The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals

Georg B Ehret; Teresa Ferreira; Daniel I Chasman; Anne U Jackson; Ellen M Schmidt; Toby Johnson; Gudmar Thorleifsson; Jian'an Luan; Lousie A Donnelly; Stavroula Kanoni; Ann-Kristin Petersen; Vasyl Pihur; Rona J Strawbridge; Dmitry Shungin; Maria F Hughes; Osorio Meirelles; Marika Kaakinen; Nabila Bouatia-Naji; Kati Kristiansson; Sonia Shah; Marcus E Kleber; Xiuqing Guo; Leo-Pekka Lyytikäinen; Cristiano Fava; Niclas Eriksson; Ilja M Nolte; Patrik K Magnusson; Elias L Salfati; Loukianos S Rallidis; Elizabeth Theusch; Andrew JP Smith; Lasse Folkersen; Kate Witkowska; Tune H Pers; Roby Joehanes; Stuart K Kim; Lazaros Lataniotis; Rick Jansen; Andrew D Johnson; Helen Warren; Young Jin Kim; Wei Zhao; Ying Wu; Bamidele O Tayo; Murielle Bochud; CHARGE-EchoGen consortium; CHARGE-HF consortium; Wellcome Trust Case Control Consortium; Devin Absher; Linda S Adair; Najaf Amin; Dan E Arking; Tomas Axelsson; Damiano Baldassarre; Beverley Balkau; Stefania Bandinelli; Michael R Barnes; Inês Barroso; Stephen Bevan; Joshua C Bis; Gyda Bjornsdottir; Michael Boehnke; Eric Boerwinkle; Lori L Bonnycastle; Dorret I Boomsma; Stefan R Bornstein; Morris J Brown; Michel Burnier; Claudia P Cabrera; John C Chambers; I-Shou Chang; Ching-Yu Cheng; Peter S Chines; Ren-Hua Chung; Francis S Collins; John M Connell; Angela Döring; Jean Dallongeville; John Danesh; Ulf de Faire; Graciela Delgado; Anna F Dominiczak; Alex SF Doney; Fotios Drenos; Sarah Edkins; John D Eicher; Roberto Elosua; Stefan Enroth; Jeanette Erdmann; Per Eriksson; Tonu Esko; Evangelos Evangelou; Alun Evans; Tove Fall; Martin Farrall; Janine F Felix; Jean Ferrières; Luigi Ferrucci; Myriam Fornage; Terrence Forrester

doi:10.1038/ng.3667

. Author manuscript; available in PMC: 2017 Mar 12.

Published in final edited form as: Nat Genet. 2016 Sep 12;48(10):1171–1184. doi: 10.1038/ng.3667

The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals

Georg B Ehret ^1,^2,^#, Teresa Ferreira ^3,^#, Daniel I Chasman ^4,⁵, Anne U Jackson ^6,⁷, Ellen M Schmidt ⁸, Toby Johnson ^9,¹⁰, Gudmar Thorleifsson ¹¹, Jian'an Luan ¹², Lousie A Donnelly ¹³, Stavroula Kanoni ¹⁴, Ann-Kristin Petersen ¹⁵, Vasyl Pihur ¹, Rona J Strawbridge ^16,¹⁷, Dmitry Shungin ^18,^19,²⁰, Maria F Hughes ²¹, Osorio Meirelles ²², Marika Kaakinen ²³, Nabila Bouatia-Naji ^24,²⁵, Kati Kristiansson ^26,²⁷, Sonia Shah ²⁸, Marcus E Kleber ²⁹, Xiuqing Guo ³⁰, Leo-Pekka Lyytikäinen ^31,³², Cristiano Fava ^33,³⁴, Niclas Eriksson ³⁵, Ilja M Nolte ³⁶, Patrik K Magnusson ³⁷, Elias L Salfati ³⁸, Loukianos S Rallidis ³⁹, Elizabeth Theusch ⁴⁰, Andrew JP Smith ⁴¹, Lasse Folkersen ¹⁶, Kate Witkowska ^9,⁴², Tune H Pers ^43,^44,^45,^46,⁴⁷, Roby Joehanes ⁴⁸, Stuart K Kim ⁴⁹, Lazaros Lataniotis ¹⁴, Rick Jansen ⁵⁰, Andrew D Johnson ^48,⁵¹, Helen Warren ^9,⁴², Young Jin Kim ⁵², Wei Zhao ⁵³, Ying Wu ⁵⁴, Bamidele O Tayo ⁵⁵, Murielle Bochud ⁵⁶; CHARGE-EchoGen consortium^110,⁵⁷; CHARGE-HF consortium^110,⁵⁷; Wellcome Trust Case Control Consortium^110,⁵⁷, Devin Absher ⁵⁸, Linda S Adair ⁵⁹, Najaf Amin ⁶⁰, Dan E Arking ¹, Tomas Axelsson ⁶¹, Damiano Baldassarre ^62,⁶³, Beverley Balkau ⁶⁴, Stefania Bandinelli ⁶⁵, Michael R Barnes ^14,⁴², Inês Barroso ^66,^67,⁶⁸, Stephen Bevan ⁶⁹, Joshua C Bis ⁷⁰, Gyda Bjornsdottir ¹¹, Michael Boehnke ^6,⁷, Eric Boerwinkle ⁷¹, Lori L Bonnycastle ⁷², Dorret I Boomsma ⁷³, Stefan R Bornstein ⁷⁴, Morris J Brown ⁷⁵, Michel Burnier ⁷⁶, Claudia P Cabrera ^9,⁴², John C Chambers ^77,^78,⁷⁹, I-Shou Chang ⁸⁰, Ching-Yu Cheng ^81,^82,⁸³, Peter S Chines ⁷², Ren-Hua Chung ⁸⁴, Francis S Collins ⁷², John M Connell ⁸⁵, Angela Döring ^86,⁸⁷, Jean Dallongeville ⁸⁸, John Danesh ^89,^66,⁹⁰, Ulf de Faire ⁹¹, Graciela Delgado ²⁹, Anna F Dominiczak ⁹², Alex SF Doney ¹³, Fotios Drenos ⁴¹, Sarah Edkins ⁶⁶, John D Eicher ^48,⁵¹, Roberto Elosua ⁹³, Stefan Enroth ^94,⁹⁵, Jeanette Erdmann ^96,⁹⁷, Per Eriksson ¹⁶, Tonu Esko ^98,^99,¹⁰⁰, Evangelos Evangelou ^77,¹⁰¹, Alun Evans ²¹, Tove Fall ¹⁰², Martin Farrall ^3,¹⁰³, Janine F Felix ¹⁰⁴, Jean Ferrières ¹⁰⁵, Luigi Ferrucci ¹⁰⁶, Myriam Fornage ¹⁰⁷, Terrence Forrester ¹⁰⁸, Nora Franceschini ¹⁰⁹, Oscar H Franco Duran ¹⁰⁴, Anders Franco-Cereceda ¹⁰¹, Ross M Fraser ^111,¹¹², Santhi K Ganesh ¹¹³, He Gao ⁷⁷, Karl Gertow ^16,¹⁷, Francesco Gianfagna ^114,¹¹⁵, Bruna Gigante ⁹¹, Franco Giulianini ⁴, Anuj Goel ^3,¹⁰³, Alison H Goodall ^116,¹¹⁷, Mark O Goodarzi ¹¹⁸, Mathias Gorski ^119,¹²⁰, Jürgen Gräßler ¹²¹, Christopher Groves ¹²², Vilmundur Gudnason ^123,¹²⁴, Ulf Gyllensten ^94,⁹⁵, Göran Hallmans ¹⁸, Anna-Liisa Hartikainen ^125,¹²⁶, Maija Hassinen ¹²⁷, Aki S Havulinna ²⁶, Caroline Hayward ¹²⁸, Serge Hercberg ¹²⁹, Karl-Heinz Herzig ^130,^131,¹³², Andrew A Hicks ¹³³, Aroon D Hingorani ²⁸, Joel N Hirschhorn ^43,^44,^45,¹³⁴, Albert Hofman ^104,¹³⁵, Jostein Holmen ¹³⁶, Oddgeir Lingaas Holmen ^136,¹³⁷, Jouke-Jan Hottenga ⁷³, Phil Howard ⁴¹, Chao A Hsiung ⁸⁴, Steven C Hunt ^138,¹³⁹, M Arfan Ikram ^104,^140,¹⁴¹, Thomas Illig ^142,^143,¹⁴⁴, Carlos Iribarren ¹⁴⁵, Richard A Jensen ^71,¹⁴⁶, Mika Kähönen ¹⁴⁷, Hyun Kang ^6,⁷, Sekar Kathiresan ^148,^149,^150,^45,¹⁵¹, Brendan J Keating ^152,¹⁵³, Kay-Tee Khaw ¹⁵⁴, Yun Kyoung Kim ⁵², Eric Kim ¹⁵⁵, Mika Kivimaki ²⁸, Norman Klopp ^142,¹⁴³, Genovefa Kolovou ¹⁵⁶, Pirjo Komulainen ¹²⁷, Jaspal S Kooner ^157,^78,⁷⁹, Gulum Kosova ^149,^148,¹⁰⁰, Ronald M Krauss ¹⁵⁸, Diana Kuh ¹⁵⁹, Zoltan Kutalik ^160,¹⁶¹, Johanna Kuusisto ¹⁶², Kirsti Kvaløy ¹³⁶, Timo A Lakka ^163,^127,¹⁶⁴, Nanette R Lee ^165,¹⁶⁶, I-Te Lee ^167,¹⁶⁸, Wen-Jane Lee ¹⁶⁹, Daniel Levy ^48,¹⁷⁰, Xiaohui Li ³⁰, Kae-Woei Liang ^171,¹⁷², Honghuang Lin ^173,⁴⁸, Li Lin ², Jaana Lindström ²⁶, Stéphane Lobbens ^174,^175,¹⁷⁶, Satu Männistö ²⁶, Gabriele Müller ¹⁷⁷, Martina Müller-Nurasyid ^15,^178,¹⁷⁹, François Mach ², Hugh S Markus ¹⁸⁰, Eirini Marouli ^14,¹⁸¹, Mark I McCarthy ¹²², Colin A McKenzie ¹⁰⁸, Pierre Meneton ¹⁸², Cristina Menni ¹⁸³, Andres Metspalu ⁹⁸, Vladan Mijatovic ¹⁸⁴, Leena Moilanen ^185,¹⁸⁶, May E Montasser ¹⁸⁷, Andrew D Morris ¹³, Alanna C Morrison ¹⁸⁸, Antonella Mulas ¹⁸⁹, Ramaiah Nagaraja ²², Narisu Narisu ⁷², Kjell Nikus ^190,¹⁹¹, Christopher J O'Donnell ^192,^48,¹⁵¹, Paul F O'Reilly ¹⁹³, Ken K Ong ¹², Fred Paccaud ⁵⁶, Cameron D Palmer ^194,^195,⁴⁵, Afshin Parsa ¹⁸⁷, Nancy L Pedersen ³⁷, Brenda W Penninx ^196,^197,¹⁹⁸, Markus Perola ^26,^27,⁹⁸, Annette Peters ⁸⁷, Neil Poulter ¹⁹⁹, Peter P Pramstaller ^133,^200,²⁰¹, Bruce M Psaty ^70,^202,^203,²⁰⁴, Thomas Quertermous ³⁸, Dabeeru C Rao ²⁰⁵, Asif Rasheed ²⁰⁶, N William NWR Rayner ^122,^3,⁶⁶, Frida Renström ^19,^207,¹⁸, Rainer Rettig ²⁰⁸, Kenneth M Rice ²⁰⁹, Robert Roberts ^210,²¹¹, Lynda M Rose ⁴, Jacques Rossouw ²¹², Nilesh J Samani ^116,²¹³, Serena Sanna ¹⁸⁹, Jouko Saramies ²¹⁴, Heribert Schunkert ^215,^216,^217,²¹⁸, Sylvain Sebert ^219,^131,¹⁶⁴, Wayne H-H Sheu ^167,^168,²²⁰, Young-Ah Shin ⁵², Xueling Sim ^6,^7,²²¹, Johannes H Smit ¹⁹⁶, Albert V Smith ^123,¹²⁴, Maria X Sosa ¹, Tim D Spector ¹⁸³, Alena Stančáková ²²², Alice Stanton ²²³, Kathleen E Stirrups ^14,²²⁴, Heather M Stringham ^6,⁷, Johan Sundstrom ⁶¹, Amy J Swift ⁷², Ann-Christine Syvänen ⁶¹, E-Shyong Tai ^225,^82,²²¹, Toshiko Tanaka ¹⁰⁶, Kirill V Tarasov ²²⁶, Alexander Teumer ²²⁷, Unnur Thorsteinsdottir ^11,¹²⁴, Martin D Tobin ²²⁸, Elena Tremoli ^62,⁶³, Andre G Uitterlinden ^104,²²⁹, Matti Uusitupa ^230,²³¹, Ahmad Vaez ^36,²³², Dhananjay Vaidya ²³³, Cornelia M van Duijn ^104,²³⁴, Erik PA van Iperen ^235,²³⁶, Ramachandran S Vasan ^48,^237,²³⁸, Germaine C Verwoert ¹⁰⁴, Jarmo Virtamo ²⁶, Veronique Vitart ¹²⁸, Benjamin F Voight ^45,²³⁹, Peter Vollenweider ²⁴⁰, Aline Wagner ²⁴¹, Louise V Wain ²²⁸, Nicholas J Wareham ¹², Hugh Watkins ^3,¹⁰³, Alan B Weder ²⁴², Harm-Jan Westra ²⁴³, Rainford Wilks ²⁴⁴, Tom Wilsgaard ^245,²⁴⁶, James F Wilson ^111,¹²⁸, Tien Y Wong ^81,^82,⁸³, Tsun-Po Yang ^14,²⁴⁷, Jie Yao ³⁰, Loic Yengo ^174,^175,¹⁷⁶, Weihua Zhang ^77,⁷⁸, Jing Hua Zhao ¹², Xiaofeng Zhu ²⁴⁸, Pascal Bovet ^249,⁵⁶, Richard S Cooper ⁵⁵, Karen L Mohlke ⁵⁴, Danish Saleheen ^250,²⁰⁶, Jong-Young Lee ⁵², Paul Elliott ^77,²⁵¹, Hinco J Gierman ^49,²⁵², Cristen J Willer ^8,^253,²⁵⁴, Lude Franke ²⁵⁵, G Kees Hovingh ²⁵⁶, Kent D Taylor ³⁰, George Dedoussis ¹⁸¹, Peter Sever ¹⁹⁹, Andrew Wong ¹⁵⁹, Lars Lind ⁶¹, Themistocles L Assimes ³⁸, Inger Njølstad ^245,²⁴⁶, Peter EH Schwarz ⁷⁴, Claudia Langenberg ¹², Harold Snieder ³⁶, Mark J Caulfield ^9,⁴², Olle Melander ³³, Markku Laakso ¹⁶², Juha Saltevo ²⁵⁷, Rainer Rauramaa ^127,¹⁶⁴, Jaakko Tuomilehto ^26,^258,^259,²⁶⁰, Erik Ingelsson ^102,³, Terho Lehtimäki ^31,³², Kristian Hveem ¹³⁶, Walter Palmas ²⁶¹, Winfried März ^262,²⁶³, Meena Kumari ²⁸, Veikko Salomaa ²⁶, Yii-Der I Chen ³⁰, Jerome I Rotter ³⁰, Philippe Froguel ^174,^175,^176,²³, Marjo-Riitta Jarvelin ^219,^131,^264,²⁵¹, Edward G Lakatta ²²⁶, Kari Kuulasmaa ²⁶, Paul W Franks ^19,^207,¹⁸, Anders Hamsten ^16,¹⁷, H-Erich Wichmann ^86,^179,²⁶⁵, Colin NA Palmer ¹³, Kari Stefansson ^11,¹²⁴, Paul M Ridker ^4,⁵, Ruth JF Loos ^12,^266,²⁶⁷, Aravinda Chakravarti ¹, Panos Deloukas ^14,²⁶⁸, Andrew P Morris ^269,^3,^#, Christopher Newton-Cheh ^148,^149,^45,^100,^#, Patricia B Munroe ^9,^42,^#

¹ Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA

² Cardiology, Department of Medicine, Geneva University Hospital, Rue Gabrielle-Perret-Gentil 4, 1211 Geneva 14, Switzerland

³ Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK

⁴ Division of Preventive Medicine, Brigham and Women's Hospital, 900 Commonwealth Ave. East, Boston, MA 02215, USA

⁵ Harvard Medical School, Boston, MA 02115, USA

⁶ Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA

⁷ Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA

⁸ Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA

⁹ Clinical Pharmacology, William Harvey Research Institute, Queen Mary University of London, London, EC1M 6BQ, UK

¹⁰ GlaxoSmithKline, Gunnels Wood Road, Stevenage SG1 2NY, UK

¹¹ deCODE Genetics/Amgen, Inc., Reykjavik, Iceland

¹² MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK

¹³ Medical Research Institute, University of Dundee, Ninewells Hospital and Medical School, Dundee, DD1 9SY, UK

¹⁴ William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK

¹⁵ Institute of Genetic Epidemiology, Helmholtz Zentrum München, Neuherberg 85764, Germany

¹⁶ Cardiovascular Research Unit, Center for Molecular Medicine L8:03, Department of Medicine, Karolinska Institutet, 171 76 Stockholm, Sweden

¹⁷ Center for Molecular Medicine, Karolinska University Hospital Solna, Stockholm, Sweden

¹⁸ Department of Public Health and Clinical Medicine, Umeå University, Sweden

¹⁹ Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Skåne University Hospital Malmö, SE-205 02 Malmö, Sweden

²⁰ Department of Odontology, Umeå University, Sweden

²¹ Centre of Excellence for Public Health, Queens University Belfast, Grosvenor Road, Belfast BT126JP, UK

²² Laboratory of Genetics, Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, Maryland 21224, USA

²³ Department of Genomics of Common Disease, School of Public Health, Imperial College London, Hammersmith Hospital, London, UK

