Association analyses of 249,796 individuals reveal eighteen new loci
associated with body mass index

doi:10.1038/ng.686

Nat Genet. Author manuscript; available in PMC 2011 May 1.

Published in final edited form as:

Nat Genet. 2010 November; 42(11): 937–948.

Published online 2010 October 10. doi: 10.1038/ng.686

PMCID: PMC3014648

NIHMSID: NIHMS237282

Association analyses of 249,796 individuals reveal eighteen new loci associated with body mass index

Elizabeth K. Speliotes,^1,²^* Cristen J. Willer,³^* Sonja I. Berndt,⁴^* Keri L. Monda,⁵^* Gudmar Thorleifsson,⁶^* Anne U. Jackson,³ Hana Lango Allen,⁷ Cecilia M. Lindgren,^8,⁹ Jian’an Luan,¹⁰ Reedik Mägi,⁸ Joshua C. Randall,⁸ Sailaja Vedantam,^1,¹¹ Thomas W. Winkler,¹² Lu Qi,^13,¹⁴ Tsegaselassie Workalemahu,¹³ Iris M. Heid,^12,¹⁵ Valgerdur Steinthorsdottir,⁶ Heather M. Stringham,³ Michael N. Weedon,⁷ Eleanor Wheeler,¹⁶ Andrew R. Wood,⁷ Teresa Ferreira,⁸ Robert J. Weyant,³ Ayellet V. Segré,^17,^18,¹⁹ Karol Estrada,^20,^21,²² Liming Liang,^23,²⁴ James Nemesh,¹⁸ Ju-Hyun Park,⁴ Stefan Gustafsson,²⁵ Tuomas O. Kilpeläinen,¹⁰ Jian Yang,²⁶ Nabila Bouatia-Naji,^27,²⁸ Tõnu Esko,^29,^30,³¹ Mary F. Feitosa,³² Zoltán Kutalik,^33,³⁴ Massimo Mangino,³⁵ Soumya Raychaudhuri,^18,³⁶ Andre Scherag,³⁷ Albert Vernon Smith,^38,³⁹ Ryan Welch,³ Jing Hua Zhao,¹⁰ Katja K. Aben,⁴⁰ Devin M. Absher,⁴¹ Najaf Amin,²⁰ Anna L. Dixon,⁴² Eva Fisher,⁴³ Nicole L. Glazer,^44,⁴⁵ Michael E. Goddard,^46,⁴⁷ Nancy L. Heard-Costa,⁴⁸ Volker Hoesel,⁴⁹ Jouke-Jan Hottenga,⁵⁰ Åsa Johansson,^51,⁵² Toby Johnson,^33,^34,^53,⁵⁴ Shamika Ketkar,³² Claudia Lamina,^15,⁵⁵ Shengxu Li,¹⁰ Miriam F. Moffatt,⁵⁶ Richard H. Myers,⁵⁷ Narisu Narisu,⁵⁸ John R.B. Perry,⁷ Marjolein J. Peters,^21,²² Michael Preuss,⁵⁹ Samuli Ripatti,^60,⁶¹ Fernando Rivadeneira,^20,^21,²² Camilla Sandholt,⁶² Laura J. Scott,³ Nicholas J. Timpson,⁶³ Jonathan P. Tyrer,⁶⁴ Sophie van Wingerden,²⁰ Richard M. Watanabe,^65,⁶⁶ Charles C. White,⁶⁷ Fredrik Wiklund,²⁵ Christina Barlassina,⁶⁸ Daniel I. Chasman,^69,⁷⁰ Matthew N. Cooper,⁷¹ John-Olov Jansson,⁷² Robert W. Lawrence,⁷¹ Niina Pellikka,^60,⁶¹ Inga Prokopenko,^8,⁹ Jianxin Shi,⁴ Elisabeth Thiering,¹⁵ Helene Alavere,²⁹ Maria T. S. Alibrandi,⁷³ Peter Almgren,⁷⁴ Alice M. Arnold,^75,⁷⁶ Thor Aspelund,^38,³⁹ Larry D. Atwood,⁴⁸ Beverley Balkau,^77,⁷⁸ Anthony J. Balmforth,⁷⁹ Amanda J. Bennett,⁹ Yoav Ben-Shlomo,⁸⁰ Richard N. Bergman,⁶⁶ Sven Bergmann,^33,³⁴ Heike Biebermann,⁸¹ Alexandra I.F. Blakemore,⁸² Tanja Boes,³⁷ Lori L. Bonnycastle,⁵⁸ Stefan R. Bornstein,⁸³ Morris J. Brown,⁸⁴ Thomas A. Buchanan,^66,⁸⁵ Fabio Busonero,⁸⁶ Harry Campbell,⁸⁷ Francesco P. Cappuccio,⁸⁸ Christine Cavalcanti-Proença,^27,²⁸ Yii-Der Ida Chen,⁸⁹ Chih-Mei Chen,¹⁵ Peter S. Chines,⁵⁸ Robert Clarke,⁹⁰ Lachlan Coin,⁹¹ John Connell,⁹² Ian N.M. Day,⁶³ Martin den Heijer,^93,⁹⁴ Jubao Duan,⁹⁵ Shah Ebrahim,^96,⁹⁷ Paul Elliott,^91,⁹⁸ Roberto Elosua,⁹⁹ Gudny Eiriksdottir,³⁸ Michael R. Erdos,⁵⁸ Johan G. Eriksson,^100,^101,^102,^103,¹⁰⁴ Maurizio F. Facheris,^105,¹⁰⁶ Stephan B. Felix,¹⁰⁷ Pamela Fischer-Posovszky,¹⁰⁸ Aaron R. Folsom,¹⁰⁹ Nele Friedrich,¹¹⁰ Nelson B. Freimer,¹¹¹ Mao Fu,¹¹² Stefan Gaget,^27,²⁸ Pablo V. Gejman,⁹⁵ Eco J.C. Geus,⁵⁰ Christian Gieger,¹⁵ Anette P. Gjesing,⁶² Anuj Goel,^8,¹¹³ Philippe Goyette,¹¹⁴ Harald Grallert,¹⁵ Jürgen Gräßler,¹¹⁵ Danielle M. Greenawalt,¹¹⁶ Christopher J. Groves,⁹ Vilmundur Gudnason,^38,³⁹ Candace Guiducci,¹ Anna-Liisa Hartikainen,¹¹⁷ Neelam Hassanali,⁹ Alistair S. Hall,⁷⁹ Aki S. Havulinna,¹¹⁸ Caroline Hayward,¹¹⁹ Andrew C. Heath,¹²⁰ Christian Hengstenberg,^121,¹²² Andrew A. Hicks,¹⁰⁵ Anke Hinney,¹²³ Albert Hofman,^20,²² Georg Homuth,¹²⁴ Jennie Hui,^71,^125,¹²⁶ Wilmar Igl,⁵¹ Carlos Iribarren,^127,¹²⁸ Bo Isomaa,^103,¹²⁹ Kevin B. Jacobs,¹³⁰ Ivonne Jarick,¹³¹ Elizabeth Jewell,³ Ulrich John,¹³² Torben Jørgensen,^133,¹³⁴ Pekka Jousilahti,¹¹⁸ Antti Jula,¹³⁵ Marika Kaakinen,^136,¹³⁷ Eero Kajantie,^101,¹³⁸ Lee M. Kaplan,^2,^70,¹³⁹ Sekar Kathiresan,^17,^18,^140,^141,¹⁴² Johannes Kettunen,^60,⁶¹ Leena Kinnunen,¹⁴³ Joshua W. Knowles,¹⁴⁴ Ivana Kolcic,¹⁴⁵ Inke R. König,⁵⁹ Seppo Koskinen,¹¹⁸ Peter Kovacs,¹⁴⁶ Johanna Kuusisto,¹⁴⁷ Peter Kraft,^23,²⁴ Kirsti Kvaløy,¹⁴⁸ Jaana Laitinen,¹⁴⁹ Olivier Lantieri,¹⁵⁰ Chiara Lanzani,⁷³ Lenore J. Launer,¹⁵¹ Cecile Lecoeur,^27,²⁸ Terho Lehtimäki,¹⁵² Guillaume Lettre,^114,¹⁵³ Jianjun Liu,¹⁵⁴ Marja-Liisa Lokki,¹⁵⁵ Mattias Lorentzon,¹⁵⁶ Robert N. Luben,¹⁵⁷ Barbara Ludwig,⁸³ MAGIC,¹⁵⁸ Paolo Manunta,⁷³ Diana Marek,^33,³⁴ Michel Marre,^159,¹⁶⁰ Nicholas G. Martin,¹⁶¹ Wendy L. McArdle,¹⁶² Anne McCarthy,¹⁶³ Barbara McKnight,⁷⁵ Thomas Meitinger,^164,¹⁶⁵ Olle Melander,¹⁶⁶ David Meyre,^27,²⁸ Kristian Midthjell,¹⁴⁸ Grant W. Montgomery,¹⁶⁷ Mario A. Morken,⁵⁸ Andrew P. Morris,⁸ Rosanda Mulic,¹⁶⁸ Julius S. Ngwa,⁶⁷ Mari Nelis,^29,^30,³¹ Matt J. Neville,⁹ Dale R. Nyholt,¹⁶⁹ Christopher J. O’Donnell,^141,¹⁷⁰ Stephen O’Rahilly,¹⁷¹ Ken K. Ong,¹⁰ Ben Oostra,¹⁷² Guillaume Paré,¹⁷³ Alex N. Parker,¹⁷⁴ Markus Perola,^60,⁶¹ Irene Pichler,¹⁰⁵ Kirsi H. Pietiläinen,^175,¹⁷⁶ Carl G.P. Platou,^148,¹⁷⁷ Ozren Polasek,^145,¹⁷⁸ Anneli Pouta,^117,¹⁷⁹ Suzanne Rafelt,¹⁸⁰ Olli Raitakari,^181,¹⁸² Nigel W. Rayner,^8,⁹ Martin Ridderstråle,¹⁶⁶ Winfried Rief,¹⁸³ Aimo Ruokonen,¹⁸⁴ Neil R. Robertson,^8,⁹ Peter Rzehak,^15,¹⁸⁵ Veikko Salomaa,¹¹⁸ Alan R. Sanders,⁹⁵ Manjinder S. Sandhu,^10,^16,¹⁵⁷ Serena Sanna,⁸⁶ Jouko Saramies,¹⁸⁶ Markku J. Savolainen,¹⁸⁷ Susann Scherag,¹²³ Sabine Schipf,^110,¹⁸⁸ Stefan Schreiber,¹⁸⁹ Heribert Schunkert,¹⁹⁰ Kaisa Silander,^60,⁶¹ Juha Sinisalo,¹⁹¹ David S. Siscovick,^45,¹⁹² Jan H. Smit,¹⁹³ Nicole Soranzo,^16,³⁵ Ulla Sovio,⁹¹ Jonathan Stephens,^194,¹⁹⁵ Ida Surakka,^60,⁶¹ Amy J. Swift,⁵⁸ Mari-Liis Tammesoo,²⁹ Jean-Claude Tardif,^114,¹⁵³ Maris Teder-Laving,^30,³¹ Tanya M. Teslovich,³ John R. Thompson,^196,¹⁹⁷ Brian Thomson,¹ Anke Tönjes,^198,¹⁹⁹ Tiinamaija Tuomi,^103,^200,²⁰¹ Joyce B.J. van Meurs,^20,^21,²² Gert-Jan van Ommen,^202,²⁰³ Vincent Vatin,^27,²⁸ Jorma Viikari,²⁰⁴ Sophie Visvikis-Siest,²⁰⁵ Veronique Vitart,¹¹⁹ Carla I. G. Vogel,¹²³ Benjamin F. Voight,^17,^18,¹⁹ Lindsay L. Waite,⁴¹ Henri Wallaschofski,¹¹⁰ G. Bragi Walters,⁶ Elisabeth Widen,⁶⁰ Susanna Wiegand,⁸¹ Sarah H. Wild,⁸⁷ Gonneke Willemsen,⁵⁰ Daniel R. Witte,²⁰⁶ Jacqueline C. Witteman,^20,²² Jianfeng Xu,²⁰⁷ Qunyuan Zhang,³² Lina Zgaga,¹⁴⁵ Andreas Ziegler,⁵⁹ Paavo Zitting,²⁰⁸ John P. Beilby,^125,^126,²⁰⁹ I. Sadaf Farooqi,¹⁷¹ Johannes Hebebrand,¹²³ Heikki V. Huikuri,^210,²¹⁰ Alan L. James,^126,²¹¹ Mika Kähönen,²¹² Douglas F. Levinson,²¹³ Fabio Macciardi,^68,²¹⁴ Markku S. Nieminen,^191,¹⁹¹ Claes Ohlsson,¹⁵⁶ Lyle J. Palmer,^71,¹²⁶ Paul M. Ridker,^69,⁷⁰ Michael Stumvoll,^198,²¹⁵ Jacques S. Beckmann,^33,²¹⁶ Heiner Boeing,⁴³ Eric Boerwinkle,²¹⁷ Dorret I. Boomsma,⁵⁰ Mark J. Caulfield,⁵⁴ Stephen J. Chanock,⁴ Francis S. Collins,⁵⁸ L. Adrienne Cupples,⁶⁷ George Davey Smith,⁶³ Jeanette Erdmann,¹⁹⁰ Philippe Froguel,^27,^28,⁸² Henrik Grönberg,²⁵ Ulf Gyllensten,⁵¹ Per Hall,²⁵ Torben Hansen,^62,²¹⁸ Tamara B. Harris,¹⁵¹ Andrew T. Hattersley,⁷ Richard B. Hayes,²¹⁹ Joachim Heinrich,¹⁵ Frank B. Hu,^13,^14,²³ Kristian Hveem,¹⁴⁸ Thomas Illig,¹⁵ Marjo-Riitta Jarvelin,^91,^136,^137,¹⁷⁹ Jaakko Kaprio,^60,^175,²²⁰ Fredrik Karpe,^9,²²¹ Kay-Tee Khaw,¹⁵⁷ Lambertus A. Kiemeney,^40,^93,²²² Heiko Krude,⁸¹ Markku Laakso,¹⁴⁷ Debbie A. Lawlor,⁶³ Andres Metspalu,^29,^30,³¹ Patricia B. Munroe,⁵⁴ Willem H. Ouwehand,^16,^194,¹⁹⁵ Oluf Pedersen,^62,^223,²²⁴ Brenda W. Penninx,^193,^225,²²⁶ Annette Peters,¹⁵ Peter P. Pramstaller,^105,^106,²²⁷ Thomas Quertermous,¹⁴⁴ Thomas Reinehr,²²⁸ Aila Rissanen,¹⁷⁶ Igor Rudan,^87,¹⁶⁸ Nilesh J. Samani,^180,¹⁹⁶ Peter E.H. Schwarz,²²⁹ Alan R. Shuldiner,^112,²³⁰ Timothy D. Spector,³⁵ Jaakko Tuomilehto,^143,^231,²³² Manuela Uda,⁸⁶ André Uitterlinden,^20,^21,²² Timo T. Valle,¹⁴³ Martin Wabitsch,¹⁰⁸ Gérard Waeber,²³³ Nicholas J. Wareham,¹⁰ Hugh Watkins,^8,¹¹³ James F. Wilson,⁸⁷ Alan F. Wright,¹¹⁹ M. Carola Zillikens,^21,²² Nilanjan Chatterjee,⁴ Steven A. McCarroll,^17,^18,¹⁹ Shaun Purcell,^17,^234,²³⁵ Eric E. Schadt,^236,²³⁷ Peter M. Visscher,²⁶ Themistocles L. Assimes,¹⁴⁴ Ingrid B. Borecki,^32,²³⁸ Panos Deloukas,¹⁶ Caroline S. Fox,²³⁹ Leif C. Groop,⁷⁴ Talin Haritunians,⁸⁹ David J. Hunter,^13,^14,²³ Robert C. Kaplan,²⁴⁰ Karen L. Mohlke,²⁴¹ Jeffrey R. O’Connell,¹¹² Leena Peltonen,^16,^60,^61,^234,²⁴² David Schlessinger,²⁴³ David P. Strachan,²⁴⁴ Cornelia M. van Duijn,^20,²² H.-Erich Wichmann,^15,^185,²⁴⁵ Timothy M. Frayling,⁷ Unnur Thorsteinsdottir,^6,²⁴⁶ Gonçalo R. Abecasis,³ Inês Barroso,^16,²⁴⁷ Michael Boehnke,³^* Kari Stefansson,^6,²⁴⁶^* Kari E. North,^5,²⁴⁸^* Mark I. McCarthy,^8,^9,²²¹^* Joel N. Hirschhorn,^1,^11,²⁴⁹^* Erik Ingelsson,²⁵^* and Ruth J.F. Loos¹⁰^*, on behalf of Procardis Consortium

