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

Mary Feitosa, Aldi Kraja, Daniel Chasman, Yun Sung, Thomas Winkler, Ioanna Ntalla, Xiuqing Guo, Nora Franceschini, Ching-Yu Cheng, Xueling Sim, et al.

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## To cite this version:

Mary Feitosa, Aldi Kraja, Daniel Chasman, Yun Sung, Thomas Winkler, et al.. Novel genetic associations for blood pressure identified via gene-alcohol interaction in up to 570K individuals across multiple ancestries. PLoS ONE, 2018, 13 (6), pp.e0198166. 10.1371/journal.pone.0198166. pasteur-03278584

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RESEARCH ARTICLE

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

Mary F. Feitosa¹*, Aldi T. Kraja¹, Daniel I. Chasman²,³,*, Yun J. Sung⁴, Thomas W. Winkler⁵,⁰, Ioanna Ntalla⁶,⁰, Xiuqing Guo⁷, Nora Franceschini⁸, Ching-Yu Cheng⁹,¹⁰,¹¹, Xueling Sim¹², Dina Vojinovic¹³, Jonathan Marten¹⁴, Solomon K. Musani¹⁵, Changwei Li¹⁶, Amy R. Bentley¹⁷, Michael R. Brown¹⁸, Karen Schwander⁴, Melissa A. Richard¹⁹, Raymond Noordam²⁰, Hugues Aschard²¹,²², Traci M. Bartz²³, Lawrence F. Bielak²⁴, Rajkumar Dorajoo²⁵, Virginia Fisher²⁶, Fernando P. Hartwig²⁷,²⁸, Andrea R. V. R. Horimoto²⁹, Kurt K. Lohman³⁰, Alisa K. Manning³¹,³², Tuomo Rankinen³³, Albert V. Smith³⁴,³⁵, Salman M. Tajuddin³⁶, Mary K. Wojczynski¹, Maris Alver³⁷, Mathilde Boissel³⁸, Qiuyin Cai³⁹, Archie Campbell⁴⁰, Jin Fang Chai¹², Xu Chen⁴¹, Jasmin Divers³⁰, Chuan Gao⁴², Anuj Goel⁴³,⁴⁴, Yanick Hagemeijer⁴⁵, Sarah E. Harris⁴⁶,⁴⁷, Meian He⁴⁸, Fang-Chi Hsu³⁰, Anne U. Jackson⁴⁹, Mika Kähönen⁵⁰,⁵¹, Anuradhani Kasturiratne⁵², Pirjo Komulainen⁵³, Brigitte Kühnel⁵⁴,⁵⁵, Federica Laguzzi⁵⁶, Jian'an Luan⁵⁷, Nana Matoba⁵⁸, Ilja M. Nolte⁵⁹, Sandosh Padmanabhan⁶⁰, Muhammad Riaz⁶¹,⁶², Rico Rueedi⁶³,⁶⁴, Antonietta Robino⁶⁵, M. Abdullah Said⁴⁵, Robert A. Scott⁵⁷, Tamar Sofer³²,⁶⁶, Alena Stančáková⁶⁷, Fumihiko Takeuchi⁶⁸, Bamidele O. Tayo⁶⁹, Peter J. van der Most⁵⁹, Tibor V. Varga⁷⁰, Veronique Vitart¹⁴, Yajuan Wang⁷¹, Erin B. Ware⁷², Helen R. Warren⁶,⁷³, Stefan Weiss⁷⁴,⁷⁵, Wanqing Wen³⁹, Lisa R. Yanek⁷⁶, Weihua Zhang⁷⁷,⁷⁸, Jing Hua Zhao⁵⁷, Saima Afaq⁷⁷, Najaf Amin¹³, Marzyeh Amini⁵⁹, Dan E. Arking⁷⁹, Tin Aung⁹,¹⁰,¹¹, Eric Boerwinkle⁸⁰,⁸¹, Ingrid Borecki¹, Ulrich Broeckel⁸², Morris Brown⁶,⁷³, Marco Brumat⁸³, Gregory L. Burke⁸⁴, Mickaël Canouil³⁸, Aravinda Chakravarti⁷⁹, Sabanayagam Charumathi⁹,¹⁰, Yii-Der Ida Chen⁷, John M. Connell⁸⁵, Adolfo Correa¹⁵, Lisa de las Fuentes⁴,⁸⁶, Renée de Mutsert⁸⁷, H. Janaka de Silva⁸⁸, Xuan Deng²⁶, Jingzhong Ding⁸⁹, Qing Duan⁹⁰, Charles B. Eaton⁹¹, Georg Ehret⁹², Ruben N. Eppinga⁴⁵, Evangelos Evangelou⁷⁷,⁹³, Jessica D. Faul⁷², Stephan B. Felix⁷⁵,⁹⁴, Nita G. Forouhi⁵⁷, Terrence Forrester⁹⁵, Oscar H. Franco¹³, Yechiel Friedlander⁹⁶, Ilaria Gandin⁸³, He Gao⁷⁷, Mohsen Ghanbari¹³,⁹⁷, Bruna Gigante⁵⁶, C. Charles Gu⁴, Dongfeng Gu⁹⁸, Saskia P. Hagenaars⁴⁶,⁹⁹, Göran Hallmans¹⁰⁰, Tamara B. Harris¹⁰¹, Jiang He¹⁰²,¹⁰³, Sami Heikkinen⁶⁷,¹⁰⁴, Chew-Kiat Heng¹⁰⁵,¹⁰⁶, Makoto Hirata¹⁰⁷, Barbara V. Howard¹⁰⁸,¹⁰⁹, M. Arfan Ikram¹³,¹¹⁰,¹¹¹, InterAct Consortium⁵⁷, Ulrich John⁷⁵,¹¹², Tomohiro Katsuya¹¹³,¹¹⁴, Chiea Chuen Khor²⁵,¹¹⁵, Tuomas O. Kilpeläinen¹¹⁶,¹¹⁷, Woon-Puay Koh¹²,¹¹⁸, José E. Krieger²⁹, Stephen B. Kritchevsky¹¹⁹, Michiaki Kubo¹²⁰, Johanna Kuusisto⁶⁷, Timo A. Lakka⁵³,¹⁰⁴,¹²¹, Carl D. Langefeld³⁰, Claudia Langenberg⁵⁷, Lenore J. Launer¹⁰¹, Benjamin Lehne⁷⁷, Cora E. Lewis¹²², Yize Li⁴, Shiow Lin¹, Jianjun Liu¹²,²⁵, Jingmin Liu¹²³, Marie Loh⁷⁷,¹²⁴, Tin Louie¹²⁵, Reedik Mägi³⁷, Colin A. McKenzie⁹⁵, Thomas Meitinger¹²⁶,¹²⁷, Andres Metspalu³⁷, Yuri Milaneschi¹²⁸, Lili Milani³⁷, Karen L. Mohlke⁹⁰, Yukihide Momozawa¹²⁹, Mike A. Nalls¹³⁰,¹³¹, Christopher P. Nelson⁶¹,⁶², Nona Sotoodehnia¹³², Jill M. Norris¹³³, Jeff R. O'Connell¹³⁴,¹³⁵, Nicholette D. Palmer¹³⁶, Thomas Perls¹³⁷, Nancy L. Pedersen⁴¹, Annette Peters⁵⁵,¹³⁸, Patricia A. Peyser²⁴, Neil Poulter¹³⁹, Leslie J. Raffel¹⁴⁰, Olli T. Raitakari¹⁴¹,¹⁴², Kathryn Roll⁷, Lynda M. Rose², Frits R. Rosendaal⁸⁷, Jerome I. Rotter⁷, Carsten O. Schmidt¹⁴³, Pamela J. Schreiner¹⁴⁴, Nicole Schupf¹⁴⁵, William R. Scott⁷⁷,¹⁴⁶, Peter S. Sever¹⁴⁶, Yuan Shi⁹, Stephen Sidney¹⁴⁷, Mario Sims¹⁵, Colleen M. Sitlani¹⁴⁸, Jennifer A. Smith²⁴,⁷², Harold Snieder⁵⁹, John M. Starr⁴⁶,¹⁴⁹, Konstantin Strauch¹⁵⁰,¹⁵¹, Heather M. Stringham⁴⁹, Nicholas Y. Q. Tan⁹, Hua Tang¹⁵², Kent D. Taylor⁷, Yik Ying Teo¹²,²⁵,¹⁵³,¹⁵⁴,¹⁵⁵, Yih Chung Tham⁹, Stephen T. Turner¹⁵⁶, André G. Uitterlinden¹³,¹⁵⁷, Peter Vollenweider¹⁵⁸, Melanie Waldenberger⁵⁴,⁵⁵, Lihua Wang¹, Ya Xing Wang¹⁵⁹,¹⁶⁰, Wen Bin Wei¹⁶⁰, Christine Williams¹, Jie Yao⁷, Caizheng Yu⁴⁸, Jian-Min Yuan¹⁶¹,¹⁶², Wei Zhao²⁴, Alan

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OPEN ACCESS

**Citation:** Feitosa MF, Kraja AT, Chasman DI, Sung YJ, Winkler TW, Ntalla I, et al. (2018) Novel genetic associations for blood pressure identified via gene-alcohol interaction in up to 570K individuals across multiple ancestries. PLoS ONE 13(6): e0198166.
https://doi.org/10.1371/journal.pone.0198166

**Editor:** Helena Kuivaniemi, Stellenbosch University
Faculty of Medicine and Health Sciences, SOUTH AFRICA

**Received:** February 27, 2018

**Accepted:** May 15, 2018

**Published:** June 18, 2018

**Copyright:** This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

**Data Availability Statement:** The meta-analysis results from this study are available at dbGAP (accession number phs000930).

**Funding:** The following authors declare commercial private and/or governmental affiliations: Bruce M. Psaty (BMP) serves on the DSMB of a clinical trial funded by Zoll Lifecor and on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. Barbara V. Howard (BVH) has a contract from
National Heart, Lung, and Blood Institute (NHLBI). Brenda W.J.H. Penninx (BWJHP) has received research funding (non-related to the work reported here) from Jansen Research and Boehringer Ingelheim. Mike A. Nalls (MAN) is supported by a consulting contract between Data Tecnica International LLC and the National Institute on Aging (NIA), National Institutes of Health (NIH), Bethesda, MD, USA. MAN also consults for Illumina Inc., the Michael J. Fox Foundation, and the University of California Healthcare. MAN also has commercial affiliation with Data Tecnica International, Glen Echo, MD, USA. Mark J. Caulfield (MJC) has commercial affiliation and is Chief Scientist for Genomics England, a UK government company. Oscar H Franco (OHF) is supported by grants from Metagenics (on women's health and epigenetics) and from Nestlé (on child health). Peter S. Sever (PSS) is financial supported from several pharmaceutical companies which manufacture either blood pressure lowering or lipid lowering agents, or both, and consultancy fees. Paul W. Franks (PWF) has been a paid consultant in the design of a personalized nutrition trial (PREDICT) as part of a private-public partnership at Kings College London, UK, and has received research support from several pharmaceutical companies as part of European Union Innovative Medicines Initiative (IMI) projects. Fimlab LTD provided support in the form of salaries for author Terho Lehtimäki (TL) but did not have any additional role in the study design to publish, or preparation of the manuscript. Gen-info Ltd provided support in the form of salaries for author Ozren Polašek (OP) but did not have any additional role in the study design to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the 'author contributions' section. There are no patents, products in development, or marked products to declare. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

**Competing interests:** The authors have read the journal's policy and the authors of this manuscript have the following competing interests: Bruce M. Psaty (BMP) serves on the DSMB of a clinical trial funded by Zoll Lifecor and on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. Barbara V. Howard (BVH) has a contract from National Heart, Lung, and Blood Institute (NHLBI). Brenda W.J.H. Penninx (BWJHP) has received research funding (non-related to the work reported here) from Jansen Research and Boehringer Ingelheim. Mike A. Nalls (MAN) is supported by a consulting contract between Data Tecnica International LLC

B. Zonderman¹⁶³, Diane M. Becker⁷⁶, Michael Boehnke⁴⁹, Donald W. Bowden¹³⁶, John C. Chambers⁷⁷,78,164,165,166, Ian J. Deary⁴⁶,⁹⁹, Tōnu Esko³⁷,¹⁶⁷, Martin Farrall⁴³,⁴⁴, Paul W. Franks⁷⁰,¹⁶⁸, Barry I. Freedman¹⁶⁹, Philippe Froguel³⁸,¹⁷⁰, Paolo Gasparini⁶⁵,⁸³, Christian Gieger⁵⁴,¹⁷¹, Jost Bruno Jonas¹⁵⁹,¹⁷², Yoichiro Kamatani⁵⁸, Norihiro Kato⁶⁸, Jaspal S. Kooner⁷⁸,¹⁴⁶,¹⁶⁵,¹⁶⁶, Zoltán Kutalik⁶⁴,¹⁷³, Markku Laakso⁶⁷, Cathy C. Laurie¹²⁵, Karin Leander⁵⁶, Terho Lehtimäki¹⁷⁴,¹⁷⁵, Lifelines Cohort Study¹⁷⁶, Patrik K. E. Magnusson⁴¹, Albertine J. Oldehinkel¹⁷⁷, Brenda W. J. H. Penninx¹²⁸, Ozren Polasek¹⁷⁸,¹⁷⁹,¹⁸⁰, David J. Porteous⁴⁰, Rainer Rauramaa⁵³, Nilesh J. Samani⁶¹,⁶², James Scott¹⁴⁶, Xiao-Ou Shu³⁹, Pim van der Harst⁴⁵,¹⁸¹, Lynne E. Wagenknecht⁸⁴, Nicholas J. Wareham⁵⁷, Hugh Watkins⁴³,⁴⁴, David R. Weir⁷², Ananda R. Wickremasinghe⁵², Tangchun Wu⁴⁸, Wei Zheng³⁹, Claude Bouchard³³, Kaare Christensen¹⁸², Michele K. Evans³⁶, Vilmundur Gudnason³⁴,³⁵, Bernardo L. Horta²⁷, Sharon L. R. Kardia²⁴, Yongmei Liu¹⁸³, Alexandre C. Pereira²⁹, Bruce M. Psaty¹⁸⁴,¹⁸⁵, Paul M. Ridker²,³, Rob M. van Dam¹²,¹⁸⁶, W. James Gauderman¹⁸⁷, Xiaofeng Zhu⁷¹, Dennis O. Mook-Kanamori⁸⁷,¹⁸⁸, Myriam Fornage¹⁸,¹⁹, Charles N. Rotimi¹⁷, L. Adrienne Cupples²⁶,¹⁸⁹, Tanika N. Kelly¹⁰², Ervin R. Fox¹⁹⁰, Caroline Hayward¹⁴, Cornelia M. van Duijn¹³, E Shyong Tai¹²,¹¹⁸,¹⁸⁶, Tien Yin Wong⁹,¹⁰,¹¹, Charles Kooperberg¹⁹¹, Walter Palmas¹⁹², Kenneth Rice¹²⁵‡, Alanna C. Morrison¹⁸‡, Paul Elliott¹⁶⁶‡, Mark J. Caulfield⁶,⁷³‡, Patricia B. Munroe⁶,⁷³‡, Dabeeru C. Rao⁴‡, Michael A. Province¹‡, Daniel Levy¹⁸⁹,¹⁹³‡*

1 Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America, 2 Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America, 3 Harvard Medical School, Boston, Massachusetts, United States of America, 4 Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, United States of America, 5 Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany, 6 Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom, 7 Genomic Outcomes, Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, California, United States of America, 8 Epidemiology, University of North Carolina Gilling School of Global Public Health, Chapel Hill, North Carolina, United States of America, 9 Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore, 10 Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore, 11 Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore, 12 Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, Singapore, 13 Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands, 14 Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom, 15 Jackson Heart Study, Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America, 16 Epidemiology and Biostatistics, University of Georgia at Athens College of Public Health, Athens, Georgia, United States of America, 17 Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America, 18 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, 19 Brown Foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America, 20 Internal Medicine, Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands, 21 Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America, 22 Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur, Paris, France, 23 Cardiovascular Health Research Unit, Biostatistics and Medicine, University of Washington, Seattle, Washington, United States of America, 24 Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America, 25 Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore, 26 Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America, 27 Postgraduate Programme in Epidemiology, Federal University of Pelotas, Pelotas, RS, Brazil, 28 Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom, 29 Laboratory of Genetics and Molecular Cardiology, Heart Institute (InCor), University of São Paulo Medical School, São Paulo, SP, Brazil, 30 Biostatistical Sciences, Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America, 31 Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, Massachusetts, United States of America, 32 Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America, 33 Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, Louisiana, United States of America
and the National Institute on Aging (NIA), National Institutes of Health (NIH), Bethesda, MD, USA. MAN also consults for Illumina Inc., the Michael J. Fox Foundation, and the University of California Healthcare. MAN also has commercial affiliation with Data Tecnica International, Glen Echo, MD, USA. Mark J. Caulfield (MJC) has commercial affiliation and is Chief Scientist for Genomics England, a UK government company. OHF is supported by grants from Metagenics (on women's health and epigenetics) and from Nestlé (on child health). Peter S. Sever (PSS) is financial supported from several pharmaceutical companies which manufacture either blood pressure lowering or lipid lowering agents, or both, and consultancy fees. Paul W. Franks (PWF) has been a paid consultant in the design of a personalized nutrition trial (PREDICT) as part of a private-public partnership at Kings College London, UK, and has received research support from several pharmaceutical companies as part of European Union Innovative Medicines Initiative (IMI) projects. Terho Lehtimäki (TL) is employed by Fimlab Ltd. Ozren Polašek (OP) is employed by Gen-info Ltd. There are no patents, products in development, or marked products to declare. All the other authors have declared no competing interests exist. This does not alter the authors' adherence to PLOS ONE policies on sharing data and materials.

