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Novel genetic associations for blood pressure identified via gene-alcohol interaction in up to 570K individuals across multiple ancestries

  • Mary F. Feitosa ,

    Contributed equally to this work with: Mary F. Feitosa, Aldi T. Kraja, Daniel I. Chasman, Yun J. Sung, Thomas W. Winkler, Ioanna Ntalla

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Supervision, Validation, Writing – original draft, Writing – review & editing

    mfeitosa@wustl.edu (MFF); levyD@nih.gov (DL)

    Affiliation Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America

  • Aldi T. Kraja ,

    Contributed equally to this work with: Mary F. Feitosa, Aldi T. Kraja, Daniel I. Chasman, Yun J. Sung, Thomas W. Winkler, Ioanna Ntalla

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Supervision, Validation, Writing – original draft, Writing – review & editing

    Affiliation Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America

  • Daniel I. Chasman ,

    Contributed equally to this work with: Mary F. Feitosa, Aldi T. Kraja, Daniel I. Chasman, Yun J. Sung, Thomas W. Winkler, Ioanna Ntalla

    Roles Data curation, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – review & editing

    Affiliations Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America, Harvard Medical School, Boston, Massachusetts, United States of America

  • Yun J. Sung ,

    Contributed equally to this work with: Mary F. Feitosa, Aldi T. Kraja, Daniel I. Chasman, Yun J. Sung, Thomas W. Winkler, Ioanna Ntalla

    Roles Conceptualization, Methodology, Writing – original draft, Writing – review & editing

    Affiliation Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, United States of America

  • Thomas W. Winkler ,

    Contributed equally to this work with: Mary F. Feitosa, Aldi T. Kraja, Daniel I. Chasman, Yun J. Sung, Thomas W. Winkler, Ioanna Ntalla

    Roles Methodology, Writing – original draft, Writing – review & editing

    Affiliation Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany

  • Ioanna Ntalla ,

    Contributed equally to this work with: Mary F. Feitosa, Aldi T. Kraja, Daniel I. Chasman, Yun J. Sung, Thomas W. Winkler, Ioanna Ntalla

    Roles Data curation, Formal analysis, Validation, Writing – review & editing

    Affiliation Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom

  • Xiuqing Guo,

    Roles Data curation, Formal analysis, Investigation, Supervision, Writing – original draft, Writing – review & editing

    Affiliation Genomic Outcomes, Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, California, United States of America

  • Nora Franceschini,

    Roles Formal analysis, Writing – review & editing

    Affiliation Epidemiology, University of North Carolina Gilling School of Global Public Health, Chapel Hill, North Carolina, United States of America

  • Ching-Yu Cheng,

    Roles Funding acquisition, Supervision

    Affiliations Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore, Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore, Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

  • Xueling Sim,

    Roles Data curation, Formal analysis, Supervision

    Affiliation Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, Singapore

  • Dina Vojinovic,

    Roles Formal analysis, Writing – review & editing

    Affiliation Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands

  • Jonathan Marten,

    Roles Data curation

    Affiliation Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom

  • Solomon K. Musani,

    Roles Data curation, Formal analysis, Investigation, Project administration, Resources, Supervision, Visualization

    Affiliation Jackson Heart Study, Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America

  • Changwei Li,

    Roles Formal analysis

    Affiliation Epidemiology and Biostatistics, University of Georgia at Athens College of Public Health, Athens, Georgia, United States of America

  • Amy R. Bentley,

    Roles Conceptualization, Formal analysis, Investigation, Resources, Writing – review & editing

    Affiliation Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America

  • Michael R. Brown,

    Roles Data curation, Formal analysis, Funding acquisition, Software

    Affiliation Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America

  • Karen Schwander,

    Roles Formal analysis, Writing – review & editing

    Affiliation Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, United States of America

  • Melissa A. Richard,

    Roles Formal analysis, Writing – review & editing

    Affiliation Brown Foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America

  • Raymond Noordam,

    Roles Formal analysis, Writing – review & editing

    Affiliation Internal Medicine, Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands

  • Hugues Aschard,

    Roles Conceptualization, Methodology, Writing – review & editing

    Affiliations Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America, Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur, Paris, France

  • Traci M. Bartz,

    Roles Formal analysis, Writing – review & editing

    Affiliation Cardiovascular Health Research Unit, Biostatistics and Medicine, University of Washington, Seattle, Washington, United States of America

  • Lawrence F. Bielak,

    Roles Data curation, Formal analysis, Software, Writing – review & editing

    Affiliation Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America

  • Rajkumar Dorajoo,

    Roles Data curation, Formal analysis

    Affiliation Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore

  • Virginia Fisher,

    Roles Formal analysis

    Affiliation Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America

  • Fernando P. Hartwig,

    Roles Data curation, Formal analysis, Investigation, Writing – review & editing

    Affiliations Postgraduate Programme in Epidemiology, Federal University of Pelotas, Pelotas, RS, Brazil, Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom

  • Andrea R. V. R. Horimoto,

    Roles Formal analysis, Investigation

    Affiliation Laboratory of Genetics and Molecular Cardiology, Heart Institute (InCor), University of São Paulo Medical School, São Paulo, SP, Brazil

  • Kurt K. Lohman,

    Roles Data curation, Formal analysis, Investigation, Writing – review & editing

    Affiliation Biostatistical Sciences, Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America

  • Alisa K. Manning,

    Roles Writing – review & editing

    Affiliations Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, Massachusetts, United States of America, Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America

  • Tuomo Rankinen,

    Roles Data curation, Formal analysis, Funding acquisition, Investigation, Project administration, Resources, Supervision, Writing – review & editing

    Affiliation Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, Louisiana, United States of America

  • Albert V. Smith,

    Roles Data curation, Formal analysis, Investigation, Validation

    Affiliations Icelandic Heart Association, Kopavogur, Iceland, Faculty of Medicine, University of Iceland, Reykjavik, Iceland

  • Salman M. Tajuddin,

    Roles Data curation, Formal analysis

    Affiliation Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States of America

  • Mary K. Wojczynski,

    Roles Data curation, Investigation, Supervision, Writing – review & editing

    Affiliation Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America

  • Maris Alver,

    Roles Data curation, Formal analysis, Investigation

    Affiliation Estonian Genome Center, University of Tartu, Tartu, Estonia

  • Mathilde Boissel,

    Roles Data curation, Formal analysis

    Affiliation CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, University of Lille, Lille, France

  • Qiuyin Cai,

    Roles Data curation, Investigation, Validation

    Affiliation Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America

  • Archie Campbell,

    Roles Data curation

    Affiliation Centre for Genomic & Experimental Medicine, Institute of Genetics & Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom

  • Jin Fang Chai,

    Roles Data curation, Formal analysis

    Affiliation Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, Singapore

  • Xu Chen,

    Roles Formal analysis

    Affiliation Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Stockholm, Sweden

  • Jasmin Divers,

    Roles Formal analysis, Writing – review & editing

    Affiliation Biostatistical Sciences, Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America

  • Chuan Gao,

    Roles Formal analysis, Writing – review & editing

    Affiliation Molecular Genetics and Genomics Program, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America

  • Anuj Goel,

    Roles Formal analysis, Project administration, Resources, Writing – review & editing

    Affiliations Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, Oxfordshire, United Kingdom, Wellcome Centre for Human Genetics, University of Oxford, Oxford, Oxfordshire, United Kingdom

  • Yanick Hagemeijer,

    Roles Data curation, Investigation

    Affiliation Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

  • Sarah E. Harris,

    Roles Data curation, Formal analysis, Investigation, Project administration, Writing – review & editing

    Affiliations Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, United Kingdom, Medical Genetics Section, Centre for Genomic and Experimental Medicine and MRC Institute of Genetics and Molecular Medicine, The University of Edinburgh, Edinburgh, United Kingdom

  • Meian He,

    Roles Formal analysis, Funding acquisition, Validation, Writing – review & editing

    Affiliation Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health for Incubating, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

  • Fang-Chi Hsu,

    Roles Formal analysis, Writing – review & editing

    Affiliation Biostatistical Sciences, Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America

  • Anne U. Jackson,

    Roles Formal analysis

    Affiliation Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, United States of America

  • Mika Kähönen,

    Roles Investigation, Resources, Writing – review & editing

    Affiliations Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland, University of Tampere, Tampere, Finland

  • Anuradhani Kasturiratne,

    Roles Investigation, Project administration, Resources, Supervision, Validation, Visualization, Writing – review & editing

    Affiliation Department of Public Health, Faculty of Medicine, University of Kelaniya, Ragama, Sri Lanka

  • Pirjo Komulainen,

    Roles Investigation, Project administration, Resources

    Affiliation Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland

  • Brigitte Kühnel,

    Roles Formal analysis, Writing – review & editing

    Affiliations Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany, Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany

  • Federica Laguzzi,

    Roles Data curation, Formal analysis, Software, Validation, Writing – review & editing

    Affiliation Unit of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden

  • Jian'an Luan,

    Roles Formal analysis

    Affiliation MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom

  • Nana Matoba,

    Roles Validation

    Affiliation Laboratory for Statistical Analysis, Center for Integrative Medical Sciences, RIKEN, Yokohama, Japan

  • Ilja M. Nolte,

    Roles Formal analysis, Writing – review & editing

    Affiliation Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

  • Sandosh Padmanabhan,

    Roles Data curation

    Affiliation Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom

  • Muhammad Riaz,

    Roles Formal analysis, Writing – review & editing

    Affiliations Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom, NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom

  • Rico Rueedi,

    Roles Data curation, Formal analysis, Software

    Affiliations Department of Computational Biology, University of Lausanne, Lausanne, Switzerland, Swiss Instititute of Bioinformatics, Lausanne, Switzerland

  • Antonietta Robino,

    Roles Formal analysis

    Affiliation Institute for Maternal and Child Health—IRCCS "Burlo Garofolo", Trieste, Italy

  • M. Abdullah Said,

    Roles Data curation, Investigation

    Affiliation Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

  • Robert A. Scott,

    Roles Formal analysis

    Affiliation MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom

  • Tamar Sofer,

    Roles Methodology, Supervision, Writing – review & editing

    Affiliations Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America, Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, United States of America

  • Alena Stančáková,

    Roles Data curation, Investigation, Writing – review & editing

    Affiliation Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland

  • Fumihiko Takeuchi,

    Roles Formal analysis

    Affiliation Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan

  • Bamidele O. Tayo,

    Roles Data curation, Formal analysis, Supervision, Writing – review & editing

    Affiliation Department of Public Health Sciences, Loyola University Chicago, Maywood, Illinois, United States of America

  • Peter J. van der Most,

    Roles Formal analysis, Writing – review & editing

    Affiliation Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

  • Tibor V. Varga,

    Roles Formal analysis, Writing – review & editing

    Affiliation Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Skåne University Hospital, Malmö, Sweden

  • Veronique Vitart,

    Roles Data curation

    Affiliation Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom

  • Yajuan Wang,

    Roles Data curation, Formal analysis, Investigation, Writing – review & editing

    Affiliation Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States of America

  • Erin B. Ware,

    Roles Data curation, Formal analysis, Project administration, Validation, Writing – review & editing

    Affiliation Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, Michigan, United States of America

  • Helen R. Warren,

    Roles Data curation, Formal analysis, Validation

    Affiliations Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom, NIHR Barts Cardiovascular Biomedical Research Unit, Queen Mary University of London, London, London, United Kingdom

  • Stefan Weiss,

    Roles Formal analysis

    Affiliations Interfaculty Institute for Genetics and Functional genomics, University Medicine Ernst Moritz Arndt University Greifsald, Greifswald, Germany, DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany

  • Wanqing Wen,

    Roles Formal analysis

    Affiliation Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America

  • Lisa R. Yanek,

    Roles Data curation, Formal analysis, Project administration

    Affiliation Division of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America

  • Weihua Zhang,

    Roles Formal analysis, Software

    Affiliations Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom, Department of Cardiology, Ealing Hospital, Middlesex, United Kingdom