¹ Metabolism Initiative and Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts 02142, USA

² Division of Gastroenterology, Massachusetts General Hospital, Boston, Massachusetts 02114, USA

³ Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA

⁴ Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland 20892, USA

⁵ Department of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514, USA

⁶ deCODE Genetics, 101 Reykjavik, Iceland

⁷ Genetics of Complex Traits, Peninsula College of Medicine and Dentistry, University of Exeter, Exeter, EX1 2LU, UK

⁸ Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK

⁹ Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, OX3 7LJ, UK

¹⁰ MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, CB2 0QQ, UK

¹¹ Divisions of Genetics and Endocrinology and Program in Genomics, Children’s Hospital, Boston, Massachusetts 02115, USA

¹² Regensburg University Medical Center, Department of Epidemiology and Preventive Medicine, 93053 Regensburg, Germany

¹³ Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts 02115, USA

¹⁴ Channing Laboratory, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA

¹⁵ Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany

¹⁶ Wellcome Trust Sanger Institute, Hinxton, Cambridge, CB10 1SA, UK

¹⁷ Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts 02114, USA

¹⁸ Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA

¹⁹ Department of Molecular Biology, Massachusetts General Hospital, Boston, Massachusetts 02114, USA

²⁰ Department of Epidemiology, Erasmus MC, Rotterdam, 3015GE, The Netherlands

²¹ Department of Internal Medicine, Erasmus MC, Rotterdam, 3015GE, The Netherlands

²² Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA)

²³ Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts 02115, USA

²⁴ Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA

²⁵ Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77 Stockholm, Sweden

²⁶ Queensland Statistical Genetics Laboratory, Queensland Institute of Medical Research, Queensland 4006, Australia

²⁷ CNRS UMR8199-IBL-Institut Pasteur de Lille, F-59019 Lille, France

²⁸ University Lille Nord de France, 59000 Lille, France

²⁹ Estonian Genome Center, University of Tartu, Tartu 50410, Estonia

³⁰ Estonian Biocenter, Tartu 51010, Estonia

³¹ Institute of Molecular and Cell Biology, University of Tartu, Tartu 51010, Estonia

³² Department of Genetics, Washington University School of Medicine, St Louis, Missouri 63110, USA

³³ Department of Medical Genetics, University of Lausanne, 1005 Lausanne, Switzerland

³⁴ Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland

³⁵ Department of Twin Research and Genetic Epidemiology, King’s College London, London, SE1 7EH, UK

³⁶ Division of Rheumatology, Immunology and Allergy, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115 USA

