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

Nature Medicine volume 28, pages 1679–1692 (2022)Cite this article

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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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Fig. 1: Design of multi-population GWAS of CAD and estimates of heritability (h2) of CAD using GREML-LDMS-I for four populations.
Fig. 2: Population-specific GWAS and multi-population meta-analysis.
Fig. 3: Local ancestry and haplotype analyses at the 9p21 susceptibility locus for CAD in the MVP.
Fig. 4: Pleiotropic assessment of 95 novel loci through extended phenome-wide association of lead SNPs.
Fig. 5: Downstream analyses to prioritize systems, pathways, tissues and cells relevant to CAD.
Fig. 6: Testing of externally derived PRS and new multi-population scores in the MVP.

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Data availability

Summary statistics for the Biobank Japan study were obtained from http://jenger.riken.jp/en/result. Summary statistics for the CARDIoGRAMplusC4D study were obtained from http://www.cardiogramplusc4d.org. Summary statistics for the UK Biobank study for CAD were obtained from https://www.cardiomics.net/download-data. The full summary level association data from the individual population association analyses in MVP as well as the multi-population meta-analysis from this report will be available via the dbGaP Study accession number phs001672. This research has been conducted using the UK Biobank Resource under Application Numbers 13721 and 19416.

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Acknowledgements

This research is based on data from the Million Veteran Program, Office of Research and Development, Veterans Health Administration, and was supported by Veterans Administration awards I01-01BX003362, I01-BX004821 (K.-M.C., P.S.T.), I01-BX003340 (K.Cho, P.W.F.W.) and VA HSR RES 13-457 (VA Informatics and Computing Infrastructure). The content of this manuscript does not represent the views of the Department of Veterans Affairs or the United States Government. The eMERGE Network was initiated and funded by the National Human Genome Research Institute (NHGRI) through the following grants: Phase III: U01HG8657 (Kaiser Permanente Washington/University of Washington); U01HG8685 (Brigham and Women’s Hospital); U01HG8672 (Vanderbilt University Medical Center); U01HG8666 (Cincinnati Children’s Hospital Medical Center); U01HG6379 (Mayo Clinic); U01HG8679 (Geisinger Clinic); U01HG8680 (Columbia University Health Sciences); U01HG8684 (Children’s Hospital of Philadelphia); U01HG8673 (Northwestern University); U01HG8701 (Vanderbilt University Medical Center serving as the Coordinating Center); U01HG8676 (Partners Healthcare/Broad Institute); and U01HG8664 (Baylor College of Medicine); Phase II: U01HG006828 (Cincinnati Children’s Hospital Medical Center/Boston Children’s Hospital); U01HG006830 (Children’s Hospital of Philadelphia); U01HG006389 (Essentia Institute of Rural Health, Marshfield Clinic Research Foundation and Pennsylvania State University); U01HG006382 (Geisinger Clinic); U01HG006375 (Group Health Cooperative/University of Washington); U01HG006379 (Mayo Clinic); U01HG006380 (Icahn School of Medicine at Mount Sinai); U01HG006388 (Northwestern University); U01HG006378 (Vanderbilt University Medical Center); and U01HG006385 (Vanderbilt University Medical Center serving as the Coordinating Center). Phase II: U01HG004438 (CIDR) and U01HG004424 (the Broad Institute) serving as Genotyping Centers. Phase I: U01-HG-004610 (Group Health Cooperative/University of Washington); U01-HG-004608 (Marshfield Clinic Research Foundation and Vanderbilt University Medical Center); U01-HG-04599 (Mayo Clinic); U01HG004609 (Northwestern University); U01-HG-04603 (Vanderbilt University Medical Center, also serving as the Administrative Coordinating Center); U01HG004438 (CIDR) and U01HG004424 (the Broad Institute) serving as Genotyping Centers. The