  • Jing Hua Zhao,

    Roles Formal analysis

    Affiliation MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom

  • Saima Afaq,

    Roles Formal analysis

    Affiliation Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom

  • Najaf Amin,

    Roles Resources, Writing – review & editing

    Affiliation Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands

  • Marzyeh Amini,

    Roles Resources, Writing – review & editing

    Affiliation Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

  • Dan E. Arking,

    Roles Data curation, Formal analysis

    Affiliation McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America

  • Tin Aung,

    Roles Funding acquisition

    Affiliations Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore, Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore, Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

  • Eric Boerwinkle,

    Roles Conceptualization, Funding acquisition, Resources

    Affiliations Department of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas School of Public Health, Houston, Texas, United States of America, Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, United States of America

  • Ingrid Borecki,

    Roles Conceptualization, Writing – review & editing

    Affiliation Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America

  • Ulrich Broeckel,

    Roles Writing – review & editing

    Affiliation Section of Genomic Pediatrics, Department of Pediatrics, Medicine and Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America

  • Morris Brown,

    Roles Funding acquisition, Investigation

    Affiliations Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom, NIHR Barts Cardiovascular Biomedical Research Unit, Queen Mary University of London, London, London, United Kingdom

  • Marco Brumat,

    Roles Formal analysis

    Affiliation Department of Medical Sciences, University of Trieste, Trieste, Italy

  • Gregory L. Burke,

    Roles Funding acquisition, Investigation, Project administration

    Affiliation Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America

  • Mickaël Canouil,

    Roles Data curation, Formal analysis, Supervision

    Affiliation CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, University of Lille, Lille, France

  • Aravinda Chakravarti,

    Roles Data curation, Funding acquisition, Project administration, Resources, Writing – review & editing

    Affiliation McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America

  • Sabanayagam Charumathi,

    Roles Funding acquisition

    Affiliations Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore, Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore

  • Yii-Der Ida Chen,

    Roles Data curation, Investigation, Supervision, Writing – review & editing

    Affiliation Genomic Outcomes, Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, California, United States of America

  • John M. Connell,

    Roles Project administration, Resources

    Affiliation Ninewells Hospital & Medical School, University of Dundee, Dundee, Scotland, United Kingdom

  • Adolfo Correa,

    Roles Data curation, Funding acquisition, Investigation, Methodology, Project administration, Resources, Writing – review & editing

    Affiliation Jackson Heart Study, Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America

  • Lisa de las Fuentes,

    Roles Formal analysis, Writing – review & editing

    Affiliations Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, United States of America, Cardiovascular Division, Department of Medicine, Washington University, St. Louis, Missouri, United States of America

  • Renée de Mutsert,

    Roles Data curation, Project administration, Writing – review & editing

    Affiliation Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands

  • H. Janaka de Silva,

    Roles Conceptualization, Data curation, Funding acquisition, Investigation, Project administration, Resources, Supervision, Validation

    Affiliation Department of Medicine, Faculty of Medicine, University of Kelaniya, Ragama, Sri Lanka

  • Xuan Deng,

    Roles Formal analysis

    Affiliation Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America

  • Jingzhong Ding,

    Roles Investigation, Writing – review & editing

    Affiliation Center on Diabetes, Obesity, and Metabolism, Gerontology and Geriatric Medicine, Wake Forest University Health Sciences, Winston-Salem, North Carolina, United States of America

  • Qing Duan,

    Roles Formal analysis

    Affiliation Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America

  • Charles B. Eaton,

    Roles Investigation, Resources, Writing – review & editing

    Affiliation Department of Family Medicine and Epidemiology, Alpert Medical School of Brown University, Providence, Rhode Island, United States of America

  • Georg Ehret,

    Roles Data curation, Funding acquisition

    Affiliation Cardiology, Geneva University Hospital, Geneva, Switzerland

  • Ruben N. Eppinga,

    Roles Data curation, Investigation

    Affiliation Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

  • Evangelos Evangelou,

    Roles Data curation, Formal analysis

    Affiliations Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom, Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece

  • Jessica D. Faul,

    Roles Data curation, Formal analysis, Project administration

    Affiliation Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, Michigan, United States of America

  • Stephan B. Felix,

    Roles Writing – review & editing

    Affiliations DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany, Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany

  • Nita G. Forouhi,

    Roles Funding acquisition, Investigation, Project administration, Resources, Writing – review & editing

    Affiliation MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom

  • Terrence Forrester,

    Roles Investigation

    Affiliation The Caribbean Institute for Health Research (CAIHR), University of the West Indies, Mona, Jamaica

  • Oscar H. Franco,

    Roles Investigation, Writing – review & editing

    Affiliation Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands

  • Yechiel Friedlander,

    Roles Data curation, Funding acquisition, Investigation, Project administration, Supervision, Writing – review & editing

    Affiliation Braun School of Public Health, Hebrew University-Hadassah Medical Center, Jerusalem, Israel

  • Ilaria Gandin,

    Roles Formal analysis

    Affiliation Department of Medical Sciences, University of Trieste, Trieste, Italy

  • He Gao,

    Roles Formal analysis

    Affiliation Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom

  • Mohsen Ghanbari,

    Roles Investigation, Writing – review & editing

    Affiliations Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands, Department of Genetics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran

  • Bruna Gigante,

    Roles Resources, Writing – review & editing

    Affiliation Unit of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden

  • C. Charles Gu,

    Roles Formal analysis, Writing – review & editing

    Affiliation Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, United States of America

  • Dongfeng Gu,

    Roles Data curation

    Affiliation Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

  • Saskia P. Hagenaars,

    Roles Formal analysis, Writing – review & editing

    Affiliations Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, United Kingdom, Psychology, The University of Edinburgh, Edinburgh, United Kingdom

  • Göran Hallmans,

    Roles Resources

    Affiliation Department of Public Health and Clinical Medicine, Nutritional Research, Umeå University, Umeå, Västerbotten, Sweden

  • Tamara B. Harris,

    Roles Conceptualization, Data curation, Project administration

    Affiliation Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, United States of America

  • Jiang He,

    Roles Data curation, Funding acquisition

    Affiliations Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America, Medicine, Tulane University School of Medicine, New Orleans, Louisiana, United States of America

  • Sami Heikkinen,

    Roles Data curation, Formal analysis

    Affiliations Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland, Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio Campus, Finland

  • Chew-Kiat Heng,

    Roles Funding acquisition

    Affiliations Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Khoo Teck Puat–National University Children's Medical Institute, National University Health System, Singapore

  • Makoto Hirata,

    Roles Validation

    Affiliation Laboratory of Genome Technology, Human Genome Center, Institute of Medical Science, The University of Tokyo, Minato-ku, Japan

  • Barbara V. Howard,

    Roles Conceptualization, Data curation, Funding acquisition, Project administration

    Affiliations MedStar Health Research Institute, Hyattsville, Maryland, United States of America, Center for Clinical and Translational Sciences and Department of Medicine, Georgetown-Howard Universities, Washington, DC, United States of America

  • M. Arfan Ikram,

    Roles Data curation, Writing – review & editing

    Affiliations Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands, Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands, Department of Neurology, Erasmus University Medical Center, Rotterdam, The Netherlands

  • InterAct Consortium,

    Roles Project administration, Validation

    Affiliation MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom

  • Ulrich John,

    Roles Funding acquisition, Project administration, Supervision

    Affiliations DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany, Institute of Social Medicine and Prevention, University Medicine Greifswald, Greifswald, Germany

  • Tomohiro Katsuya,

    Roles Data curation, Investigation, Resources

    Affiliations Department of Clinical Gene Therapy, Osaka University Graduate School of Medicine, Suita, Japan, Department of Geriatric Medicine and Nephrology, Osaka University Graduate School of Medicine, Suita, Japan

  • Chiea Chuen Khor,

    Roles Resources

    Affiliations Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore, Department of Biochemistry, National University of Singapore, Singapore, Singapore

  • Tuomas O. Kilpeläinen,

    Roles Conceptualization, Data curation, Methodology, Writing – review & editing

    Affiliations Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark, Department of Environmental Medicine and Public Health, The Icahn School of Medicine at Mount Sinai, New York, New York, United States of America

  • Woon-Puay Koh,

    Roles Data curation, Funding acquisition, Investigation, Project administration, Writing – review & editing

    Affiliations Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, Singapore, Duke-NUS Medical School, Singapore, Singapore

  • José E. Krieger,

    Roles Funding acquisition, Investigation, Project administration

    Affiliation Laboratory of Genetics and Molecular Cardiology, Heart Institute (InCor), University of São Paulo Medical School, São Paulo, SP, Brazil

  • Stephen B. Kritchevsky,

    Roles Data curation, Funding acquisition, Resources, Writing – review & editing

    Affiliation Sticht Center for Healthy Aging and Alzheimer's Prevention, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America

  • Michiaki Kubo,

    Roles Validation

    Affiliation Center for Integrative Medical Sciences, RIKEN, Yokohama, Japan

  • Johanna Kuusisto,

    Roles Data curation, Investigation, Writing – review & editing

    Affiliation Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland

  • Timo A. Lakka,

    Roles Writing – review & editing

    Affiliations Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland, Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio Campus, Finland, Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland

  • Carl D. Langefeld,

    Roles Formal analysis, Writing – review & editing

    Affiliation Biostatistical Sciences, Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America

  • Claudia Langenberg,

    Roles Funding acquisition

    Affiliation MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom

  • Lenore J. Launer,

    Roles Resources, Writing – review & editing

    Affiliation Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, United States of America

  • Benjamin Lehne,

    Roles Formal analysis

    Affiliation Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom

  • Cora E. Lewis,

    Roles Funding acquisition, Investigation, Project administration

    Affiliation Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States of America

  • Yize Li,

    Roles Formal analysis

    Affiliation Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, United States of America

  • Shiow Lin,

    Roles Data curation, Formal analysis, Validation, Writing – review & editing

    Affiliation Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America

  • Jianjun Liu,

    Roles Resources

    Affiliations Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, Singapore, Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore

  • Jingmin Liu,

    Roles Formal analysis

    Affiliation WHI CCC, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America

  • Marie Loh,

    Roles Formal analysis

    Affiliations Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom, Translational Laboratory in Genetic Medicine, Agency for Science, Technology and Research, Singapore

  • Tin Louie,

    Roles Formal analysis

    Affiliation Department of Biostatistics, University of Washington, Seattle, Washington, United States of America

  • Reedik Mägi,

    Roles Data curation, Formal analysis, Investigation, Supervision

    Affiliation Estonian Genome Center, University of Tartu, Tartu, Estonia

  • Colin A. McKenzie,

    Roles Writing – review & editing

    Affiliation The Caribbean Institute for Health Research (CAIHR), University of the West Indies, Mona, Jamaica

  • Thomas Meitinger,

    Roles Writing – review & editing

    Affiliations Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany, Institute of Human Genetics, Technische Universität München, Munich, Germany

  • Andres Metspalu,

    Roles Funding acquisition, Project administration, Resources, Supervision

    Affiliation Estonian Genome Center, University of Tartu, Tartu, Estonia

  • Yuri Milaneschi,

    Roles Data curation, Formal analysis

    Affiliation Department of Psychiatry, Amsterdam Neuroscience and Amsterdam Public Health Research Institute, VU University Medical Center, Amsterdam, The Netherlands

  • Lili Milani,

    Roles Data curation, Investigation, Resources

    Affiliation Estonian Genome Center, University of Tartu, Tartu, Estonia

  • Karen L. Mohlke,

    Roles Data curation, Funding acquisition, Writing – review & editing

    Affiliation Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America

  • Yukihide Momozawa,

    Roles Investigation

    Affiliation Laboratory for Genotyping Development, Center for Integrative Medical Sciences, RIKEN, Yokohama, Japan

  • Mike A. Nalls,

    Roles Data curation, Formal analysis, Supervision

    Affiliations Data Tecnica International, Glen Echo, Maryland, United States of America, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, Maryland, United States of America

  • Christopher P. Nelson,

    Roles Data curation, Writing – review & editing

    Affiliations Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom, NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom

  • Nona Sotoodehnia,

    Roles Writing – review & editing

    Affiliation Cardiovascular Health Research Unit, Division of Cardiology, University of Washington, Seattle, Washington, United States of America

  • Jill M. Norris,

    Roles Funding acquisition, Project administration, Resources, Supervision

    Affiliation Department of Epidemiology, Colorado School of Public Health, Aurora, Colorado, United States of America

