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LARGE-SCALE GENETIC STUDIES OF BODY MASS INDEX PROVIDE INSIGHT
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INTO THE BIOLOGICAL BASIS OF OBESITY
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Adam E Locke1,*, Bratati Kahali2,*, Sonja I Berndt3,*, Anne E Justice4,*, Tune H Pers5,6,7,8,*,
Felix R Day9, Corey Powell2, Sailaja Vedantam5,6, Martin L Buchkovich10, Jian Yang11,12,
Damien C Croteau-Chonka10,13, Tonu Esko5,6,7,14, Tove Fall15,16, Teresa Ferreira17, Stefan
Gustafsson15, Zoltan Kutálik18,19,20, Jian'an Luan9, Reedik Mägi14,17, Joshua C Randall17,21,
Thomas W Winkler22, Andrew R Wood23, Tsegaselassie Workalemahu24, Jessica D
Faul25, Jennifer A Smith26, Jing Hua Zhao9, Wei Zhao26, Jin Chen27, Rudolf Fehrmann28,
Åsa K Hedman15,17, Juha Karjalainen28, Ellen M Schmidt29, Devin Absher30, Najaf Amin31,
Denise Anderson32, Marian Beekman33,34, Jennifer L Bolton35, Jennifer L BraggGresham1, Steven Buyske36,37, Ayse Demirkan31,38, Guohong Deng39,40,41, Georg B
Ehret42,43, Bjarke Feenstra44, Mary F Feitosa45, Krista Fischer14, Anuj Goel17,46, Jian
Gong47, Anne U Jackson1, Stavroula Kanoni48, Marcus E Kleber49,50, Kati Kristiansson51,
Unhee Lim52, Vaneet Lotay53, Massimo Mangino54, Irene Mateo Leach55, Carolina
Medina-Gomez56,57,58, Sarah E Medland59, Michael A Nalls60, Cameron D Palmer5,6,
Dorota Pasko23, Sonali Pechlivanis61, Marjolein J Peters56,58, Inga Prokopenko17,62,63,
Dmitry Shungin64,65,66, Alena Stančáková67, Rona J Strawbridge68, Yun Ju Sung69,
Toshiko Tanaka70, Alexander Teumer71, Stella Trompet72,73, Sander W van der Laan74,
Jessica van Setten75, Jana V Van Vliet-Ostaptchouk76, Zhaoming Wang77,78, Loïc
Yengo79,80,81, Weihua Zhang39,82, Aaron Isaacs31,83, Eva Albrecht84, Johan Ärnlöv15,85,
Gillian M Arscott86, Antony P Attwood87,88, Stefania Bandinelli89, Amy Barrett62, Isabelita
N Bas90, Claire Bellis91, Amanda J Bennett62, Christian Berne92, Roza Blagieva93,
Matthias Blüher94,95, Stefan Böhringer33,96, Lori L Bonnycastle97, Yvonne Böttcher94,
Heather A Boyd44, Marcel Bruinenberg98, Ida H Caspersen99, Jin Chen27, Robert
Clarke100, E Warwick Daw45, Anton JM de Craen73, Graciela Delgado de Moissl49, Josh C
Denny101, Maria Dimitriou102, Alex SF Doney103, Niina Eklund51,104, Karol Estrada6,58,105,
Elodie Eury79,80,81, Lasse Folkersen68, Ross M Fraser35, Melissa E Garcia106, Frank
Geller44, Vilmantas Giedraitis107, Bruna Gigante108, Alan S Go109, Alain Golay110, Alison H
Goodall111,112, Scott D Gordon59, Mathias Gorski22,113, Hans-Jörgen Grabe114,115, Harald
Grallert116, Tanja B Grammer49, Jürgen Gräßler117, Henrik Grönberg16, Christopher J
Groves62, Gaëlle Gusto118, Jeffrey Haessler47, Per Hall16, Toomas Haller14, Goran
Hallmans119, Catharina A Hartman120, Maija Hassinen121, Caroline Hayward122, Nancy L
Heard-Costa123,124, Quinta Helmer33,96,125, Christian Hengstenberg126,127, Oddgeir
Holmen128, Jouke-Jan Hottenga129, Alan L James130,131, Janina M. Jeff53, Åsa
Johansson132, Jennifer Jolley87,88, Thorhildur Juliusdottir17, Abel N Kho133, Leena
Kinnunen51, Wolfgang Koenig50, Markku Koskenvuo134, Wolfgang Kratzer135, Jaana
Laitinen136, Claudia Lamina137, Karin Leander108, Nanette R Lee90, Peter Lichtner138, Lars
Lind139, Jaana Lindström51, Ken Sin Lo140, Stéphane Lobbens79,80,81, Roberto Lorbeer141,
Yingchang Lu53,142, François Mach43, Patrik KE Magnusson16, Anubha Mahajan17, Wendy
L McArdle143, Stela McLachlan35, Cristina Menni54, Sigrun Merger93, Evelin Mihailov14,144,
Lili Milani14, Alireza Moayyeri54, Keri L Monda4,145, Mario A Morken97, Antonella Mulas146,
Gabriele Müller147, Martina Müller-Nurasyid84,148,149, Arthur W Musk150, Ramaiah
Nagaraja151, Markus M Nöthen152,153, Ilja M Nolte154, Stefan Pilz155,156, Nigel W
Rayner17,21,62, Frida Renstrom64, Rainer Rettig157, Janina S Ried84, Stephan Ripke105,158,
Neil R Robertson17,62, Lynda M Rose159, Serena Sanna146, Hubert Scharnagl160, Salome
Scholtens154, Fredrick R Schumacher161, William R Scott39,82, Thomas Seufferlein135,
Jianxin Shi162, Albert Vernon Smith163,164, Joanna Smolonska28,154, Alice V Stanton165,
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Valgerdur Steinthorsdottir166, Kathleen Stirrups21,48, Heather M Stringham1, Johan
Sundström139, Morris A Swertz28, Amy J Swift97, Ann-Christine Syvänen15, Sian-Tsung
Tan39,167, Bamidele O Tayo168, Barbara Thorand169, Gudmar Thorleifsson166, Jonathan P
Tyrer170, Hae-Won Uh33,96, Floor VA van Oort171, Liesbeth Vandenput172, Sita H
Vermeulen173,174, Niek Verweij55, Judith M Vonk154, Lindsay L Waite30, Helen R Warren175,
Dawn Waterworth176, Michael N Weedon23, Lynne R Wilkens52, Christina
Willenborg177,178, Tom Wilsgaard179, Mary K Wojczynski45, Andrew Wong180, Alan F
Wright122, Qunyuan Zhang45, The LifeLines Cohort Study181,182, Eoin P. Brennan183,
Murim Choi184, Zari Dastani185, Alexander W Drong17, Per Eriksson68, Anders FrancoCereceda186, Jesper Gådin68, Ali G Gharavi187, Michael E Goddard188,189, Robert E
Handsaker6,7, Jinyan Huang190, Fredrik Karpe62,191, Sekar Kathiresan6,192, Sarah
Keildson17, Krzysztof Kiryluk187, Michiaki Kubo193, Jong-Young Lee194, Liming Liang190,195,
Richard P Lifton196, Baoshan Ma190,197, Steven A McCarroll6,7,158, Amy J McKnight198,
Josine L Min143, Miriam F Moffatt167, Grant W Montgomery59, Joanne M Murabito123,199,
George Nicholson200,201, Dale R Nyholt59, Yukinori Okada202,203, John RB Perry17,23,54,
Rajkumar Dorajoo204, Eva Reinmaa14, Rany M Salem5,6,7, Niina Sandholm205,206,207,
Robert A Scott9, Lisette Stolk33,58, Atsushi Takahashi202, Toshihiro Tanaka203,208,209,
Ferdinand M van ´t Hooft68, Anna AE Vinkhuyzen11, Harm-Jan Westra28, Wei Zheng210,
Krina T Zondervan17,211, The ADIPOGen Consortium182,212, The AGEN-BMI Working
Group182,213, The CARDIOGRAMplusC4D Consortium, The CKDGen Consortium, The
eMERGE Consortium214, The GLGC215, The ICBP182,216, The MAGIC Investigators217,
The MuTHER Consortium182,218, The MIGen Consortium182,219, The PAGE
Consortium182,220, The ReproGen Consortium, The GENIE Consortium182,221, The
International Endogene Consortium182, Andrew C Heath222, Dominique Arveiler223,
Stephan JL Bakker224, John Beilby86,225, Richard N Bergman226, John Blangero91, Pascal
Bovet227,228, Harry Campbell35, Mark J Caulfield175, Giancarlo Cesana229, Aravinda
Chakravarti42, Daniel I Chasman159,230, Peter S Chines97, Francis S Collins97, Dana C
Crawford231,232, L Adrienne Cupples233, Daniele Cusi234,235, John Danesh236, Ulf de
Faire108, Hester M den Ruijter74,237, Anna F Dominiczak238, Raimund Erbel239, Jeanette
Erdmann177,178, Johan G Eriksson51,240,241, Martin Farrall17,46, Stephan B Felix77,242, Ele
Ferrannini243,244, Jean Ferrières245, Ian Ford246, Nita G Forouhi9, Terrence Forrester247,
Oscar H Franco56,57, Ron T Gansevoort224, Pablo V Gejman248, Christian Gieger84, Omri
Gottesman53, Vilmundur Gudnason163,164, Ulf Gyllensten132, Alistair S Hall249, Tamara B
Harris106, Andrew T Hattersley250, Andrew A Hicks251,252, Lucia A Hindorff253, Aroon D
Hingorani254, Albert Hofman56,57, Georg Homuth71, G Kees Hovingh255, Steve E
Humphries256, Steven C Hunt257, Elina Hypponen258,259,260, Thomas Illig116,261, Kevin B
Jacobs3,262, Marjo-Riitta Jarvelin82,263,264,265,266,267, Karl-Heinz Jöckel61, Berit Johansen99,
Pekka Jousilahti51, J Wouter Jukema72,268,269, Antti M Jula51, Jaakko Kaprio51,104,134, John
JP Kastelein255, Sirkka M Keinanen-Kiukaanniemi270,271, Lambertus A Kiemeney173,272,
Paul Knekt51, Jaspal S Kooner39,167,273, Charles Kooperberg47, Peter Kovacs94,95, Aldi T
Kraja45, Meena Kumari274, Johanna Kuusisto275, Timo A Lakka121,276, Claudia
Langenberg9,274, Loic Le Marchand52, Terho Lehtimäki277, Valeriya Lyssenko278, Satu
Männistö51, André Marette279,280, Tara C Matise37, Colin A McKenzie247, Barbara
McKnight281, Frans L Moll282, Andrew D Morris103, Andrew P Morris14,17,283, Jeffrey C
Murray284, Mari Nelis14, Claes Ohlsson172, Albertine J Oldehinkel120, Ken K Ong9,180,
Pamela AF Madden222, Gerard Pasterkamp74, John F Peden285, Annette Peters116,126,169,
Dirkje S Postma286, Peter P Pramstaller251,252,287, Jackie F Price35, Lu Qi13,288, Olli T
Raitakari289,290, Tuomo Rankinen291, DC Rao45,69,222, Treva K Rice69,222, Paul M
Ridker159,230, John D Rioux140,292, Marylyn D. Ritchie293, Igor Rudan35,294, Veikko
Salomaa51, Nilesh J Samani111,112, Jouko Saramies295, Mark A Sarzynski291, Heribert
Schunkert126,127, Peter EH Schwarz117,296, Peter Sever297, Alan R Shuldiner298,299, Juha
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Sinisalo300, Ronald P Stolk154, Konstantin Strauch84,149, Anke Tönjes94,95, DavidAlexandre Trégouët301,302,303,Angelo Tremblay304, Elena Tremoli305, Jarmo Virtamo51,
Marie-Claude Vohl280,306, Uwe Völker71,77, Gérard Waeber307, Gonneke Willemsen129,
Jacqueline C Witteman57, Wolfgang Koenig50, M Carola Zillikens56,58, Linda S Adair308,
Philippe Amouyel309, Folkert W Asselbergs254,268,310 Themistocles L Assimes311, Murielle
Bochud227,228, Bernhard O Boehm93,312, Eric Boerwinkle313, Stefan R Bornstein117, Erwin
P Bottinger53, Claude Bouchard291, Stéphane Cauchi79,80,81, John C Chambers39,82,273,
Stephen J Chanock3, Richard S Cooper168, Paul IW de Bakker75,314,315, George
Dedoussis102, Luigi Ferrucci70, Paul W Franks64,65,288, Philippe Froguel63,79,80,81, Leif C
Groop104,316, Christopher A Haiman161, Anders Hamsten68, M Geoffrey Hayes133, Jennie
Hui86,225,258, David J. Hunter13,190,288, Kristian Hveem128, Robert C Kaplan317, Mika
Kivimaki274, Diana Kuh180, Markku Laakso275, Yongmei Liu318, Nicholas G Martin59,
Winfried März49,160,319, Mads Melbye44, Andres Metspalu14,144, Susanne Moebus61,
Patricia B Munroe175, Inger Njølstad179, Ben A Oostra31,83,320, Colin NA Palmer103, Nancy
L Pedersen16, Markus Perola14,51,104, Louis Pérusse280,304, Ulrike Peters47, Chris Power260,
Thomas Quertermous311, Rainer Rauramaa121,321, Fernando Rivadeneira56,57,58, Timo E
Saaristo322,323, Danish Saleheen236,324,325, Naveed Sattar326, Eric E Schadt327, David
Schlessinger151, P Eline Slagboom33,34, Harold Snieder154, Tim D Spector54, Kari
Stefansson166,328, Michael Stumvoll94,95, Jaakko Tuomilehto51,329,330,331, André G
Uitterlinden56,57,58, Matti Uusitupa332,333, Pim van der Harst28,55,268, Mark Walker334, Henri
Wallaschofski77,78, Nicholas J Wareham9, Hugh Watkins17,46, David R Weir25, H-Erich
Wichmann335,336,337, James F Wilson35, Pieter Zanen338, Ingrid B Borecki45, Panos
Deloukas21,48,339, Caroline S Fox123, Iris M Heid22,84, Jeffrey R O'Connell298, David P
Strachan340, Unnur Thorsteinsdottir166,328, Cornelia M van Duijn31,56,57,83, Gonçalo R
Abecasis1, Lude Franke28, Timothy M Frayling23, Mark I McCarthy17,62,341, Peter M
Visscher11,12, André Scherag61,342, Cristen J Willer27,29,343, Michael Boehnke1, Karen L
Mohlke10, Cecilia M Lindgren6,17, Jacques S. Beckmann19,20,344, Inês Barroso21,345,346, Kari
E North4,347,§, Erik Ingelsson15,17,§, Joel N Hirschhorn5,6,7,§, Ruth JF Loos9,53,142,348,§,
Elizabeth K Speliotes2,§
Affiliations
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Center for Statistical Genetics, Department of Biostatistics, University of Michigan,
Ann Arbor, MI 48109, USA
Department of Internal Medicine, Division of Gastroenterology, and Department of
Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI
48109
Division of Cancer Epidemiology and Genetics, National Cancer Institute, National
Institutes of Health, Bethesda, MD 20892, USA
Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel
Hill, NC 27599, USA
Divisions of Endocrinology and Genetics and Center for Basic and Translational
Obesity Research, Boston Children's Hospital, Boston, MA 02115, USA
Broad Institute of the Massachusetts Institute of Technology and Harvard
University, Cambridge 02142, MA, USA
Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
Center for Biological Sequence Analysis, Department of Systems Biology,
Technical University of Denmark, Lyngby 2800, Denmark
MRC Epidemiology Unit, University of Cambridge, Institute of Metabolic Science,
Addenbrooke’s Hospital, Hills Road, Cambridge, CB2 0QQ, UK
Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
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Queensland Brain Institute, The University of Queensland, Brisbane 4072,
Australia
The University of Queensland Diamantina Institute, The Translation Research
Institute, Brisbane 4012, Australia
Channing Division of Network Medicine, Department of Medicine, Brigham and
Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
Estonian Genome Center, University of Tartu, Tartu 51010, Estonia
Department of Medical Sciences, Molecular Epidemiology and Science for Life
Laboratory, Uppsala University, Uppsala 75185, Sweden
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet,
Stockholm 17177, Sweden
Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN,
UK
Institute of Social and Preventive Medicine (IUMSP), Centre Hospitalier
Universitaire Vaudois (CHUV), Lausanne 1010, Switzerland
Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
Department of Medical Genetics, University of Lausanne, Lausanne 1005,
Switzerland
Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK
Department of Genetic Epidemiology, Institute of Epidemiology and Preventive
Medicine, University of Regensburg, Regensburg, Germany, D-93053 Regensburg,
Germany
Genetics of Complex Traits, University of Exeter Medical School, University of
Exeter, Exeter EX1 2LU, UK
Harvard School of Public Health, Department of Nutrition, Harvard University,
Boston, MA 2115, USA
Survey Research Center, Institute for Social Research, University of Michigan, Ann
Arbor, MI 48104, USA
Department of Epidemiology, University of Michigan, Ann Arbor, MI 48109, USA
Department of Internal Medicine, Division of Cardiovascular Medicine, University of
Michigan, Ann Arbor, MI, USA
Department of Genetics, University Medical Center Groningen, University of
Groningen, 9700 RB Groningen, The Netherlands
Department of Computational Medicine and Bioinformatics, University of Michigan,
Ann Arbor, MI, USA
HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, USA
Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University
Medical Center, 3015 GE Rotterdam, The Netherlands
Telethon Institute for Child Health Research, Centre for Child Health Research,
The University of Western Australia, Western Australia 6008, Australia
Netherlands Consortium for Healthy Aging (NCHA), Leiden University Medical
Center, Leiden 2300 RC, The Netherlands
Department of Molecular Epidemiology, Leiden University Medical Center, 2300
RC Leiden, The Netherlands
Centre for Population Health Sciences, University of Edinburgh, Teviot Place,
Edinburgh, EH8 9AG, Scotland, UK
Department of Statistics & Biostatistics, Rutgers University, Piscataway, N.J. USA
Department of Genetics, Rutgers University, Piscataway, N.J. USA.