²⁴ INSERM UMR970, Paris Cardiovascular Research Center PARCC, 56 rue Leblanc, 75015 Paris, France

²⁵ University Paris-Descartes, Sorbonne Paris Cité, 12 rue de l'Ecole de medicine, F-75006 Paris, France

²⁶ National Institute for Health and Welfare, FI-00271 Helsinki, Finland

²⁷ Institute for Molecular Medicine Finland FIMM, University of Helsinki, 00290 Helsinki, Finland

²⁸ Genetic Epidemiology Group, Dept. Epidemiology and Public Health, UCL, London, WC1E 6BT, UK

²⁹ Vth Department of Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany

³⁰ Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, 1124 W. Carson Street, Torrance, CA 90502, USA

³¹ Department of Clinical Chemistry, Fimlab Laboratories, Tampere 33520, Finland

³² Department of Clinical Chemistry, University of Tampere School of Medicine, Tampere 33014, Finland

³³ University of Lund, Dept Internal Medicine, Malmo, SE 20502, Sweden

³⁴ University of Verona, Dept of Internal Medicine, Verona, Italy 37134

³⁵ Uppsala University, Uppsala Clinical Research Center, SE-75185 Uppsala, Sweden

³⁶ Department of Epidemiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands

³⁷ Dept of Medical Epidemiology and Biostatistics, Karolinska Institutet, Box 281, SE-171 77 Stockholm, Sweden

³⁸ Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA

³⁹ Second Department of Cardiology, Attikon Hospital, School of Medicine, University of Athens, Athens, Greece

⁴⁰ Children's Hospital Oakland Research Institute, Oakland, CA 94609, USA

⁴¹ Department of Cardiovascular Genetics, Institute of Cardiovascular Sciences, University College London, London WC1E 6JF, UK

⁴² NIHR Barts Cardiovascular Biomedical Research Unit, Queen Mary University of London, London, EC1M 6BQ, UK

⁴³ Division of Endocrinology, Boston Children's Hospital, Boston, MA 02115, USA

⁴⁴ Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA 02115, USA

⁴⁵ Program in Medical and Population Genetics, Broad Institute, 7 Cambridge Center, Cambridge, MA 02142, USA

⁴⁶ Novo Nordisk Foundation Centre for Basic Metabolic Research, Section of Metabolic, Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2100, Denmark

⁴⁷ Department of Epidemiology Research, Statens Serum Institut, 2300, Copenhagen, Denmark

⁴⁸ National Heart, Lung and Blood Institute's Framingham Heart Study, Framingham, MA 01702, USA

⁴⁹ Dept. Dev. Bio. And Genetics, Stanford University Medical Center, Stanford, CA 94305, USA

⁵⁰ Department of Psychiatry, VU University Medical Center, Amsterdam, The Netherlands

⁵¹ National Heart, Lung and Blood Institute, Cardiovascular Epidemiology and Human Genomics Branch, Bethesda, MD 20814, USA

⁵² Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do, Republic of Korea

⁵³ Division of Translational Medicine and Human Genetics, Department of Medicine, University of Pennyslvania, USA

⁵⁴ Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA

⁵⁵ Department of Preventive Medicine and Epidemiology, Loyola University Chicago Stritch School of Medicine, Maywood, IL, 60153, USA

⁵⁶ Institute of Social and Preventive Medicine (IUMSP), Centre Hospitalier Universitaire Vaudois and University of Lausanne, Route de la Corniche 10, 1010 Lausanne, Switzerland

⁵⁸ HudsonAlpha Institute for Biotechnology, Huntsville, AL 35086, USA

⁵⁹ Department of Nutrition, University of North Carolina, Chapel Hill, NC 27599, USA

⁶⁰ Genetic Epidemiology Unit, Department of Epidemiology, Erasmus MC, Rotterdam, 3015CN, The Netherlands

⁶¹ Uppsala University, Department of Medical Sciences, SE-75185 Uppsala, Sweden

⁶² Dipartimento di Scienze Farmacologiche e Biomolecolari, Università di Milano, Milan, Italy

⁶³ Centro Cardiologico Monzino, IRCCS, Milan, Italy

⁶⁴ INSERM Centre for Research in Epidemiology and Population Health, U1018, Villejuif, France University Paris-Sud, URMS 1018, Villejuif, France

⁶⁵ Geriatric Unit, Azienda Sanitaria Firenze (ASF), Florence, Italy

⁶⁶ Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, CB10 1SA, Hinxton, UK

⁶⁷ University of Cambridge Metabolic Research Laboratories, Level 4, Institute of Metabolic Science Box 289 Addenbrookes Hospital Cambridge CB2 OQQ, UK

⁶⁸ NIHR Cambridge Biomedical Research Centre, Level 4, Institute of Metabolic Science Box 289 Addenbrookes Hospital Cambridge CB2 OQQ, UK

⁶⁹ School of Life Science, University of Lincoln, Joseph Banks Laboratories, Lincoln LN6 7DL, UK

⁷⁰ Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA 98101, USA

⁷¹ Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, 1200 Pressler St., Suite 453E, Houston, TX 77030, USA

⁷² Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA

⁷³ Department of Biological Psychology, VU University, Amsterdam, The Netherlands

⁷⁴ Dept of Medicine III, University of Dresden, Medical Faculty Carl Gustav Carus, Fetscherstrasse 74, 01307 Dresden, Germany

⁷⁵ The Barts Heart Centre, William Harvey Research Institute, Queen Mary University of London, London EC1M 6BQ, UK

⁷⁶ Nephrology, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Bugnon 17, 1005 Lausanne, Switzerland

⁷⁷ Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK

⁷⁸ Department of Cardiology, Ealing Hospital NHS Trust, Uxbridge Road, Southall, Middlesex UB1 3EU, UK

⁷⁹ Imperial College Healthcare NHS Trust, London, UK

⁸⁰ National Institute of Cancer Research, National Health Research Institutes. 35 Keyan Rd., Zhunan Town, Miaoli County 350, Taiwan

⁸¹ Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 168751, Singapore

⁸² Duke-NUS Graduate Medical School Singapore, Singapore 169857, Singapore

⁸³ Department of Ophthalmology, National University of Singapore and National University Health System, Singapore 119228

⁸⁴ Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes. 35 Keyan Rd., Zhunan Town, Miaoli County 350, Taiwan

⁸⁵ University of Dundee, Ninewells Hospital and Medical School, Dundee, DD1 9SY, UK

⁸⁶ Institute of Epidemiology I, Helmholtz Zentrum München, Neuherberg 85764, Germany

⁸⁷ Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg 85764, Germany

⁸⁸ UMR744 Inserm-Lille2-Institut Pasteur Lille, France

⁸⁹ Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK

⁹⁰ NIHR Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK

⁹¹ Division of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden

⁹² BHF Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow, 126 University Place, Glasgow, G12 8QT, UK

⁹³ Cardiovascular Epidemiology and Genetics. IMIM (Institut Hospital del Mar d'Investigacions Mèdiques), Barcelona, Spain

⁹⁴ Department of Immunology, Genetics and Pathology, University of Uppsala, Box 815, Biomerical center, 751 08 Uppsala, Sweden

⁹⁵ Science for Life Laboratory, University of Uppsala, Box 815, Biomerical center, 751 08 Uppsala, Sweden

⁹⁶ Institut für Integrative und Experimentelle Genomik, Universiät zu Lübeck, RatzeburgerAllee 160, 23538 Lübeck, Germany

⁹⁷ Deutsches Zentrum für Herz-Kreislauf-Forschung (DZHK), partner site Hamburg, Kiel, Lübeck, Universität zu Lübeck, Lübeck, Germany

⁹⁸ Estonian Genome Center, University of Tartu, Tartu, 51010, Estonia

⁹⁹ Divisions of Endocrinology/Children's Hospital, Boston, MA 02115, USA

¹⁰⁰ Broad Institute of Harvard and MIT, Cambridge, MA 02139 USA

¹⁰¹ Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, 45110, Greece

¹⁰² Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden

¹⁰³ Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 9DU, UK

¹⁰⁴ Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, P.O.Box 2040, 3000 CA Rotterdam, The Netherlands

¹⁰⁵ Toulouse University School of Medicine, Rangueil University Hospital, INSERM UMR1027, Toulouse, France

¹⁰⁶ Translational Gerontology Branch, National Institute on Aging, Baltimore MD, USA

¹⁰⁷ Institute of Molecular Medicine, University of Texas Health Science Center at Houston, TX, USA

¹⁰⁸ Tropical Metabolism Research Unit, Tropical Medicine Research Institute, University of the West Indies, Mona, Kingston 7, Jamaica

¹⁰⁹ Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA

¹¹⁰ Cardiothoracic Surgery Unit, Department of Molecular Medicine and Surgery, Karolinska Institutet, 171 76 Stockholm, Sweden

¹¹¹ Institute for Population Health Sciences and Informatics, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, Scotland

¹¹² Synpromics Ltd, 9 Bioquarter, Little France Road, Edinburgh, EH16 4UX, Scotland

¹¹³ University of Michigan Medical School, 7220 MSRB III, Ann Arbor MI 48109, USA

¹¹⁴ EPIMED Research Centre - Epidemiology and Preventive Medicine, Department of Clinical and Experimental Medicine, University of Insubria, Varese, Italy

¹¹⁵ Department of Epidemiology and Prevention, IRCCS Istituto Neurologico Mediterraneo NEUROMED, 86077 Pozzilli, Italy

¹¹⁶ Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Leicester LE3 9QP, UK

¹¹⁷ National Institute for Health Research Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester LE3 9QP, UK

¹¹⁸ Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA

¹¹⁹ Department of Genetic Epidemiology, Institute of Epidemiology and Preventive Medicine, University of Regensburg, Franz-Josef-Strauss-Allee 11, 93053 Regensburg, Germany

¹²⁰ Department of Nephrology, University Hospital Regensburg, Franz-Josef-Strauss-Allee 11, 93053 Regensburg, Germany

¹²¹ Department of Medicine III, Division Pathobiochemistry, Technische Universität Dresden, Dresden, Germany

¹²² Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK

¹²³ Icelandic Heart Association, Kopavogur, Iceland

¹²⁴ Faculty of Medicine, University of Iceland, Reykjavik, Iceland

¹²⁵ Institute of Clinical Medicine/Obstetrics and Gynaecology, University of Oulu, Oulu, Finland

¹²⁶ Medical Research Center, Oulu University Hospital, Oulu, Finland

¹²⁷ Kuopio Research Institute of Exercise Medicine, Kuopio, Finland

¹²⁸ Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, EH4 2XU Scotland, UK

¹²⁹ UREN, INSERM U557, INRA U1125, CNAM, SMBH, Sorbonne Paris Cité, Université Paris 13, Bobigny, France

¹³⁰ Institute of Biomedicine, University of Oulu, Medical Research Center Oulu and Oulu University Hospital, Finland

¹³¹ Biocenter Oulu, P.O.Box 5000, Aapistie 5A, FI-90014 University of Oulu, Finland

¹³² Department of Gastroenterology and Metabolism, Poznan University of Medical Sciences, Poznan, Poland

¹³³ Center for Biomedicine, European Academy Bozen/Bolzano (EURAC), Bolzano, 39100, Italy - affiliated institute of the University of Lübeck, Germany

¹³⁴ Department of Genetics, Harvard Medical School, Boston, 02115, USA

¹³⁵ Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA

¹³⁶ HUNT Research Centre, Department of Public Health and General Practice, Norwegian University of Science and Technology, 7600 Levanger, Norway

¹³⁷ St. Olav Hospital, Trondheim University Hospital, Trondheim, Norway

¹³⁸ Cardiovascular Genetics Division, University of Utah School of Medicine, Salt Lake City, Utah, USA

¹³⁹ Department of Genetic Medicine, Weill Cornell Medical College Qatar, Doha, Qatar

¹⁴⁰ Department of Radiology, Erasmus MC, The Netherlands

¹⁴¹ Department of Neurology, Erasmus MC, University Medical Center Rotterdam, P.O.Box 2040, 3000 CA Rotterdam, The Netherlands

¹⁴² Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg 85764, Germany

¹⁴³ Hannover Unified Biobank, Hannover Medical School, Hannover 30625, Germany

¹⁴⁴ Hannover Medical School, Institute for Human Genetics, Carl-Neuberg-Strasse 1, 30625 Hanover, Germany

¹⁴⁵ Kaiser Permanente, Division of Research, Oakland, CA 94612, USA

¹⁴⁶ Department of Medicine, University of Washington, Seattle, Washington 98101, USA

¹⁴⁷ Department of Clinical Physiology, Tampere University Hospital, Tampere 33521, Finland

¹⁴⁸ Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA 02114, USA

¹⁴⁹ Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA

¹⁵⁰ Department of Medicine, Harvard Medical School, Boston, MA, USA

¹⁵¹ Cardiology Division, Department of Medicine, Massachusetts General Hospital

¹⁵² Division of Transplantation, Department of Surgery, University of Pennsylvania, PA 19104 USA

¹⁵³ Department of Pediatrics, University of Pennsylvania, Philadelphia, PA, USA

¹⁵⁴ Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge, Cambridge CB2 2SR, UK

¹⁵⁵ Institute for Translational Genomics and Population Sciences, Department of Pediatrics, LABioMed at Harbor-UCLA Medical Center, 1124 W. Carson Street, Torrance, CA 90502, USA

¹⁵⁶ 1st Cardiology Department, Onassis Cardiac Surgery Center 356, Sygrou Ave, Athens, Greece

¹⁵⁷ National Heart and Lung Institute, Imperial College London, Hammersmith Hospital Campus, Ducane Road, London W12 0NN, UK

¹⁵⁸ Department of Medicine, Children's Hospital Oakland Research Institute, Oakland, CA 94609, USA

¹⁵⁹ MRC Unit for Lifelong Health and Ageing at UCL, London, WC1B 5JU, UK

¹⁶⁰ Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland

¹⁶¹ Swiss Institute of Bioinformatics, Lausanne, Switzerland

¹⁶² Department of Medicine, University of Eastern Finland and Kuopio University Hospital, 70210 Kuopio, Finland

¹⁶³ Institute of Biomedicine/Physiology, University of Eastern Finland, Kuopio Campus, Finland

¹⁶⁴ Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland

¹⁶⁵ Office of Population Studies Foundation Inc., Talamban, Cebu City, 6000, Philippines

¹⁶⁶ Department of Anthropology, Sociology, and History, University of San Carlos, Talamban, Cebu City, 6000, Philippines

¹⁶⁷ Division of Endocrine and Metabolism, Department of Internal Medicine, Chichung Veterans General Hospital, Taichung 40705, Taiwan

¹⁶⁸ School of Medicine, National Yang-Ming University, Taipei, Taiwan

¹⁶⁹ Department of Medical Research, Taichung Veterans General Hospital, Taichung 407, Taiwan

¹⁷⁰ Population Sciences Branch, National Heart Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA

¹⁷¹ Cardiovascular Center, Taichung Veterans General Hospital, Taichung, 40705, Taiwan

¹⁷² Institute of Clinical Medicine, National Yang Ming University School of Medicine, Taipei 112, Taiwan

¹⁷³ Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, 02446 MA, USA

¹⁷⁴ European Genomic Institute for Diabetes (EGID), FR 3508 Lille, France

¹⁷⁵ Centre National de la Recherche Scientifique (CNRS) UMR 8199, Lille Pasteur Institute, 1 rue du Prof Calmette, 59019 Lille Cedex, France

¹⁷⁶ Lille 2 University, Lille, France

¹⁷⁷ Center for Evidence-based Healthcare, University of Dresden, Medical Faculty Carl Gustav Carus, Fetscherstrasse 74, 01307 Dresden, Germany

¹⁷⁸ Department of Medicine I, University Hospital Grosshadern, Ludwig-Maximilians University, Munich, Germany

¹⁷⁹ Institute of Medical Informatics, Biometry and Epidemiology, Chair of Epidemiology, Ludwig-Maximilians-Universität, München 81377, Germany

¹⁸⁰ Neurology Unit, University of Cambridge, R3, Box 83, Cambridge Biomedical Campus, Cambridge, Cb2 0QQ, UK

¹⁸¹ Department of Dietetics-Nutrition, Harokopio University, 70 El. Venizelou Str, Athens, Greece

¹⁸² INSERM U1142 LIMICS, UMR_S 1142 Sorbonne Universités, UPMC Université Paris 06, Université Paris 13, Paris, France

¹⁸³ Department of Twin Research and Genetic Epidemiology, King's College London, London, UK

¹⁸⁴ Department of Life and Reproduction Sciences, University of Verona, Strada le Grazie 8, 37134 Verona, Italy

¹⁸⁵ Department of Medicine, Kuopio University Hospital, Kuopio, Finland

¹⁸⁶ Unit of General Practice, Oulu University Hospital, Oulu, Finland

¹⁸⁷ Department of Medicine, Program for Personalized and Genomic Medicine, University of Maryland, School of Medicine, Baltimore, Maryland 21201, USA

¹⁸⁸ Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, 1200 Pressler St., Suite 453E, Houston, TX 77030, USA

¹⁸⁹ Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche, c/o Cittadella Universitaria di Monseratto, Monserrato, Cagliari 09042, Italy

¹⁹⁰ Department of Cardiology, School of Medicine, University of Tampere, Tampere 33014, Finland

¹⁹¹ School of Medicine, University of Tampere, Tampere 33014, Finland

¹⁹² National Heart, Lung and Blood Institute, Division of Intramural Research, Bethesda, MD, USA

¹⁹³ Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK

¹⁹⁴ Divisions of Endocrinology, Children's Hospital Boston, Massachusetts 02115, USA

¹⁹⁵ Genetics and Program in Genomics, Children's Hospital Boston, Massachusetts 02115, USA

¹⁹⁶ Department of Psychiatry, EMGO Institute, Neuroscience Campus, VU University Medical Centre, Van der Boechorststraat 7, 1081 BT Amsterdam, The Netherlands

¹⁹⁷ Department of Psychiatry, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands

¹⁹⁸ Department of Psychiatry, Leiden University Medical Centre, P.O. Box 9600, 2300 RC Leiden, The Netherlands