  • Jeff R. O'Connell,

    Roles Methodology, Software

    Affiliations Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, Maryland, United States of America, Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States of America

  • Nicholette D. Palmer,

    Roles Project administration, Supervision, Writing – review & editing

    Affiliation Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America

  • Thomas Perls,

    Roles Funding acquisition, Project administration, Writing – review & editing

    Affiliation Geriatrics Section, Boston University Medical Center, Boston, Massachusetts, United States of America

  • Nancy L. Pedersen,

    Roles Funding acquisition, Supervision

    Affiliation Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Stockholm, Sweden

  • Annette Peters,

    Roles Data curation, Funding acquisition, Resources, Writing – review & editing

    Affiliations Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany, DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Neuherberg, Germany

  • Patricia A. Peyser,

    Roles Supervision, Writing – review & editing

    Affiliation Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America

  • Neil Poulter,

    Roles Writing – review & editing

    Affiliation School of Public Health, Imperial College London, London, London, United Kingdom

  • Leslie J. Raffel,

    Roles Resources

    Affiliation Division of Genetic and Genomic Medicine, Department of Pediatrics, University of California, Irvine, California, United States of America

  • Olli T. Raitakari,

    Roles Funding acquisition, Project administration, Resources

    Affiliations Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland, Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland

  • Kathryn Roll,

    Roles Data curation, Project administration

    Affiliation Genomic Outcomes, Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, California, United States of America

  • Lynda M. Rose,

    Roles Data curation, Formal analysis, Resources

    Affiliation Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America

  • Frits R. Rosendaal,

    Roles Funding acquisition, Project administration, Writing – review & editing

    Affiliation Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands

  • Jerome I. Rotter,

    Roles Funding acquisition, Investigation, Project administration, Resources, Writing – review & editing

    Affiliation Genomic Outcomes, Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, California, United States of America

  • Carsten O. Schmidt,

    Roles Investigation, Project administration, Writing – review & editing

    Affiliation Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany

  • Pamela J. Schreiner,

    Roles Resources

    Affiliation Epidemiology & Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota, United States of America

  • Nicole Schupf,

    Roles Funding acquisition, Investigation, Writing – review & editing

    Affiliation Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Medical Center, New York, New York, United States of America

  • William R. Scott,

    Roles Formal analysis

    Affiliations Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom, National Heart and Lung Institute, Imperial College London, London, United Kingdom

  • Peter S. Sever,

    Roles Project administration, Resources

    Affiliation National Heart and Lung Institute, Imperial College London, London, United Kingdom

  • Yuan Shi,

    Roles Data curation

    Affiliation Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore

  • Stephen Sidney,

    Roles Resources

    Affiliation Division of Research, Kaiser Permanente of Northern California, Oakland, California, United States of America

  • Mario Sims,

    Roles Formal analysis

    Affiliation Jackson Heart Study, Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America

  • Colleen M. Sitlani,

    Roles Methodology, Writing – review & editing

    Affiliation Cardiovascular Health Research Unit, Medicine, University of Washington, Seattle, Washington, United States of America

  • Jennifer A. Smith,

    Roles Data curation, Project administration, Supervision, Writing – review & editing

    Affiliations Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America, Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, Michigan, United States of America

  • Harold Snieder,

    Roles Supervision, Writing – review & editing

    Affiliation Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

  • John M. Starr,

    Roles Funding acquisition, Resources, Writing – review & editing

    Affiliations Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, United Kingdom, Alzheimer Scotland Dementia Research Centre, The University of Edinburgh, Edinburgh, United Kingdom

  • Konstantin Strauch,

    Roles Data curation, Funding acquisition, Writing – review & editing

    Affiliations Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany, Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU, Munich, Germany

  • Heather M. Stringham,

    Roles Formal analysis

    Affiliation Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, United States of America

  • Nicholas Y. Q. Tan,

    Roles Project administration

    Affiliation Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore

  • Hua Tang,

    Roles Writing – review & editing

    Affiliation Department of Genetics, Stanford University, Stanford, California, United States of America

  • Kent D. Taylor,

    Roles Investigation

    Affiliation Genomic Outcomes, Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, California, United States of America

  • Yik Ying Teo,

    Roles Funding acquisition

    Affiliations Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, Singapore, Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore, Life Sciences Institute, National University of Singapore, Singapore, Singapore, NUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore, Singapore, Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore

  • Yih Chung Tham,

    Roles Project administration

    Affiliation Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore

  • Stephen T. Turner,

    Roles Writing – review & editing

    Affiliation Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, United States of America

  • André G. Uitterlinden,

    Roles Data curation, Investigation, Writing – review & editing

    Affiliations Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands, Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands

  • Peter Vollenweider,

    Roles Project administration, Resources

    Affiliation Service of Internal Medicine, Department of Internal Medicine, University Hospital, Lausanne, Switzerland

  • Melanie Waldenberger,

    Roles Data curation, Supervision, Writing – review & editing

    Affiliations Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany, Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany

  • Lihua Wang,

    Roles Data curation, Formal analysis, Validation, Writing – review & editing

    Affiliation Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America

  • Ya Xing Wang,

    Roles Data curation, Resources

    Affiliations Beijing Institute of Ophthalmology, Beijing Ophthalmology and Visual Science Key Lab, Beijing Tongren Eye Center, Capital Medical University, Beijing, China, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China

  • Wen Bin Wei,

    Roles Data curation, Resources

    Affiliation Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China

  • Christine Williams,

    Roles Data curation, Formal analysis

    Affiliation Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America

  • Jie Yao,

    Roles Data curation, Formal analysis, Investigation, Writing – review & editing

    Affiliation Genomic Outcomes, Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, California, United States of America

  • Caizheng Yu,

    Roles Formal analysis, Investigation

    Affiliation Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health for Incubating, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

  • Jian-Min Yuan,

    Roles Funding acquisition, Investigation, Resources, Validation, Writing – review & editing

    Affiliations Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America, Division of Cancer Control and Population Sciences, UPMC Hillman Cancer, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America

  • Wei Zhao,

    Roles Data curation, Formal analysis, Software, Writing – review & editing

    Affiliation Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America

  • Alan B. Zonderman,

    Roles Data curation, Funding acquisition, Project administration, Resources, Writing – review & editing

    Affiliation Behavioral Epidemiology Section, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States of America

  • Diane M. Becker,

    Roles Funding acquisition, Project administration, Resources, Supervision

    Affiliation Division of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America

  • Michael Boehnke,

    Roles Funding acquisition, Project administration, Resources

    Affiliation Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, United States of America

  • Donald W. Bowden,

    Roles Funding acquisition, Project administration, Writing – review & editing

    Affiliation Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America

  • John C. Chambers,

    Roles Data curation, Funding acquisition, Investigation, Project administration, Resources, Supervision, Validation, Visualization

    Affiliations Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom, Department of Cardiology, Ealing Hospital, Middlesex, United Kingdom, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore, Imperial College Healthcare NHS Trust, London, United Kingdom, MRC-PHE Centre for Environment and Health, Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, United Kingdom

  • Ian J. Deary,

    Roles Funding acquisition, Resources, Supervision, Writing – review & editing

    Affiliations Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, United Kingdom, Psychology, The University of Edinburgh, Edinburgh, United Kingdom

  • Tõnu Esko,

    Roles Funding acquisition, Project administration, Resources, Supervision

    Affiliations Estonian Genome Center, University of Tartu, Tartu, Estonia, Broad Institute of the Massachusetts Institute of Technology and Harvard University, Boston, Massachusetts, United States of America

  • Martin Farrall,

    Roles Funding acquisition, Project administration, Resources, Supervision, Writing – review & editing

    Affiliations Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, Oxfordshire, United Kingdom, Wellcome Centre for Human Genetics, University of Oxford, Oxford, Oxfordshire, United Kingdom

  • Paul W. Franks,

    Roles Funding acquisition, Resources, Supervision, Writing – review & editing

    Affiliations Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Skåne University Hospital, Malmö, Sweden, Harvard T. H. Chan School of Public Health, Department of Nutrition, Harvard University, Boston, Massachusetts, United States of America

  • Barry I. Freedman,

    Roles Data curation, Funding acquisition, Project administration, Writing – review & editing

    Affiliation Nephrology, Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America

  • Philippe Froguel,

    Roles Investigation, Supervision

    Affiliations CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, University of Lille, Lille, France, Department of Genomics of Common Disease, Imperial College London, London, United Kingdom

  • Paolo Gasparini,

    Roles Resources

    Affiliations Institute for Maternal and Child Health—IRCCS "Burlo Garofolo", Trieste, Italy, Department of Medical Sciences, University of Trieste, Trieste, Italy

  • Christian Gieger,

    Roles Data curation, Funding acquisition, Supervision, Writing – review & editing

    Affiliations Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany, German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany

  • Jost Bruno Jonas,

    Roles Data curation, Resources

    Affiliations Beijing Institute of Ophthalmology, Beijing Ophthalmology and Visual Science Key Lab, Beijing Tongren Eye Center, Capital Medical University, Beijing, China, Department of Ophthalmology, Medical Faculty Mannheim, University Heidelberg, Mannheim, Germany, Germany

  • Yoichiro Kamatani,

    Roles Validation

    Affiliation Laboratory for Statistical Analysis, Center for Integrative Medical Sciences, RIKEN, Yokohama, Japan

  • Norihiro Kato,

    Roles Data curation, Funding acquisition, Resources, Supervision

    Affiliation Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan

  • Jaspal S. Kooner,

    Roles Data curation, Funding acquisition, Investigation, Project administration, Resources, Supervision, Validation, Visualization

    Affiliations Department of Cardiology, Ealing Hospital, Middlesex, United Kingdom, National Heart and Lung Institute, Imperial College London, London, United Kingdom, Imperial College Healthcare NHS Trust, London, United Kingdom, MRC-PHE Centre for Environment and Health, Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, United Kingdom

  • Zoltán Kutalik,

    Roles Formal analysis

    Affiliations Swiss Instititute of Bioinformatics, Lausanne, Switzerland, Institute of Social and Preventive Medicine, Lausanne University Hospital, Lausanne, Switzerland

  • Markku Laakso,

    Roles Data curation, Funding acquisition, Investigation, Project administration, Resources, Writing – review & editing

    Affiliation Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland

  • Cathy C. Laurie,

    Roles Data curation, Funding acquisition, Project administration, Supervision, Writing – review & editing

    Affiliation Department of Biostatistics, University of Washington, Seattle, Washington, United States of America

  • Karin Leander,

    Roles Data curation, Project administration, Resources, Supervision

    Affiliation Unit of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden

  • Terho Lehtimäki,

    Roles Writing – review & editing

    Affiliations Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland, Department of Clinical Chemistry, Finnish Cardiovascular Research Center—Tampere, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland

  • Lifelines Cohort Study,

    Roles Investigation, Resources

    Affiliation Lifelines Cohort, Groningen, The Netherlands

  • Patrik K. E. Magnusson,

    Roles Project administration, Supervision

    Affiliation Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Stockholm, Sweden

  • Albertine J. Oldehinkel,

    Roles Resources

    Affiliation Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

  • Brenda W. J. H. Penninx,

    Roles Funding acquisition, Project administration, Supervision

    Affiliation Department of Psychiatry, Amsterdam Neuroscience and Amsterdam Public Health Research Institute, VU University Medical Center, Amsterdam, The Netherlands

  • Ozren Polasek,

    Roles Data curation, Investigation, Methodology, Project administration, Supervision

    Affiliations Department of Public Health, Department of Medicine, University of Split, Split, Croatia, Psychiatric Hospital "Sveti Ivan", Zagreb, Croatia, Gen-info Ltd, Zagreb, Croatia

  • David J. Porteous,

    Roles Data curation

    Affiliation Centre for Genomic & Experimental Medicine, Institute of Genetics & Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom

  • Rainer Rauramaa,

    Roles Funding acquisition, Investigation, Project administration, Resources, Supervision

    Affiliation Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland

  • Nilesh J. Samani,

    Roles Funding acquisition, Supervision, Writing – review & editing

    Affiliations Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom, NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom

  • James Scott,

    Roles Funding acquisition

    Affiliation National Heart and Lung Institute, Imperial College London, London, United Kingdom