Department of Human Genetics, Leiden University Medical Center, 2333 ZC
Leiden, The Netherlands
Ealing Hospital NHS Trust, Middlesex UB1 3HW, UK
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Department of Gastroenterology and Hepatology, Imperial College London,
London W2 1PG, UK
Institute of infectious Diseases, Southwest Hospital, Third Military Medical
University, Chongqing, China
Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic
Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205,
USA
Cardiology, Department of Specialties of Internal Medicine, Geneva University
Hospital, Geneva 1211, Switzerland
Department of Epidemiology Research, Statens Serum Institut, Copenhagen DK2300, Denmark
Department of Genetics, Washington University School of Medicine, St. Louis, MO
63110, USA
Division of Cardiovacular Medicine, Radcliffe Department of Medicine, University of
Oxford, Oxford OX3 9DU, UK
Division of Public Health Sciences, Fred Hutchinson Cancer Research Center,
Seattle, WA 98109, USA
William Harvey Research Institute, Barts and The London School of Medicine and
Dentistry, Queen Mary University of London, EC1M 6BQ UK
Institute of Public Health, Social and Preventive Medicine, Mannheim Medical
Faculty, Heidelberg University, D-68167 Mannheim, Germany
Department of Internal Medicine II, Ulm University Medical Centre, D-89081 Ulm,
Germany
National Institute for Health and Welfare, FI-00271 Helsinki, Finland
Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI USA
The Charles Bronfman Institute for Personalized Medicine, Icahn School of
Medicine at Mount Sinai, New York, NY 10029, USA
Department of Twin Research and Genetic Epidemiology, King’s College London,
London SE1 7EH, UK
Department of Cardiology, University Medical Center Groningen, University of
Groningen, 9700RB Groningen, The Netherlands
Netherlands Consortium for Healthy Aging (NCHA), 3015GE Rotterdam, The
Netherlands
Department of Epidemiology, Erasmus Medical Center, 3015GE Rotterdam, The
Netherlands
Department of Internal Medicine, Erasmus Medical Center, 3015GE Rotterdam,
The Netherlands
QIMR Berghofer Medical Research Institute, Queensland 4006, Australia
Laboratory of Neurogenetics, National Institute on Aging, National Institutes of
Health, Bethesda, MD 20892, USA
Institute for Medical Informatics, Biometry and Epidemiology (IMIBE), University
Hospital Essen, Essen, Germany
Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford,
Oxford OX3 7LJ, UK
Department of Genomics of Common Disease, School of Public Health, Imperial
College London, Hammersmith Hospital, London, UK
Department of Clinical Sciences, Genetic & Molecular Epidemiology Unit, Lund
University Diabetes Center, Skåne University Hosptial, Malmö 205 02, Sweden
Department of Public Health and Clinical Medicine, Unit of Medicine, Umeå
University, Umeå 901 87, Sweden
Department of Odontology, Umeå University, Umeå 901 85, Sweden
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University of Eastern Finland, FI-70210 Kuopio, Finland
Atherosclerosis Research Unit, Center for Molecular Medicine, Department of
Medicine, Karolinska Institutet, Stockholm 17176, Sweden
Division of Biostatistics, Washington University School of Medicine, St. Louis, MO
63110, USA
Translational Gerontology Branch, National institute on Aging, Baltimore MD 21225,
USA
Interfaculty Institute for Genetics and Functional Genomics, University Medicine
Greifswald, D-17475 Greifswald, Germany
Department of Cardiology, Leiden University Medical Center, 2300 RC Leiden, The
Netherlands
Department of Gerontology and Geriatrics, Leiden University Medical Center, 2300
RC Leiden, The Netherlands
Experimental Cardiology Laboratory, Division Heart and Lungs, University Medical
Center Utrecht, 3584 CX Utrecht, The Netherlands
Department of Medical Genetics, University Medical Center Utrecht, 3584 CX
Utrecht, The Netherlands
Department of Endocrinology, University of Groningen, University Medical Center
Groningen, Groningen, 9700 RB, The Netherlands
Deutsches Forschungszentrum für Herz-Kreislauferkrankungen (DZHK) (German
Centre for Cardiovascular Research), partner site Greifswald, D-17475 Greifswald,
Germany
Institute of Clinical Chemistry and Laboratory Medicine, University Medicine
Greifswald, D-17475 Greifswald, Germany
CNRS UMR 8199, F-59019 Lille, France
European Genomic Institute for Diabetes, F-59000 Lille, France
Université de Lille 2, F-59000 Lille, France
Department of Epidemiology and Biostatistics, Imperial College London, London
W2 1PG, UK
Center for Medical Sytems Biology, Leiden, The Netherlands
Institute of Genetic Epidemiology, Helmholtz Zentrum München - German
Research Center for Environmental Health, D-85764 Neuherberg, Germany
School of Health and Social Studies, Dalarna University, Falun, Sweden
PathWest Laboratory Medicine of Western Australia, NEDLANDS, Western
Australia 6009, Australia
Department of Haematology, University of Cambridge, Cambridge CB2 0PT, UK
NHS Blood and Transplant, Cambridge CB2 0PT, UK
Geriatric Unit, Azienda Sanitaria Firenze (ASF), Florence, Italy
USC-Office of Population Studies Foundation, Inc., University of San Carlos, Cebu
City 6000, Philippines
Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX,
USA
Department of Medical Sciences, Endocrinology, Diabetes and Metabolism,
Uppsala University, Uppsala 75185, Sweden
Division of Endocrinology, Diabetes and Metabolism, Ulm University Medical
Centre, D-89081 Ulm, Germany
IFB Adiposity Diseases, University of Leipzig, D-04103 Leipzig, Germany
Department of Medicine, University of Leipzig, D-04103 Leipzig, Germany
Department of Medical Statistics and Bioinformatics, Leiden University Medical
Center, 2300 RC Leiden, The Netherlands
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Genome Technology Branch, National Human Genome Research Institute, NIH,
Bethesda, MD 20892, USA
LifeLines, University Medical Center Groningen, University of Groningen, 9700 RB
Groningen, The Netherlands
Department of Biology, Norwegian University of Science and Technology,
Trondheim, Norway
Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of
Population Health, University of Oxford, Oxford OX3 7LF, UK
Department of Biomedical Informatics, Vanderbilt University, Nashville, TN 37232,
USA
Department of Dietetics-Nutrition, Harokopio University, Athens, Greece
Medical Research Institute, University of Dundee, Ninewells Hospital and Medical
School, Dundee DD1 9SY, UK
Institute for Molecular Medicine, University of Helsinki, FI-00014 Helsinki, Finland
Analytic and Translational Genetics Unit, Massachusetts General Hospital and
Harvard Medical School, Boston, MA, USA
Laboratory of Epidemiology and Population Sciences, National Institute on Aging,
NIH, Bethesda, MD 20892, USA
Department of Public Health and Caring Sciences, Geriatrics, Uppsala University,
Uppsala 75185, Sweden
Division of Cardiovascular Epidemiology, Institute of Environmental Medicine,
Karolinska Institutet, Stockholm, Sweden, Stockholm 17177, Sweden
Kaiser Permanente, Division of Research, Oakland, CA 94612, USA
Service of Therapeutic Education for Diabetes, Obesity and Chronic Diseases,
Geneva University Hospital, Geneva CH-1211, Switzerland
Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital,
Leicester LE3 9QP, UK
National Institute for Health Research (NIHR) Leicester Cardiovascular Disease
Biomedical Research Unit, Glenfield Hospital, Leicester, LE3 9QP, UK
Department of Internal Medicine II, University Medical Center Regensburg,
Regensburg, Germany, D-93053 Regensburg, Germany
Department of Psychiatry and Psychotherapy, University Medicine Greifswald,
HELIOS-Hospital Stralsund, D-17475 Greifswald, Germany
German Center for Neurodegenerative Diseases (DZNE), Rostock, Greifswald, D17475 Greifswald, Germany
Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German
Research Center for Environmental Health, D-85764 Neuherberg, Germany
Department of Medicine III, University of Dresden, Medical Faculty Carl Gustav
Carus, D-01307 Dresden, Germany
Institut inter Régional pour la Santé, Synergies, F-37520 La Riche, France
Department of Public Health and Primary Care, Units of Nutritional Research,
Umeå University Hosptial, Umeå 90187, Sweden
Department of Psychiatry, University of Groningen, University Medical Center
Groningen, Groningen, The Netherlands
Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine,
University of Edinburgh, Western General Hospital, Edinburgh, EH4 2XU, Scotland,
UK
National Heart, Lung, and Blood Institute, the Framingham Heart Study,
Framingham MA 01702, USA
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124. Department of Neurology, Boston University School of Medicine, Boston, MA
02118, USA
125. Faculty of Psychology and Education, VU University Amsterdam, Amsterdam, The
Netherlands
126. Deutsches Forschungszentrum für Herz-Kreislauferkrankungen (DZHK) (German
Research Centre for Cardiovascular Research), Munich Heart Alliance, D-80636
Munich, Germany
127. Deutsches Herzzentrum München, Technische Universität München, D-80636
Munich, Germany
128. Department of Public Health and General Practice, Norwegian University of
Science and Technology, Trondheim 7489, Norway
129. Biological Psychology, VU University Amsterdam, 1081BT Amsterdam, The
Netherlands
130. Department of Pulmonary Physiology and Sleep Medicine, NEDLANDS, Western
Australia 6009, Australia
131. School of Medicine and Pharmacology, University of Western Australia, CRAWLEY
6009, Australia
132. Uppsala University, Department of Immunology, Genetics, Pathology, SciLifeLab,
Rudbeck Laboratory, SE-751 85, Uppsala, Sweden
133. Department of Medicine, Northwestern University Feinberg School of Medicine,
Chicago, IL 60611, USA
134. Hjelt Institute Department of Public Health, University of Helsinki, FI-00014 Helsinki,
Finland
135. Department of Internal Medicine I, Ulm University Medical Centre, D-89081 Ulm,
Germany
136. Finnish Institute of Occupational Health, Oulu, Finland
137. Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and
Clinical Pharmacology, Innsbruck Medical University, 6020 Innsbruck, Austria
138. Institute of Human Genetics, Helmholtz Zentrum München - German Research
Center for Environmental Health, D-85764 Neuherberg, Germany
139. Department of Medical Sciences, Cardiovascular Epidemiology, Uppsala University,
Uppsala 75185, Sweden
140. Montreal Heart Institute, Montreal, Quebec H1T 1C8, Canada
141. Institute for Community Medicine, University Medicine Greifswald, D-17475
Greifswald, Germany
142. The Genetics of Obesity and Related Metabolic Traits Program, The Icahn School
of Medicine at Mount Sinai, New York, NY 10029, USA
143. School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN,
UK
144. Institute of Molecular and Cell Biology, University of Tartu, Tartu 51010, Estonia
145. The Center for Observational Research, Amgen, Inc., Thousand Oaks, CA 91320,
USA
146. Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle
Ricerche, Cagliari, Sardinia 09042, Italy
147. Institute for Medical Informatics and Biometrics, University of Dresden, Medical
Faculty Carl Gustav Carus, D-01307 Dresden, Germany
148. Department of Medicine I, University Hospital Grosshadern, Ludwig-MaximiliansUniversität, D-81377 Munich, Germany
149. Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic
Epidemiology, Ludwig-Maximilians-Universität, D-85764 Neuherberg, Germany
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150. Department of Respiratory Medicine, Sir Charles Gairdner Hospital, NEDLANDS,
Western Australia 6009, Australia
151. Laboratory of Genetics, National Institute on Aging, Baltimore, MD 21224, USA
152. Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
153. Institute of Human Genetics, University of Bonn, Bonn, Germany
154. Department of Epidemiology, University Medical Center Groningen, University of
Groningen, 9700 RB Groningen, The Netherlands
155. Department of Epidemiology and Biostatistics, EMGO Institute for Health and Care
Research, VU University Medical Center, Amsterdam, The Netherlands
156. Department of Internal Medicine, Division of Endocrinology and Metabolism,
Medical University of Graz, 8036 Graz, Austria
157. Institute of Physiology, University Medicine Greifswald, D-17495 Karlsburg,
Germany
158. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard,
Cambridge, MA 02142, USA
159. Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA
02215, USA
160. Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical
University of Graz, Graz 8036, Austria
161. Department of Preventive Medicine, Keck School of Medicine, University of
Southern California, Los Angeles, CA, USA
162. National Cancer Institute, Bethesda, MD, USA
163. Icelandic Heart Association, Kopavogur 201, Iceland
164. University of Iceland, Reykjavik 101, Iceland
165. Molecular & Cellular Therapeutics, Royal College of Surgeons in Ireland, 123 St
Stephens Green, Dublin 2, Ireland
166. deCODE Genetics, Amgen inc., Reykjavik 101, Iceland
167. National Heart and Lung Institute, Imperial College, London W12 0NN, UK
168. Department of Public Health Sciences, Stritch School of Medicine, Loyola
University of Chicago, Maywood, IL 61053, USA
169. Institute of Epidemiology II, Helmholtz Zentrum München - German Research
Center for Environmental Health, Neuherberg, Germany, D-85764 Neuherberg,
Germany
170. Department of Ocology, University of Cambridge, Cambridge CB2 0QQ, UK
171. Department of Child and Adolescent Psychiatry, Psychology, Erasmus University
Medical Centre, 3000 CB Rotterdam, The Netherlands
172. Department of Internal Medicine, Institute of Medicine, Sahlgrenska Academy,
University of Gothenburg, Gothenburg 413 45, Sweden
173. Department for Health Evidence, Radboud University Medical Centre, 6500 HB
Nijmegen, The Netherlands
174. Department of Genetics, Radboud University Medical Centre, 6500 HB Nijmegen,
The Netherlands
175. 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
176. Genetics, GlaxoSmithKline, King of Prussia, PA, USA
177. Deutsches Forschungszentrum für Herz-Kreislauferkrankungen (DZHK) (German
Centre for Cardiovascular Research), partner site Hamburg, Kiel, D-23562 Lübeck,
Germany
178. Institut für Integrative und Experimentelle Genomik, Universität zu Lübeck, D23562 Lübeck, Germany
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179. Department of Clinical Medicine, Faculty of Health Sciences, University of Tromsø,