States of America, 34 Icelandic Heart Association, Kopavogur, Iceland, 35 Faculty of Medicine, University of Iceland, Reykjavik, Iceland, 36 Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States of America, 37 Estonian Genome Center, University of Tartu, Tartu, Estonia, 38 CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, University of Lille, Lille, France, 39 Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America, 40 Centre for Genomic & Experimental Medicine, Institute of Genetics & Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom, 41 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Stockholm, Sweden, 42 Molecular Genetics and Genomics Program, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America, 43 Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, Oxfordshire, United Kingdom, 44 Wellcome Centre for Human Genetics, University of Oxford, Oxford, Oxfordshire, United Kingdom, 45 Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands, 46 Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, United Kingdom, 47 Medical Genetics Section, Centre for Genomic and Experimental Medicine and MRC Institute of Genetics and Molecular Medicine, The University of Edinburgh, Edinburgh, United Kingdom, 48 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, 49 Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, United States of America, 50 Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland, 51 University of Tampere, Tampere, Finland, 52 Department of Public Health, Faculty of Medicine, University of Kelaniya, Ragama, Sri Lanka, 53 Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland, 54 Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany, 55 Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany, 56 Unit of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden, 57 MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom, 58 Laboratory for Statistical Analysis, Center for Integrative Medical Sciences, RIKEN, Yokohama, Japan, 59 Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands, 60 Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom, 61 Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom, 62 NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom, 63 Department of Computational Biology, University of Lausanne, Lausanne, Switzerland, 64 Swiss Institute of Bioinformatics, Lausanne, Switzerland, 65 Institute for Maternal and Child Health—IRCCS "Burlo Garofolo", Trieste, Italy, 66 Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, United States of America, 67 Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland, 68 Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan, 69 Department of Public Health Sciences, Loyola University Chicago, Maywood, Illinois, United States of America, 70 Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Skåne University Hospital, Malmö, Sweden, 71 Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States of America, 72 Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, Michigan, United States of America, 73 NIHR Barts Cardiovascular Biomedical Research Unit, Queen Mary University of London, London, United Kingdom, 74 Interfaculty Institute for Genetics and Functional genomics, University Medicine Ernst Moritz Arndt University Greifsald, Greifswald, Germany, 75 DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany, 76 Division of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America, 77 Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom, 78 Department of Cardiology, Ealing Hospital, Middlesex, United Kingdom, 79 McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America, 80 Department of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas School of Public Health, Houston, Texas, United States of America, 81 Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, United States of America, 82 Section of Genomic Pediatrics, Department of Pediatrics, Medicine and Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America, 83 Department of Medical Sciences, University of Trieste, Trieste, Italy, 84 Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America, 85 Ninewells Hospital & Medical School, University of Dundee, Dundee, Scotland, United Kingdom, 86 Cardiovascular Division, Department of Medicine, Washington University, St. Louis, Missouri, United States of America, 87 Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands, 88 Department of Medicine, Faculty of Medicine, University of Kelaniya, Ragama, Sri Lanka, 89 Center on Diabetes, Obesity, and Metabolism, Gerontology and Geriatric Medicine, Wake Forest University Health Sciences, Winston-
Salem, North Carolina, United States of America, **90** Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America, **91** Department of Family Medicine and Epidemiology, Alpert Medical School of Brown University, Providence, Rhode Island, United States of America, **92** Cardiology, Geneva University Hospital, Geneva, Switzerland, **93** Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece, **94** Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany, **95** The Caribbean Institute for Health Research (CAIHR), University of the West Indies, Mona, Jamaica, **96** Braun School of Public Health, Hebrew University-Hadassah Medical Center, Jerusalem, Israel, **97** Department of Genetics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran, **98** 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, **99** Psychology, The University of Edinburgh, Edinburgh, United Kingdom, **100** Department of Public Health and Clinical Medicine, Nutritional Research, Umeå University, Umeå, Västerbotten, Sweden, **101** Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, United States of America, **102** Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America, **103** Medicine, Tulane University School of Medicine, New Orleans, Louisiana, United States of America, **104** Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio Campus, Finland, **105** Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, **106** Khoo Teck Puat-National University Children's Medical Institute, National University Health System, Singapore, **107** Laboratory of Genome Technology, Human Genome Center, Institute of Medical Science, The University of Tokyo, Minato-ku, Japan, **108** MedStar Health Research Institute, Hyattsville, Maryland, United States of America, **109** Center for Clinical and Translational Sciences and Department of Medicine, Georgetown-Howard Universities, Washington, DC, United States of America, **110** Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands, **111** Department of Neurology, Erasmus University Medical Center, Rotterdam, The Netherlands, **112** Institute of Social Medicine and Prevention, University Medicine Greifswald, Greifswald, Germany, **113** Department of Clinical Gene Therapy, Osaka University Graduate School of Medicine, Suita, Japan, **114** Department of Geriatric Medicine and Nephrology, Osaka University Graduate School of Medicine, Suita, Japan, **115** Department of Biochemistry, National University of Singapore, Singapore, Singapore, **116** Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark, **117** Department of Environmental Medicine and Public Health, The Icahn School of Medicine at Mount Sinai, New York, New York, United States of America, **118** Duke-NUS Medical School, Singapore, Singapore, **119** 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, **120** Center for Integrative Medical Sciences, RIKEN, Yokohama, Japan, **121** Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland, **122** Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States of America, **123** WHI CCC, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America, **124** Translational Laboratory in Genetic Medicine, Agency for Science, Technology and Research, Singapore, **125** Department of Biostatistics, University of Washington, Seattle, Washington, United States of America, **126** Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany, **127** Institute of Human Genetics, Technische Universität München, Munich, Germany, **128** Department of Psychiatry, Amsterdam Neuroscience and Amsterdam Public Health Research Institute, VU University Medical Center, Amsterdam, The Netherlands, **129** Laboratory for Genotyping Development, Center for Integrative Medical Sciences, RIKEN, Yokohama, Japan, **130** Data Tecnica International, Glen Echo, Maryland, United States of America, **131** Laboratory of Neurogenetics, National Institute on Aging, Bethesda, Maryland, United States of America, **132** Cardiovascular Health Research Unit, Division of Cardiology, University of Washington, Seattle, Washington, United States of America, **133** Department of Epidemiology, Colorado School of Public Health, Aurora, Colorado, United States of America, **134** Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, Maryland, United States of America, **135** Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States of America, **136** Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America, **137** Geriatrics Section, Boston University Medical Center, Boston, Massachusetts, United States of America, **138** DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Neuherberg, Germany, **139** School of Public Health, Imperial College London, London, United Kingdom, **140** Division of Genetic and Genomic Medicine, Department of Pediatrics, University of California, Irvine, California, United States of America, **141** Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland, **142** Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland, **143** Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany, **144** Epidemiology & Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota, United States of America, **145** Taub Institute for Research on Alzheimer's Disease
and the Aging Brain, Columbia University Medical Center, New York, New York, United States of America, **146** National Heart and Lung Institute, Imperial College London, London, United Kingdom, **147** Division of Research, Kaiser Permanente of Northern California, Oakland, California, United States of America, **148** Cardiovascular Health Research Unit, Medicine, University of Washington, Seattle, Washington, United States of America, **149** Alzheimer Scotland Dementia Research Centre, The University of Edinburgh, Edinburgh, United Kingdom, **150** Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany, **151** Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU, Munich, Germany, **152** Department of Genetics, Stanford University, Stanford, California, United States of America, **153** Life Sciences Institute, National University of Singapore, Singapore, Singapore, **154** NUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore, Singapore, **155** Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore, **156** Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, United States of America, **157** Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands, **158** Service of Internal Medicine, Department of Internal Medicine, University Hospital, Lausanne, Switzerland, **159** Beijing Institute of Ophthalmology, Beijing Ophthalmology and Visual Science Key Lab, Beijing Tongren Eye Center, Capital Medical University, Beijing, China, **160** Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China, **161** Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America, **162** Division of Cancer Control and Population Sciences, UPMC Hillman Cancer, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America, **163** Behavioral Epidemiology Section, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States of America, **164** Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore, **165** Imperial College Healthcare NHS Trust, London, United Kingdom, **166** MRC-PHE Centre for Environment and Health, Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, United Kingdom, **167** Broad Institute of the Massachusetts Institute of Technology and Harvard University, Boston, Massachusetts, United States of America, **168** Harvard T. H. Chan School of Public Health, Department of Nutrition, Harvard University, Boston, Massachusetts, United States of America, **169** Nephrology, Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America, **170** Department of Genomics of Common Disease, Imperial College London, London, United Kingdom, **171** German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany, **172** Department of Ophthalmology, Medical Faculty Mannheim, University Heidelberg, Mannheim, Germany, **173** Institute of Social and Preventive Medicine, Lausanne University Hospital, Lausanne, Switzerland, **174** Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland, **175** Department of Clinical Chemistry, Finnish Cardiovascular Research Center—Tampere, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland, **176** Lifelines Cohort, Groningen, The Netherlands, **177** Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands, **178** Department of Public Health, Department of Medicine, University of Split, Split, Croatia, **179** Psychiatric Hospital "Sveti Ivan", Zagreb, Croatia, **180** Gen-info Ltd, Zagreb, Croatia, **181** Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands, **182** The Danish Aging Research Center, Institute of Public Health, University of Southern Denmark, Odense, Denmark, **183** Public Health Sciences, Epidemiology and Prevention, Wake Forest University Health Sciences, Winston-Salem, North Carolina, United States of America, **184** Cardiovascular Health Research Unit, Epidemiology, Medicine and Health Services, University of Washington, Seattle, Washington, United States of America, **185** Kaiser Permanente Washington, Health Research Institute, Seattle, Washington, United States of America, **186** Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore, **187** Biostatistics, Preventive Medicine, University of Southern California, Los Angeles, California, United States of America, **188** Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands, **189** The Framingham Heart Study, Framingham, Massachusetts, United States of America, **190** Cardiology, Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America, **191** Fred Hutchinson Cancer Research Center, University of Washington School of Public Health, Seattle, Washington, United States of America, **192** Medicine, Columbia University Medical Center, New York, New York, United States of America, **193** The Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States of America

e These authors contributed equally to this work.

‡ These authors also contributed equally to this work.

* mfeitosa@wustl.edu (MFF); levyD@nih.gov (DL)
## 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[8–14] and about 30 SNVs for alcohol consumption[15–17]. 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 (N = 4,415), European ancestry (N = 91,102), and Hispanic ancestry (N = 4,056) (S1-S4 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, S5-S8 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 S6-S15 Figs, for Stage 1 and Stage 2 combined meta-analyses of European, African and Asian ancestries.

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),
<table><caption>Table 1. Novel SNVs/Genes associated with BP traits in European ancestry.</caption><thead><tr><th rowspan="2">SNV</th><th rowspan="2">Chr</th><th rowspan="2">Position</th><th rowspan="2">Gene</th><th rowspan="2">Near Gene</th><th rowspan="2">Role</th><th rowspan="2">A1/2</th><th rowspan="2">Frq1</th><th rowspan="2">Trait</th><th rowspan="2">Drink</th><th colspan="3">Stage 1 (S1)</th><th colspan="3">Stage 2 (S2)</th><th rowspan="2">S1 & S2</th></tr><tr><th>b_M</th><th>b_I</th><th>P-Value</th><th>b_M</th><th>b_I</th><th>P-Value</th></tr></thead><tbody><tr><td>rs2979172</td><td>8</td><td>8452998</td><td>LOC107986913</td><td>SGK223</td><td></td><td>C/G</td><td>0.48</td><td>PP</td><td>LHD</td><td>0.24</td><td>0.25</td><td>7.59 x 10<sup>-6</sup></td><td>0.32</td><td>-0.20</td><td>5.13 X 10<sup>-6</sup></td><td>6.17 X 10<sup>-10</sup></td></tr><tr><td>rs2921064</td><td>8</td><td>8459127</td><td>LOC107986913</td><td>SGK223</td><td></td><td>T/C</td><td>0.45</td><td>PP</td><td>CURD</td><td>0.19</td><td>0.10</td><td>7.76 X 10<sup>-6</sup></td><td>0.24</td><td>-0.02</td><td>3.63 X 10<sup>-9</sup></td><td>2.69 x 10<sup>-14</sup></td></tr><tr><td>rs2979181</td><td>8</td><td>8465578</td><td>LOC107986913</td><td>SGK223</td><td></td><td>A/T</td><td>0.52</td><td>SBP</td><td>CURD</td><td>-0.25</td><td>-0.23</td><td>9.33 x 10<sup>-8</sup></td><td>-0.35</td><td>0.01</td><td>1.15 x 10<sup>-10</sup></td><td>7.41 x 10<sup>-18</sup></td></tr><tr><td>rs2979181</td><td>8</td><td>8465578</td><td>LOC107986913</td><td>SGK223</td><td></td><td>A/T</td><td>0.52</td><td>SBP</td><td>LHD</td><td>-0.47</td><td>-0.14</td><td>5.37 x 10<sup>-7</sup></td><td>-0.42</td><td>0.16</td><td>4.79 x 10<sup>-5</sup></td><td>3.98 x 10<sup>-11</sup></td></tr><tr><td>rs2980755</td><td>8</td><td>8506173</td><td>LOC105379224</td><td>SGK223</td><td></td><td>A/G</td><td>0.55</td><td>PP</td><td>LHD</td><td>-0.28</td><td>-0.20</td><td>4.17 x 10<sup>-6</sup></td><td>-0.32</td><td>0.17</td><td>4.90 x 10<sup>-6</sup></td><td>1.35 x 10<sup>-10</sup></td></tr><tr><td>rs2980755</td><td>8</td><td>8506173</td><td>LOC105379224</td><td>SGK223</td><td></td><td>A/G</td><td>0.55</td><td>SBP</td><td>LHD</td><td>-0.49</td><td>-0.20</td><td>2.63 x 10<sup>-7</sup></td><td>-0.42</td><td>0.12</td><td>5.25 x 10<sup>-5</sup></td><td>2.51 x 10<sup>-11</sup></td></tr><tr><td>rs13270194</td><td>8</td><td>8520592</td><td>LOC105379224</td><td>SGK223</td><td></td><td>T/C</td><td>0.51</td><td>SBP</td><td>CURD</td><td>-0.26</td><td>-0.24</td><td>2.46 x 10<sup>-8</sup></td><td>-0.42</td><td>0.05</td><td>1.23 x 10<sup>-12</sup></td><td>2.34 x 10<sup>-20</sup></td></tr><tr><td>rs6995407</td><td>8</td><td>8527137</td><td>LOC105379224</td><td>SGK223</td><td></td><td>C/G</td><td>0.51</td><td>PP</td><td>CURD</td><td>-0.16</td><td>-0.15</td><td>7.59 x 10<sup>-7</sup></td><td>-0.25</td><td>0.02</td><td>2.34 x 10<sup>-10</sup></td><td>2.34 x 10<sup>-16</sup></td></tr><tr><td>rs453301</td><td>8</td><td>9172877</td><td>LOC102724880</td><td>PPP1R3B</td><td></td><td>T/G</td><td>0.51</td><td>SBP</td><td>CURD</td><td>-0.17</td><td>-0.33</td><td>1.59 x 10<sup>-6</sup></td><td>-0.27</td><td>-0.08</td><td>8.13 x 10<sup>-10</sup></td><td>1.23 x 10<sup>-15</sup></td></tr><tr><td>rs11774915</td><td>8</td><td>9331252</td><td>LOC157273</td><td></td><td>Intron</td><td>T/C</td><td>0.33</td><td>SBP</td><td>CURD</td><td>0.45</td><td>0.01</td><td>1.02 x 10<sup>-7</sup></td><td>0.35</td><td>-0.05</td><td>7.94 x 10<sup>-8</sup></td><td>8.91 x 10<sup>-15</sup></td></tr><tr><td>rs6601302</td><td>8</td><td>9381948</td><td>LOC105379231</td><td>LOC157273</td><td>Intron</td><td>T/G</td><td>0.24</td><td>SBP</td><td>CURD</td><td>0.35</td><td>0.17</td><td>7.94 x 10<sup>-7</sup></td><td>0.20</td><td>0.06</td><td>7.59 x 10<sup>-5</sup></td><td>2.57 x 10<sup>-10</sup></td></tr><tr><td>rs35231275</td><td>8</td><td>9762399</td><td>TNKS</td><td></td><td>Intron</td><td>A/T</td><td>0.31</td><td>PP</td><td>CURD</td><td>-0.38</td><td>0.03</td><td>1.26 x 10<sup>-6</sup></td><td>-0.05</td><td>-0.12</td><td>3.31 x 10<sup>-4</sup></td><td>1.35 x 10<sup>-8</sup></td></tr><tr><td>rs1976671</td><td>8</td><td>9822124</td><td>TNKS</td><td></td><td></td><td>A/G</td><td>0.62</td><td>SBP</td><td>CURD</td><td>-0.21</td><td>-0.31</td><td>4.68 x 10<sup>-8</sup></td><td>-0.37</td><td>-0.02</td><td>2.24 x 10<sup>-10</sup></td><td>7.24 x 10<sup>-18</sup></td></tr><tr><td>rs55868514</td><td>8</td><td>9822890</td><td>TNKS</td><td></td><td></td><td>T/C</td><td>0.38</td><td>DBP</td><td>CURD</td><td>0.20</td><td>0.09</td><td>1.32 x 10<sup>-6</sup></td><td>0.17</td><td>0.01</td><td>1.20 x 10<sup>-7</sup></td><td>1.70 x 10<sup>-13</sup></td></tr><tr><td>rs483916</td><td>8</td><td>9936091</td><td>MIR124-1</td><td></td><td></td><td>A/C</td><td>0.47</td><td>DBP</td><td>CURD</td><td>0.25</td><td>0.01</td><td>1.18 x 10<sup>-6</sup></td><td>0.04</td><td>0.14</td><td>1.29 x 10<sup>-6</sup></td><td>5.89 x 10<sup>-12</sup></td></tr><tr><td>rs483916</td><td>8</td><td>9936091</td><td>MIR124-1</td><td></td><td></td><td>A/C</td><td>0.47</td><td>PP</td><td>CURD</td><td>0.20</td><td>0.09</td><td>7.94 x 10<sup>-6</sup></td><td>0.16</td><td>0.03</td><td>4.68 x 10<sup>-12</sup></td><td>6.61 x 10<sup>-17</sup></td></tr><tr><td>rs483916</td><td>8</td><td>9936091</td><td>MIR124-1</td><td></td><td></td><td>A/C</td><td>0.47</td><td>SBP</td><td>CURD</td><td>0.38</td><td>0.17</td><td>1.05 x 10<sup>-9</sup></td><td>0.21</td><td>0.16</td><td>3.24 x 10<sup>-11</sup></td><td>3.31 x 10<sup>-20</sup></td></tr><tr><td>rs615632</td><td>8</td><td>9938811</td><td>MIR124-1</td><td></td><td></td><td>T/C</td><td>0.53</td><td>SBP</td><td>LHD</td><td>-0.50</td><td>-0.30</td><td>7.41 x 10<sup>-9</sup></td><td>-0.40</td><td>0.09</td><td>1.07 x 10<sup>-4</sup></td><td>3.63 x 10<sup>-12</sup></td></tr><tr><td>rs9650622</td><td>8</td><td>9946782</td><td>LOC105379235</td><td>MIR124-1</td><td></td><td>T/G</td><td>0.53</td><td>DBP</td><td>CURD</td><td>-0.24</td><td>-0.01</td><td>4.07 x 10<sup>-6</sup></td><td>-0.12</td><td>-0.07</td><td>1.10 x 10<sup>-7</sup></td><td>4.27 x 10<sup>-13</sup></td></tr><tr><td>rs56243511</td><td>8</td><td>9948185</td><td>LOC105379235</td><td>MIR124-1</td><td></td><td>T/C</td><td>0.47</td><td>SBP</td><td>CURD</td><td>0.37</td><td>0.11</td><td>2.57 x 10<sup>-8</sup></td><td>0.27</td><td>0.14</td><td>1.91 x 10<sup>-13</sup></td><td>1.74 x 10<sup>-21</sup></td></tr><tr><td>rs656319</td><td>8</td><td>9956901</td><td>LOC105379235</td><td>MIR124-1</td><td></td><td>A/G</td><td>0.45</td><td>MAP</td><td>LHD</td><td>0.29</td><td>0.20</td><td>1.29 x 10<sup>-6</sup></td><td>0.24</td><td>0.06</td><td>6.03 x 10<sup>-5</sup></td><td>7.59 x 10<sup>-11</sup></td></tr><tr><td>rs656319</td><td>8</td><td>9956901</td><td>LOC105379235</td><td>MIR124-1</td><td></td><td>A/G</td><td>0.45</td><td>SBP</td><td>LHD</td><td>0.39</td><td>0.35</td><td>8.71 x 10<sup>-7</sup></td><td>0.43</td><td>0.01</td><td>1.62 x 10<sup>-6</sup></td><td>1.59 x 10<sup>-12</sup></td></tr><tr><td>rs11786677</td><td>8</td><td>10406750</td><td>MSRA</td><td></td><td>Intron</td><td>A/G</td><td>0.58</td><td>SBP</td><td>CURD</td><td>-0.25</td><td>-0.22</td><td>2.57 x 10<sup>-7</sup></td><td>-0.40</td><td>0.03</td><td>1.35 x 10<sup>-42</sup></td><td>5.62 x 10<sup>-49</sup></td></tr><tr><td>rs2062331</td><td>8</td><td>10122482</td><td>MSRA</td><td></td><td>Intron</td><td>A/G</td><td>0.54</td><td>DBP</td><td>CURD</td><td>-0.18</td><td>-0.15</td><td>2.00 x 10<sup>-8</sup></td><td>-0.18</td><td>0.00</td><td>7.59 x 10<sup>-8</sup></td><td>5.01 x 10<sup>-15</sup></td></tr><tr><td>rs11993089</td><td>8</td><td>10152442</td><td>MSRA</td><td></td><td>Intron</td><td>T/G</td><td>0.42</td><td>PP</td><td>CURD</td><td>0.24</td><td>0.05</td><td>5.25 x 10<sup>-6</sup></td><td>0.32</td><td>-0.13</td><td>4.68 x 10<sup>-18</sup></td><td>6.17 x 10<sup>-23</sup></td></tr><tr><td>rs7832708</td><td>8</td><td>10332530</td><td>MSRA</td><td></td><td>Intron</td><td>T/C</td><td>0.49</td><td>SBP</td><td>LHD</td><td>0.55</td><td>0.07</td><td>2.19 x 10<sup>-8</sup></td><td>0.42</td><td>-0.09</td><td>2.19 x 10<sup>-5</sup></td><td>5.89 x 10<sup>-13</sup></td></tr><tr><td>rs4841409</td><td>8</td><td>10658864</td><td>RP1L1</td><td></td><td></td><td>A/G</td><td>0.44</td><td>MAP</td><td>CURD</td><td>0.18</td><td>0.14</td><td>7.59 x 10<sup>-7</sup></td><td>0.27</td><td>-0.12</td><td>9.77 x 10<sup>-6</sup></td><td>5.13 x 10<sup>-11</sup></td></tr><tr><td>rs4841409</td><td>8</td><td>10658864</td><td>RP1L1</td><td></td><td></td><td>A/G</td><td>0.44</td><td>MAP</td><td>LHD</td><td>0.37</td><td>-0.14</td><td>6.03 x 10<sup>-6</sup></td><td>0.36</td><td>-0.19</td><td>2.14 x 10<sup>-6</sup></td><td>6.46 x 10<sup>-12</sup></td></tr><tr><td>rs4841409</td><td>8</td><td>10658864</td><td>RP1L1</td><td></td><td></td><td>A/G</td><td>0.44</td><td>SBP</td><td>CURD</td><td>0.23</td><td>0.25</td><td>1.91 x 10<sup>-7</sup></td><td>0.32</td><td>0.01</td><td>9.55 x 10<sup>-16</sup></td><td>4.90 x 10<sup>-23</sup></td></tr><tr><td>rs10096777</td><td>8</td><td>10660990</td><td>RP1L1</td><td></td><td></td><td>A/G</td><td>0.56</td><td>SBP</td><td>LHD</td><td>-0.52</td><td>0.10</td><td>1.55 x 10<sup>-6</sup></td><td>-0.60</td><td>0.39</td><td>2.88 x 10<sup>-8</sup></td><td>3.80 x 10<sup>-14</sup></td></tr><tr><td>rs7814795</td><td>8</td><td>10661775</td><td>MIR4286</td><td></td><td></td><td>T/C</td><td>0.55</td><td>MAP</td><td>CURD</td><td>-0.18</td><td>-0.14</td><td>7.59 x 10<sup>-7</sup></td><td>-0.22</td><td>0.08</td><td>1.45 x 10<sup>-4</sup></td><td>9.77 x 10<sup>-10</sup></td></tr><tr><td>rs7814795</td><td>8</td><td>10661775</td><td>MIR4286</td><td></td><td></td><td>T/C</td><td>0.55</td><td>SBP</td><td>CURD</td><td>-0.22</td><td>-0.26</td><td>1.78 x 10<sup>-7</sup></td><td>-0.2</td><td>-0.15</td><td>2.29 x 10<sup>-14</sup></td><td>1.48 x 10<sup>-21</sup></td></tr><tr><td>rs7814795</td><td>8</td><td>10661775</td><td>MIR4286</td><td></td><td></td><td>T/C</td><td>0.55</td><td>SBP</td><td>LHD</td><td>-0.50</td><td>0.06</td><td>2.04 x 10<sup>-6</sup></td><td>-0.59</td><td>0.38</td><td>3.80 x 10<sup>-8</sup></td><td>7.76 x 10<sup>-14</sup></td></tr></tbody></table>