  • Xiao-Ou Shu,

    Roles Data curation, Funding acquisition, Investigation, Resources, Writing – review & editing

    Affiliation Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America

  • Pim van der Harst,

    Roles Investigation, Resources, Supervision

    Affiliations Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands, Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

  • Lynne E. Wagenknecht,

    Roles Funding acquisition, Project administration, Writing – review & editing

    Affiliation Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America

  • Nicholas J. Wareham,

    Roles Funding acquisition

    Affiliation MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom

  • Hugh Watkins,

    Roles Funding acquisition, Project administration, Resources, Supervision, Writing – review & editing

    Affiliations Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, Oxfordshire, United Kingdom, Wellcome Centre for Human Genetics, University of Oxford, Oxford, Oxfordshire, United Kingdom

  • David R. Weir,

    Roles Investigation, Resources

    Affiliation Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, Michigan, United States of America

  • Ananda R. Wickremasinghe,

    Roles Data curation, Funding acquisition, Project administration, Writing – review & editing

    Affiliation Department of Public Health, Faculty of Medicine, University of Kelaniya, Ragama, Sri Lanka

  • Tangchun Wu,

    Roles Funding acquisition, Investigation, Project administration, Supervision, Writing – review & editing

    Affiliation Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health for Incubating, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

  • Wei Zheng,

    Roles Data curation, Resources

    Affiliation Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America

  • Claude Bouchard,

    Roles Funding acquisition, Investigation, Resources, Supervision, Writing – review & editing

    Affiliation Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, Louisiana, United States of America

  • Kaare Christensen,

    Roles Investigation, Writing – review & editing

    Affiliation The Danish Aging Research Center, Institute of Public Health, University of Southern Denmark, Odense, Denmark

  • Michele K. Evans,

    Roles Funding acquisition, Investigation, Project administration, Resources, Supervision

    Affiliation Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States of America

  • Vilmundur Gudnason,

    Roles Writing – review & editing

    Affiliations Icelandic Heart Association, Kopavogur, Iceland, Faculty of Medicine, University of Iceland, Reykjavik, Iceland

  • Bernardo L. Horta,

    Roles Data curation

    Affiliation Postgraduate Programme in Epidemiology, Federal University of Pelotas, Pelotas, RS, Brazil

  • Sharon L. R. Kardia,

    Roles Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Resources, Writing – review & editing

    Affiliation Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America

  • Yongmei Liu,

    Roles Data curation, Funding acquisition, Investigation, Resources, Supervision, Writing – review & editing

    Affiliation Public Health Sciences, Epidemiology and Prevention, Wake Forest University Health Sciences, Winston-Salem, North Carolina, United States of America

  • Alexandre C. Pereira,

    Roles Data curation, Investigation, Supervision, Writing – review & editing

    Affiliation Laboratory of Genetics and Molecular Cardiology, Heart Institute (InCor), University of São Paulo Medical School, São Paulo, SP, Brazil

  • Bruce M. Psaty,

    Roles Conceptualization, Data curation, Funding acquisition, Supervision, Writing – review & editing

    Affiliations Cardiovascular Health Research Unit, Epidemiology, Medicine and Health Services, University of Washington, Seattle, Washington, United States of America, Kaiser Permanente Washington, Health Research Institute, Seattle, Washington, United States of America

  • Paul M. Ridker,

    Roles Funding acquisition, Project administration, Resources

    Affiliations Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America, Harvard Medical School, Boston, Massachusetts, United States of America

  • Rob M. van Dam,

    Roles Funding acquisition

    Affiliations Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, Singapore, Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

  • W. James Gauderman,

    Roles Writing – review & editing

    Affiliation Biostatistics, Preventive Medicine, University of Southern California, Los Angeles, California, United States of America

  • Xiaofeng Zhu,

    Roles Funding acquisition, Investigation, Resources, Supervision

    Affiliation Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States of America

  • Dennis O. Mook-Kanamori,

    Roles Funding acquisition, Project administration, Writing – review & editing

    Affiliations Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands, Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands

  • Myriam Fornage,

    Roles Resources, Supervision, Writing – review & editing

    Affiliations Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America, Brown Foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America

  • Charles N. Rotimi,

    Roles Conceptualization, Resources, Supervision, Writing – review & editing

    Affiliation Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America

  • L. Adrienne Cupples,

    Roles Conceptualization, Funding acquisition, Methodology, Supervision, Writing – review & editing

    Affiliations Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America, The Framingham Heart Study, Framingham, Massachusetts, United States of America

  • Tanika N. Kelly,

    Roles Formal analysis, Supervision

    Affiliation Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America

  • Ervin R. Fox,

    Roles Investigation, Supervision, Writing – review & editing

    Affiliation Cardiology, Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America

  • Caroline Hayward,

    Roles Data curation

    Affiliation Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom

  • Cornelia M. van Duijn,

    Roles Conceptualization, Data curation, Supervision, Writing – review & editing

    Affiliation Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands

  • E Shyong Tai,

    Roles Funding acquisition, Supervision

    Affiliations Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, Singapore, Duke-NUS Medical School, Singapore, Singapore, Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

  • Tien Yin Wong,

    Roles Data curation

    Affiliations Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore, Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore, Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

  • Charles Kooperberg,

    Roles Data curation, Project administration, Supervision, Writing – review & editing

    Affiliation Fred Hutchinson Cancer Research Center, University of Washington School of Public Health, Seattle, Washington, United States of America

  • Walter Palmas,

    Roles Investigation, Supervision, Writing – review & editing

    Affiliation Medicine, Columbia University Medical Center, New York, New York, United States of America

  • Kenneth Rice ,

    Roles Conceptualization, Methodology, Writing – review & editing

    ‡ These authors also contributed equally to this work.

    Affiliation Department of Biostatistics, University of Washington, Seattle, Washington, United States of America

  • Alanna C. Morrison ,

    Roles Conceptualization, Formal analysis, Supervision, Writing – review & editing

    ‡ These authors also contributed equally to this work.

    Affiliation Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America

  • Paul Elliott ,

    Roles Supervision, Writing – review & editing

    ‡ These authors also contributed equally to this work.

    Affiliation MRC-PHE Centre for Environment and Health, Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, United Kingdom

  • Mark J. Caulfield ,

    Roles Project administration, Resources, Supervision

    ‡ These authors also contributed equally to this work.

    Affiliations Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom, NIHR Barts Cardiovascular Biomedical Research Unit, Queen Mary University of London, London, London, United Kingdom

  • Patricia B. Munroe ,

    Roles Project administration, Resources, Supervision, Writing – review & editing

    ‡ These authors also contributed equally to this work.

    Affiliations Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom, NIHR Barts Cardiovascular Biomedical Research Unit, Queen Mary University of London, London, London, United Kingdom

  • Dabeeru C. Rao ,

    Roles Conceptualization, Funding acquisition, Methodology, Writing – review & editing

    ‡ These authors also contributed equally to this work.

    Affiliation Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, United States of America

  • Michael A. Province ,

    Roles Funding acquisition, Investigation, Methodology, Resources, Validation, Writing – original draft, Writing – review & editing

    ‡ These authors also contributed equally to this work.

    Affiliation Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America

  •  [ ... ],
  • Daniel Levy

    Roles Conceptualization, Funding acquisition, Investigation, Writing – original draft, Writing – review & editing

    mfeitosa@wustl.edu (MFF); levyD@nih.gov (DL)

    ‡ These authors also contributed equally to this work.

    Affiliations The Framingham Heart Study, Framingham, Massachusetts, United States of America, The Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States of America

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Novel genetic associations for blood pressure identified via gene-alcohol interaction in up to 570K individuals across multiple ancestries

  • Mary F. Feitosa, 
  • Aldi T. Kraja, 
  • Daniel I. Chasman, 
  • Yun J. Sung, 
  • Thomas W. Winkler, 
  • Ioanna Ntalla, 
  • Xiuqing Guo, 
  • Nora Franceschini, 
  • Ching-Yu Cheng, 
  • Xueling Sim
PLOS
x

Abstract

Heavy alcohol consumption is an established risk factor for hypertension; the mechanism by which alcohol consumption impact blood pressure (BP) regulation remains unknown. We hypothesized that a genome-wide association study accounting for gene-alcohol consumption interaction for BP might identify additional BP loci and contribute to the understanding of alcohol-related BP regulation. We conducted a large two-stage investigation incorporating joint testing of main genetic effects and single nucleotide variant (SNV)-alcohol consumption interactions. In Stage 1, genome-wide discovery meta-analyses in ≈131K individuals across several ancestry groups yielded 3,514 SNVs (245 loci) with suggestive evidence of association (P < 1.0 x 10−5). In Stage 2, these SNVs were tested for independent external replication in ≈440K individuals across multiple ancestries. We identified and replicated (at Bonferroni correction threshold) five novel BP loci (380 SNVs in 21 genes) and 49 previously reported BP loci (2,159 SNVs in 109 genes) in European ancestry, and in multi-ancestry meta-analyses (P < 5.0 x 10−8). For African ancestry samples, we detected 18 potentially novel BP loci (P < 5.0 x 10−8) in Stage 1 that warrant further replication. Additionally, correlated meta-analysis identified eight novel BP loci (11 genes). Several genes in these loci (e.g., PINX1, GATA4, BLK, FTO and GABBR2) have been previously reported to be associated with alcohol consumption. These findings provide insights into the role of alcohol consumption in the genetic architecture of hypertension.

Introduction

Hypertension is a major risk factor for cardiovascular disease (CVD)[1], which in 2015 alone was estimated to cause about 10.7 million deaths worldwide[2]. The prevalence of hypertension in the US is ~46% for those of African ancestry compared to ~33% for European ancestry and ~30% for Hispanic ancestry[3] based on previous blood pressure (BP) guidelines (The Seventh Report of the Joint National Committee on Prevention)[4]. Recently, based on the 2017 American College of Cardiology/ American Heart Association high BP guideline, the overall prevalence of hypertension among US adults is estimated at 45.6%[5]. Blood pressure levels are influenced by alcohol consumption independently of adiposity, sodium intake, smoking and socio-economic status[6]. Alcohol shows a dose-dependent effect on systolic BP (SBP) after adjusting for environmental confounders[7].

Genome-wide association studies (GWAS) have identified more than 400 single nucleotide variants (SNVs) for BP[814] and about 30 SNVs for alcohol consumption[1517]. However, few studies have explored SNV-alcohol interactions in relation to BP[18, 19], in part due to the large sample sizes required to obtain adequate power[18, 20]. SNVs, which effect differ by level of alcohol consumption, can harbor modest marginal effects and might therefore be missed by standard marginal effects association screening. As previously demonstrated, a joint test of main genetic effect and gene-environmental interaction can have higher power[21] to identify such variants.

Within the CHARGE Gene-Lifestyle Interactions Working Group[22, 23], we studied a total of 571,652 adults across multiple ancestries to identify variants associated with SBP, diastolic BP (DBP), mean arterial pressure (MAP), and pulse pressure (PP). We tested a model that included a joint model of SNV main effect on BP and SNV-alcohol consumption interaction, in each ancestry and across ancestries. Alcohol consumption was defined by two categories: (I) as current drinking (yes/no), and (II) in the subset of drinkers, as light/heavy drinking (1–7 drinks/week or ≥8 drinks/week). Individual cohort results were meta-analyzed using a modified version of METAL applicable to the statistics summary results accounting for interactions[24]. We also performed multi-trait correlated meta-analyses[25, 26] in participants of European ancestry using the joint model P-values from each meta-analysis of all four BP traits.

Results

Genetic associations for BP identified via gene-alcohol interaction

The overall description of the CHARGE Gene-Lifestyle Interactions Working Group was previously reported[22, 23]. We studied the joint model of SNV main effect and SNV-alcohol consumption interaction for BP in a two-stage study design, as depicted in S1 Fig. GWAS discovery (Stage 1), was conducted in each of 47 multi-ancestry cohorts including a total of 130,828 individuals of African ancestry (N = 21,417), Asian ancestry (N = 9,838), Brazilian (4,415), European ancestry (N = 91,102), and Hispanic ancestry (N = 4,056) (S1S4 Tables and S1 Note). A total of 3,514 SNVs (245 loci) attained P < 1.0 x 10−5 in Stage 1 meta-analyses (for at least one combination of BP trait and alcohol consumption status in one ancestry or multi-ancestries). We considered a locus to be independent, if our lead variant (i.e., most significant) was in low linkage disequilibrium (LD, r2 ≤ 0.2) and at least 500 kb away from any variant associated with BP in previous GWAS (P ≤ 5.0 x 10−8). The meta-analysis distributions of–log10 P-values of observed versus–log10 P-values expected (QQ plots) are shown in S2 and S3 Figs.