¹⁹⁹ International Centre for Circulatory Health, Imperial College London, W2 1PG, UK

²⁰⁰ Department of Neurology, General Central Hospital, Bolzano, 39100, Italy

²⁰¹ Department of Neurology, University of Lübeck, Lübeck, Germany

²⁰² Department of Epidemiology, University of Washington, Seattle, WA, USA

²⁰³ Department of Health Services, University of Washington, Seattle, WA

²⁰⁴ Group Health Research Institute, Group Health Cooperative, Seattle, WA

²⁰⁵ Division of Biostatistics, Washington University School of Medicine, Saint Louis, MO, 63110, USA

²⁰⁶ Center for Non-Communicable Diseases, Karachi, Pakistan

²⁰⁷ Department of Nutrition, Harvard School of Public Health, Boston, MA, USA

²⁰⁸ Institute of Physiology, University Medicine Greifswald, Greifswald, Germany

²⁰⁹ Department of Biostatistics, University of Washington, Seattle, WA, USA

²¹⁰ University of Ottawa Heart Institute, Cardiovascular Research Methods Centre Ontario, Canada

²¹¹ Ruddy Canadian Cardiovascular Genetics Centre, Ontario, Canada

²¹² National Heart, Lung, and Blood Institute, 6701 Rockledge Ave., Bethesda, MD 20892, USA

²¹³ Leicester NIHR Biomedical Research Unit in Cardiovascular Disease, Glenfield Hospital, Leicester LE3 9QP, UK

²¹⁴ South Karelia Central Hospital, Lappeenranta, Finland

²¹⁵ Deutsches Herzzentrum München, Germany

²¹⁶ Technische Universität München, Germany

²¹⁷ Deutsches Zentrum für Herz-Kreislauf-Forschung (DZHK), München, Germany

²¹⁸ Munich Heart Alliance, Germany

²¹⁹ Center For Life-course Health Research, P.O.Box 5000, FI-90014 University of Oulu, Finland

²²⁰ College of Medicine, National Defense Medical Center, Taipei, Taiwan

²²¹ Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117597

²²² University of Eastern Finland and Kuopio University Hospital, 70210 Kuopio, Finland

²²³ Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin 4, Ireland

²²⁴ Department of Haematology, University of Cambridge, Cambridge, UK

²²⁵ Department of Medicine, National University of Singapore and National University Health System, Singapore 119228, Singapore

²²⁶ Laboratory of Cardiovascular Science, Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, 21224, USA

²²⁷ Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany

²²⁸ Department of Health Sciences, University of Leicester, University Rd, Leicester LE1 7RH, UK

²²⁹ Department of internal medicine, Erasmus MC, Rotterdam, 3000CA, The Netherlands

²³⁰ Department of Public Health and Clinical Nutrition, University of Eastern Finland, Finland

²³¹ Research Unit, Kuopio University Hospital, Kuopio, Finland

²³² Research Institute for Primordial Prevention of Non-communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran

²³³ Johns Hopkins Medical Institutions, 1830 East Monument St., Baltimore, MD 21287, USA

²³⁴ Centre of Medical Systems Biology (CMSB 1-2), NGI Erasmus Medical Center, Rotterdam, The Netherlands

²³⁵ Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, Amsterdam, The Netherlands

²³⁶ Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, Utrecht, The Netherlands

²³⁷ Section of Preventive medicine, Department of Medicine, Boston University School of Medicine, Boston, 02446 MA, USA

²³⁸ Cardiology, Department of Medicine, Boston University School of Medicine, Boston, 02446 MA, USA

²³⁹ Department of Pharmacology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA

²⁴⁰ Department of Internal medicine, University Hospital Lausanne, Lausanne, Switzerland

²⁴¹ Department of Epidemiology and Public Health, EA3430, University of Strasbourg, Strasbourg, France

²⁴² Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA

²⁴³ University Medical Center Groningen, University of Groningen, Groningen, 9700RB, The Netherlands

²⁴⁴ Epidemiology Research Unit, Tropical Medicine Research Institute, University of the West Indies, Mona, Kingston 7, Jamaica

²⁴⁵ Department of Community Medicine, Faculty of Health Sciences, University of Tromsø, Tromsø, Norway

²⁴⁶ Department of Clinical Medicine, Faculty of Health Sciences, University of Tromsø, Tromsø, Norway

²⁴⁷ MRC Cancer Unit, University of Cambridge, Cambridge, UK

²⁴⁸ Department of Epidemiology and Biostatistics, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA

²⁴⁹ Ministry of Health, Victoria, Republic of Seychelles

²⁵⁰ Department of Biostatistics and Epidemiology, University of Pennsylvania, USA

²⁵¹ MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK

²⁵² Enterprise Informatics, Illumina Inc., Santa Clara CA, 95050, USA

²⁵³ Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI 48109, USA

²⁵⁴ Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, USA

²⁵⁵ Department of Genetics, University of Groningen, University Medical Centre Groningen, Groningen, 9711, The Netherlands

²⁵⁶ Dept Vascular Medicine, Academic Medical Center, Amsterdam, The Netherlands

²⁵⁷ Department of Medicine, Central Finland Health Care District, Jyväskylä, Finland

²⁵⁸ Dasman Diabetes Institute, Dasman, 15462 Kuwait

²⁵⁹ Saudi Diabetes Research Group, King Abdulaziz University, 21589 Jeddah, Saudi Arabia

²⁶⁰ Centre for Vascular Prevention, Danube-University Krems, 3500 Krems, Austria

²⁶¹ Department of Medicine, Columbia University, 622 West 168th St., New York, NY 10032, USA

²⁶² Synlab Academy, Synlab Services GmbH, P5, 7, 68161 Mannheim, Germany

²⁶³ Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, 8036 Graz, Austria

²⁶⁴ Unit of Primary Care, Oulu University Hospital, Kajaanintie 50, P.O.Box 20, FI-90220 Oulu, 90029 OYS, Finland

²⁶⁵ Grosshadern, Klinikum, München 81377, Germany

²⁶⁶ The Charles Bronfman Institute for Personalized Medicine, The Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA

²⁶⁷ Mindich Child health Development Institute, The Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA

²⁶⁸ Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders (PACER-HD), King Abdulaziz University, Jeddah 21589, Saudi Arabia

²⁶⁹ Department of Biostatistics, University of Liverpool, Liverpool L69 3GA, UK

These authors jointly supervised this work.

⁵⁷

A list of members and affiliations appears in the Supplementary Note

^✉

Corresponding authors: Christopher Newton-Cheh (cnewtoncheh@mgh.harvard.edu) and Patricia B. Munroe (p.b.munroe@qmul.ac.uk).

Contributed equally.

PMCID: PMC5042863 NIHMSID: NIHMS810971 PMID: 27618452

Abstract

To dissect the genetic architecture of blood pressure and assess effects on target-organ damage, we analyzed 128,272 SNPs from targeted and genome-wide arrays in 201,529 individuals of European ancestry and genotypes from an additional 140,886 individuals were used for validation. We identified 66 blood pressure loci, of which 17 were novel and 15 harbored multiple distinct association signals. The 66 index SNPs were enriched for cis-regulatory elements, particularly in vascular endothelial cells, consistent with a primary role in blood pressure control through modulation of vascular tone across multiple tissues. The 66 index SNPs combined in a risk score showed comparable effects in 64,421 individuals of non-European descent. The 66-SNP blood pressure risk score was significantly associated with target-organ damage in multiple tissues, with minor effects in the kidney. Our findings expand current knowledge of blood pressure pathways and highlight tissues beyond the classic renal system in blood pressure regulation.

INTRODUCTION

There are considerable physiological, clinical and genetic data that point to the kidney as the major regulator of blood pressure (BP) and to renal damage as a consequence of long-term BP elevation. However, alternative hypotheses, such as increasing systemic vascular resistance, are also serious contenders to explain the rise of BP with increasing age, but with limited genetic support. The genetic basis of elevated blood pressure or hypertension (HTN) involves many loci that have been identified using large-scale analyses of candidate genes^1,2, linkage studies, and genome-wide association studies (GWAS)^3-12. The genes underlying BP regulation can help resolve many of the open questions regarding BP (patho-) physiology. While ~40-50% of BP variability is heritable^13,14, the genetic variation identified to date explains only ~2%^1-12.

The Cardio-MetaboChip is a custom genotyping microarray designed to facilitate cost-effective follow-up of nominal associations for metabolic and cardiovascular traits, including BP. This array comprises 196,725 variants, including ~5,000 SNPs with nominal (P <0.016) evidence of BP association in our previous GWAS meta-analysis⁵. Furthermore, the array includes several dense scaffolds for fine mapping of selected loci spanning, on average, genomic regions of 350 kilobases^5,16, of which 24 include genome-wide significant BP association in the current study^5,16.

RESULTS

Novel genetic loci associated with systolic and diastolic BP

We performed meta-analyses of association summary statistics from a total of 201,529 individuals of European (EUR) ancestry from 74 studies: (i) 109,096 individuals from 46 studies genotyped on Cardio-MetaboChip; and (ii) 92,433 individuals from 28 studies with imputed genotype data from genome-wide genotyping at variants included on the Cardio-MetaboChip. Twenty-four of the 28 studies with genome-wide genotyping data had contributed to previous analyses (Supplementary Tables 1-3)^5,7.

BP was measured using standardized protocols in all studies^5,17 (Supplementary Table 1, Online methods). Association statistics for systolic and diastolic BP (SBP and DBP) in models adjusting for age, age², sex, and body mass index (BMI), were obtained for each study separately, with study-specific genomic control applied to correct for possible population structure. Fixed-effects meta-analysis proceeded in 4 stages, separately for the following SNP associations: Stage 1, using results based on 46 studies using Cardio-MetaboChip genotypes of 109,096 participants; Stage 2, using additional results based on imputed genotypes from genome-wide genotyping arrays in 4 previously unpublished studies; Stage 3 using imputed genotypes from genome-wide genotyping arrays in 24 previously published studies⁵; and Stage 4, the joint meta-analysis of Stages 1-3 including a total of 201,529 independent individuals (Supplementary Figure 1, Supplementary Tables 2-3, Supplementary Note). To account for population structure between studies in Stages 1-3 of our meta-analysis, genomic control correction was applied to meta-analysis results from each of these stages in an approach aggregating summary statistics from GWAS and Cardio-MetaboChip studies^18,19.

After stage 4, 67 loci attained genome-wide significance (P < 5 × 10⁻⁸), 18 of which were not previously reported in the literature (Supplementary Table 4). Quantile-quantile plots of the stage 4 meta-analysis showed an excess of small P values, with an elevated genomic control lambda estimate that was persistent, albeit attenuated, after excluding all 66 loci (Supplementary Figure 2). This observation is compatible with either residual uncorrected population stratification or the presence of a large number of variants that are truly associated with BP but fail to achieve genome-wide significance in the current meta-analysis. The Cardio-MetaboChip array's inclusion of SNPs from a prior BP GWAS⁵ does not appear to be the sole explanation, as we did not observe a significant decrease of the excess of small P values after exclusion of all SNPs that were included on the Cardio-MetaboChip based on nominal BP association (Supplementary Figures 3 and 4). Since the quantile-quantile plots continued to show deviation from the null expectation, we sought additional validation for 18 variants attaining genome-wide significance, but without prior support in the literature, in up to 140,886 individuals of European ancestry from UK Biobank²⁰. For these SNPs, we performed a stage 5 meta-analysis combining the association summary statistics from stage 4 and UK Biobank, in a total of up to 342,415 individuals (Supplementary Table 5).

Upon stage 5 meta-analysis, 17 of 18 variants retained genome-wide significance for the primary trait (SBP or DBP result with the lower P value). The one variant that was not genome-wide significant had a borderline P value of 4.49 × 10⁻⁸ at stage 4. These findings are consistent with appropriate calibration of the association test statistics at stage 4 such that observing one failure among 18 validation tests is consistent with the use of a threshold (P < 5 × 10⁻⁸) designed to have a 1 in 20 chance of a result as or more extreme solely due to chance. In total, 66 loci attained genome-wide significance: 13 loci for SBP only, 12 loci for DBP only, and 41 loci for both traits. Of these, 17 BP loci were novel, while 49 were previously reported at genome-wide significance (Table 1 and Figure 1).

Table 1.

SBP and DBP association at 66 loci.

Locus no.	Locus name	Lead SNP	Chr	Position (hg19)	CA /NC	Coded allele freq	Traits	SBP				DBP
Locus no.	Locus name	Lead SNP	Chr	Position (hg19)	CA /NC	Coded allele freq	Traits	Effect	SE	P value	Total N	Effect	SE	P value	Total N^#
NEW 1	*HIVEP3*	rs7515635	1	42,408,070	T/C	0.468	SBP	0.307	0.0444	4.81E-12	340,969	0.1365	0.0263	2.05E-07	340,934
NEW 2	*PNPT1*	rs1975487	2	55,809,054	A/G	0.464	DBP	−0.2107	0.045	2.81E-06	337,522	−0.1602	0.0266	1.75E-09	337,517
NEW 3	*FGD5*	rs11128722	3	14,958,126	A/G	0.563	SBP & DBP	−0.3103	0.0469	3.61E-11	310,430	−0.1732	0.0279	5.16E-10	310,429
NEW 4	*ADAMTS9*	rs918466	3	64,710,253	A/G	0.406	DBP	−0.0865	0.0459	5.94E-02	336,671	−0.1819	0.027	1.73E-11	336,653
NEW 5	*TBC1D1-FLJ13197*	rs2291435	4	38,387,395	T/C	0.524	SBP & DBP	−0.3441	0.0449	1.90E-14	331,382	−0.156	0.0266	4.26E-09	331,389
NEW 6	*TRIM36*	rs10077885	5	114,390,121	A/C	0.501	SBP & DBP	−0.284	0.0444	1.64E-10	338,328	−0.1735	0.0263	3.99E-11	338,323
NEW 7	*CSNK1G3*	rs6891344	5	123,136,656	A/G	0.819	DBP	0.2811	0.058	1.24E-06	338,688	0.2311	0.0343	1.58E-11	338,678
NEW 8	*CHST12-LFNG*	rs2969070	7	2,512,545	A/G	0.639	SBP & DBP	−0.2975	0.0464	1.44E-10	335,991	−0.1821	0.0274	2.92E-11	335,972
NEW 9	*ZC3HC1*	rs11556924	7	129,663,496	T/C	0.384	SBP & DBP	−0.2705	0.0468	7.64E-09	325,929	−0.2141	0.0276	8.15E-15	325,963
NEW 10	*PSMD5*	rs10760117	9	123,586,737	T/G	0.415	SBP	0.283	0.0457	6.10E-10	333,377	0.0999	0.0269	2.08E-04	333,377
NEW 11	*DBH*	rs6271^*	9	136,522,274	T/C	0.072	SBP & DBP	−0.5911	0.0899	4.89E-11	306,394	−0.4646	0.0532	2.42E-18	306,463
NEW 12	*RAPSN, PSMC3, SLC39A13*	rs7103648	11	47,461,783	A/G	0.614	SBP & DBP	−0.3349	0.0462	4.43E-13	335,614	−0.2409	0.0272	9.03E-19	335,592
NEW 13	*LRRC10B*	rs751984	11	61,278,246	T/C	0.879	SBP & DBP	0.4074	0.0691	3.80E-09	334,583	0.3755	0.0409	4.20E-20	334,586
NEW 14	*SETBP1*	rs12958173	18	42,141,977	A/C	0.306	SBP & DBP	0.3614	0.0489	1.43E-13	331,007	0.1789	0.0289	5.87E-10	331,010
NEW 15	*INSR*	rs4247374	19	7,252,756	T/C	0.143	SBP & DBP	−0.5933	0.0673	1.23E-18	302,458	−0.3852	0.0396	2.08E-22	302,459
NEW 16	*ELAVL3*	rs17638167	19	11,584,818	T/C	0.047	DBP	−0.4784	0.1066	7.13E-06	333,137	−0.3479	0.0632	3.71E-08	333,107
NEW 17	*CRYAA-SIK1*	rs12627651	21	44,760,603	A/G	0.288	SBP & DBP	0.3905	0.0513	2.69E-14	310,738	0.2037	0.0301	1.36E-11	310,722