The most significantly associated SNVs are shown per gene for each Blood Pressure (BP) trait and alcohol status. Abbreviations: SNV, single nucleotide variant; Chr, chromosome; Position, Gene, and Role in dbSNP build 150 (hg38); Role: Intronic, Non-coding transcript (NCT) 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; S1 & S2, 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; P-Meta, modified-interaction METAL P-Value of Meta-analysis in combined Stage 1 and Stage 2.

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

the above five novel loci (14 genes, Tables 1 and 2), and the 22 previously reported BP loci (49 genes).

## 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
<table><caption>Table 2. Novel SNVs/Genes associated with BP traits in European ancestry.</caption><thead><tr><th rowspan="2">SNV</th><th rowspan="2">Chr</th><th rowspan="2">Position</th><th rowspan="2">Gene</th><th rowspan="2">Near Gene</th><th rowspan="2">Role</th><th rowspan="2">A1/2</th><th rowspan="2">Frq1</th><th rowspan="2">Trait</th><th rowspan="2">Drink</th><th colspan="3">Stage 1 (S1)</th><th colspan="3">Stage 2 (S2)</th><th colspan="2">S1 & S2</th></tr><tr><th>b_M</th><th>b_I</th><th>P-Value</th><th>b_M</th><th>b_I</th><th>P-Value</th><th>P-Meta</th></tr></thead><tbody><tr><td>rs28680211</td><td>8</td><td>10661935</td><td>MIR4286</td><td></td><td></td><td>A/T</td><td>0.55</td><td>MAP</td><td>LHD</td><td>-0.36</td><td>0.13</td><td>7.76 x 10<sup>-6</sup></td><td>-0.35</td><td>0.19</td><td>3.98 x 10<sup>-6</sup></td><td>1.59 x 10<sup>-11</sup></td></tr><tr><td>rs13276026</td><td>8</td><td>10752445</td><td>LOC102723313</td><td>SOX7</td><td>Intron</td><td>A/G</td><td>0.56</td><td>SBP</td><td>CURD</td><td>-0.23</td><td>-0.23</td><td>5.62 x 10<sup>-7</sup></td><td>-0.26</td><td>-0.19</td><td>2.29 x 10<sup>-15</sup></td><td>3.98 x 10<sup>-22</sup></td></tr><tr><td>rs7814757</td><td>8</td><td>10817678</td><td>PINX1</td><td></td><td>Intron</td><td>T/C</td><td>0.40</td><td>SBP</td><td>CURD</td><td>0.24</td><td>0.22</td><td>7.94 x 10<sup>-7</sup></td><td>0.21</td><td>0.26</td><td>8.71 x 10<sup>-16</sup></td><td>2.63 x 10<sup>-22</sup></td></tr><tr><td>rs4841465</td><td>8</td><td>10962344</td><td>XKR6</td><td></td><td>Intron</td><td>T/C</td><td>0.52</td><td>SBP</td><td>CURD</td><td>-0.21</td><td>-0.27</td><td>6.17 x 10<sup>-7</sup></td><td>-0.21</td><td>-0.21</td><td>6.03 x 10<sup>-14</sup></td><td>1.41 x 10<sup>-20</sup></td></tr><tr><td>rs4841465</td><td>8</td><td>10962344</td><td>XKR6</td><td></td><td>Intron</td><td>T/C</td><td>0.52</td><td>SBP</td><td>LHD</td><td>-0.51</td><td>-0.10</td><td>3.89 x 10<sup>-7</sup></td><td>-0.43</td><td>0.04</td><td>4.07 x 10<sup>-6</sup></td><td>1.23 x 10<sup>-12</sup></td></tr><tr><td>rs9969423</td><td>8</td><td>11398066</td><td>FAM167A-AS1</td><td>C8orf12</td><td>Intron</td><td>A/C</td><td>0.50</td><td>SBP</td><td>CURD</td><td>0.21</td><td>0.2</td><td>3.98 X 10<sup>-6</sup></td><td>0.29</td><td>0.01</td><td>1.20 x 10<sup>-7</sup></td><td>5.37 x 10<sup>-13</sup></td></tr><tr><td>rs9969423</td><td>8</td><td>11398066</td><td>FAM167A-AS1</td><td>C8orf12</td><td>Intron</td><td>A/C</td><td>0.50</td><td>SBP</td><td>LHD</td><td>0.52</td><td>-0.09</td><td>4.90 X 10<sup>-6</sup></td><td>0.38</td><td>-0.07</td><td>1.95 X 10<sup>-4</sup></td><td>8.13 X 10<sup>-10</sup></td></tr><tr><td>rs12156009</td><td>8</td><td>11427710</td><td>FAM167A</td><td>C8orf12</td><td>Intron</td><td>A/C</td><td>0.51</td><td>SBP</td><td>CURD</td><td>0.29</td><td>0.21</td><td>1.66 X 10<sup>-7</sup></td><td>0.17</td><td>0.10</td><td>1.02 X 10<sup>-5</sup></td><td>5.37 X 10<sup>-12</sup></td></tr><tr><td>rs13255193</td><td>8</td><td>11451683</td><td>FAM167A</td><td>FAM167A</td><td>Intron</td><td>T/C</td><td>0.46</td><td>SBP</td><td>LHD</td><td>0.53</td><td>-0.11</td><td>6.76 X 10<sup>-7</sup></td><td>0.36</td><td>-0.11</td><td>7.76 X 10<sup>-4</sup></td><td>6.17 X 10<sup>-10</sup></td></tr><tr><td>rs6983727</td><td>8</td><td>11558303</td><td>BLK</td><td></td><td>Intron</td><td>T/C</td><td>0.48</td><td>PP</td><td>CURD</td><td>-0.15</td><td>-0.15</td><td>4.68 X 10<sup>-6</sup></td><td>-0.17</td><td>-0.08</td><td>1.66 X 10<sup>-10</sup></td><td>5.89 X 10<sup>-16</sup></td></tr><tr><td>rs6983727</td><td>8</td><td>11558303</td><td>BLK</td><td></td><td>Intron</td><td>T/C</td><td>0.48</td><td>PP</td><td>LHD</td><td>-0.24</td><td>-0.25</td><td>5.89 X 10<sup>-6</sup></td><td>-0.26</td><td>0.07</td><td>6.03 X 10<sup>-5</sup></td><td>1.74 X 10<sup>-9</sup></td></tr><tr><td>rs6983727</td><td>8</td><td>11558303</td><td>BLK</td><td></td><td>Intron</td><td>T/C</td><td>0.48</td><td>SBP</td><td>LHD</td><td>-0.47</td><td>-0.17</td><td>4.27 X 10<sup>-7</sup></td><td>-0.34</td><td>0.00</td><td>1.55 X 10<sup>-4</sup></td><td>1 X 10<sup>-10</sup></td></tr><tr><td>rs34190028</td><td>8</td><td>11559641</td><td>BLK</td><td></td><td>Intron</td><td>T/G</td><td>0.48</td><td>SBP</td><td>CURD</td><td>-0.16</td><td>-0.31</td><td>5.13 X 10<sup>-7</sup></td><td>-0.36</td><td>-0.04</td><td>3.47 X 10<sup>-13</sup></td><td>1.26 X 10<sup>-19</sup></td></tr><tr><td>rs899366</td><td>8</td><td>11572976</td><td>LINC00208</td><td></td><td></td><td>A/G</td><td>0.33</td><td>MAP</td><td>CURD</td><td>0.15</td><td>0.18</td><td>3.39 X 10<sup>-6</sup></td><td>0.28</td><td>0.00</td><td>3.47 X 10<sup>-79</sup></td><td>1.51 X 10<sup>-82</sup></td></tr><tr><td>rs7464263</td><td>8</td><td>11576667</td><td>LINC00208</td><td></td><td>NCT</td><td>A/T</td><td>0.48</td><td>SBP</td><td>LHD</td><td>0.48</td><td>0.24</td><td>6.03 X 10<sup>-8</sup></td><td>0.41</td><td>-0.08</td><td>3.72 X 10<sup>-5</sup></td><td>4.37 X 10<sup>-12</sup></td></tr><tr><td>rs1478894</td><td>8</td><td>11591245</td><td>LINC00208</td><td></td><td></td><td>T/C</td><td>0.36</td><td>SBP</td><td>CURD</td><td>0.33</td><td>0.21</td><td>1.00 X 10<sup>-8</sup></td><td>0.24</td><td>0.16</td><td>3.31 X 10<sup>-11</sup></td><td>2.51 X 10<sup>-19</sup></td></tr><tr><td>rs4841569</td><td>8</td><td>11594668</td><td>LINC00208</td><td></td><td></td><td>A/G</td><td>0.42</td><td>PP</td><td>CURD</td><td>-0.10</td><td>-0.28</td><td>1.95 X 10<sup>-7</sup></td><td>-0.07</td><td>-0.18</td><td>1.23 X 10<sup>-10</sup></td><td>4.17 X 10<sup>-17</sup></td></tr><tr><td>rs4841569</td><td>8</td><td>11594668</td><td>LINC00208</td><td></td><td></td><td>A/G</td><td>0.42</td><td>PP</td><td>LHD</td><td>-0.27</td><td>-0.44</td><td>2.88 X 10<sup>-8</sup></td><td>-0.28</td><td>0.08</td><td>2.40 X 10<sup>-5</sup></td><td>4.79 X 10<sup>-11</sup></td></tr><tr><td>rs17807624</td><td>8</td><td>11605506</td><td>LINC00208</td><td></td><td></td><td>T/C</td><td>0.35</td><td>DBP</td><td>CURD</td><td>0.11</td><td>0.20</td><td>5.37 X 10<sup>-6</sup></td><td>0.14</td><td>0.05</td><td>8.13 X 10<sup>-8</sup></td><td>6.03 X 10<sup>-13</sup></td></tr><tr><td>rs17807624</td><td>8</td><td>11605506</td><td>LINC00208</td><td></td><td></td><td>T/C</td><td>0.35</td><td>MAP</td><td>LHD</td><td>0.45</td><td>-0.22</td><td>5.13 X 10<sup>-7</sup></td><td>0.32</td><td>-0.16</td><td>6.03 X 10<sup>-5</sup></td><td>2.57 X 10<sup>-11</sup></td></tr><tr><td>rs13280442</td><td>8</td><td>11610048</td><td>LOC105379242</td><td>LINC00208</td><td></td><td>C/G</td><td>0.55</td><td>MAP</td><td>CURD</td><td>0.23</td><td>0.11</td><td>1.29 X 10<sup>-6</sup></td><td>0.28</td><td>-0.17</td><td>4.90 X 10<sup>-4</sup></td><td>1.62 X 10<sup>-8</sup></td></tr><tr><td>rs13280442</td><td>8</td><td>11610048</td><td>LOC105379242</td><td>LINC00208</td><td></td><td>C/G</td><td>0.55</td><td>MAP</td><td>LHD</td><td>0.40</td><td>-0.11</td><td>3.39 X 10<sup>-6</sup></td><td>0.28</td><td>-0.01</td><td>5.25 X 10<sup>-5</sup></td><td>1.38 X 10<sup>-10</sup></td></tr><tr><td>rs13280442</td><td>8</td><td>11610048</td><td>LOC105379242</td><td>LINC00208</td><td></td><td>C/G</td><td>0.55</td><td>SBP</td><td>CURD</td><td>0.30</td><td>0.24</td><td>8.32 X 10<sup>-8</sup></td><td>0.48</td><td>-0.03</td><td>1.91 X 10<sup>-16</sup></td><td>9.12 X 10<sup>-24</sup></td></tr><tr><td>rs13280442</td><td>8</td><td>11610048</td><td>LOC105379242</td><td>LINC00208</td><td></td><td>C/G</td><td>0.55</td><td>SBP</td><td>LHD</td><td>0.57</td><td>0.10</td><td>1.38 X 10<sup>-7</sup></td><td>0.50</td><td>-0.10</td><td>4.68 X 10<sup>-7</sup></td><td>5.01 X 10<sup>-14</sup></td></tr><tr><td>rs13250871</td><td>8</td><td>11610254</td><td>LOC105379242</td><td>LINC00208</td><td></td><td>A/G</td><td>0.4</td><td>PP</td><td>CURD</td><td>-0.10</td><td>-0.27</td><td>8.51 X 10<sup>-7</sup></td><td>-0.21</td><td>-0.10</td><td>2.63 X 10<sup>-17</sup></td><td>1.91 X 10<sup>-23</sup></td></tr><tr><td>rs13250871</td><td>8</td><td>11610254</td><td>LOC105379242</td><td>LINC00208</td><td></td><td>A/G</td><td>0.39</td><td>PP</td><td>LHD</td><td>-0.24</td><td>-0.49</td><td>7.59 X 10<sup>-8</sup></td><td>-0.29</td><td>0.10</td><td>2.69 X 10<sup>-5</sup></td><td>2.14 X 10<sup>-10</sup></td></tr><tr><td>rs36038176</td><td>8</td><td>11752486</td><td>GATA4</td><td></td><td>Intron</td><td>T/C</td><td>0.28</td><td>SBP</td><td>CURD</td><td>-0.21</td><td>-0.29</td><td>1.07 X 10<sup>-6</sup></td><td>-0.39</td><td>0.15</td><td>3.89 X 10<sup>-5</sup></td><td>3.24 X 10<sup>-10</sup></td></tr><tr><td>rs55872725</td><td>16</td><td>53775211</td><td>FTO</td><td></td><td>Intron</td><td>T/C</td><td>0.41</td><td>SBP</td><td>CURD</td><td>0.69</td><td>-0.31</td><td>3.39 X 10<sup>-9</sup></td><td>0.36</td><td>-0.16</td><td>2.14 X 10<sup>-5</sup></td><td>2.40 X 10<sup>-13</sup></td></tr><tr><td>rs7185735</td><td>16</td><td>53788739</td><td>FTO</td><td></td><td>Intron</td><td>A/G</td><td>0.59</td><td>PP</td><td>CURD</td><td>-0.36</td><td>0.07</td><td>6.31 X 10<sup>-8</sup></td><td>-0.25</td><td>0.14</td><td>3.31 X 10<sup>-4</sup></td><td>2.09 X 10<sup>-10</sup></td></tr></tbody></table>

The most significantly associated SNVs are shown per gene for each Blood Pressure (BP) trait and alcohol status. Abbreviations: SNV, single nucleotide variant; Chr, chromosome; Position, Gene, and Role in dbSNP build 150 (hg38); Role: Intrinsic, Non-coding transcript (NCT) 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; S1 & S2, 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; P-Meta, modified-interaction METAL P-Value of Meta-analysis in combined Stage 1 and Stage 2.