Population Architecture Using Genomics and Epidemiology (PAGE) program is funded by the NHGRI with co-funding from the National Institute on Minority Health and Health Disparities (NIMHD), supported by U01HG007416 (CALiCo), U01HG007417 (ISMMS), U01HG007397 (MEC), U01HG007376 (WHI), and U01HG007419 (Coordinating Center). The MultiEthnic Study (MEC) was supported by U01 CA164973. The Women’s Health Initiative (WHI) program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, US Department of Health and Human Services through contracts HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C and HHSN271201100004C. Scientific Computing Infrastructure at Fred Hutch is funded by ORIP grant S10OD028685. Funding support for the ‘Exonic variants and their relation to complex traits in minorities of the WHI study is provided through the NHGRI PAGE program (U01HG004790). The Atherosclerosis Risk in Communities (ARIC) Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C and HHSN268201100012C). The Cardiovascular Health Study (CHS) was supported by NHLBI contracts HHSN268201200036C, HHSN268200800007C, HHSN268201800001C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, 75N92021D00006; and NHLBI grants U01HL080295, R01HL085251, R01HL087652, R01HL105756, R01HL103612, R01HL120393 and U01HL130114 with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided through R01AG023629 from the National Institute on Aging (NIA). A full list of principal CHS investigators and institutions can be found at CHS-NHLBI.org. The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR001881, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DRC) grant DK063491 to the Southern California Diabetes Endocrinology Research Center. BioBank Japan (BBJ) was supported by the Tailor-Made Medical Treatment Program of the Ministry of Education, Culture, Sports, Science, and Technology and Japan Agency for Medical Research (AMED) under grant numbers JP17km0305002 and JP17km0305001. Healthy Aging in Neighborhoods of Diversity across the Life Span (HANDLS) was funded by the Interlaboratory Proposal Funding of the Intramural Research Program of the National Institute on Aging (NIA), the National Institutes of Health (NIH), Baltimore, Maryland. Funding number: [AG000989]. X.Z. was supported by the Stein Fellowship from Stanford University and Institute for Computational and Data Sciences Seed Grant from The Pennsylvania State University. S.M.D., J.A.L. and K.M.L. were supported by the US Department of Veterans Affairs (IK2-CX001780). Y.L. is supported by NIH R56HL150186. S.Ko. and K.It. were supported by AMED under Grant Numbers JP20km0405209 and JP20ek0109487. K.E.N. is supported by NIH R01HL142302. R.D. is supported by NIH R35GM124836 and R01HL139865. F.C. is supported by NCI T32CA229110. B.F.V. was supported by the NIH R01DK101478 and a Linda Pechenik Montague Investigator Award. P.N. is supported by grants from the NIH/NHLBI (R01HL142711, R01HL148050, R01HL127564, R01HL151152), NIH/NHGRI (U01HG011719), Fondation Leducq (TNE-18CVD04) and Massachusetts General Hospital (Fireman Chair). Support for title page creation and format was provided by AuthorArranger, a tool developed at the National Cancer Institute. The authors thank C. D. Bustamante for his review and feedback of specific cross-population analyses involving the 9p21 region. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author information

Author notes

  1. These authors contributed equally: Catherine Tcheandjieu, Xiang Zhu, Austin T Hilliard, Shoa L. Clarke.

  2. These authors jointly supervised this work: Yan V. Sun, Philip S. Tsao, Christopher J. O’Donnell, Themistocles L. Assimes.

  3. A list of members and their affiliations appears in the Supplementary Information.

Authors and Affiliations

  1. VA Palo Alto Health Care System, Palo Alto, CA, USA

    Catherine Tcheandjieu, Xiang Zhu, Austin T. Hilliard, Shoa L. Clarke, Derek Klarin, Nasa Sinnott-Armstrong, Jennifer S. Lee, Philip S. Tsao & Themistocles L. Assimes

