Large-scale genome-wide association study of coronary artery disease in genetically diverse populations

doi:10.1038/s41591-022-01891-3

. 2022 Aug;28(8):1679-1692.

doi: 10.1038/s41591-022-01891-3. Epub 2022 Aug 1.

Large-scale genome-wide association study of coronary artery disease in genetically diverse populations

Catherine Tcheandjieu^#^{1

2

3

4}, Xiang Zhu^#^{5

6

7

8}, Austin T Hilliard^#⁵, Shoa L Clarke^#^{5

9}, Valerio Napolioni^{10

11}, Shining Ma⁶, Kyung Min Lee¹², Huaying Fang¹³, Fei Chen¹⁴, Yingchang Lu¹⁵, Noah L Tsao¹⁶, Sridharan Raghavan^{17

18}, Satoshi Koyama¹⁹, Bryan R Gorman^{20

21}, Marijana Vujkovic^{22

23}, Derek Klarin^{5

20

24

25

26

27}, Michael G Levin^{22

23}, Nasa Sinnott-Armstrong^{5

13}, Genevieve L Wojcik²⁸, Mary E Plomondon^{29

30}, Thomas M Maddox^{31

32}, Stephen W Waldo^{29

30

33}, Alexander G Bick³⁴, Saiju Pyarajan^{20

35}, Jie Huang^{20

36

37}, Rebecca Song²⁰, Yuk-Lam Ho²⁰, Steven Buyske³⁸, Charles Kooperberg³⁹, Jeffrey Haessler³⁹, Ruth J F Loos⁴⁰, Ron Do^{40

41}, Marie Verbanck^{40

41

42}, Kumardeep Chaudhary^{40

41}, Kari E North⁴³, Christy L Avery⁴³, Mariaelisa Graff⁴³, Christopher A Haiman¹⁴, Loïc Le Marchand⁴⁴, Lynne R Wilkens⁴⁴, Joshua C Bis⁴⁵, Hampton Leonard^{46

47}, Botong Shen⁴⁸, Leslie A Lange^{49

50

51}, Ayush Giri^{52

53}, Ozan Dikilitas⁵⁴, Iftikhar J Kullo⁵⁴, Ian B Stanaway⁵⁵, Gail P Jarvik^{56

57}, Adam S Gordon⁵⁸, Scott Hebbring⁵⁹, Bahram Namjou^{60

61}, Kenneth M Kaufman⁶⁰, Kaoru Ito¹⁹, Kazuyoshi Ishigaki⁶², Yoichiro Kamatani^{62

63}, Shefali S Verma^{64

65}, Marylyn D Ritchie^{64

65}, Rachel L Kember^{22

66}, Aris Baras⁶⁷, Luca A Lotta⁶⁷; Regeneron Genetics Center; CARDIoGRAMplusC4D Consortium; Biobank Japan; Million Veteran Program; Sekar Kathiresan^{25