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

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
<table><caption>Table 3. Potential novel SNVs/Genes associated with BP traits in African ancestry.</caption><thead><tr><th rowspan="2">SNV</th><th rowspan="2">Chr</th><th rowspan="2">Position</th><th rowspan="2">Gene</th><th rowspan="2">Near Gene</th><th rowspan="2">Role</th><th rowspan="2">A1/2</th><th rowspan="2">Frq1</th><th rowspan="2">Trait</th><th rowspan="2">Drink</th><th colspan="3">Stage 1 (S1)</th><th colspan="3">Stage 2 (S2)</th><th rowspan="2">S1 & S2</th></tr><tr><th>b_M</th><th>b_I</th><th>P-Value</th><th>b_M</th><th>b_I</th><th>P-Value</th><th>P-Meta</th></tr></thead><tbody><tr><td>rs80158983</td><td>6</td><td>65489746</td><td>EYS</td><td>EYS</td><td>intron</td><td>T/C</td><td>0.02</td><td>SBP</td><td>CURD</td><td>3.53</td><td>-10.05</td><td>1.29 x 10<sup>-8</sup></td><td>0.95</td><td>-3.08</td><td>8.32 x 10<sup>-1</sup></td><td>6.92 x 10<sup>-9</sup></td></tr><tr><td>rs76987554</td><td>6</td><td>133759717</td><td>TARID</td><td>MGC34034, SGK1</td><td>intron</td><td>T/C</td><td>0.09</td><td>SBP</td><td>CURD</td><td>-2.45</td><td>0.80</td><td>2.19 x 10<sup>-8</sup></td><td>-1.48</td><td>-0.42</td><td>2.09 x 10<sup>-1</sup></td><td>1.86 x 10<sup>-9</sup></td></tr><tr><td>rs79505281</td><td>8</td><td>35841899</td><td>UNC5D</td><td></td><td></td><td>A/C</td><td>0.02</td><td>PP</td><td>CURD</td><td>-5.66</td><td>1.26</td><td>6.03 x 10<sup>-7</sup></td><td>1.50</td><td>-6.67</td><td>2.82 x 10<sup>-3</sup></td><td>3.24 x 10<sup>-9</sup></td></tr><tr><td>rs115888294</td><td>8</td><td>94105161</td><td>CDH17</td><td></td><td></td><td>T/C</td><td>0.93</td><td>PP</td><td>CURD</td><td>-1.18</td><td>-0.55</td><td>1.59 x 10<sup>-7</sup></td><td>-0.71</td><td>-0.84</td><td>2.19 x 10<sup>-1</sup></td><td>1.29 x 10<sup>-8</sup></td></tr><tr><td>rs73655199</td><td>9</td><td>98145201</td><td>CORO2A</td><td>GABBR2</td><td>intron</td><td>A/G</td><td>0.01</td><td>PP</td><td>CURD</td><td>-5.09</td><td>-0.13</td><td>3.16 x 10<sup>-9</sup></td><td>-0.45</td><td>-2.71</td><td>2.95 x 10<sup>-1</sup></td><td>1.41 x 10<sup>-9</sup></td></tr><tr><td>rs4253197</td><td>10</td><td>49473111</td><td>ERCC6</td><td>CHAT</td><td>intron</td><td>A/G</td><td>0.89</td><td>PP</td><td>CURD</td><td>0.66</td><td>0.67</td><td>6.61 x 10<sup>-7</sup></td><td>-0.80</td><td>2.57</td><td>3.63 x 10<sup>-2</sup></td><td>4.90 x 10<sup>-8</sup></td></tr><tr><td>rs11200509</td><td>10</td><td>122256927</td><td>TACC2</td><td></td><td></td><td>C/G</td><td>0.17</td><td>PP</td><td>LHD</td><td>-0.27</td><td>-4.05</td><td>6.76 x 10<sup>-9</sup></td><td>1.72</td><td>-2.92</td><td>1.45 x 10<sup>-1</sup></td><td>1.00 x 10<sup>-8</sup></td></tr><tr><td>rs10741534</td><td>11</td><td>11233360</td><td>GALNT18</td><td></td><td></td><td>T/C</td><td>0.09</td><td>SBP</td><td>CURD</td><td>2.34</td><td>-3.76</td><td>8.32 x 10<sup>-8</sup></td><td>0.94</td><td>-2.76</td><td>2.29 x 10<sup>-1</sup></td><td>1.18 x 10<sup>-8</sup></td></tr><tr><td>rs139077481</td><td>11</td><td>107579224</td><td>ELMOD1</td><td></td><td></td><td>T/C</td><td>0.99</td><td>PP</td><td>CURD</td><td>-3.18</td><td>10.41</td><td>1.32 x 10<sup>-7</sup></td><td>-0.81</td><td>4.67</td><td>3.47 x 10<sup>-1</sup></td><td>3.39 x 10<sup>-8</sup></td></tr><tr><td>rs140520944</td><td>18</td><td>29508647</td><td>LOC105372045</td><td>MIR302F</td><td></td><td>T/G</td><td>0.02</td><td>PP</td><td>CURD</td><td>-0.49</td><td>-4.83</td><td>1 x 10<sup>-12</sup></td><td>1.94</td><td>-3.30</td><td>6.03 x 10<sup>-1</sup></td><td>4.07 x 10<sup>-13</sup></td></tr><tr><td>rs142673685</td><td>19</td><td>31669942</td><td>LOC105372361</td><td>THEG5</td><td></td><td>T/C</td><td>0.01</td><td>PP</td><td>CURD</td><td>-3.04</td><td>-2.20</td><td>5.01 x 10<sup>-8</sup></td><td>-2.92</td><td>2.29</td><td>4.47 x 10<sup>-1</sup></td><td>3.63 x 10<sup>-8</sup></td></tr><tr><th rowspan="2">SNV</th><th rowspan="2">Chr</th><th rowspan="2">Position</th><th rowspan="2">Gene</th><th rowspan="2">Near Gene</th><th rowspan="2">Role</th><th rowspan="2">A1/2</th><th rowspan="2">Frq1</th><th rowspan="2">Trait</th><th rowspan="2">Drink</th><th colspan="3">Stage 1 (S1)</th><th colspan="3">No Stage 2 (S2)</th></tr><tr><th>b_M</th><th>b_I</th><th>P-Value</th><th>b_M</th><th>b_I</th><th>P-Value</th></tr><tr><td>rs9862344</td><td>3</td><td>178283140</td><td>LOC105374235</td><td>KCNMB2, KCNMB2-IT1</td><td></td><td>T/C</td><td>0.02</td><td>SBP</td><td>CURD</td><td>3.53</td><td>-10.05</td><td>1.29 x 10<sup>-8</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>rs73884351</td><td>3</td><td>178287933</td><td>LOC105374235</td><td>KCNMB2, KCNMB2-IT1</td><td></td><td>T/C</td><td>0.09</td><td>SBP</td><td>CURD</td><td>-2.45</td><td>0.80</td><td>2.19 x 10<sup>-8</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>rs145429126</td><td>4</td><td>47000363</td><td>GABRB1</td><td>GABRA4</td><td>intron</td><td>A/C</td><td>0.02</td><td>PP</td><td>CURD</td><td>-5.66</td><td>1.26</td><td>6.03 x 10<sup>-7</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>rs61494734</td><td>9</td><td>29196976</td><td>LINGO2</td><td></td><td>intron</td><td>T/C</td><td>0.93</td><td>PP</td><td>CURD</td><td>-1.18</td><td>-0.55</td><td>1.59 x 10<sup>-7</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>rs201383951</td><td>10</td><td>119468517</td><td>GRK5</td><td>BAG3</td><td></td><td>A/G</td><td>0.01</td><td>PP</td><td>CURD</td><td>-5.09</td><td>-0.13</td><td>3.16 x 10<sup>-9</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>rs186331780</td><td>12</td><td>61317029</td><td>LOC105369793</td><td>FAM19A2</td><td></td><td>A/G</td><td>0.89</td><td>PP</td><td>CURD</td><td>0.66</td><td>0.67</td><td>6.61 x 10<sup>-7</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>rs187888844</td><td>13</td><td>67705907</td><td>LOC105370250</td><td>PCDH9</td><td></td><td>C/G</td><td>0.17</td><td>PP</td><td>LHD</td><td>-0.27</td><td>-4.05</td><td>6.76 x 10<sup>-9</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>rs116464496</td><td>13</td><td>105934773</td><td>LINC00343</td><td></td><td></td><td>T/C</td><td>0.09</td><td>SBP</td><td>CURD</td><td>2.34</td><td>-3.76</td><td>8.32 x 10<sup>-8</sup></td><td></td><td></td><td></td><td></td></tr></tbody></table>

The most significantly associated SNVs are shown per gene for each Blood Pressure (BP) trait and alcohol status. Abbreviations: SNV, single nucleotide variant; Chr, chromosome; Position, Gene, and Role in dbSNP build 150 (hg38); 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), LHD, Light(1-7 drinks/week) or heavy (≥8 drinks/week) drinker; Stage 1, Discovery cohorts; Stage 2, Replication cohorts; S1 & S2, 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; P-Meta, modified-interaction METAL P-Value of Meta-analysis in combined Stage 1 and Stage 2

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

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 “Cardio-megaly” (*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<sup>-20</sup>).

## 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
<table><caption>Table 4. Novel SNVs/Genes associated with BP traits in Multi-ancestry meta-analysis in combined Stage 1 and Stage 2.</caption><thead><tr><th rowspan="2">SNV</th><th rowspan="2">Chr</th><th rowspan="2">Position</th><th rowspan="2">Gene</th><th rowspan="2">Near Gene</th><th rowspan="2">Role</th><th rowspan="2">A1/2</th><th rowspan="2">Frq1</th><th rowspan="2">Ancestry</th><th rowspan="2">Trait</th><th rowspan="2">Drink</th><th colspan="4">Stage 1 and Stage 2</th></tr><tr><th>b_M</th><th>b_I</th><th>P-Meta</th><th>N</th></tr></thead><tbody><tr><td>rs10092965</td><td>8</td><td>8515975</td><td>LOC105379224</td><td>SGK223</td><td></td><td>A/G</td><td>0.53</td><td>EA, HA</td><td>DBP</td><td>CURD</td><td>-0.19</td><td>0.01</td><td>1.74 x 10<sup>-12</sup></td><td>373,915</td></tr><tr><td>rs7823056</td><td>8</td><td>8525195</td><td>LOC105379224</td><td>SGK223</td><td></td><td>A/G</td><td>0.5</td><td>AA, EA</td><td>PP</td><td>LHD</td><td>-0.31</td><td>0.10</td><td>3.31 x 10<sup>-11</sup></td><td>161,080</td></tr><tr><td>rs7823056</td><td>8</td><td>8525195</td><td>LOC105379224</td><td>SGK223</td><td></td><td>A/G</td><td>0.41</td><td>AA, EA</td><td>SBP</td><td>LHD</td><td>-0.44</td><td>0.11</td><td>1.38 x 10<sup>-11</sup></td><td>214,814</td></tr><tr><td>rs453301</td><td>8</td><td>9172877</td><td>LOC102724880</td><td>PPP1R3B</td><td></td><td>T/G</td><td>0.5</td><td>EA, HA</td><td>DBP</td><td>CURD</td><td>-0.13</td><td>-0.07</td><td>4.90 x 10<sup>-12</sup></td><td>365,537</td></tr><tr><td>rs10503387</td><td>8</td><td>9293015</td><td>LOC157273</td><td></td><td></td><td>T/C</td><td>0.37</td><td>AA, EA</td><td>SBP</td><td>CURD</td><td>0.32</td><td>0.03</td><td>1.07 x 10<sup>-14</sup></td><td>381,431</td></tr><tr><td>rs11781008</td><td>8</td><td>9295729</td><td>LOC157273</td><td></td><td></td><td>T/G</td><td>0.37</td><td>EA, HA</td><td>DBP</td><td>CURD</td><td>0.13</td><td>0.07</td><td>1.05 x 10<sup>-11</sup></td><td>373,915</td></tr><tr><td>rs4383974</td><td>8</td><td>9761838</td><td>TNKS</td><td></td><td>intron</td><td>C/G</td><td>0.7</td><td>AA, EA</td><td>SBP</td><td>CURD</td><td>-0.28</td><td>-0.08</td><td>2.04 x 10<sup>-13</sup></td><td>381,431</td></tr><tr><td>rs9286060</td><td>8</td><td>9795635</td><td>TNKS</td><td></td><td></td><td>A/C</td><td>0.38</td><td>AA, EA</td><td>DBP</td><td>CURD</td><td>0.21</td><td>-0.02</td><td>2.29 x 10<sup>-13</sup></td><td>371,053</td></tr><tr><td>rs34919878</td><td>8</td><td>10241994</td><td>MSRA</td><td></td><td>intron</td><td>A/G</td><td>0.41</td><td>EA, HA</td><td>DBP</td><td>CURD</td><td>-0.18</td><td>-0.05</td><td>5.75 x 10<sup>-17</sup></td><td>365,537</td></tr><tr><td>rs4841294</td><td>8</td><td>10247558</td><td>MSRA</td><td></td><td>intron</td><td>A/C</td><td>0.43</td><td>AA, EA</td><td>SBP</td><td>LHD</td><td>-0.40</td><td>0.01</td><td>2.69 x 10<sup>-10</sup></td><td>166,956</td></tr><tr><td>rs17693945</td><td>8</td><td>10248500</td><td>MSRA</td><td></td><td>intron</td><td>T/C</td><td>0.41</td><td>AA, EA</td><td>MAP</td><td>LHD</td><td>-0.30</td><td>0.08</td><td>1.51 x 10<sup>-9</sup></td><td>166,054</td></tr><tr><td>rs13276026</td><td>8</td><td>10752445</td><td>LOC102723313</td><td>PINX1</td><td>intron</td><td>A/G</td><td>0.55</td><td>EA, HA</td><td>DBP</td><td>CURD</td><td>-0.11</td><td>-0.10</td><td>4.47 x 10<sup>-14</sup></td><td>373,915</td></tr><tr><td>rs13276026</td><td>8</td><td>10752445</td><td>LOC102723313</td><td>PINX1</td><td>intron</td><td>A/G</td><td>0.55</td><td>EA, HA</td><td>MAP</td><td>CURD</td><td>-0.15</td><td>-0.03</td><td>4.68 x 10<sup>-9</sup></td><td>373,911</td></tr><tr><td>rs13276026</td><td>8</td><td>10752445</td><td>LOC102723313</td><td>PINX1</td><td>intron</td><td>A/G</td><td>0.55</td><td>EA, HA</td><td>SBP</td><td>CURD</td><td>-0.22</td><td>-0.24</td><td>3.89 x 10<sup>-23</sup></td><td>373,919</td></tr><tr><td>rs4551304</td><td>8</td><td>10807559</td><td>PINX1</td><td></td><td>intron</td><td>A/G</td><td>0.4</td><td>EA, HA</td><td>DBP</td><td>CURD</td><td>0.10</td><td>0.12</td><td>1.70 x 10<sup>-14</sup></td><td>373,915</td></tr><tr><td>rs4551304</td><td>8</td><td>10807559</td><td>PINX1</td><td></td><td>intron</td><td>A/G</td><td>0.4</td><td>EA, HA</td><td>MAP</td><td>CURD</td><td>0.15</td><td>0.03</td><td>2.24 x 10<sup>-8</sup></td><td>373,911</td></tr><tr><td>rs9969436</td><td>8</td><td>10985149</td><td>XKR6</td><td></td><td>intron</td><td>T/G</td><td>0.47</td><td>AA, EA</td><td>MAP</td><td>LHD</td><td>0.28</td><td>-0.01</td><td>3.09 x 10<sup>-9</sup></td><td>165,894</td></tr><tr><td>rs2409784</td><td>8</td><td>11539347</td><td>BLK</td><td></td><td>intron</td><td>A/C</td><td>0.51</td><td>EA, HA</td><td>DBP</td><td>CURD</td><td>-0.11</td><td>-0.09</td><td>5.62 x 10<sup>-12</sup></td><td>374,975</td></tr><tr><td>rs2244894</td><td>8</td><td>11591150</td><td>LINC00208</td><td></td><td></td><td>C/G</td><td>0.44</td><td>ASA, EA</td><td>PP</td><td>CURD</td><td>-0.07</td><td>-0.19</td><td>3.24 x 10<sup>-15</sup></td><td>493,402</td></tr><tr><td>rs13249843</td><td>8</td><td>11601509</td><td>LINC00208</td><td></td><td></td><td>T/G</td><td>0.33</td><td>EA, HA</td><td>DBP</td><td>CURD</td><td>0.18</td><td>0.04</td><td>2.51 x 10<sup>-15</sup></td><td>398,330</td></tr><tr><td>rs3735814</td><td>8</td><td>11749887</td><td>GATA4</td><td></td><td>intron</td><td>A/G</td><td>0.52</td><td>EA, HA</td><td>SBP</td><td>CURD</td><td>0.09</td><td>0.22</td><td>2.14 x 10<sup>-10</sup></td><td>373,919</td></tr><tr><td>rs9928094</td><td>16</td><td>53765993</td><td>FTO</td><td></td><td>intron</td><td>A/G</td><td>0.63</td><td>ASA, EA</td><td>PP</td><td>CURD</td><td>-0.33</td><td>0.19</td><td>2.63 x 10<sup>-15</sup></td><td>499,179</td></tr><tr><td>rs62033406</td><td>16</td><td>53790314</td><td>FTO</td><td></td><td>intron</td><td>A/G</td><td>0.55</td><td>ASA, EA</td><td>MAP</td><td>CURD</td><td>-0.22</td><td>0.12</td><td>3.31 x 10<sup>-8</sup></td><td>511,074</td></tr></tbody></table>

The most significantly associated SNVs are shown per gene for each Blood Pressure (BP) trait and alcohol status. Abbreviations: 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), 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-Meta, modified-interaction METAL P-Value of Meta-analysis in combined Stage 1 and Stage 2; N, Number of individuals.