The 3,514 SNVs were taken forward to replication, Stage 2, which included 440,824 individuals from 68 cohorts of African ancestry (N = 5,041), Asian ancestry (N = 141,026), European ancestry (N = 281,380), and Hispanic ancestry (N = 13,377, S5S8 Tables and S1 Note). We identified and replicated (Stage 2, at Bonferroni correction P < 0.0002) five novel BP loci in European ancestry, four loci on 8p23.1 and one locus (FTO) on 16q12.2, which included 380 SNVs in 21 genes. These findings achieved genome-wide statistical significance (P < 5.0 x 10−8) in Stage 1 and Stage 2 combined meta-analyses. Tables 1 and 2 show the most significant SNVs per BP trait, per alcohol consumption and gene for European ancestry participants. The loci containing novel BP associations at 8p23.1 were detected for all four BP traits in current drinkers and in light/heavy drinkers. The regional association plots on chromosomes 8p23 and 16q12 in European ancestry are shown in S4 and S5 Figs. For African ancestry, 18 potentially novel BP loci were found in discovery (P ≤ 5.0 x 10−8), but without replication (Table 3). Further, we performed combined meta-analyses of Stage 1 and Stage 2 across all ancestries, which reproduced our European ancestry findings (P ≤ 5.0 x 10−8, Table 4 and S9 Table). We also identified and replicated 49 previously reported BP loci (2,159 SNVs in 109 genes) for European ancestry participants (S10 Table). For African Ancestry, and multi-ancestry analyses, additional reported BP loci were significant (P < 5.0 x 10−8) in Stage 1 and Stage 2 combined meta-analyses (S11 and S12 Tables). Manhattan plots for BP trait and alcohol consumption status are shown in S6S15 Figs, for Stage 1 and Stage 2 combined meta-analyses of European, African and Asian ancestries.

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Table 1. Novel SNVs/Genes associated with BP traits in European ancestry.

https://doi.org/10.1371/journal.pone.0198166.t001

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Table 2. Novel SNVs/Genes associated with BP traits in European ancestry.

https://doi.org/10.1371/journal.pone.0198166.t002

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Table 3. Potential novel SNVs/Genes associated with BP traits in African ancestry.

https://doi.org/10.1371/journal.pone.0198166.t003

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Table 4. Novel SNVs/Genes associated with BP traits in Multi-ancestry meta-analysis in combined Stage 1 and Stage 2.

https://doi.org/10.1371/journal.pone.0198166.t004

Finally, we leveraged the added power of correlated meta-analysis[25, 26] for BP traits to detect additional variants. We performed correlated meta-analysis on P-values from METAL-meta-analysis[24] of DBP, SBP, MAP and PP traits separately for current drinkers and light/heavy drinkers in Stage 1 European ancestry cohorts. A variant was considered pleiotropic if the P- METAL-meta reached P ≤ 0.0001 in two or more BP traits and the correlated meta-analysis P-value was P ≤ 5.0 x 10−8[27]. We identified eight novel BP loci (11 genes, Table 5), the above five novel loci (14 genes, Tables 1 and 2), and the 22 previously reported BP loci (49 genes).

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Table 5. Novel SNVs/Genes associated with BP traits from correlated meta-analysis in European ancestry in Stage 1.

https://doi.org/10.1371/journal.pone.0198166.t005

Gene transcription regulation

HaploReg[28, 29], RegulomeDB[30, 31], GTEx[32], GWAS3D[33], and GRASP[34] provided evidence that several SNVs on 8p23.1 have regulatory features (S13 and S14 Tables). From the analyses with GTEx, a total of 227 (56 novel and 171 BP-known S14 Tables) SNVs had tissue specific eQTL results. Seven out of 56 novel SNVs were associated with eQTLs that have expression in brain, thyroid, and/or blood. From 171 BP-known SNVs, 44 were significantly associated with eQTLs with expression in adipose, artery, esophagus, lung, pancreas, thyroid and/or fibroblasts. In addition, GWAS3D analyses suggested trans-regulation features for our BP candidate SNVs. It identified 215 SNVs with long-range interactions.

BP genes show enrichment for alcohol and cardiovascular disease

We used GeneGO[35] and Literature Lab[36] to perform enrichment analyses for the full set of novel and reported (179 BP candidate) genes identified from our analyses. Literature Lab, based on 106,967 abstracts for “Drinking” Physiology from MeSH (Medical Subject Headings), identified enrichment (P < 0.00001) related to ALDH2 (known to be associated with alcohol dependence)[15] and several other genes, including our novel finding for ERCC6, CATSPER2, GABRB1 and GATA4. The main contributor for “Angiotensin II” (P < 0.00001) was AGT and ACE for “Hypertension” (P = 0.0002). AGT and ACE are part of Renin-Angiotensin System pathway (KEGG, map04614), involved in BP homeostasis, fluid-electrolyte balance, and essential hypertension[37, 38].

Our results were significantly enriched for cardiovascular disease-related biological functions. For example, “Cardiovascular Diseases” (P = 0.0034) enriched with genes AGT, NPPA, ACE, NOS3, ADRB1, MTHFR, FBN1 and GATA4. “Heart Failure” (P = 0.0003) and “Cardiomegaly” (P = 0.0003); from Pathological Conditions: “Hypertrophy” (P = 0.0001); from Anatomy MeSH: “Heart” (P = 0.0001), “Cardiovascular System” (P = 0.0002) and “Aorta” (P = 0.0002); and from domain Tissue Type MeSH: “Myocardium” (P = 0.0008) enriched with NPPA, GATA4, AGT, ADRB1, NOS3, ACE and KCNJ11. GeneGO identified an additional term “Cardiac Arrhythmias” (P-FDR = 3.2 x 10−20).

Protein-protein interactions and pathways enriched for BP genes

The protein-protein interactions (PPI) analyses showed that several novel gene proteins are important hubs in interaction with many other proteins. For example, MAPKAPK2 (1q32.1, Table 5) interacts among others with BAG2, LISP1 and ELAVL1. ELAVL1 interacts also with novel XKR6 from 8p23.1 (S16 Fig). Of the novel genes GRK5, MAPKAPK2, BLK, EFEMP2 and ERCC6 ranked the highest in protein-protein interconnectivity (degree), while MAPKAPK2, PINX1, EFEMP2, FAM167A and GRK5 were ranked the highest for important interconnections based on PageRank algorithm. Further, we entered the gene labels of the combined PPI network into the GeneGo software and found enrichment for Cytoskeleton Remodeling/TGF/ Wnt (P-FDR = 1.7 x 10−17), among other pathways.

Discussion

This is the first large-scale study to systematically evaluate the role of joint effect of main gene and gene-alcohol interaction on BP in a very large meta-analysis across multiple ancestries.

BP genes interacting with alcohol show association with alcohol metabolism or dependence

The 8p23.1 containing novel BP associations spans ~3.3 Mb from LOC107986913-SGK223 (8,452,998 bp) to GATA4 (11,752,486 bp) (Tables 1 and 2). Chromosome 8p23.1 is a complex region of deletions and replications, with repeated inverse structures[39, 40]. We identified four LD blocks in 8p23.1 (Fig 1). The significant GWAS results on 8p23.1 are from European ancestry participants in Stage 1, Stage 2 follow up, and combined Stage 1 and Stage 2 meta-analyses. For this region, the evidence of genetic associations was identified from all four BP traits at both current drinking and light/heavy drinking status (Tables 1 and 2). The association on 8p23.1 found in the large European ancestry sample may also occur in other ancestries. The genome-wide significance levels in meta-analysis of European ancestry combined with African (5 genes), Asian (2 genes), and/or Hispanic (9 genes) ancestries have shown small improvements in their P-values compared to European ancestry meta-analysis alone (Tables 4 and S9). For some of these associated SNVs on 8p23.1, the allele frequencies in European ancestry are higher than in African ancestry (e.g., rs4841294: 0.44 versus 0.25, respectively), and Hispanic Ancestry (e.g., rs34919878: 0.42 versus 0.25, respectively). These findings suggest the presence of cross-population association patterns between European, African, and Hispanic ancestries, although they are not genome-wide significant in African and Hispanic ancestries presumably because of small sample sizes.

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Fig 1. Identification of four independent LD blocks in the 8p23.1 region (~3.3 MBs).

https://doi.org/10.1371/journal.pone.0198166.g001

Several of the genes residing on 8p23.1 have been reported for alcohol metabolism and/or dependence. Overexpression of PINX1 was reported to be associated with alcohol-related cirrhosis and fibrosis[41]. The transcription factor GATA4 has been reported to be associated with alcohol dependence in several studies[4245]. GATA4 was suggested to regulate atrial natriuretic peptide (ANP, officially known as NPPA) modulating the amygdala’s response to alcohol dependence[39] and is associated with BP[46]. In addition, a suggestive GWAS finding was observed between a variant near BLK-LINC00208 with alcohol dependence[47]. The S2 Note provides a comprehensive summary of novel and neighboring genes and their potential biological relevance.

FTO (16q12.2) variants in interaction with alcohol consumption were significant for BP in European ancestry (Table 2) and in combined meta-analysis of European and Asian ancestries (Table 4). FTO is involved in the regulation of thermogenesis and the control of adipocyte differentiation into brown or white fat cells[48]. FTO variants have been associated in diverse ancestries with obesity-related traits[49, 50], as well as alcohol consumption and alcohol dependency[51, 52]. Frequency of alcohol consumption was suggested to modify the effect of FTO variants on body mass index[53].

IL10 (interleukin 10, ~49 Kb upstream of rs3813963, Table 5) has been associated with hypertension[54] and with alcoholic cirrhosis[55]. MALAT1 (ncRNA, ~390 Kb upstream of rs201407003) is upregulated in the cerebellum, hippocampus and brain stem of alcoholics[56], which may represent an important mechanism for alcohol actions in the central nervous system.

It is worth to note that the allele frequencies for several potential SNVs in African ancestry (Table 3) are low (<0.10) but they are monomorphic in Europeans, which may suggest African-specific associations. Even though we did not have true replications for African ancestry associations (some of them due to missing SNVs or very low sample size in Stage 2), the identified candidate loci include genes previously related to alcohol consumption and dependence (Table 3). GABRB1[57] (4p12) and GABBR2[58] (9q22.33, 143 kb upstream of rs73655199) are major neurotransmitters in the vertebrate brain, representing ligand-gated ion channels and have been shown to associate with alcohol dependence. EYS (6q12) displayed association with alcohol dependence in multi-ancestry population studies for rare[59] and common[60] variants. LINGO2 (9p21.1) was reported to be associated with age at onset of alcohol dependence in the Collaborative Study on the Genetics of Alcoholism[16]. ERCC6 (10q11.23) participates in DNA repair in response to oxidative stress[61]. Carriers of Arg1230Pro at ERCC6 had a decreased risk for laryngeal cancer, strongest in heavy smokers and high alcohol consumers[62]. CHAT (10q11.23, 136 kb downstream of rs4253197) encodes an enzyme that catalyzes the biosynthesis of the neurotransmitter acetylcholine, and binge ethanol in adolescents was reported to decrease CHAT expression[63]. BAG3 (10q26.11, 183 Kb downstream of rs201383951) was also suggested to contribute to alcohol-induced neurodegenerations[64]. A mouse study suggested that BAG3 exerts a vaso-relaxing effect through the activation of the PI3K/Akt/eNOS signaling pathway, and may influence BP regulation[64]. A GWAS identified association of BAG3 with dilated cardiomyopathy[65], and suggestive association with alcohol dependence[44]. SGK1 (409 kb upstream of rs76987554) is associated with increased BP[66] and may contribute to the mechanisms underlying behavioral response to chronic ethanol exposure[67]. In addition, our two potential genes by alcohol interaction, TARID (rs76987554) and CDH17 (rs115888294), have been recently reported association with BP in African ancestry, which supports our findings[68].