  2. Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA

    Catherine Tcheandjieu, Shoa L. Clarke & Themistocles L. Assimes

  3. Gladstone Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA

    Catherine Tcheandjieu

  4. Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA

    Catherine Tcheandjieu

  5. Department of Statistics, Stanford University, Stanford, CA, USA

    Xiang Zhu & Shining Ma

  6. Department of Statistics, The Pennsylvania State University, University Park, PA, USA

    Xiang Zhu

  7. Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, USA

    Xiang Zhu

  8. School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy

    Valerio Napolioni

  9. Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA

    Valerio Napolioni

  10. VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA

    Kyung Min Lee

  11. Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA

    Huaying Fang, Nasa Sinnott-Armstrong & Hua Tang

  12. Department of Preventive Medicine, Center for Genetic Epidemiology, University of Southern California, Los Angeles, CA, USA

    Fei Chen & Christopher A. Haiman

  13. Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA

    Yingchang Lu

  14. Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Noah L. Tsao & Scott M. Damrauer

  15. Medicine Service, VA Eastern Colorado Health Care System, Aurora, CO, USA

    Sridharan Raghavan

  16. Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

    Sridharan Raghavan

  17. Laboratory for Cardiovascular Genomics and Informatics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan

    Satoshi Koyama & Kaoru Ito

  18. VA Boston Healthcare System, Boston, MA, USA

    Bryan R. Gorman, Derek Klarin, Saiju Pyarajan, Jie Huang, Rebecca Song, Yuk-Lam Ho, Kelly Cho, J. Michael Gaziano, Jennifer E. Huffman & Christopher J. O’Donnell

  19. Booz Allen Hamilton, McLean, VA, USA

    Bryan R. Gorman

  20. Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA

    Marijana Vujkovic, Michael G. Levin, Rachel L. Kember, Danish Saleheen, Benjamin F. Voight, Kyong-Mi Chang & Scott M. Damrauer

  21. Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Marijana Vujkovic, Michael G. Levin, Daniel J. Rader & Kyong-Mi Chang

  22. Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA

    Derek Klarin

  23. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Derek Klarin, Sekar Kathiresan & Pradeep Natarajan

  24. Division of Vascular Surgery and Endovascular Therapy, University of Florida School of Medicine, Gainesville, FL, USA

    Derek Klarin

  25. Stanford University School of Medicine, Stanford, CA, USA

    Derek Klarin

  26. Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA

    Genevieve L. Wojcik

  27. Department of Medicine, Rocky Mountain Regional VA Medical Center, Aurora, CO, USA

    Mary E. Plomondon & Stephen W. Waldo

  28. CART Program, VHA Office of Quality and Patient Safety, Washington, DC, USA

    Mary E. Plomondon & Stephen W. Waldo

  29. Healthcare Innovation Lab, JC HealthCare/Washington University School of Medicine, St Louis, MO, USA

    Thomas M. Maddox

  30. Division of Cardiology, Washington University School of Medicine, St Louis, MO, USA

    Thomas M. Maddox

  31. Division of Cardiology, University of Colorado School of Medicine, Aurora, CO, USA

    Stephen W. Waldo

  32. Department of Biomedical Informatics, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

    Alexander G. Bick

  33. Department of Medicine, Brigham Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Saiju Pyarajan, Kelly Cho, J. Michael Gaziano & Christopher J. O’Donnell

  34. Department of Global Health, Peking University School of Public Health, Beijing, China

    Jie Huang

  35. School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen, China

    Jie Huang

  36. Department of Statistics, Rutgers University, Piscataway, NJ, USA

    Steven Buyske

  37. Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA

    Charles Kooperberg & Jeffrey Haessler

  38. Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Ruth J. F. Loos, Ron Do, Marie Verbanck & Kumardeep Chaudhary

  39. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Ron Do, Marie Verbanck & Kumardeep Chaudhary

