Edinburgh Research Explorer Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index Citation for published version: Speliotes, EK, Willer, CJ, Berndt, SI, Monda, KL, Thorleifsson, G, Jackson, AU, Allen, HL, Lindgren, CM, Luan, J, Mägi, R, Randall, JC, Vedantam, S, Winkler, TW, Qi, L, Workalemahu, T, Heid, IM, Steinthorsdottir, V, Stringham, HM, Weedon, MN, Wheeler, E, Wood, AR, Ferreira, T, Weyant, RJ, Segrè, AV, Estrada, K, Liang, L, Nemesh, J, Park, J-H, Gustafsson, S, Kilpeläinen, TO, Yang, J, Bouatia-Naji, N, Esko, T, Feitosa, MF, Kutalik, Z, Mangino, M, Raychaudhuri, S, Scherag, A, Smith, AV, Welch, R, Zhao, JH, Campbell, H, Hayward, C, Vitart, V, Wild, SH, Zgaga, L, Rudan, I, Wilson, JF, Wright, AF, Visscher, PM & MAGIC 2010, 'Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index', Nature Genetics, vol. 42, no. 11, pp. 937-48. https://doi.org/10.1038/ng.686 Digital Object Identifier (DOI): 10.1038/ng.686 Link: Link to publication record in Edinburgh Research Explorer Document Version: Peer reviewed version Published In: Nature Genetics General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact openaccess@ed.ac.uk providing details, and we will remove access to the work immediately and investigate your claim. Download date: 17. Apr. 2021
NIH Public Access Author Manuscript Published in final edited form as: Nat Genet. 2010 November ; 42(11): 937 948. doi:10.1038/ng.686. 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. Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms Correspondence should be addressed to Michael Boehnke (boehnke@umich.edu), Kari Stefansson (kstefans@decode.is), Kari North (kari_north@unc.edu), Mark McCarthy (mark.mccarthy@drl.ox.ac.uk), Joel Hirschhorn (joelh@broadinstitute.org), Erik Ingelsson (erik.ingelsson@ki.se), and Ruth Loos (ruth.loos@mrc-epid.cam.ac.uk). * These authors contributed equally to this work. 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.
Speliotes et al. Page 2 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
Speliotes et al. Page 3 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. Loos 10,* 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
Speliotes et al. Page 4 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
Speliotes et al. Page 5 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-
Speliotes et al. Page 6 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
Speliotes et al. Page 7 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
Speliotes et al. Page 8 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 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 disease 1,2. While lifestyle changes have driven its prevalence to epidemic proportions, heritability studies provide evidence for a substantial genetic contribution (h 2 ~40 70%) to obesity risk 3,4. BMI is an inexpensive, non-invasive measure of obesity that predicts the risk of related complications 5. 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,
Speliotes et al. Page 9 Results GNPDA2, BDNF, NEGR1, SH2B1, ETV5, MTCH2, and KCTD15 6 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 obesity 9. 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 obesity 11 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 variants 9. 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. 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 BMI 6 10, two loci previously associated with body weight 10 (FAIM2 and SEC16B) and one locus previously associated with waist circumference 14 (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. Stage 2 follow-up leads to additional novel loci for BMI To identify additional BMI-associated loci and to validate the loci that reached genomewide 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; r 2 ) 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 circumference 15 (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 weight 10 or waist circumference 14,15, 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 BMI 6 10 and waist circumference 14,15, four loci identified by GWA studies for early-onset or adult morbid obesity 16,17 [at NPC1 (rs1805081; P = 0.0025), MAF (rs1424233; P = 0.25), PTER
Speliotes et al. Page 10 (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 geneticsusceptibility score, approximately equivalent to one additional risk allele, BMI increased by 0.17 kg/m 2, 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 geneticsusceptibility 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/m 2, 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/m 2 ) using age, age 2 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 ). All 32 confirmed BMI-increasing alleles showed directionally consistent effects on risk of being overweight (BMI 25 kg/m 2 ) or obese ( 30 kg/m 2 ) 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 metaanalysis 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.