EST 1	*CASZ1*	rs880315	1	10,796,866	T/C	0.641	SBP & DBP	−0.475	0.062	2.09E-14	184,226	−0.257	0.038	1.34E-11	184,212
EST 2	*MTHFR-NPPB*	rs17037390	1	11,860,843	A/G	0.155	SBP & DBP	−0.908	0.081	5.95E-29	195,493	−0.499	0.05	1.20E-23	195,481
EST 3	*ST7L-CAPZA1-MOV10*	rs1620668	1	113,023,980	A/G	0.822	SBP & DBP	−0.535	0.076	1.45E-12	197,966	−0.285	0.047	9.00E-10	197,948
EST 4	*MDM4*	rs4245739	1	204,518,842	A/C	0.737	DBP	0.326	0.068	1.37E-06	191,594	0.243	0.041	4.63E-09	191,578
EST 5	*AGT*	rs2493134^*	1	230,849,359	T/C	0.579	SBP & DBP	−0.413	0.058	9.65E-13	199,505	−0.275	0.036	9.53E-15	199,502
EST 6	*KCNK3*	rs2586886	2	26,932,031	T/C	0.599	SBP & DBP	−0.404	0.059	5.94E-12	197,269	−0.254	0.036	1.92E-12	197,272
EST 7	*NCAPH*	rs772178	2	96,963,684	A/G	0.64	DBP	−0.072	0.061	2.39E-01	192,513	−0.208	0.038	3.58E-08	192,501
EST 8	*FIGN-GRB14*	rs1371182	2	165,099,215	T/C	0.443	SBP & DBP	−0.444	0.058	1.89E-14	196,262	−0.252	0.036	1.50E-12	196,240
EST 9	*HRH1-ATG7*	rs2594992	3	11,360,997	A/C	0.607	SBP	−0.334	0.06	2.31E-08	189,895	−0.136	0.037	2.20E-04	189,854
EST 10	*SLC4A7*	rs711737	3	27,543,655	A/C	0.604	SBP	0.334	0.058	9.93E-09	200,282	0.17	0.036	2.24E-06	200,260
EST 11	*ULK4*	rs2272007^*	3	41,996,136	T/C	0.18	DBP	−0.11	0.077	1.52E-01	193,915	0.328	0.047	3.94E-12	193,900
EST 12	*MAP4*	rs6442101^*	3	48,130,893	T/C	0.692	SBP & DBP	0.396	0.062	1.62E-10	200,543	0.303	0.038	1.60E-15	200,534
EST 13	*MECOM*	rs6779380	3	169,111,915	T/C	0.539	SBP & DBP	−0.439	0.06	1.85E-13	186,535	−0.239	0.037	6.87E-11	186,521
EST 14	*FGF5*	rs1458038	4	81,164,723	T/C	0.3	SBP & DBP	0.659	0.065	5.36E-24	188,136	0.392	0.04	7.36E-23	188,088
EST 15	*ARHGAP24*	rs17010957	4	86,719,165	T/C	0.857	SBP	−0.498	0.082	1.51E-09	196,325	−0.173	0.051	6.63E-04	196,292
EST 16	*SLC39A8*	rs13107325	4	103,188,709	T/C	0.07	SBP & DBP	−0.837	0.127	4.69E-11	175,292	−0.602	0.078	1.63E-14	175,372
EST 17	*GUCY1A3-GUCY1B3*	rs4691707	4	156,441,314	A/G	0.652	SBP	−0.349	0.06	7.10E-09	198,246	−0.163	0.037	1.08E-05	198,226
EST 18	*NPR3-C5orf23*	rs12656497	5	32,831,939	T/C	0.403	SBP & DBP	−0.487	0.06	3.85E-16	194,831	−0.228	0.037	4.73E-10	194,829
EST 19	*EBF1*	rs11953630	5	157,845,402	T/C	0.366	SBP & DBP	−0.38	0.065	3.91E-09	167,698	−0.23	0.04	8.07E-09	167,708
EST 20	*HFE*	rs1799945^*	6	26,091,179	C/G	0.857	SBP & DBP	−0.598	0.086	3.28E-12	185,306	−0.43	0.053	3.10E-16	185,273
EST 21	*BAT2-BAT5*	rs2187668	6	32,605,884	T/C	0.126	DBP	−0.291	0.092	1.60E-03	189,806	−0.372	0.057	4.31E-11	189,810
EST 22	*ZNF318-ABCC10*	rs6919440	6	43,352,898	A/G	0.57	SBP	−0.337	0.058	4.92E-09	200,733	−0.125	0.035	4.25E-04	200,730
EST 23	*RSPO3*	rs1361831	6	127,181,089	T/C	0.541	SBP & DBP	−0.482	0.058	7.38E-17	197,027	−0.271	0.036	2.34E-14	197,012
EST 24	*PLEKHG1*	rs17080093	6	150,997,440	T/C	0.075	DBP	−0.564	0.111	3.83E-07	194,728	−0.411	0.068	1.71E-09	194,734
EST 25	*HOTTIP-EVX*	rs3735533	7	27,245,893	T/C	0.081	SBP & DBP	−0.798	0.106	6.48E-14	197,881	−0.445	0.065	1.09E-11	197,880
EST 26	*PIK3CG*	rs12705390	7	106,410,777	A/G	0.227	SBP	0.619	0.069	2.69E-19	198,297	0.059	0.042	1.63E-01	198,290
EST 27	*BLK-GATA4*	rs2898290	8	11,433,909	T/C	0.491	SBP	0.377	0.058	8.85E-11	197,759	0.167	0.036	3.17E-06	197,726
EST 28	*CACNB2*	rs12243859	10	18,740,632	T/C	0.326	SBP & DBP	−0.402	0.061	6.13E-11	199,136	−0.335	0.038	8.11E-19	199,124
EST 29	*C10orf107*	rs7076398	10	63,533,663	A/T	0.188	SBP & DBP	−0.563	0.076	1.72E-13	187,013	−0.409	0.047	2.55E-18	187,024
EST 30	*SYNPO2L*	rs12247028	10	75,410,052	A/G	0.611	SBP	−0.364	0.063	8.16E-09	180,194	−0.159	0.039	3.89E-05	180,094
EST 31	*PLCE1*	rs932764^*	10	95,895,940	A/G	0.554	SBP & DBP	−0.495	0.059	6.88E-17	195,577	−0.224	0.036	6.28E-10	195,547
EST 32	*CYP17A1-NT5C2*	rs943037	10	104,835,919	T/C	0.087	SBP & DBP	−1.133	0.105	2.35E-27	193,818	−0.482	0.064	4.48E-14	193,799
EST 33	*ADRB1*	rs740746	10	115,792,787	A/G	0.73	SBP & DBP	0.486	0.067	4.59E-13	184,835	0.32	0.041	8.63E-15	184,868
EST 34	*LSP1-TNNT3*	rs592373	11	1,890,990	A/G	0.64	SBP & DBP	0.484	0.063	2.02E-14	177,149	0.282	0.039	3.61E-13	177,134
EST 35	*ADM*	rs1450271	11	10,356,115	T/C	0.468	SBP & DBP	0.413	0.059	3.40E-12	191,246	0.199	0.036	4.11E-08	191,221
EST 36	*PLEKHA7*	rs1156725	11	16,307,700	T/C	0.804	SBP & DBP	−0.447	0.072	5.65E-10	200,889	−0.292	0.044	3.67E-11	200,899
EST 37	*SIPA1*	rs3741378^*	11	65,408,937	T/C	0.137	SBP	−0.486	0.084	8.04E-09	194,563	−0.183	0.052	4.17E-04	194,551
EST 38	*FLJ32810-TMEM133*	rs633185	11	100,593,538	C/G	0.715	SBP & DBP	0.522	0.067	6.97E-15	183,845	0.288	0.041	2.38E-12	183,825
EST 39	*PDE3A*	rs3752728	12	20,192,972	A/G	0.737	DBP	0.331	0.066	4.32E-07	200,440	0.319	0.04	2.35E-15	200,408
EST 40	*ATP2B1*	rs11105354	12	90,026,523	A/G	0.84	SBP & DBP	0.909	0.081	3.88E-29	195,206	0.459	0.05	2.61E-20	195,195
EST 41	*SH2B3*	rs3184504^*	12	111,884,608	T/C	0.475	SBP & DBP	0.498	0.062	9.97E-16	177,067	0.362	0.038	1.28E-21	177,122
EST 42	*TBX5-TBX3*	rs2891546	12	115,552,499	A/G	0.11	DBP	−0.529	0.1	1.36E-07	172,012	−0.38	0.061	4.71E-10	171,980
EST 43	*CYP1A1-ULK3*	rs936226	15	75,069,282	T/C	0.722	SBP & DBP	−0.549	0.067	3.06E-16	187,238	−0.363	0.041	1.03E-18	187,221
EST 44	*FURIN-FES*	rs2521501	15	91,437,388	A/T	0.684	SBP & DBP	−0.639	0.069	3.35E-20	164,272	−0.358	0.042	1.85E-17	164,255
EST 45	*PLCD3*	rs7213273	17	43,155,914	A/G	0.658	SBP	−0.413	0.066	4.71E-10	164,795	−0.185	0.041	7.23E-06	164,788
EST 46	*GOSR2*	rs17608766	17	45,013,271	T/C	0.854	SBP	−0.658	0.083	2.27E-15	188,895	−0.218	0.051	1.95E-05	188,928
EST 47	*ZNF652*	rs12940887	17	47,402,807	T/C	0.38	DBP	0.321	0.06	7.06E-08	192,546	0.261	0.037	1.07E-12	192,524
EST 48	*JAG1*	rs1327235	20	10,969,030	A/G	0.542	SBP & DBP	−0.395	0.059	2.23E-11	192,680	−0.308	0.036	1.78E-17	192,659
EST 49	*GNAS-EDN3*	rs6026748	20	57,745,815	A/G	0.125	SBP & DBP	0.867	0.089	3.15E-22	192,338	0.552	0.055	4.86E-24	192,327

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Meta-analysis results of up to 342,415 individuals of European ancestry for SBP and DBP: Established and new loci are grouped separately. Nearest genes are shown as locus labels but this should not be interpreted as support that the causal gene is the nearest gene. The lead SNP with the lowest P value for either BP trait is shown as the lead SNP and both SBP and DBP results are presented even if both are not genome-wide significant. The SNP effects are shown according to the effect in mm Hg per copy of the coded allele (that is the allele coded 0, 1, 2) under an additive genetic model.

in the lead SNP column indicates a non-synonymous coding SNP (either the SNP itself or another SNP in r² >0.8).

Established loci have smaller total sample sizes relative to novel loci (see Supplementary Note).

P values (expressed as -log₁₀P) are plotted by physical genomic position labeled by chromosome. SNPs in new loci (3.5MB window around the index SNP), identified in this study, are labeled in dark red (SBP) or dark blue (DBP); SNPs in previously known loci are labeled in orange (SBP) or light blue (DBP). The locus names are indicated. The grey crosses indicate genomic positions at which the y-axis was truncated (SNPs with P < 10⁻¹⁵).

Compared with previously reported BP variants^5,7,21, the average absolute effect size of the newly discovered variants is smaller, with comparable minor allele frequency (MAF), presumably owing to the increased power of a larger sample size (Table 2). As expected from the high correlation between SBP and DBP effects, the observed directions of effects for the two traits were generally concordant (Supplementary Figure 5), and the absolute effect sizes were inversely correlated with MAF (Table 1 and Supplementary Figure 6). The 66 BP SNPs explained 3.46% and 3.36% of SBP and DBP variance, respectively, a modest increase from 2.95% and 2.78% for SBP and DBP, respectively, for the 49 previously reported SNPs (Supplementary Note). The low percent variance explained is consistent with estimates that large numbers of common variants with weak effects at a large number of loci influence BP⁵.

Table 2.

Overview of novel and known BP variant properties.

	17 new loci	49 established loci	66 loci
Minor allele frequency (mean, range)	32.1% [5%-50%]	28.9% [7%-49%]	29.8% [5%-50%]
Effect size SBP [mmHg] (range, mean)	0.09-0.59, 0.34	0.07-1.13, 0.5	0.07-1.13, 0.46
Effect size DBP [mmHg] (range, mean)	0.1-0.46, 0.23	0.06-0.60, 0.3	0.06-0.6, 0.28
Variance explained SBP	0.52%	2.95%	3.46%
Variance explained DBP	0.58%	2.78%	3.36%

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Key characteristics of the novel and established BP loci are shown. MAF and effect size estimates are derived from the Cardio-MetaboChip data. Variance explained estimates are estimated from one large study (Supplementary Note). Novel loci are classified as previously unknown to be linked to BP by a systematic PubMed review of all genes in a 200kb window (Supplementary Note).

Signal refinement at the 66 BP loci

To identify distinct signals of association at the 66 BP loci and the variants most likely to be causal for each, we started with an approximate conditional analysis using a model selection procedure implemented in the GCTA-COJO package^22,23 as well as a detailed literature review of all published BP association studies. GCTA-COJO analysis was performed using the association summary statistics for SBP and DBP from the Stage 4 EUR ancestry meta-analyses, with the linkage disequilibrium (LD) between variants estimated on the basis of Cardio-MetaboChip genotype data from 7,006 individuals of EUR ancestry from the GoDARTS cohort²⁴. More than one distinct BP association signal was identified at 13 loci at P < 5 × 10⁻⁸ (Supplementary Table 6, Supplementary Figures 7, and Supplementary Note). At six loci, the distinct signals were identified for both SBP and DBP analyzed separately; these trait-specific associations were represented by the same or highly correlated (r² > 0.8) SNPs at 5 of the 6 loci (Supplementary Tables 7 and 8). We repeated GCTA-COJO analyses using the same summary association results, but with a different reference sample for LD estimates (WTCCC1-T2D/58BC, N = 2,947, Supplementary Note) and observed minimal differences arising from minor fluctuations in the association P value in the joint regression models (Supplementary Tables 7 and 8). LD-based comparisons of published association signals at established BP loci, and the current study's findings suggested that at 10 loci, the signals identified by the single-SNP and the GCTA-COJO analyses were distinct from those reported in the literature (Supplementary Table 9).

We then performed multivariable regression modeling in a single large cohort (Women's Genome Health Study, WGHS, N = 23,047) with simultaneous adjustment for both 1) all combinations of putative index SNPs for each distinct signal from the GCTA-COJO conditional analyses, and 2) all index SNPs for all potential distinct signals identified by our literature review (Supplementary Table 9, Supplementary Note). Although WGHS is very large as a single study, power is reduced in a single sample compared to that in the overall meta-analysis (23k vs. 342k individuals) and consequently the failure to reach significance does not represent non-replication for individual SNPs. The WGHS analysis supported two distinct association signals at eight of 13 loci identified in the GCTA-COJO analysis, but could not provide support for the remaining five (Supplementary Table 10). The joint SNP modeling in WGHS additionally supported two distinct signals of association at three other loci (GUCY1A3-GUCY1B3, SYNPO2L and TBX5-TBX3), at which the SNP identified in the current study is distinct from that previously reported in the literature^5,11.

We sought to refine the localization of likely functional variants at loci with high-density coverage on the Cardio-MetaboChip. We followed a Bayesian approach to define, for each signal, credible sets of variants that have 99% probability of containing or tagging the causal variant (Supplementary Note). To improve the resolution of the method, the analyses were restricted to 24 regions selected to fine map (FM) genetic associations, and that included at least one SNP reaching genome-wide significance in the current meta-analyses (Supplementary Table 11). Twenty-one of the Cardio-MetaboChip FM regions were BP loci in the original design, with three of the newly discovered BP loci in FM regions that were originally selected for other non-BP traits. We observed that the 99% credible SNP sets at five BP loci spanned <20kb. The greatest refinement was observed at the SLC39A8 locus for SBP and DBP, and at the ZC3HC1 and PLCE1 loci for DBP, where the 99% credible sets included only the index variants (Supplementary Table 12). Although SNPs in credible sets were primarily non-coding, they included one synonymous and seven non-synonymous variants that attained high posterior probability of driving seven distinct association signals at six BP loci (Supplementary Table 12). Of these, three variants alone account for more than 95% of the posterior probability of driving the association signal observed at each of three loci (Supplementary Table 12 and 13). Despite reduced statistical power, the analyses restricted to the samples with Cardio-MetaboChip genotypes only (N = 109,096) identified the majority of SNPs identified in the GWAS+Cardio-MetaboChip data (Supplementary Table 12). The full list of SNPs in the 99% credible sets are listed in Supplementary Table 13.

What do the BP variants do?

Index SNPs or their proxies (r² > 0.8) altered amino acid sequence at 11 of 66 BP loci (Table 1). Thus, the majority of BP-association signals are likely driven by non-coding variants hypothesized to regulate expression of some nearby gene in cis. To characterize their effects, we first sought SNPs associated with gene expression (eSNPs) from a range of available expression data which included hypertension target end organs and cells of the circulatory system (heart tissue, kidney tissue, brain tissue, aortic endothelial cells, blood vessels) and other tissue/cell types (CD4⁺ macrophages, monocytes lymphoblastoid cell lines, skin tissue, fat tissue, and liver tissue). Fourteen BP-associated SNPs at the MTHFR-NPPB, MDM4, ULK4, CYP1A1-ULK3, ADM, FURIN-FES, FIGN, and PSMD5 loci were eSNPs across different tissues (Supplementary Table 14). Of these 14 eSNPs, three were also predicted to alter the amino acid sequence at the MTHFR-NPPB, MAP4 and ULK4 loci, providing two potential mechanisms to explore in functional studies. Second, we used gene expression levels measured in whole blood in two different samples each including >5,000 individuals of EUR descent. We tested whether the lead BP SNP was associated with expression of any transcript in cis (<1Mb from the lead SNP at each locus) at a false discovery rate (FDR) of < 0.05, accounting for all possible cis-transcript association tests genome-wide. It is likely that we did not genotype the causal genetic variant underlying each BP association signal; a nearby SNP-transcript association, due to LD, may therefore reflect an independent genetic effect on expression that is unrelated to the BP effect. Consequently, we assumed that the lead BP SNP and the most significant eSNP for a given transcript should be highly correlated (r² > 0.7). Furthermore, we assumed that the significance of the transcript association with the lead BP SNP should be substantially reduced in a conditional model adjusting for the best eSNP for a given transcript. Eighteen SNPs at 15 loci were associated with 22 different transcripts, with a total of 23 independent SNP-transcript associations (three SNPs were associated with two transcripts each, Supplementary Table 15, Supplementary Note). The genes expressed in a BP SNP allele-specific manner are clearly high-priority candidates to mediate the BP association. In whole blood, these genes included obvious biological candidates such as GUCY1A3, encoding the alpha subunit of the soluble guanylate cyclase protein, and ADM, encoding adrenomedullin, both of which are known to induce vasodilation^25,26. There was some overlap of eSNPs between the whole blood and other tissue datasets at the MTHFR-NPPB, MDM4, PSMD5, ULK4 and CYP1A1-ULK3 loci, illustrating additional potentially causal genes for further study.

An alternative method for understanding the effect on BP of non-coding variants is to determine whether they fall within DNaseI hypersensitivity sites (DHSs). We performed two analyses to investigate whether BP SNPs or their LD proxies (r² > 0.8) were enriched in DHSs in a cell-type-specific manner (Supplementary Note). First, we used Epigenomics Roadmap and ENCODE DHS data from 123 adult cell lines or tissues^27-29 to estimate the fold increase in the proportion of BP SNPs mapping to DHSs compared to SNPs associated at genome-wide significance with non-BP phenotypes from the NHGRI GWAS catalog³⁰. We observed that 7 out of the 10 cell types with the greatest relative enrichment of BP SNPs mapping to DHSs were from blood vessels (vascular or micro-vascular endothelial cell-lines or cells) and 11 of the 12 endothelial cells were among the top quarter most enriched among the 123 cell types (Figure 2 and Supplementary Table 16). In a second analysis of an expanded set of tissues and cell lines, in which cell types were grouped into tissues (Supplementary Table 17), BP-associated SNP enrichment in DHSs in blood vessels was again observed (P = 1.2 × 10⁻⁹), as well as in heart samples (P = 5.3 × 10⁻⁸; Supplementary Table 18).