  40. EA 7537 BioSTM, Université de Paris, Paris, France

    Marie Verbanck

  41. Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA

    Kari E. North, Christy L. Avery & Mariaelisa Graff

  42. Cancer Epidemiology Program, University of Hawaii Cancer Center, University of Hawaii, Honolulu, HI, USA

    Loïc Le Marchand & Lynne R. Wilkens

  43. Department of Medicine, Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA

    Joshua C. Bis

  44. Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA

    Hampton Leonard

  45. Data Tecnica Int’l, LLC, Glen Echo, MD, USA

    Hampton Leonard

  46. Health Disparities Research Section, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA

    Botong Shen

  47. Department of Medicine, Division of Biomedical Informatics and Personalized Medicine, Aurora, CO, USA

    Leslie A. Lange

  48. Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, Aurora, CO, USA

    Leslie A. Lange

  49. Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

    Leslie A. Lange

  50. Department of Medicine, Division of Epidemiology, Vanderbilt University Medical Center, Nashville, TN, USA

    Ayush Giri

  51. Department of Obstetrics and Gynecology, Division of Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN, USA

    Ayush Giri

  52. Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA

    Ozan Dikilitas & Iftikhar J. Kullo

  53. Department of Medicine, Division of Nephrology, University of Washington, Seattle, WA, USA

    Ian B. Stanaway

  54. Department of Medicine, Medical Genetics, University of Washington School of Medicine, Seattle, WA, USA

    Gail P. Jarvik

  55. Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA

    Gail P. Jarvik

  56. Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    Adam S. Gordon

  57. Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, WI, USA

    Scott Hebbring

  58. Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Bahram Namjou & Kenneth M. Kaufman

  59. Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA

    Bahram Namjou

  60. Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan

    Kazuyoshi Ishigaki & Yoichiro Kamatani

  61. Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences – The University of Tokyo, Tokyo, Japan

    Yoichiro Kamatani

  62. Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Shefali S. Verma, Marylyn D. Ritchie, Benjamin F. Voight & Scott M. Damrauer

  63. Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Shefali S. Verma & Marylyn D. Ritchie

  64. Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Rachel L. Kember

  65. Regeneron Genetics Center, Tarrytown, NY, USA

    Aris Baras & Luca A. Lotta

  66. Department of Medicine, Harvard Medical School, Boston, MA, USA

    Sekar Kathiresan

  67. Department of Medicine, Cardiology Division, Massachusetts General Hospital, Boston, MA, USA

    Sekar Kathiresan & Pradeep Natarajan

  68. Verve Therapeutics, Cambridge, MA, USA

    Sekar Kathiresan

  69. Cooperative Studies Program Epidemiology Center–Durham, Durham VA Health Care System, Durham, NC, USA

    Elizabeth R. Hauser

  70. Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA

    Elizabeth R. Hauser

  71. Center for Healthcare Organization and Implementation Research, Bedford VA Healthcare System, Bedford, MA, USA

    Donald R. Miller

  72. Center for Population Health, Department of Biomedical and Nutritional Sciences, University of Massachusetts, Lowell, MA, USA