68

69

70}, Elizabeth R Hauser^{71

72}, Donald R Miller^{73

74}, Jennifer S Lee^{5

75}, Danish Saleheen^{22

76}, Peter D Reaven^{77

78}, Kelly Cho^{20

35}, J Michael Gaziano^{20

35}, Pradeep Natarajan^{25

69

79}, Jennifer E Huffman²⁰, Benjamin F Voight^{22

64

80

81}, Daniel J Rader²³, Kyong-Mi Chang^{22

23}, Julie A Lynch^{82

83}, Scott M Damrauer^{16

22

64}, Peter W F Wilson^{84

85}, Hua Tang¹³, Yan V Sun^{86

87}, Philip S Tsao^{5

75

88}, Christopher J O'Donnell^{20

35}, Themistocles L Assimes^{89

90

91

92}

Affiliations

¹ VA Palo Alto Health Care System, Palo Alto, CA, USA. catherine.tcheandjieu@gladstone.ucsf.edu.
² Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA. catherine.tcheandjieu@gladstone.ucsf.edu.
³ Gladstone Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA. catherine.tcheandjieu@gladstone.ucsf.edu.
⁴ Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA. catherine.tcheandjieu@gladstone.ucsf.edu.
⁵ VA Palo Alto Health Care System, Palo Alto, CA, USA.
⁶ Department of Statistics, Stanford University, Stanford, CA, USA.
⁷ Department of Statistics, The Pennsylvania State University, University Park, PA, USA.
⁸ Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, USA.
⁹ Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA.
¹⁰ School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy.
¹¹ Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.
¹² VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA.
¹³ Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
¹⁴ Department of Preventive Medicine, Center for Genetic Epidemiology, University of Southern California, Los Angeles, CA, USA.
¹⁵ Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
¹⁶ Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
¹⁷ Medicine Service, VA Eastern Colorado Health Care System, Aurora, CO, USA.
¹⁸ Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
¹⁹ Laboratory for Cardiovascular Genomics and Informatics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan.
²⁰ VA Boston Healthcare System, Boston, MA, USA.
²¹ Booz Allen Hamilton, McLean, VA, USA.
²² Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA.
²³ Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
²⁴ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
²⁵ Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
²⁶ Division of Vascular Surgery and Endovascular Therapy, University of Florida School of Medicine, Gainesville, FL, USA.
²⁷ Stanford University School of Medicine, Stanford, CA, USA.
²⁸ Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
²⁹ Department of Medicine, Rocky Mountain Regional VA Medical Center, Aurora, CO, USA.
³⁰ CART Program, VHA Office of Quality and Patient Safety, Washington, DC, USA.
³¹ Healthcare Innovation Lab, JC HealthCare/Washington University School of Medicine, St Louis, MO, USA.
³² Division of Cardiology, Washington University School of Medicine, St Louis, MO, USA.
³³ Division of Cardiology, University of Colorado School of Medicine, Aurora, CO, USA.
³⁴ Department of Biomedical Informatics, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
³⁵ Department of Medicine, Brigham Women's Hospital, Harvard Medical School, Boston, MA, USA.
³⁶ Department of Global Health, Peking University School of Public Health, Beijing, China.
³⁷ School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen, China.
³⁸ Department of Statistics, Rutgers University, Piscataway, NJ, USA.
³⁹ Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA.
⁴⁰ Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
⁴¹ Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
⁴² EA 7537 BioSTM, Université de Paris, Paris, France.
⁴³ Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.
⁴⁴ Cancer Epidemiology Program, University of Hawaii Cancer Center, University of Hawaii, Honolulu, HI, USA.
⁴⁵ Department of Medicine, Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA.
⁴⁶ Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA.
⁴⁷ Data Tecnica Int'l, LLC, Glen Echo, MD, USA.
⁴⁸ Health Disparities Research Section, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
⁴⁹ Department of Medicine, Division of Biomedical Informatics and Personalized Medicine, Aurora, CO, USA.
⁵⁰ Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, Aurora, CO, USA.
⁵¹ Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
⁵² Department of Medicine, Division of Epidemiology, Vanderbilt University Medical Center, Nashville, TN, USA.
⁵³ Department of Obstetrics and Gynecology, Division of Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
⁵⁴ Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
⁵⁵ Department of Medicine, Division of Nephrology, University of Washington, Seattle, WA, USA.
⁵⁶ Department of Medicine, Medical Genetics, University of Washington School of Medicine, Seattle, WA, USA.
⁵⁷ Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA.
⁵⁸ Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
⁵⁹ Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, WI, USA.
⁶⁰ Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
⁶¹ Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
⁶² Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan.
⁶³ Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences - The University of Tokyo, Tokyo, Japan.
⁶⁴ Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
⁶⁵ Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
⁶⁶ Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
⁶⁷ Regeneron Genetics Center, Tarrytown, NY, USA.
⁶⁸ Department of Medicine, Harvard Medical School, Boston, MA, USA.
⁶⁹ Department of Medicine, Cardiology Division, Massachusetts General Hospital, Boston, MA, USA.
⁷⁰ Verve Therapeutics, Cambridge, MA, USA.
⁷¹ Cooperative Studies Program Epidemiology Center-Durham, Durham VA Health Care System, Durham, NC, USA.
⁷² Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA.
⁷³ Center for Healthcare Organization and Implementation Research, Bedford VA Healthcare System, Bedford, MA, USA.
⁷⁴ Center for Population Health, Department of Biomedical and Nutritional Sciences, University of Massachusetts, Lowell, MA, USA.
⁷⁵ Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
⁷⁶ Department of Medicine, Division of Cardiology, Columbia University, New York, NY, USA.
⁷⁷ Phoenix VA Health Care System, Phoenix, AZ, USA.
⁷⁸ College of Medicine, University of Arizona, Phoenix, AZ, USA.
⁷⁹ Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.
⁸⁰ Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
⁸¹ Institute of Translational Medicine and Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
⁸² VA Salt Lake City Health Care System, Salt Lake City, UT, USA.
⁸³ College of Nursing and Health Sciences, University of Massachusetts, Boston, MA, USA.
⁸⁴ Atlanta VA Medical Center, Atlanta, GA, USA.
⁸⁵ Division of Cardiology, Emory University School of Medicine, Atlanta, GA, USA.
⁸⁶ Atlanta VA Health Care System, Atlanta, GA, USA.
⁸⁷ Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA.
⁸⁸ Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA.
⁸⁹ VA Palo Alto Health Care System, Palo Alto, CA, USA. tassimes@stanford.edu.
⁹⁰ Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA. tassimes@stanford.edu.
⁹¹ Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA. tassimes@stanford.edu.
⁹² Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA. tassimes@stanford.edu.