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

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<sup>-17</sup>), 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.
<table><caption>Table 5. Novel SNVs/Genes associated with BP traits from correlated meta-analysis in European ancestry in Stage 1.</caption><caption>Associations NOT Present in Tables 1 and 2, in Current Drinkers</caption><thead><tr><th>SNV</th><th>Chr</th><th>Position</th><th>Gene</th><th>Near Gene</th><th>Role</th><th>Frql</th><th>P-Correlated Meta</th><th>P-DBP</th><th>P-SBP</th><th>P-MAP</th><th>P-PP</th><th>N</th></tr></thead><tbody><tr><td>rs200124401</td><td>1</td><td>83336112</td><td>LOC107985037</td><td>TTLL7</td><td>intron</td><td>0.70</td><td>4.29 x 10<sup>-8</sup></td><td>1.82 x 10<sup>-5</sup></td><td>1.86 x 10<sup>-6</sup></td><td>1.20 x 10<sup>-6</sup></td><td>4.68 x 10<sup>-4</sup></td><td>89,035</td></tr><tr><td>rs3813963</td><td>1</td><td>206648224</td><td>DYRK3</td><td>DYRK3, IL10</td><td>Synon</td><td>0.99</td><td>2.95 x 10<sup>-8</sup></td><td>1.66 x 10<sup>-4</sup></td><td>8.32 x 10<sup>-8</sup></td><td>8.13 x 10<sup>-7</sup></td><td>3.72 x 10<sup>-4</sup></td><td>39,497</td></tr><tr><td>rs80169249</td><td>1</td><td>206683281</td><td>LOC105372875</td><td>MAPKAPK2</td><td></td><td>0.99</td><td>3.52 x 10<sup>-8</sup></td><td>2.45 x 10<sup>-4</sup></td><td>7.41 x 10<sup>-8</sup></td><td>1.00 x 10<sup>-6</sup></td><td>3.39 x 10<sup>-4</sup></td><td>39,497</td></tr><tr><td>rs185597356</td><td>4</td><td>161336738</td><td>FSTL5</td><td>FSTL5</td><td></td><td>0.99</td><td>1.77 x 10<sup>-8</sup></td><td>7.24 x 10<sup>-7</sup></td><td>8.71 x 10<sup>-7</sup></td><td>4.37 x 10<sup>-8</sup></td><td>1.00 x 10<sup>-2</sup></td><td>55,056</td></tr><tr><td>rs77779142</td><td>11</td><td>65832185</td><td>SNX32</td><td>SNX32</td><td></td><td>0.84</td><td>3.89 x 10<sup>-8</sup></td><td>8.32 x 10<sup>-5</sup></td><td>1.12 x 10<sup>-6</sup></td><td>2.88 x 10<sup>-6</sup></td><td>7.08 x 10<sup>-5</sup></td><td>90,689</td></tr><tr><td>rs11227333</td><td>11</td><td>65874946</td><td>EFEMP2</td><td>EFEMP2</td><td></td><td>0.80</td><td>2.34 x 10<sup>-8</sup></td><td>3.24 x 10<sup>-5</sup></td><td>5.89 x 10<sup>-7</sup></td><td>1.15 x 10<sup>-6</sup></td><td>2.00 x 10<sup>-4</sup></td><td>86,262</td></tr><tr><td>rs201407003</td><td>11</td><td>65894964</td><td>FOSL1</td><td>FOSL1, MALAT1</td><td>intron</td><td>0.85</td><td>1.76 x 10<sup>-8</sup></td><td>2.09 x 10<sup>-5</sup></td><td>6.31 x 10<sup>-7</sup></td><td>7.94 x 10<sup>-7</sup></td><td>2.04 x 10<sup>-4</sup></td><td>86,262</td></tr></tbody></table>

<table><caption>Associations Present in Tables 1 and 2, in Current Drinkers</caption><thead><tr><th>SNV</th><th>Chr</th><th>Position</th><th>Gene</th><th>Near Gene</th><th>Role</th><th>Frql</th><th>P-Correlated Meta</th><th>P-DBP</th><th>P-SBP</th><th>P-MAP</th><th>P-PP</th><th>N</th></tr></thead><tbody><tr><td>rs2980755</td><td>8</td><td>8506173</td><td>LOC107986913</td><td>SGK223</td><td></td><td>0.55</td><td>4.59 x 10<sup>-9</sup></td><td>5.13 x 10<sup>-4</sup></td><td>4.27 x 10<sup>-8</sup></td><td>1.74 x 10<sup>-6</sup></td><td>1.15 x 10<sup>-6</sup></td><td>90,691</td></tr><tr><td>rs13270194</td><td>8</td><td>8520592</td><td>LOC105379224</td><td>CLDN23</td><td></td><td>0.51</td><td>1.59 x 10<sup>-9</sup></td><td>2.14 x 10<sup>-4</sup></td><td>2.45 x 10<sup>-8</sup></td><td>8.13 x 10<sup>-7</sup></td><td>8.51 x 10<sup>-7</sup></td><td>90,691</td></tr><tr><td>rs1976671</td><td>8</td><td>9822124</td><td>TNKS</td><td>TNKS</td><td></td><td>0.62</td><td>2.01 x 10<sup>-9</sup></td><td>1.58 x 10<sup>-6</sup></td><td>4.68 x 10<sup>-8</sup></td><td>3.02 x 10<sup>-8</sup></td><td>1.26 x 10<sup>-3</sup></td><td>90,691</td></tr><tr><td>rs483916</td><td>8</td><td>9936091</td><td>MIR124-1</td><td>MIR124-1</td><td></td><td>0.47</td><td>1.55 x 10<sup>-11</sup></td><td>1.17 x 10<sup>-6</sup></td><td>1.05 x 10<sup>-9</sup></td><td>3.55 x 10<sup>-9</sup></td><td>7.94 x 10<sup>-6</sup></td><td>90,691</td></tr><tr><td>rs2062331</td><td>8</td><td>10122482</td><td>MSRA</td><td>MSRA</td><td>intron</td><td>0.54</td><td>5.49 x 10<sup>-13</sup></td><td>2.00 x 10<sup>-8</sup></td><td>1.70 x 10<sup>-10</sup></td><td>1.20 x 10<sup>-10</sup></td><td>1.32 x 10<sup>-5</sup></td><td>90,691</td></tr><tr><td>rs10096777</td><td>8</td><td>10660990</td><td>RP1L1</td><td>RP1L1</td><td></td><td>0.44</td><td>7.58 x 10<sup>-9</sup></td><td>9.77 x 10<sup>-5</sup></td><td>1.91 x 10<sup>-7</sup></td><td>9.55 x 10<sup>-7</sup></td><td>1.51 x 10<sup>-5</sup></td><td>90,691</td></tr><tr><td>rs7814795</td><td>8</td><td>10661775</td><td>MIR4286</td><td>MIR4286</td><td></td><td>0.45</td><td>6.86 x 10<sup>-9</sup></td><td>7.76 x 10<sup>-5</sup></td><td>1.78 x 10<sup>-7</sup></td><td>7.59 x 10<sup>-7</sup></td><td>2.00 x 10<sup>-5</sup></td><td>90,691</td></tr><tr><td>rs13276026</td><td>8</td><td>10752445</td><td>LOC102723313</td><td>SOX7</td><td>intron</td><td>0.44</td><td>4.79 x 10<sup>-8</sup></td><td>1.38 x 10<sup>-4</sup></td><td>5.62 x 10<sup>-7</sup></td><td>1.58 x 10<sup>-6</sup></td><td>1.91 x 10<sup>-4</sup></td><td>90,691</td></tr><tr><td>rs12156009</td><td>8</td><td>11427710</td><td>FAM167A</td><td>FAM167A</td><td>intron</td><td>0.51</td><td>9.49 x 10<sup>-9</sup></td><td>1.82 x 10<sup>-4</sup></td><td>1.66 x 10<sup>-7</sup></td><td>1.32 x 10<sup>-6</sup></td><td>1.07 x 10<sup>-5</sup></td><td>90,691</td></tr><tr><td>rs1478894</td><td>8</td><td>11591245</td><td>LINC00208</td><td>LINC00208</td><td></td><td>0.64</td><td>3.69 x 10<sup>-10</sup></td><td>1.66 x 10<sup>-5</sup></td><td>1.00 x 10<sup>-8</sup></td><td>8.51 x 10<sup>-8</sup></td><td>8.32 x 10<sup>-6</sup></td><td>90,691</td></tr><tr><td>rs13280442</td><td>8</td><td>11610048</td><td>LOC105379242</td><td>GATA4</td><td></td><td>0.45</td><td>5.23 x 10<sup>-9</sup></td><td>1.86 x 10<sup>-4</sup></td><td>8.32 x 10<sup>-8</sup></td><td>1.29 x 10<sup>-6</sup></td><td>4.47 x 10<sup>-6</sup></td><td>90,691</td></tr><tr><td>rs9937521</td><td>16</td><td>53765384</td><td>FTO</td><td>FTO</td><td>intron</td><td>0.61</td><td>2.89 x 10<sup>-10</sup></td><td>8.13 x 10<sup>-5</sup></td><td>4.68 x 10<sup>-9</sup></td><td>6.46 x 10<sup>-7</sup></td><td>2.04 x 10<sup>-7</sup></td><td>90,691</td></tr></tbody></table>

<table><caption>Associations NOT Present in Tables 1 and 2, in Light / Heavy Drinkers</caption><thead><tr><th>SNV</th><th>Chr</th><th>Position</th><th>Gene</th><th>Near Gene</th><th>Role</th><th>Frql</th><th>P-Correlated Meta</th><th>P-DBP</th><th>P-SBP</th><th>P-MAP</th><th>P-PP</th><th>N</th></tr></thead><tbody><tr><td>rs117519896</td><td>15</td><td>43645473</td><td>CATSPER2</td><td>CATSPER2</td><td>intron</td><td>0.98</td><td>8.25 x 10<sup>-9</sup></td><td>7.76 x 10<sup>-5</sup></td><td>2.88 x 10<sup>-7</sup></td><td>9.77 x 10<sup>-7</sup></td><td>2.75 x 10<sup>-5</sup></td><td>13,141</td></tr><tr><td>rs2957398</td><td>17</td><td>53625691</td><td>LOC107984982</td><td>LOC107984982</td><td></td><td>0.29</td><td>1.11 x 10<sup>-8</sup></td><td>8.91 x 10<sup>-5</sup></td><td>1.23 x 10<sup>-7</sup></td><td>2.69 x 10<sup>-6</sup></td><td>3.80 x 10<sup>-5</sup></td><td>54,785</td></tr><tr><td>rs146091319</td><td>18</td><td>71962177</td><td>LOC102725148</td><td>LOC102725148</td><td></td><td>0.99</td><td>1.50 x 10<sup>-8</sup></td><td>1.26 x 10<sup>-3</sup></td><td>1.74 x 10<sup>-8</sup></td><td>3.39 x 10<sup>-6</sup></td><td>1.26 x 10<sup>-5</sup></td><td>26,187</td></tr><tr><td>rs111700101</td><td>19</td><td>11433340</td><td>CCDC151</td><td>CCDC151</td><td>intron</td><td>0.94</td><td>2.78 x 10<sup>-8</sup></td><td>3.80 x 10<sup>-6</sup></td><td>8.13 x 10<sup>-7</sup></td><td>3.80 x 10<sup>-7</sup></td><td>3.55 x 10<sup>-3</sup></td><td>37,996</td></tr></tbody></table>

<table><caption>Associations Present in Tables 1 and 2, in Light / Heavy Drinkers</caption><thead><tr><th>SNV</th><th>Chr</th><th>Position</th><th>Gene</th><th>Near Gene</th><th>Role</th><th>Frql</th><th>P-Correlated Meta</th><th>P-DBP</th><th>P-SBP</th><th>P-MAP</th><th>P-PP</th><th>N</th></tr></thead><tbody><tr><td>rs34062996</td><td>8</td><td>9802688</td><td>TNKS</td><td>TNKS</td><td></td><td>0.39</td><td>2.26 x 10<sup>-9</sup></td><td>6.17 x 10<sup>-5</sup></td><td>2.40 x 10<sup>-8</sup></td><td>3.24 x 10<sup>-7</sup></td><td>3.47 x 10<sup>-5</sup></td><td>54,785</td></tr><tr><td>rs615632</td><td>8</td><td>9938811</td><td>MIR124-1</td><td>MIR124-1</td><td></td><td>0.47</td><td>4.18 x 10<sup>-10</sup></td><td>1.78 x 10<sup>-5</sup></td><td>7.41 x 10<sup>-9</sup></td><td>8.13 x 10<sup>-8</sup></td><td>2.34 x 10<sup>-5</sup></td><td>54,785</td></tr><tr><td>rs7843924</td><td>8</td><td>10119030</td><td>MSRA</td><td>MSRA</td><td>intron</td><td>0.54</td><td>2.46 x 10<sup>-13</sup></td><td>1.38 x 10<sup>-8</sup></td><td>1.58 x 10<sup>-10</sup></td><td>1.58 x 10<sup>-10</sup></td><td>6.46 x 10<sup>-6</sup></td><td>54,785</td></tr><tr><td>rs11250099</td><td>8</td><td>10961147</td><td>XKR6</td><td>XKR6</td><td>intron</td><td>0.48</td><td>4.13 x 10<sup>-8</sup></td><td>1.82 x 10<sup>-4</sup></td><td>3.98 x 10<sup>-7</sup></td><td>2.19 x 10<sup>-6</sup></td><td>1.62 x 10<sup>-4</sup></td><td>54,785</td></tr><tr><td>rs13255193</td><td>8</td><td>11451683</td><td>FAM167A</td><td>FAM167A</td><td>intron</td><td>0.46</td><td>2.41 x 10<sup>-8</sup></td><td>7.76 x 10<sup>-5</sup></td><td>6.76 x 10<sup>-7</sup></td><td>1.66 x 10<sup>-6</sup></td><td>9.77 x 10<sup>-5</sup></td><td>54,785</td></tr><tr><td>rs4841559</td><td>8</td><td>11559376</td><td>BLK</td><td>BLK</td><td>intron</td><td>0.51</td><td>4.12 x 10<sup>-8</sup></td><td>4.79 x 10<sup>-4</sup></td><td>4.47 x 10<sup>-7</sup></td><td>9.55 x 10<sup>-6</sup></td><td>1.35 x 10<sup>-5</sup></td><td>54,785</td></tr><tr><td>rs4840573</td><td>8</td><td>11605721</td><td>LINC00208</td><td>LINC00208</td><td></td><td>0.60</td><td>3.94 x 10<sup>-9</sup></td><td>1.15 x 10<sup>-3</sup></td><td>7.76 x 10<sup>-8</sup></td><td>7.59 x 10<sup>-6</sup></td><td>4.57 x 10<sup>-8</sup></td><td>53,371</td></tr><tr><td>rs13280442</td><td>8</td><td>11610048</td><td>LOC105379242</td><td>GATA4</td><td></td><td>0.45</td><td>6.26 x 10<sup>-9</sup></td><td>2.40 x 10<sup>-4</sup></td><td>1.38 x 10<sup>-7</sup></td><td>3.39 x 10<sup>-6</sup></td><td>2.24 x 10<sup>-6</sup></td><td>54,785</td></tr></tbody></table>

The most significantly associated SNVs are shown per gene for correlated BP traits and alcohol status: Current drinker (yes/no), and Light (1–7 drinks/week) or heavy (≥8 drinks/week) drinker. The “NOT Present in Tables 1 and 2” represents the associations detected using correlated meta-approach, otherwise the associations were already presented in Tables 1 and 2 using modified-interaction METAL approach. Abbreviations: SNV, single nucleotide variant; Chr, chromosome; Position, Gene, and Role in dbSNP build 150 (hg38); Role: Intronic, synonymous codon (Synon), or intergenic (blank space) SNV; Near gene reflects genes at up to +/-500 kb and related to BP / alcohol; Frql, Frequency of coded allele; P-Correlated Meta, P-Value of BP-correlated meta-analysis; P-DBP, modified-interaction METAL P-Value for Diastolic BP; P-SBP, modified-interaction METAL P-Value for Systolic BP; P-MAP, modified-interaction METAL P-Value for Mean Arterial Pressure; P-PP, modified-interaction METAL P-Value for Pulse Pressure; N, Number of individuals.
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.

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

<figure><img src="image_5.png" /><figcaption>Fig 1. Identification of four independent LD blocks in the 8p23.1 region (~3.3 MBs).</figcaption></figure>

https://doi.org/10.1371/journal.pone.0198166.g001
cirrhosis and fibrosis[41]. The transcription factor GATA4 has been reported to be associated with alcohol dependence in several studies[42–45]. 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 ($P \le 5.0 \times 10^{-8}$) 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 $\ge 140$ mm Hg, (ii) DBP $\ge 90$ 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 $\ge 8$ 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 S1–S4 Tables, and for Stage 2 in S5–S8 Tables.
## Interaction association analysis

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

$$E(Y) = b_0 + b_GSNV + b_EE + b_{GE}SNV * E + b_CC,$$

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 $b_G$ and $b_{GE}$, 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 ($b_G$) 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 $P < 1.0 \times 10^{-6}$ 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 $-\log_{10} P$-values of observed versus $-\log_{10} P$-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 $-2 \ln(p_i)$, approximately chi-square ($X^2$) with two degrees of freedom. In the case of correlated GWAS, this sum is no longer distributed as a simple $X^2$. Instead, the correlated meta-analysis method[25, 26]
uses an inverse-normal transform, $Z_i = \theta^{-1}(p_i)$ forming the N dimensional vector Z of all $Z_i$ s. Then, the method applies the basic theorem of multidimensional statistics for the matrix D, if $Z \sim N(O, E)$ then $DZ \sim N(O, E\Sigma D')$. In particular, when D is a $1 \times N$ vector of all 1's, $SUM(Z) = D Z \sim N(0, SUM(\Sigma))$, whose tail probability gives the Z meta-analysis P-value. In this case, for estimating $\Sigma$, the SNV P-values are dichotomized across the genome as ($P \le 0.5$; $P > 0.5$). 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 1–5) 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 (web-site 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).
(DOCX)

**S2 Note. Summary of biological description for novel BP loci.** Information summary of the nearest genes for blood pressure novel loci.
(DOCX)

**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.
(TIF)

**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).
(TIF)

**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).
(TIF)

**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).
(TIF)
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.
(TIF)

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.
(TIF)

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.
(TIF)

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.
(TIF)

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.
(TIF)

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.
(TIF)

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.
(TIF)

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.
(TIF)

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.
(TIF)

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.
(TIF)

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.
(TIF)

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).
(TIF)

**S17 Fig. Protein-protein interactions between tankyrase and beta-catenin.** Tankyrase (from *TNKS* gene) and β-catenin (from *CTNNB1* gene).
(TIF)

**S18 Fig. Wnt signaling KEGG pathway.** *TNKS* interacts with *CTNNB1*.
(TIF)

**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.
(XLSX)

**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.
(XLSX)

**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.
(XLSX)

**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).
(XLSX)

**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.
(XLSX)

**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.
(XLSX)

**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.
(XLSX)

**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.
(XLSX)

**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.
(XLSX)

**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.
(XLSX)

**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.
(XLSX)

**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.
(XLSX)

**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.
(XLSX)

**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.

(XLSX)

**S15 Table. Data analysis tools and databases.**

(DOCX)

**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 Bio-wulf 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 cooperation of our Diabetes Heart Study (DHS) and AA-DHS participants.