Regulatory features of BP genes

Analysis of our significant BP variants for cis- transcription regulation via HaploReg[29] (S13 Table) showed that in total about 11% of variants were localized in promoter histone marks, 55% in enhancer histone marks, 34% at DNAse hypersensitive sites, 10% located at protein regulatory binding sites, and 88% were predicted to change regulatory protein binding motifs. These feature findings are inflated, because several variants are in LD blocks. Several of our variants had P-values ≤ 5.0 x 10−8 for being eQTLs for one or more target genes. The rs2921053 is the best eSNV regulating the transcription of SGK223 in thyroid tissue (P-value = 1.04 x 10−67). Thyroid hormones are known to affect BP, heart and cardiovascular system[69].

Pathways enriched for BP genes

Our findings, TNKS (Table 1), FSTL5 and MAPKAPK2 (Table 5) and many other genes from PPI networks (S17 Fig), are part of Wnt/beta-catenin[70] signaling pathway. The TNKS forms a complex for degrading β-catenin (CTNNB1)[70] in interaction with AXIN1, AXIN2, and glycogen synthase kinase 3β (GSK-3β) (S17 and S18 Figs). The Wnt/beta-catenin pathway is known to be involved in renal injury and fibrosis induced by hypertension[71]. In addition, TNKS is involved in the regulation of GLUT4 trafficking in adipocytes[72]. Other findings from correlated meta-analysis also contributed to pathways. For example, rs206648224 is intronic to DYRK3, 37 Kb upstream of MAPKAPK2, and 119 Kb downstream of IL10. MAPKAPK2 is a stress-activated serine/threonine-protein kinase involved in cytokine production especially for TNF and IL6, and phosphorylates among others LSP1, already identified in association with BP[9]. MAPKAPK2[73] augments and FSTL5[74] diminishes the expression of Wnt/β-catenin signaling pathway.

Limitations

Despite large sample sizes in Stages 1 and 2 (≈131K individuals and ≈440K individuals, respectively), our novel variants (8p23 and 16q12) are common in their allele frequencies. For an analysis of gene by alcohol interactions in BP, even larger sample sizes are required to have sufficient power for detecting (and replicating) variants with lower allele frequency in the genome.

Our findings were based on a joint test of the main and interaction effects, which limits our ability to statistically differentiate the effect of interaction from the main effect. However, there is evidence that several of our novel and previously reported findings suggest association with alcohol consumption and dependency.

For African ancestry, the findings were not replicated, due to low sample size in Stage 2 (≈3K individuals) versus Stage 1 (≈21K individuals) and because seven potential variants for African ancestry were not available in Stage 2.

There are fewer associations of SNVs interacting with light/heavy drinkers compared to current drinkers, which is probably due to the reduced sample size in light/heavy drinkers. We also found an association in light/heavy drinkers which is not present in current drinkers. The LOC105374235 gene interacts with light/heavy drinkers for SBP but does not interact with current drinkers for SBP in African ancestry (Table 3 and S10 Fig). These findings suggest that novel loci for BP can be expected to be discovered when increasing the sample size for light/heavy drinkers.

The two Brazilian cohorts (from discovery only) were included in the multi-ancestry meta-analyses. However, their association results did not contribute to SNV-alcohol interactions for BP traits, which could be in part to the relative small sample size (4,415 subjects) affecting the power of associations in the joint gene-environmental interaction model.

Conclusion

We identified and replicated five novel loci (380 SNVs in 21 genes) via joint test of main genetic effect and gene-alcohol interaction, and eight novel loci (11 genes) using correlated meta-analysis in European ancestry. We also found 18 potentially novel BP loci in discovery (P ≤ 5.0 x 10−8) in gene-alcohol interaction model in African ancestry participants, but without replication. In addition, we identified 49 loci previously reported for BP (2,159 SNVs in 109 genes) using the joint test for interaction in European and multi-ancestries meta-analyses. Several of these SNVs/genes are related to alcohol metabolism and dependence, have evidence for regulatory features, and are enriched in pathways for cardiovascular disease, hypertension and blood pressure homeostasis. Our findings provide novel insights into mechanisms of BP regulation and may highlight new therapeutic targets.

Methods

Individuals between the ages of 18–80, who participated in the studies, provided written informed consent and approval by their research ethics committees and/or institutional review boards. The description of each participating study cohort is shown in S1 Note.

Phenotypes, alcohol consumption, and study cohorts

SBP (in mmHg) and diastolic BP (DBP in mmHg) were measured at resting or sitting positions by averaging up to three BP readings at the same clinical visit. To account for the reduction in BP levels due to anti-hypertensive medication use, the BP levels were adjusted by adding 15 mm Hg to SBP and 10 mm Hg to DBP values. After adjustment, mean arterial pressure (MAP) was defined as the sum of two-thirds of DBP and one-third of SBP, and pulse pressure (PP) was estimated as the difference between SBP and DBP. Hypertension was defined whether participants presented: (i) SBP ≥ 140 mm Hg, (ii) DBP ≥ 90 mm Hg, and/or (iii) taking anti-hypertensive medication. For quality control (QC), SE-N (i.e., inverse of the median standard error versus the square root of the sample size) plots were produced[75]. If cohort-specific analytical problems existed, they were corrected.

Definition of “a dose or a drink” is about 17.7 grams of ethanol, which is the amount of a typical beverage of 12 oz. (354.882 ml) bottle or can of beer, a 5 oz. (147.868 ml) glass of wine, or a standard 1.5 oz. (44.3603 ml) shot of 80-proof spirits, such as gin, vodka, or whiskey[76]. Alcohol consumption was defined by two categories: (I) as current drinking (yes/no), and (II) in the subset of drinkers, as light/heavy drinking (1–7 drinks/week or ≥8 drinks/week).

Genotyping

Genotyping was performed using Illumina (San Diego, CA, USA) or Affymetrix (Santa Clara, CA, USA) arrays. 1000 Genomes Imputation was implemented using MACH and Minimac, IMPUTE2, and/or BEAGLE software, based on the cosmopolitan panel from Phase I Integrated Release Version 3 Haplotypes (2010–11 data freeze, 2012-03-14 haplotypes). Dosages from 1000 Genomes were used in 106 cohorts out of 115 Stage 1 and Stage 2 cohorts. If 1000 Genomes were not available in a cohort, dosages based on HapMap Phase II / III reference panel (2 Stage 1 cohorts and 4 Stage 2 cohorts) or genotyped data (3 Stage 2 cohorts) were used in the analyses. Information of study characteristics, genotyping, imputation, covariates, and analyses are summarized for Stage 1 in S1S4 Tables, and for Stage 2 in S5S8 Tables.

Interaction association analysis

Each Stage 1 and Stage 2 cohort conducted a joint statistical model analysis[24]:

where SNV is the dosage of the genetic (G) variant, E is the alcohol consumption (current drinker or light/heavy drinker) effect, SNV*E is SNV-alcohol interaction effect, b values are the respective beta coefficients from regression analysis and C represents covariates (age, sex, principal components (PCs), and other study-specific covariates). The joint model provides estimates of bG and bGE, robust estimates of the corresponding standard errors (SEs) and covariance, and P-values from the joint 2 degree-of-freedom Wald test. The SNV effect (bG) is context-dependent and thus should not be interpreted as the “main effect”[23]. Principal components were derived from genotyped SNVs and used for controlling population stratification and genomic confounding effects. Each cohort decided the number of PCs to be included in the joint statistical model analysis, as shown in S4 Table (Discovery, in Stage 1) and S8 Table (Replication, in Stage 2). Particularly for African ancestry, it was required to include at the least the first PC and additional PCs as appropriate.

The association analyses were implemented by programming in R or using ProbABEL[77] for studies of unrelated individuals, or by GenABEL/MixABEL[78] or MMAP (O’Connell, unpublished; personal communication), which account for family relatedness.

Meta-analysis and quality control

We employed a modified METAL software[24] to perform 2 degrees of freedom joint meta-analysis, using the inverse-variance weighted fixed-effects approach. We applied multiple steps of QC, both at cohort association analysis and at meta-analysis level, implemented with EasyQC, an R package[75]. They included filtering of markers with imputation quality < 0.5; with minor allele frequency < 1%; minor allele count ≤ 10; if alleles were mismatched when comparing the cohort’s alleles with the 1000 Genomes cosmopolitan panel; and/or if the allele frequencies were different from those of the 1000 Genomes. In addition, a cohort participated in the meta-analysis if it had more than 50 individuals consuming alcohol. The meta-analysis results were reported if they had more than 5,000 individuals and if at least two studies for each SNV contributed to the analysis. Markers with meta-heterogeneity P < 1.0 x 10−6 were dropped. We used (double) study- and meta- level genomic control corrections to account for population stratification accumulated across studies or due to unaccounted relatedness. Distributions of–log10 P-values of observed versus–log10 P-values expected (QQ plots) are shown in S2 and S3 Figs.

Correlated meta-analysis

The genome (millions of SNPs) are under the null hypothesis of no genotype-phenotype association, which is only mildly contaminated with a relatively smaller set of SNVs that are under the alternative. The correlated meta-analysis[25, 26] performs a large sampling of genome and produces the polychoric correlation estimator (using SAS PROC FREQ). The estimator measures the relation degree of any non-independence between scans. The correlated meta-analysis corrects the inference for it, retaining the proper type I error structure. The correlated meta-analysis[25, 26] uses the Fisher’s 1925 method by combining P-values at each location of the genome. This technique uses the fact that for number of scans, sum of −2 ln (pi), approximately chi-square (X2) with two degrees of freedom. In the case of correlated GWAS, this sum is no longer distributed as a simple X2. Instead, the correlated meta-analysis method[25, 26] uses an inverse-normal transform, Zi = θ−1 (pi) forming the N dimensional vector Z of all Zi s. Then, the method applies the basic theorem of multidimensional statistics for the matrix D, if Z~N(O, E) then DZ~N(O, ED’). In particular, when D is a 1×N vector of all 1’s, SUM(Z) = D Z ~ N(0, SUM(∑)), whose tail probability gives the Z meta-analysis P-value. In this case, for estimating ∑, the SNV P-values are dichotomized across the genome as (P ≤ 0.5; P > 0.5). The software was developed in SAS.

Bioinformatics analyses

The annotation of variants was sourced from NCBI dbSNP build 138 (hg19) during the analyses and updated to dbSNP build 150 (hg38) for reporting results. Our candidate SNVs for BP were questioned if they resided in any of regulatory marks, analyzing information from the NCBI Entrez gene, dbSNP, Encyclopedia of DNA Elements Consortium (ENCODE) project and the Roadmap Epigenomics Mapping Consortium (ROADMAP), as summarized by HaploReg[28, 29], and RegulomeDB[30, 31].

HaploReg (v.4.1) queries were used to identify functional annotations including the chromatin state segmentation on the Roadmap reference epigenomes, conserved regions by GERP and SiPhy, the experiments of DNAse hypersensitivity and ChIP-seq experiments from ENCODE. UCSC Genome Browser and GENCODE were used for gene annotations. We calculated the proximity of each variant to a gene.

RegulomeDB (v. 1.1, accessed on 06.15.2017) provided regulatory information of gene expression via ChIP factors, DNase sensitivity, and transcription factor (TF) binding sites from ENCODE. RegulomeDB uses the Position-Weight Matrix for TF binding, and databases JASPAR CORE, TRANSFAC and UniPROBE[79]. RegulomeDB reported Chromatin States from ROADMAP, eQTLs from several tissue types, DNase footprinting[80, 81], differentially methylated regions[82], manually curated regions and validated functional SNVs.

GWAS3D[33] (accessed on 03.15.2017) was used to analyze genetic variants that may affect regulatory elements, by integrating annotations from cell type-specific chromatin states, epigenetic modifications, sequence motifs and cross-species conservation. The regulatory elements are inferred from the genome-wide chromosome interaction data, chromatin marks in different cell types measured by high-throughput chromosome conformation capture technologies (5C, ChIA-PET and Hi-C) from ENCODE, Gene Expression Omnibus (GEO) database, published resources and regulatory factor motifs. We gathered also evidence for eQTLs based on GTEx (v. 7), GRASP software and special gene expression reported results[83, 84].