^# Contributed equally.

PMID: 35915156
PMCID: PMC9419655
DOI: 10.1038/s41591-022-01891-3

Large-scale genome-wide association study of coronary artery disease in genetically diverse populations

Catherine Tcheandjieu et al. Nat Med. 2022 Aug.

. 2022 Aug;28(8):1679-1692.

doi: 10.1038/s41591-022-01891-3. Epub 2022 Aug 1.

Authors

Affiliations

¹ VA Palo Alto Health Care System, Palo Alto, CA, USA. catherine.tcheandjieu@gladstone.ucsf.edu.
² Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA. catherine.tcheandjieu@gladstone.ucsf.edu.
³ Gladstone Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA. catherine.tcheandjieu@gladstone.ucsf.edu.
⁴ Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA. catherine.tcheandjieu@gladstone.ucsf.edu.
⁵ VA Palo Alto Health Care System, Palo Alto, CA, USA.
⁶ Department of Statistics, Stanford University, Stanford, CA, USA.
⁷ Department of Statistics, The Pennsylvania State University, University Park, PA, USA.
⁸ Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, USA.
⁹ Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA.
¹⁰ School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy.
¹¹ Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.
¹² VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA.
¹³ Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
¹⁴ Department of Preventive Medicine, Center for Genetic Epidemiology, University of Southern California, Los Angeles, CA, USA.
¹⁵ Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
¹⁶ Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
¹⁷ Medicine Service, VA Eastern Colorado Health Care System, Aurora, CO, USA.
¹⁸ Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
¹⁹ Laboratory for Cardiovascular Genomics and Informatics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan.
²⁰ VA Boston Healthcare System, Boston, MA, USA.
²¹ Booz Allen Hamilton, McLean, VA, USA.
²² Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA.
²³ Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
²⁴ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
²⁵ Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
²⁶ Division of Vascular Surgery and Endovascular Therapy, University of Florida School of Medicine, Gainesville, FL, USA.
²⁷ Stanford University School of Medicine, Stanford, CA, USA.
²⁸ Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
²⁹ Department of Medicine, Rocky Mountain Regional VA Medical Center, Aurora, CO, USA.
³⁰ CART Program, VHA Office of Quality and Patient Safety, Washington, DC, USA.
³¹ Healthcare Innovation Lab, JC HealthCare/Washington University School of Medicine, St Louis, MO, USA.
³² Division of Cardiology, Washington University School of Medicine, St Louis, MO, USA.
³³ Division of Cardiology, University of Colorado School of Medicine, Aurora, CO, USA.
³⁴ Department of Biomedical Informatics, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
³⁵ Department of Medicine, Brigham Women's Hospital, Harvard Medical School, Boston, MA, USA.
³⁶ Department of Global Health, Peking University School of Public Health, Beijing, China.
³⁷ School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen, China.
³⁸ Department of Statistics, Rutgers University, Piscataway, NJ, USA.
³⁹ Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA.
⁴⁰ Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
⁴¹ Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
⁴² EA 7537 BioSTM, Université de Paris, Paris, France.
⁴³ Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.
⁴⁴ Cancer Epidemiology Program, University of Hawaii Cancer Center, University of Hawaii, Honolulu, HI, USA.
⁴⁵ Department of Medicine, Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA.
⁴⁶ Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA.
⁴⁷ Data Tecnica Int'l, LLC, Glen Echo, MD, USA.
⁴⁸ Health Disparities Research Section, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
⁴⁹ Department of Medicine, Division of Biomedical Informatics and Personalized Medicine, Aurora, CO, USA.
⁵⁰ Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, Aurora, CO, USA.
⁵¹ Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
⁵² Department of Medicine, Division of Epidemiology, Vanderbilt University Medical Center, Nashville, TN, USA.
⁵³ Department of Obstetrics and Gynecology, Division of Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
⁵⁴ Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