    Donald R. Miller

  73. Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA

    Jennifer S. Lee & Philip S. Tsao

  74. Department of Medicine, Division of Cardiology, Columbia University, New York, NY, USA

    Danish Saleheen

  75. Phoenix VA Health Care System, Phoenix, AZ, USA

    Peter D. Reaven

  76. College of Medicine, University of Arizona, Phoenix, AZ, USA

    Peter D. Reaven

  77. Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA

    Pradeep Natarajan

  78. Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Benjamin F. Voight

  79. Institute of Translational Medicine and Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Benjamin F. Voight

  80. VA Salt Lake City Health Care System, Salt Lake City, UT, USA

    Julie A. Lynch

  81. College of Nursing and Health Sciences, University of Massachusetts, Boston, MA, USA

    Julie A. Lynch

  82. Atlanta VA Medical Center, Atlanta, GA, USA

    Peter W. F. Wilson

  83. Division of Cardiology, Emory University School of Medicine, Atlanta, GA, USA

    Peter W. F. Wilson

  84. Atlanta VA Health Care System, Atlanta, GA, USA

    Yan V. Sun

  85. Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA

    Yan V. Sun

  86. Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA

    Philip S. Tsao & Themistocles L. Assimes

  87. Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA

    Themistocles L. Assimes

Authors
  1. Catherine Tcheandjieu
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  2. Xiang Zhu
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  3. Austin T. Hilliard
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  4. Shoa L. Clarke
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  5. Valerio Napolioni
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  6. Shining Ma
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  7. Kyung Min Lee
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  8. Huaying Fang
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  9. Fei Chen
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  10. Yingchang Lu
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  11. Noah L. Tsao
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  12. Sridharan Raghavan
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  13. Satoshi Koyama
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  14. Bryan R. Gorman
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  15. Marijana Vujkovic
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  16. Derek Klarin
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  17. Michael G. Levin
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  18. Nasa Sinnott-Armstrong
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  19. Genevieve L. Wojcik
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  20. Mary E. Plomondon
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  21. Thomas M. Maddox
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  22. Stephen W. Waldo
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  23. Alexander G. Bick
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  24. Saiju Pyarajan
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  25. Jie Huang
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  26. Rebecca Song
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  27. Yuk-Lam Ho
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  28. Steven Buyske
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  29. Charles Kooperberg
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  30. Jeffrey Haessler
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  31. Ruth J. F. Loos
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  32. Ron Do
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  33. Marie Verbanck
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  34. Kumardeep Chaudhary
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  35. Kari E. North
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  36. Christy L. Avery
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  37. Mariaelisa Graff
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  38. Christopher A. Haiman
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  39. Loïc Le Marchand
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  40. Lynne R. Wilkens
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  41. Joshua C. Bis
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  42. Hampton Leonard
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  43. Botong Shen
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  44. Leslie A. Lange
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  45. Ayush Giri
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  46. Ozan Dikilitas
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  47. Iftikhar J. Kullo
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  48. Ian B. Stanaway
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  49. Gail P. Jarvik
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  50. Adam S. Gordon
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  51. Scott Hebbring
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  52. Bahram Namjou
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  53. Kenneth M. Kaufman
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  54. Kaoru Ito
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  55. Kazuyoshi Ishigaki
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  56. Yoichiro Kamatani
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  57. Shefali S. Verma
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  58. Marylyn D. Ritchie
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  59. Rachel L. Kember
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  60. Aris Baras
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  61. Luca A. Lotta
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  62. Sekar Kathiresan
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  63. Elizabeth R. Hauser
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  64. Donald R. Miller
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  66. Danish Saleheen
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  67. Peter D. Reaven
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  68. Kelly Cho
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  69. J. Michael Gaziano
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  71. Jennifer E. Huffman
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  73. Daniel J. Rader
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  74. Kyong-Mi Chang
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  75. Julie A. Lynch
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  77. Peter W. F. Wilson
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  78. Hua Tang
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  79. Yan V. Sun
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  80. Philip S. Tsao
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  81. Christopher J. O’Donnell
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  82. Themistocles L. Assimes
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Consortia

Regeneron Genetics Center

  • Aris Baras
  •  & Luca A. Lotta

CARDIoGRAMplusC4D Consortium

  • Satoshi Koyama
  • , Iftikhar J. Kullo
  • , Kaoru Ito
  • , Kazuyoshi Ishigaki
  • , Yoichiro Kamatani
  • , Sekar Kathiresan
  • , Christopher J. O’Donnell
  •  & Themistocles L. Assimes

Biobank Japan

  • Satoshi Koyama
  • , Kaoru Ito
  • , Kazuyoshi Ishigaki
  •  & Yoichiro Kamatani

Million Veteran Program

  • Elizabeth R. Hauser
  • , Kelly Cho
  • , J. Michael Gaziano
  • , Kyong-Mi Chang
  • , Peter W. F. Wilson
  • , Yan V. Sun
  •  & Philip S. Tsao

