Thiazide diuretics, commonly used antihypertensives, may cause QT interval (QT) prolongation, a risk factor for highly fatal and difficult to predict ventricular arrhythmias. We examined whether common single-nucleotide polymorphisms (SNPs) modified the association between thiazide use and QT or its component parts (QRS interval, JT interval) by performing ancestry-specific, trans-ethnic and cross-phenotype genome-wide analyses of European (66%), African American (15%) and Hispanic (19%) populations (N=78 199), leveraging longitudinal data, incorporating corrected standard errors to account for underestimation of interaction estimate variances and evaluating evidence for pathway enrichment. Although no loci achieved genome-wide significance (P<5 × 10−8), we found suggestive evidence (P<5 × 10−6) for SNPs modifying the thiazide-QT association at 22 loci, including ion transport loci (for example, NELL1, KCNQ3). The biologic plausibility of our suggestive results and simulations demonstrating modest power to detect interaction effects at genome-wide significant levels indicate that larger studies and innovative statistical methods are warranted in future efforts evaluating thiazide–SNP interactions.

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  1. 1.

    , , . Prescription drug use continues to increase: U.S. prescription drug data for 2007-2008. NCHS Data Brief 2010; 42: 1–8.

  2. 2.

    , , , , . Customizing dosage drugs what contribution in therapeutic drug monitoring? Ann Biol Clin 2014; 72: 15–24.

  3. 3.

    , , . Variability in response to cardiovascular drugs. Curr Clin Pharmacol 2006; 1: 35–46.

  4. 4.

    , . Sources of interindividual variability. Methods Mol Biol 2014; 1113: 363–415.

  5. 5.

    , , , , , . QT-interval duration and mortality rate: results from the Third National Health and Nutrition Examination Survey. Arch Intern Med 2011; 171: 1727–1733.

  6. 6.

    Arizona Center for Education and Research on Therapeutics. QTDrugs Lists. Available at . Accessed November 17, 2014.

  7. 7.

    , . Mayo Clinic Cardiology Concise Textbook and Mayo Clinic Cardiology Board Review Questions & Answers: (TEXT AND Q&A SET). Taylor & Francis: Boca Raton, FL, 2007.

  8. 8.

    . Drug-Induced prolongation of the QT interval. N Engl J Med 2004; 350: 1013–1022.

  9. 9.

    , , , . What clinicians should know about the QT interval. JAMA 2003; 289: 2120–2127.

  10. 10.

    , . Cardiac Electrophysiology: From Cell to Bedside 4th Edition ed. Elsevier Inc: Philadelphia, 2004.

  11. 11.

    , , , , , et al. The emerging era of pharmacogenomics: current successes, future potential, and challenges. Clin Genet 2014; 86: 21–28.

  12. 12.

    , , , . Genetic epidemiology with a capital E, ten years after. Genet Epidemiol 2011; 35: 845–852.

  13. 13.

    , . Pharmacogenomics update in pancreatic cancer. JOP 2014; 15: 114–117.

  14. 14.

    , , , , , et al. Clinical pharmacogenetics implementation: Approaches, successes, and challenges. Am J Med Genet 2014; 166: 56–67.

  15. 15.

    . Using pharmacogenetics in real time to guide therapy: the warfarin example. Clin Genet 2014; 85: 533–534.

  16. 16.

    , , , , , et al. Pathway analysis of genome-wide data improves warfarin dose prediction. BMC Genomics 2013; 14: S11.

  17. 17.

    , . Pharmacogenomic testing and the prospect of individualized treatment. N C Med J 2013; 74: 485–493.

  18. 18.

    , , , , , et al. Functional characterization of 32 CYP2C9 allelic variants. Pharmacogenomics J 2014; 14: 107–114.

  19. 19.

    , , , , , et al. Genetic variants associated with warfarin dose in African-American individuals: a genome-wide association study. Lancet 2013; 382: 790–796.

  20. 20.

    , , , , , et al. ITPA gene variants protect against anaemia in patients treated for chronic hepatitis C. Nature 2010; 464: 405–408.