⁵⁵ Department of Medicine, Division of Nephrology, University of Washington, Seattle, WA, USA.
⁵⁶ Department of Medicine, Medical Genetics, University of Washington School of Medicine, Seattle, WA, USA.
⁵⁷ Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA.
⁵⁸ Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
⁵⁹ Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, WI, USA.
⁶⁰ Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
⁶¹ Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
⁶² Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan.
⁶³ Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences - The University of Tokyo, Tokyo, Japan.
⁶⁴ Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
⁶⁵ Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
⁶⁶ Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
⁶⁷ Regeneron Genetics Center, Tarrytown, NY, USA.
⁶⁸ Department of Medicine, Harvard Medical School, Boston, MA, USA.
⁶⁹ Department of Medicine, Cardiology Division, Massachusetts General Hospital, Boston, MA, USA.
⁷⁰ Verve Therapeutics, Cambridge, MA, USA.
⁷¹ Cooperative Studies Program Epidemiology Center-Durham, Durham VA Health Care System, Durham, NC, USA.
⁷² Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA.
⁷³ Center for Healthcare Organization and Implementation Research, Bedford VA Healthcare System, Bedford, MA, USA.
⁷⁴ Center for Population Health, Department of Biomedical and Nutritional Sciences, University of Massachusetts, Lowell, MA, USA.
⁷⁵ Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
⁷⁶ Department of Medicine, Division of Cardiology, Columbia University, New York, NY, USA.
⁷⁷ Phoenix VA Health Care System, Phoenix, AZ, USA.
⁷⁸ College of Medicine, University of Arizona, Phoenix, AZ, USA.
⁷⁹ Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.
⁸⁰ Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
⁸¹ Institute of Translational Medicine and Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
⁸² VA Salt Lake City Health Care System, Salt Lake City, UT, USA.
⁸³ College of Nursing and Health Sciences, University of Massachusetts, Boston, MA, USA.
⁸⁴ Atlanta VA Medical Center, Atlanta, GA, USA.
⁸⁵ Division of Cardiology, Emory University School of Medicine, Atlanta, GA, USA.
⁸⁶ Atlanta VA Health Care System, Atlanta, GA, USA.
⁸⁷ Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA.
⁸⁸ Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA.
⁸⁹ VA Palo Alto Health Care System, Palo Alto, CA, USA. tassimes@stanford.edu.
⁹⁰ Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA. tassimes@stanford.edu.
⁹¹ Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA. tassimes@stanford.edu.
⁹² Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA. tassimes@stanford.edu.

^# Contributed equally.

PMID: 35915156
PMCID: PMC9419655
DOI: 10.1038/s41591-022-01891-3

Abstract

We report a genome-wide association study (GWAS) of coronary artery disease (CAD) incorporating nearly a quarter of a million cases, in which existing studies are integrated with data from cohorts of white, Black and Hispanic individuals from the Million Veteran Program. We document near equivalent heritability of CAD across multiple ancestral groups, identify 95 novel loci, including nine on the X chromosome, detect eight loci of genome-wide significance in Black and Hispanic individuals, and demonstrate that two common haplotypes at the 9p21 locus are responsible for risk stratification in all populations except those of African origin, in which these haplotypes are virtually absent. Moreover, in the largest GWAS for angiographically derived coronary atherosclerosis performed to date, we find 15 loci of genome-wide significance that robustly overlap with established loci for clinical CAD. Phenome-wide association analyses of novel loci and polygenic risk scores (PRSs) augment signals related to insulin resistance, extend pleiotropic associations of these loci to include smoking and family history, and precisely document the markedly reduced transferability of existing PRSs to Black individuals. Downstream integrative analyses reinforce the critical roles of vascular endothelial, fibroblast, and smooth muscle cells in CAD susceptibility, but also point to a shared biology between atherosclerosis and oncogenesis. This study highlights the value of diverse populations in further characterizing the genetic architecture of CAD.