³⁷ Institute for Medical Informatics, Biometry and Epidemiology, University of Duisburg-Essen, 45122 Essen, Germany

³⁸ Icelandic Heart Association, Kopavogur, Iceland

³⁹ University of Iceland, Reykjavik, Iceland

⁴⁰ Comprehensive Cancer Center East, 6501 BG Nijmegen, The Netherlands

⁴¹ Hudson Alpha Institute for Biotechnology, Huntsville, Alabama 35806, USA

⁴² Department of Pharmacy and Pharmacology, University of Bath, Bath, BA1 1RL, UK

⁴³ Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, 14558 Nuthetal, Germany

⁴⁴ Department of Medicine, University of Washington, Seattle, Washington 98101, USA

⁴⁵ Cardiovascular Health Research Unit, University of Washington, Seattle, Washington 98101, USA

⁴⁶ University of Melbourne, Parkville 3010, Australia

⁴⁷ Department of Primary Industries, Melbourne, Victoria 3001, Australia

⁴⁸ Department of Neurology, Boston University School of Medicine, Boston, Massachusetts 02118, USA

⁴⁹ Technical University Munich, Chair of Biomathematics, Boltzmannstrasse 3, 85748 Garching

⁵⁰ Department of Biological Psychology, VU University Amsterdam, 1081 BT Amsterdam, The Netherlands

⁵¹ Department of Genetics and Pathology, Rudbeck Laboratory, University of Uppsala, SE-75185 Uppsala, Sweden

⁵² Department of Cancer Research and Molecular Medicine, Faculty of Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, N-7489, Norway

⁵³ Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary, University of London, London, UK

⁵⁴ Clinical Pharmacology and Barts and The London Genome Centre, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK

⁵⁵ Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, 6020 Innsbruck, Austria

⁵⁶ National Heart and Lung Institute, Imperial College London, London SW3 6LY, UK

⁵⁷ Department of Neurology, Boston University School of Medicine, Boston, Massachusetts 02118, USA

⁵⁸ National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland 20892, USA

⁵⁹ Institut fur Medizinische Biometrie und Statistik, Universitat zu Lubeck, Universitatsklinikum Schleswig-Holstein, Campus Lubeck, 23562 Lubeck, Germany

⁶⁰ Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00014, Helsinki, Finland

⁶¹ National Institute for Health and Welfare, Department of Chronic Disease Prevention, Unit of Public Health Genomics, 00014, Helsinki, Finland

⁶² Hagedorn Research Institute, 2820 Gentofte, Denmark

⁶³ MRC Centre for Causal Analyses in Translational Epidemiology, Department of Social Medicine, Oakfield House, Bristol, BS8 2BN, UK

⁶⁴ Department of Oncology, University of Cambridge, Cambridge, CB1 8RN, UK

⁶⁵ Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California 90089, USA

⁶⁶ Department of Physiology and Biophysics, Keck School of Medicine, University of Southern California, Los Angeles, California 90033, USA

⁶⁷ Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts 02118, USA

⁶⁸ University of Milan, Department of Medicine, Surgery and Dentistry, 20139 Milano, Italy

⁶⁹ Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, Massachusetts 02215, USA

⁷⁰ Harvard Medical School, Boston, Massachusetts 02115, USA

⁷¹ Centre for Genetic Epidemiology and Biostatistics, University of Western Australia, Crawley, Western Australia 6009, Australia

⁷² Department of Physiology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, 405 30 Gothenburg, Sweden

⁷³ University Vita-Salute San Raffaele, Division of Nephrology and Dialysis, 20132 Milan, Italy

⁷⁴ Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, 20502 Malmö, Sweden

⁷⁵ Departments of Biostatistics, University of Washington, Seattle, Washington 98195, USA

⁷⁶ Collaborative Health Studies Coordinating Center, Seattle, Washington 98115, USA

⁷⁷ INSERM CESP Centre for Research in Epidemiology and Public Health U1018, Epidemiology of diabetes, obesity and chronic kidney disease over the lifecourse, 94807 Villejuif, France

⁷⁸ University Paris Sud 11, UMRS 1018, 94807 Villejuif, France

⁷⁹ Multidisciplinary Cardiovascular Research Centre (MCRC), Leeds Institute of Genetics, Health and Therapeutics (LIGHT), University of Leeds, Leeds LS2 9JT, UK

⁸⁰ Department of Social Medicine, University of Bristol, Bristol, BS8 2PS, UK

⁸¹ Institute of Experimental Paediatric Endocrinology, Charite Universitatsmedizin Berlin, 13353 Berlin, Germany

⁸² Department of Genomics of Common Disease, School of Public Health, Imperial College London, W12 0NN, London, UK

⁸³ Department of Medicine III, University of Dresden, 01307 Dresden, Germany

⁸⁴ Clinical Pharmacology Unit, University of Cambridge, Addenbrooke’s Hospital, Hills Road, Cambridge CB2 2QQ, UK

⁸⁵ Division of Endocrinology, Keck School of Medicine, University of Southern California, Los Angeles, California 90033, USA

⁸⁶ Istituto di Neurogenetica e Neurofarmacologia del CNR, Monserrato, 09042, Cagliari, Italy

⁸⁷ Centre for Population Health Sciences, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, Scotland

⁸⁸ University of Warwick, Warwick Medical School, Coventry, CV2 2DX, UK

⁸⁹ Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, USA

⁹⁰ Clinical Trial Service Unit, Richard Doll Building, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK

⁹¹ Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, W2 1PG, UK

⁹² University of Dundee, Ninewells Hospital & Medical School, Dundee, DD1 9SY, UK

⁹³ Department of Epidemiology, Biostatistics and HTA, Radboud University Nijmegen Medical Centre, 6500 HB Nijmegen, The Netherlands

⁹⁴ Department of Endocrinology, Radboud University Nijmegen Medical Centre, 6500 HB Nijmegen, The Netherlands

⁹⁵ Northshore University Healthsystem, Evanston, Ilinois 60201, USA

⁹⁶ The London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK

⁹⁷ South Asia Network for Chronic Disease

⁹⁸ MRC-HPA Centre for Environment and Health, London W2 1PG, UK

⁹⁹ Cardiovascular Epidemiology and Genetics, Institut Municipal D’investigacio Medica and CIBER Epidemiologia y Salud Publica, Barcelona, Spain

¹⁰⁰ Department of General Practice and Primary health Care, University of Helsinki, Helsinki, Finland

¹⁰¹ National Institute for Health and Welfare, 00271 Helsinki, Finland

¹⁰² Helsinki University Central Hospital, Unit of General Practice, 00280 Helsinki, Finland

¹⁰³ Folkhalsan Research Centre, 00250 Helsinki, Finland

¹⁰⁴ Vasa Central Hospital, 65130 Vasa, Finland

¹⁰⁵ Institute of Genetic Medicine, European Academy Bozen/Bolzano (EURAC), Bolzano/Bozen, 39100, Italy. Affiliated Institute of the University of Lubeck, Lubeck, Germany

¹⁰⁶ Department of Neurology, General Central Hospital, Bolzano, Italy

¹⁰⁷ Department of Internal Medicine B, Ernst-Moritz-Arndt University, 17475 Greifswald, Germany

¹⁰⁸ Pediatric Endocrinology, Diabetes and Obesity Unit, Department of Pediatrics and Adolescent Medicine, 89075 Ulm, Germany

¹⁰⁹ Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis Minnesota 55454, USA

¹¹⁰ Institut fur Klinische Chemie und Laboratoriumsmedizin, Universitat Greifswald, 17475 Greifswald, Germany

¹¹¹ Center for Neurobehavioral Genetics, University of California, Los Angeles, California 90095, USA

¹¹² Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland 21201, USA

¹¹³ Department of Cardiovascular Medicine, University of Oxford, Level 6 West Wing, John Radcliffe Hospital, Headley Way, Headington, Oxford, OX3 9DU

¹¹⁴ Montreal Heart Institute, Montreal, Quebec, H1T 1C8, Canada

¹¹⁵ Department of Medicine III, Pathobiochemistry, University of Dresden, 01307 Dresden, Germany

¹¹⁶ Merck Research Laboratories, Merck & Co., Inc., Boston, Massachusetts 02115, USA

¹¹⁷ Department of Clinical Sciences/Obstetrics and Gynecology, University of Oulu, 90014 Oulu, Finland

¹¹⁸ National Institute for Health and Welfare, Department of Chronic Disease Prevention, Chronic Disease Epidemiology and Prevention Unit, 00014, Helsinki, Finland

¹¹⁹ MRC Human Genetics Unit, Institute for Genetics and Molecular Medicine, Western General Hospital, Edinburgh, EH4 2XU, Scotland, UK

¹²⁰ Department of Psychiatry and Midwest Alcoholism Research Center, Washington University School of Medicine, St Louis, Missouri 63108, USA

¹²¹ Klinik und Poliklinik fur Innere Medizin II, Universitat Regensburg, 93053 Regensburg, Germany

¹²² Regensburg University Medical Center, Innere Medizin II, 93053 Regensburg, Germany

¹²³ Department of Child and Adolescent Psychiatry, University of Duisburg-Essen, 45147 Essen, Germany

¹²⁴ Interfaculty Institute for Genetics and Functional Genomics, Ernst-Moritz-Arndt-University Greifswald, 17487 Greifswald, Germany

¹²⁵ PathWest Laboratory of Western Australia, Department of Molecular Genetics, J Block, QEII Medical Centre, Nedlands, Western Australia 6009, Australia

¹²⁶ Busselton Population Medical Research Foundation Inc., Sir Charles Gairdner Hospital, Nedlands, Western Australia 6009, Australia

¹²⁷ Division of Research, Kaiser Permanente Northern California, Oakland, California 94612, USA