Contributions

Concept and design: C.T., X.Z., A.T.H., S.L.C., V.N., D.J.R., K.-M.C., J.A.L., S.M.D., P.W.F.W., H.T., Y.V.S., P.S.T., C.J.O., T.L.A. Acquisition, analysis or interpretation of data: C.T., X.Z., A.T.H., S.L.C., V.N., S.M., B.R.G., K.M.L., R.S., K.M.K., H.F., F.C., Y.L., S.Ko., N.L.T., M.Vu., S.R., M.E.P., T.M.M., S.W.W., A.G.B., M.G.L., S.P., J.Hu., N.S.-A., Y.-L.H., G.L.W., S.B., C.K., J.Ha., R.J.F.L., R.D., M.Ve., K.Cha., K.E.N., C.L.A., M.G., C.A.H., L.L.M., L.R.W., J.C.B., H.L., B.S., L.A.La., A.G., O.D., I.J.K., I.B.S., G.P.J., A.S.G., S.H., B.N., K.It., K.Is., Y.K., S.S.V., M.D.R., R.L.K., A.B., L.A.Lo., S.Ka., E.R.H., D.R.M., J.S.L., D.S., P.D.R., K.Cho, J.M.G., J.E.H., B.F.V., D.J.R., K.-M.C., J.A.L., S.M.D., P.W.F.W, H.T., Y.V.S., P.S.T., C.J.O., T.L.A. Drafting of the manuscript: C.T., T.L.A. Critical revision of the manuscript for important intellectual content: X.Z., A.T.H., S.L.C., V.N., M.Vu., D.K., S.R., M.G.L., R.D., K.E.N., C.K., J.C.B., I.J.K., M.D.R., P.N., B.F.V., J.A.L., S.M.D., P.W.F.W, H.T., Y.V.S., P.S.T, C.J.O.

Corresponding authors

Correspondence to Catherine Tcheandjieu or Themistocles L. Assimes.

Ethics declarations

Competing interests

A.B. and L.A.Lo. are employees of Regeneron Pharmaceuticals. R.D. has received grants from AstraZeneca, grants and non-financial support from Goldfinch Bio, is a scientific co-founder, consultant and equity holder for Pensieve Health and 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 of the JF Maddox Foundation in New Mexico. S.Ka. 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 the initial submission of the 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 and Roche/Genentech, and is a co-founder of TenSixteen Bio, a shareholder of geneXwell, TenSixteen Bio and Vertex, a scientific advisory board member of geneXwell and TenSixteen Bio, and reports 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. All other authors have no competing interests.

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Extended data

Extended Data Fig. 1 LocusZoom plots of loci reaching genome-wide significance in Black participants and Hispanic participants.

Sets of LocusZoom plots for five loci in Black participants and 3 loci in Hispanic participants 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 white participants vs. subgroups of MVP Black participants 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 Hispanic participants + 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 Hispanic participants 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.