  21. 21.

    , , , , . Potential role of pharmacogenomics in reducing adverse drug reactions: a systematic review. JAMA 2001; 286: 2270–2279.

  22. 22.

    , . Genetics and variable drug response. JAMA 2011; 306: 306–307.

  23. 23.

    , , , , , et al. Clinical correlates and heritability of QT interval duration in blacks: the Jackson Heart Study. Circ Arrhythm Electrophysiol 2009; 2: 427–432.

  24. 24.

    , , , , , et al. QT interval in twins. J Hum Hypertens 2000; 14: 389–390.

  25. 25.

    , , , , , et al. Genetic factors in the electrocardiogram and heart rate of twins reared apart and together. Am J Cardiol 1989; 63: 606–609.

  26. 26.

    , , , , , et al. Association of NOS1AP genetic variants with QT interval duration in families from the diabetes heart study. Diabetes 2008; 57: 1108–1114.

  27. 27.

    , , , , , et al. Heritabilities, proportions of heritabilities explained by GWAS findings, and implications of cross-phenotype effects on PR interval. Hum Genet 2015; 134: 1211–1219.

  28. 28.

    , , , . Trends in antihypertensive medication use and blood pressure control among United States adults with hypertension: the National Health And Nutrition Examination Survey, 2001 to 2010. Circulation 2012; 126: 2105–2114.

  29. 29.

    , , , , , et al. Association of chromosome 12 locus with antihypertensive response to hydrochlorothiazide may involve differential YEATS4 expression. Pharmacogenom J 2013; 13: 257–263.

  30. 30.

    , , , , , et al. [Effect of CYP11B2 gene -344T/C polymorphism on renin-angiotensin-aldosterone system activity and blood pressure response to hydrochlorothiazide]. Zhonghua yi xue yi chuan xue za zhi 2012; 29: 68–71.

  31. 31.

    , , , , , et al. Interaction of ACE and CYP11B2 genes on blood pressure response to hydrochlorothiazide in Han Chinese hypertensive patients. Clin Exp Hypertens 2011; 33: 141–146.

  32. 32.

    , , , , , et al. Association of variants in NEDD4L with blood pressure response and adverse cardiovascular outcomes in hypertensive patients treated with thiazide diuretics. J Hypertens 2013; 31: 698–704.

  33. 33.

    , , , , , et al. Genomic association analysis suggests chromosome 12 locus influencing antihypertensive response to thiazide diuretic. Hypertension 2008; 52: 359–365.

  34. 34.

    , , , , , et al. Genomic association analysis of common variants influencing antihypertensive response to hydrochlorothiazide. Hypertension 2013; 62: 391–397.

  35. 35.

    Centers for Disease Control Prevention. Vital signs: prevalence, treatment, and control of hypertension—United States, 1999-2002 and 2005-2008. MMWR 2011; 60: 103–108.

  36. 36.

    , , , , , et al. Heart disease and stroke statistics—2013 update: a report from the American Heart Association. Circulation 2013; 127: e6–e245.

  37. 37.

    , , , , , et al. Drug-gene interactions and the search for missing heritability: a cross-sectional pharmacogenomics study of the QT interval. Pharmacogenom J 2014; 14: 6–13.

  38. 38.

    , , , , , et al. Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium: design of prospective meta-analyses of genome-wide association studies from 5 cohorts. Circ Cardiovasc Genet 2009; 2: 73–80.

  39. 39.

    , , , , , et al. Generalized estimating equations for genome-wide association studies using longitudinal phenotype data. Stat Med 2014; 34: 118–130.

  40. 40.

    International HapMap Consortium. The International HapMap Project. Nature 2003; 426: 789–796.

  41. 41.

    International HapMap Consortium. A haplotype map of the human genome. Nature 2005; 437: 1299–1320.

  42. 42.

    International HapMap ConsortiumInternational HapMap Consortium International HapMap Consortium International HapMap Consortium International HapMap Consortium International HapMap Consortium et al. Integrating common and rare genetic variation in diverse human populations. Nature 2010; 467: 52–58.