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Conflict of interest statement

Ethics declarations - Competing interests

A.B. and L.A.L. are employees of Regeneron Pharmaceuticals. R.D. has received grants from AstraZeneca, grants and nonfinancial support from Goldfinch Bio, being a scientific co-founder, consultant and equity holder for Pensieve Health and being a consultant for Variant Bio. T. M. M. is an employee of the Healthcare Innovation Lab at BJC HealthCare / Washington University School of Medicine, an advisor of Myia Labs, and a compensated director the J.F Maddox Foundation in New Mexico. S.K. is an is an employee of Verve Therapeutics, holds equity in Verve Therapeutics and Maze Therapeutics, and has served as a consultant for Acceleron, Eli Lilly, Novartis, Merck, Novo Nordisk, Novo Ventures, Ionis, Alnylam, Aegerion, Haug Partners, Noble Insights, Leerink Partners, Bayer Healthcare, Illumina, Color Genomics, MedGenome, Quest, and Medscape. D.J.R. is on the Scientific Advisory Board of Alnylam, Novartis, and Verve Therapeutics. M.D.R. is on the scientific advisory board for Goldfinch Bio and Cipherome. C.J.O became an employee of Novartis after initial submission of manuscript. P.N. reports investigator-initiated grants from Amgen, Apple, AstraZeneca, Boston Scientific, and Novartis, personal fees from Apple, AstraZeneca, Blackstone Life Sciences, Invitae, Foresite Labs, Novartis, Roche / Genentech, is a co-founder of TenSixteen Bio, is a shareholder of geneXwell, TenSixteen Bio, and Vertex, scientific advisory board member of geneXwell and TenSixteen Bio, and spousal employment at Vertex, all unrelated to the present work. S.M.D. receives research support from RenalytixAI to his institution and consulting fees from Calico Labs. A.G.B. is a scientific co-founder and equity holder in TenSixteen Bio. The remaining authors declare no competing interests.

Figures

**Extended Data Fig. 1. LocusZoom plots of loci reaching genome wide significance in Blacks and Hispanics**
Sets of LocusZoom plots for five loci in Blacks and 3 loci in Hispanics reaching genome wide significance after two-stage meta-analysis with external cohorts. Each set of plots show the association results for a locus for all three populations using the same chromosome location scale (x-axis) but not the same p-value scale (y-axis). P values are derived from inverse variance weighted meta-analysis using METAL and are two-sided.

**Extended Data Fig. 2. Allele frequencies and association results at the 9p21 locus among Black in the Million Veteran Program stratified by local ancestry status**
Top panels show plots of corresponding allelic frequencies at the 9p21 susceptibility locus observed in MVP Whites vs. subgroups of MVP Blacks including those with a. two African chromosomes (chr), b. one African chr, and c. no African chr at the locus. Corresponding LocusZoom plots for each group are in the panels immediately below. Association testing was performed using logistic regression with adjustment on sex and principal component as implemented in PLINK. P values were derived from a Wald test and are two-sided.

**Extended Data Fig. 3. LocusZoom plots of SNP association at the 9p21 susceptibility locus for CAD.**
Top panel plots the results for MVP GWAS of all Hispanics + Stage 2 cohort meta-analysis. P values are derived from inverse variance weighted meta-analysis using METAL and are two-sided. Bottom panel plots the subset of MVP Hispanics with no African derived chromosomes at 9p21 based on local ancestry assessment using RFMix (5,298 cases / 20,556 controls). Association testing was performed using logistic regression with adjustment on sex and principal component as implemented in PLINK. P values were derived from a Wald test and are two-sided.