Tromsø, Norway
180. MRC Unit for Lifelong Health and Ageing at University College London, London
WC1B 5JU, UK
181. The LifeLines Cohort Study, University of Groningen, University Medical Center
Groningen, Groningen, The Netherlands
182. Membership to this consortium is provided below.
183. Diabetes Complications Research Centre, Conway Institute, School of Medicine
and Medical Sciences, University College Dublin, Dublin, Ireland
184. Department of Biomedical Sciences, Seoul National University College of Medicine,
Seoul, Korea
185. Lady Davis Institute, Departments of Human Genetics, Epidemiology and
Biostatistics, McGill University, Montréal, Québec H3T1E2, Canada
186. Cardiothoracic Surgery Unit, Department of Molecular Medicine and Surgery,
Karolinska Institutet, Stockholm 17176, Sweden
187. Department of Medicine, Columbia University College of Physicians and Surgeons,
New York NY, USA
188. Biosciences Research Division, Department of Primary Industries, Victoria 3083,
Australia
189. Department of Food and Agricultural Systems, University of Melbourne, Victoria
3010, Australia
190. Harvard School of Public Health, Department of Epidemiology, Harvard University,
Boston, MA 2115, USA
191. NIHR Oxford Biomedical Research Centre, OUH Trust, Oxford OX3 7LE, UK
192. Cardiovascular Research Center, Massachusetts General Hospital, Harvard
Medical School, Boston, MA, USA
193. Laboratory for Genotyping Development, RIKEN Center for Integrative Medical
Sciences , Yokohama, Japan
194. Center for Genome Science, National Institute of Health, Chungcheongbuk-do,
Chungbuk 363-951, Republic of Korea
195. Harvard School of Public Health, Department of Biostatistics, Harvard University,
Boston, MA 2115, USA
196. Department of Genetics, Howard Hughes Medical Institute, Yale University School
of Medicine, New Haven, New Haven CT, USA
197. College of Information Science and Technology, Dalian Maritime University, Dalian,
Liaoning 116026, China
198. Nephrology Research, Centre for Public Health, Queen's University of Belfast,
Belfast, Co. Down BT9 7AB, UK
199. Section of General Internal Medicine, Boston University School of Medicine,
Boston, MA 2118, USA
200. Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1
3TG, UK
201. MRC Harwell, Harwell Science and Innovation Campus, Harwell, UK
202. Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences,
Yokohama, Japan
203. Department of Human Genetics and Disease Diversity, Graduate School of
Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
204. Genome Institute of Singapore, Agency for Science, Technology and Research,
Singapore
205. Department of Biomedical Engineering and Computational Science, Aalto
University School of Science, Helsinki, Finland
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206. Department of Medicine, Division of Nephrology, Helsinki University Central
Hospital, FI-00290 Helsinki, Finland
207. Folkhälsan Institute of Genetics, Folkhälsan Research Center, FI-00290 Helsinki,
Finland
208. Laboratory for Cardiovascular Diseases, RIKEN Center for Integrative Medical
Sciences, Yokohama, Japan
209. Division of Disease Diversity, Bioresource Research Center, Tokyo Medical and
Dental University
210. Division of Epidemiology, Department of Medicine; Vanderbilt Epidemiology
Center; and Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical
Center, Nashville, TN 37075, USA
211. Nuffield Department of Obstetrics & Gynaecology, University of Oxford, Oxford
OX3 7BN, UK
212. Adiponectin Genetic Consortium
213. The Asian Genetic Epidemiology Network - BMI Working Group
214. The electronic medical records and genomics (eMERGE) consortium
215. The Global Lipids Genetics Consortium
216. The International Consortium for Blood Pressure Genome-Wide Association
Studies
217. Meta-Analyses of Glucose and Insulin-related traits Consortium Investigators
218. The Multiple Tissue Human Expression Resource Consortium
219. Myocardial Infarction Genetics (MIGen) Consortium
220. Population Architecture using Genomics and Epidemiology Consortium
221. GEnetics of Nephropathy - an International Effort Consortium
222. Department of Psychiatry, Washington University School of Medicine, St. Louis,
MO 63110, USA
223. Department of Epidemiology and Public Health, EA3430, University of Strasbourg,
Faculty of Medicine, Strasbourg, France
224. Department of Internal Medicine, University Medical Center Groningen, University
of Groningen, 9700RB Groningen, The Netherlands
225. Pathology and Laboratory Medicine, The University of Western Australia, Western
Australia 6009, Australia
226. Diabetes and Obesity Research Institute, Cedars-Sinai Medical Center, Los
Angeles, California, USA
227. Institute of Social and Preventive Medicine (IUMSP), Centre Hospitalier
Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
228. Ministry of Health, Victoria, Republic of Seychelles
229. University of Milano, Bicocca, Italy
230. Harvard Medical School, Boston, MA 02115, USA
231. Center for Human Genetics Research, Vanderbilt University Medical Center,
Nashville TN 37203, USA
232. Department of Molecular Physiology and Biophysics, Vanderbilt University,
Nashville, TN 37232, USA
233. Department of Biostatistics, Boston University School of Public Health, Boston, MA
02118, USA
234. Department of Health Sciences, University of Milano, I 20142, Italy
235. Fondazione Filarete, Milano I 20139, Italy
236. Department of Public Health and Primary Care, University of Cambridge,
Cambridge, UK
237. Julius Center for Health Sciences and Primary Care, University Medical Center
Utrecht, 3584 CX Utrecht, The Netherlands
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238. Institute of Cardiovascular and Medical Sciences, Faculty of Medicine, University of
Glasgow, Glasgow G12 8TA, UK
239. Clinic of Cardiology, West-German Heart Centre, University Hospital Essen, Essen,
Germany
240. Department of General Practice and Primary Health Care, University of Helsinki,
FI-00290 Helsinki, Finland
241. Unit of General Practice, Helsinki University Central Hospital, Helsinki 00290,
Finland
242. Department of Internal Medicine B, University Medicine Greifswald, D-17475
Greifswald, Germany
243. Department of Internal Medicine, University of Pisa, Pisa, Italy
244. CNR Institute of Clinical Physiology, University of Pisa, Pisa, Italy
245. Department of Cardiology, Toulouse University School of Medicine, Rangueil
Hospital, Toulouse, France
246. Robertson Center for Biostatistics, University of Glasgow, Glasgow, UK
247. Tropical Metabolism Research Unit, Tropical Medicine Research Institute, The
University of the West Indies, Mona, Kingston 7, Jamaica
248. NorthShore University HealthSystem, Evanston, IL, University of Chicago, Chicago,
IL, USA
249. Division of Epidemiology, Leeds Institute of Genetics, Health and Therapeutics,
University of Leeds, UK
250. Institute of Biomedical & Clinical Science, University of Exeter, Barrack Road,
Exeter, EX2 5DW
251. Center for Biomedicine, European Academy Bozen, Bolzano (EURAC), Bolzano
39100, Italy
252. Affiliated Institute of the University of Lübeck, D-23562 Lübeck, Germany
253. Division of Genomic Medicine, National Human Genome Research Institute,
National Institutes of Health, Bethesda, MD, USA
254. Institute of Cardiovascular Science, University College London, WC1E 6BT, UK
255. Department of Vascular Medicine, Academic Medical Center, Amsterdam, The
Netherlands
256. Centre for Cardiovascular Genetics, Institute Cardiovascular Sciences, University
College London, London WC1E 6JJ, UK
257. Cardiovascular Genetics Division, Department of Internal Medicine, University of
Utah, Salt Lake City, Utah 84108, USA
258. School of Population Health and Sansom Institute for Health Research, University
of South Australia, Adelaide 5000, Australia
259. South Australian Health and Medical Research Institute, Adelaide, Australia
260. Centre for Paediatric Epidemiology and Biostatistics, UCL Institute of Child Health,
London WC1N 1EH, UK
261. Hannover Unified Biobank, Hannover Medical School, Hannover, Germany, D30625 Hannover, Germany
262. Core Genotyping Facility, SAIC-Frederick, Inc., NCI-Frederick, Frederick, MD
21702, USA
263. National Institute for Health and Welfare, FI-90101 Oulu, Finland
264. Department of Epidemiology and Biostatistics, MRC Health Protection Agency
(HPE) Centre for Environment and Health, School of Public Health, Imperial
College London, UK
265. Unit of Primary Care, Oulu University Hospital, FI-90220 Oulu, Finland
266. Biocenter Oulu, University of Oulu, FI-90014 Oulu, Finland
267. Institute of Health Sciences, FI-90014 University of Oulu, Finland
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268. Durrer Center for Cardiogenetic Research, Interuniversity Cardiology Institute
Netherlands-Netherlands Heart Institute, 3501 DG Utrecht, The Netherlands
269. Interuniversity Cardiology Institute of the Netherlands (ICIN), Utrecht, the
Netherlands
270. Faculty of Medicine, Institute of Health Sciences, University of Oulu, Oulu, Finland
271. Unit of General Practice, Oulu University Hospital, Oulu, Finland
272. Department of Urology, Radboud University Medical Centre, 6500 HB Nijmegen,
The Netherlands
273. Imperial College Healthcare NHS Trust, London W12 0HS, UK
274. Department of Epidemiology and Public Health, UCL London, WC1E 6BT, UK
275. Department of Medicine, University of Eastern Finland and Kuopio University
Hospital, FI-70210 Kuopio, Finland
276. Department of Physiology, Institute of Biomedicine, University of Eastern Finland,
Kuopio Campus, Kuopio, Finland
277. Department of Clinical Chemistry, Fimlab Laboratories and School of Medicine
University of Tampere, FI-33520 Tampere, Finland
278. Steno Diabetes Center A, S, Gentofte DK-2820, Denmark
279. Institut Universitaire de Cardiologie et de Pneumologie de Québec, Faculty of
Medicine, Laval University, Quebec, QC G1V 0A6, Canada
280. Institute of Nutrition and Functional Foods, Laval University, Quebec, QC G1V 0A6,
Canada
281. Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
282. Department of Surgery, University Medical Center Utrecht, 3584 CX Utrecht, The
Netherlands
283. Department of Biostatistics, University of Liverpool, Liverpool L69 3GA, UK
284. Department of Pediatrics, University of Iowa, Iowa City, Iowa IA 52242, USA
285. Illumina, Inc, Little Chesterford CB10 1XL, UK
286. University of Groningen, University Medical Center Groningen, Department of
Pulmonary Medicine and Tuberculosis, Groningen, The Netherlands
287. Department of Neurology, General Central Hospital, Bolzano 39100, Italy
288. Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts,
USA
289. Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital,
FI-20521 Turku, Finland
290. Research Centre of Applied and Preventive Cardiovascular Medicine, University of
Turku, FI-20521 Turku, Finland
291. Human Genomics Laboratory, Pennington Biomedical Research Center, Baton
Rouge, LA 70808, USA
292. Université de Montréal, Montreal, Quebec H1T 1C8, Canada
293. Center for Systems Genomics, The Pennsylvania State University, University Park,
PA 16802, USA
294. Croatian Centre for Global Health, Faculty of Medicine, University of Split, 21000
Split, Croatia
295. South Carelia Central Hospital. 53130 Lappeenranta. Finland
296. Paul Langerhans Institute Dresden, German Center for Diabetes Research (DZD),
Dresden, Germany"
297. International Centre for Circulatory Health, Imperial College London, London W2
1PG, UK
298. Program for Personalized and Genomic Medicine, and Division of Endocrinology,
Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD
21201, USA
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299. Geriatric Research and Education Clinical Center, Vetrans Administration Medical
Center, Baltimore, MD 21201, USA
300. HUCH Heart and Lungcenter, Department of Medicine, Helsinki University Central
Hospital, FI-00290 Helsinki, Finland
301. Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1166 ,F-75013, Paris, France
302. INSERM, UMR S 1166, Team Genomics and Physiopathology of Cardiovascular
Diseases, F-75013, Paris, France
303. ICAN Institute for Cardiometabolism And Nutrition, F-75013, Paris, France
304. Department of Kinesiology, Laval University, Quebec, QC G1V 0A6, Canada
305. Dipartimento di Scienze Farmacologiche e Biomolecolari, Università di Milano &
Centro Cardiologico Monzino, IRCCS, Milan 20133, italy
306. Department of Food Science and Nutrition, Laval University, Quebec, QC G1V 0A6,
Canada
307. Department of Internal Medicine, University Hospital (CHUV) and University of
Lausanne, 1011, Switzerland
308. Department of Nutrition, University of North Carolina, Chapel Hill, NC 27599, USA
309. Institut Pasteur de Lille; INSERM, U744; Université de Lille 2; F-59000 Lille, France
310. Department of Cardiology, Division Heart and Lungs, University Medical Center
Utrecht, 3584 CX Utrecht, The Netherlands
311. Department of Medicine, Stanford University School of Medicine, Stanford, CA
94305, USA
312. Lee Kong Chian School of Medicine, Imperial College London and Nanyang
Technological University, Singapore, 637553 Singapore, Singapore
313. Health Science Center at Houston, University of Texas, Houston, TX, USA
314. Department of Medicine, Division of Genetics, Brigham and Women's Hospital,
Harvard Medical School, Boston, MA 02115, USA
315. Department of Epidemiology, University Medical Center Utrecht, Utrecht, The
Netherlands
316. Lund University Diabetes Centre and Department of Clinical Science, Diabetes &
Endocrinology Unit, Lund University, Malmö 221 00, Sweden
317. Albert Einstein College of Medicine. Department of epidemiology and population
health, Belfer 1306, NY 10461, USA
318. Center for Human Genetics, Division of Public Health Sciences, Wake Forest
School of Medicine, Winston-Salem, NC 27157, USA
319. Synlab Academy, Synlab Services GmbH, Mannheim, Germany
320. Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam,
The Netherlands
321. Department of Clinical Physiology and Nuclear Medicine, Kuopio University
Hospital, Kuopio, Finland
322. Finnish Diabetes Association, Kirjoniementie 15, FI-33680 Tampere, Finland
323. Pirkanmaa Hospital District, Tampere, Finland
324. Center for Non-Communicable Diseases, Karatchi, Pakistan
325. Department of Medicine, University of Pennsylvania, Philadelphia, USA
326. BHF Glasgow Cardiovascular Research Centre, Division of Cardiovascular and
Medical Sciences, University of Glasgow, Glasgow G12 8TA, UK
327. Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at
Mount Sinai, NY 10580, USA
328. Faculty of Medicine, University of Iceland, Reykjavik 101, Iceland
329. Instituto de Investigacion Sanitaria del Hospital Universario LaPaz (IdiPAZ), Madrid,
Spain
330. Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
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331. Centre for Vascular Prevention, Danube-University Krems, 3500 Krems, Austria
332. Department of Public Health and Clinical Nutrition, University of Eastern Finland,
Finland
333. Research Unit, Kuopio University Hospital, Kuopio, Finland
334. Institute of Cellular Medicine, Newcastle University, Newcastle NE1 7RU, UK
335. Institute of Medical Informatics, Biometry and Epidemiology, Chair of Epidemiology,
Ludwig-Maximilians-Universität, D-85764 Munich, Germany
336. Klinikum Grosshadern, D-81377 Munich, Germany
337. Institute of Epidemiology I, Helmholtz Zentrum München - German Research
Center for Environmental Health, Neuherberg, Germany, D-85764 Neuherberg,
Germany
338. Department of Pulmonology, University Medical Center Utrecht, Utrecht, The
Netherlands
339. King Abdulaziz University, Jeddah 21589, Saudi Arabia
340. Division of Population Health Sciences & Education, St George's, University of
London, London SW17 0RE, UK
341. Oxford NIHR Biomedical Research Centre, Oxford University Hospitals NHS Trust,
Oxford, OX3 7LJ, UK
342. Clinical Epidemiology, Integrated Research and Treatment Center, Center for
Sepsis Control and Care (CSCC), Jena University Hospital, Jena, Germany
343. Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
344. Service of Medical Genetics, CHUV University Hospital, Lausanne, Switzerland
345. University of Cambridge Metabolic Research Laboratories, Institute of Metabolic
Science, Addenbrooke’s Hospital, Cambridge CB2 OQQ, UK
346. NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science,
Addenbrooke’s Hospital, Cambridge CB2 OQQ, UK
347. Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill,
Chapel Hill, NC 27599, USA
348. The Mindich Child Health and Development Institute, Icahn School of Medicine at
Mount Sinai, New York, NY 10029, USA
*
These authors contributed equally to this work.