  43. 43.

    The 1000 Genomes Project Consortium. A map of human genome variation from population-scale sequencing. Nature 2010; 467: 1061–1073.

  44. 44.

    The 1000 Genomes Project Consortium. An integrated map of genetic variation from 1,092 human genomes. Nature 2012; 491: 56–65.

  45. 45.

    , , , , , et al. The Human Genome Browser at UCSC. Genome Res 2002; 12: 996–1006.

  46. 46.

    UCSC Human Genome Browser Lift Genome Annotations. Available at .

  47. 47.

    . An approximate distribution of estimates of variance components. Biometrics 1946; 2: 110–114.

  48. 48.

    , , . METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 2010; 26: 2190–2191.

  49. 49.

    , , , , , et al. Pharmacogenomics, ancestry and clinical decision making for global populations. Pharmacogenom J 2013; 14: 217–222.

  50. 50.

    . Gene–environment-wide association studies: emerging approaches. Nat Rev Genet 2010; 11: 259–272.

  51. 51.

    . Transethnic meta-analysis of genomewide association studies. Genet Epidemiol 2011; 35: 809–822.

  52. 52.

    , , , , , et al. A multi-trait, meta-analysis for detecting pleiotropic polymorphisms for stature, fatness and reproduction in beef cattle. PLoS Genet 2014; 10: e1004198.

  53. 53.

    , , , , . GPA: a statistical approach to prioritizing GWAS results by integrating pleiotropy and annotation. PLoS Genet 2014; 10: e1004787.

  54. 54.

    , , . An adaptive association test for multiple phenotypes with GWAS summary statistics. Genet Epidemiol 2015; 39: 651–663.

  55. 55.

    , , , . Comparisons of seven algorithms for pathway analysis using the WTCCC Crohn's disease dataset. BMC Res Notes 2011; 4: 386.

  56. 56.

    The Network Pathway Analysis Subgroup of the Psychiatric Genomics Consortium. Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways. Nat Neurosci 2015; 18: 199–209.

  57. 57.

    , , . Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods. Nucleic Acids Res 2013; 41: 4378–4391.

  58. 58.

    , , , . MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput Biol 2015; 11: e1004219.

  59. 59.

    , , , . Causal analysis approaches in ingenuity pathway analysis. Bioinformatics 2014; 30: 523–530.

  60. 60.

    , . PANTHER pathway: an ontology-based pathway database coupled with data analysis tools. Methods Mol Biol 2009; 563: 123–140.

  61. 61.

    , , , , . KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res 2012; 40: D109–D114.

  62. 62.

    , , , , , . ConsensusPathDB: toward a more complete picture of cell biology. Nucleic acids Res 2011; 39: D712–D717.

  63. 63.

    , , , . ConsensusPathDB—a database for integrating human functional interaction networks. Nucleic Acids Res 2009; 37: D623–D628.

  64. 64.

    , , , , , et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat Commun 2015; 6: 5890.

  65. 65.

    , , , , , et al. Genetic association study of QT interval highlights role for calcium signaling pathways in myocardial repolarization. Nat Genet 2014; 46: 826–836.

  66. 66.

    , , , , . QT interval prolongation and the risk of torsades de pointes: essentials for clinicians. Curr Med Res Opin 2013; 29: 1719–1726.

  67. 67.

    , , , , , et al. Genome-wide association analyses suggest NELL1 influences adverse metabolic response to HCTZ in African Americans. Pharmacogenom J 2014; 14: 35–40.

  68. 68.

    , , , , , et al. Pitx2, an atrial fibrillation predisposition gene, directly regulates ion transport and intercalated disc genes. Circ Cardiovasc Genet 2014; 7: 23–32.

  69. 69.

    , , , , , . Stanniocalcin 2 is a negative modulator of store-operated calcium entry. Mol Cell Biol 2011; 31: 3710–3722.

  70. 70.