**ASCOT (Anglo-Scandinavian Cardiac Outcomes Trial):** We thank all ASCOT trial participants, physicians, nurses, and practices in the participating countries for their important contribution to the study. In particular, we thank Clare Muckian and David Toomey for their help in DNA extraction, storage, and handling. We would also like to acknowledge the Barts and The London Genome Centre staff for genotyping the Exome chip array. P.B.M, M.J.C and H.
R.W. wish to acknowledge the support of the NIHR Cardiovascular Biomedical Research Centre at Barts and Queen Mary University of London, UK.

**BBJ (Biobank Japan Project):** We thank all the participants, medical coordinators of the cooperating hospitals for collecting samples and clinical information in the project.

**BRIGHT (British Genetics of Hypertension):** The BRIGHT study is extremely grateful to all the patients who participated in the study and the BRIGHT nursing team. P.B.M, M.J.C and H.R.W wish to acknowledge the support of the NIHR Cardiovascular Biomedical Research Centre at Barts and Queen Mary University of London, UK.

**CoLaus (Cohorte Lausannoise Study):** The authors would like to thank all the people who participated in the recruitment of the participants, data collection and validation, particularly Nicole Bonvin, Yolande Barreau, Mathieu Firmann, François Bastardot, Julien Vaucher, Panagiotis Antiochos and Cédric Gubelmann.

**DESIR (Data from an Epidemiological Study on the Insulin Resistance):** The DESIR Study Group is composed of Inserm-U1018 (Paris: B. Balkau, P. Ducimetière, E. Eschwège), Inserm-U367 (Paris: F. Alhenc-Gelas), CHU d'Angers (A. Girault), Bichat Hospital (Paris: F. Fumeron, M. Marre, R. Roussel), CHU de Rennes (F. Bonnet), CNRS UMR-8199 (Lille: A. Bonnefond, P. Froguel), Medical Examination Services (Alençon, Angers, Blois, Caen, Chartres, Chateau-roux, Cholet, LeMans, Orléans and Tours), Research Institute for General Medicine (J. Cogneau), the general practitioners of the region and the Cross-Regional Institute for Health (C. Born, E. Caces, M. Cailleau, N. Copin, J.G. Moreau, F. Rakotozafy, J. Tichet, S. Vol).

**DHS (Diabetes Heart Study):** The authors thank the investigators, staff, and participants of the DHS for their valuable contributions.

**EGCUT Estonian Genome Center—University of Tartu (Estonian Biobank):** Data analyzes were carried out in part in the High Performance Computing Center of University of Tartu.

**EPIC (European Prospective Investigation into Cancer and Nutrition)-Norfolk:** We thank all EPIC participants and staff for their contribution to the study.

**FENLAND (The Fenland Study):** We are grateful to all the volunteers for their time and help, and to the General Practitioners and practice staff for assistance with recruitment. We thank the Fenland Study Investigators, Fenland Study Co-ordination team and the Epidemiology Field, Data and Laboratory teams. We further acknowledge support from the Medical research council (MC_UU_12015/1).

**GeneSTAR (Genetic Studies of Atherosclerosis Risk):** We are very grateful to all of our participants for their long-term involvement.

**GLACIER (Gene x Lifestyle Interactions and Complex Traits Involved in Elevated Disease Risk):** We thank the participants, health professionals and data managers involved in the Västerbottens Intervention Project. We are also grateful to the staff of the Northern Sweden Biobank for preparing materials and to K Enqvist and T Johansson (Västerbottens County Council, Umeå, Sweden) for DNA preparation.

**HCHS/SOL (Hispanic Community Health Study/Study of Latinos):** We thank the participants and staff of the HCHS/SOL study for their contributions to this study.

**HRS (Health & Retirement Study):** Our genotyping was conducted by the NIH Center for Inherited Disease Research (CIDR) at Johns Hopkins University. Genotyping quality control and final preparation of the data were performed by the Genetics Coordinating Center at the University of Washington.

**HyperGEN-AXIOM (Hypertension Genetic Epidemiology Network—Axiom Chip GWAS):** We thank the study investigators, staff and participants for their value contributions.

**INGI (Italian Network Genetic Isolate):** We thank all the inhabitants who participated to the projects.
**InterAct (The EPIC-InterAct Case-Cohort Study):** We thank all EPIC participants and staff for their contribution to the study.

**IRAS (Insulin Resistance Atherosclerosis Study):** The authors thank study investigators, staff, and participants for their valuable contributions.

**KORA (Cooperative Health Research in the Augsburg Region):** We thank all KORA participants and staff for their contribution to the study.

**LBC1921 (Lothian Birth Cohort 1921):** We thank the LBC1921 cohort participants and team members who contributed to these studies. Funding from the Biological Sciences Research Council (BBSRC) and Medical Research Council (MRC) is gratefully acknowledged.

**LBC1936 (Lothian Birth Cohort 1936):** We thank the LBC1936 cohort participants and team members who contributed to these studies. Funding from the Biological Sciences Research Council (BBSRC) and Medical Research Council (MRC) is gratefully acknowledged.

**LifeLines (Lifelines Cohort Study):** The authors wish to acknowledge the services of the Lifelines, the contributing research centers delivering data to Lifelines, and all the study participants. The authors wish to acknowledge the services of the Lifelines, the contributing research centers delivering data to Lifelines, and all the study participants. Also, Lifelines acknowledges the contributions from Behrooz Z Alizadeh (Department of Epidemiology, University of Groningen, University Medical Center Groningen, The Netherlands), H Marike Boezen (Department of Epidemiology, University of Groningen, University Medical Center Groningen, The Netherlands), Lude Franke (Department of Genetics, University of Groningen, University Medical Center Groningen, The Netherlands), Pim van der Harst (Department of Cardiology, University of Groningen, University Medical Center Groningen, The Netherlands), Gerjan Navis (Department of Internal Medicine, Division of Nephrology, University of Groningen, University Medical Center Groningen, The Netherlands), Marianne Rots (Department of Medical Biology, University of Groningen, University Medical Center Groningen, The Netherlands), Harold Snieder (Department of Epidemiology, University of Groningen, University Medical Center Groningen, The Netherlands), Morris Swertz (Department of Genetics, University of Groningen, University Medical Center Groningen, The Netherlands), Bruce HR Wolffenbuttel (Department of Endocrinology, University of Groningen, University Medical Center Groningen, The Netherlands), Cisca Wijmenga (Department of Genetics, University of Groningen, University Medical Center Groningen, The Netherlands).

**LLFS (Long Life Family Study):** The LLFS would like to thank the participants and research staff who make the study possible.

**LOLIPOP (London Life Sciences Prospective Population Study):** We acknowledge support of the MRC-PHE Centre for Environment and Health, and the NIHR Health Protection Research Unit on Health Impact of Environmental Hazards. The work was carried out in part at the NIHR/Wellcome Trust Imperial Clinical Research Facility. The views expressed are those of the author(s) and not necessarily those of the Imperial College Healthcare NHS Trust, the NHS, the NIHR or the Department of Health. We thank the participants and research staff who made the study possible.

**PROCARDIS (Precocious Coronary Artery Disease):** The PROCARDIS researchers thank the patients for their selfless participation in this project.

**RHS (Ragama Health Study):** The RHS was supported by the Grant of National Center for Global Health and Medicine (NCGM), Japan.

**SWHS/SMHS (Shanghai Women's Health Study/ Shanghai Men's Health Study):** We thank all the individuals who took part in these studies and all the researchers who have enabled this work to be carried out.

**TRAILS (TRacking Adolescents' Individual Lives Survey):** TRAILS is a collaborative project involving various departments of the University Medical Center and University of Groningen,
the Erasmus University Medical Center Rotterdam, the University of Utrecht, the Radboud
Medical Center Nijmegen, and the Parnassia Bavo group, all in the Netherlands. We are grate-
ful to all adolescents who participated in this research and to everyone who worked on this
project and made it possible.

**UKB (United Kingdom Biobank, www.ukbiobank.ac.uk)**: This research has been conducted using the UK Biobank Resource. The UK Biobank data were analyzed from the data set corresponding to UK Biobank access application no. 236, application title “Genome-wide association study of blood pressure”, with Paul Elliott as the PI/applicant. This work was supported by the UK-CMC and the BP working group.

Author Contributions

**Conceptualization:** Mary F. Feitosa, Aldi T. Kraja, Yun J. Sung, Amy R. Bentley, Hugues Aschard, Eric Boerwinkle, Ingrid Borecki, H. Janaka de Silva, Tamara B. Harris, Barbara V. Howard, Tuomas O. Kilpeläinen, Sharon L. R. Kardia, Bruce M. Psaty, Charles N. Rotimi, L. Adrienne Cupples, Cornelia M. van Duijn, Kenneth Rice, Alanna C. Morrison, Dabeeru C. Rao, Daniel Levy.

**Data curation:** Mary F. Feitosa, Aldi T. Kraja, Daniel I. Chasman, Ioanna Ntalla, Xiuqing Guo, Xueling Sim, Jonathan Marten, Solomon K. Musani, Michael R. Brown, Lawrence F. Bielak, Rajkumar Dorajoo, Fernando P. Hartwig, Kurt K. Lohman, Tuomo Rankinen, Albert V. Smith, Salman M. Tajuddin, Mary K. Wojczynski, Maris Alver, Mathilde Boissel, Qiuyin Cai, Archie Campbell, Jin Fang Chai, Yanick Hagemeijer, Sarah E. Harris, Federica Laguzzi, Sandosh Padmanabhan, Rico Rueedi, M. Abdullah Said, Alena Stančáková, Bamidele O. Tayo, Veronique Vitart, Yajuan Wang, Erin B. Ware, Helen R. Warren, Lisa R. Yanek, Dan E. Arking, Mickaël Canouil, Aravinda Chakravarti, Yii-Der Ida Chen, Adolfo Correa, Renée de Mutsert, H. Janaka de Silva, Georg Ehret, Ruben N. Eppinga, Evangelos Evangelou, Jessica D. Faul, Yechiel Friedlander, Dongfeng Gu, Tamara B. Harris, Jiang He, Sami Heikkinen, Barbara V. Howard, M. Arfan Ikram, Tomohiro Katsuya, Tuomas O. Kilpeläinen, Woon-Puay Koh, Stephen B. Kritchevsky, Johanna Kuusisto, Shiow Lin, Reedik Mägi, Yuri Milaneschi, Lili Milani, Karen L. Mohlke, Mike A. Nalls, Christopher P. Nelson, Annette Peters, Kathryn Roll, Lynda M. Rose, Yuan Shi, Jennifer A. Smith, Konstantin Strauch, André G. Uitterlinden, Melanie Waldenberger, Lihua Wang, Ya Xing Wang, Wen Bin Wei, Christine Williams, Jie Yao, Wei Zhao, Alan B. Zonderman, John C. Chambers, Barry I. Freedman, Christian Gieger, Jost Bruno Jonas, Norihiro Kato, Jaspal S. Kooner, Markku Laakso, Cathy C. Laurie, Karin Leander, Ozren Polasek, David J. Porteous, Xiao-Ou Shu, Ananda R. Wickremasinghe, Wei Zheng, Bernardo L. Horta, Sharon L. R. Kardia, Yongmei Liu, Alexandre C. Pereira, Bruce M. Psaty, Caroline Hayward, Cornelia M. van Duijn, Tien Yin Wong, Charles Kooperberg.

**Formal analysis:** Mary F. Feitosa, Aldi T. Kraja, Ioanna Ntalla, Xiuqing Guo, Nora Franceschini, Xueling Sim, Dina Vojinovic, Solomon K. Musani, Changwei Li, Amy R. Bentley, Michael R. Brown, Karen Schwander, Melissa A. Richard, Raymond Noordam, Traci M. Bartz, Lawrence F. Bielak, Rajkumar Dorajoo, Virginia Fisher, Fernando P. Hartwig, Andrea R. V. R. Horimoto, Kurt K. Lohman, Tuomo Rankinen, Albert V. Smith, Salman M. Tajuddin, Maris Alver, Mathilde Boissel, Jin Fang Chai, Xu Chen, Jasmin Divers, Chuan Gao, Anuj Goel, Sarah E. Harris, Meian He, Fang-Chi Hsu, Anne U. Jackson, Brigitte Kühnel, Federica Laguzzi, Jian'an Luan, Ilja M. Nolte, Muhammad Riaz, Rico Rueedi, Antonietta Robino, Robert A. Scott, Fumihiko Takeuchi, Bamidele O. Tayo, Peter J. van der Most, Tibor V. Varga, Yajuan Wang, Erin B. Ware, Helen R. Warren, Stefan Weiss,
Wanqing Wen, Lisa R. Yanek, Weihua Zhang, Jing Hua Zhao, Saima Afaq, Dan E. Arking, Marco Brumat, Mickaël Canouil, Lisa de las Fuentes, Xuan Deng, Qing Duan, Evangelos Evangelou, Jessica D. Faul, Ilaria Gandin, He Gao, C. Charles Gu, Saskia P. Hagenaars, Sami Heikkinen, Carl D. Langefeld, Benjamin Lehne, Yize Li, Shiow Lin, Jingmin Liu, Marie Loh, Tin Louie, Reedik Mägi, Yuri Milaneschi, Mike A. Nalls, Lynda M. Rose, William R. Scott, Mario Sims, Heather M. Stringham, Lihua Wang, Christine Williams, Jie Yao, Caizheng Yu, Wei Zhao, Zoltán Kutalik, Tanika N. Kelly, Alanna C. Morrison.

**Funding acquisition:** Daniel I. Chasman, Ching-Yu Cheng, Michael R. Brown, Tuomo Rankinen, Meian He, Tin Aung, Eric Boerwinkle, Morris Brown, Gregory L. Burke, Aravinda Chakravarti, Sabanayagam Charumathi, Adolfo Correa, H. Janaka de Silva, Georg Ehret, Nita G. Forouhi, Yechiel Friedlander, Jiang He, Chew-Kiat Heng, Barbara V. Howard, Ulrich John, Woon-Puay Koh, José E. Krieger, Stephen B. Kritchevsky, Claudia Langenberg, Cora E. Lewis, Andres Metspalu, Karen L. Mohlke, Jill M. Norris, Thomas Perls, Nancy L. Pedersen, Annette Peters, Olli T. Raitakari, Frits R. Rosendaal, Jerome I. Rotter, Nicole Schupf, John M. Starr, Konstantin Strauch, Yik Ying Teo, Jian-Min Yuan, Alan B. Zonderman, Diane M. Becker, Michael Boehnke, Donald W. Bowden, John C. Chambers, Ian J. Deary, Tōnu Esko, Martin Farrall, Paul W. Franks, Barry I. Freedman, Christian Gieger, Norihiro Kato, Jaspal S. Kooner, Markku Laakso, Cathy C. Laurie, Brenda W. J. H. Penninx, Rainer Rauramaa, Nilesh J. Samani, James Scott, Xiao-Ou Shu, Lynne E. Wagenknecht, Nicholas J. Wareham, Hugh Watkins, Ananda R. Wickremasinghe, Tangchun Wu, Claude Bouchard, Michele K. Evans, Sharon L. R. Kardia, Yongmei Liu, Bruce M. Psaty, Paul M. Ridker, Rob M. van Dam, Xiaofeng Zhu, Dennis O. Mook-Kanamori, L. Adrienne Cupples, E Shyong Tai, Dabeeru C. Rao, Michael A. Province, Daniel Levy.

**Investigation:** Mary F. Feitosa, Aldi T. Kraja, Daniel I. Chasman, Xiuqing Guo, Solomon K. Musani, Amy R. Bentley, Fernando P. Hartwig, Andrea R. V. R. Horimoto, Kurt K. Lohman, Tuomo Rankinen, Albert V. Smith, Mary K. Wojczynski, Maris Alver, Qiuyin Cai, Yanick Hagemeijer, Sarah E. Harris, Mika Kähönen, Anuradhani Kasturiratne, Pirjo Komulainen, M. Abdullah Said, Alena Stančáková, Yajuan Wang, Morris Brown, Gregory L. Burke, Yii-Der Ida Chen, Adolfo Correa, H. Janaka de Silva, Jingzhong Ding, Charles B. Eaton, Ruben N. Eppinga, Nita G. Forouhi, Terrence Forrester, Oscar H. Franco, Yechiel Friedlander, Mohsen Ghanbari, Tomohiro Katsuya, Woon-Puay Koh, José E. Krieger, Johanna Kuusisto, Cora E. Lewis, Reedik Mägi, Lili Milani, Yukihide Momozawa, Jerome I. Rotter, Carsten O. Schmidt, Nicole Schupf, Kent D. Taylor, André G. Uitterlinden, Jie Yao, Caizheng Yu, Jian-Min Yuan, John C. Chambers, Philippe Froguel, Jaspal S. Kooner, Markku Laakso, Lifelines Cohort Study, Ozren Polasek, Rainer Rauramaa, Xiao-Ou Shu, Pim van der Harst, David R. Weir, Tangchun Wu, Claude Bouchard, Kaare Christensen, Michele K. Evans, Sharon L. R. Kardia, Yongmei Liu, Alexandre C. Pereira, Xiaofeng Zhu, Ervin R. Fox, Walter Palmas, Michael A. Province, Daniel Levy.