**Fig. 1:. Design of multi-population genome wide association study (GWAS) of coronary artery disease (CAD) and estimates of heritability (h²) of CAD using GREML-LDMS-I for four populations**
a, Study design. GWAS was first performed stratified by population group. GWAS for Whites was then meta-analyzed with 2 existing GWAS for initial discovery among Whites. The GWAS for MVP Hispanics and MVP Blacks as well as the Biobank Japan GWAS of CAD was further incorporated into a single multi-population meta-analysis. Two-stage joint meta-analysis of the most promising SNPs was performed for the Hispanics and Blacks with multiple external cohorts for population-specific discovery. **b-d,** Heritability (h²) analyses for CAD in four major racial groups using GREML-LDMS-I. b. Principal component analysis of MVP participants combined with 1000 genomes was first performed to identify a random subset of 19,395 Hispanics with the highest proportion of Indigenous American ancestry (pink). A random subset of the 19,392 least admixed Whites (dark green) and the 19,392 least admixed Blacks (dark blue), respectively, were then matched 1:1 on case-control status, age of first EHR evidence of CAD, type of CAD presentation, and age of controls to the Hispanics. Similar matching was performed for 18,747 participants from the Biobank Japan study. c, Observed narrow-sense h² within each cohort defined in b using a multi-component model, GREML-LDMS-I, implemented in GCTA, with age, sex, and a genetic relatedness matrix as covariates. h² estimate and respective standard error (SE) of that estimate is shown for each of 24 bins of imputed SNPs defined by linkage disequilibrium score quartiles and six minor allele frequency thresholds (top panel) with the corresponding absolute number of SNPs contributing to this h² shown on the bottom panel. Total h² is calculated by summing 24 estimates with SE for this estimate calculated by delta method. d, h² on the liability scale for each population in c as a function of a range of presumed population prevalence of CAD. Error bars denote +/− one SE around each point estimate.

**Fig. 2:. Population-specific GWAS and multi-population meta-analysis**
a, Circos plot indicating the −log₁₀(P) for association with CAD for population-specific and multi-population GWAS meta-analyses. See Figure 1a for sample sizes. P values are derived from inverse variance weighted meta-analysis using METAL or GWAMA and are two-sided. The inner track plots the 2-stage meta-analysis association results for Blacks in red and Hispanics (HISP) in green, while the middle track plots the results for the meta-analysis of Whites in black and the multi-population metanalysis further incorporating the GWAS of MVP Blacks, MVP Hispanics, and of Biobank Japan in blue. The red line indicates genome-wide significance (GWS) (P = 5.0 × 10⁻⁸). The outer track lists the nearest mapped gene to the lead SNPs reaching GWS in each of these four meta-analyses including five loci in Blacks (red font), three loci in Hispanics (green font), 33 novel loci among Whites (black font), and 62 additional novel loci after the multi-population meta-analysis (blue font). b, Example of X-ray image from an angiogram of the right coronary artery used to estimate the burden of coronary atherosclerosis. The image shows 2 high-grade obstructions (arrows) as contrast agent is injected into the blood vessel (Adobe Stock FILE #: 413211903). Manhattan plot (right) of multi-population meta-analysis of GWAS (n=41,507) for burden of coronary atherosclerosis as estimated by the number of arteries with obstructions >50% on an angiogram. P values are derived from inverse variance weighted meta-analysis using METAL and are two-sided.