¹²⁸ Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California 94107, USA

¹²⁹ Department of Social Services and Health Care, 68601 Jakobstad, Finland

¹³⁰ Core Genotyping Facility, SAIC-Frederick, Inc., NCI-Frederick, Frederick, Maryland 21702, USA

¹³¹ Institute of Medical Biometry and Epidemiology, University of Marburg, 35037 Marburg, Germany

¹³² Institut fur Epidemiologie und Sozialmedizin, Universitat Greifswald, 17475 Greifswald, Germany

¹³³ Research Centre for Prevention and Health, Glostrup University Hospital, 2600 Glostrup, Denmark

¹³⁴ Faculty of Health Science, University of Copenhagen, 2100 Copenhagen, Denmark

¹³⁵ National Institute for Health and Welfare, Department of Chronic Disease Prevention, Population Studies Unit, 20720 Turku, Finland

¹³⁶ Institute of Health Sciences, University of Oulu, 90014 Oulu, Finland

¹³⁷ Biocenter Oulu, University of Oulu, 90014 Oulu, Finland

¹³⁸ Hospital for Children and Adolescents, Helsinki University Central Hospital and University of Helsinki, 00029 HUS, Finland

¹³⁹ MGH Weight Center, Massachusetts General Hospital, Boston, Massachusetts 02114, USA

¹⁴⁰ Cardiovascular Research Center and Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts 02114, USA

¹⁴¹ Framingham Heart Study of the National, Heart, Lung, and Blood Institute and Boston University, Framingham, Massachusetts 01702, USA

¹⁴² Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA

¹⁴³ National Institute for Health and Welfare, Diabetes Prevention Unit, 00271 Helsinki, Finland

¹⁴⁴ Department of Medicine, Stanford University School of Medicine, Stanford, California 94305, USA

¹⁴⁵ Andrija Stampar School of Public Health, Medical School, University of Zagreb, 10000 Zagreb, Croatia

¹⁴⁶ Interdisciplinary Centre for Clinical Research, University of Leipzig, 04103 Leipzig, Germany

¹⁴⁷ Department of Medicine, University of Kuopio and Kuopio University Hospital, 70210 Kuopio, Finland

¹⁴⁸ HUNT Research Centre, Department of Public Health and General Practice, Norwegian University of Science and Technology, 7600 Levanger, Norway

¹⁴⁹ Finnish Institute of Occupational Health, 90220 Oulu, Finland

¹⁵⁰ Institut inter-regional pour la sante (IRSA), F-37521 La Riche, France

¹⁵¹ Laboratory of Epidemiology, Demography, Biometry, National Institute on Aging, National Institutes of Health, Bethesda, Maryland 20892, USA

¹⁵² Department of Clinical Chemistry, University of Tampere and Tampere University Hospital, 33520 Tampere, Finland

¹⁵³ Department of Medicine, Universite de Montreal, Montreal, Quebec, H3T 1J4, Canada

¹⁵⁴ Human Genetics, Genome Institute of Singapore, Singapore 138672, Singapore

¹⁵⁵ Transplantation Laboratory, Haartman Institute, University of Helsinki, 00014, Helsinki, Finland

¹⁵⁶ Department of Internal Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, 413 45 Gothenburg, Sweden

¹⁵⁷ Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge, Cambridge CB2 2SR, UK

¹⁵⁸ On behalf of the MAGIC (Meta-Analyses of Glucose and Insulin-related traits Consortium) investigators

¹⁵⁹ Department of Endocrinology, Diabetology and Nutrition, Bichat-Claude Bernard University Hospital, Assistance Publique des Hopitaux de Paris, F-75018 Paris, France

¹⁶⁰ Cardiovascular Genetics Research Unit, Universite Henri Poincare-Nancy 1, 54000, Nancy, France

¹⁶¹ Genetic Epidemiology Laboratory, Queensland Institute of Medical Research, Queensland 4006, Australia

¹⁶² Avon Longitudinal Study of Parents and Children (ALSPAC) Laboratory, Department of Social Medicine, University of Bristol, Bristol, BS8 2BN, UK

¹⁶³ Division of Health, Research Board, An Bord Taighde Slainte, Dublin, 2, Ireland

¹⁶⁴ Institute of Human Genetics, Klinikum rechts der Isar der Technischen Universitat Munchen, 81675 Munich, Germany

¹⁶⁵ Institute of Human Genetics, Helmholtz Zentrum Munchen - German Research Center for Environmental Health, 85764 Neuherberg, Germany

¹⁶⁶ Department of Clinical Sciences, Lund University, 20502 Malmo, Sweden

¹⁶⁷ Molecular Epidemiology Laboratory, Queensland Institute of Medical Research, Queensland 4006, Australia

¹⁶⁸ Croatian Centre for Global Health, School of Medicine, University of Split, Split 21000, Croatia

¹⁶⁹ Neurogenetics Laboratory, Queensland Institute of Medical Research, Queensland 4006, Australia

¹⁷⁰ National, Lung, and Blood Institute, National Institutes of Health, Framingham, Massachusetts 01702, USA

¹⁷¹ University of Cambridge Metabolic Research Laboratories, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge CB2 0QQ, UK

¹⁷² Department of Clinical Genetics, Erasmus MC, Rotterdam, 3015GE, The Netherlands

¹⁷³ Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario L8N3Z5, Canada

¹⁷⁴ Amgen, Cambridge, Massachusetts 02139, USA

¹⁷⁵ Finnish Twin Cohort Study, Department of Public Health, University of Helsinki, 00014, Helsinki, Finland

¹⁷⁶ Obesity Research unit, Department of Psychiatry, Helsinki University Central Hospital, Helsinki, Finland

¹⁷⁷ Department of Medicine, Levanger Hospital, The Nord-Trøndelag Health Trust, 7600 Levanger, Norway

¹⁷⁸ Gen-Info Ltd, 10000 Zagreb, Croatia

¹⁷⁹ National Institute for Health and Welfare, 90101 Oulu, Finland

¹⁸⁰ Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Leicester, LE3 9QP, UK

¹⁸¹ Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, 20520 Turku, Finland

¹⁸² The Department of Clinical Physiology, Turku University Hospital, 20520 Turku, Finland

¹⁸³ Clinical Psychology and Psychotherapy, University of Marburg, 35032 Marburg, Germany

¹⁸⁴ Department of Clinical Sciences/Clinical Chemistry, University of Oulu, 90014 Oulu, Finland

¹⁸⁵ Ludwig-Maximilians-Universität, Institute of Medical Informatics, Biometry and Epidemiology, Chair of Epidemiology, 81377 Munich, Germany

¹⁸⁶ South Karelia Central Hospital, 53130 Lappeenranta, Finland

¹⁸⁷ Department of Clinical Sciences/Internal Medicine, University of Oulu, 90014 Oulu, Finland

¹⁸⁸ Institut für Community Medicine, 17489 Greifswald, Germany

¹⁸⁹ Christian-Albrechts-University, University Hospital Schleswig-Holstein, Institute for Clinical Molecular Biology and Department of Internal Medicine I, 24105 Kiel, Germany

¹⁹⁰ Universität zu Lübeck, Medizinische Klinik II, 23562 Lübeck, Germany

¹⁹¹ Division of Cardiology, Cardiovascular Laboratory, Helsinki University Central Hospital, 00029 Helsinki, Finland

¹⁹² Departments of Medicine and Epidemiology, University of Washington, Seattle, Washington 98195, USA

¹⁹³ Department of Psychiatry/EMGO Institute, VU University Medical Center, 1081 BT Amsterdam, The Netherlands

¹⁹⁴ Department of Haematology, University of Cambridge, Cambridge CB2 0PT, UK

¹⁹⁵ NHS Blood and Transplant, Cambridge Centre, Cambridge, CB2 0PT, UK

¹⁹⁶ Leicester NIHR Biomedical Research Unit in Cardiovascular Disease, Glenfield Hospital, Leicester, LE3 9QP, UK

¹⁹⁷ Department of Health Sciences, University of Leicester, University Road, Leicester, LE1 7RH, UK

¹⁹⁸ Department of Medicine, University of Leipzig, 04103 Leipzig, Germany

¹⁹⁹ Coordination Centre for Clinical Trials, University of Leipzig, Härtelstr. 16-18, 04103 Leipzig, Germany

²⁰⁰ Department of Medicine, Helsinki University Central Hospital, 00290 Helsinki, Finland

²⁰¹ Research Program of Molecular Medicine, University of Helsinki, 00014 Helsinki, Finland

²⁰² Department of Human Genetics, Leiden University Medical Center, 2333 ZC Leiden, the Netherlands

²⁰³ Center of Medical Systems Biology, Leiden University Medical Center, 2333 ZC Leiden, the Netherlands

²⁰⁴ Department of Medicine, University of Turku and Turku University Hospital, 20520 Turku, Finland

²⁰⁵ INSERM Cardiovascular Genetics team, CIC 9501, 54000 Nancy, France

²⁰⁶ Steno Diabetes Center, 2820 Gentofte, Denmark

²⁰⁷ Center for Human Genomics, Wake Forest University, Winston-Salem, North Carolina 27157, USA

²⁰⁸ Department of Physiatrics, Lapland Central Hospital, 96101 Rovaniemi, Finland

²⁰⁹ School of Pathology and Laboratory Medicine, University of Western Australia, Nedlands, Western Australia 6009, Australia

²¹⁰ Department of Internal Medicine, University of Oulu, 90014 Oulu, Finland

²¹¹ School of Medicine and Pharmacology, University of Western Australia, Perth, Western Australia 6009, Australia

²¹² Department of Clinical Physiology, University of Tampere and Tampere University Hospital, 33520 Tampere, Finland;

²¹³ Stanford University School of Medicine, Stanford, California 93405, USA

²¹⁴ Department of Psychiatry and Human Behavior, University of California, Irvine (UCI), Irvine, California 92617, USA