Enrichment analyses of SBP or DBP associated loci according to discovery P value using narrow peaks (panel A) or broad peaks (panel B). SNPs were selected according to different P value cutoffs (x-axis) and a fold enrichment of overlap with DNAse hypersensitive sites compared to unrelated GWAS SNPs was calculated (y-axis) (see **Supplementary Note**). The 12 endothelial cell-lines are indicated in color and for each endothelial cell-type the rank using the 10⁻¹⁴ P value cutoff is indicated. EC denotes endothelial cells.

We next tested whether there was enrichment of BP SNPs in H3K4me3³¹ sites, a methylation mark associated with both promoter and enhancer DNA. We observed significant enrichment in a range of cell types including CD34 primary cells, adult kidney cells, and muscle satellite cultured cells(Supplementary Table 19). Enrichment of BP SNPs in predicted strong and weak enhancer states and in active promoters³² in a range of cell types was also observed (Supplementary Table 20, Supplementary Figure 8).

We used Meta-Analysis Gene-set Enrichment of variaNT Associations (MAGENTA)³³ to attempt to identify pathways over-represented in the BP association results. No gene sets meeting experiment-wide significance for enrichment for BP association were identified by MAGENTA after correction for multiple testing, although some attained nominal significance (Supplementary Table 21, Supplementary Note). We also adapted the DEPICT³⁴ pathway analysis tool (Data-driven Expression Prioritized Integration for Complex Traits) to identify assembled gene-sets that are enriched for genes near associated variants, and to assess whether genes from associated loci were highly expressed in particular tissues or cell types. Using the extended BP locus list based on genome-wide significant loci from this analysis and previously published SNPs that may not have reached genome-wide significance in the current analysis (Supplementary Table 9), we identified five significant (FDR ≤ 5%) gene sets: abnormal cardiovascular system physiology, G Alpha 1213 signaling events, embryonic growth retardation, prolonged QT interval, and abnormal vitelline vasculature morphology. We also found that suggestive SBP and DBP associations (P < 1 × 10⁻⁵) were enriched for reconstituted gene-sets at DBP loci (mainly related to developmental pathways), but not at SBP loci (Supplementary Table 22, Supplementary Note). In a final analysis, we assessed Cardio-MetaboChip SNPs at the fine-mapping loci using formaldehyde-assisted isolation of regulatory elements (FAIRE-gen) in lymphoblastoid cell lines³⁵. Our results provided support for two SNPs, one of which SNP (rs7961796 at the TBX5-TBX3 locus) was located in a regulatory site. Although the other SNP (rs3184504 at the SH2B3 locus) is a non-synonymous variant, there was also a regulatory site indicated by DNaseI and H3K4me1 signatures at the locus, making the SNP a potential regulatory variant (Supplementary Table 23)³⁶. Both SNPs were included in the list of 99% credible SNPs at each locus.

Asian- and African ancestry BP SNP association

We tested the 66 lead SNPs at the established and novel loci for association with BP in up to 20,875 individuals of South Asian (SAS) ancestry (PROMIS and RACE studies), 9,637 individuals of East Asian (EAS) ancestry (HEXA, HALST, CLHNS, DRAGON, and TUDR studies), and 33,909 individuals of African (AFR) ancestry (COGENT-BP consortium, Jupiter, SPT, Seychelles, GXE, and TANDEM studies). As expected, the effect allele frequencies are very similar across studies of the same ethnicity, but markedly different across different ancestry groups (Supplementary Figure 9). Many associations of individual SNPs failed to reach P < 0.05 for the BP trait with the lower P value (Supplementary Table 24), which could potentially be due to the much lower statistical power at the sample sizes available, different patterns of LD at each locus across ancestries, variability in allele frequency, or true lack of association in individuals of a given non-European ancestry. The low statistical power for the great majority of SNPs tested is visible considering SNP-by-SNP power calculations using European ancestry effect sizes (Supplementary Table 24). However, concordant directions of allelic effects for both SBP and DBP were observed for 45/66 SNPs in SAS, 36/60 SNPs in EAS, and 42/66 SNPs in AFR samples: the strongest concordance with SAS may not be surprising because South Asians are more closely related to Europeans than are East Asians or Africans. Moreover, strong correlation of effect sizes was observed between EUR samples with SAS, EAS, or AFR samples (r = 0.55, 0.60, and 0.48, respectively). A 66-SNP SBP or DBP risk score were significant predictors of SBP and DBP in all samples. A 1 mm Hg higher SBP or DBP risk score in EUR samples was associated with a 0.58/0.50 mm Hg higher SBP/DBP in SAS samples (SBP P = 1.5 × 10⁻¹⁹, DBP P = 3.2 × 10⁻¹⁵), 0.49/0.50 mm Hg higher SBP/DBP in EAS samples (SBP P = 1.9 × 10⁻¹⁰, DBP P = 1.3 × 10⁻⁷), and 0.51/0.47 mm Hg higher SBP/DBP in AFR samples (SBP P = 2.2 × 10⁻²¹, DBP P = 6.5 × 10⁻¹⁹). The attenuation of the genetic risk score estimates in non-European ancestries is presumably due to inclusion of a subset of variants that lack association in the non-European or admixed samples.

We subsequently performed a trans-ethnic meta-analysis of the 66 SNPs in all 64,421 samples across the three non-European ancestries. After correcting for 66 tests, 12/66 SNPs were significantly associated with either SBP or DBP (P < 7.6 × 10⁻⁴), with a correlation of EUR and non-EUR effect estimates of 0.77 for SBP and 0.67 for DBP; the European-ancestry SBP or DBP risk score was associated with 0.53/0.48 mm Hg higher BP per predicted mm Hg SBP/DBP respectively (SBP P < 6.6 × 10⁻⁴⁸, DBP P < 1.3 × 10⁻³⁸). For 7 of the 12 significant SNPs, no association has previously been reported in genome-wide studies of non-European ancestry. Some heterogeneity of effects was observed between European and non-European effect estimates (Supplementary Table 24). Taken together, these findings suggest that, in aggregate, BP loci identified using data from individuals of EUR ancestry are also predictive of BP in non-EUR samples, but larger non-European sample sizes will be needed to establish precisely which individual SNPs are associated in a given ethnic group.

Impact on hypertensive target organ damage

Long-term elevated BP causes target organ damage, especially in the heart, kidney, brain, large blood vessels, and the retinal vessels³⁷. Consequently, the genetic effect of the 66 SBP and DBP SNPs on end-organ outcomes can be directly tested using the risk score, although some outcomes lacked results for a small number of SNPs. Interestingly, BP risk scores significantly predicted (Supplementary Note) coronary artery disease risk, left ventricular mass and wall thickness, stroke, urinary albumin/creatinine ratio, carotid intima-medial thickness and central retinal artery caliber, but not heart failure or other kidney phenotypes, after accounting for the number of outcomes examined (Table 3). Because outlier effects can affect risk scores, we repeated the risk score analysis removing iteratively SNPs that contributed to statistical heterogeneity (SNP-trait effects relative to SNP-BP effects). Heterogeneity was defined based on a multiple testing adjusted significance threshold for Cochran's Q test of homogeneity of effects (Supplementary Note). The risk score analyses restricted to the subset of SNPs showing no heterogeneity of effect revealed essentially identical results, with the exception that urinary albumin/creatinine ratio was no longer significant. The per-SNP results are provided in Supplementary Table 25 and Supplementary Figures 10. Because large-scale GWAS of non-BP cardiovascular risk factors are available, we examined the BP risk scores as predictors of other cardiovascular risk factors: LDL-cholesterol, HDL-cholesterol, triglycerides, type 2 diabetes, BMI, and height. We observed nominal (P <0.05) associations of the BP risk scores with risk factors, although mostly in the opposite direction to the risk factor-CVD association (Supplementary Table 26). The failure to demonstrate an effect of BP risk scores on heart failure may reflect limited power from a modest sample size, but the lack of significant effects on renal measures suggests that the epidemiologic relationship of higher BP and worse renal function may not reflect direct consequences of BP elevation.

Table 3.

Prediction of hypertensive target organ damage by a multi-BP SNP score.

Phenotype	Var. type (cont./ dic.)	Eth.	Consort.	Total N or no. ca/co	Total #SNPs	SBP_score					DBP_score
Phenotype	Var. type (cont./ dic.)	Eth.	Consort.	Total N or no. ca/co	Total #SNPs	effect (all)	P value (all)	het. P value (all)	P value (p)	# SNPs rem.	effect (all)	P value (all)	het. P value (all)	P value (p)	# SNPs rem.
HEART
CAD	dich.	EUR SAS	CARDIoGRAMplusC4D	63,746/130,681	61	1.042	1.72E-44	1.75E-25	4.08E-32	10	1.069	1.19E-42	6.63E-27	2.2E-38	10
heart failure	dich.	EUR	CHARGE	2,526/18,400	66	1.021	2.77E-02	1.63E-01	2.77E-02	0	1.035	2.31E-02	1.70E-01	2.31E-02	0
LV mass	cont.	EUR	CHARGE	11,273	66	0.480	6.43E-04	3.58E-01	6.43E-04	0	0.754	1.23E-03	3.21E-01	1.23E-03	0
LV wall thickness	cont.	EUR	CHARGE	11,311	66	0.004	4.45E-06	5.83E-02	4.45E-06	0	0.007	3.19E-06	6.40E-02	3.19E-06	0
KIDNEY
CKD	dich.	EUR	CHARGE	6,271/68,083	65	1.010	1.37E-01	1.77E-03	2.65E-01	1	1.008	4.49E-01	1.25E-03	7.69E-01	1
eGFR (based on cr)	cont.	EUR	CHARGE	74,354	65	0.000	7.07E-01	3.12E-05	3.22E-01	2	0.000	9.41E-01	3.02E-05	9.65E-01	2
eGFR (based on cystatin)	cont.	EUR	CHARGE	74,354	65	0.001	9.05E-02	9.28E-06	4.11E-01	1	0.001	3.30E-01	5.64E-06	6.9E-01	1
creatinine	cont.	EUR	KidneyGEN	23,812	66	0.000	9.42E-01	6.31E-03	9.42E-01	0	0.000	4.11E-01	7.16E-03	4.11E-01	0
microalbuminuria	dich.	EUR	CHARGE	2,499/29,081	65	0.011	2.10E-01	4.79E-02	2.1E-01	0	0.023	1.02E-01	5.66E-02	1.02E-02	0
urinary albumin/cr ratio	cont.	EUR	CHARGE	31,580	65	0.009	2.52E-03	3.02E-04	0.53E-03	1	0.015	2.40E-03	3.08E-04	8.31E-03	1
STROKE
stroke, all subtypes	dich.	EUR	CHARGE	1,544/18,058	66	0.056	6.11E-06	8.26E-02	6.11E-06	0	0.085	3.79E-05	4.98E-02	3.79E-05	0
stroke, ischemic subtype	dich.	EUR	CHARGE	1,164/18,438	66	0.067	3.33E-06	1.75E-01	3.33E-06	0	0.096	5.63E-05	8.82E-02	5.63E-05	0
stroke, ischemic subtype	dich.	EUR	MetaStroke	11,012/40,824	66	0.036	1.69E-10	4.72E-02	1.69E-10	0	0.056	1.29E-09	2.51E-02	1.29E-09	0
VASCULATURE
cIMT	cont.	EUR	CHARGE	27,610	66	0.004	4.80E-15	5.06E-08	7.32E-10	4	0.005	4.15E-11	3.84E-10	6.2E-07	5
EYE
mild retinop.	dich.	EUR	CHARGE	1,122/18,289	66	1.021	1.37E-01	6.01E-03	1.37E-01	0	1.046	5.78E-02	7.81E-03	5.78E-02	0
central retinal artery caliber	cont.	EUR	CHARGE	18,576	66	0.343	3.29E-14	2.56E-06	2.06E-13	2	0.570	3.61E-14	2.44E-06	7.05E-13	3
mild retinop.	dich.	EAS	SEED	289/5,419	66	1.033	2.55E-01	2.42E-01	2.55E-01	0	1.087	8.55E-02	2.87E-01	8.55E-02	0
central retinal artery caliber	cont.	EAS	SEED	6,976	63	0.320	1.39E-04	9.07E-01	1.39E-04	0	0.533	2.19E-04	8.91E-01	2.19E-04	0

Open in a new tab

Shown are the estimated effects of a BP risk score comprised of up to 66 SNPs (see column “Total #SNPs”) on risk of dichotomous outcome (as odds ratios) or increment in continuous measures per predicted mmHg of the SBP or DBP score. The effect sizes are expressed as incremental change in the phenotype for quantitative traits and natural logarithm of the odds ratio for binary traits, per 1 mmHg predicted increase in SBP or DBP. P values are bolded if they meet an analysis-wide significance threshold (< 0.05/18 = 0.0028). Results for all SNPs (“all”) and for pruned results (“p”) are shown. The pruned results were obtained by iterative removal of SNPs from the risk score starting with the SNP with lowest heterogeneity P value. Iterations to remove SNPs were continued until the heterogeneity P value was < 0.0028 (see Supplementary Note). The number of SNPs removed when calculating the pruned results is indicated by “# SNPs rem.”. The results per individual SNP can be found in Supplementary Table 15. CAD: coronary artery disease, LV: left ventricle, CKD: chronic kidney disease, eGFR: estimated glomerular filtration rate, cr: creatinine, cIMT: carotid intima: media thickness. Var. type denotes the variable type and cont. for continuous, or dic. for dichotomous. Eth. = Ethnicity, Consort. = Consortium, EUR = European ancestry, EAS = East Asian ancestry.

DISCUSSION

The study reported here is the largest to date to investigate the genomics of BP in multiple continental ancestries. Our results highlight four major features of inter-individual variation in BP: (1) we identified 66 (17 novel) genome-wide significant loci for SBP and DBP by targeted genotyping in up to 342,415 individuals of European ancestry that cumulatively explain ~3.5% of the trait; (2) the variants were enriched for cis-regulatory elements, particularly in vascular endothelial cells; (3) the variants had broadly comparable BP effects in South Asians, East Asian and Africans, albeit in smaller sample sizes; and, (4) a 66 SNP risk-score predicted target organ damage in the heart, cerebral vessels, carotid artery and the eye with little evidence for an effect in kidneys. Overall, there was no enrichment of a single genetic pathway in our data; rather, our results are consistent with the effects of BP arising from multiple tissues and organs.

Genetic and molecular analyses of Mendelian syndromes of hypertension and hypotension point largely to a renal origin, involving multiple rare deleterious mutations in proteins that regulate salt-water balance³⁸. This is strong support for Guyton's hypothesis that the regulation of sodium excretion by the kidney and its effects on extracellular volume are a prime pathway determining intra-arterial pressure³⁹. However, our genetic data from unselected individuals in the general community argues against a single dominant renal effect. The 66 SNPs we identified are not chance effects, but have a global distribution and impact on BP that are consistent as measured by their effects across the many studies meta-analyzed. That they are polymorphic across all continental ancestries argues for their origin and functional effects prior to human continental differentiation.

However several of the 17 novel loci contain strong positional biological candidates, these are described in greater detail in Supplementary Table 27 and the Supplementary Note. The single most common feature we identified was the enrichment of regulatory elements for gene expression in vascular endothelial cells. The broad distribution of these cells across both large and small vessels and across all tissues and organs suggest that functional variation in these cells affects endothelial permeability or vascular smooth muscle cell contractility via multiple pathways. These hypotheses will need to be rigorously tested in appropriate models, to assess the contribution of these pathways to BP control, and these pathways could also be targets for systemic anti-hypertensive therapy as they are for the pulmonary circulation⁴².

In summary, these genetic observations may contribute to an improved understanding of BP biology and a re-evaluation of the pathways considered relevant for therapeutic BP control.

ONLINE METHODS

Cohorts contributing to systolic (SBP) and diastolic blood pressure (DBP) analyses

Studies contributing to BP association discovery including community- and population-based collections as well as studies of non-BP traits, analyzed as case and control samples separately. Details on each of the studies including study design and BP measurement are provided in Supplementary Table 1, genotyping information in Supplementary Table 2, and participant characteristics in Supplementary Table 3. All participants provided written informed consent and the studies were approved by local Research Ethics Committees and/or Institutional Review Boards.

European ancestry meta-analysis

BP was measured using standardized protocols in all studies regardless of whether the primary focus was BP or another trait. We initially analyzed affected and unaffected individuals from samples selected as cases (e.g. type 2 diabetes) or controls, separately. However, because sensitivity analyses did not reveal any significant difference in BP effect size estimates between case and control samples (data not shown), we analyzed all samples combined. When available, the average of two BP measurements was used for association analyses (Supplementary Table 1). If an individual was taking a BP-lowering treatment, the underlying systolic BP (SBP) and diastolic BP (DBP) were estimated by adding 15 mmHg and 10 mmHg, respectively, to the measured values, as done in prior analyses.

A meta-analysis of 340,934 individuals of European descent was undertaken in four stages with subsequent validation in an independent cohort. Because stage 1 Cardio-MetaboChip samples included many SNPs selected on the basis of association with BP in earlier GWAS, we performed genomic control using a set of putative null SNPs based on P > 0.10 in earlier GWAS of SBP and DBP or both. Stage 2 samples with genome-wide genotyping used the entire genome-wide set of SNPs for genomic control given the lack of ascertainment. The study design is summarized in Supplementary Figure 1, and further details are provided in Supplementary Tables 2-5 and the Supplementary Note.