**Fig. 3:. Local ancestry and haplotype analyses at the 9p21 susceptibility locus for CAD in the Million Veteran Program**
**a-c,** Black (n=17,247 cases / 60,578 controls) and Hispanic (n=6,388 / 24,479) MVP participants were stratified into groups based on the degree of African ancestry at the 9p21 locus for CAD as determined by RFMix. Whites (n=11,170 / 39,706) were analyzed as a single non-admixed group. The three subgroups among Blacks formed includes subjects with a high probability of having inherited two African (Black_AFR+/+, n=11,173 / 39,706) derived chromosomes in the 9p21 region, one African and one European (Black_AFR+/−, n=5,136 / 17,451), or two European chromosomes (Black_AFR−/−, n=654 / n=2,101). The two subgroups among Hispanics included those with high probability of having either 1 or 2 African chromosomes (Hisp_AFR+/−|+/+, n=985 / 3,943) vs. those without any African ancestry in this region (Hisp_AFR−/−, n=5,298 / 20,556). Among SNPs in the high-risk region of 9p21 that reached genome wide significance among Whites, six SNPs with a minor allele frequency >10% in Black_AFR+/+ were used to infer haplotypes in the region. Each column along the x-axis represents a haplotype, named by the alleles of the six defining SNPs. a, frequency of 17 observed haplotypes overall in each population and by subgroup of Blacks and Hispanics. **b-c,** odds ratio (OR) of CAD and −log10(p-value) obtained through a haplotype trend regression analysis where AACATT is the reference haplotype in b and AGTTCA is the reference haplotype in c.

**Fig. 4:. Pleiotropic assessment of 95 novel loci through extended phenome wide association of lead SNPs**
Network plot of genotype-phenotype associations reaching significance at FDR<0.05 among 194,022 White participants in MVP without CAD for the lead SNPs in the 95 novel loci. Nodes are labelled either with the mapped gene for a lead SNP (purple font) or a phenotype tested in the PheWAS (black font). To highlight most pleiotropic SNPs and facilitate interpretation, the plot is restricted to lead SNPs associated with at least three distinct phenotypes. Distinct colors of nodes and edges represent a group of genotypes and phenotypes in the same dominant network. The thickness of the edges is correlated with the strength of the SNP-phenotype association (z-score). The size of the labels is dictated by the number of connections to phenotypes or genes and the strength of association. Network plot was created using Yifan Yu proportional and Atlas 2 layout algorithms as implemented in Gephi software.

**Fig. 5:. Downstream analyses to prioritize systems, pathways, tissues, and cells relevant to CAD**
**a-c**, MAGMA gene-property analyses to test relationship between expressed genes in specific cells or tissues and genetic associations (meta-analysis of Whites) as implemented in FUMA. The gene-property analysis is based on the regression model, Z∼β₀+E_tβ_E+Aβ_A+Bβ_B+ϵ where Z is a gene-based Z-score converted from the gene-based P-value, B is a matrix of technical confounders, Et is the gene expression value of a testing tissue type c and A is the average expression across tissue types in a data set. A one-sided test (β_E>0) is performed testing the positive relationship between tissue specificity and genetic association of genes. Data in a are restricted to three mouse single-cell RNA-seq (sc-RNA) datasets involving a broad range of cell types/organs while data in b are restricted to human datasets mostly involving the brain but also the pancreas and blood. Results show only independent cell-type associations based on within-dataset conditional analyses ordered by p-value across datasets. Data in c shows results for 54 specific tissue from the GTEx RNA-seq dataset v8 in order of p-value significance with red bars and font highlighting statistically significant tissues after adjusting for multiple testing (horizontal black dashed line) while remaining tissues are in blue. **d-f**, DEPICT following standard algorithm on the same GWAS used for MAGMA analyses in **a-c**. A tissue/cell type expression matrix was constructed by averaging gene expression levels of microarray samples with the same Medical Subject Heading tissue and cell type annotation. In this matrix, each column includes relative and normalized expression values of genes across 209 tissue/cell types. Enrichment in a tissue/cell type is then quantified by summing z-scores of the expression of genes with variants reaching genome wide significance in our meta-analysis of Whites. Z-scores are adjusted for confounding factors using 200 precomputed null GWAS in the Diabetes Genetics Initiative (DGI). Type 1 error rates were calculated by replacing null GWAS in DGI with simulated GWAS with positive signals but no underlying biological basis. DEPICT results are separated into d, cells e, tissues, and f, systems. −log10(p-value) for a false discovery rate (FDR) of <0.05 is demarcated by red dashed line while the FDR <0.2 threshold is shown in blue. Only cells/tissues reaching an FDR<0.2 are labelled.