The importance of our novel and potential novel BP genes (Tables 15) were mined by means of four methods: enrichment analysis, protein- protein interactions (PPI), analytical gene expression cis-regulation, and analytical gene expression trans-regulation.

The GeneGO and Literature Lab of ACUMENTA software (accessed on 03.15. 2017) were used for enrichment analysis. We tested if novel genes were significantly enriched among pre-specified gene sets defined in pathways, or by shared roles in particular diseases or biological processes from Gene Ontology. The GeneGO enrichment analysis consists of matching unique gene symbols of possible targets for the "common", "similar" and "unique" sets with gene symbols in functional ontologies. The probability of a random intersection between a set of gene symbols, the size of target list with ontology entities, is estimated by P-value of a hypergeometric intersection. The lower P-value means higher relevance of the entity to the dataset, which shows in higher rating for the entity.

Literature Lab is an interface between experimentally-derived gene lists and scientific literature in a curated vocabulary of 24,000 biological and biochemical terms. It employs statistical and clustering analysis on over 17.5 million PubMed abstracts (from 01.01.1990 to the present) to identify pathways (809 pathways), diseases, compounds, cell biology and other areas of biology and biochemistry. The analysis engine compares statistically the submitted gene set to 1,000 random gene sets generated in the analysis to identify term relationships that are associated with the gene set more than by chance alone.

The BP candidate genes were assessed via PPI of databases from Biological General Repository for Interaction Datasets (BioGrid), Escherichia coli K-12 (EcoCyc), and Human Protein Database (HPRD) as summarized by the National Center for Biotechnology Information (NCBI, accessed on 02.28.2017). The gene list from PPI was evaluated using igraph package[85]. The network was built using our programs in SAS, to a Pajek format and imported into igraph in R language. “Google” PageRank algorithm provided the importance of genes (website pages) in a network, which was implemented by igraph.

Information of data analysis tools and databases, including their website links (when available) and the corresponding literature citations, are provided in S15 Table.

Supporting information

S1 Note. Description of participating studies.

Study descriptions of discovery cohorts (Stage 1) and replication cohorts (Stage 2).

https://doi.org/10.1371/journal.pone.0198166.s001

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S2 Note. Summary of biological description for novel BP loci.

Information summary of the nearest genes for blood pressure novel loci.

https://doi.org/10.1371/journal.pone.0198166.s002

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S1 Fig. Study design of SNV x alcohol interactions for BP.

Schematic study design of the joint model of SNV main effect and SNV-alcohol consumption interaction; Blood pressure (BP) traits: systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP), and pulse pressure (PP); Alcohol consumption was defined by two categories: (I) as current drinking (yes/no), and (II), in the subset of drinkers, as light/heavy drinking (1–7 drinks/week or ≥8 drinks/week); Meta-analysis using a modified version of METAL: Stage 1 (discovery), Stage 2 (replication) and combined Stage 1 and Stage 2; Cohorts: European ancestry (EA), African ancestry, Asian ancestry (ASA), Hispanic ancestry (HA), Brazilian (BRA); Correlated meta-analysis in EA for four BP traits; Number of BP loci (genes), novel and reported.

https://doi.org/10.1371/journal.pone.0198166.s003

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S2 Fig. QQ plots for BP traits for current drinkers.

Meta-analysis distributions of–log10 P-values of observed versus–log10 P-values expected (QQ plots) for current drinkers (yes/no) European ancestry (A) and in African ancestry (B).

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S3 Fig. QQ plots for BP traits for light/heavy drinkers.

Meta-analysis distributions of–log10 P-values of observed versus–log10 P-values expected (QQ plots) for light/heavy drinkers (1–7 drinks/week or ≥8 drinks/week) in European ancestry (A) and in African ancestry (B).

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S4 Fig. Regional association plots on 8p23.

SNV x current drinker interaction for SBP (A), DBP (B), MAP (C) and PP (D) in European Ancestry; four linkage disequilibrium (LD) blocks (see also Fig 1).

https://doi.org/10.1371/journal.pone.0198166.s006

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S5 Fig. Regional association plots on 16q12.

SNV x current drinker interaction for SBP (A), DBP (B), MAP (C) and PP (D) in European Ancestry.

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S6 Fig.

Manhattan plots of combined Stage 1 and Stage 2 meta-analysis for SBP in current drinkers (A) and in light/heavy drinkers (B) in European ancestry. Novel loci are highlighted in blue.

https://doi.org/10.1371/journal.pone.0198166.s008

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S7 Fig.

Manhattan plots of combined Stage 1 and Stage 2 meta-analysis for DBP in current drinkers (A) and in light/heavy drinkers (B) in European ancestry. Novel loci are highlighted in blue.

https://doi.org/10.1371/journal.pone.0198166.s009

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S8 Fig.

Manhattan plots of combined Stage 1 and Stage 2 meta-analysis for MAP in current drinkers (A) and in light/heavy drinkers (B) in European ancestry. Novel loci are highlighted in blue.

https://doi.org/10.1371/journal.pone.0198166.s010

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S9 Fig.

Manhattan plots of combined Stage 1 and Stage 2 meta-analysis for PP in current drinkers (A) and in light/heavy drinkers (B) in European ancestry. Novel loci are highlighted in blue.

https://doi.org/10.1371/journal.pone.0198166.s011

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S10 Fig.

Manhattan plots of combined Stage 1 and Stage 2 meta-analysis for SBP in current drinkers (A) and in light/heavy drinkers (B) in African ancestry. Novel loci are highlighted in blue.

https://doi.org/10.1371/journal.pone.0198166.s012

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S11 Fig.

Manhattan plots of combined Stage 1 and Stage 2 meta-analysis for DBP in current drinkers (A) and in light/heavy drinkers (B) in African ancestry.

https://doi.org/10.1371/journal.pone.0198166.s013

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S12 Fig.

Manhattan plots of combined Stage 1 and Stage 2 meta-analysis for MAP in current drinkers (A) and in light/heavy drinkers (B) in African ancestry.

https://doi.org/10.1371/journal.pone.0198166.s014

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S13 Fig.

Manhattan plots of combined Stage 1 and Stage 2 meta-analysis for PP in current drinkers (A) and in light/heavy drinkers (B) in African ancestry. Novel loci are highlighted in blue.

https://doi.org/10.1371/journal.pone.0198166.s015

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S14 Fig.

Manhattan plots of combined Stage 1 and Stage 2 meta-analysis for SBP (A) and DBP (B) in current drinkers in Asian ancestry.

https://doi.org/10.1371/journal.pone.0198166.s016

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S15 Fig.

Manhattan plots of combined Stage 1 and Stage 2 meta-analysis for MAP (A) and PP (B) in current drinkers in Asian ancestry.

https://doi.org/10.1371/journal.pone.0198166.s017

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S16 Fig. Protein-protein interactions network.

In the figure, ellipses in black represent all novel genes; ellipses in red represent novel from EA; squares in blue represent potential novel findings from African ancestry; and triangles in black from correlated-meta. Labeled with A and B free-hand circles are proteins that have two connections, while labeled within C are proteins that have three-five connections with our findings. APP interacts with five of our BP candidate novel genes TTLL7, SOX7, PINX1, LINGO2 and KCNMB2 (circle C).

https://doi.org/10.1371/journal.pone.0198166.s018

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S17 Fig. Protein-protein interactions between tankyrase and beta-catenin.

Tankyrase (from TNKS gene) and β-catenin (from CTNNB1 gene).

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S18 Fig. Wnt signaling KEGG pathway.

TNKS interacts with CTNNB1.

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S1 Table. Descriptive analyses for discovery data (Stage 1) in current drinkers.

Characteristics of blood pressure (BP) in current drinkers (yes or no), within sub-sample of individuals with or without anti-hypertensive (BP Lowering) medications, and in combined samples; SBP, systolic BP; DBP, diastolic BP; MAP, mean arterial pressure; PP, pulse pressure; N, number of individuals; mean, mean levels; SD, standard deviation of mean; Min, minimum value; Max, maximum value; For each BP trait (SBP, DBP, MAP, and PP), the extreme BP values were winsorised if a BP value was greater than 6 SD, above or below the mean, setting the BP value exactly at 6 SDs from the mean.

https://doi.org/10.1371/journal.pone.0198166.s021

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S2 Table. Descriptive analyses for discovery data (Stage 1) in light/heavy drinkers.

Characteristics of blood pressure (BP) in light/heavy drinkers (1–7 drinks/week or ≥8 drinks/week), within sub-sample of individuals with or without anti-hypertensive (BP Lowering) medications, and in combined samples; SBP, systolic BP; DBP, diastolic BP; MAP, mean arterial pressure; PP, pulse pressure; N, number of individuals; mean, mean levels; SD, standard deviation of mean; Min, minimum value; Max, maximum value; For each BP trait (SBP, DBP, MAP, and PP), the extreme BP values were winsorised if a BP value was greater than 6 SD, above or below the mean, setting the BP value exactly at 6 SDs from the mean.

https://doi.org/10.1371/journal.pone.0198166.s022

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S3 Table. Descriptive analyses for blood pressure (BP) stratified by alcohol consumption for discovery data (Stage 1).

Characteristics of systolic BP and diastolic BP, after correcting for BP lowering medication and winsorizing observations.

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S4 Table. Characteristics of each study and their genotype data for discovery data (Stage 1).

Study design, population-based or cohort-unrelated; Principal components used; Other covariates entered in the model; Genotyping platforms; Genotyping calling algorithm; Quality Control Filters; Imputation reference panel; Number of SNVs (single nucleotide variants).

https://doi.org/10.1371/journal.pone.0198166.s024

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S5 Table. Descriptive analyses for replication data (Stage 2) in current drinkers.

Characteristics of blood pressure (BP) within current drinkers (CURD: yes or no), and in alcohol combined samples; SBP, systolic BP; DBP, diastolic BP; MAP, mean arterial pressure; PP, pulse pressure; N, number of individuals; mean, mean levels; SD, standard deviation of mean; Min, minimum value; Max, maximum value.

https://doi.org/10.1371/journal.pone.0198166.s025

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S6 Table. Descriptive analyses for replication data (Stage 2) in light/heavy drinkers.

Characteristics of blood pressure (BP) within light/heavy drinkers (LHD: 1–7 drinks/week or ≥8 drinks/week), and in alcohol combined samples; SBP, systolic BP; DBP, diastolic BP; MAP, mean arterial pressure; PP, pulse pressure; N, number of individuals; mean, mean levels; SD, standard deviation of mean; Min, minimum value; Max, maximum value.

https://doi.org/10.1371/journal.pone.0198166.s026

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S7 Table. Demographic statistics for replication data (Stage 2).

N, Number of subjects; % Hypertensive, defined whether participants presented: (i) SBP ≥ 140 mm Hg, (ii) DBP ≥ 90 mm Hg, and/or (iii) taking anti-hypertensive medication; Mean, age mean; SD, standard deviation of mean; Min, minimum age; Max, maximum age.

https://doi.org/10.1371/journal.pone.0198166.s027

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S8 Table. Characteristics of each study and their genotype data for replication data (Stage 2).

Study design, population-based or cohort-unrelated; Principal components used; Other covariates entered in the model; Genotyping platforms; Genotyping calling algorithm; Imputation reference panel; NCBI dbSNP build; Analysis software; Robust or model-based statistics; Family studies: Method of handling relatedness.

https://doi.org/10.1371/journal.pone.0198166.s028

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S9 Table. Novel SNVs/ genes associated with BP traits in multi-ancestry and specific-ancestry meta-combined results.

Top significant associated SNVs are shown per gene for each trait and alcohol exposure.

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S10 Table. SNVs/genes associated with BP traits in European ancestry.