    , , , , , et al. Nuclear membrane R-type calcium channels mediate cytosolic ET-1-induced increase of nuclear calcium in human vascular smooth muscle cells. Can J Physiol Pharmacol 2015; 93: 291–297.

  71. 71.

    , . Trafficking mechanisms and regulation of TRPC channels. Cell Calcium 2014; 56: 43–50.

  72. 72.

    , , , , , et al. Missense mutations in plakophilin-2 cause sodium current deficit and associate with a Brugada syndrome phenotype. Circulation 2014; 129: 1092–1103.

  73. 73.

    , , , , , et al. Disrupted-in-schizophrenia-1 (DISC1) regulates endoplasmic reticulum calcium dynamics. Sci Rep 2015; 5: 8694.

  74. 74.

    . Gene-environment interactions in common mental disorders: an update and strategy for a genome-wide search. Soc Psychiatry Psychiatr Epidemiol 2014; 49: 3–14.

  75. 75.

    , , . Gene-environment and gene-treatment interactions in type 2 diabetes: progress, pitfalls, and prospects. Diabetes Care 2013; 36: 1413–1421.

  76. 76.

    , , , , , . A partition-based approach to identify gene-environment interactions in genome wide association studies. BMC Proc 2014; 8: S60.

  77. 77.

    , , , , , et al. Diuretic therapy for hypertension and the risk of primary cardiac arrest. N Engl J Med 1994; 330: 1852–1857.

  78. 78.

    , , , , . Correlates of QT prolongation in older adults (the Cardiovascular Health Study). Cardiovascular Health Study Collaborative Research Group. Am J Cardiol 1994; 73: 999–1002.

  79. 79.

    , , . Diuretics in the treatment of hypertension. Part 1: thiazide and thiazide-like diuretics. Exp Opin Pharmacother 2014; 15: 527–547.

  80. 80.

    , , , , , et al. Modulators of normal ECG intervals identified in a large electronic medical record. Heart Rhythm 2011; 8: 271–277.

  81. 81.

    , , , , . Selection bias in rheumatic disease research. Nat Rev Rheumatol 2014; 10: 403–412.

  82. 82.

    , Methodological Issues Relevant to Observational Studies, Registries, and Administrative Health Databases in RheumatologyUnderstanding Evidence-Based Rheumatology. Springer: New York, NY, pp 209–2282014.

  83. 83.

    . Gene-environment interactions in human diseases. Nat Rev Genet 2005; 6: 287–298.

  84. 84.

    , , . Selection bias in the assessment of gene-environment interaction in case-control studies. Am J Epidemiol 2003; 158: 259–263.

  85. 85.

    , , , , . The reliability of medication inventory methods compared to serum levels of cardiovascular drugs in the elderly. J Clin Epidemiol 1999; 52: 143–146.

  86. 86.

    , , , , , et al. Electronic medical records for genetic research: results of the eMERGE consortium. Sci Transl Med 2011; 3: 79re1.

  87. 87.

    , , , , , et al. The use of a DNA biobank linked to electronic medical records to characterize pharmacogenomic predictors of tacrolimus dose requirement in kidney transplant recipients. Pharmacogenet Genomics 2012; 22: 32–42.

  88. 88.

    , . A review of uses of health care utilization databases for epidemiologic research on therapeutics. J Clin Epidemiol 2005; 58: 323–337.

  89. 89.

    , , , , , et al. Validation of a population-based method to assess drug-induced alterations in the QT interval: a self-controlled crossover study. Pharmacoepidemiol Drug Safety 2013; 22: 1222–1232.

  90. 90.

    FDA. Guidance for Industry: E14 Clinical Evaluation of QT/QTc Interval Prolongation and Proarrhythmic Potential for Non-Antiarrhythmic Drugs. In: Services DoHaH, editor. 2005.