Systematic PubMed search +/− 100kb of each newly discovered index SNP

All genes with any overlap with a 200kb region centered around each of the 17 newly discovered lead SNPs were identified using the UCSC Genome Browser. A search term was constructed for each gene including the short and long gene name and the terms “blood pressure” and “hypertension” (e.g. for NPPA on chr 1: “NPPA OR natriuretic peptide A AND (blood pressure OR hypertension)”) and the search results of each search term from PubMed were individually reviewed.

Trait variance explained

The trait variance explained by 66 lead SNPs at novel and known loci was evaluated in one study that contributed to the discovery effort: the Atherosclerosis Risk in Communities (ARIC) study. We constructed a linear regression model with all 66 or the subset of 49 known SNPs as a set of predictors of the BP residual after adjustment for covariates of the adjusted treatment-corrected BP phenotype (SBP or DBP). The r² from the regression model was used as the estimate of trait variance explained.

European ancestry GCTA-COJO analysis

To identify multiple distinct association signals at any given BP locus, we undertook approximate conditional analyses using a model selection procedure implemented in the GCTA-COJO software package^44,45. To evaluate the robustness of the GCTA-COJO results to the choice of reference data set, model selection was performed using the LD between variants in separate analyses from two datasets of European descent, both with individuals from the UK with Cardio-MetaboChip genotype data: GoDARTS with 7,006 individuals and WTCCC1-T2D/58BC with 2,947 individuals. Assuming that the LD between SNPs more than 10 Mb away or on different chromosomes is zero, we undertook the GCTA-COJO step wise model selection to select SNPs that were conditionally-independently associated with SBP and DBP, in turn, at a genome-wide significance, given by P < 5×10⁻⁸ (Supplementary Tables 6-8) using the stage 4 combined European GWAS+ Cardio-MetaboChip meta-analysis.

Conditional analyses in the Women's Genome Health Study (WGHS)

Multivariable regression modeling was performed for each possible combination of putative independent SNPs from a) model selection implemented in GCTA-COJO and b) a comprehensive manual review of the literature (Supplementary Table 9). Any SNP with P < 5×10⁻⁸ in a previous reported BP GWAS was considered. A total of 46 SNPs were examined (Supplementary Table 10). Genome-wide genotyping data imputed to 1000 Genomes in the WGHS (N = 23,047) were used. Regression modeling was performed in the R statistical language (Supplementary Table 10).

Fine mapping and determination of credible sets of causal SNPs

The GCTA-COJO and WGHS conditional analyses identified multiple distinct signals of association at multiple loci (Supplementary Tables 6 and 10). Of the 24 loci considered in fine-mapping analyses, 16 had no evidence for the existence of multiple distinct association signals, so it is reasonable to assume that there is a single causal SNP and therefore the credible sets of variants could be constructed using the association summary statistics from the unconditional meta-analyses. However, in the remaining eight loci, where evidence of secondary signals was observed from GCTA-COJO, we performed approximate conditional analyses across the region by conditioning on each index SNP (Supplementary Table 11). By adjusting for the other index SNPs at the locus, we can therefore assume a single variant is driving each “conditionally-independent” association signal, and we can construct the 99% credible set of variants on the basis of the approximate conditional analysis from GCTA-COJO (Supplementary Tables 12-13). At five of the eight loci with multiple distinct signals of association, one index SNP mapped outside of the fine-mapping region, so a credible set could not be constructed.

eQTL analysis: Whole Blood

NESDA/NTR: Whole blood eQTL analyses were performed in samples from the Netherlands Study of Depression and Anxiety (NESDA)⁴⁶ and the Netherlands Twin Registry (NTR)⁴⁷ studies. RNA expression analysis was performed in the statistical software R. The residuals resulting from the linear regression analysis of the probe set intensity values onto the covariates sex, age, body mass index (kg/m²), smoking status coded as a categorical covariate, several technical covariates, and three principal components were used. The eQTL effects were detected using a linear mixed model approach, including for each probe set the expression level (normalized, residualized and without the first 50 expression PCs) as dependent variable; the SNP genotype values as fixed effects; and family identifier and zygosity (in the case of twins) as random effects to account for family and twin relations⁴⁸.

The eQTL effects were defined as cis when probe set–SNP pairs were at distance < 1M base pairs. At a FDR of 0.01 applied genome-wide, not just for candidate SNPs, the P value threshold was 1×10⁻⁴ for the cis-eQTL analysis. For each probe set that displayed a statistically significant association with at least one SNP located within its cis region, we identified the most significantly associated SNP and denoted this as the top cis-eQTL SNP. See Supplementary Note for details.

eQTL analysis: Selected published eQTL datasets

Lead BP SNP and proxies (r² > 0.8) were searched against a collected database of expression SNP (eSNP) results. The reported eSNP results met criteria for statistical thresholds for association with gene transcript levels as described in the original papers. The non-blood cell tissue eQTLs searched included aortic endothelial cells⁴⁹, left ventricle of the heart ⁵⁰, cd14+ monocytes ⁵¹ and the brain ⁵². The results are presented in Supplementary Tables 14-15.

Enrichment analyses: Analysis of cell-specific DNase hypersensitivity sites (DHSs) using an OR method

The overlap of Cardio-MetaboChip SNPs with DHSs was examined using publicly available data from the Epigenomics Roadmap Project and ENCODE, choosing different cutoffs of Cardio-MetaboChip P values. The DHS mappings were available for 123 mostly adult cells and tissues ⁵³ (downloaded from The DHS mappings were specified as both “narrow” and “broad” peaks, referring to reduction of the experimental data to peak calls at 0.1% and 1.0% FDR thresholds, respectively. Thus, the “narrow” peaks are largely nested within the “broad” peaks. Experimental replicates of the DHS mappings (typically duplicates) were also available for the majority of cells and tissues.

SNPs from the Cardio-MetaboChip genome-wide scan were first clumped in PLINK in windows of 100kb and maximum r² = 0.1 among LD relationships from the 1000 Genomes European data. Then, the resulting index SNPs at each P value threshold were tagged with r² = 0.8 in windows of 100kb, again using LD relationships in the 1000 Genomes, restricted to SNPs with MAF > 1% and also present in the HapMap2 CEU population. A reference set of SNPs was constructed using the same clumping and tagging procedures applied to GWAS catalog SNPs (available at http://www.genome.gov/gwastudies/, accessed 3/13/2013)⁵⁴ with discovery P < 5×10⁻⁸ in European populations. A small number of reference SNPs or their proxies overlapping the BP SNPs or their proxies were excluded. After LD pruning and exclusions, there were a total of 1,196 reference SNPs. For each cell type and P value threshold, the enrichment of SBP or DBP SNPs (or their LD proxies) mapping to DHSs was expressed as an odds ratio (OR) relative to the GWAS catalog reference SNPs (or their LD proxies), using logistic mixed effect models treating the replicate peak determinations as random effects (glmer package in R). The significance of the enrichment ORs was derived from the significance of beta coefficients for the main effects in the mixed models (Figure 2, Supplementary Table 16).

Enrichment analyses: Analysis of tissue-specific enrichment of BP variants and H3K4me3 sites

An analysis to test for significant cell-specific enrichment in the overlap of BP SNPs (or their proxies) with H3K4me3 sites was performed as described in Trynka et al, 2013⁵⁵. The measure of overlap is a “score” that is constructed by dividing the height of an H3K4me3 ChIP signal in a particular cell by the distance between the nearest test SNP. The significance of the scores (i.e. P value) for all SNPs was determined by a permutation approach that compares the observed scores to scores of SNPs with similar properties to the test SNPs, essentially in terms of LD and proximity to genes (Supplementary Note). The number of permutations determined the number of significant digits in the P values and we conducted 10,000 iterations. Results are shown in Supplementary Table 19.

Enrichment analyses: Analysis of tissue-specific DHSs and chromatin states using GREGOR

The DNase-seq ENCODE data for all available cell types were downloaded in the processed “narrowPeak” format. The local maxima of the tag density in broad, variable-sized “hotspot” regions of chromatin accessibility were thresholded at FDR 1% with peaks set to a fixed width of 150bp. Individual cell types were further grouped into 41 broad tissue categories by taking the union of DHSs for all related cell types and replicates. For each GWAS locus, a set of matched control SNPs was selected based on three criteria: 1) number of variants in LD (r² > 0.7; ± 8 variants), 2) MAF (± 1%), and 3) distance to nearest gene (± 11,655 bp). To calculate the distance to the nearest gene, the distance to the 5’ flanking gene (start and end position) and to the 3’ flanking gene was calculated and the minimum of these 4 values was used. If the SNP fell within the transcribed region of a gene, the distance was 0. The probability that a set of GWAS loci overlap with a regulatory feature more often than we expect by chance was estimated.

Enrichment analyses: FAIRE analysis of BP variants in fine-mapping regions in lymphoblastoid cell lines

FAIRE analysis was performed on a sample of 20 lymphoblastoid cell lines of European origin. All samples were genotyped using the Cardio-MetaboChip genotyping array, and BP SNPs and LD proxies (r² > 0.8) at the fine mapping loci (N = 24, see Supplementary Table 23) were assessed to identify heterozygous imbalance between non-treated and FAIRE-treated chromatin. A paired t-test was used to compare the B allele frequency (BAF) arising from formaldehyde-fixed chromatin sheared by sonication and DNA purified to the BAF when the same chromatin sample underwent FAIRE to enrich for open chromatin. Three hundred and fifty-seven Cardio-MetaboChip BP SNPs were directly genotyped across the fine mapping regions. The Bonferroni-corrected threshold of significance is P < 0.0001 (0.05/357). The results for SNPs with P < 0.05 are reported in (Supplementary Table 23). FAIRE results were not available for some SNPs with missing data due to genotype failure or not having >3 heterozygous individuals for statistical analysis. Therefore there are no results for three lower frequency BP loci (SLC39A8, CYP17A1-NT5C2 and GNAS-EDN3) and for the second signal at the following loci: MTHFR-NPPB (rs2272803), MECOM (rs2242338) and HFE rs1800562).

Pathway analyses: MAGENTA

MAGENTA tests for enrichment of gene sets from a precompiled library derived from GO, KEGG, PATHTER, REACTOME, INGENUITY, and BIOCARTA was performed as described by Segré et al, 2010⁵⁶. Enrichment of significant gene-wide P values in gene sets is assessed by 1) using LD and distance criteria to define the span of each gene, 2) selecting the smallest P value among SNPs mapping to the gene span, and 3) adjusting this P value using a regression method that accounts for the number of SNPs, the LD, etc. In the second step, MAGENTA examines the distribution of these adjusted P values and defines thresholds for the 75%ile and the 95%ile. In the third step, MAGENTA calculates an enrichment for each gene set by comparing the number of genes in the gene set with P value less than either the 75th or 95th %ile to the number of genes in the gene set with P value greater than either the 75th or 95th %ile, and then comparing this quotient to the same quotient among genes not in the gene set. This gene-set quotient is assigned a P value based on reference to a hypergeometric distribution. The results based on our analyses are indicated in Supplementary Table 21.

Pathway analyses: DEPICT

We applied the DEPICT ⁵⁷ analysis separately on genome-wide significant loci from the overall blood pressure (BP) Cardio-MetaboChip analysis including published blood pressure loci (see Supplementary Table 22). SNPs at the HFE and BAT2-BAT5 loci (rs1799945, rs1800562, rs2187668, rs805303, rs9268977) could not be mapped. As a secondary analysis, we additionally included associated loci (P < 1×10⁻⁵) from the Cardio-MetaboChip stage 4 combined meta-analyses of SBP and the DBP. DEPICT assigned genes to associated regions if they overlapped or resided within associated LD blocks with r² > 0.5 to a given associated SNP.

Literature review for genes at the newly discovered loci

Recognizing that the most significantly associated SNP at a locus may not be located in the causal gene and that the functional consequences of a SNP often extends beyond 100kb, we conducted a literature review of genes in extended regions around newly discovered BP index SNPs. The genes for this extensive review were identified by DEPICT (Supplementary Table 22).

Non-European meta-analysis

To assess the association of the 66 significant loci from the European ancestry meta-analysis in non-European ethnicities, we obtained lookup results for the 66 index SNPs for participants of South-Asian ancestry (8 datasets, total N = 20,875), East-Asian ancestry (5 datasets, total N = 9,637), and African- and African-American ancestry (6 datasets, total N = 33,909). The association analyses were all conducted with the same covariates (age, age², sex, BMI) and treatment correction (+15/10 mm Hg in the presence of any hypertensive medication) as the association analyses for the discovery effort in Europeans. Tests for heterogeneity across effect estimates in European, South Asian, East Asian and African derived samples were performed using GWAMA⁵⁸.

Genetic risk score and cardiovascular outcomes

The gtx package for the R statistical programming language was used to estimate the effect of the SNP-risk score on the response variable in a regression model⁵⁹.

Supplementary Material

NIHMS810971-supplement-1.pdf^{(11.9MB, pdf)}

NIHMS810971-supplement-3.xlsx^{(476KB, xlsx)}

SUMMARY STATISTICS.

Full summary statistics (P values) are in the online version of the paper (file “ICBPCMfinalMeta.csv.zip”).

ACKNOWLEDGEMENTS

We thank all the study participants of this study for their contributions. Detailed acknowledgment of funding sources is provided in the Supplementary Note.

AUTHOR CONTRIBUTIONS

Analysis group

Design of secondary analyses: G.B.E., T.Ferreira, T.J., A.P.M., P.B.M., C.N.-C. Computation of secondary analyses: G.B.E., T.Ferreira, T.J., A.P.M., P.B.M., C.N.-C. Paper writing: A.C., G.B.E., T.Ferreira, T.J., A.P.M., P.B.M., C.N.-C. Study management: P.B.M., C.N.-C.

Cardio-MetaboChip or new GWAS

WGHS: Study phenotyping: P.M.R., D.I.C., L.M.R. Genotyping or analysis: P.M.R., D.I.C., L.M.R., F.Giulianini Study PI: P.M.R.

JUPITER: Study phenotyping: P.M.R., D.I.C., L.M.R. Genotyping or analysis: D.I.C., L.M.R., F.Giulianini Study PI: P.M.R., D.I.C.

deCODE: Study phenotyping: G.B. Genotyping or analysis: G.T. Study PI: K.S., U.T.

GoDARTS: Study phenotyping: C.N.A.P., L.A.D., A.D.M., A.S.F.D. Genotyping or analysis: C.N.A.P., L.A.D., A.D.M., M.I.M., C.G., N.W.W.R.R. Study PI: C.N.A.P., A.D.M.

KORA F3/F4: Study phenotyping: A.D., H.Schunkert, J.E. Genotyping or analysis: A.-K.P., M.M.-N., N.K., T.I. Study PI: H.-E.W., A.Peters

GLACIER: Study phenotyping: F.R., G.H. Genotyping or analysis: P.W.F., D.Shungin, I.B., S.Edkins, F.R. Study PI: P.W.F.

B58C: Genotyping or analysis: S.Kanoni, K.E.S., Wellcome Trust Case Control Consortium, E.M., T.Ferreira, T.J. Study PI: P.D.

MORGAM: Study phenotyping: K.Kuulasmaa, F.Gianfagna, A.Wagner, J.Dallongeville Genotyping or analysis: M.F.H., F.Gianfagna Study PI: J.V., J.F., A.E.

SardiNIA: Study phenotyping: E.G.L. Genotyping or analysis: E.G.L., O.Meirelles, S.Sanna, R.N., A.Mulas, K.V.T.

NFBC1986: Study phenotyping: M.R.J., S.Sebert, K.H.H., A.L.H. Genotyping or analysis: M.Kaakinen, A.L.H. Study PI: M.R.J.

DESIR: Genotyping or analysis: N.B.-N., L.Y., S.L. Study PI: P.F., N.B.-N., B.B.

DILGOM: Study phenotyping: S.M. Genotyping or analysis: K.Kristiansson, M.P., A.S.H. Study PI: V.S.

IMPROVE: Study phenotyping: D.B. Genotyping or analysis: R.J.S., K.G. Study PI: A.Hamsten, E.Tremoli

HyperGEN: Study phenotyping: S.C.H., D.C.R. Genotyping or analysis: A.C., V.P., G.B.E. Study PI: S.C.H.

FENLAND (MetaboChip): Study phenotyping: R.J.F.L., J.a.L., N.J.W., K.K.O. Genotyping or analysis: R.J.F.L., J.a.L., N.J.W., K.K.O. Study PI: N.J.W.

Whitehall II: Study phenotyping: M.Kumari Genotyping or analysis: M.Kumari, S.Shah, C.L. Study PI: A.Hingorani, M.Kivimaki

LURIC: Genotyping or analysis: M.E.K., G.Delgado Study PI: W.M.

MESA: Study phenotyping: W.P. Genotyping or analysis: W.P., X.G., J.Y., V.D., K.D.T., J.I.R., Y.-D.C. Study PI: W.P.

HUNT2: Study phenotyping: K.Kvaløy, J.H., O.L.H. Genotyping or analysis: A.U.J. Study PI: K.H.

FINCAVAS: Genotyping or analysis: T.L., L.-P.L., K.N., M.Kähönen Study PI: T.L., M.Kähönen

GenNet: Study phenotyping: R.S.C., A.B.W. Genotyping or analysis: A.C., V.P., M.X.S., D.E.A., G.B.E. Study PI: A.C., R.S.C., A.B.W.

SCARFSHEEP: Study phenotyping: B.G. Genotyping or analysis: R.J.S. Study PI: A.Hamsten, U.d.F.

DPS: Study phenotyping: J.L. Genotyping or analysis: A.U.J., P.S.C. Study PI: J.T., M.U.

DR's EXTRA: Study phenotyping: P.K. Genotyping or analysis: A.U.J., M.H. Study PI: R.Rauramaa, T.A.L.

FIN-D2D 2007: Genotyping or analysis: A.U.J., L.L.B. Study PI: J.Saltevo, L.M.

METSIM: Study phenotyping: H.M.S. Genotyping or analysis: A.U.J., A.Stančáková Study PI: M.L., J.K.