**Fig. 6:. Testing of externally derived polygenic risk scores and new multi-population scores in the Million Veteran Program**
a, Performance of four externally derived and previously validated polygenic risk scores (PRS) in Whites, Blacks, and Hispanics, respectively, included in the MVP GWAS (see Fig. 1a for sample sizes of the three cohorts and methods for details on the origins of these PRS). Odds ratios and 95% confidence intervals per standard deviation (SD) increase in PRS are shown derived from logistic regression. In addition to all cases combined, subgroups of incident only cases (after enrollment), severe cases with evidence of either a myocardial infarction (AMI) and/or a revascularization (Revasc) procedure, and younger vs older onset cases (divided by median age of onset) were tested. b, externally derived PRS were tested for burden of coronary atherosclerosis among 25,600 Whites who underwent coronary angiography using multinomial logistic regression. Subjects with normal coronaries on angiography serve as the reference group and are compred to each of four progressively higher burdens of disease including non-obstructive disease (‘Non-obs.’), 1-vessel disease (1V), 2-vessel disease (2V), and 3-vessel or left main disease (3V/LM). Odds ratio and 95% confidence intervals are reported per SD increase in PRS. c, The best performing score in a and b, the metaGRS, was tested for association with Phecodes, clinical labs and anthropomorphic measures, as well as selected components of the baseline questionnaires among up to 164,534 Whites with no EHR evidence of atherosclerosis related complications at the end of EHR follow up. P-value are derived from a t-test implemented in the GLM and LM functions in R and are two-sided. d, New multi-population PRSs were developed using the pruning and thresholding strategy applied to the multi-population meta-analysis. These PRSs were tuned on an independent set of prevalent cases and controls in MVP, using population-specific tuning. Score performance of each score is shown in an independent set of incident cases and controls. Odds ratio and 95% confidence intervals are reported per SD increase in PRS and compared to performance of the metaGRS.

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References

1. Roth GA et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019: Update From the GBD 2019 Study. J Am Coll Cardiol 76, 2982–3021 (2020). - PMC - PubMed
1. Statistics;, N.C.f.H. Health, United States Spotlight: Racial and Ethnic Disparities in Heart Disease (Centers for Disease Control and Prevention, 2019).
1. Churchwell K et al. Call to Action: Structural Racism as a Fundamental Driver of Health Disparities: A Presidential Advisory From the American Heart Association. Circulation 142, e454–e468 (2020). - PubMed
1. Popejoy AB & Fullerton SM Genomics is failing on diversity. Nature 538, 161–164 (2016). - PMC - PubMed
1. Martin AR et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet 51, 584–591 (2019). - PMC - PubMed

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[1] Roth GA et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019: Update From the GBD 2019 Study. J Am Coll Cardiol 76, 2982–3021 (2020). - PMC - PubMed

[2] Roth GA et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019: Update From the GBD 2019 Study. J Am Coll Cardiol 76, 2982–3021 (2020). - PMC - PubMed

[3] Statistics;, N.C.f.H. Health, United States Spotlight: Racial and Ethnic Disparities in Heart Disease (Centers for Disease Control and Prevention, 2019).

[4] Statistics;, N.C.f.H. Health, United States Spotlight: Racial and Ethnic Disparities in Heart Disease (Centers for Disease Control and Prevention, 2019).

[5] Churchwell K et al. Call to Action: Structural Racism as a Fundamental Driver of Health Disparities: A Presidential Advisory From the American Heart Association. Circulation 142, e454–e468 (2020). - PubMed

[6] Churchwell K et al. Call to Action: Structural Racism as a Fundamental Driver of Health Disparities: A Presidential Advisory From the American Heart Association. Circulation 142, e454–e468 (2020). - PubMed

[7] Popejoy AB & Fullerton SM Genomics is failing on diversity. Nature 538, 161–164 (2016). - PMC - PubMed

[8] Popejoy AB & Fullerton SM Genomics is failing on diversity. Nature 538, 161–164 (2016). - PMC - PubMed

[9] Martin AR et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet 51, 584–591 (2019). - PMC - PubMed

[10] Martin AR et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet 51, 584–591 (2019). - PMC - PubMed

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Large-scale genome-wide association study of coronary artery disease in genetically diverse populations

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Large-scale genome-wide association study of coronary artery disease in genetically diverse populations

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