,522 Indian individuals, and a framework was tested for projecting individuals onto this static ancestry and GPSCAD reference distribution using 1,800 CAD cases and 1,163 control subjects newly recruited in India. RESULTS: The GPSCAD, containing 6,630,150 common DNA variants, had an odds ratio (OR) per SD of 1.58 in South Asian UK Biobank participants and 1.60 in the Bangladeshi study (p < 0.001 for each). Next, individuals of the Indian case-control study were projected onto static reference distributions, observing an OR/SD of 1.66 (p < 0.001). Compared with the middle quintile, risk for CAD was most pronounced for those in the top 5% of the GPSCAD distribution-ORs of 4.16, 2.46, and 3.22 in the South Asian UK Biobank, Bangladeshi, and Indian studies, respectively (p < 0.05 for each). CONCLUSIONS: The new GPSCAD has been developed and tested using 3 distinct South Asian studies, and provides a generalizable framework for ancestry-specific GPS assessment.
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Authors: Tian Ge; Marguerite R Irvin; Amit Patki; Vinodh Srinivasasainagendra; Yen-Feng Lin; Hemant K Tiwari; Nicole D Armstrong; Barbara Benoit; Chia-Yen Chen; Karmel W Choi; James J Cimino; Brittney H Davis; Ozan Dikilitas; Bethany Etheridge; Yen-Chen Anne Feng; Vivian Gainer; Hailiang Huang; Gail P Jarvik; Christopher Kachulis; Eimear E Kenny; Atlas Khan; Krzysztof Kiryluk; Leah Kottyan; Iftikhar J Kullo; Christoph Lange; Niall Lennon; Aaron Leong; Edyta Malolepsza; Ayme D Miles; Shawn Murphy; Bahram Namjou; Renuka Narayan; Mark J O'Connor; Jennifer A Pacheco; Emma Perez; Laura J Rasmussen-Torvik; Elisabeth A Rosenthal; Daniel Schaid; Maria Stamou; Miriam S Udler; Wei-Qi Wei; Scott T Weiss; Maggie C Y Ng; Jordan W Smoller; Matthew S Lebo; James B Meigs; Nita A Limdi; Elizabeth W Karlson Journal: Genome Med Date: 2022-06-29 Impact factor: 15.266