Variants previously reported for blood pressure (BP) in genome-wide association studies. The most significant associated SNVs are shown per gene for each Blood Pressure (BP) trait and alcohol status. Abbreviations: Nb, order number based on genes; SNV, single nucleotide variant; Chr, chromosome; Position, Gene, and Role in dbSNP build 150 (hg38) annotation; Role: Intronic, missense, up-stream or downstream, or intergenic (blank space) SNV; Near gene reflects genes at up to +/-500 kb and related to BP / alcohol; A1/2, Coded and non-coded alleles; Frq1, Frequency of coded allele; Trait, SBP: Systolic BP, DBP: Diastolic BP, MAP: Mean Arterial Pressure, PP: Pulse Pressure; Drink: Alcohol consumption, CURD, Current drinker (yes/no), LHD, Light(1–7 drinks/week) or heavy (≥8 drinks/week) drinker; Stage 1, Discovery cohorts; Stage 2, Replication cohorts; Stage 1 & Stage 2, Discovery and Replication combined; b_M(S.E.), beta coefficient of SNV (standard error); b_I(S.E.): SNV*E is SNV-alcohol interaction effect (standard error); P-Value: modified-interaction METAL P-Value; N, Number of subjects; P-Meta, P-Meta, modified-interaction METAL P-Value of Meta-analysis in combined Stage 1 and Stage 2; Het-P value, Heterogeneity P-Value. * These genes were detected also via correlated meta-analysis.

https://doi.org/10.1371/journal.pone.0198166.s030

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S11 Table. SNVs/genes associated with BP traits in African ancestry.

Variants previously reported for blood pressure (BP) in genome-wide association studies. The most significant associated SNVs are shown per gene for each Blood Pressure (BP) trait and alcohol status. Abbreviations: Nb, order number based on genes; SNV, single nucleotide variant; Chr, chromosome; Position, Gene, and Role in dbSNP build 150 (hg38) annotation; Role: Intronic or intergenic (blank space) SNV; Near gene reflects genes at up to +/-500 kb and related to BP / alcohol; A1/2, Coded and non-coded alleles; Frq1, Frequency of coded allele; Trait, SBP: Systolic BP, DBP: Diastolic BP, MAP: Mean Arterial Pressure, PP: Pulse Pressure; Drink: Alcohol consumption, CURD, Current drinker (yes/no); Stage 1, Discovery cohorts; Stage 2, Replication cohorts; Stage 1 & Stage 2, Discovery and Replication combined; b_M(S.E.), beta coefficient of SNV (standard error); b_I(S.E.): SNV*E is SNV-alcohol interaction effect (standard error); P-Value: modified-interaction METAL P-Value; N, Number of subjects; P-Meta, P-Meta, modified-interaction METAL P-Value of Meta-analysis in combined Stage 1 and Stage 2; Het-P value, Heterogeneity P-Value. * These genes were detected also via correlated meta-analysis.

https://doi.org/10.1371/journal.pone.0198166.s031

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S12 Table. SNVs/genes associated with BP traits in multi-ancestry meta-analysis in combined Stage 1 and Stage 2.

Variants previously reported for blood pressure (BP) in genome-wide association studies. The most significant associated SNVs are shown per gene for each Blood Pressure (BP) trait and alcohol status. Abbreviations: Nb, order number based on genes; SNV, single nucleotide variant; Chr, chromosome; Position, Gene, and Role in dbSNP build 150 (hg38) annotation; Role: Intronic, missense, up-stream or downstream, or intergenic (blank space) SNV; Near gene reflects genes at up to +/-500 kb and related to BP / alcohol; A1/2, Coded and non-coded alleles; Frq1, Frequency of coded allele; Ancestry, EA: European Ancestry, AA: African American Ancestry, ASA: Asian American Ancestry, HA: Hispanic Ancestry; Trait, SBP: Systolic BP, DBP: Diastolic BP, MAP: Mean Arterial Pressure, PP: Pulse Pressure; Drink: Alcohol consumption, CURD, Current drinker (yes/no), LHD, Light(1–7 drinks/week) or heavy (≥8 drinks/week) drinker; Stage 1 and Stage 2, Combined Discovery and Replication; b_M, beta coefficient of SNV; b_I: SNV*E is SNV-alcohol interaction effect; P-Value, modified-interaction METAL P-Value of meta-analysis in combined Stage 1 and Stage 2; N, Number of subjects; Het-P value, Heterogeneity P-Value.

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S13 Table. SNVs/genes associated with BP traits for regulatory features using HaploReg and RegulomeDB.

Association findings from European Ancestry (novel), African Ancestry (potential) and correlated meta-analysis (novel variants). The annotation of variants was sourced from NCBI dbSNP build 138 (hg19) during the analyses and updated to dbSNP build 150 (hg38) for reporting results. Abbreviations: Nb, order number based on SNVs; Position, dbSNP build 150 (hg38) annotation; Variant, single nucleotide variant (SNV); Ref, reference allele; Alt, alternative allele; AFR Freq, Freq of Ref in African ancestry; ASN Freq, Freq of Ref in East Asian ancestry; EUR Freq, Freq of Ref in European ancestry; GERP cons and Siphy cons, measured conserved regions. RegulomeDB scoring has classes defined as 1b, 1d and 1f: likely to affect binding and linked to expression of a gene target, with details: 1b (eQTL + TF binding + any motif + DNase footprint + DNase peak); 1d (eQTL + TF binding + any motif + DNase peak); 1f (eQTL + TF binding/DNase peak), 2a and 2b: likely to affect binding, 3a: less likely to affect binding, 4, 5, and 6: minimal binding evidence, and 7: no data. This software was accessed on 06.15.2017. Regulatory function measured by Promoter histone marks, Enhancer histone marks, DNase (DNAse hypersensitivity), Proteins bound, Motifs changed.

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S14 Table. Novel SNVs/genes associated with BP traits for eSNV/eQTL using GTEx.

Target genes (Tissues and P-Values). Association findings from European Ancestry (novel) and correlated meta-analysis (novel variants). The annotation of variants was sourced from NCBI dbSNP build 138 (hg19) during the analyses and updated to dbSNP build 150 (hg38) for reporting results. Abbreviations: Nb, order number based on SNVs; Position, dbSNP build 150 (hg38) annotation; Variant, single nucleotide variant (SNV); Ref, reference allele; Alt, alternative allele; AFR Freq, Freq of Ref in African ancestry; ASN Freq, Freq of Ref in East Asian ancestry; EUR Freq, Freq of Ref in European ancestry. * RegulomeDB scoring has classes defined as 1b, 1d and 1f: likely to affect binding and linked to expression of a gene target, with details: 1b (eQTL + TF binding + any motif + DNase footprint + DNase peak); 1d (eQTL + TF binding + any motif + DNase peak); 1f (eQTL + TF binding/DNase peak), 2a and 2b: likely to affect binding, 3a: less likely to affect binding, 4, 5, and 6: minimal binding evidence, and 7: no data. This software was accessed on 06.15.2017. Regulatory function measured by Promoter histone marks, Enhancer histone marks, DNase (DNAse hypersensitivity), Proteins bound, Motifs changed.

https://doi.org/10.1371/journal.pone.0198166.s034

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S15 Table. Data analysis tools and databases.

https://doi.org/10.1371/journal.pone.0198166.s035

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Acknowledgments

Discovery:

AGES (Age Gene/Environment Susceptibility Reykjavik 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.

ARIC (Atherosclerosis Risk in Communities): The authors thank the staff and participants of the ARIC study for their important contributions.

CARDIA (Coronary Artery Risk Development in Young Adults): This manuscript has been reviewed and approved by CARDIA for scientific content.

CHS (Cardiovascular Health Study): A full list of principal CHS investigators and institutions can be found at CHS-NHLBI.org. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

IGMM (Institute of Genetics and Molecular Medicine): CROATIA-Korcula: We would like to acknowledge the staff of several institutions in Croatia that supported the field work, including but not limited to The University of Split and Zagreb Medical Schools and the Croatian Institute for Public Health. We would like to acknowledge the invaluable contributions of the recruitment team in Korcula, the administrative teams in Croatia and Edinburgh and the participants. The SNP genotyping for the CROATIA-Korcula cohort was performed in Helmholtz Zentrum München, Neuherberg, Germany. CROATIA-Vis: We would like to acknowledge the staff of several institutions in Croatia that supported the field work, including but not limited to The University of Split and Zagreb Medical Schools, the Institute for Anthropological Research in Zagreb and Croatian Institute for Public Health. The SNP genotyping for the CROATIA-Vis cohort was performed in the core genotyping laboratory of the Wellcome Trust Clinical Research Facility at the Western General Hospital, Edinburgh, Scotland. GS:SFHS: Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates [CZD/16/6] and the Scottish Funding Council [HR03006]. Genotyping of the GS:SFHS samples was carried out by the Genetics Core Laboratory at the Wellcome Trust Clinical Research Facility, Edinburgh, Scotland.

ERF (Erasmus Rucphen Family study): We are grateful to all study participants and their relatives, general practitioners and neurologists for their contributions and to P. Veraart for her help in genealogy, J. Vergeer for the supervision of the laboratory work, P. Snijders for his help in data collection and E.M. van Leeuwen for genetic imputation.

GENOA (Genetic Epidemiology Network of Arteriopathy): Genotyping was performed at the Mayo Clinic (Stephen T. Turner, MD, Mariza de Andrade PhD, Julie Cunningham, PhD). We thank Eric Boerwinkle, PhD and Megan L. Grove from the Human Genetics Center and Institute of Molecular Medicine and Division of Epidemiology, University of Texas Health Science Center, Houston, Texas, USA for their help with genotyping. We would also like to thank the families that participated in the GENOA study.

HANDLS (Healthy Aging in Neighborhoods of Diversity across the Life Span): Data analyses for the HANDLS study utilized the high-performance computational resources of the Biowulf Linux cluster at the National Institutes of Health, Bethesda, MD. http://hpc.nih.gov

HUFS (Howard University Family Study): We thank the participants of the study. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official view of the National Institutes of Health.

HyperGEN (Hypertension Genetic Epidemiology Network): The study involves: University of Utah: (Network Coordinating Center, Field Center, and Molecular Genetics Lab); Univ. of Alabama at Birmingham: (Field Center and Echo Coordinating and Analysis Center); Medical College of Wisconsin: (Echo Genotyping Lab); Boston University: (Field Center); University of Minnesota: (Field Center and Biochemistry Lab); University of North Carolina: (Field Center); Washington University: (Data Coordinating Center); Weil Cornell Medical College: (Echo Reading Center); National Heart, Lung, & Blood Institute. For a complete list of HyperGEN Investigators: http://www.biostat.wustl.edu/hypergen/Acknowledge.html

JHS (Jackson Heart Study): The authors wish to thank the staffs and participants of the JHS.

MESA (Multi-Ethnic Study of Atherosclerosis): MESA and the MESA SHARe project are conducted in collaboration with MESA investigators. Genotyping was performed at Affymetrix (Santa Clara, California, USA) and the Broad Institute of Harvard and MIT (Boston, Massachusetts, USA) using the Affymetrix Genome-Wide Human SNP Array 6.0.

NEO (The Netherlands Epidemiology of Obesity study): 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, Petra Noordijk, Pat van Beelen and Ingeborg de Jonge for the coordination, lab and data management of the NEO study.

RS (Rotterdam Study) was funded by Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. The authors are grateful to the study participants, the staff from the Rotterdam Study and the participating general practitioners and pharmacists. The generation and management of GWAS genotype data for the Rotterdam Study was executed by the Human Genotyping Facility of the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands. We thank Pascal Arp, Mila Jhamai, Marijn Verkerk, Lizbeth Herrera, Marjolein Peters and Carolina Medina-Gomez for their help in creating the GWAS database, and Karol Estrada, Yurii Aulchenko and Carolina Medina-Gomez for the creation and analysis of imputed data.

WHI (Women’s Health Initiative): The authors thank the WHI investigators and staff for their dedication, and the study participants for making the program possible. A full listing of WHI investigators can be found at: http://www.whi.org/researchers/Documents%20%20Write%20a%20Paper/WHI%20Investigator%20Short%20List.pdf

Replication:

AA-DHS (African American Diabetes Heart Study): The investigators acknowledge the