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Age, Gene/Environment Susceptibility—Reykjavik Study (AGES): This study has been funded by NIH contracts N01-AG-1-2100 and 271201200022C, the NIA Intramural Research Program, Hjartavernd (the Icelandic Heart Association), and the Althingi (the Icelandic Parliament). The study is approved by the Icelandic National Bioethics Committee, VSN: 00-063. The researchers are indebted to the participants for their willingness to participate in the study.

Atherosclerosis Risk in Communities (ARIC): The Atherosclerosis Risk in Communities 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), R01HL087641, R01HL59367 and R01HL086694; National Human Genome Research Institute Contract U01HG004402; and National Institutes of Health Contract HHSN268200625226C. We thank the staff and participants of the ARIC study for their important contributions. Infrastructure was partly supported by Grant no. UL1RR025005, a component of the National Institutes of Health and NIH Roadmap for Medical Research. AAS was supported by NHLBI Training grants T32HL7055 and T32HL07779.

Cardiovascular Health Study (CHS): This CHS research was supported by NHLBI contracts HHSN268201200036C, HHSN268200800007C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086; and NHLBI grants U01HL080295, R01HL087652, R01HL105756, R01HL103612, R01HL120393, HL130114, and R01HL085251with 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 UL1TR000124, 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. NS was supported by R01HL116747 and RO1HL111089.

Erasmus Rucphen Family Study (ERF): The ERF study, as a part of EUROSPAN (European Special Populations Research Network), was supported by European Commission FP6 STRP grant number 018947 (LSHG-CT-2006-01947) and also received funding from the European Community's Seventh Framework Programme (FP7/2007-2013)/grant agreement HEALTH-F4-2007-201413 by the European Commission under the programme ‘Quality of Life and Management of the Living Resources’ of 5th Framework Programme (no. QLG2-CT-2002-01254). The ERF study was further supported by ENGAGE consortium and CMSB. High-throughput analysis of the ERF data was supported by joint grant from Netherlands Organisation for Scientific Research and the Russian Foundation for Basic Research (NWO-RFBR 047.017.043). Exome sequencing in ERF was supported by the ZonMw grant (project 91111025). We are grateful to all study participants and their relatives, general practitioners and neurologists for their contributions to the ERF study and to P. Veraart for her help in genealogy, J. Vergeer for the supervision of the laboratory work and P. Snijders for his help in data collection.

Framingham Heart Study (FHS): FHS work was supported by the National Heart Lung and Blood Institute of the National Institutes of Health and Boston University School of Medicine (Contract No. N01-HC-25195 and Contract No. HHSN268201500001I), its contract with Affymetrix for genotyping services (Contract No. N02-HL-6-4278), based on analyses by FHS investigators participating in the SNP Health Association Resource (SHARe) project. A portion of this research was conducted using the Linux Cluster for Genetic Analysis (LinGA-II), funded by the Robert Dawson Evans Endowment of the Department of Medicine at Boston University School of Medicine and Boston Medical Center. Measurement of the Gen 3 ECGs was supported by grants from the Doris Duke Charitable Foundation and the Burroughs Wellcome Fund (Newton-Cheh) and the NIH (HL080025, Newton-Cheh).

Health 2000: Supported by the Orion-Farmos Research Foundation (KK and KP), the Finnish Foundation for Cardiovascular Research (KK, KP) and the Academy of Finland (Grant Nos. 129494 and 139635 to VS).

Health, Aging, and Body Composition (Health ABC): This research was supported by NIA Contracts N01AG62101, N01AG62103 and N01AG62106. The genome-wide association study was funded by NIA Grant 1R01AG032098-01A1 to Wake Forest University Health Sciences and genotyping services were provided by the Center for Inherited Disease Research (CIDR). CIDR is fully funded through a federal contract from the National Institutes of Health to The Johns Hopkins University, Contract No. HHSN268200782096C. This research was supported in part by the Intramural Research Program of the NIH, National Institute on Aging.