Extended Data Table 1 Demographic characteristics of the Million Veteran Program participants included in the genome-wide association study analyses of the clinical coronary artery disease phenotype. SD: standard deviation. AMI: acute myocardial infarction. HARE: Harmonizing Genetic Ancestry and Self-identified Race/Ethnicity algorithm. CAD: coronary artery disease
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Extended Data Table 2 Demographic characteristics of the white, Black, and Hispanic participants from the Million Veteran Program and the Japanese from Biobank Japan used in GREML-LDMS-I heritability analyses. N: number in stratum. SD: standard deviation
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Extended Data Table 3 Characteristics of Stage 1 and Stage 2 Black and Hispanic cohorts. PAGE: Population Architecture through Genomics and Environment Study (funded by the National Institutes of Health - NHGRI). ARIC: Atherosclerosis Risk in Communities Study (funded by the National institutes of Health - NHLBI). MEC: Multiethnic Study (funded by the National Cancer Institute). WHI: Women’s Health Initiative study (funded by the National institutes of Health - NHLBI). MEGA (WHI): PAGE substudy in the WHI genotyped with the Illumina Multi-Ethnic Genotyping Array. SHARE: SNP Health Association Resource substudy in WHI genotyped using Affymetrix 6.0 array. GARNET: Genomics and Randomized Trials Network substudy in WHI genotyped using Illumina HumanOmni1-Quad v1-0 B. BioVU: Vanderbilt’s biorepository of DNA extracted from discarded blood collected during routine clinical testing and linked to de-identified medical records in the Synthetic Derivative. CHS: Cardiovascular Health Study (funded by the National institutes of Health - NHLBI). Health ABC: The Health, Aging and Body Composition Study (funded by the National institutes of Health - NIA). HANDLS: The Healthy Aging in Neighborhoods of Diversity across the Life Span study (funded by the National Institutes of Health - NIA). JHS: The Jackson Heart Study (funded by the National Institutes of Health - NHLBI and NIMHD). eMERGE: The electronics Medical records and Genomics consortium (funded by the National Institutes of Health - NHGRI). Penn Biobank: The Penn Medicine BioBank (Institute for Translational Medicine and Therapeutics at the University of Pennsylvania). UK-Biobank: The UK Biobank Study. BioME: The BioME Biobank Program (The Institute for Personalized Medicine at the Icahn School of Medicine at Mount Sinai)
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Extended Data Table 4 Loci reaching genome-wide significance after two-stage meta-analysis in Black participants and Hispanic participants. *The 8p22 locus in Black participants was carefully examined for the possibility of a false-positive association as was not even a hint of a genetic signal in this region for white participants despite more than adequate power due to much larger sample size and a substantially higher frequency of the lead SNPs. There was no obvious link between the signal and the status of the inversion in the immediately adjacent 8p23 region which we called using principal component analyses of SNPs within the inversion site. The region reached genome-wide significance in MVP Black participants based solely on imputed genotypes. We determined that neighboring genotypes in the region were not reliably called due to an unrecognized African specific deletion in the region subsequently reported by the gnoMAD consortium. The deletion also affected the reliability of the imputed SNPs. Even after recalling the genotypes in this region taking into consideration the presence of the deletion, no genotyped SNPs were genome-wide significant for CAD. P values are derived from inverse variance weighted meta-analysis using METAL and are two-sided
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Extended Data Table 5 Derivation of the VA Clinical Assessment Reporting and Tracking (CART) sub cohort for genome-wide association study. 1 V = 1 vessel >50% obstruction, 2 V = 2 vessels with >50% obstruction, 3 V/LM = 3 vessels and/or left main disease >50% obstruction. *age assigned to last procedure. **age assigned to earliest procedure. ***age assigned to the earliest procedure showing the most severe disease. ****n with history of a cardiac transplant = 137, major age discrepancy between CART and CDW derived age = 2, and undefined HARE assignment = 595
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Extended Data Table 6 Age and sex characteristics of VA Clinical Assessment Reporting and Tracking (CART) subcohort by Harmonizing Genetic Ancestry and Self-identified Race/Ethnicity algorithm (HARE) assigned populations and severity of disease. *did not proceed with genetic association in this group because of low numbers
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Extended Data Table 7 Odds ratio of CAD per standard deviation increase of the externally derived LDPred and metaGRS scores in the MVP white, Black, and Hispanic cohorts followed by relative efficiency estimates based on ratios of betas. *LDPred score for CAD as previously described22 **metaGRS for CAD as previously described23 ***AMI/Revasc: subset of cases with evidence of discharge diagnosis of acute myocardial infarction or revascularization procedure in the EHR. All CAD: further include participants with CAD codes that are not AMI or revascularization **** Relative efficiency: ratio of log ORs (beta coefficients) between MVP and UK Biobank. n/a: not applicable as this broader phenotype not reported in the LDpred and metaGRS reports. glm function in R for logistic regression covariates: age, sex, genotyping batch and top 10 genotype-based PCs. p-value corresponding to the z ratio based on a Standard Normal reference distribution, 2-sided
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Tcheandjieu, C., Zhu, X., Hilliard, A.T. et al. Large-scale genome-wide association study of coronary artery disease in genetically diverse populations. Nat Med 28, 1679–1692 (2022). https://doi.org/10.1038/s41591-022-01891-3

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