Speliotes et al. Page 11 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 diabetes 18, fasting glucose, fasting insulin, indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) 19, and blood lipid levels 20 ) 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 BMIassociated 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 weight 23,24, 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 MC4R 12,25, BDNF 26, and SH2B1 11,27. 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 obesity 24,28,29. 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 product 30, and rare mutations in POMC also cause human obesity 23,29,31. 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 (r 2 = 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 obesity 32. 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.
Speliotes et al. Page 12 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 MAGENTA 33. 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 (r 2 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 NEGR1 9. 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 (r 2 = 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. 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 liver 34 36 (Online Methods, Table 1 and Supplementary Table 7). Significant cisassociations, 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
Speliotes et al. Page 13 Discussion 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. 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 (r 2 = 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-analyses 39,40. Furthermore, mutations at the MC4R locus are known to influence early-onset obesity 24,41, supporting the notion that allelic heterogeneity may be a frequent phenomenon in the genetic architecture of obesity. 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.
Speliotes et al. Page 14 A primary goal of human genetic discovery is to improve understanding of the biology of conditions such as obesity 42. 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 finemapping 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 obesity 43,44, new insights into biology are critically needed to guide the development and application of future therapies and interventions. Supplementary Material Acknowledgments Refer to Web version on PubMed Central for supplementary material. 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
Speliotes et al. Page 15 References 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. 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Speliotes et al. Page 19 Figure 1. Genome-wide association results for the BMI meta-analysis (a) Manhattan plot showing the significance of association between all SNPs and BMI in the stage 1 meta-analysis, highlighting SNPs previously reported to show genome-wide significant association with BMI (blue), weight or waist circumference (green), and the 18 new regions described here (red). The 19 SNPs that reached genome-wide significance at Stage 1 (13 previously reported and 6 new) are listed in Table 1). (b) Quantile-quantile (Q- Q) plot of SNPs in stage 1 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 SNPs from the four loci for waist/weight (green) and after excluding SNPs from all 32 confirmed loci (red). The plot was abridged at the Y-axis (at P < 10 20 ) to better visualise the excess of small P-values after excluding the 32 confirmed loci (Supplementary Fig. 3 shows full-scale Q-Q plot). The shaded region is the 95% concentration band. (c) Plot of effect size (in inverse normally transformed units (invbmi)) versus effect allele frequency of newly identified and previously identified BMI variants after stage 1 + stage 2 analysis; including the 10 previously identified BMI loci (blue), the four previously identified waist and weight loci (green) and the 18 newly identified BMI loci (blue). The dotted lines represent the minimum effect sizes that could be identified for a given effectallele frequency with 80% (upper line), 50% (middle line), and 10% (lower line) power, assuming a sample size of 123,000 individuals and a α-level of 5 10 8.
Speliotes et al. Page 20 Figure 2. Combined impact of risk alleles on BMI/obesity (a) Combined effect of risk alleles on average BMI in the population-based Atherosclerosis Risk in Communities (ARIC) study (n = 8,120 individuals of European descent). For each individual, the number of best guess replicated (n = 32) risk alleles from imputed data (0,1,2) per SNP was weighted for their relative effect sizes estimated from the stage 2 data. Weighted risk alleles were summed for each individual and the overall individual sum was rounded to the nearest integer to represent the individual s risk allele score (range 16 44). Along the x-axis, individuals in each risk allele category are shown (grouped 21 and 38 at the extremes), and the mean BMI (+/ SEM) is plotted (y axis on right), with the line representing the regression of the mean BMI values across the risk-allele scores. The histogram (y-axis on left) represents the number of individuals in each risk-score category. (b) The area under the ROC curve (AUC) of two different models predicting the risk of obesity (BMI = 30 kg/m 2 ) in the n = 8,120 genotyped individuals of European descent in the ARIC Study. Model 1, represented by the solid line, includes age, age 2, and sex (AUC = 0.515, P = 0.023 for difference from AUC null = 0.50). Model 2, represented by the dashed line, includes age, age 2, sex, and the n = 32 confirmed BMI SNPs (AUC = 0.0575, P < 10 5 for difference from AUC null = 0.50). The difference between both AUCs is significant (P < 10 4 ).