Hispanic Community Health Study/Study of Latinos (HCHS/SOL): We thank the participants and staff of the HCHS/SOL study for their contributions to this study. The baseline examination of HCHS/SOL was carried out as a collaborative study supported by contracts from the National Heart, Lung, and Blood Institute (NHLBI) to the University of North Carolina (N01-HC65233), University of Miami (N01-HC65234), Albert Einstein College of Medicine (N01-HC65235), Northwestern University (N01-HC65236), and San Diego State University (N01-HC65237). The following Institutes/Centers/Offices contributed to the first phase of HCHS/SOL through a transfer of funds to the NHLBI: National Institute on Minority Health and Health Disparities, National Institute on Deafness and Other Communication Disorders, National Institute of Dental and Craniofacial Research (NIDCR), National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Neurological Disorders and Stroke, NIH Institution-Office of Dietary Supplements. The Genetic Analysis Center at University of Washington was supported by NHLBI and NIDCR contracts (HHSN268201300005C AM03 and MOD03). Genotyping efforts were supported by NHLBI HSN 26220/20054C, NCATS CTSI grant UL1TR000124, and NIDDK Diabetes Research Center (DRC) grant DK063491.

Jackson Heart Study (JHS): We thank the Jackson Heart Study (JHS) participants and staff for their contributions to this work. The JHS is supported by contracts HHSN268201300046C, HHSN268201300047C, HSN268201300048C, HHSN268201300049C, HHSN268201300050C from the National Heart, Lung, and Blood Institute and the National Institute on Minority Health and Health Disparities.

Multi-Ethnic Study of Atherosclerosis (MESA): MESA and MESA SNP Health Association Resource (SHARe) are conducted and supported by the National Heart, Lung and Blood Institute (NHLBI) in collaboration with MESA investigators. Support is provided by grants and contracts N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169 and RR-024156. Additional funding was supported in part by the Clinical Translational Science Institute grant UL1RR033176 and is now at the National Center for Advancing Translational Sciences, CTSI grant UL1TR000124. We also thank the other investigators, the staff and the participants of the MESA study for their valuable contributions. A full list of participating MESA investigators and institutions can be found at http://www.mesa-nhlbi.org.

The Netherlands Epidemiology of Obesity (NEO): The authors of the NEO study thank all individuals who participated in the Netherlands Epidemiology in Obesity study, all participating general practitioners for inviting eligible participants and all research nurses for collection of the data. We thank the NEO study group, Pat van Beelen, Petra Noordijk and Ingeborg de Jonge for the coordination, lab and data management of the NEO study. The genotyping in the NEO study was supported by the Centre National de Génotypage (Paris, France), headed by Jean-Francois Deleuze. The NEO study is supported by the participating Departments, the Division and the Board of Directors of the Leiden University Medical Center, and by the Leiden University, Research Profile Area Vascular and Regenerative Medicine. Dennis Mook-Kanamori is supported by Dutch Science Organization (ZonMW-VENI Grant 916.14.023).

Prospective Study of Pravastatin in the Elderly at Risk (PROSPER): The PROSPER study was supported by an investigator initiated grant obtained from Bristol-Myers Squibb. Professor Dr J W Jukema is an Established Clinical Investigator of the Netherlands Heart Foundation (Grant no. 2001 D 032). Support for genotyping was provided by the seventh framework program of the European commission (Grant no. 223004) and by the Netherlands Genomics Initiative (Netherlands Consortium for Healthy Aging Grant 050-060-810).

Rotterdam Study (RS): The RS is supported by the Erasmus Medical Center and Erasmus University Rotterdam; The Netherlands Organization for Scientific Research; The Netherlands Organization for Health Research and Development (ZonMw); the Research Institute for Diseases in the Elderly; The Netherlands Heart Foundation; the Ministry of Education, Culture and Science; the Ministry of Health Welfare and Sports; the European Commission; and the Municipality of Rotterdam. Support for genotyping was provided by The Netherlands Organization for Scientific Research (NWO) (175.010.2005.011, 911.03.012) and Research Institute for Diseases in the Elderly (RIDE). This study was supported by The Netherlands Genomics Initiative (NGI)/Netherlands Organization for Scientific Research (NWO) Project No. 050-060-810. This collaborative effort was supported by an award from the National Heart, Lung and Blood Institute (R01-HL-103612, PI BMP). CLA was supported in part by Grant R00-HL-098458 from the National Heart, Lung, and Blood Institute.