²¹⁵ LIFE Study Centre, University of Leipzig, Leipzig, Germany

²¹⁶ Service of Medical Genetics, Centre Hospitalier Universitaire Vaudois (CHUV) University Hospital, 1011 Lausanne, Switzerland

²¹⁷ Human Genetics Center and Institute of Molecular Medicine, University of Texas Health Science Center, Houston, Texas 77030, USA

²¹⁸ Faculty of Health Science, University of Southern Denmark, 5000 Odense, Denmark

²¹⁹ New York University Medical Center, New York, New York 10016, USA

²²⁰ National Institute for Health and Welfare, Department of Mental Health and Substance Abuse Services, Unit for Child and Adolescent Mental Health, 00271 Helsinki, Finland

²²¹ NIHR Oxford Biomedical Research Centre, Churchill Hospital, Oxford, OX3 7LJ, UK

²²² Department of Urology, Radboud University Nijmegen Medical Centre, 6500 HB Nijmegen, The Netherlands

²²³ Institute of Biomedical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark

²²⁴ Faculty of Health Science, University of Aarhus, 8000 Aarhus, Denmark

²²⁵ Department of Psychiatry, Leiden University Medical Centre, 2300 RC Leiden, The Netherlands

²²⁶ Department of Psychiatry, University Medical Centre Groningen, 9713 GZ Groningen, The Netherlands

²²⁷ Department of Neurology, University of Lübeck, Lübeck, Germany

²²⁸ Institute for Paediatric Nutrition Medicine, Vestische Hospital for Children and Adolescents, University of Witten-Herdecke, 45711 Datteln, Germany

²²⁹ Department of Medicine III, Prevention and Care of Diabetes, University of Dresden, 01307 Dresden, Germany

²³⁰ Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, Maryland 21201, USA

²³¹ Hjelt Institute, Department of Public Health, University of Helsinki, 00014 Helsinki, Finland

²³² South Ostrobothnia Central Hospital, 60220 Seinajoki, Finland

²³³ Department of Internal Medicine, Centre Hospitalier Universitaire Vaudois (CHUV) University Hospital, 1011 Lausanne, Switzerland

²³⁴ The Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA

²³⁵ Department of Psychiatry, Harvard Medical School, Boston, Massachusetts 02115, USA

²³⁶ Pacific Biosciences, Menlo Park, California 94025, USA

²³⁷ Sage Bionetworks, Seattle, Washington 98109, USA

²³⁸ Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri 63110, USA

²³⁹ Division of Intramural Research, National Heart, Lung and Blood Institute, Framingham Heart Study, Framingham, Massachusetts 01702, USA

²⁴⁰ Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York 10461, USA

²⁴¹ Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599, USA

²⁴² Department of Medical Genetics, University of Helsinki, 00014 Helsinki, Finland

²⁴³ Laboratory of Genetics, National Institute on Aging, Baltimore, Maryland 21224, USA

²⁴⁴ Division of Community Health Sciences, St George’s, University of London, London, SW17 0RE, UK

²⁴⁵ Klinikum Grosshadern, 81377 Munich, Germany

²⁴⁶ Faculty of Medicine, University of Iceland, 101 Reykjavík, Iceland

²⁴⁷ University of Cambridge Metabolic Research Labs, Institute of Metabolic Science Addenbrooke’s Hospital, CB2 OQQ, Cambridge, UK

²⁴⁸ Carolina Center for Genome Sciences, School of Public Health, University of North Carolina Chapel Hill, Chapel Hill, North Carolina 27514, USA

²⁴⁹ Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA

Correspondence should be addressed to Michael Boehnke (Email: boehnke/at/umich.edu), Kari Stefansson (Email: kstefans/at/decode.is), Kari North (Email: kari_north/at/unc.edu), Mark McCarthy (Email: mark.mccarthy/at/drl.ox.ac.uk), Joel Hirschhorn (Email: joelh/at/broadinstitute.org), Erik Ingelsson (Email: erik.ingelsson/at/ki.se), and Ruth Loos (Email: ruth.loos/at/mrc-epid.cam.ac.uk)

^*These authors contributed equally to this work.

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Abstract

Obesity is globally prevalent and highly heritable, but the underlying genetic factors remain largely elusive. To identify genetic loci for obesity-susceptibility, we examined associations between body mass index (BMI) and ~2.8 million SNPs in up to 123,865 individuals, with targeted follow-up of 42 SNPs in up to 125,931 additional individuals. We confirmed 14 known obesity-susceptibility loci and identified 18 new loci associated with BMI (P<5×10⁻⁸), one of which includes a copy number variant near GPRC5B. Some loci (MC4R, POMC, SH2B1, BDNF) map near key hypothalamic regulators of energy balance, and one is near GIPR, an incretin receptor. Furthermore, genes in other newly-associated loci may provide novel insights into human body weight regulation.

Obesity is a major and increasingly prevalent risk factor for multiple disorders, including type 2 diabetes and cardiovascular disease¹^,². While lifestyle changes have driven its prevalence to epidemic proportions, heritability studies provide evidence for a substantial genetic contribution (h²~40–70%) to obesity risk³^,⁴. BMI is an inexpensive, non-invasive measure of obesity that predicts the risk of related complications⁵. Identifying genetic determinants of BMI could lead to a better understanding of the biological basis of obesity.

Genome-wide association (GWA) studies of BMI have previously identified ten loci with genome-wide significant (P < 5×10⁻⁸) associations in or near FTO, MC4R, TMEM18, GNPDA2, BDNF, NEGR1, SH2B1, ETV5, MTCH2, and KCTD15⁶^–¹⁰. Many of these genes are expressed or known to act in the central nervous system, highlighting a likely neuronal component to the predisposition to obesity⁹. This pattern is consistent with results in animal models and studies of monogenic human obesity, where neuronal genes, particularly those expressed in the hypothalamus and involved in regulation of appetite or energy balance, are known to play a major role in susceptibility to obesity¹¹^–¹³.

The ten previously identified loci account for only a small fraction of the variation in BMI. Furthermore, power calculations based on the effect sizes of established variants have suggested that increasing the sample size would likely lead to the discovery of additional variants⁹. To identify more loci associated with BMI, we expanded the GIANT (Genetic Investigation of ANtropometric Traits) consortium GWA meta-analysis to include a total of 249,769 individuals of European ancestry.

Results

Stage 1 GWA studies identify novel loci associated with BMI

We first conducted a meta-analysis of GWA studies of BMI and ~2.8 million imputed or genotyped SNPs using data from 46 studies including up to 123,865 individuals (Online Methods, Supplementary Fig. 1 and Supplementary Note). This stage 1 analysis revealed 19 loci associated with BMI at P < 5×10⁻⁸ (Table 1, Fig. 1a and Supplementary Table 1). These 19 loci included all ten loci from previous GWA studies of BMI⁶^–¹⁰, two loci previously associated with body weight¹⁰ (FAIM2 and SEC16B) and one locus previously associated with waist circumference¹⁴ (near TFAP2B). The remaining six loci, near GPRC5B, MAP2K5/LBXCOR1, TNNI3K, LRRN6C, FLJ35779/HMGCR, and PRKD1, have not previously been associated with BMI or other obesity-related traits.

Table 1

Stage 1 and stage 2 results of the 32 SNPs that were associated with BMI at genome-wide significance (P < 5.10⁻⁸) levels.

Figure 1

Genome-wide association results for the BMI meta-analysis

Stage 2 follow-up leads to additional novel loci for BMI

To identify additional BMI-associated loci and to validate the loci that reached genome-wide significance in stage 1 analyses, we examined SNPs representing 42 independent loci (including the 19 genome-wide significant loci) with stage 1 P < 5×10⁻⁶. Variants were considered to be independent if the pair-wise linkage disequilibrium (LD; r²) was less than 0.1 and if they were separated by at least 1 Mb. In stage 2, we examined these 42 SNPs in up to 125,931 additional individuals (79,561 newly genotyped individuals from 16 different studies and 46,370 individuals from 18 additional studies for which GWA data were available; Table 1, Supplementary Note, and Online Methods). In a joint analysis of stage 1 and stage 2 results, 32 of the 42 SNPs reached P < 5×10⁻⁸. Even after excluding SNPs within these 32 confirmed BMI loci, we still observed an excess of small P-values compared to the distribution expected under the null hypothesis (Fig. 1b), suggesting that more BMI loci remain to be uncovered.

The 32 confirmed associations included all 19 loci with P < 5×10⁻⁸ at stage 1, 12 additional novel loci near RBJ/ADCY3/POMC, QPCTL/GIPR, SLC39A8, TMEM160, FANCL, CADM2, LRP1B, PTBP2, MTIF3/GTF3A, ZNF608, RPL27A/TUB, NUDT3/HMGA1, and one locus (NRXN3) previously associated with waist circumference¹⁵ (Table 1, Supplementary Table 1, Supplementary Fig. 1 and 2). In all, our study increased the number of loci robustly associated with BMI from 10 to 32. Four of the 22 new loci were previously associated with body weight¹⁰ or waist circumference¹⁴^,¹⁵, whereas 18 loci had not previously associated with any obesity-related trait in the general population. Whilst we confirmed all loci previously established by large-scale GWA studies for BMI⁶^–¹⁰ and waist circumference¹⁴^,¹⁵, four loci identified by GWA studies for early-onset or adult morbid obesity¹⁶^,¹⁷ [at NPC1 (rs1805081; P = 0.0025), MAF (rs1424233; P = 0.25), PTER (rs10508503; P = 0.64), and TNKS/MSRA (rs473034; P = 0.23)] showed limited or no evidence of association with BMI in our study.