MDC-CVA: Study phenotyping: O.Melander Genotyping or analysis: O.Melander, C.F. Study PI: O.Melander

BRIGHT: Study phenotyping: A.F.D., M.J.B., N.J.S., J.M.C. Genotyping or analysis: T.J., P.B.M. Study PI: M.J.C., A.F.D., M.J.B., N.J.S., J.M.C., P.B.M.

NESDA: Study phenotyping: J.H.S. Genotyping or analysis: H.Snieder, I.M.N. Study PI: B.W.P.

EPIC (MetaboChip): Study phenotyping: R.J.F.L., J.a.L., N.J.W. Genotyping or analysis: J.a.L., N.J.W. Study PI: N.J.W., K.-T.K.

ELY: Study phenotyping: C.L., J.a.L., N.J.W. Genotyping or analysis: C.L., J.a.L., N.J.W. Study PI: N.J.W.

DIAGEN: Study phenotyping: J.G., G.M. Genotyping or analysis: A.U.J., G.M. Study PI: P.E.S., S.R.B.

GOSH: Study phenotyping: P.K.M., N.L.P. Genotyping or analysis: E.I., P.K.M., N.L.P., T.Fall Study PI: E.I.

Tromsø: Study phenotyping: T.W. Genotyping or analysis: A.U.J., A.J.S., N. Study PI: I.N.

ADVANCE: Study phenotyping: T.L.A., C.I. Genotyping or analysis: T.L.A., E.L.S., T.Q. Study PI: T.L.A., T.Q., C.I.

ULSAM: Study phenotyping: E.I., J.Sundstrom Genotyping or analysis: E.I., N.E., J.Sundstrom, A.-C.S. Study PI: J.Sundstrom

PIVUS: Study phenotyping: L.Lind, J.Sundstrom Genotyping or analysis: L.Lind, N.E., J.Sundstrom, T.A. Study PI: L.Lind, J.Sundstrom

MRC NSHD: Study phenotyping: D.K. Genotyping or analysis: A.Wong, J.a.L., D.K., K.K.O. Study PI: D.K.

ASCOT: Study phenotyping: A.Stanton, N.P. Genotyping or analysis: T.J., M.J.C., P.B.M. Study PI: P.S., M.J.C.

THISEAS: Genotyping or analysis: L.S.R., S.Kanoni, E.M., G.Kolovou Study PI: G.Dedoussis, P.D.

PARC: Study phenotyping: R.M.K. Genotyping or analysis: K.D.T., E.Theusch, J.I.R., X.L., M.O.G., Y.D.I.C. Study PI: R.M.K.

AMC-PAS: Genotyping or analysis: G.K.H., P.D. Study PI: G.K.H.

CARDIOGENICS: Genotyping or analysis: S.Kanoni, A.H.G. Study PI: P.D., A.H.G., J.E., N.J.S., H.Schunkert

Secondary analyses

Allele-specific FAIRE: Design of secondary analysis: A.J.P.S. Computation of secondary analysis: A.J.P.S., F.D., P.H.

ASAP eQTL: Design of secondary analysis: A.F.C. Computation of secondary analysis: L.Folkersen, P.Eriksson

CARDIOGENICS eQTL: Computation of secondary analysis: L.Lataniotis

CM design: P.B.M., C.N.-C., T.J., B.F.V.

Comprehensive literature review: Design of secondary analysis: P.B.M. Computation of secondary analysis: K.W., P.B.M.

DEPICT: Design of secondary analysis: L.Franke, T.H.P., J.N.H. Computation of secondary analysis: T.H.P.

DHS and methylation analysis by tissue:Design of secondary analysis: C.J.W. Computation of secondary analysis: E.M.S.

DHS and methylation by cell-line: Design of secondary analysis: D.I.C. Computation of secondary analysis: D.I.C., F.Giulianini

FHS eSNP: Design of secondary analysis: R.Joehanes Computation of secondary analysis: R.Joehanes

ICBP SC: C.N.-C., M.J.C., P.B.M., A.C., K.M.R., P.-O'R., W.P., D.L., M.D.T., B.M.P., A.D.J., P.Elliott, C.M.v.D., D.I.C., A.V.S., M.Bochud, L.V.W., H.Snieder, G.B.E.

Kidney eQTL: Computation of secondary analysis: H.J.G., S.K.K.

MAGENTA: Design of secondary analysis: D.I.C. Computation of secondary analysis: D.I.C.

Miscellaneous: Computation of secondary analysis: H.Warren

MuTHER eQTL: Design of secondary analysis: P.D. Computation of secondary analysis: L.Lataniotis, T.-P.Y.

NESDA eQTL: Design of secondary analysis: R.Jansen Computation of secondary analysis: R.Jansen, A.V.

NTR eQTL: Design of secondary analysis: R.Jansen Computation of secondary analysis: R.Jansen, J.-J.H. Study PI: D.I.B.

eQTL, EGCUT:Design of secondary analysis: A.Metspalu Computation of secondary analysis: T.E., A.Metspalu

eQTL, Groningen:Design of secondary analysis: L.Franke Computation of secondary analysis: H.J.W., L.Franke

Public eSNP and methylation: Design of secondary analysis: A.D.J., J.D.E. Computation of secondary analysis: A.D.J., J.D.E.

PubMed search: Design of secondary analysis: G.B.E. Computation of secondary analysis: G.B.E., L.Lin

WGHS conditional: Design of secondary analysis: D.I.C. Computation of secondary analysis: D.I.C., F.Giulianini, L.M.R.

Lookup of Cardio-MetaboChip variants

HEXA: Genotyping or analysis: Y.J.K., Y.K.K., Y.-A.S. Study PI: J.-Y.L.

RACe: Study phenotyping: D.Saleheen, W.Zhao, A.R., A.R. Genotyping or analysis: W.Zhao, A.R., A.R. Study PI: D.Saleheen

HALST: Study phenotyping: C.A.H. Genotyping or analysis: J.I.R., Y.-D.C., C.A.H., R.-H.C., I.-S.C. Study PI: C.A.H.

CLHNS: Study phenotyping: N.R.L., L.S.A. Genotyping or analysis: Y.W., N.R.L., L.S.A. Study PI: K.L.M., L.S.A.

GxE/Spanish Town: Study phenotyping: B.O.T., C.A.M., R.W. Genotyping or analysis: C.D.P. Study PI: R.S.C., C.A.M., R.W., T.Forrester, J.N.H.

DRAGON: Study phenotyping: W.-J.L., W.H.-H.S., K.-W.L., I-Te Lee Genotyping or analysis: J.I.R., Y.-D.C., E.K., D.A., K.D.T., X.G. Study PI: W.H.-H.S.

SEY: Study phenotyping: P.B. Genotyping or analysis: M.Bochud, G.B.E., F.M. Study PI: P.B., M.Bochud, M.Burnier, F.P.

TUDR: Study phenotyping: W.H.-H.S., I-Te Lee, W.-J.L. Genotyping or analysis: J.I.R., Y.-D.C., E.K., K.D.T., X.G. Study PI: W.H.-H.S.

TANDEM: Study phenotyping: P.B., M.Bochud Genotyping or analysis: G.B.E., F.M. Study PI: P.B., M.Bochud, M.Burnier, F.P.

Imputed genotypes

FHS: Study phenotyping: D.L. Genotyping or analysis: D.L. Study PI: D.L.

ARIC: Study phenotyping: E.B. Genotyping or analysis: G.B.E., E.B., A.C.M., A.C., S.K.G. Study PI: E.B., A.C.

RS: Genotyping or analysis: G.C.V., A.G.U. Study PI: A.Hofman, A.G.U., O.H.F.D.

CoLaus: Study phenotyping: P.V. Genotyping or analysis: Z.K. Study PI: P.V.

NFBC1966: Study phenotyping: M.R.J. Genotyping or analysis: P.O.R. Study PI: M.R.J.

SHIP: Study phenotyping: R.Rettig Genotyping or analysis: A.T.

CHS: Study phenotyping: B.M.P. Genotyping or analysis: K.M.R. Study PI: B.M.P.

EPIC (GWAS): Study phenotyping: N.J.W., R.J.F.L., J.a.L. Genotyping or analysis: N.J.W., J.H.Z., J.a.L. Study PI: N.J.W., K.-T.K.

SU.VI.MAX: Study phenotyping: S.H. Genotyping or analysis: S.H., P.M. Study PI: P.M.

Amish: Genotyping or analysis: M.E.M. Study PI: A.Parsa

FENLAND (GWAS): Study phenotyping: N.J.W., J.a.L., R.J.F.L., K.K.O. Genotyping or analysis: N.J.W., J.a.L., R.J.F.L., K.K.O. Study PI: N.J.W.

DGI: Study phenotyping: C.N.C. Genotyping or analysis: C.N.C., G.Kosova Study PI: C.N.C.

ERF (EUROSPAN): Genotyping or analysis: N.A. Study PI: C.M.v.D.

MIGEN: Study phenotyping: S.Kathiresan, R.E. Genotyping or analysis: S.Kathiresan, R.E. Design of secondary analysis: S.Kathiresan, R.E.

MICROS: Study phenotyping: P.P.P. Genotyping or analysis: A.A.H. Study PI: A.A.H., P.P.P.

FUSION: Genotyping or analysis: A.U.J. Study PI: M.Boehnke, F.S.C., K.L.M., J.Saramies

TwinsUK: Genotyping or analysis: C.M. Study PI: T.D.S.

PROCARDIS: Genotyping or analysis: M.Farrall, A.G. Study PI: M.Farrall

BLSA: Study phenotyping: L.Ferrucci Genotyping or analysis: T.T. Study PI: L.Ferrucci

ORCADES: Study phenotyping: J.F.W. Study PI: J.F.W.

Croatia-Vis: Genotyping or analysis: V.V., C.H. Study PI: V.V., C.H.

NSPHS: Genotyping or analysis: S.Enroth Study PI: U.G.

InCHIANTI: Genotyping or analysis: T.T. Study PI: S.Bandinelli

AGES Reykjavik: Study phenotyping: V.G. Genotyping or analysis: A.V.S. Study PI: V.G.

Lookup

CARDIoGRAMplusC4D: Genotyping or analysis: P.D. Study PI: J.Danesh, H.Schunkert, T.L.A., J.E., S.Kathiresan, R.Roberts, N.J.S., P.D.

CHARGE cIMT: Genotyping or analysis: C.O'D., J.C.B.

CHARGE EYE: Genotyping or analysis: T.Y.W., X.S., R.A.J. Study PI: T.Y.W.

CHARGE-HF consortium: Study phenotyping: R.S.V., J.F.F. Genotyping or analysis: H.L., J.F.F. Study PI: R.S.V.

CKDGen: Genotyping or analysis: M.G., V.M.

COGENT: Study phenotyping: N.F., J.R. Genotyping or analysis: N.F., X.Z., B.J.K., B.O.T., J.R.

EchoGen consortium: Study phenotyping: R.S.V., J.F.F. Genotyping or analysis: H.L., J.F.F. Study PI: R.S.V.

KidneyGen Consortium: Study phenotyping: J.C.C., J.S.K., P.Elliott Genotyping or analysis: W.Zhang, J.C.C., J.S.K. Study PI: J.C.C., J.S.K.

MetaStroke: Genotyping or analysis: S.Bevan, H.S.M.

NeuroCHARGE: Genotyping or analysis: M.Fornage, M.A.I. Study PI: M.A.I.

PROMIS: Study phenotyping: D.Saleheen, W.Zhao, J.Danesh Genotyping or analysis: W.Zhao Study PI: D.Saleheen

SEED: Study phenotyping: T.Y.W., C.-Y.C. Genotyping or analysis: E.-S.T, C.-Y.C., C.-Y.C. Study PI: C.-Y.C., T.Y.W.

UK Biobank: BP group leaders: Mark Caulfield, P.Elliott Genotyping or analysis: M.R.B., H.Warren, Claudia Cabrera, Evangelos Evangelou, He Gao.

Footnotes

SUPPLEMENTARY NOTE

Supplementary Note is available in the online version of the paper.

URLs

http://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeUwDnase for enrichment analyses. Accessed 3/13/2013.

http://www.genome.gov/gwastudies for enrichment analyses. Accessed 3/13/2013.

http://genome.ucsc.edu/ENCODE/cellTypes.html for enrichment analyses. Accessed 3/13/2013.

COMPETING FINANCIAL INTERESTS

The authors declare competing financial interests (see corresponding section in the Supplementary Note).

REFERENCES

1.Johnson T, et al. Blood Pressure Loci Identified with a Gene-Centric Array. The American Journal of Human Genetics. 2011;89:1–13. doi: 10.1016/j.ajhg.2011.10.013. [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Newton-Cheh C, et al. Association of common variants in NPPA and NPPB with circulating natriuretic peptides and blood pressure. Nature genetics. 2009;41:348–53. doi: 10.1038/ng.328. [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Franceschini N, et al. Genome-wide association analysis of blood-pressure traits in African- ancestry individuals reveals common associated genes in African and non-African populations. Am J Hum Genet. 2013;93:545–54. doi: 10.1016/j.ajhg.2013.07.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Ganesh SK, et al. Effects of long-term averaging of quantitative blood pressure traits on the detection of genetic associations. Am J Hum Genet. 2014;95:49–65. doi: 10.1016/j.ajhg.2014.06.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Ehret GB, et al. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature. 2011;478:103–109. doi: 10.1038/nature10405. [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Wain LV, et al. Genome-wide association study identifies six new loci influencing pulse pressure and mean arterial pressure. Nat Genet. 2011;43:1005–11. doi: 10.1038/ng.922. [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Newton-Cheh C, et al. Genome-wide association study identifies eight loci associated with blood pressure. Nat Genet. 2009;41:666–76. doi: 10.1038/ng.361. [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Simino J, et al. Gene-age interactions in blood pressure regulation: a large-scale investigation with the CHARGE, Global BPgen, and ICBP Consortia. Am J Hum Genet. 2014;95:24–38. doi: 10.1016/j.ajhg.2014.05.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Tragante V, et al. Gene-centric meta-analysis in 87,736 individuals of European ancestry identifies multiple blood-pressure-related loci. Am J Hum Genet. 2014;94:349–60. doi: 10.1016/j.ajhg.2013.12.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Wang Y, et al. From the Cover: Whole-genome association study identifies STK39 as a hypertension susceptibility gene. Proc Natl Acad Sci U S A. 2009;106:226–31. doi: 10.1073/pnas.0808358106. [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Kato N, et al. Meta-analysis of genome-wide association studies identifies common variants associated with blood pressure variation in east Asians. Nature genetics. 2011;43:531–8. doi: 10.1038/ng.834. [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Padmanabhan S, et al. Genome-wide association study of blood pressure extremes identifies variant near UMOD associated with hypertension. PLoS genetics. 2010;6:e1001177. doi: 10.1371/journal.pgen.1001177. [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Miall WE, Oldham PD. The hereditary factor in arterial blood-pressure. Br Med J. 1963;1:75–80. doi: 10.1136/bmj.1.5323.75. [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Levy D, et al. Framingham Heart Study 100K Project: genome-wide associations for blood pressure and arterial stiffness. BMC Med Genet. 2007;8(Suppl 1):S3. doi: 10.1186/1471-2350-8-S1-S3. [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Teslovich TM, et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature. 2010;466:707–13. doi: 10.1038/nature09270. [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Voight BF, et al. The metabochip, a custom genotyping array for genetic studies of metabolic, cardiovascular, and anthropometric traits. PLoS Genet. 2012;8:e1002793. doi: 10.1371/journal.pgen.1002793. [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Tobin MD, Sheehan NA, Scurrah KJ, Burton PR. Adjusting for treatment effects in studies of quantitative traits: antihypertensive therapy and systolic blood pressure. Stat Med. 2005;24:2911–35. doi: 10.1002/sim.2165. [DOI] [PubMed] [Google Scholar]
18.Locke AE, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518:197–206. doi: 10.1038/nature14177. [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Shungin D, et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature. 2015;518:187–96. doi: 10.1038/nature14132. [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Sudlow C, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12:e1001779. doi: 10.1371/journal.pmed.1001779. [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Levy D, et al. Genome-wide association study of blood pressure and hypertension. Nat Genet. 2009;41:677–87. doi: 10.1038/ng.384. [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Yang J, et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat Genet. 2012;44:369–75. S1–3. doi: 10.1038/ng.2213. [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet. 2011;88:76–82. doi: 10.1016/j.ajhg.2010.11.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Kimber CH, et al. TCF7L2 in the Go-DARTS study: evidence for a gene dose effect on both diabetes susceptibility and control of glucose levels. Diabetologia. 2007;50:1186–91. doi: 10.1007/s00125-007-0661-9. [DOI] [PubMed] [Google Scholar]
25.Erdmann J, et al. Dysfunctional nitric oxide signalling increases risk of myocardial infarction. Nature. 2013;504:432–6. doi: 10.1038/nature12722. [DOI] [PubMed] [Google Scholar]
26.Hirata Y, et al. Mechanisms of adrenomedullin-induced vasodilation in the rat kidney. Hypertension. 1995;25:790–5. doi: 10.1161/01.hyp.25.4.790. [DOI] [PubMed] [Google Scholar]
27.Epigenomics Roadmap et al. Integrative analysis of 111 reference human epigenomes. Nature. 2015;518:317–30. doi: 10.1038/nature14248. [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Consortium EP. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489:57–74. doi: 10.1038/nature11247. [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Maurano MT, et al. Systematic localization of common disease-associated variation in regulatory DNA. Science. 2012;337:1190–5. doi: 10.1126/science.1222794. [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Welter D, et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 2014;42:D1001–6. doi: 10.1093/nar/gkt1229. [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Trynka G, et al. Chromatin marks identify critical cell types for fine mapping complex trait variants. Nat Genet. 2013;45:124–30. doi: 10.1038/ng.2504. [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Ernst J, et al. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature. 2011;473:43–9. doi: 10.1038/nature09906. [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Segre AV, et al. Common inherited variation in mitochondrial genes is not enriched for associations with type 2 diabetes or related glycemic traits. PLoS Genet. 2010;6 doi: 10.1371/journal.pgen.1001058. [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Pers TH, et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat Commun. 2015;6:5890. doi: 10.1038/ncomms6890. [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Giresi PG, Kim J, McDaniell RM, Iyer VR, Lieb JD. FAIRE (Formaldehyde-Assisted Isolation of Regulatory Elements) isolates active regulatory elements from human chromatin. Genome Res. 2007;17:877–85. doi: 10.1101/gr.5533506. [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Stergachis AB, et al. Conservation of trans-acting circuitry during mammalian regulatory evolution. Nature. 2014;515:365–70. doi: 10.1038/nature13972. [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Mancia G, et al. 2013 ESH/ESC guidelines for the management of arterial hypertension: the Task Force for the Management of Arterial Hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC). Eur Heart J. 2013;34:2159–219. doi: 10.1093/eurheartj/eht151. [DOI] [PubMed] [Google Scholar]
38.Lifton R, Somlo S, Giebisch G, Seldin D. Genetic Diseases of the Kidney. Academic Press; 2009. [Google Scholar]
39.Coffman TM, Crowley SD. Kidney in hypertension: guyton redux. Hypertension. 2008;51:811–6. doi: 10.1161/HYPERTENSIONAHA.105.063636. [DOI] [PubMed] [Google Scholar]
40.Kim CH, et al. Mutations in the dopamine beta-hydroxylase gene are associated with human norepinephrine deficiency. American journal of medical genetics. 2002;108:140–7. [PubMed] [Google Scholar]
41.Deinum J, et al. DBH gene variants that cause low plasma dopamine beta hydroxylase with or without a severe orthostatic syndrome. Journal of medical genetics. 2004;41:e38. doi: 10.1136/jmg.2003.009282. [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Ghofrani HA, et al. Riociguat for the treatment of pulmonary arterial hypertension. N Engl J Med. 2013;369:330–40. doi: 10.1056/NEJMoa1209655. [DOI] [PubMed] [Google Scholar]
44.Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet. 2011;88:76–82. doi: 10.1016/j.ajhg.2010.11.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Yang J, et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat Genet. 2012;44:369–75. S1–3. doi: 10.1038/ng.2213. [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Penninx BW, et al. The Netherlands Study of Depression and Anxiety (NESDA): rationale, objectives and methods. Int J Methods Psychiatr Res. 2008;17:121–40. doi: 10.1002/mpr.256. [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Boomsma DI, et al. Netherlands Twin Register: from twins to twin families. Twin Res Hum Genet. 2006;9:849–57. doi: 10.1375/183242706779462426. [DOI] [PubMed] [Google Scholar]
48.Visscher PM, Benyamin B, White I. The use of linear mixed models to estimate variance components from data on twin pairs by maximum likelihood. Twin Res. 2004;7:670–4. doi: 10.1375/1369052042663742. [DOI] [PubMed] [Google Scholar]
49.Romanoski CE, et al. Network for activation of human endothelial cells by oxidized phospholipids: a critical role of heme oxygenase 1. Circ Res. 2011;109:e27–41. doi: 10.1161/CIRCRESAHA.111.241869. [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Koopmann TT, et al. Genome-wide identification of expression quantitative trait loci (eQTLs) in human heart. PLoS One. 2014;9:e97380. doi: 10.1371/journal.pone.0097380. [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Fairfax BP, et al. Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression. Science. 2014;343:1246949. doi: 10.1126/science.1246949. [DOI] [PMC free article] [PubMed] [Google Scholar]
52.Ramasamy A, et al. Genetic variability in the regulation of gene expression in ten regions of the human brain. Nat Neurosci. 2014;17:1418–28. doi: 10.1038/nn.3801. [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Maurano MT, et al. Systematic localization of common disease-associated variation in regulatory DNA. Science. 2012;337:1190–5. doi: 10.1126/science.1222794. [DOI] [PMC free article] [PubMed] [Google Scholar]
54.Welter D, et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 2014;42:D1001–6. doi: 10.1093/nar/gkt1229. [DOI] [PMC free article] [PubMed] [Google Scholar]
55.Trynka G, et al. Chromatin marks identify critical cell types for fine mapping complex trait variants. Nat Genet. 2013;45:124–30. doi: 10.1038/ng.2504. [DOI] [PMC free article] [PubMed] [Google Scholar]
56.Segre AV, et al. Common inherited variation in mitochondrial genes is not enriched for associations with type 2 diabetes or related glycemic traits. PLoS Genet. 2010;6 doi: 10.1371/journal.pgen.1001058. [DOI] [PMC free article] [PubMed] [Google Scholar]
57.Pers TH, et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat Commun. 2015;6:5890. doi: 10.1038/ncomms6890. [DOI] [PMC free article] [PubMed] [Google Scholar]
58.Magi R, Morris AP. GWAMA: software for genome-wide association meta-analysis. BMC Bioinformatics. 2010;11:288. doi: 10.1186/1471-2105-11-288. [DOI] [PMC free article] [PubMed] [Google Scholar]
59.Johnson T. Efficient Calculation for Multi-SNP Genetic Risk Scores. ASHG 2012 Annual Meeting. poster presentation. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