**Methodology:** Mary F. Feitosa, Aldi T. Kraja, Daniel I. Chasman, Yun J. Sung, Thomas W. Winkler, Hugues Aschard, Tamar Sofer, Adolfo Correa, Tuomas O. Kilpeläinen, Jeff R. O'Connell, Colleen M. Sitlani, Ozren Polasek, Sharon L. R. Kardia, L. Adrienne Cupples, Kenneth Rice, Dabeeru C. Rao, Michael A. Province.

**Project administration:** Daniel I. Chasman, Solomon K. Musani, Tuomo Rankinen, Anuj Goel, Sarah E. Harris, Anuradhani Kasturiratne, Pirjo Komulainen, Erin B. Ware, Lisa R. Yanek, Gregory L. Burke, Aravinda Chakravarti, John M. Connell, Adolfo Correa, Renée de Mutsert, H. Janaka de Silva, Jessica D. Faul, Nita G. Forouhi, Yechiel Friedlander, Tamara B. Harris, Barbara V. Howard, InterAct Consortium, Ulrich John, Woon-Puay Koh, José E.
Krieger, Cora E. Lewis, Andres Metspalu, Jill M. Norris, Nicholette D. Palmer, Thomas Perls, Olli T. Raitakari, Kathryn Roll, Frits R. Rosendaal, Jerome I. Rotter, Carsten O. Schmidt, Peter S. Sever, Jennifer A. Smith, Nicholas Y. Q. Tan, Yih Chung Tham, Peter Vollenweider, Alan B. Zonderman, Diane M. Becker, Michael Boehnke, Donald W. Bowden, John C. Chambers, Tōnu Esko, Martin Farrall, Barry I. Freedman, Jaspal S. Kooner, Markku Laakso, Cathy C. Laurie, Karin Leander, Patrik K. E. Magnusson, Brenda W. J. H. Penninx, Ozren Polasek, Rainer Rauramaa, Lynne E. Wagenknecht, Hugh Watkins, Ana-nda R. Wickremasinghe, Tangchun Wu, Michele K. Evans, Sharon L. R. Kardia, Paul M. Ridker, Dennis O. Mook-Kanamori, Charles Kooperberg, Mark J. Caulfield, Patricia B. Munroe.

**Resources:** Daniel I. Chasman, Solomon K. Musani, Amy R. Bentley, Tuomo Rankinen, Anuj Goel, Mika Kähönen, Anuradhani Kasturiratne, Pirjo Komulainen, Najaf Amin, Marzyeh Amini, Eric Boerwinkle, Aravinda Chakravarti, John M. Connell, Adolfo Correa, H. Janaka de Silva, Charles B. Eaton, Nita G. Forouhi, Bruna Gigante, Göran Hallmans, Tomohiro Katsuya, Chiea Chuen Khor, Stephen B. Kritchevsky, Lenore J. Launer, Jianjun Liu, Andres Metspalu, Lili Milani, Jill M. Norris, Annette Peters, Leslie J. Raffel, Olli T. Raitakari, Lynda M. Rose, Jerome I. Rotter, Pamela J. Schreiner, Peter S. Sever, Stephen Sidney, John M. Starr, Peter Vollenweider, Ya Xing Wang, Wen Bin Wei, Jian-Min Yuan, Alan B. Zonderman, Diane M. Becker, Michael Boehnke, John C. Chambers, Ian J. Deary, Tōnu Esko, Martin Farrall, Paul W. Franks, Paolo Gasparini, Jost Bruno Jonas, Norihiro Kato, Jaspal S. Kooner, Markku Laakso, Karin Leander, Lifelines Cohort Study, Albertine J. Oldehinkel, Rainer Rauramaa, Xiao-Ou Shu, Pim van der Harst, Hugh Watkins, David R. Weir, Wei Zheng, Claude Bouchard, Michele K. Evans, Sharon L. R. Kardia, Yongmei Liu, Paul M. Ridker, Xiaofeng Zhu, Myriam Fornage, Charles N. Rotimi, Mark J. Caulfield, Patricia B. Munroe, Michael A. Province.

**Software:** Michael R. Brown, Lawrence F. Bielak, Federica Laguzzi, Rico Rueedi, Weihua Zhang, Jeff R. O'Connell, Wei Zhao.

**Supervision:** Mary F. Feitosa, Aldi T. Kraja, Daniel I. Chasman, Xiuqing Guo, Ching-Yu Cheng, Xueling Sim, Solomon K. Musani, Tuomo Rankinen, Mary K. Wojczynski, Anuradhani Kasturiratne, Tamar Sofer, Bamidele O. Tayo, Mickaël Canouil, Yii-Der Ida Chen, H. Janaka de Silva, Yechiel Friedlander, Ulrich John, Reedik Mägi, Andres Metspalu, Mike A. Nalls, Jill M. Norris, Nicholette D. Palmer, Nancy L. Pedersen, Patricia A. Peyser, Jennifer A. Smith, Harold Snieder, Melanie Waldenberger, Diane M. Becker, John C. Chambers, Ian J. Deary, Tōnu Esko, Martin Farrall, Paul W. Franks, Philippe Froguel, Christian Gieger, Norihiro Kato, Jaspal S. Kooner, Cathy C. Laurie, Karin Leander, Patrik K. E. Magnusson, Brenda W. J. H. Penninx, Ozren Polasek, Rainer Rauramaa, Nilesh J. Samani, Pim van der Harst, Hugh Watkins, Tangchun Wu, Claude Bouchard, Michele K. Evans, Yongmei Liu, Alexandre C. Pereira, Bruce M. Psaty, Xiaofeng Zhu, Myriam Fornage, Charles N. Rotimi, L. Adrienne Cupples, Tanika N. Kelly, Ervin R. Fox, Cornelia M. van Duijn, E Shyong Tai, Charles Kooperberg, Walter Palmas, Alanna C. Morrison, Paul Elliott, Mark J. Caulfield, Patricia B. Munroe.

**Validation:** Mary F. Feitosa, Aldi T. Kraja, Ioanna Ntalla, Albert V. Smith, Qiuyin Cai, Meian He, Anuradhani Kasturiratne, Federica Laguzzi, Nana Matoba, Erin B. Ware, Helen R. Warren, H. Janaka de Silva, Makoto Hirata, InterAct Consortium, Michiaki Kubo, Shiow Lin, Lihua Wang, Jian-Min Yuan, John C. Chambers, Yoichiro Kamatani, Jaspal S. Kooner, Michael A. Province.
**Visualization:** Solomon K. Musani, Anuradhani Kasturiratne, John C. Chambers, Jaspal S. Kooner.

**Writing – original draft:** Mary F. Feitosa, Aldi T. Kraja, Yun J. Sung, Thomas W. Winkler, Xiuqing Guo, Michael A. Province, Daniel Levy.

**Writing – review & editing:** Mary F. Feitosa, Aldi T. Kraja, Daniel I. Chasman, Yun J. Sung, Thomas W. Winkler, Ioanna Ntalla, Xiuqing Guo, Nora Franceschini, Dina Vojinovic, Amy R. Bentley, Karen Schwander, Melissa A. Richard, Raymond Noordam, Hugues Aschard, Traci M. Bartz, Lawrence F. Bielak, Fernando P. Hartwig, Kurt K. Lohman, Alisa K. Manning, Tuomo Rankinen, Mary K. Wojczynski, Jasmin Divers, Chuan Gao, Anuj Goel, Sarah E. Harris, Meian He, Fang-Chi Hsu, Mika Kähönen, Anuradhani Kasturiratne, Brigitte Kühnel, Federica Laguzzi, Ilja M. Nolte, Muhammad Riaz, Tamar Sofer, Alena Stančáková, Bamidele O. Tayo, Peter J. van der Most, Tibor V. Varga, Yajuan Wang, Erin B. Ware, Najaf Amin, Marzyeh Amini, Ingrid Borecki, Ulrich Broeckel, Aravinda Chakravarti, Yii-Der Ida Chen, Adolfo Correa, Lisa de las Fuentes, Renée de Mutsert, Jingzhong Ding, Charles B. Eaton, Stephan B. Felix, Nita G. Forouhi, Oscar H. Franco, Yechiel Friedlander, Mohsen Ghanbari, Bruna Gigante, C. Charles Gu, Saskia P. Hagenaars, M. Arfan Ikram, Tuomas O. Kilpeläinen, Woon-Puay Koh, Stephen B. Kritchevsky, Johanna Kuusisto, Timo A. Lakka, Carl D. Langefeld, Lenore J. Launer, Shiow Lin, Colin A. McKenzie, Thomas Meitinger, Karen L. Mohlke, Christopher P. Nelson, Nona Sotoodehnia, Nicholette D. Palmer, Thomas Perls, Annette Peters, Patricia A. Peyser, Neil Poulter, Frits R. Rosendaal, Jerome I. Rotter, Carsten O. Schmidt, Nicole Schupf, Colleen M. Sitlani, Jennifer A. Smith, Harold Snieder, John M. Starr, Konstantin Strauch, Hua Tang, Stephen T. Turner, André G. Uitterlinden, Melanie Waldenberger, Lihua Wang, Jie Yao, Jian-Min Yuan, Wei Zhao, Alan B. Zonderman, Donald W. Bowden, Ian J. Deary, Martin Farrall, Paul W. Franks, Barry I. Freedman, Christian Gieger, Markku Laakso, Cathy C. Laurie, Terho Lehtimäki, Nilesh J. Samani, Xiao-Ou Shu, Lynne E. Wagenknecht, Hugh Watkins, Ananda R. Wickremasinghe, Tangchun Wu, Claude Bouchard, Kaare Christensen, Vilmundur Gudnason, Sharon L. R. Kardia, Yongmei Liu, Alexandre C. Pereira, Bruce M. Psaty, W. James Gauderman, Dennis O. Mook-Kanamori, Myriam Fornage, Charles N. Rotimi, L. Adrienne Cupples, Ervin R. Fox, Cornelia M. van Duijn, Charles Kooperberg, Walter Palmas, Kenneth Rice, Alanna C. Morrison, Paul Elliott, Patricia B. Munroe, Dabeeru C. Rao, Michael A. Province, Daniel Levy.

## References

1. O'Donnell MJ, Chin SL, Rangarajan S, Xavier D, Liu L, Zhang H, et al. Global and regional effects of potentially modifiable risk factors associated with acute stroke in 32 countries (INTERSTROKE): a case-control study. Lancet. 2016; 388(10046):761–75. https://doi.org/10.1016/S0140-6736(16)30506-2 PMID: 27431356.

2. Collaborators GBDRF. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016; 388(10053):1659–724. https://doi.org/10.1016/S0140-6736(16)31679-8 PMID: 27733284.

3. Writing Group M, Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, et al. Heart Disease and Stroke Statistics-2016 Update: A Report From the American Heart Association. Circulation. 2016; 133 (4):e38–360. https://doi.org/10.1161/CIR.0000000000000350 PMID: 26673558.

4. Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL Jr., et al. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA. 2003; 289(19):2560–72. https://doi.org/10.1001/jama.289.19.2560 PMID: 12748199.

5. Muntner P, Carey RM, Gidding S, Jones DW, Taler SJ, Wright JT Jr., et al. Potential U.S. Population Impact of the 2017 American College of Cardiology/American Heart Association High Blood Pressure
Guideline. Circulation. 2017. https://doi.org/10.1161/CIRCULATIONAHA.117.032582 PMID: 29133599.

6. Passaglia P, Ceron CS, Mecawi AS, Antunes-Rodrigues J, Coelho EB, Tirapelli CR. Angiotensin type 1 receptor mediates chronic ethanol consumption-induced hypertension and vascular oxidative stress. Vascul Pharmacol. 2015; 74:49–59. https://doi.org/10.1016/j.vph.2015.04.002 PMID: 25872164.

7. Lawlor DA, Nordestgaard BG, Benn M, Zuccolo L, Tybjaerg-Hansen A, Davey Smith G. Exploring causal associations between alcohol and coronary heart disease risk factors: findings from a Mendelian randomization study in the Copenhagen General Population Study. Eur Heart J. 2013; 34(32):2519–28. https://doi.org/10.1093/eurheartj/eht081 PMID: 23492672.

8. Liu C, Kraja AT, Smith JA, Brody JA, Franceschini N, Bis JC, et al. Meta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci. Nat Genet. 2016; 48(10):1162–70. https://doi.org/10.1038/ng.3660 PMID: 27618448.

9. Ganesh SK, Tragante V, Guo W, Guo Y, Lanktree MB, Smith EN, et al. Loci influencing blood pressure identified using a cardiovascular gene-centric array. Hum Mol Genet. 2013; 22(8):1663–78. https://doi.org/10.1093/hmg/dds555 PMID: 23303523; PubMed Central PMCID: PMCPMC3657476.

10. Tragante V, Barnes MR, Ganesh SK, Lanktree MB, Guo W, Franceschini N, et al. Gene-centric meta-analysis in 87,736 individuals of European ancestry identifies multiple blood-pressure-related loci. Am J Hum Genet. 2014; 94(3):349–60. https://doi.org/10.1016/j.ajhg.2013.12.016 PMID: 24560520; PubMed Central PMCID: PMCPMC3951943.

11. Ehret GB, Ferreira T, Chasman DI, Jackson AU, Schmidt EM, Johnson T, et al. The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals. Nat Genet. 2016; 48(10):1171–84. https://doi.org/10.1038/ng.3667 PMID: 27618452; PubMed Central PMCID: PMCPMC5042863.

12. Surendran P, Drenos F, Young R, Warren H, Cook JP, Manning AK, et al. Trans-ancestry meta-analyses identify rare and common variants associated with blood pressure and hypertension. Nat Genet. 2016; 48(10):1151–61. https://doi.org/10.1038/ng.3654 PMID: 27618447; PubMed Central PMCID: PMCPMC5056636.

13. Warren HR, Evangelou E, Cabrera CP, Gao H, Ren M, Mifsud B, et al. Genome-wide association analysis identifies novel blood pressure loci and offers biological insights into cardiovascular risk. Nat Genet. 2017; 49(3):403–15. https://doi.org/10.1038/ng.3768 PMID: 28135244.

14. Hoffmann TJ, Ehret GB, Nandakumar P, Ranatunga D, Schaefer C, Kwok PY, et al. Genome-wide association analyses using electronic health records identify new loci influencing blood pressure variation. Nat Genet. 2017; 49(1):54–64. https://doi.org/10.1038/ng.3715 PMID: 27841878; PubMed Central PMCID: PMCPMC5370207.

15. Quillen EE, Chen XD, Almasy L, Yang F, He H, Li X, et al. ALDH2 is associated to alcohol dependence and is the major genetic determinant of "daily maximum drinks" in a GWAS study of an isolated rural Chinese sample. Am J Med Genet B Neuropsychiatr Genet. 2014; 165B(2):103–10. https://doi.org/10.1002/ajmg.b.32213 PMID: 24277619; PubMed Central PMCID: PMCPMC4149216.

16. Kapoor M, Wang JC, Wetherill L, Le N, Bertelsen S, Hinrichs AL, et al. Genome-wide survival analysis of age at onset of alcohol dependence in extended high-risk COGA families. Drug Alcohol Depend. 2014; 142:56–62. https://doi.org/10.1016/j.drugalcdep.2014.05.023 PMID: 24962325; PubMed Central PMCID: PMCPMC4127128.

17. Gelernter J, Kranzler HR, Sherva R, Almasy L, Koesterer R, Smith AH, et al. Genome-wide association study of alcohol dependence: significant findings in African- and European-Americans including novel risk loci. Mol Psychiatry. 2014; 19(1):41–9. https://doi.org/10.1038/mp.2013.145 PMID: 24166409; PubMed Central PMCID: PMCPMC4165335.

18. Simino J, Sung YJ, Kume R, Schwander K, Rao DC. Gene-alcohol interactions identify several novel blood pressure loci including a promising locus near SLC16A9. Front Genet. 2013; 4:277. https://doi.org/10.3389/fgene.2013.00277 PMID: 24376456; PubMed Central PMCID: PMCPMC3860258.

19. Owusu D, Pan Y, Xie C, Harirforoosh S, Wang KS. Polymorphisms in PDLIM5 gene are associated with alcohol dependence, type 2 diabetes, and hypertension. J Psychiatr Res. 2017; 84:27–34. https://doi.org/10.1016/j.jpsychires.2016.09.015 PMID: 27693979.

20. Clarke GM, Morris AP. A comparison of sample size and power in case-only association studies of gene-environment interaction. Am J Epidemiol. 2010; 171(4):498–505. https://doi.org/10.1093/aje/kwp398 PMID: 20047976; PubMed Central PMCID: PMCPMC2816730.

21. Kraft P, Yen YC, Stram DO, Morrison J, Gauderman WJ. Exploiting gene-environment interaction to detect genetic associations. Hum Hered. 2007; 63(2):111–9. https://doi.org/10.1159/000099183 PMID: 17283440.

22. Rao DC, Sung YJ, Winkler TW, Schwander K, Borecki I, Cupples LA, et al. Multiancestry Study of Gene-Lifestyle Interactions for Cardiovascular Traits in 610 475 Individuals From 124 Cohorts: Design
and Rationale. Circ Cardiovasc Genet. 2017; 10(3). https://doi.org/10.1161/CIRCGENETICS.116.001649 PMID: 28620071.

23. Sung YJ, Winkler TW, Manning AK, Aschard H, Gudnason V, Harris TB, et al. An Empirical Comparison of Joint and Stratified Frameworks for Studying G x E Interactions: Systolic Blood Pressure and Smoking in the CHARGE Gene-Lifestyle Interactions Working Group. Genet Epidemiol. 2016; 40(5):404–15. https://doi.org/10.1002/gepi.21978 PMID: 27230302; PubMed Central PMCID: PMCPMC4911246.

24. Manning AK, LaValley M, Liu CT, Rice K, An P, Liu Y, et al. Meta-analysis of gene-environment interaction: joint estimation of SNP and SNP x environment regression coefficients. Genet Epidemiol. 2011; 35 (1):11–8. https://doi.org/10.1002/gepi.20546 PMID: 21181894; PubMed Central PMCID: PMCPMC3312394.

25. Province MA, Kardia SL, Ranade K, Rao DC, Thiel BA, Cooper RS, et al. A meta-analysis of genome-wide linkage scans for hypertension: the National Heart, Lung and Blood Institute Family Blood Pressure Program. Am J Hypertens. 2003; 16(2):144–7. PMID: 12559682.

26. Province MA, Borecki IB. A correlated meta-analysis strategy for data mining "OMIC" scans. Pac Symp Biocomput. 2013:236–46. PMID: 23424128; PubMed Central PMCID: PMCPMC3773990.

27. Kraja AT, Chasman DI, North KE, Reiner AP, Yanek LR, Kilpelainen TO, et al. Pleiotropic genes for metabolic syndrome and inflammation. Mol Genet Metab. 2014; 112(4):317–38. https://doi.org/10.1016/j.ymgme.2014.04.007 PMID: 24981077; PubMed Central PMCID: PMCPMC4122618.