As expected, the effect sizes of the 18 newly discovered loci are slightly smaller, for a given minor allele frequency, than those of the previously identified variants (Table 1 and Fig. 1c). The increased sample size also brought out more signals with low minor allele frequency. The BMI-increasing allele frequencies for the 18 newly identified variants ranged from 4% to 87%, covering more of the allele frequency spectrum than previous, smaller GWA studies of BMI (24%–83%)⁹^,¹⁰ (Table 1 and Fig. 1c).

We tested for evidence of non-additive (dominant or recessive) effects, SNP×SNP interaction effects and heterogeneity by sex or study among the 32 BMI-associated SNPs (Online Methods). We found no evidence for any such effects (P > 0.001, no significant results after correcting for multiple testing) (Supplementary Tables 1 and Supplementary Note).

Impact of 32 confirmed loci on BMI, obesity, body size, and other metabolic traits

Together, the 32 confirmed BMI loci explained 1.45% of the inter-individual variation in BMI of the stage 2 samples, with the FTO SNP accounting for the largest proportion of the variance (0.34%) (Table 1). To estimate the cumulative effect of the 32 variants on BMI, we constructed a genetic-susceptibility score that sums the number of BMI-increasing alleles weighted by the overall stage 2 effect sizes in the ARIC study (N = 8,120), one of our largest population-based studies (Online Methods). For each unit increase in the genetic-susceptibility score, approximately equivalent to one additional risk allele, BMI increased by 0.17 kg/m², equivalent to a 435–551 g gain in body weight in adults of 160–180 cm in height. The difference in average BMI between individuals with a high genetic-susceptibility score (≥38 BMI-increasing alleles, 1.5% (n=124) of the ARIC sample) and those with a low genetic-susceptibility score (≤21 BMI-increasing alleles, 2.2% (n=175) of the ARIC sample) was 2.73 kg/m², equivalent to a 6.99 to 8.85 kg body weight difference in adults 160–180 cm in height (Fig. 2a). Still, we note that the predictive value for obesity risk and BMI of the 32 variants combined was modest, although statistically significant (Fig. 2b, Supplementary Fig. 4). The area under the receiver operating characteristic (ROC) curve for prediction of risk of obesity (BMI ≥ 30 kg/m²) using age, age² and sex only was 0.515 (P = 0.023 compared to AUC of 0.50), which increased to 0.575 (P < 10⁻⁵) when also the 32 confirmed SNPs were included in the model (Fig. 2b). The area under the ROC for the 32 SNPs only was 0.574 (P < 10⁻⁵).

Figure 2

Combined impact of risk alleles on BMI/obesity

All 32 confirmed BMI-increasing alleles showed directionally consistent effects on risk of being overweight (BMI ≥25 kg/m²) or obese (≥30 kg/m²) in stage 2 samples, with 30 of 32 variants achieving at least nominally significant associations. The BMI-increasing alleles increased the odds of overweight by 1.013 to 1.138-fold, and the odds for being obese by 1.016- to 1.203-fold (Supplementary Table 2). In addition, 30 of the 32 loci also showed directionally consistent effects on the risk of extreme and early-onset obesity in a meta-analysis of seven case-control studies of adults and children (binomial sign test P = 1.3×10⁻⁷) (Supplementary Table 3). The BMI-increasing allele observed in adults also increased the BMI in children and adolescents with directionally consistent effects observed for 23 of the 32 SNPs (binomial sign test P = 0.01). Furthermore, in family-based studies, the BMI-increasing allele was over-transmitted to the obese offspring for 24 of the 32 SNPs (binomial sign test P = 0.004) (Supplementary Table 3). As these studies in extreme obesity cases, children and families were relatively small (N_range = 354 − 15,251) compared to the overall meta-analyses, their power was likely insufficient to confirm association for all 32 loci. Nevertheless, these results show that the effects are unlikely to reflect population stratification and that they extend to BMI differences throughout the life course.

All BMI-increasing alleles were associated with increased body weight, as expected from the correlation between BMI and body weight (Supplementary Table 2). To confirm an effect of the loci on adiposity rather than general body size, we tested association with body fat percentage, which was available in a subset of the stage 2 replication samples (n = 5,359–28,425) (Supplementary Table 2). The BMI-increasing allele showed directionally consistent effects on body fat percentage at 31 of the 32 confirmed loci (binomial sign test P = 1.54×10⁻⁸) (Supplementary Table 2).

We also examined the association of the BMI loci with metabolic traits (type 2 diabetes¹⁸, fasting glucose, fasting insulin, indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR)¹⁹, and blood lipid levels²⁰) and with height (Supplementary Tables 2 and 4). Although many nominal associations are expected because of known correlations between BMI and most of these traits and because of overlap in samples, several associations stand out as possible examples of pleiotropic effects of the BMI-associated variants. Particularly interesting is the variant in the GIPR locus where the BMI-increasing allele is also associated with increased fasting glucose levels and lower 2-hour glucose levels (Supplementary Table 4)¹⁹^,²¹. The direction of the effect is opposite to what would be expected due to the correlation between obesity and glucose intolerance, but is consistent with the suggested roles of GIPR in glucose and energy metabolism (see below)²². Three loci show strong associations (P < 10⁻⁴) with height (MC4R, RBJ/ADCY3/POMC and MTCH2/NDUFS3). Because BMI is weakly correlated with height (and indeed, the BMI-associated variants as a group show no consistent effect on height), these associations are also suggestive of pleiotropy. Interestingly, analogous to the effects of severe mutations in POMC and MC4R on height and weight²³^,²⁴, the BMI-increasing alleles of the variants near these genes were associated with decreased (POMC) and increased (MC4R) height, respectively (Supplementary Table 2).

Potential functional roles and pathways analyses

Although associated variants typically implicate genomic regions rather than individual genes, we note that some of the 32 loci include candidate genes with established connections to obesity. Several of the 10 previously identified loci are located in or near genes that encode neuronal regulators of appetite or energy balance, including MC4R¹²^,²⁵, BDNF²⁶, and SH2B1¹¹^,²⁷. Each of these genes has been tied to obesity, not only in animal models, but also by rare human variants that disrupt each of these genes and lead to severe obesity²⁴^,²⁸^,²⁹. Using the automated literature search programme, Snipper (Online Methods), we identified various genes within the novel loci with potential biological links to obesity-susceptibility (Supplementary Note). Among the novel loci, the location of rs713586 near POMC provides further support for a role of neuroendocrine circuits that regulate energy balance in susceptibility to obesity. POMC encodes several polypeptides including α-MSH, a ligand of the MC4R gene product³⁰, and rare mutations in POMC also cause human obesity²³^,²⁹^,³¹.

In contrast, the locus near GIPR, which encodes a receptor of gastric inhibitory polypeptide (GIP), suggests a role for peripheral biology in obesity. GIP, which is expressed in the K cell of the duodenum and intestine, is an incretin hormone that mediates incremental insulin secretion in response to oral intake of glucose. The variant associated with BMI is in strong LD (r² = 0.83) with a missense SNP in GIPR (rs1800437, Glu354Gln) that has recently been shown to influence the glucose and insulin response to an oral glucose challenge ²¹. Although no human phenotype is known to be caused by mutations in GIPR, mice with disruption of Gipr are resistant to diet-induced obesity³². The association of a variant in GIPR with BMI suggests that there may be a link between incretins/insulin secretion and body weight regulation in humans as well.

To systematically identify biological connections among the genes located near the 32 confirmed SNPs, and to potentially identify new pathways associated with BMI, we performed pathway-based analyses using MAGENTA³³. Specifically, we tested for enrichment of BMI genetic associations in biological processes or molecular functions that contain at least one gene from the 32 confirmed BMI loci (Online Methods). Using annotations from the KEGG, Ingenuity, PANTHER, and Gene Ontology databases, we found evidence of enrichment for pathways involved in the platelet-derived growth factor (PDGF) signaling (PANTHER, P = 0.0008, FDR = 0.0061), translation elongation (PANTHER, P = 0.0008, FDR = 0.0066), hormone or nuclear hormone receptor binding (Gene Ontology, P < 0.0005, FDR < 0.0085), homeobox transcription (PANTHER, P = 0.0001, FDR = 0.011), regulation of cellular metabolism (Gene Ontology, P = 0.0002, FDR = 0.031), neurogenesis and neuron differentiation (Gene Ontology, P < 0.0002, FDR < 0.034), protein phosphorylation (PANTHER, P = 0.0001, FDR = 0.045) and numerous other pathways related to growth, metabolism, immune and neuronal processes (Gene Ontology, P < 0.002, FDR < 0.046) (Supplementary Table 5).

Identifying possible functional variants

We used data from the 1000 Genomes Project and the HapMap Consortium to explore whether the 32 confirmed BMI SNPs were in LD (r² ≥ 0.75) with common missense SNPs or copy number variants (CNVs) (Online Methods). Non-synonymous variants in LD with our signals were present in the BDNF, SLC39A8, FLJ35779/HMGCR, QPCTL/GIPR, MTCH2, ADCY3, and LBXCOR1 genes. In addition, the rs7359397 signal was in LD with coding variants in several genes including SH2B1, ATNX2L, APOB48R, SULT1A2, and AC138894.2 (Table 1, Fig. 3, Supplementary Table 6 and Supplementary Fig. 2). Furthermore, two SNPs tagged common CNVs. The first CNV was previously identified and is a 45-kb deletion near NEGR1⁹. The second CNV is a 21-kb deletion that lies 50kb upstream of GPRC5B; the deletion allele is tagged by the T-allele of rs12444979 (r² = 1) (Fig. 3). Although the correlations with potentially functional variants does not prove that these variants are indeed causal, these provide first clues as to which genes and variants at these loci might be prioritized for fine-mapping and functional follow-up.