§
These authors jointly directed this work.
Correspondence should be addressed to E.K.S. ([email protected]), R.J.F.L.
([email protected]), and J.N.H. ([email protected]).
Page 15 of 68
745
746
SUMMARY
Obesity is heritable and predisposes to many diseases. To better understand the genetic
747
basis of obesity, we conducted a genome-wide association study and Metabochip meta-
748
analysis of body mass index (BMI), a measure commonly used to define obesity and
749
assess adiposity, in up to 339,224 individuals. This analysis identified 97 BMI-associated
750
loci (P < 5×10-8), of which 56 were novel. Five loci demonstrate clear evidence of
751
multiple independent association signals, and many loci have significant effects on other
752
metabolic phenotypes. The 97 loci account for ~2.7% of BMI variation, and genome-wide
753
estimates suggest common variation accounts for >20% of BMI variation. Pathway
754
analyses provide strong support for a role of the central nervous system in obesity
755
susceptibility and implicate new genes and pathways, including those related to synaptic
756
function, glutamate signaling, insulin secretion/action, energy metabolism, lipid biology,
757
and adipogenesis.
758
Page 16 of 68
759
Obesity is a worldwide epidemic associated with increased morbidity and mortality that
760
imposes an enormous burden on individual and public health. Forty to 70% of inter-
761
individual variability in body mass index (BMI), commonly used to assess obesity, has
762
been attributed to genetic factors1-3. At least 77 loci have previously been associated
763
with any obesity measure4-23, including 32 loci from our previous meta-analysis of BMI
764
genome-wide association studies (GWAS)24. Nevertheless, much of the genetic
765
variability in BMI remains unexplained. Moreover, while analyses of previous genetic
766
association results have suggested intriguing biological processes underlying obesity
767
susceptibility, only a few specific genes supported these pathways8,24. For the vast
768
majority of loci, the likely causal gene(s) and relevant pathways remain unknown.
769
770
To further expand the catalog of BMI susceptibility loci and gain a better understanding
771
of the genes and biological pathways influencing obesity, we performed the largest
772
GWAS meta-analysis to date for BMI. Compared to our previous BMI meta-analysis24,
773
this work doubles of the number of individuals contributing GWAS results, incorporates
774
results from >100,000 samples genotyped with the Metabochip25, and nearly doubles the
775
number of loci associated with BMI. In addition, comprehensive assessment of meta-
776
analysis results using integrative approaches provides multiple lines of evidence
777
supporting candidate genes at many loci and highlights pathways that both reinforce and
778
expand our understanding of the biological processes underlying obesity.
779
780
RESULTS
781
Identification of 97 genome-wide significant loci
782
This BMI meta-analysis included genetic association results for up to 339,224 individuals
783
from 125 separate studies. Of these, 82 studies contributed GWAS results for up to
784
236,231 individuals and 43 additional studies contributed BMI association results for up
Page 17 of 68
785
to 103,047 individuals based on the Metabochip (Extended Data Table 1,
786
Supplementary Tables 1-3). Taking age and sex into account and after inverse normal
787
transformation of the residuals, we carried out association analyses with genotypes or
788
imputed genotype dosages. GWAS were meta-analyzed together, as were Metabochip
789
studies, followed by a combined GWAS + Metabochip meta-analysis (Extended Data
790
Fig. 1). In total, we analyzed data from up to 322,154 individuals of European-descent
791
(88,137 with Metabochip data) and up to 17,072 individuals, mostly of non-European-
792
descent (14,910 with Metabochip data).
793
794
Our primary meta-analysis of individuals of European-descent from both GWAS and
795
Metabochip (GWAS+Metabochip) studies (N=322,154) identified 77 loci reaching
796
genome-wide significance (GWS) and separated by at least 500 kb (Table 1 and
797
Supplementary Figs. 1 & 2). We carried out additional analyses to explore the effects
798
of power and heterogeneity. The inclusion of 17,072 non-European-descent individuals
799
(total N=339,224) identified an additional ten loci, while secondary analyses of
800
European-descent men (N=152,893) and women (N=171,977) separately, and of
801
population-based studies only (N=209,521) identified another ten GWS loci (Table 2,
802
Supplementary Tables 4-8, Supplementary Figs. 3-9). Of the 97 BMI associated loci,
803
41 have previously been associated with one or more obesity measure11,20,22-24,26. Thus,
804
our current analyses identified 56 novel loci associated with BMI (Tables 1 & 2).
805
806
Effects of associated loci on BMI
807
Newly identified loci generally have lower minor allele frequency (MAF) and/or smaller
808
effect size estimates than previously known loci (Figs. 1A & B). Based on effect
809
estimates from the discovery data set, which can be inflated due to winner’s curse, the
810
97 loci account for 2.7% of the phenotypic variance in BMI (Tables 1 & 2; Figs. 1A & B).
Page 18 of 68
811
We conservatively used only GWS SNPs after strict double genomic control (GC)
812
correction, which likely over-corrects association statistics given the lack of evidence for
813
population stratification in family-based analyses (Extended Data Figs. 2 & 3,
814
Extended Data Table 1; A.R.W. et al., submitted). Polygene analyses suggested that
815
SNPs with association P values well below the GWS threshold added significantly to the
816
phenotypic variance explained. For example, 2,346 SNPs selected from conditional and
817
joint multiple SNP analysis with P < 5×10-3 explained 6.6% (SE = 1.1%) of variance,
818
compared to 21.6% (SE = 2.2%) of the variance explained by all SNPs in HapMap3 (31-
819
54% of heritability, Fig. 1C). Further, of the 1,909 independent SNPs (pairwise distance
820
>500 kb and r2 < 0.1) included on Metabochip for replication of potential BMI
821
associations, 1,458 (76.4%) have directionally consistent effects with our previous
822
GWAS meta-analysis24 and the non-overlapping samples in the current meta-analysis
823
(see Methods, Fig. 1D). Based on the significant excess of these directionally consistent
824
observations (sign test P = 2.5×10-123), we estimate approximately 1,007 of the 1,909
825
SNPs represent true associations with BMI (see Methods).
826
827
We compared the effects of our 97 BMI-associated SNPs between the sexes, between
828
ethnicities, and across multiple cross-sections of our data (Methods, Supplementary
829
Tables 4-11, Extended Data Fig. 4). Two previously identified loci, near SEC16B (P =
830
5.2×10-5) and ZFP64 (P = 9.1×10-5) showed evidence of heterogeneity between men
831
and women. Both have stronger effects in women (Supplementary Table 10). We also
832
observed significantly stronger effects at TCF7L2 in T2D case/control studies than in
833
population-based studies (Phet = 5.7×10-7), likely driven by effects of ascertainment in the
834
T2D cases (Phet = 1.3×10-11; Supplementary Tables 10 & 11). We also observed
835
marginally stronger effects at KCNK3 in T2D case/control studies (Phet = 2.6×10-4) and
836
stronger effects in population-based studies than in ascertained studies at CALCR (Phet
Page 19 of 68
837
= 3.9×10-4). Two SNPs, near NEGR1 (P = 9.1×10-5) and PRKD1 (P = 1.9×10-5),
838
exhibited significant evidence for heterogeneity of effect between European and African-
839
descent samples, and one SNP, near GBE1 (P = 1.3×10-4), exhibited evidence for
840
heterogeneity between European and East Asian individuals (Supplementary Table 9).
841
These findings may reflect true heterogeneity at these loci, but is most likely due to
842
linkage disequilibrium (LD) differences across ancestries. Effect estimates for 79% of
843
BMI-associated SNPs in African-descent samples (P = 9.2×10-9) and 91% in East Asian
844
samples (P = 1.8×10-15) showed directional consistency with those observed in our
845
European-only analyses. These results suggest that common SNPs associated with BMI
846
have comparable effects across ancestry groups and between sexes.
847
848
We also took advantage of LD differences across populations to fine-map association
849
signals using the Bayesian methods of Maller et al.27 and Wakefield28. At ten of 27 loci
850
fine-mapped for BMI on Metabochip, the addition of non-European individuals into the
851
meta-analysis either 1) narrowed the genomic region containing the 99% credible set, or
852
2) decreased the number of SNPs in the credible set (Supplementary Table 12 and
853
Supplementary Fig. 10). At TCF7L2, the region decreased from >54 kb to 14 kb,
854
containing 4 SNPs, where the most likely SNP corresponds with the type 2 diabetes
855
(T2D) associated variant rs7093146. At the SEC16B and FTO loci, the all ancestries
856
credible set includes a single SNP, though the SNP we highlight at FTO (rs1558902)
857
differs from that identified by a recent fine-mapping effort in African American cohorts29.
858
Fine-mapping efforts using larger, more diverse study samples will further narrow
859
association signals. Regardless of fine-mapping methodology or sample diversity, a
860
complete catalogue of variation for the region of interest is necessary to identify causal
861
variants. For example, a known missense variant at the SH2B1 locus thought to be the
Page 20 of 68
862
causal variant was not captured by the Metabochip, and thus is not included in the
863
credible interval.
864
865
We examined the combined effects of the lead SNP at the 97 loci in an independent
866
sample of 8,164 European-descent individuals from the Health and Retirement Study30.
867
We observed an average increase of 0.1 BMI units (kg/m2) per BMI-increasing allele,
868
equivalent to 260 to 320 grams for an individual 160-180 cm in height. There was a 1.8
869
kg/m2 difference in mean BMI between the 145 individuals (1.78%) carrying the most of
870
BMI-increasing alleles (>104 alleles) and those carrying the mean number of BMI-
871
increasing alleles in the sample (91 alleles, Fig. 1E), corresponding to a difference of 4.6
872
to 5.8 kg for an individual 160 to 180 cm in height, and a 1.5 kg/m2 difference (3.8 to 4.9
873
kg difference) in mean BMI between the 95 individuals (1.16%) carrying the fewest BMI-
874
increasing alleles (<78 alleles) and those carrying the mean number. Such differences
875
are medically significant in predisposing to development of metabolic disease31. For
876
predicting obesity (BMI ≥ 30 kg/m2), adding the genetic risk score to a model also
877
including age, age2, sex, and four genotype-based principal components significantly
878
increases the area under the receiver-operating characteristic (ROC) curve from 0.576
879
to 0.601.
880
881
Additional associated variants at BMI loci
882
To identify additional SNPs with independent BMI associations at the 97 established loci,
883
we used GCTA32 to perform an approximate joint and conditional association analysis33
884
using summary level results from European sex-combined meta-analysis after removing
885
family-based validation studies (TwinGene and QIMR; See Methods). GCTA analysis
886
confirmed two signals at MC4R that had previously been identified using exact
887
conditional analyses in individual cohorts24, and identified a total of five loci harboring
Page 21 of 68
888
variants with independent evidence of association from the index SNPs (Table 3):
889
second signals near FLJ30838, NLRC3/ADCY9, GPRC5B/GP2, and BDNF, and a third
890
signal near MC4R (rs9944545) located between the two known signals (Fig. 1F). Joint
891
conditional analyses at two genomic regions separated by >500 kb (AGBL4/ELAVL4 on
892
chromosome 1 and ATP2A1/SBK1 on chromosome 16), indicate that these pairs of
893
signals may not be independent due to extended LD.
894
895
Effects of BMI variants on other traits
896
We tested for associations between our 97 BMI-associated index SNPs and other
897
metabolic phenotypes (Supplementary Tables 13-15, Fig. 2, Extended Data Fig. 5).
898
Thirteen of the 23 phenotypes tested had more SNPs with effects in the anticipated
899
direction than expected by chance (Supplementary Table 16). These results
900
corroborate the epidemiological relationships of BMI with metabolic traits. Whether this
901
reflects a common genetic etiology or a causal relationship of BMI on these traits
902
requires further investigation.
903
904
Interestingly, some individual loci showed significant association with traits in the
905
opposite direction than expected based on their phenotypic correlation with BMI (Fig. 2).
906
For example, at HHIP, the BMI-increasing allele is associated with decreased risk for
907
T2D and higher high-density lipoprotein (HDL). At LOC646736/IRS1, the BMI-increasing
908
allele is associated with reduced risk of CAD and diabetic nephropathy, decreased
909
triglyceride (TG) levels, increased HDL, higher adiponectin, and lower fasting insulin.
910
This may be due to increased subcutaneous fat and possible production of metabolic
911
mediators that are protective against the development of metabolic disease despite
912
increased adiposity22. These unexpected associations may help us to better understand
913
the complex pathophysiology underlying these traits and may indicate potential
Page 22 of 68
914
additional benefits or potential side effects if these regions are targets of therapeutic
915
intervention. Further, of our 97 GWS loci, 35 (binomial P = 0.0019) were in high LD (r2 >
916
0.7) with one or more GWS SNPs in the NHGRI GWAS catalog (P < 5×10-8), even after
917
removing SNPs associated with anthropometric traits. These SNPs were associated not
918
only with cardiometabolic traits, but also with schizophrenia, smoking behavior, irritable
919
bowel syndrome, and Alzheimer’s disease, suggesting intriguing genetic links between
920
BMI and a diverse group of traits (Supplementary Tables 17A & B, Extended Data Fig.
921
5).
922
923
Relevant BMI tissues, biological pathways, and gene sets
924
We anticipated that the expanded sample size would not only identify additional BMI-
925
associated variants and loci, but would also more clearly highlight the biology implicated
926
by genetic studies of BMI. By applying multiple complementary methods, we identified
927
biologically relevant tissues, pathways and gene sets, and also highlighted genes likely
928
to be causal contributors at associated loci. These approaches included systematic
929
methods incorporating diverse data types, including a novel approach called DEPICT
930
(Data-driven Enrichment-Prioritized Integration for Complex Traits, see Methods), and
931
extensive manual review of the published literature.
932
933
DEPICT used 37,000 human gene expression microarray samples to identify tissues
934
and cell types in which genes near top BMI-associated SNPs are highly expressed, and
935
then tested for enrichment of specific tissues by comparing results with randomly
936
selected loci matched for gene density (see Methods). Twenty-seven of 31 significantly
937
enriched tissues were in the central nervous system (out of 290 tested). Current results
938
are not sufficient to isolate specific brain regions important in regulating BMI, however
939
we observe enrichment not only in the hypothalamus/pituitary gland -- key sites of
Page 23 of 68
940
central appetite regulation -- but even more strongly in the hippocampus/limbic system,
941
tissues that play a role in learning, cognition, emotion, and memory (Fig. 3A;
942
Supplementary Table 18). Similar analyses for other anthropometric traits showed
943
enrichment in different tissues suggesting these results are specific to BMI (A.R.W.et al.
944
and D. Shungin et al.,submitted).
945
946
As a complementary approach to evaluate whether specific cell types/tissues could be
947
implicated by BMI-associated variants, we examined overlap of associated variants at
948
the 97 loci (r2 > 0.7 with the lead SNP) with five regulatory marks found in most of the 14
949
selected cell types from brain, blood, liver, pancreatic islet, and adipose tissue from the
950
Encyclopedia of DNA Elements (ENCODE) Consortium34 and Roadmap Epigenomics
951
Project35 (Supplementary Tables 19A, B & C). We found evidence of enrichment (P <
952
1.2×10-3) in 24 of 41 datasets examined. The strongest enrichment was observed with
953
promoter (H3K4me3, H3K9ac) and enhancer (H3K4me1, HeK27ac) marks detected in
954
mid-frontal lobe, anterior caudate, astrocytes, and substantia nigra, further corroborating
955
neuronal tissues in regulation of BMI.
956
957
To identify pathways or gene sets implicated by the BMI-associated loci, we first used
958
MAGENTA36, which takes as input pre-annotated gene sets, and then tests for
959
overrepresentation of the genes in each gene set at BMI-associated loci. We found
960
enrichment (false discovery rate (FDR) < 0.05) of seven gene sets, including
961
neurotrophin signaling. Other highlighted gene sets were related to general growth and
962
patterning: basal cell carcinoma, acute myeloid leukemia, and hedgehog signaling
963
(Supplementary Tables 20 A & B).