NIHMS810971-supplement-1.pdf^{(11.9MB, pdf)}

NIHMS810971-supplement-3.xlsx^{(476KB, xlsx)}

[R1] 1.Johnson T, et al. Blood Pressure Loci Identified with a Gene-Centric Array. The American Journal of Human Genetics. 2011;89:1–13. doi: 10.1016/j.ajhg.2011.10.013. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R2] 2.Newton-Cheh C, et al. Association of common variants in NPPA and NPPB with circulating natriuretic peptides and blood pressure. Nature genetics. 2009;41:348–53. doi: 10.1038/ng.328. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R3] 3.Franceschini N, et al. Genome-wide association analysis of blood-pressure traits in African- ancestry individuals reveals common associated genes in African and non-African populations. Am J Hum Genet. 2013;93:545–54. doi: 10.1016/j.ajhg.2013.07.010. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R4] 4.Ganesh SK, et al. Effects of long-term averaging of quantitative blood pressure traits on the detection of genetic associations. Am J Hum Genet. 2014;95:49–65. doi: 10.1016/j.ajhg.2014.06.002. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R5] 5.Ehret GB, et al. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature. 2011;478:103–109. doi: 10.1038/nature10405. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R6] 6.Wain LV, et al. Genome-wide association study identifies six new loci influencing pulse pressure and mean arterial pressure. Nat Genet. 2011;43:1005–11. doi: 10.1038/ng.922. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R7] 7.Newton-Cheh C, et al. Genome-wide association study identifies eight loci associated with blood pressure. Nat Genet. 2009;41:666–76. doi: 10.1038/ng.361. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R8] 8.Simino J, et al. Gene-age interactions in blood pressure regulation: a large-scale investigation with the CHARGE, Global BPgen, and ICBP Consortia. Am J Hum Genet. 2014;95:24–38. doi: 10.1016/j.ajhg.2014.05.010. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R9] 9.Tragante V, et al. Gene-centric meta-analysis in 87,736 individuals of European ancestry identifies multiple blood-pressure-related loci. Am J Hum Genet. 2014;94:349–60. doi: 10.1016/j.ajhg.2013.12.016. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R10] 10.Wang Y, et al. From the Cover: Whole-genome association study identifies STK39 as a hypertension susceptibility gene. Proc Natl Acad Sci U S A. 2009;106:226–31. doi: 10.1073/pnas.0808358106. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R11] 11.Kato N, et al. Meta-analysis of genome-wide association studies identifies common variants associated with blood pressure variation in east Asians. Nature genetics. 2011;43:531–8. doi: 10.1038/ng.834. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R12] 12.Padmanabhan S, et al. Genome-wide association study of blood pressure extremes identifies variant near UMOD associated with hypertension. PLoS genetics. 2010;6:e1001177. doi: 10.1371/journal.pgen.1001177. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R13] 13.Miall WE, Oldham PD. The hereditary factor in arterial blood-pressure. Br Med J. 1963;1:75–80. doi: 10.1136/bmj.1.5323.75. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R14] 14.Levy D, et al. Framingham Heart Study 100K Project: genome-wide associations for blood pressure and arterial stiffness. BMC Med Genet. 2007;8(Suppl 1):S3. doi: 10.1186/1471-2350-8-S1-S3. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R15] 15.Teslovich TM, et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature. 2010;466:707–13. doi: 10.1038/nature09270. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R16] 16.Voight BF, et al. The metabochip, a custom genotyping array for genetic studies of metabolic, cardiovascular, and anthropometric traits. PLoS Genet. 2012;8:e1002793. doi: 10.1371/journal.pgen.1002793. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R17] 17.Tobin MD, Sheehan NA, Scurrah KJ, Burton PR. Adjusting for treatment effects in studies of quantitative traits: antihypertensive therapy and systolic blood pressure. Stat Med. 2005;24:2911–35. doi: 10.1002/sim.2165. [DOI] [PubMed] [Google Scholar]

[R18] 18.Locke AE, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518:197–206. doi: 10.1038/nature14177. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R19] 19.Shungin D, et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature. 2015;518:187–96. doi: 10.1038/nature14132. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R20] 20.Sudlow C, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12:e1001779. doi: 10.1371/journal.pmed.1001779. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R21] 21.Levy D, et al. Genome-wide association study of blood pressure and hypertension. Nat Genet. 2009;41:677–87. doi: 10.1038/ng.384. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R22] 22.Yang J, et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat Genet. 2012;44:369–75. S1–3. doi: 10.1038/ng.2213. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R23] 23.Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet. 2011;88:76–82. doi: 10.1016/j.ajhg.2010.11.011. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R24] 24.Kimber CH, et al. TCF7L2 in the Go-DARTS study: evidence for a gene dose effect on both diabetes susceptibility and control of glucose levels. Diabetologia. 2007;50:1186–91. doi: 10.1007/s00125-007-0661-9. [DOI] [PubMed] [Google Scholar]

[R25] 25.Erdmann J, et al. Dysfunctional nitric oxide signalling increases risk of myocardial infarction. Nature. 2013;504:432–6. doi: 10.1038/nature12722. [DOI] [PubMed] [Google Scholar]

[R26] 26.Hirata Y, et al. Mechanisms of adrenomedullin-induced vasodilation in the rat kidney. Hypertension. 1995;25:790–5. doi: 10.1161/01.hyp.25.4.790. [DOI] [PubMed] [Google Scholar]

[R27] 27.Epigenomics Roadmap et al. Integrative analysis of 111 reference human epigenomes. Nature. 2015;518:317–30. doi: 10.1038/nature14248. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R28] 28.Consortium EP. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489:57–74. doi: 10.1038/nature11247. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R29] 29.Maurano MT, et al. Systematic localization of common disease-associated variation in regulatory DNA. Science. 2012;337:1190–5. doi: 10.1126/science.1222794. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R30] 30.Welter D, et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 2014;42:D1001–6. doi: 10.1093/nar/gkt1229. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R31] 31.Trynka G, et al. Chromatin marks identify critical cell types for fine mapping complex trait variants. Nat Genet. 2013;45:124–30. doi: 10.1038/ng.2504. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R32] 32.Ernst J, et al. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature. 2011;473:43–9. doi: 10.1038/nature09906. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R33] 33.Segre AV, et al. Common inherited variation in mitochondrial genes is not enriched for associations with type 2 diabetes or related glycemic traits. PLoS Genet. 2010;6 doi: 10.1371/journal.pgen.1001058. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R34] 34.Pers TH, et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat Commun. 2015;6:5890. doi: 10.1038/ncomms6890. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R35] 35.Giresi PG, Kim J, McDaniell RM, Iyer VR, Lieb JD. FAIRE (Formaldehyde-Assisted Isolation of Regulatory Elements) isolates active regulatory elements from human chromatin. Genome Res. 2007;17:877–85. doi: 10.1101/gr.5533506. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R36] 36.Stergachis AB, et al. Conservation of trans-acting circuitry during mammalian regulatory evolution. Nature. 2014;515:365–70. doi: 10.1038/nature13972. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R37] 37.Mancia G, et al. 2013 ESH/ESC guidelines for the management of arterial hypertension: the Task Force for the Management of Arterial Hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC). Eur Heart J. 2013;34:2159–219. doi: 10.1093/eurheartj/eht151. [DOI] [PubMed] [Google Scholar]

[R38] 38.Lifton R, Somlo S, Giebisch G, Seldin D. Genetic Diseases of the Kidney. Academic Press; 2009. [Google Scholar]

[R39] 39.Coffman TM, Crowley SD. Kidney in hypertension: guyton redux. Hypertension. 2008;51:811–6. doi: 10.1161/HYPERTENSIONAHA.105.063636. [DOI] [PubMed] [Google Scholar]

[R40] 40.Kim CH, et al. Mutations in the dopamine beta-hydroxylase gene are associated with human norepinephrine deficiency. American journal of medical genetics. 2002;108:140–7. [PubMed] [Google Scholar]

[R41] 41.Deinum J, et al. DBH gene variants that cause low plasma dopamine beta hydroxylase with or without a severe orthostatic syndrome. Journal of medical genetics. 2004;41:e38. doi: 10.1136/jmg.2003.009282. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R42] 42.Ghofrani HA, et al. Riociguat for the treatment of pulmonary arterial hypertension. N Engl J Med. 2013;369:330–40. doi: 10.1056/NEJMoa1209655. [DOI] [PubMed] [Google Scholar]

[R43] 44.Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet. 2011;88:76–82. doi: 10.1016/j.ajhg.2010.11.011. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R44] 45.Yang J, et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat Genet. 2012;44:369–75. S1–3. doi: 10.1038/ng.2213. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R45] 46.Penninx BW, et al. The Netherlands Study of Depression and Anxiety (NESDA): rationale, objectives and methods. Int J Methods Psychiatr Res. 2008;17:121–40. doi: 10.1002/mpr.256. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R46] 47.Boomsma DI, et al. Netherlands Twin Register: from twins to twin families. Twin Res Hum Genet. 2006;9:849–57. doi: 10.1375/183242706779462426. [DOI] [PubMed] [Google Scholar]

[R47] 48.Visscher PM, Benyamin B, White I. The use of linear mixed models to estimate variance components from data on twin pairs by maximum likelihood. Twin Res. 2004;7:670–4. doi: 10.1375/1369052042663742. [DOI] [PubMed] [Google Scholar]

[R48] 49.Romanoski CE, et al. Network for activation of human endothelial cells by oxidized phospholipids: a critical role of heme oxygenase 1. Circ Res. 2011;109:e27–41. doi: 10.1161/CIRCRESAHA.111.241869. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R49] 50.Koopmann TT, et al. Genome-wide identification of expression quantitative trait loci (eQTLs) in human heart. PLoS One. 2014;9:e97380. doi: 10.1371/journal.pone.0097380. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R50] 51.Fairfax BP, et al. Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression. Science. 2014;343:1246949. doi: 10.1126/science.1246949. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R51] 52.Ramasamy A, et al. Genetic variability in the regulation of gene expression in ten regions of the human brain. Nat Neurosci. 2014;17:1418–28. doi: 10.1038/nn.3801. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R52] 53.Maurano MT, et al. Systematic localization of common disease-associated variation in regulatory DNA. Science. 2012;337:1190–5. doi: 10.1126/science.1222794. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R53] 54.Welter D, et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 2014;42:D1001–6. doi: 10.1093/nar/gkt1229. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R54] 55.Trynka G, et al. Chromatin marks identify critical cell types for fine mapping complex trait variants. Nat Genet. 2013;45:124–30. doi: 10.1038/ng.2504. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R55] 56.Segre AV, et al. Common inherited variation in mitochondrial genes is not enriched for associations with type 2 diabetes or related glycemic traits. PLoS Genet. 2010;6 doi: 10.1371/journal.pgen.1001058. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R56] 57.Pers TH, et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat Commun. 2015;6:5890. doi: 10.1038/ncomms6890. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R57] 58.Magi R, Morris AP. GWAMA: software for genome-wide association meta-analysis. BMC Bioinformatics. 2010;11:288. doi: 10.1186/1471-2105-11-288. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R58] 59.Johnson T. Efficient Calculation for Multi-SNP Genetic Risk Scores. ASHG 2012 Annual Meeting. poster presentation. [Google Scholar]

PERMALINK

The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals

Georg B Ehret

Teresa Ferreira

Daniel I Chasman

Anne U Jackson

Ellen M Schmidt

Toby Johnson

Gudmar Thorleifsson

Jian'an Luan

Lousie A Donnelly

Stavroula Kanoni

Ann-Kristin Petersen

Vasyl Pihur

Rona J Strawbridge

Dmitry Shungin

Maria F Hughes

Osorio Meirelles

Marika Kaakinen

Nabila Bouatia-Naji

Kati Kristiansson

Sonia Shah

Marcus E Kleber

Xiuqing Guo

Leo-Pekka Lyytikäinen

Cristiano Fava

Niclas Eriksson

Ilja M Nolte

Patrik K Magnusson

Elias L Salfati

Loukianos S Rallidis

Elizabeth Theusch

Andrew JP Smith

Lasse Folkersen

Kate Witkowska

Tune H Pers

Roby Joehanes

Stuart K Kim

Lazaros Lataniotis

Rick Jansen

Andrew D Johnson

Helen Warren

Young Jin Kim

Wei Zhao

Ying Wu

Bamidele O Tayo

Murielle Bochud

Devin Absher

Linda S Adair

Najaf Amin

Dan E Arking

Tomas Axelsson

Damiano Baldassarre

Beverley Balkau

Stefania Bandinelli

Michael R Barnes

Inês Barroso

Stephen Bevan

Joshua C Bis

Gyda Bjornsdottir

Michael Boehnke

Eric Boerwinkle

Lori L Bonnycastle

Dorret I Boomsma

Stefan R Bornstein

Morris J Brown

Michel Burnier

Claudia P Cabrera

John C Chambers

I-Shou Chang

Ching-Yu Cheng

Peter S Chines

Ren-Hua Chung

Francis S Collins

John M Connell

Angela Döring

Jean Dallongeville

John Danesh

Ulf de Faire

Graciela Delgado