Figure 3

Regional plots of selected replicating BMI loci with missense and CNV variants

As many of the 32 BMI loci harbor multiple genes, we examined whether gene expression (eQTL) analyses could also direct us to positional candidates. Gene expression data were available for human brain, lymphocytes, blood, subcutaneous and visceral adipose tissue, and liver³⁴^–³⁶ (Online Methods, Table 1 and Supplementary Table 7). Significant cis-associations, defined at the tissue-specific level, were observed between 14 BMI-associated alleles and expression levels (Table 1 and Supplementary Table 7). In several cases, the BMI-associated SNP was the most significant SNP or explained a substantial proportion of the association with the most significant SNP for the gene transcript in conditional analyses (P_adj>0.05). These significant associations included NEGR1, ZC3H4, TMEM160, MTCH2, NDUFS3, GTF3A, ADCY3, APOB48R, SH2B1, TUFM, GPRC5B, IQCK, SLC39A8, SULT1A1, and SULT1A2 (Table 1 and Supplementary Table 7), making these genes higher priority candidates within the associated loci. However, we note that some BMI-associated variants were correlated with the expression of multiple nearby genes, making it difficult to determine the most relevant gene.

Evidence for the existence of additional associated variants

Because the variants identified by this large study explain only 1.45% of the variance in BMI (2–4% of genetic variance based on an estimated heritability of 40–70%), we considered how much the explained phenotypic variance could be increased by including more SNPs at various degrees of significance in a polygene model using an independent validation set (Online Methods)³⁷. We found that including SNPs associated with BMI at lower significance levels (up to P > 0.05) increased the explained phenotypic variance in BMI to 2.5%, or 4% to 6% of genetic variance (Fig. 4a). In a separate analysis, we estimated the total number of independent BMI-associated variants that are likely to exist with similar effect sizes to the 32 confirmed here (Online Methods)³⁸. Based on the effect size and allele frequencies of the 32 replicated loci observed in stage 2 and the power to detect association in the combined stage 1 and stage 2, we estimated that there are 284 (95% CI: 132–510) loci with similar effect sizes as the currently observed ones, which together would account for 4.5% (95% CI: 3.1–6.8%) of the variation in BMI or 6–11% of the genetic variation (based on an estimated heritability of 40–70%) (Supplementary Table 8). In order to detect 95% of these loci, a sample size of approximately 730,000 subjects would be needed (Fig. 4b). This method does not account for the potential of loci of smaller effect than those identified here to explain even more of the variance and thus provides an estimated lower bound of explained variance. These two analyses strongly suggest that larger GWA studies will continue to identify additional novel associated loci, but also indicate that even extremely large studies focusing on variants with allele frequencies above 5% will not account for a large fraction of the genetic contribution to BMI.

Figure 4

Phenotypic variance explained by common variants

We examined whether selecting only a single variant from each locus for follow-up led us to underestimate the fraction of phenotypic variation explained by the associated loci. To search for additional independent loci at each of the 32 associated BMI loci, we repeated our GWA meta-analysis, conditioning on the 32 confirmed SNPs. Using a significance threshold of 5 × 10⁻⁶ for SNPs at known loci, we identified one apparently independent signal at the MC4R locus; rs7227255 was associated with BMI (P = 6.56 × 10⁻⁷) even after conditioning for the most strongly associated variant near MC4R (rs571312) (Fig. 5). Interestingly, rs7227255 is in perfect LD (r² = 1) with a relatively rare MC4R missense variant (rs2229616, V103I, minor allele frequency = 1.7%) that has been associated with BMI in two independent meta-analyses³⁹^,⁴⁰. Furthermore, mutations at the MC4R locus are known to influence early-onset obesity²⁴^,⁴¹, supporting the notion that allelic heterogeneity may be a frequent phenomenon in the genetic architecture of obesity.

Figure 5

Second signal at the MC4R locus contributing to BMI

Discussion

Using a two-stage genome-wide association meta-analysis of up to 249,796 individuals of European descent, we have identified 18 additional loci that are associated with BMI at genome-wide significance, bringing the total number of such loci to 32. We estimate that more than 250 (i.e. 284 predicted loci – 32 confirmed loci) common variant loci with effects on BMI similar to those described here remain to be discovered, and even larger numbers of loci with smaller effects. A substantial proportion of these loci should be identifiable through larger GWA studies and/or by targeted follow-up of top signals selected from our stage 1 analysis. The latter approach is already being implemented through large-scale genotyping of samples informative for BMI using a custom array (the Metabochip) designed to support follow-up of thousands of promising variants in hundreds of thousands of individuals.

The combined effect on BMI of the associated variants at the 32 loci is modest, and even when we try to account for as-yet-undiscovered variants with similar properties, we estimate that these common variant signals account for only 6–11% of the genetic variation in BMI. There is a strong expectation that additional variance and biology will be explained using complementary approaches that capture variants not examined in the current study, such as lower frequency variants and short insertion-deletion polymorphisms. There is good reason to believe (based on our findings at MC4R and other loci – POMC, BDNF, SH2B1 – which feature both common and rare variant associations) that a proportion of such low-frequency and rare causal variation will map to the loci already identified by GWA studies.

A primary goal of human genetic discovery is to improve understanding of the biology of conditions such as obesity⁴². One particularly interesting finding in this regard is the association between BMI and common variants near GIPR, which may indicate a causal contribution of variation in postprandial insulin secretion to the development of obesity. In most cases, the loci identified by the present study harbor few, if any, annotated genes with clear connections to the biology of weight regulation. This reflects our still limited understanding of the biology of BMI and obesity-related traits and is in striking contrast with the results from equivalent studies of certain other traits (such as autoimmune diseases or lipid levels). Thus, these results suggest that much novel biology remains to be uncovered, and that GWA studies may provide an important entry point. In particular, further examination of the associated loci through a combination of resequencing and fine-mapping to find causal variants, and genomic and experimental studies designed to assign function, could uncover novel insights into the biology of obesity.

In conclusion, we have performed GWA studies in large samples to identify numerous genetic loci associated with variation in BMI, a common measure of obesity. Because current lifestyle interventions are largely ineffective in addressing the challenges of growing obesity⁴³^,⁴⁴, new insights into biology are critically needed to guide the development and application of future therapies and interventions.

Supplementary Material

Supplementary Figure 1 Study design. Stage 1 - Meta analysis of genome-wide association data was performed in stage 1 across 46 studies of white European Ancestry. A total of 42 SNPs representing the best associating (P < 10⁻⁶) loci (shown) were taken forward for replication. Nineteen of these SNPs (loci in bold) reached already genome-wide significance at stage 1. Stage 2 – The 42 SNPs were genotyped in 16 de novo replication studies and extracted from 18 in silico replication studies, all adults of European ancestry and were tested for association with BMI. In a joint analyses of stage 1 and stage 2 data, 32 SNPs (loci in bold) reached genome-wide significance (P < 5×10⁻⁸). Follow-up analyses – The 32 confirmed loci were taken forward for additional analyses.

Supplementary Figure 2 Regional plots of the 32 confirmed BMI loci with missense and CNV variants. SNPs are plotted by position on chromosome against association with BMI (−log₁₀ P-value). The SNP name shown on the plot was the most significant SNP after stage 1 meta-analysis. Estimated recombination rates (from HapMap) are plotted in cyan to reflect the local LD structure. The SNPs surrounding the most significant SNP are color-coded to reflect their LD with this SNP (taken from pairwise r² values from the HapMap CEU database, www.hapmap.org). Genes, position of exons, and direction of transcription from UCSC genome browser (genome.ucsc.edu) are noted. Hash-marks represent SNP positions available in the meta-analysis. Plots were generated using LocusZoom (http://csg.sph.umich.edu/locuszoom).

Supplementary Figure 3 Quantile-quantile plot of SNPs at stage 1 GIANT meta-analysis (black) and after removing any SNPs within 1 Mb of the 10 previously reported genome-wide significant hits for BMI (blue), after additionally excluding the four loci for waist/weight (green) and after excluding all 32 confirmed loci (red).

Supplementary Figure 4 Relationship between the predicted BMI, based on the 32 confirmed BMI loci combined, and the actual BMI in the ARIC Study (N=8,120). Panel (a) shows the comparison of the predicted BMI (grey), with the actual BMI (green) and quartile ranges (orange) in sets of 500 individuals, suggesting good average predictions. Panel (b) shows the comparison of the individual predicted and observed BMI values, suggesting poor individual predictions.

Supplementary Table 1 The 42 SNPs, associated with BMI at P < 5.10⁻⁶ at stage 1, that were taken forward for replication in stage 2.

Supplementary Table 2 Association of 32 replicated SNPs with other anthropometric traits.

Supplementary Table 3 Association between 32 replicated SNPs with risk of extreme obesity in children and adults, and with BMI in population-based childhood studies.

Supplementary Table 4 Association of the 32 confirmed BMI SNPs with metabolic traits.

Supplementary Table 5 Gene set enrichment analysis (MAGENTA) of biological pathways with one or more genes from the 32 confirmed BMI loci, using the BMI meta-analysis.

Supplementary Table 6 Non-synonymous or splice-site variants in linkage disequilibrium (r² > 0.75) with lead SNPs.

Supplementary Table 7 Significant associations between BMI SNPs and cis gene expression (cis-eQTLs) in lymphocyte, blood, adipose and brain tissues.

Supplementary Table 8 Estimated number of BMI loci for each of the effect sizes observed in Stage 2 for the SNPs that reached a genome-wide significance of 5×10⁻⁸ in the joint analysis of stage 1 and stage 2, given the power to detect the association in the joint analysis of stage 1 and stage 2.

Click here to view.^{(5.9M, pdf)}

Acknowledgments

A full list of acknowledgments appears in the Supplementary Note.