Women’s Health Initiative Clinical Trial (WHI CT): The Women’s Health Initiative clinical trials were funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services through contracts HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, and HHSN271201100004C. All contributors to WHI science are listed at https://www.whi.org/researchers/Documents%20%20Write%20a%20Paper/WHI%20Investigator%20Long%20List.pdf. ELB was supported in part by a grant from the National Cancer Institute (5T32CA009001). WHI GARNET: Within the Genomics and Randomized Trials Network, a GWAS of Hormone Treatment and CVD and Metabolic Outcomes in the WHI was funded by the National Human Genome Research Institute, National Institutes of Health, U.S. Department of Health and Human Services through cooperative agreement U01HG005152 (Reiner). All contributors to GARNET science are listed at https://www.garnetstudy.org/Home. WHI MOPMAP: The Modification of PM-Mediated Arrhythmogenesis in Populations was funded by the National Institute of Environmental Health Sciences, National Institutes of Health, U.S. Department of Health and Human Services through grant R01ES017794 (Whitsel). WHI SHARe: The SNP Health Association Resource project was funded by the National Heart, Lung and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services through contract N02HL64278 (Kooperberg). WHI WHIMS: The Women's Health Initiative Memory Study (WHIMS+) Genome-Wide Association Study was funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services through contract HHSN268201100046C (Anderson).

Author information


  1. Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA

    • A A Seyerle
    • , H M Highland
    • , J D Stewart
    • , R Méndez-Giráldez
    • , M D Napier
    • , K E North
    • , T Stürmer
    • , E A Whitsel
    •  & C L Avery
  2. Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA

    • A A Seyerle
  3. Department of Medicine, University of Washington, Seattle, WA, USA

    • C M Sitlani
    • , J C Bis
    • , J A Brody
    • , J S Floyd
    • , T Sofer
    • , K L Wiggins
    •  & B M Psaty
  4. Department of Epidemiology, Erasmus MC—University Medical Center Rotterdam, Rotterdam, The Netherlands

    • R Noordam
    • , A Isaacs
    • , A Hofman
    • , C van Duijn
    •  & B H Stricker
  5. Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands

    • R Noordam
    •  & S Trompet
  6. Department of Biostatistics, University of Washington, Seattle, WA, USA

    • S M Gogarten
    • , A M Stilp
    • , K F Kerr
    • , K M Rice
    •  & C C Laurie
  7. Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Palo Alto, CA, USA

    • J Li
  8. Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA

    • X Li
    • , H J Lin
    • , K D Taylor
    • , Y-DI Chen
    •  & J I Rotter
  9. California Pacific Medical Center Research Institute, San Francisco, CA, USA

    • D S Evans
    •  & S R Cummings
  10. Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA

    • F Sun
    • , G M Peloso
    •  & L A Cupples
  11. Department of Health, THL-National Institute for Health and Welfare, Helsinki, Finland

    • M A Laaksonen
    • , K Kristiansson
    •  & V Salomaa
  12. CARIM School of Cardiovascular Diseases, Maastricht Centre for Systems Biology (MaCSBio), and Department of Biochemistry, Maastricht University, Maastricht, The Netherlands

    • A Isaacs
  13. Carolina Population Center, University of North Carolina, Chapel Hill, NC, USA

    • J D Stewart
    •  & C L Avery
  14. Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, Bethesda, MD, USA

    • T B Harris
    •  & L J Launer
  15. Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands

    • S Trompet
    •  & J W Jukema
  16. Department of Internal Medicine, Erasmus MC-University Medical Center Rotterdam, Rotterdam, The Netherlands

    • L Broer
    •  & A G Uitterlinden
  17. Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    • E L Busch
  18. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    • E L Busch
  19. Department of Genetics, University of North Carolina, Chapel Hill, NC, USA