964
Page 24 of 68
965
Second, we employed DEPICT to perform gene set enrichment analysis. DEPICT first
966
reconstituted predefined gene sets based on data from over 77,000 gene expression
967
microarrays for close to 20,000 genes. DEPICT then incorporated all genes near top
968
BMI-associated SNPs and used the reconstituted gene sets as a reference to prioritize
969
genes and reconstituted gene sets at and across BMI-associated loci (see Methods).
970
After merging highly correlated gene sets, nearly 500 gene sets were significantly
971
enriched (FDR < 0.05) for genes in BMI-associated loci (Fig. 3B; Extended Data Fig. 6;
972
Supplementary Tables 21A & B). The most strongly enriched gene sets highlight
973
potentially novel pathways in the central nervous system (CNS). These include gene
974
sets related to synaptic function, long term potentiation, and neurotransmitter signaling
975
(glutamate signaling in particular, but also norepinephrine, dopamine, and serotonin
976
release cycles, and gamma-aminobutyric acid receptor activity; Fig. 3C). Potentially
977
relevant mouse behavioral phenotypes, such as physical activity and impaired
978
coordination were also highly enriched by DEPICT (Fig. 3B; Supplementary Table
979
21A). Several gene sets previously linked to obesity, such as integration of energy
980
metabolism, polyphagia, secretion and action of insulin and related hormones (e.g.,
981
“regulation of insulin secretion by glucagon-like peptide-1” and “glucagon signaling in
982
metabolic regulation”), MTOR signaling (which affects cell growth in response to nutrient
983
intake via insulin and growth factors37), and gene sets overlapping the neurotrophin
984
signaling pathway identified by MAGENTA were also enriched in DEPICT, though not as
985
significantly as CNS processes (Fig. 3D). DEPICT also identified significant enrichment
986
for other cellular components and processes: calcium channels, MAP kinase activity,
987
chromatin organization/modification, and ubiquitin ligases.
988
989
Finally, we manually reviewed published literature related to 405 genes within the 97
990
associated loci (all genes within 500 kb and r2 > 0.2 to the index SNPs; see Methods).
Page 25 of 68
991
We classified these genes into one or more biological categories (see Methods), and
992
observed 25 categories containing three or more genes (Supplementary Table 22). The
993
largest category comprised genes involved in neuronal processes, including monogenic
994
obesity genes involved in hypothalamic function/energy homeostasis and genes involved
995
in neuronal transmission and neuronal development. Other processes highlighted by the
996
manual literature review included glucose and lipid homeostasis as well as limb
997
development, which were less notable in the above methods, but may still be related to
998
the underlying biology of BMI.
999
1000
To identify specific genes that may account for the BMI association, we considered each
1001
of the following to represent supportive evidence for a gene within a locus: a) the gene
1002
nearest to the index SNP38,39, b) genes harboring missense, nonsense, or copy number
1003
variants, or a cis-expression quantitative trait locus (eQTL) in LD with the index SNP40,41,
1004
c) prioritized by integrative methods implemented in DEPICT, d) prioritized by
1005
connections in published abstracts as implemented in GRAIL42, or e) biologically related
1006
to obesity, related metabolic disease, or energy expenditure based on manual literature
1007
review (Table 1 & 2, Extended Data Tables 2 & 3, Supplementary Tables 23, 24, &
1008
25). We first focused on the 64 genes in associated loci with more than one consistent
1009
line of supporting evidence. As expected, many of these genes overlap with CNS
1010
processes, including those gene sets identified by the above pathway methods. ELAVL4,
1011
GRID1, CADM2, NRXN3, NEGR1, and SCG3 are implicated in synaptic function, cell-
1012
cell adhesion, and glutamate signaling. The pathways implicated also include genes that
1013
cause monogenic obesity syndromes (MC4R, BDNF, BBS4, POMC), or function in
1014
extreme/early onset obesity in humans and mouse models (SH2B1, NEGR1)8,43,44. Other
1015
genes with multiple lines of supporting evidence are related to insulin secretion/action,
1016
energy metabolism, lipid biology, and/or adipogenesis (TCF7L2, GIPR, IRS1, FOXO3,
Page 26 of 68
1017
ASB4, RPTOR, NPC1, CREB1, FAM57B, APOB48, HSD17B12), encode RNA
1018
binding/processing proteins (PTBP2, ELAVL4, CELF1, RALYL), are in the MAP kinase
1019
signaling pathway (MAP2K5, MAPK3), or regulate cell proliferation or cell survival
1020
(FAIM2, PARK2, OLFM4).
1021
1022
Other loci contain good candidates either highlighted by DEPICT only or, alternatively,
1023
fall short of the DEPICT FDR threshold but have other lines of supporting evidence
1024
(Table 1 & 2). For example, PCDH9, TAOK2, and STX1B are prioritized by DEPICT and
1025
are related to synaptic function and/or glutamate signaling. Several other associated loci
1026
contain likely relevant genes that are only supported by manual review of the literature,
1027
including several related to glucose, energy, or lipid/cholesterol metabolism (KLF7, GRP,
1028
ADPGK, APOE, HMGCR). Although we cannot be certain that any individual gene is
1029
related to the association at a given locus, the strong enrichment of pathways among
1030
genes within associated loci argues for a causal role for these pathways, prioritizes
1031
specific genes for follow-up experiments, and provides the strongest genetic evidence to
1032
date for a role of particular biological and CNS processes in the regulation of human
1033
body mass.
1034
1035
Discussion
1036
Our meta-analysis of nearly 340,000 individuals identified 97 GWS loci associated with
1037
BMI, including 56 novel loci. Together these loci account for 2.7% of the variation in BMI,
1038
and suggest that as much as 21% of BMI variation can be accounted for by common
1039
genetic variation. Our analyses now provide robust data to implicate particular genes
1040
and pathways affecting BMI, including those involved in synaptic function and glutamate
1041
receptor signaling. Synaptic plasticity and glutamate receptor activity respond to
1042
changes in feeding and fasting, are regulated by key obesity-related molecules such as
Page 27 of 68
1043
BDNF and MC4R, and impinge on key hypothalamic circuits45-48. Also, these pathways
1044
overlap with one of the several proposed mechanisms of action of topiramate, which is a
1045
component of one of two weight-loss drugs approved by the United States Food and
1046
Drug Administration49,50. This observation suggests the relevant site of action for this
1047
drug may be glutamate receptor activity, which supports the idea that these genes
1048
and pathways could reveal more targets for weight-loss therapies. BMI-associated loci
1049
also overlap with genes and pathways implicated in neurodevelopmental processes
1050
(Supplementary Tables 21A & B, 22). Finally, consistent with previous work and with
1051
findings from monogenic obesity syndromes, we confirm a role for the central nervous
1052
system -- particularly genes expressed in the hypothalamus -- in regulation of body mass.
1053
1054
Examining the genes at BMI-associated loci in the context of gene expression, molecular
1055
pathways, eQTL results, mutational evidence, and positional data (i.e., the gene nearest
1056
the signal) provides multiple complementary avenues through which to prioritize genes
1057
for relevance in BMI biology. Genes, such as NPC1 and ELAVL4, are implicated by
1058
multiple lines of evidence (literature, mutational, eQTL, and DEPICT) and become strong
1059
candidate genes in their respective regions. However, in most regions, only one or a few
1060
lines of evidence prioritize a particular gene. Literature review also helps identify high
1061
quality candidate genes and processes, particularly related to metabolism. These genes
1062
highlight insulin biology, energy homeostasis, lipid biology, and adipogenesis. It is
1063
important to recognize that pathway methods and literature reviews are limited by
1064
current data sets and knowledge, and thus provide only a working model of obesity
1065
biology. For example, little is known about host genetic factors that regulate the
1066
microbiome. Variation in immune-related genes such as TLR4 could presumably exert
1067
an influence on obesity through the microbiome51. Together, our results underscore the
Page 28 of 68
1068
heterogeneous etiology of obesity and its links with multiple related metabolic diseases
1069
and processes.
1070
1071
BMI variants are generally associated with related cardiometabolic traits in accord with
1072
established epidemiological relationships. This could be due to shared genetic effects or
1073
to other causes of cross-phenotypic correlations. However, some BMI-associated
1074
variants have effects on related traits counter to epidemiological expectations. Once
1075
better understood, these mechanisms may not only help to explain why not all obese
1076
individuals develop related metabolic diseases, but may also suggest possible
1077
mechanisms to prevent development of metabolic disease in those who are already
1078
obese.
1079
1080
Larger studies of common genetic variation, studies of rare variation (including those
1081
based on imputation, exome chips, and sequencing), and improved computation tools
1082
will continue to identify genetic variants associated with BMI and further refine the
1083
biology of obesity. Already, the 97 loci identified here represent an important step in
1084
understanding the physiological mechanisms leading to obesity. These findings
1085
strengthen the connection between obesity and other metabolic diseases, enhance our
1086
appreciation of the tissues, physiological processes, and molecular pathways that
1087
contribute to obesity, and will guide future research aimed at unraveling the complex
1088
biology of obesity.
1089
Page 29 of 68
1090
Methods Summary
1091
Main Analyses We conducted a two-stage of meta-analysis of genetic association
1092
studies for BMI in European-descent adults (Supplementary Fig. 1). Secondary meta-
1093
analyses included: 1) all ancestries (European + Non-European), 2) European men, 3)
1094
European women, and 4) European population-based studies. Each study performed
1095
linear regression assuming an additive genetic model on inverse normally transformed
1096
BMI (kg/m2). QC following study level analyses was conducted following procedures
1097
outlined elsewhere (T.W.W., in press). Fixed effects inverse-variance weighted meta-
1098
analyses were conducted using METAL52, with significance evaluated at P < 5×10-8.
1099
Heterogeneity was assessed between groups using methods outlined elsewhere15 and
1100
using P < 5×10-4 to assess significance. Fine-mapping Comparing meta-analyses
1101
across ethnic populations, we calculated credible sets of SNPs likely to contain the
1102
causal variant27,28. Risk, variance explained, and secondary signals We assessed the
1103
cumulative effects of the 97 GWS loci on mean BMI and to predict obesity (BMI ≥30
1104
kg/m2) using the c statistic from logistic regression. Polygene analyses and approximate,
1105
conditional analyses were performed using GCTA32,33. We performed within-family
1106
prediction analysis using full-sib pairs selected from independent families and selected
1107
SNPs were used to calculate the percent of phenotypic variance explained and risk
1108
prediction. The SNP-derived predictor was calculated using PLINK and estimation
1109
analyses were performed using GCTA32,53,54. Enrichment of Metabochip SNPs We
1110
used the 1,909 independent SNPs included for BMI replication on the Metabochip to
1111
determine the number of SNPs with directional consistency between Speliotes et al.24
1112
and the current analysis. Functional variants All variants within 500 kb and in LD (r2 >
1113
0.7) with a BMI SNP were annotated for protein-coding effects based on RefSeq
1114
transcripts. CNVs We used a list of SNPs in high LD (r2 > 0.8) with known CNVs in
1115
European populations55 to test if CNVs account for BMI associations. eQTLs We
Page 30 of 68
1116
examined cis associations between BMI loci and expression of nearby genes in several
1117
tissues56-63. We report associations with a study-specific FDR of 5% (or 1% for some
1118
datasets), that are in LD (r2 > 0.7) with the BMI SNP, and with P > 0.05 for the peak
1119
expression SNP after conditioning on the BMI SNP. Pathway analyses MAGENTA was
1120
used to identify predefined gene sets enriched for association signals36. GRAIL was
1121
used to identify genes near BMI loci co-occurring in the published scientific text42. We
1122
used DEPICT to identify the most likely causal gene at a given locus, gene sets enriched
1123
in genetic associations, tissues and cell types in which nearby genes are highly
1124
expressed, and to detect significantly enriched pathways for GWS results (T. H. Pers et
1125
al., in preparation). Cross-trait lookups Association results for the 97 BMI SNPs were
1126
requested from 13 consortia with GWAS on related cardiometabolic traits. We employed
1127
the meta-regression technique to determine the joint effect of all 97 BMI-increasing loci
1128
on other cardiometabolic phenotypes64. For each cardiometabolic trait, we converted the
1129
effect estimates and standard errors (or P values) from meta-analysis to Z-scores
1130
oriented with respect to the BMI-increasing allele, then classified each BMI SNP as
1131
having a positive, negative, or non-significant effect on each of the traits, and generated
1132
a heat map using Euclidean distance and complete linkage clustering to order both loci
1133
and traits (Fig. 2). A bubble plot (Extended Data Fig. 5) was generated using all BMI
1134
loci that reached P < 5.15×10-4 (0.05/97) for each related cardiometabolic trait. NHGRI
1135
GWAS Catalog Lookups We used the NHGRI GWAS Catalog65 and manual curated
1136
genome-wide publications to identify associations within 500 kb and r2 > 0.7 with a BMI-
1137
index SNP. Regulatory variation We tested variants in LD (r2 > 0.7) with BMI SNPs for
1138
global enrichment of regulatory marks using permutation-based tests in a subset of 41
1139
markers of open chromatin, histone modification, and transcription factor binding35,66.
1140
Marks were considered significantly enriched if P < 1.2×10-3.
1141
Page 31 of 68
1142
1143
Full Methods and any associated references are available in the online version of the
1144
paper at www.nature.com/nature
1145
1146
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www.nature.com/nature.
Acknowledgments A full list of acknowledgments can be found in the Supplementary
Information.