Academy of Finland (10404, 77299, 104781, 114382, 117797, 120315, 121584, 124243, 126775, 126925, 127437, 129255, 129269, 129306, 129494, 129680, 130326, 209072, 210595, 213225, 213506, 216374); ADA Mentor-Based Postdoctoral Fellowship; Amgen; Agency for Science, Technology and Research of Singapore (A*STAR); ALF/LUA research grant in Gothenburg; Althingi (the Icelandic Parliament); AstraZeneca; Augustinus Foundation; Australian National Health and Medical Research Council (241944, 389875, 389891, 389892, 389938, 442915, 442981, 496739, 496688, 552485 and 613672); Australian Research Council (ARC grant DP0770096); Becket Foundation; Biocenter (Finland); Biomedicum Helsinki Foundation, Boston Obesity Nutrition Research Center; British Diabetes Association (1192); British Heart Foundation (97020; PG/02/128); Busselton Population Medical Research Foundation; Cambridge Institute for Medical Research; Cambridge NIHR Comprehensive Biomedical Research Centre; CamStrad (UK); Cancer Research UK; Centre for Medical Systems Biology (The Netherlands); Centre for Neurogenomics and Cognitive Research (The Netherlands); Chief Scientist Office of the Scottish Government; Contrat Plan Etat Région (France); Danish Centre for Health Technology Assessment; Danish Diabetes Association; Danish Heart Foundation; Danish Pharmaceutical Association; Danish Research Council; Deutsche Forschungsgemeinschaft (DFG; HE 1446/4-1); Department of Health (UK); Diabetes UK; Diabetes & Inflammation Laboratory; Donald W. Reynolds Foundation; Dresden University of Technology Funding Grant; Emil and Vera Cornell Foundation; Erasmus Medical Center (Rotterdam); Erasmus University (Rotterdam); European Commission (DG XII; QLG1-CT-2000-01643, QLG2-CT-2002-01254, LSHC-CT-2005, LSHG-CT-2006-018947, LSHG-CT-2004-518153, LSH-2006-037593, LSHM-CT-2007-037273, HEALTH-F2-2008-ENGAGE, HEALTH-F4-2007-201413, HEALTH-F4-2007-201550, FP7/2007-2013, 205419, 212111, 245536, SOC 95201408 05F02, WLRT-2001-01254); Federal Ministry of Education and Research (Germany) (01AK803, 01EA9401, 01GI0823, 01GI0826, 01GP0209, 01GP0259, 01GS0820, 01GS0823, 01GS0824, 01GS0825, 01GS0830, 01GS0831, 01IG07015, 01KU0903, 01ZZ9603, 01ZZ0103, 01ZZ0403, 03ZIK012); Federal State of Mecklenburg-West Pomerania; European Social Fund; Eve Appeal; Finnish Diabetes Research Foundation; Finnish Foundation for Cardiovascular Research; Finnish Foundation for Pediatric Research, Finnish Medical Society; Finska Läkaresällskapet, Päivikki and Sakari Sohlberg Foundation, Folkhalsan Research Foundation; Fond Européen pour le Développement Régional (France); Fondation LeDucq (Paris, France); Foundation for Life and Health in Finland; Foundation for Strategic Research (Sweden); Genetic Association Information Network; German Research Council (KFO-152) German National Genome Research Net ‘NGFNplus’ (FKZ 01GS0823); German Research Center for Environmental Health; Giorgi-Cavaglieri Foundation; GlaxoSmithKline; Göteborg Medical Society; Great Wine Estates Auctions; Gyllenberg Foundation; Health Care Centers in Vasa, Närpes and Korsholm; Healthway, Western Australia; Helmholtz Center Munich; Helsinki University Central Hospital, Hjartavernd (the Icelandic Heart Association); INSERM (France); Ib Henriksen Foundation; IZKF (B27); Jalmari and Rauha Ahokas Foundation; Juho Vainio Foundation; Juvenile Diabetes Research Foundation International (JDRF); Karolinska Institute; Knut and Alice Wallenberg Foundation; Leenaards Foundation; Lundbeck Foundation Centre of Applied Medical Genomics for Personalized Disease Prediction, Prevention and Care (LUCAMP); Lundberg Foundation; Marie Curie Intra-European Fellowship; Medical Research Council (UK) (G0000649, G0000934, G9521010D, G0500539, G0600331, G0601261, PrevMetSyn); Ministry of Cultural Affairs and Social Ministry of the Federal State of Mecklenburg-West Pomerania; Ministry for Health, Welfare and Sports (Netherlands); Ministry of Education (Finland); Ministry of Education, Culture and Science (Netherlands); Ministry of Internal Affairs and Health (Denmark); Ministry of Science, Education and Sport of the Republic of Croatia (216-1080315-0302); Ministry of Science, Research and the Arts Baden-Württemberg; Montreal Heart Institute Foundation; Municipal Health Care Center and Hospital in Jakobstad; Municipality of Rotterdam; Närpes Health Care Foundation; National Cancer Institute; National Health and Medical Research Council of Australia; National Institute for Health Research Cambridge Biomedical Research Centre; National Institute for Health Research Oxford Biomedical Research Centre; National Institute for Health Research comprehensive Biomedical Research Centre; National Institutes of Health (263-MA-410953, AA07535, AA10248, AA014041, AA13320, AA13321, AA13326, CA047988, CA65725, CA87969, CA49449, CA67262, CA50385, DA12854, DK58845, DK46200, DK062370, DK063491, DK072193, HG002651, HL084729, HHSN268200625226C, HL71981, K23-DK080145, K99-HL094535, M01-RR00425, MH084698, N01-AG12100, NO1-AG12109, N01-HC15103, N01-HC25195, N01-HC35129, N01-HC45133, N01-HC55015, N01-HC55016, N01-HC55018, N01-HC55019, N01-HC55020, N01-N01HC-55021, N01-HC55022, N01-HC55222, N01-HC75150, N01-HC85079, N01-HC85080, N01-HG-65403, N01-HC85081, N01-HC85082; N01-HC85083; N01-HC85084; N01-HC85085; N01-HC85086, N02-HL64278, P30-DK072488, R01-AG031890, R01-DK073490, R01-DK075787, R01DK068336, R01DK075681, R01-HL59367, R01-HL086694, R01-HL087641, R01-HL087647, R01-HL087652, R01-HL087676, R01-HL087679, R01-HL087700, R01-HL088119, R01-MH59160, R01-MH59565, R01-MH59566, R01-MH59571, R01-MH59586, R01-MH59587, R01-MH59588, R01-MH60870, R01-MH60879, R01-MH61675, R01-MH63706, R01-MH67257, R01-MH79469, R01-MH79470, R01-MH81800, RL1-MH083268, UO1-CA098233, U01-DK062418, U01-GM074518, U01-HG004402, U01-HG004399, U01-HL72515, U01-HL080295, U01-HL084756, U54-RR020278, T32-HG00040, UL1-RR025005, Z01-HG000024); National Alliance for Research on Schizophrenia and Depression (NARSAD); Netherlands Genomics Initiative/Netherlands Consortium for Healthy Aging (050-060-810); Netherlands Organisation for Scientific Research (NWO) (904-61-090, 904-61-193, 480-04-004, 400-05-717, SPI 56-464-1419, 175.010.2005.011, 911-03-012); Nord-Trøndelag County Council; Nordic Center of Excellence in Disease Genetics; Novo Nordisk Foundation; Norwegian Institute of Public Health; Ollqvist Foundation; Oxford NIHR Biomedical Research Centre; Organization for the Health Research and Development (10-000-1002); Paavo Nurmi Foundation; Paul Michael Donovan Charitable Foundation; Perklén Foundation; Petrus and Augusta Hedlunds Foundation; Pew Scholar for the Biomedical Sciences; Public Health and Risk Assessment, Health & Consumer Protection (2004310); Research Foundation of Copenhagen County; Research Institute for Diseases in the Elderly (014-93-015; RIDE2); Robert Dawson Evans Endowment; Royal Society (UK); Royal Swedish Academy of Science; Sahlgrenska Center for Cardiovascular and Metabolic Research (CMR, no. A305: 188); Siemens Healthcare, Erlangen, Germany; Sigrid Juselius Foundation; Signe and Ane Gyllenberg Foundation; Science Funding programme (UK); Social Insurance Institution of Finland; Söderberg’s Foundation; South Tyrol Ministry of Health; South Tyrolean Sparkasse Foundation; State of Bavaria; Stockholm County Council (560183); Susan G. Komen Breast Cancer Foundation; Swedish Cancer Society; Swedish Cultural Foundation in Finland; Swedish Foundation for Strategic Research; Swedish Heart-Lung Foundation; Swedish Medical Research Council (8691, K2007-66X-20270-01-3, K2010-54X-09894-19-3, K2010-54X-09894-19-3, 2006-3832); Swedish Research Council; Swedish Society of Medicine; Swiss National Science Foundation (33CSCO-122661, 310000-112552, 3100AO-116323/1); Torsten and Ragnar Söderberg’s Foundation; Université Henri Poincaré-Nancy 1, Région Lorraine, Communauté Urbaine du Grand Nancy; University Hospital Medical funds to Tampere; University Hospital Oulu; University of Oulu, Finland (75617); Västra Götaland Foundation; Walter E. Nichols, M.D., and Eleanor Nichols endowments; Wellcome Trust (068545, 072960, 075491, 076113, 077016, 079557, 079895, 081682, 083270, 085301, 086596); Western Australian DNA Bank; Western Australian Genetic Epidemiology Resource; Yrjö Jahnsson Foundation.

Footnotes

Author contributions

A full list of author contributions appears in the Supplementary Note.

Competing interests statement

The authors declare competing financial interests. A full list of competing interests appears in the Supplementary Note.

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