28. Ernst J, Kheradpour P, Mikkelsen TS, Shoresh N, Ward LD, Epstein CB, et al. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature. 2011; 473(7345):43–9. Epub 2011/03/29. https://doi.org/10.1038/nature09906 PMID: 21441907; PubMed Central PMCID: PMCPMC3088773.

29. Ward LD, Kellis M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 2012; 40 part 1 (Database issue): D930–4. https://doi.org/10.1093/nar/gkr917 PMID: 22064851; PubMed Central PMCID: PMCPMC3245002.

30. Xie D, Boyle AP, Wu L, Zhai J, Kawli T, Snyder M. Dynamic trans-acting factor colocalization in human cells. Cell. 2013; 155(3):713–24. https://doi.org/10.1016/j.cell.2013.09.043 PMID: 24243024; PubMed Central PMCID: PMCPMC4079469.

31. Boyle AP, Araya CL, Brdlik C, Cayting P, Cheng C, Cheng Y, et al. Comparative analysis of regulatory information and circuits across distant species. Nature. 2014; 512(7515):453–6. Epub 2014/08/29. https://doi.org/10.1038/nature13668 PMID: 25164757; PubMed Central PMCID: PMCPMC4336544.

32. Gamazon ER, Wheeler HE, Shah KP, Mozaffari SV, Aquino-Michaels K, Carroll RJ, et al. A gene-based association method for mapping traits using reference transcriptome data. Nat Genet. 2015; 47 (9):1091–8. Epub 2015/08/11. https://doi.org/10.1038/ng.3367 PMID: 26258848; PubMed Central PMCID: PMCPMC4552594.

33. Li MJ, Wang LY, Xia Z, Sham PC, Wang J. GWAS3D: Detecting human regulatory variants by integrative analysis of genome-wide associations, chromosome interactions and histone modifications. Nucleic Acids Res. 2013; 41 (Web Server issue): W150–8. https://doi.org/10.1093/nar/gkt456 PMID: 23723249; PubMed Central PMCID: PMCPMC3692118.

34. Leslie R, O'Donnell CJ, Johnson AD. GRASP: analysis of genotype-phenotype results from 1390 genome-wide association studies and corresponding open access database. Bioinformatics. 2014; 30 (12):i185–94. Epub 2014/06/17. https://doi.org/10.1093/bioinformatics/btu273 PMID: 24931982; PubMed Central PMCID: PMCPMC4072913.

35. Nikolsky Y, Bryant J. Protein networks and pathway analysis. Preface. Methods Mol Biol. 2009; 563:v–vii. PMID: 19760825.

36. Febbo PG, Mulligan MG, Slonina DA, Stegmaier K, Di Vizio D, Martinez PR, et al. Literature Lab: a method of automated literature interrogation to infer biology from microarray analysis. BMC Genomics. 2007; 8:461. https://doi.org/10.1186/1471-2164-8-461 PMID: 18088408; PubMed Central PMCID: PMCPMC2244637.

37. Castellon R, Hamdi HK. Demystifying the ACE polymorphism: from genetics to biology. Curr Pharm Des. 2007; 13(12):1191–8. PMID: 17504229.

38. Turner AJ, Hooper NM. The angiotensin-converting enzyme gene family: genomics and pharmacology. Trends Pharmacol Sci. 2002; 23(4):177–83. PMID: 11931993.

39. Jorde A, Bach P, Witt SH, Becker K, Reinhard I, Vollstadt-Klein S, et al. Genetic variation in the atrial natriuretic peptide transcription factor GATA4 modulates amygdala responsiveness in alcohol dependence. Biol Psychiatry. 2014; 75(10):790–7. https://doi.org/10.1016/j.biopsych.2013.10.020 PMID: 24314346.

40. Lo MT, Hinds DA, Tung JY, Franz C, Fan CC, Wang Y, et al. Genome-wide analyses for personality traits identify six genomic loci and show correlations with psychiatric disorders. Nat Genet. 2017; 49
(1):152-6. https://doi.org/10.1038/ng.3736 PMID: 27918536; PubMed Central PMCID: PMCPMC5278898.

41. El Idrissi M, Hervieu V, Merle P, Mortreux F, Wattel E. Cause-specific telomere factors deregulation in hepatocellular carcinoma. J Exp Clin Cancer Res. 2013; 32:64. https://doi.org/10.1186/1756-9966-32-64 PMID: 24020493; PubMed Central PMCID: PMCPMC3850108.

42. Karpyak VM, Winham SJ, Biernacka JM, Cunningham JM, Lewis KA, Geske JR, et al. Association of GATA4 sequence variation with alcohol dependence. Addict Biol. 2014; 19(2):312-5. https://doi.org/10.1111/j.1369-1600.2012.00482.x PMID: 22862823; PubMed Central PMCID: PMCPMC3504631.

43. Kiefer F, Witt SH, Frank J, Richter A, Treutlein J, Lemenager T, et al. Involvement of the atrial natriuretic peptide transcription factor GATA4 in alcohol dependence, relapse risk and treatment response to acamprosate. Pharmacogenomics J. 2011; 11(5):368-74. https://doi.org/10.1038/tpj.2010.51 PMID: 20585342.

44. Edenberg HJ, Koller DL, Xuei X, Wetherill L, McClintick JN, Almasy L, et al. Genome-wide association study of alcohol dependence implicates a region on chromosome 11. Alcohol Clin Exp Res. 2010; 34 (5):840-52. https://doi.org/10.1111/j.1530-0277.2010.01156.x PMID: 20201924; PubMed Central PMCID: PMCPMC2884073.

45. Treutlein J, Cichon S, Ridinger M, Wodarz N, Soyka M, Zill P, et al. Genome-wide association study of alcohol dependence. Arch Gen Psychiatry. 2009; 66(7):773-84. https://doi.org/10.1001/archgenpsychiatry.2009.83 PMID: 19581569; PubMed Central PMCID: PMCPMC4229246.

46. Newton-Cheh C, Larson MG, Vasan RS, Levy D, Bloch KD, Surti A, et al. Association of common variants in NPPA and NPPB with circulating natriuretic peptides and blood pressure. Nat Genet. 2009; 41 (3):348-53. https://doi.org/10.1038/ng.328 PMID: 19219041; PubMed Central PMCID: PMCPMC2664511.

47. McGue M, Zhang Y, Miller MB, Basu S, Vrieze S, Hicks B, et al. A genome-wide association study of behavioral disinhibition. Behav Genet. 2013; 43(5):363-73. https://doi.org/10.1007/s10519-013-9606-x PMID: 23942779; PubMed Central PMCID: PMCPMC3886341.

48. Claussnitzer M, Dankel SN, Kim KH, Quon G, Meuleman W, Haugen C, et al. FTO Obesity Variant Circuitry and Adipocyte Browning in Humans. N Engl J Med. 2015; 373(10):895-907. https://doi.org/10.1056/NEJMoa1502214 PMID: 26287746; PubMed Central PMCID: PMCPMC4959911.

49. Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU, et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet. 2010; 42 (11):937-48. https://doi.org/10.1038/ng.686 PMID: 20935630; PubMed Central PMCID: PMCPMC3014648.

50. Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015; 518(7538):197-206. https://doi.org/10.1038/nature14177 PMID: 25673413; PubMed Central PMCID: PMCPMC4382211.

51. Wang L, Liu X, Luo X, Zeng M, Zuo L, Wang KS. Genetic variants in the fat mass- and obesity-associated (FTO) gene are associated with alcohol dependence. J Mol Neurosci. 2013; 51(2):416-24. https://doi.org/10.1007/s12031-013-0044-2 PMID: 23771786.

52. Corella D, Ortega-Azorin C, Sorli JV, Covas MI, Carrasco P, Salas-Salvado J, et al. Statistical and biological gene-lifestyle interactions of MC4R and FTO with diet and physical activity on obesity: new effects on alcohol consumption. PLoS One. 2012; 7(12):e52344. https://doi.org/10.1371/journal.pone.0052344 PMID: 23284998; PubMed Central PMCID: PMCPMC3528751.

53. Young AI, Wauthier F, Donnelly P. Multiple novel gene-by-environment interactions modify the effect of FTO variants on body mass index. Nat Commun. 2016; 7:12724. Epub 2016/09/07. https://doi.org/10.1038/ncomms12724 PMID: 27596730; PubMed Central PMCID: PMCPMC5025863 LLP. The remaining authors declare no competing financial interests.

54. Park HK, Kim DH, Yun DH, Ban JY. Association between IL10, IL10RA, and IL10RB SNPs and ischaemic stroke with hypertension in Korean population. Mol Biol Rep. 2013; 40(2):1785-90. https://doi.org/10.1007/s11033-012-2232-5 PMID: 23096091.

55. Roy N, Mukhopadhyay I, Das K, Pandit P, Majumder PP, Santra A, et al. Genetic variants of TNFalpha, IL10, IL1beta, CTLA4 and TGFbeta1 modulate the indices of alcohol-induced liver injury in East Indian population. Gene. 2012; 509(1):178-88. https://doi.org/10.1016/j.gene.2012.07.077 PMID: 22902304.

56. Kryger R, Fan L, Wilce PA, Jaquet V. MALAT-1, a non protein-coding RNA is upregulated in the cerebellum, hippocampus and brain stem of human alcoholics. Alcohol. 2012; 46(7):629-34. https://doi.org/10.1016/j.alcohol.2012.04.002 PMID: 22560368.

57. Parsian A, Zhang ZH. Human chromosomes 11p15 and 4p12 and alcohol dependence: possible association with the GABRB1 gene. Am J Med Genet. 1999; 88(5):533-8. PMID: 10490712.

58. Caputo F, Ciminelli BM, Jodice C, Blasi P, Vignoli T, Cibin M, et al. Alcohol use disorder and GABAB receptor gene polymorphisms in an Italian sample: haplotype frequencies, linkage disequilibrium and
association studies. Ann Hum Biol. 2017:1-5. https://doi.org/10.1080/03014460.2017.1287307 PMID: 28118741.

59. Zuo L, Zhang X, Deng HW, Luo X. Association of rare PTP4A1-PHF3-EYS variants with alcohol dependence. J Hum Genet. 2013; 58(3):178-9. https://doi.org/10.1038/jhg.2012.153 PMID: 23324950.

60. Zuo L, Wang K, Wang G, Pan X, Zhang X, Zhang H, et al. Common PTP4A1-PHF3-EYS variants are specific for alcohol dependence. Am J Addict. 2014; 23(4):411-4. https://doi.org/10.1111/j.1521-0391.2013.12115.x PMID: 24961364; PubMed Central PMCID: PMCPMC4111256.

61. Wei L, Levine AS, Lan L. Transcription-coupled homologous recombination after oxidative damage. DNA Repair (Amst). 2016; 44:76-80. Epub 2016/05/29. https://doi.org/10.1016/j.dnarep.2016.05.009 PMID: 27233112.

62. Abbasi R, Ramroth H, Becher H, Dietz A, Schmezer P, Popanda O. Laryngeal cancer risk associated with smoking and alcohol consumption is modified by genetic polymorphisms in ERCC5, ERCC6 and RAD23B but not by polymorphisms in five other nucleotide excision repair genes. Int J Cancer. 2009; 125(6):1431-9. Epub 2009/05/16. https://doi.org/10.1002/ijc.24442 PMID: 19444904.

63. Vetreno RP, Broadwater M, Liu W, Spear LP, Crews FT. Adolescent, but not adult, binge ethanol exposure leads to persistent global reductions of choline acetyltransferase expressing neurons in brain. PLoS One. 2014; 9(11):e113421. https://doi.org/10.1371/journal.pone.0113421 PMID: 25405505; PubMed Central PMCID: PMCPMC4236188.

64. Carrizzo A, Damato A, Ambrosio M, Falco A, Rosati A, Capunzo M, et al. The prosurvival protein BAG3: a new participant in vascular homeostasis. Cell Death Dis. 2016; 7(10):e2431. https://doi.org/10.1038/cddis.2016.321 PMID: 27763645; PubMed Central PMCID: PMCPMC5133988.

65. Villard E, Perret C, Gary F, Proust C, Dilanian G, Hengstenberg C, et al. A genome-wide association study identifies two loci associated with heart failure due to dilated cardiomyopathy. Eur Heart J. 2011; 32(9):1065-76. https://doi.org/10.1093/eurheartj/ehr105 PMID: 21459883; PubMed Central PMCID: PMCPMC3086901.

66. Li C, Yang X, He J, Hixson JE, Gu D, Rao DC, et al. A gene-based analysis of variants in the serum/glu-cocorticoid regulated kinase (SGK) genes with blood pressure responses to sodium intake: the GenSalt Study. PLoS One. 2014; 9(5):e98432. https://doi.org/10.1371/journal.pone.0098432 PMID: 24878720; PubMed Central PMCID: PMCPMC4039502.

67. Costin BN, Dever SM, Miles MF. Ethanol regulation of serum glucocorticoid kinase 1 expression in DBA2/J mouse prefrontal cortex. PLoS One. 2013; 8(8):e72979. https://doi.org/10.1371/journal.pone.0072979 PMID: 23991167; PubMed Central PMCID: PMCPMC3750005.

68. Liang J, Le TH, Edwards DRV, Tayo BO, Gaulton KJ, Smith JA, et al. Single-trait and multi-trait genome-wide association analyses identify novel loci for blood pressure in African-ancestry populations. PLoS Genet. 2017; 13(5):e1006728. Epub 2017/05/13. https://doi.org/10.1371/journal.pgen.1006728 PMID: 28498854; PubMed Central PMCID: PMCPMC5446189.

69. Danzi S, Klein I. Thyroid disease and the cardiovascular system. Endocrinol Metab Clin North Am. 2014; 43(2):517-28. https://doi.org/10.1016/j.ecl.2014.02.005 PMID: 24891175.

70. Huang SM, Mishina YM, Liu S, Cheung A, Stegmeier F, Michaud GA, et al. Tankyrase inhibition stabilizes axin and antagonizes Wnt signalling. Nature. 2009; 461(7264):614-20. https://doi.org/10.1038/nature08356 PMID: 19759537.

71. Wei SY, Wang YX, Zhang QF, Zhao SL, Diao TT, Li JS, et al. Multiple Mechanisms are Involved in Salt-Sensitive Hypertension-Induced Renal Injury and Interstitial Fibrosis. Sci Rep. 2017; 7:45952. https://doi.org/10.1038/srep45952 PMID: 28383024; PubMed Central PMCID: PMCPMC5382679.

72. Scherag A, Dina C, Hinney A, Vatin V, Scherag S, Vogel CI, et al. Two new Loci for body-weight regulation identified in a joint analysis of genome-wide association studies for early-onset extreme obesity in French and german study groups. PLoS Genet. 2010; 6(4):e1000916. https://doi.org/10.1371/journal.pgen.1000916 PMID: 20421936; PubMed Central PMCID: PMCPMC2858696.

73. Shangguan L, Ning G, Luo Z, Zhou Y. Fibulin-4 reduces extracellular matrix production and suppresses chondrocyte differentiation via DKK1-mediated canonical Wnt/beta-catenin signaling. Int J Biol Macromol. 2017; 99:293-9. https://doi.org/10.1016/j.ijbiomac.2017.02.087 PMID: 28238906.

74. Zhang D, Ma X, Sun W, Cui P, Lu Z. Down-regulated FSTL5 promotes cell proliferation and survival by affecting Wnt/beta-catenin signaling in hepatocellular carcinoma. Int J Clin Exp Pathol. 2015; 8(3):3386-94. PMID: 26045876; PubMed Central PMCID: PMCPMC4440185.

75. Winkler TW, Day FR, Croteau-Chonka DC, Wood AR, Locke AE, Magi R, et al. Quality control and conduct of genome-wide association meta-analyses. Nat Protoc. 2014; 9(5):1192-212. https://doi.org/10.1038/nprot.2014.071 PMID: 24762786; PubMed Central PMCID: PMCPMC4083217.

76. Maisch B. Alcoholic cardiomyopathy: The result of dosage and individual predisposition. Herz. 2016; 41(6):484-93. https://doi.org/10.1007/s00059-016-4469-6 PMID: 27582365; PubMed Central PMCID: PMCPMC5013142.
77. Aulchenko YS, Struchalin MV, van Duijn CM. ProbABEL package for genome-wide association analysis of imputed data. BMC Bioinformatics. 2010; 11:134. https://doi.org/10.1186/1471-2105-11-134 PMID: 20233392; PubMed Central PMCID: PMCPMC2846909.

78. Aulchenko YS, Ripke S, Isaacs A, van Duijn CM. GenABEL: an R library for genome-wide association analysis. Bioinformatics. 2007; 23(10):1294–6. https://doi.org/10.1093/bioinformatics/btm108 PMID: 17384015.

79. Jolma A, Yan J, Whitington T, Toivonen J, Nitta KR, Rastas P, et al. DNA-binding specificities of human transcription factors. Cell. 2013; 152(1–2):327–39. Epub 2013/01/22. https://doi.org/10.1016/j.cell.2012.12.009 PMID: 23332764.

80. Degner JF, Pai AA, Pique-Regi R, Veyrieras JB, Gaffney DJ, Pickrell JK, et al. DNase I sensitivity QTLs are a major determinant of human expression variation. Nature. 2012; 482(7385):390–4. Epub 2012/02/07. https://doi.org/10.1038/nature10808 PMID: 22307276; PubMed Central PMCID: PMCPMC3501342.

81. Boyle AP, Hong EL, Hariharan M, Cheng Y, Schaub MA, Kasowski M, et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 2012; 22(9):1790–7. https://doi.org/10.1101/gr.137323.112 PMID: 22955989; PubMed Central PMCID: PMCPMC3431494.

82. Kuleshov V, Xie D, Chen R, Pushkarev D, Ma Z, Blauwkamp T, et al. Whole-genome haplotyping using long reads and statistical methods. Nat Biotechnol. 2014; 32(3):261–6. Epub 2014/02/25. https://doi.org/10.1038/nbt.2833 PMID: 24561555; PubMed Central PMCID: PMCPMC4073643.

83. Westra HJ, Peters MJ, Esko T, Yaghootkar H, Schurmann C, Kettunen J, et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat Genet. 2013; 45(10):1238–43. https://doi.org/10.1038/ng.2756 PMID: 24013639; PubMed Central PMCID: PMCPMC3991562.

84. Lappalainen T, Sammeth M, Friedlander MR, t Hoen PA, Monlong J, Rivas MA, et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature. 2013; 501(7468):506–11. https://doi.org/10.1038/nature12531 PMID: 24037378; PubMed Central PMCID: PMCPMC3918453.

85. Csardi G, Nepusz T. The igraph software package for complex network research, InterJournal, Complex Systems 1695. 2006.