    • Q Duan
    • , Y Li
    • , K Wilhelmsen
    •  & L A Lange
  20. Department of Medicine, Harvard University, Boston, MA, USA

    • C J O'Donnell
  21. National Heart, Lung, and Blood Institute Framingham Heart Study, Framingham, MA, USA

    • C J O'Donnell
    • , L A Cupples
    •  & R S Vasan
  22. Cardiology Section, Boston Veterans Administration Healthcare, Boston, MA, USA

    • C J O'Donnell
  23. Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK

    • P W Macfarlane
    •  & C Newton-Cheh
  24. Department of Epidemiology, University of Washington, Seattle, WA, USA

    • J S Floyd
    • , S R Heckbert
    • , A P Reiner
    • , N Sotoodehnia
    •  & B M Psaty
  25. Department of Medical Informatics, Erasmus MC-University Medical Center Rotterdam, Rotterdam, The Netherlands

    • J A Kors
  26. Division of Medical Genetics, Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, USA

    • H J Lin
  27. Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands

    • R Li-Gao
    • , R de Mutsert
    • , F R Rosendaal
    •  & D O Mook-Kanamori
  28. Robertson Center for Biostatistics, University of Glasgow, Glasgow, UK

    • I Ford
  29. Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA

    • Y Li
  30. Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA

    • Y Li
  31. Division of Cardiology, Heart and Lung Center, Helsinki University Central Hospital, Helsinki, Finland

    • K Porthan
  32. Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA

    • C Newton-Cheh
  33. Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA

    • C Newton-Cheh
  34. Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA

    • C Newton-Cheh
  35. Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    • A P Reiner
  36. Research Computing Center, University of North Carolina, Chapel Hill, NC, USA

    • J Roach
  37. Department of Pharmacology and Therapeutics, University College Cork, Cork, Ireland

    • B M Buckley
  38. Epidemiology Cardiology Research Center (EPICARE), Wake Forest School of Medicine, Winston-Salem, NC, USA

    • E Z Soliman
  39. Division of Cardiology, University of Washington, Seattle, WA, USA

    • N Sotoodehnia
  40. Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA

    • N Sotoodehnia
  41. Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA

    • C R Lee
  42. Icelandic Heart Association, Kopavogur, Iceland

    • V Gudnason
    •  & A V Smith
  43. Department of Medicine, University of Iceland, Reykjavik, Iceland

    • V Gudnason
    •  & A V Smith
  44. Center for Pharmacoepidemiology, University of North Carolina, Chapel Hill, NC, USA

    • T Stürmer
  45. Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, USA

    • J G Wilson
  46. Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA

    • R C Kaplan
  47. The Renaissance Computing Institute, Chapel Hill, NC, USA

    • K Wilhelmsen
  48. Durrer Center for Cardiogenetic Research, Amsterdam, The Netherlands

    • J W Jukema
  49. Interuniversity Cardiology Institute of the Netherlands, Utrecht, The Netherlands

    • J W Jukema
  50. Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University, Winston-Salem, NC, USA

    • Y Liu
  51. Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, the Netherlands

    • D O Mook-Kanamori
  52. Department of BESC, Epidemiology Section, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia

    • D O Mook-Kanamori
  53. Division of Preventive Medicine and Epidemiology, Department of Epidemiology, Boston University School of Medicine, Boston, MA, USA

    • R S Vasan
  54. Inspectorate of Health Care, Utrecht, The Netherlands

    • B H Stricker
  55. Department of Medicine, University of North Carolina, Chapel Hill, NC, USA

    • E A Whitsel
  56. Department of Health Services, University of Washington, Seattle, WA, USA

    • B M Psaty
  57. Group Health Research Institute, Group Health Cooperative, Seattle, WA, USA

    • B M Psaty


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Competing interests

The authors declare no conflict of interest.

Corresponding author

Correspondence to A A Seyerle.

Supplementary information


The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services.

Supplementary Information accompanies the paper on the The Pharmacogenomics Journal website (http://www.nature.com/tpj)