Academy of Finland; Agence Nationale de la Recherche; Agency for Science,
Technology and Research of Singapore (A*STAR); Althingi (the Icelandic Parliament);
Ardix Medical; Association Diabète Risque Vasculaire; AstraZenec; Augustinus
Foundation; Australian National Health and Medical Research Council; Australian
Research Council; Avera Institute; Bayer Diagnostics; Becton Dickinson; Biobanking and
Biomolecular Resources Research Infrastructure; Biotechnology and Biological Sciences
Research Council; Boehringer Ingelheim Foundation; Boston University School of
Medicine; British Heart Foundation; Bristol-Myers Squibb; Cancer Research UK;
Cardionics; Canadian Institutes of Health Research; Cavadis B.V.; Celiac Disease
Consortium; Center for Medical Systems Biology ; Center of Excellence in Genomics
(EXCEGEN); Central Norway Health Authority; Centre for Medical Systems Biology;
Centre of Excellence Baden-Wuerttemberg; Chief Scientist Office of the Scottish
Government; City of Kuopio and Social Insurance Institution of Finland; Clinic
Department of Dermatology, University Hospital Essen; CNAMTS ("Caisse Nationale de
l’Assurance Maladie des Travailleurs Salariés"); Commission of the European
Communities; CVON; Danish National Research Foundation; Danish Pharmacists’ Fund;
deCODE Genetics; Deutsche Forschungsgemeinschaft; Diabetes Hilfs- und
Forschungsfonds Deutschland; Diabetes UK; Directorate C - Public Health and Risk
Assessment; Directorate C-Public Health; Dresden University of Technology; Dutch
Brain Foundation; Dutch Diabetes Research Foundation; Dutch Digestive Disease
Foundation; Dutch Government; Dutch Inter University Cardiology Institute Netherlands;
Dutch Kidney Foundation; Dutch Ministry of Justice; Economic Structure Enhancing
Fund (FES) of the Dutch government; Egmont Foundation; Emil Aaltonen Foundation;
Erasmus University; Estonian Government; European Commission Framework
Programme 6; European Community's Seventh Framework Programme; European
Network for Genetic and Genomic Epidemiology; European Science Council; European
Science Foundation; European Special Populations Research Network; European Union
framework program 6 EUROSPAN project; European Union Ingenious HyperCare
Consortium; Faculty of Biology and Medicine of Lausanne, Switzerland; Federal Ministry
of Education and Research (BMBF); Federal Ministry of Education and Research
(German Obesity Biomaterial Bank); Fédération Française de Cardiologie; French
Ministry of Research; Finland’s Slottery Machine Association; Finnish Academy SALVE
program ‘‘Pubgensense’’; Finnish Diabetes Association; Finnish Diabetes Research
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Foundation; Finnish Foundation of Cardiovascular Research and Finnish Cultural
Foundation; Finnish Funding Agency for Technology and Innovation; Finnish Medical
Society; Finnish National Public Health Institute; Finska Läkaresällskapet; Flemish
League against Cancer; Folkhälsan Research Foundation; Fondation de France;
Foundation against Cancer; Foundation for Life and Health in Finland; Foundation for
Strategic Research and the Stockholm County Council; Foundation Heart and Arteries;
French Research Agency; GlaxoSmithKline; G. Ph. Verhagen Foundation; Geestkracht
program of the Netherlands Organization for Health Research and Development;
Genetic Association Information Network; GenomEUtwin; German Diabetes Association;
German Federal Ministry of Education and Research; German National Genome
Research Network ; German Research Council; The Great Wine Estates of the Margaret
River region of Western Australia; Healthway; Greek General Secretary of Research and
Technology; Gyllenberg Foundation; Health & Consumer Protection; Health Care
Centers in Vasa; Helmholtz Zentrum München - German Research Center for
Environmental Health; Helsinki University Central Hospital Research Funds (EVO);
Hjartavernd (the Icelandic Heart Association); hospital districts of Pirkanmaa, South
Ostrobothnia, and Central Finland; HYPERGENES Consortium; Innovation-Oriented
Research Program on Genomics; INSERM ("Réseaux en Santé Publique Interactions
entre les determinants de la santé"); INTEROMICS (MIUR - CNR Italian Flagship
Project); Interuniversity Cardiology Institute of the Netherlands; Italian Ministry of Health;
Juho Vainio Foundation; Juselius Foundation; Juvenile Diabetes Research Foundation;
Juvenile Diabetes Research Foundation International (JDRF); King's College London;
Knut and Alice Wallenberg Foundation; Kuopio University Hospital from Ministry of
Health and Social Affairs; Leo Laboratories; Medical Research Council (MRC); Lilly; Liv
och Hälsa Foundation; Louis-Jeantet Foundation; Lundberg Foundation; March of Dimes
Birth Defects Foundation; Medical Research Council of Canada; Medical Research
Council, UK; Medical Research Foundation of Umeå University; Merck Santé; Merck
Sharp and Dohme-Chibret Laboratory; Ministry for Health, Welfare and Sports; Ministry
of Cultural Affairs; Ministry of Economic Affairs; Ministry of Education; Ministry of
Education and Culture of Finland; Ministry of Education, Culture and Science; Ministry of
Health of the Republic of Seychelles; Ministry of Science, Education and Sport of the
Republic of Croatia; Ministry of Social Affairs and Health in Finland; MRC Centre for
Obesity and Related Metabolic Diseases; MRC Human Genetics Unit, Arthritis Research
UK; MRC-GlaxoSmithKline; Munich Center of Health Sciences; Municipal Health Care
Center and Hospital in Jakobstad; Municipality of Rotterdam; Närpes and Korsholm;
Närpes Health Care Foundation; National Alliance for Research on Schizophrenia and
Depression; National Cancer Institute; National Center for Advancing Translational
Sciences; National Center for Research Resources; National Human Genome Research
Institute (NHGRI); National Heart, Lung, and Blood Institute (NHLBI); National Institute
for Health Research (NIHR); National Institute of Allergy and Infectious Diseases
(NIAID); National Institute on Aging; National Institute of Child Health and Human
Development (NICHD); National Institute of Diabetes and Digestive and Kidney
Diseases (NIDDK); National Institutes of Health (NIH); National Institutes of Mental
Health; National Institute of Neurological Disorders and Stroke (NINDS); National
Research Initiative; Netherlands Consortium for Healthy Aging; Netherlands Genomics
Initiative; Netherlands Organisation for Health Research and Development; Netherlands
Organization for Scientific Research; Neuroscience Campus Amsterdam; NIA Intramural
Research Program; NIH Roadmap for Medical Research; Nordic Center of Excellence in
Disease Genetics; Nordic Centre of Excellence on Systems; Nord-Trøndelag County
Council; Northern Ireland Research Development; Northern Netherlands Collaboration of
Provinces (SNN); Norwegian Institute of Public Health; Norwegian Research Council;
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Norwegian University of Science and Technology; Novartis Pharma; Novo Nordisk; Novo
Nordisk Foundation; Ollqvist Foundation; Onivins; Orion-Farmos Research Foundation;
Paavo Nurmi Foundation; Päivikki and Sakari Sohlberg Foundation; Paul Michael
Donovan Charitable Foundation; Perklén Foundation; Pfizer; Pierre Fabre; Province of
Groningen, University Medical Center Groningen, the University of Groningen; Radboud
University Medical Centre, Nijmegen; Research Institute for Diseases in the Elderly;
Reynold's Foundation; Roche; Royal Swedish Academy of Science; sanofi-aventis;
Science for Life Laboratory – Uppsala and the Swedish Society for Medical Research;
Science Foundation Ireland; Servier Research Group; Siemens Healthcare, Erlangen;
Signe and Ane Gyllenberg Foundation; Sigrid Juselius Foundation; 6th Framework
Program of the European Union; Social Insurance Institution of Finland; Social Ministry
of the Federal State of Mecklenburg-West Pomerania; South Tyrolean Sparkasse
Foundation; Stanford University; State of Bavaria; Stockholm County Council; Strategic
Cardiovascular Program of Karolinska Institutet and Stockholm County Council;
Strategic Cardiovascular Programme of Karolinska Institutet; Stroke Association; Susan
G. Komen Breast Cancer Foundation; Société Francophone du Diabète; Swedish
Cancer Society; Swedish Cultural Foundation in Finland; Swedish Diabetes Association;
Swedish Foundation for Strategic Research; Swedish Heart-Lung Foundation; Swedish
Research Council; Swedish Medical Research Council; Swedish Society of Medicine;
Swiss National Science Foundation; SYSDIET; Tampere and Turku University Hospital;
Tampere Tuberculosis Foundation; Tekes; The Andrea and Charles Bronfman
Philanthropies; The Finnish Diabetes Research Foundation; Topcon; Torsten and
Ragnar Söderberg Foundation; UK Medical Research Council; United Kingdom NIHR
Cambridge Biomedical Research Centre; University and Research of the Autonomous
Province of Bolzano; University Hospital Oulu, Biocenter, University of Oulu; University
of Geneva; University of Maryland General Clinical Research Center; University of Tartu;
University of Ulm; USDA National Institute of Food and Agriculture; VU University’s
Institute for Health and Care Research; Wilhelm and Else Stockmann Foundation; Yrjö
Jahnsson Foundation; Zorg Onderzoek Nederland-Medische Wetenschappen;
Wellcome Trust.
Author Contributions
Steering Committee Overseeing the Consortium
Gonçalo R. Abecasis, Themistocles Assimes, Ines Barroso, Sonja I. Berndt, Michael
Boehnke, Ingrid Borecki, Panagiotis Deloukas, Caroline S. Fox, Timothy M. Frayling, Leif
Groop, Iris M. Heid, Joel N. Hirschhorn, David Hunter, Erik Ingelsson, Robert Kaplan,
Ruth J.F. Loos, Mark I. McCarthy, Karen L. Mohlke, Kari E. North, Jeffrey R. O'Connell,
David Schlessinger, David Strachan, Unnur Thorsteinsdottir, Cornelia M. van Duijn
Writing Group
Ines Barroso, Jaques S. Beckmann, Sonja I. Berndt, Martin L. Buchkovich, Damien C.
Croteau-Chonka, Felix R. Day, Stefan Gustafsson, Joel N. Hirschhorn, Erik Ingelsson,
Anne E. Justice, Bratati Kahali, Cecilia M. Lindgren, Adam E. Locke, Ruth J.F. Loos,
Karen L. Mohlke, Kari E. North, Tune H. Pers, Corey Powell, André Scherag, Elizabeth
K. Speliotes, Sailaja Vedantam, Cristen J. Willer
Data Cleaning and Preparation
Damien C. Croteau-Chonka, Felix R. Day, Tonu Esko, Tove Fall, Teresa Ferreira, Stefan
Gustafsson, Zoltán Kutalik, Adam E. Locke, Jian'an Luan, Reedik Mägi, Joshua C.
Randall, André Scherag, Sailaja Vedantam, Thomas W. Winkler, Andrew R. Wood,
Tsegaselassie Workalemahu
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GWAS Look-ups in Other Consortia
(ADIPOGen
Consortium)
Zari
Dastani,
ADIPOGen
Consortium;
(CARDIOGRAMplusC4D) CARDIOGRAMplusC4D, Panos Deloukas, Stavroula Kanoni,
Sekar Kathiresan; (ENDOMETRIOSIS GWAS) Grant W. Montgomery, Dale R. Nyholt,
Krina T. Zondervan, The International Endogene Consortium; (FinnDiane/GENIE) Niina
Sandholm; (GENIE) Eoin P. Brennan, Amy Jayne McKnight, Rany M. Salem; (GENIE
look up) The GENIE Consortium; (GLOBAL LIPIDS look up) The GLGC, (ICBP look up)
The IBPC; (CNV) Robert E. Handsaker, Steven A. McCarroll; (IgA Nephropathy)
Krzysztof Kiryluk, Richard P. Lifton; (MAGIC look up) Robert A. Scott, MAGIC (MetaAnalyses of Glucose and Insulin-Related Traits Consortium) investigators; (ReproGen)
Joanne M. Murabito, John R.B. Perry, Lisette Stolk, The ReproGen Consortium;
(CKDGen) CKDGen Consortium
Gene Expression (eQTL) Analyses
(Brain Eqtl) Ruth J.F. Loos, Jing Hua Zhao; (EGCUT) Tonu Esko, Andres Metspalu, Eva
Reinmaa; (eQTL Liver/Omental/Subq eSNPs) Eric E. Schadt; (MolOBB) Alexander
Werner Drong, Fredrik Karpe, Josine L. Min, George Nicholson; (MuTHER) Åsa K.
Hedman, Sarah Keildson, MuTHER Consortium
Other Analyses and Contributions
(Health and Retirement Study) Wei Zhao, Jennifer A. Smith, Jessica D. Faul, David R.
Weir; (DEPICT) Rudolf Fehrmann, Lude Franke, Joel N. Hirschhorn; Juha Karjalainen,
Tune H. Pers; (ENCODE) Martin L. Buchkovich, Jin Chen, Ellen M. Schmidt, Cristen J.
Willer; (QIMR cohort) Michael E. Goddard, Anna A.E. Vinkhuyzen, Peter M. Visscher,
Jian Yang
Project Design, Management and Coordination of Contributing Studies
METABOCHIP STUDIES
(ADVANCE) Themistocles L. Assimes, Joshua W. Knowles, Thomas Quertermous;
(AMCPAS) John Kastelein, Panos Deloukas; (ARIC Metabochip) Eric Boerwinkle, Kari E.
North;(B1958C) Elina Hypponen, Chris Power; (BHS MC) John Beilby, Jennie Hui;
(CARDIOGENICS) Panos Deloukas; (CLHNS) Linda S. Adair, Karen L. Mohlke; (DESIR)
Stéphane Cauchi, Philippe Froguel; (DIAGEN) Stefan R. Bornstein, Peter E.H. Schwarz;
(DILGOM) Pekka Jousilahti, Antti M. Jula, Satu Männistö, Markus Perola, Veikko
Salomaa;(DPS) Matti Uusitupa; (DR's EXTRA) Timo A. Lakka, Rainer Rauramaa;
(Dundee – GoDarts) Colin Neil Alexander Palmer; (EAS) Jackie F. Price; (EGCUT)
Andres Metspalu; (ELY) Nita G. Forouhi, Claudia Langenberg, Ruth J.F. Loos, Ken K.
Ong, Robert A. Scott, Nicholas J. Wareham; (EMIL (SWABIA)) Bernhard O. Boehm;
(EPIC-Norfolk) Nita G. Forouhi, Claudia Langenberg, Ruth J.F. Loos, Ken K. Ong,
Robert A Scott, Nicholas J Wareham;(FBPP) Aravinda Chakravarti, Richard Cooper,
Steven C. Hunt;(Fenland) Nita G. Forouhi, Claudia Langenberg, Ruth J.F. Loos, Ken K.
Ong, Robert A. Scott, Nicholas J. Wareham; (FIN-D2D 2007) Sirkka M. KeinanenKiukaanniemi, Timo E. Saaristo; (FUSION stage 2) Francis S. Collins, Jouko Saramies,
Jaakko Tuomilehto;(GLACIER) Paul W. Franks; (GxE) Richard S. Cooper, Joel N.
Hirschhorn, Colin A. McKenzie; (HNR) Raimund Erbel, Karl-Heinz Jöckel, Susanne
Moebus; (HUNT 2) Kristian Hveem; (IMPROVE) Ulf de Faire, Anders Hamsten, Steve
Humphries, Elena Tremoli; (KORA S3 (MetaboChip)) Iris M. Heid, Annette Peters,
Konstantin Strauch, H.-Erich Wichmann; (Leipzig adults) Michael Stumvoll; (LURIC),
Winfried März; (MEC Metabochip) Christopher Haiman, Loic Le Marchand; (METSIM)
Johanna Kuusisto, Markku Laakso; (MORGAM) Philippe Amouyel, Dominique Arveiler,
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Giancarlo Cesana, Jean Ferrières, David-Alexandre Trégouët, Jarmo Virtamo; (MRC
NSHD) Diana Kuh; (PIVUS) Erik Ingelsson; (PROMIS) John Danesh, Panos Deloukas,
Danish Saleheen; (SardiNIA) Gonçalo R. Abecasis, David Schlessinger; (ScarfSheep)
Ulf de Faire, Anders Hamsten; (SPT) Richard S. Cooper, Joel N. Hirschhorn, Colin A.
McKenzie; (STR) Erik Ingelsson; (Tandem) Murielle Bochud, Pascal Bovet; (THISEAS)
George Dedoussis, Panos Deloukas; (Tromsø) Inger Njølstad; (ULSAM) Erik Ingelsson;
(WHI Metabochip) Charles Kooperberg, Ulrike Peters; (Whitehall) Aroon D. Hingorani,
Mika Kivimaki, Nick Wareham; (WTCCC-T2D) Mark I. McCarthy, Cecilia M. Lindgren;
(DietGeneExpression (DGE)) Berit Johansen
NEW GWAS
(All LOLIPOP Studies) John C. Chambers, Jaspal S. Kooner; (ASCOT) Mark J. Caulfield,
Peter Sever; (Athero-Express Biobank Studies) Folkert W. Asselbergs, Hester M. de
Ruijter, Frans L. Moll, Gerard Pasterkamp; (Busselton Health Study) John Beilby, Jennie
Hui; (COROGENE) Markus Perola, Juha Sinisalo; (DESIR) Stéphane Cauchi, Philippe
Froguel; (DNBC) Mads Melbye, Jeffrey C. Murray; (EGCUT) Andres Metspalu; (Emerge)
M. Geoffrey Hayes; (ERF) Ben A. Oostra, Cornelia M. van Duijn; (FamHS) Ingrid B.
Borecki; (FINGESTURE) John D. Rioux; (GOOD) Claes Ohlsson; (HBCS) Johan G
Eriksson; (Health ABC) Tamara B. Harris, Yongmei Liu; (HERITAGE Family Study)
Claude Bouchard, D.C. Rao, Mark A. Sarzynski; (HYPERGENES) Daniele Cusi; (IPM
BioMe) Erwin P. Bottinger, Ruth J.F. Loos; (LifeLines) The Lifelines Cohort Study; (LLS)
P. Eline Slagboom; (MGS) Pablo V. Gejman; (NELSON) Paul I.W. de Bakker, Pieter
Zanen; (PLCO2) Sonja I. Berndt, Stephen J. Chanock; (PREVEND) Pim van der Harst;
(PROCARDIS) Martin Farrall, Hugh Watkins; (PROSPER/PHASE) Ian Ford, J. Wouter
Jukema, Naveed Sattar; (QFS) Claude Bouchard, André Marette, Louis Pérusse, Angelo
Tremblay, Marie-Claude Vohl; (QIMR Polygene) Heath C. Andrew, Nicholas G. Martin,
Madden A.F. Pamela; (RISC) Timothy M. Frayling, Mark Walker; (RSII) Oscar H. Franco,
Albert Hofman, Fernando Rivadeneira, André G. Uitterlinden, Cornelia M. van Duijn,
Jacqueline C. Witteman, M. Carola Zillikens; (RSIII) Oscar H. Franco, Albert Hofman,
Fernando Rivadeneira, André G. Uitterlinden, Cornelia M. van Duijn, Jacqueline C.
Witteman, M. Carola Zillikens; (SHIP-TREND) Henri Wallaschofski; (Sorbs) Anke
Tönjes; (TRAILS) Albertine J. Oldehinkel, Harold Snieder; (TWINGENE) Erik Ingelsson;
(TwinsUK) Tim D. Spector; (WGHS) Paul M. Ridker
PREVIOUS GWAS
(AGES) Vilmundur Gudnasson, Tamara B. Harris; (Amish) Alan R. Shudiner; (ARIC
GWAS) Kari E. North; (B58C T1D CONTROLS) David P. Strachan; (B58C WTCCC)
David P. Strachan; (BRIGHT) Anna F. Dominiczak, Martin Farrall; (CAPS) Erik
Ingelsson; (COLAUS) Gérard Waeber, Dawn Waterworth; (CROATIA-Vis) Igor Rudan;
(deCODE) Kari Stefansson, Unnur Thorsteinsdottir; (DGI) Leif C. Groop; (EGCUT)
Andres Metspalu; (EPIC-Norfolk) Jing Hua Zhao; (Fenland) Nicholas J. Wareham;
(Finnish Twin Cohort) Jaakko Kaprio; (FRAM) L. Adrienne Cupples; (FUSION (GWAS))
Richard N. Bergman, Michael Boehnke; (GerMIFS I) Jeanette Erdmann, Christian
Hengstenberg, Heribert Schunkert; (Health 2000) Paul Knekt; (HPFS) David Hunter;
(KORA S4 (GWA)) Christian Gieger; (MICROS) Andrew A. Hicks, Peter P. Pramstaller;
(NFBC66) Marjo-Riitta Jarvelin; (NHS) David Hunter; (NSPHS) Ulf Gyllensten;
(ORCADES) Harry Campbell; (PLCO) Sonja I. Berndt, Stephen J. Chanock; (RSI) Oscar
H. Franco, Albert Hofman, Fernando Rivadeneira, André G. Uitterlinden, Cornelia M. van
Duijn, Jacqueline C. Witteman, M. Carola Zillikens; (RUNMC) Lambertus A. Kiemeney;
(SASBAC) Erik Ingelsson; (SHIP) Henri Wallaschofski; (WTCCC-CAD) Alistair S. Hall,
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Nilesh J. Samani; (WTCCC-T2D) Mark I. McCarthy, Cecilia Lindgren; (Young Finns
Study (YFS)) Terho Lehtimäki, Olli T. Raitakari
Genotyping of Contributing Studies
METABOCHIP STUDIES
(ADVANCE) Devin Absher, Themistocles L. Assimes, Joshua W. Knowles, Thomas
Quertermous; (AMCPAS) Kathleen Stirrups; (ARIC Metabochip) Eric Boerwinkle, Kari E.
North; (B1958C) Neil R. Robertson, Christopher J. Groves, Thorhildur Juliusdottir; (BHS
MC) Gillian M. Arscott, Jennie Hui; (CARDIOGENICS) Kathleen Stirrups; (CLHNS)
Damien C. Croteau-Chonka; (DESIR) Elodie Eury, Stéphane LOBBENS; (DIAGEN) Amy
J. Swift; (Dundee – GoDarts) Nigel William Rayner, Amanda J. Bennett, Colin Neil
Alexander Palmer; (EAS) James F. Wilson; (EGCUT) Tõnu Esko, Lili Milani; (ELY)
Claudia Langenberg, Ruth J.F. Loos, Ken K. Ong, Nicholas J. Wareham; (EMIL
(SWABIA)) Bernhard O. Boehm; (EPIC-Norfolk) Claudia Langenberg, Ruth J.F. Loos,
Ken K. Ong, Nicholas J. Wareham; (FBPP) Aravinda Chakravarti; (Fenland) Claudia
Langenberg, Ruth J.F. Loos, Ken K. Ong, Nicholas J. Wareham; (FIN-D2D 2007) Peter
S. Chines; (FUSION stage 2) Leena Kinnunen; (GLACIER) Ines Barroso; (HNR) Markus
M. Noethen; (HUNT 2) Mario A. Morken; (KORA S4 (MetaboChip)), Harald Grallert,Peter
Lichtner; (Leipzig adults) Yvonne Böttcher, Peter Kovacs; (LURIC) Marcus E. Kleber;
(MEC Metabochip), Christopher Haiman; (MRC NSHD) Diana Kuh, Ken K. Ong, Andrew
Wong; (PIVUS) Christian Berne, Erik Ingelsson, Lars Lind, Johan Sundström, Kathleen
Stirrups; (SardiNIA) Ramaiah Nagaraja, Serena Sanna; (ScarfSheep) Bruna Gigante;
(STR) Nancy L. Pedersen; (Tandem) Georg B. EHRET, François Mach; (THISEAS)
Kathleen Stirrups; (Tromsø) Lori L Bonnycastle; (ULSAM) Johan Ärnlöv, Erik Ingelsson,
Ann-Christine Syvänen; (WHI Metabochip), Charles Kooperberg, Ulrike Peters;
(Whitehall) Claudia Langenberg; (WTCCC-T2D) Mark I. McCarthy, Andrew Tym
Hattersley; (DietGeneExpression (DGE)) Berit Johansen
NEW GWAS
(All LOLIPOP Studies) John C. Chambers Jaspal S. Kooner; (ASCOT) Patricia B.
Munroe; (Athero-Express Biobank Study) Sander W. van der Laan; (Busselton Health
Study) John Beilby, Jennie Hui; (DESIR) Elodie EURY, Stéphane LOBBENS; (EGCUT)
Tõnu Esko, Lili Milani; (Emerge) Dana C. Crawford, M. Geoffrey Hayes; (ERF) Aaron
Isaacs, Ben A. Oostra, Cornelia M. van Duijn; (FamHS) Ingrid B. Borecki, Warwick E.
Daw, Mary F. Feitosa, Aldi T. Kraja, Mary K. Wojczynski, Qunyuan Zhang; (GOOD)
Claes Ohlsson; (Health ABC) Yongmei Liu; (HERITAGE Family Study) Mark A.
Sarzynski; (IPM BioMe) Erwin P. Bottinger; (LifeLines) Morris A. Swertz, The LifeLines
Cohort Study; (LLS) Quinta Helmer; (MGS) Pablo V. Gejman; (NELSON) Joanna
Smolonska; (PLCO2) Stephen J. Chanock, Kevin B. Jacobs, Zhaoming Wang;
(PREVEND) Folkert W. Asselbergs, Irene Mateo Leach, Pim van der Harst;
(PROCARDIS) John F. Peden; (PROSPER/PHASE) J. Wouter Jukema, P. Eline
Slagboom, Stella Trompet; (QFS) Claire Bellis, John Blangero; (RSII) Karol Estrada,
Fernando Rivadeneira, André G. Uitterlinden; (RSIII) Karol Estrada, Fernando
Rivadeneira, André G. Uitterlinden; (SHIP-TREND) Georg Homuth, Uwe Völker;
(TRAILS) Marcel Bruinenberg, Catharina A. Hartman; (TWINGENE) Anders Hamsten,
Nancy L. Pedersen; (TwinsUK) Massimo Mangino, Alireza Moayyeri; (WGHS) Daniel I.
Chasman, Lynda M. Rose;
PREVIOUS GWAS
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(AGES) Albert Vernon Smith; (Amish) Jeffrey R. O'Connell; (B58C T1D CONTROLS)
Wendy L. McArdle; (B58C WTCCC) Wendy L. McArdle; (BRIGHT) Martin Farrall;
(CAPS) Henrik Grönberg; (COLAUS) Dawn Waterworth; (CROATIA-Vis) Caroline
Hayward; (EGCUT) Mari Nelis; (Fenland) Nicholas J. Wareham; (Finnish Twin Cohort)
Jaakko Kaprio; (KORA S3 (GWA)) Thomas Illig; (KORA S4 (GWA)) Martina MüllerNurasid; (MICROS) Andrew A. Hicks; (NFBC66) Marjo-Riitta Jarvelin; (ORCADES) Alan
F. Wright; (PLCO) Stephen J. Chanock; (RSI) Karol Estrada, Fernando Rivadeneira,
André G. Uitterlinden; (SASBAC) Per Hall; (SHIP) Georg Homuth, Uwe Völker;
(WTCCC-CAD) Alistair S. Hall, Nilesh J. Samani; (WTCCC-T2D) Mark I. McCarthy,
Andrew Tym Hattersley; (Young Finns Study (YFS)) Terho Lehtimäki, Olli T. Raitakari
Phenotype Coordination of Contributing Studies
METABOCHIP STUDIES
(ADVANCE) Alan S Go, Thomas Quertermous; (AMC-PAS) Kees G Hovingh; (ARIC
Metabochip) Eric Boerwinkle; (B1958C) Elina Hypponen, Chris Power; (BHS MC) Alan L
James, Arthur Willian (Bill) Musk; (CARDIOGENICS) Alison H Goodall, Christian
Hengstenberg; (CLHNS) Isabelita N Bas, Nanette R Lee; (DESIR) Gaëlle Gusto;
(DIAGEN) Jürgen Gräßler, Gabriele Müller; (DPS) Jaana Lindström; (DR's EXTRA)
Maija Hassinen; (Dundee – GoDarts) Andrew David Morris, Colin Neil Alexander Palmer,
Alex Surendra Fleetwood DoneyEAS, Stela McLachlan; (EGCUT) Tõnu Esko, Andres
Metspalu; (ELY) Nita G Forouhi, Nicholas J Wareham; (EMIL (SWABIA)), Roza
Blagieva,Bernhard O Boehm,Wolfgang Kratzer, Sigrun Merger, Thomas Seufferlein,
Koenig Wolfgang; (EPIC-Norfolk) Nita G Forouhi, Nicholas J Wareham (FBPP), Richard
Cooper, Steven C Hunt; (Fenland) Nita G Forouhi, Nicholas J Wareham; (GLACIER)
Goran Hallmans; (GxE) Terrence Forrester, Bamidele O Tayo; (HNR) Raimund Erbel,
Karl-Heinz Jöckel, Susanne Moebus; (HUNT 2) Oddgeir Holmen; (KORA S3
(MetaboChip)) Wolfgang Koenig, Barbara Thorand, Annette Peters, H.-Erich Wichmann;
(Leipzig adults) Matthias Blüher; (MEC Metabochip) Lynne Wilkens; (METSIM) Heather
M Stringham; (MRC NSHD) Diana Kuh; (PIVUS) Christian Berne, Erik Ingelsson, Lars
Lind, Johan Sundström; (PROMIS) Danish Saleheen; (SardinNIA) Antonella Mulas;
(ScarfSheep) Karin Leander; (SPT) Terrence Forrester, Bamidele O Tayo, Nancy L
Pedersen; (Tandem) Murielle Bochud, Pascal Bovet; (THISEAS) Maria Dimitriou;
(Tromsø) Tom Wilsgaard; (ULSAM) Johan Ärnlöv, Vilmantas Giedraitis, Erik Ingelsson;
(WHI Metabochip) Charles, Ulrike Peters; (Whitehall) Meena Kumari; (WTCCC-T2D)
Andrew Tym Hattersley; (DietGeneExpression (DGE)) Ida H Caspersen, Berit Johansen
NEW GWAS
(All LOLIPOP Studies) John C Chambers, Jaspal S Kooner, William R Scott, Sian-Tsung
Tan; (ASCOT) Mark J Caulfield, Peter Sever, Alice V Stanton; (Athero-Express Biobank
Study) Frans L. Moll; (Busselton Health Study) John Beilby, Jennie Hui; (DESIR) Gaëlle
Gusto; (DNBC) Heather Allison Boyd, Bjarke Feenstra, Frank Geller; (EGCUT) Tõnu
Esko, Andres Metspalu; (Emerge) Josh C. Denny, Abel N. Kho; (ERF) Ben A Oostra,
Cornelia M van Duijn; (FamHS) Ingrid B Borecki, Mary F Feitosa; (GOOD) Claes
Ohlsson, Liesbeth Vandenput; (Health ABC) Melissa E Garcia, Tamara B Harris,
Michael A Nalls; (HBCS) Johan G Eriksson; (HERITAGE Family Study) Claude
Bouchard; (HYPERGENES) Daniele Cusi; (IPM BioMe) Omri Gottesman; (LifeLines)
Salome Scholtens, Morris A Swertz, Judith M Vonk, The LifeLines Cohort Study; (LLS)
Anton JM de Craen; (MGS) Pablo V. Gejman; (NELSON) Dirkje S. Postma; (PLCO2)
Sonja I Berndt; (PREVEND) Stephan JL Bakker, Ron T Gansevoort; (PROCARDIS)
Robert Clarke, Anders Hamsten; (PROSPER/PHASE) Anton JM de Craen, Ian Ford, J
Wouter Jukema, Naveed Sattar; (QFS) Claude Bouchard, Angelo Trembay; (QIMR
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1771
1772
1773
1774
1775
1776
Polygene) Heath C. Andrew, Nicholas G. Martin, Madden A.F. Pamela; (RSII) Oscar H.
Franco; Albert Hofman, Fernando Rivadeneira, André G. Uitterlinden, Cornelia M. van
Duijn, Jacqueline C. Witteman; (RSIII) Oscar H. Franco, Albert Hofman, Fernando
Rivadeneira, André G. Uitterlinden, Cornelia M. van Duijn, Jacqueline C. Witteman;
(SHIP-TREND) Stephan B. Felix, Hans-Jörgen Grabe, Roberto Lorbeer, Rainer Rettig;
(Sorbs) Anke Tönjes; (TRAILS) Catharina A Hartman, Ronald P Stolk, Floor V Van Oort;
(TWINGENE) Patrik KE Magnusson, Nancy L Pedersen; (TwinsUK) Massimo Mangino,
Cristina Menni; (WGHS) Daniel I. Chasman, Lynda M. Rose
PREVIOUS GWAS
(Amish) Alan R Shudiner; (B58C T1D CONTROLS) David P Strachan; (B58C WTCCC)
David P Strachan; (BRIGHT) Anna F Dominiczak; (CAPS) Henrik Grönberg; (CHS) YiiDer Ida Chen; (COLAUS) Gérard Waeber, Dawn Waterworth; (CROATIA-Vis) Igor
Rudan; (DGI) Valeriya Lyssenko; (EGCUT) Andres Metspalu; (Fenland) Nicholas J
Wareham; (Finnish Twin Cohort) Jaakko Kaprio, Markku Koskenvuo; (NFBC66) MarjoRiitta Jarvelin, Jaana Laitinen; (NTRNESDA) Gonneke Willemsen; (ORCADES) Alan F
Wright; (PLCO) Sonja I Berndt; (RSI) Oscar H. Franco, Albert Hofman, Fernando
Rivadeneira, André G. Uitterlinden, Cornelia M. van Duijn, Jacqueline C. Witteman;
(SASBAC) Per Hall; (SHIP) Stephan B. Felix, Hans-Jörgen Grabe, Roberto Lorbeer,
Rainer Rettig; (UKBS-CC) Jennifer Jolley; (WTCCC-CAD) Alistair S Hall, Nilesh J
Samani; (WTCCC-T2D) Andrew Tym Hattersley; (Young Finns Study (YFS)) Terho
Lehtimäki, Olli T Raitakari
Data Analysis
METABOCHIP STUDIES
(ADVANCE) Devin Absher, Themistocles L. Assimes, Lindsay L. Waite; (AMCPAS)
Stavroula Kanoni; (ARIC Metabochip) Steven Buyske, Anne E. Justice, Kari E. North;
(B1958C) Teresa Ferreira; (BHS MC) Denise Anderson; (CARDIOGENICS) Stavroula
Kanoni; (CLHNS) Damien C. Croteau-Chonka; (DESIR) Stéphane Cauchi, Loïc YENGO;
(DGE DietGeneExpression) Ida H. Caspersen; (DIAGEN) Anne U. Jackson, Gabriele
Müller; (DILGOM) Kati Kristiansson; (Dundee – GoDarts) Teresa Ferreira; (EAS)
Jennifer L. Bolton, Ross M. Fraser; (EGCUT) Tõnu Esko, Krista Fischer, Evelin Mihailov;
(ELY) Jian'an Luan; (EMIL (SWABIA)) Bernhard O. Boehm, Wolfgang Kratzer; (EPICNorfolk) Jian'an Luan; (FBPP) Aravinda Chakravarti, Georg B. Ehret; (Fenland) Jian'an
Luan; (GLACIER) Frida Renstrom, Dmitry Shungin; (GxE) Cameron D Palmer; (HNR)
Sonali Pechlivanis, André Scherag; (IMPROVE) Lasse Folkersen, Rona J. Strawbridge;
(KORA S3 (MetaboChip)), Mathias Gorski, Janina S. Ried, Thomas W. Winkler; (KORA
S4 (MetaboChip)) Eva Albrecht; (Leipzig adults) Anubha Mahajan, Inga Prokopenko;
(LURIC) Graciela Delgado de Moissl, Tanja B. Grammer, Marcus E. Kleber, Stefan Pilz,
Hubert Scharnagl; (MEC Metabochip) Unhee Lim, Fred Schumacher; (METSIM) Alena
Stančáková; (MRC NSHD), Jian'an Luan, Andrew Wong; (PIVUS) Stefan Gustafsson,
Erik Ingelsson; (PROMIS) Stavroula KanoniSardi; (SardiNIA) Jennifer L. BraggGresham; (ScarfSheep) Lasse Folkersen, Rona J Strawbridge; (SPT) Cameron D
Palmer, Stefan Gustafsson, Erik Ingelsson; (Tandem) Georg B. EHRET, François Mach;
(THISEAS) Stavroula Kanoni; (ULSAM) Stefan Gustafsson, Erik Ingelsson; (WHI
Metabochip) Jian Gong, Jeffrey Haessler; (Whitehall) Jian'An Luan; (WTCCC-T2D)
Andrew P. Morris, Teresa Ferreira, Anubha Mahajan, Reedik Mägi;
NEW GWAS
(Athero-Express Biobank Studies) Sander W. van der Laan; (DESIR) Stéphane Cauchi,
Loïc YENGO; (DNBC) Bjarke Feenstra, Frank Geller; (EGCUT) Tõnu Esko, Krista
Page 45 of 68
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
Fischer, Toomas Haller, Reedik Mägi; (Emerge) M. Geoffrey Hayes; (ERF) Najaf Amin,
Ayse Demirkan; (FamHS) Mary F. Feitosa; (FINGESTURE) Ken Sin Lo; (GOOD) Claes
Ohlsson, Liesbeth Vandenput; (HBCS) Niina Eklund; (Health ABC) Michael A. Nalls;
(HERITAGE Family Study) Claude Bouchard, Tuomo Rankinen, D.C. Rao, Treva Rice,
Mark A. Sarzynski, Yun Ju Sung; (HYPERGENES) Daniele Cusi, Zoltán Kutalik;
(InCHIANTI) Andrew R. Wood, Dorota Pasko; (IPM BioMe) Janina Jeff, Vaneet Lotay,
Yingchang Lu; (LifeLines) Ilja M. Nolte, Jana V. Van Vliet-Ostaptchouk; (LLS) Marian
Beekman, Stefan Böhringer, HaeWon Uh; (LOLIPOP) Guohong Deng, Weihua Zhang;
(MGS) Jianxin Shi; (NELSON) Stephan Ripke, Jessica van Setten; (PLCO2) Sonja I.
Berndt, Zhaoming Wang; (PREVEND) Irene Mateo Leach, Pim van der Harst, Niek
Verweij; (PROCARDIS) Anuj Goel, John F. Peden; (PROSPER/PHASE) Anton J.M. de
Craen, Ian Ford, Stella Trompet; (QFS) John Blangero, Louis Pérusse; (QIMR Polygene)
Scott D. Gordon, Sarah E. Medland, Dale R. Nyholt; (RISC) Dorota Pasko, Andrew R.
Wood; (RSII) Karol Estrada, Carolina Medina-Gomez, Marjolein Peters, Fernando
Rivadeneira, André G. Uitterlinden; (RSIII) Karol Estrada, Carolina Medina-Gomez,
Marjolein Peters, Fernando Rivadeneira, André G. Uitterlinden; (SHIP-TREND)
Alexander Teumer; (Sorbs) Reedik Mägi; (TRAILS) Harold Snieder; (TWINGENE)
Stefan Gustafsson, Erik Ingelsson; (TwinsUK) Massimo Mangino; (WGHS) Daniel I.
Chasman, Lynda M. Rose
1812
Author Information
1813
G.T., V.S., U.T., and K.S. are employed by deCODE Genetics/Amgen, Inc.
1814
I.B. and spouse own stock in GlaxoSmithKline and Incyte, Ltd. C.B. is a
1815
consultant for Weight Watchers, Pathway Genomics, NIKE, and Gatorade
1816
PepsiCo.
PREVIOUS GWAS
(AGES) Albert Vernon Smith; (Amish) Jeffrey R. O'Connell; (ARIC GWAS) Keri L.
Monda, Kari E. North; (B58C T1D CONTROLS) David P. Strachan; (B58C WTCCC)
David P. Strachan; (CAPS) Erik Ingelsson; (CHS) Yii-Der Ida Chen, Barbara McKnight;
(CROATIA-Vis) Caroline Hayward; (deCODE) Valgerdur Steinthorsdottir, Gudmar
Thorleifsson; (EGCUT) Mari Nelis; (Fenland) Jian'an Luan; (FRAM) L. Adrienne Cupples,
Nancy L. Heard-Costa; (GerMIFS II) Christina Willenborg; (Health 2000) Niina Eklund;
(HPFS) Lu Qi; (KORA S3 (GWA)) Claudia Lamina; (NHS) Lu Qi; (NSPHS) Åsa
Johansson; (NTRNESDA) Jouke-Jan Hottenga; (PLCO) Sonja I. Berndt; (RSI) Karol
Estrada, Carolina Medina-Gomez, Marjolein Peters, Fernando Rivadeneira, André G.
Uitterlinden; (RUNMC) Sita H. Vermeulen; (SASBAC) Erik Ingelsson; (SEARCH)
Jonathan P. Tyrer; (SHIP) Alexander Teumer; (UKBS-CC) Antony Paul Attwood;
(WTCCC-T2D) Andrew P. Morris, Teresa Ferreira, Anubha Mahajan, Reedik Mägi
1817
1818
Page 46 of 68
Table 1 | Loci reaching genome-wide significance (P < 5 x 10-8) for BMI in European sexcombined analysis
Alleles
SNP
Chr:position
(bp)
Nearest
gene
*Notable gene(s)
Effect/other
Effect
allele
frequency
β
SE
Variance
explained
N
P value
Novel loci
rs657452
1:49,362,434
AGBL4
-
A/G
0.394
0.023
0.003
0.025%
313,651
5.48E-13
rs12286929
11:114,527,614
CADM1
-
G/A
0.523
0.022
0.003
0.023%
321,903
1.31E-12
rs7903146
10:114,748,339
TCF7L2
TCF7L2(B)
C/T
0.713
0.023
0.003
0.022%
322,130
1.11E-11
rs10132280
14:24,998,019
STXBP6
-
C/A
0.682
0.023
0.003
0.023%
321,797
1.14E-11
rs17094222
10:102,385,430
HIF1AN
-
C/T
0.211
0.025
0.004
0.021%
321,770
5.94E-11
rs7599312
2:213,121,476
ERBB4
ERBB4(D)
G/A
0.724
0.022
0.003
0.019%
322,024
1.17E-10
rs2365389
3:61,211,502
FHIT
-
C/T
0.582
0.020
0.003
0.019%
316,768
1.63E-10
rs2820292
1:200,050,910
NAV1
-
C/A
0.555
0.020
0.003
0.019%
321,707
1.83E-10
rs12885454
14:28,806,589
PRKD1
-
C/A
0.642
0.021
0.003
0.020%
320,823
1.94E-10
rs16851483
3:142,758,126
RASA2
-
T/G
0.066
0.048
0.008
0.029%
233,929
3.55E-10
rs1167827
7:75,001,105
HIP1
HIP1(B); PMS2L3(B,Q);
PMS2P5(Q);
WBSCR16(Q)
G/A
0.553
0.020
0.003
0.020%
306,238
6.33E-10
rs758747
16:3,567,359
NLRC3
-
T/C
0.265
0.023
0.004
0.020%
308,688
7.47E-10
rs1928295
9:119,418,304
TLR4
TLR4(B)
T/C
0.548
0.019
0.003
0.018%
321,979
7.91E-10
A/G
0.620
0.019
0.003
0.017%
318,385
8.11E-10
rs9925964
16:31,037,396
KAT8
ZNF646(M,Q);
VKORC1(Q);
ZNF668(Q); STX1B(D);
FBXL19(D)
rs11126666
2:26,782,315
KCNK3
KCNK3(D)
A/G
0.283
0.021
0.003
0.017%
321,979
1.33E-09
rs2650492
16:28,240,912
SBK1
SBK1(D); APOB48R(B)
A/G
0.303
0.021
0.004
0.018%
319,464
1.92E-09
rs6804842
3:25,081,441
RARB
RARB(B)
G/A
0.575
0.019
0.003
0.017%
321,463
2.48E-09
rs4740619
9:15,624,326
C9orf93
C9orf93(C,M)
T/C
0.542
0.018
0.003
0.016%
321,887
4.56E-09
rs13191362
6:162,953,340
PARK2
PARK2(B,D)
A/G
0.879
0.028
0.005
0.016%
321,902
7.34E-09
rs3736485
15:49,535,902
DMXL2
SCG3(B,D); DMXL2(M)
A/G
0.454
0.018
0.003
0.015%
321,398
7.41E-09
G/C
0.153
0.031
0.005
0.024%
233,722
7.76E-09
C/T
0.089
0.031
0.005
0.015%
321,893
8.45E-09
-
T/C
0.631
0.018
0.003
0.015%
321,924
1.20E-08
SCARB2
NUP54(M);
SCARB2(Q)
CYP17A1(B);
SFXN2(Q)
rs17001654
4:77,348,592
rs11191560
10:104,859,028
NT5C2
rs1528435
2:181,259,207
UBE2E3
rs1000940
17:5,223,976
RABEP1
-
G/A
0.320
0.019
0.003
0.016%
321,836
1.28E-08
rs2033529
6:40,456,631
TDRG1
LRFN2(D)
G/A
0.293
0.019
0.003
0.015%
321,917
1.39E-08
rs11583200
1:50,332,407
ELAVL4
ELAVL4(B,Q,D)
C/T
0.396
0.018
0.003
0.015%
322,095
1.48E-08
rs9400239
6:109,084,356
FOXO3
FOXO3(B);
AI057453(unspliced
EST)(Q)
C/T
0.688
0.019
0.003
0.015%
321,988
1.61E-08
rs10733682
9:128,500,735
LMX1B
LMX1B(B)
A/G
0.478
0.017
0.003
0.015%
320,727
1.83E-08
rs11688816
2:62,906,552
EHBP1
EHBP1(B)
G/A
0.525
0.017
0.003
0.015%
322,051
1.89E-08
rs11057405
12:121,347,850
CLIP1
-
G/A
0.901
0.031
0.006
0.017%
314,111
2.02E-08
rs11727676
4:145,878,514
HHIP
HHIP(B)
T/C
0.910
0.036
0.006
0.021%
296,401
2.55E-08
rs3849570
3:81,874,802
GBE1
GBE1(B,M)
A/C
0.359
0.019
0.003
0.016%
284,339
2.60E-08
rs6477694
9:110,972,163
EPB41L4B
C9orf4(D)
C/T
0.365
0.017
0.003
0.014%
322,048
2.67E-08
rs7899106
10:87,400,884
GRID1
GRID1(B)
G/A
0.052
0.040
0.007
0.015%
321,770
2.96E-08
rs2176598
11:43,820,854
HSD17B12
HSD17B12(B,M)
T/C
0.251
0.020
0.004
0.015%
316,848
2.97E-08
rs2245368
7:76,446,079
PMS2L11
-
C/T
0.180
0.032
0.006
0.030%
205,675
3.19E-08
rs17724992
19:18,315,825
PGPEP1
GDF15(B); PGPEP1(Q)
A/G
0.746
0.019
0.004
0.014%
319,588
3.42E-08
rs7243357
18:55,034,299
GRP
GRP(B,G)
T/G
0.812
0.022
0.004
0.014%
322,107
3.86E-08
rs2033732
8:85,242,264
RALYL
RALYL(D)
C/T
0.747
0.019
0.004
0.014%
321,406
4.89E-08
Page 47 of 68
Previously identified loci
rs1558902
16:52,361,075
FTO
FTO(B)
A/T
0.415
0.082
0.003
0.325%
320,073
7.51E-153
rs6567160
18:55,980,115
MC4R
MC4R(B)
C/T
0.236
0.056
0.004
0.111%
321,958
3.93E-53
2:622,348
TMEM18
-
G/A
0.828
0.060
0.004
0.103%
318,287
1.11E-50
rs13021737
rs10938397
4:44,877,284
GNPDA2
GABRG1(B)
G/A
0.434
0.040
0.003
0.079%
320,955
3.21E-38
rs543874
1:176,156,103
SEC16B
-
G/A
0.193
0.048
0.004
0.072%
322,008
2.62E-35
rs2207139
6:50,953,449
TFAP2B
TFAP2B(B)
G/A
0.177
0.045
0.004
0.058%
322,019
4.13E-29
rs11030104
11:27,641,093
BDNF
BDNF(B,M)
A/G
0.792
0.041
0.004
0.056%
322,103
5.56E-28
rs3101336
1:72,523,773
NEGR1
NEGR1(B,C,D)
C/T
0.613
0.033
0.003
0.053%
316,872
2.66E-26
rs7138803
12:48,533,735
BCDIN3D
FAIM2(D)
A/G
0.384
0.032
0.003
0.047%
322,092
8.15E-24
G/A
0.462
0.031
0.003
0.047%
321,759
8.78E-24
A/C
0.403
0.031
0.003
0.046%
321,930
3.14E-23
rs10182181
2:25,003,800
ADCY3
ADCY3(B,M,Q);
POMC(B,G);
NCOA1(B)
rs3888190
16:28,796,987
ATP2A1
SH2B1(B,M,Q);
APOB48R(M,Q);
ATXN2L(Q);
SBK1(Q,D);
SULT1A2(Q); TUFM(Q)
rs1516725
3:187,306,698
ETV5
-
C/T
0.872
0.045
0.005
0.045%
320,644
1.89E-22
rs12446632
16:19,842,890
GPRC5B
GPRC5B(C); IQCK(Q)
G/A
0.865
0.040
0.005
0.038%
316,758
1.48E-18
rs2287019
19:50,894,012
QPCTL
GIPR(B,M)
C/T
0.804
0.036
0.004
0.041%
300,921
4.59E-18
rs16951275
15:65,864,222
MAP2K5
MAP2K5(B,D);
LBXCOR1(M)
T/C
0.784
0.031
0.004
0.033%
322,098
1.91E-17
rs3817334
11:47,607,569
MTCH2
MTCH2(M,Q);
C1QTNF4(Q,I);
SPI1(Q); CELF1(D)
T/C
0.407
0.026
0.003
0.033%
321,959
5.15E-17
rs2112347
5:75,050,998
POC5
POC5(M); HMGCR(B);
COL4A3BP(B)
T/G
0.629
0.026
0.003
0.032%
322,019
6.19E-17
rs12566985
1:74,774,781
FPGT-TNNI3K
-
G/A
0.446
0.024
0.003
0.029%
319,282
3.28E-15
rs3810291
19:52,260,843
ZC3H4
ZC3H4(Q,D)
A/G
0.666
0.028
0.004
0.036%
296,261
4.81E-15
rs7141420
14:78,969,207
NRXN3
NRXN3(D)
T/C
0.527
0.024
0.003
0.028%
321,970
1.23E-14
rs13078960
3:85,890,280
CADM2
CADM2(D)
G/T
0.196
0.030
0.004
0.028%
322,135
1.74E-14
rs10968576
9:28,404,339
LINGO2
LINGO2(D)
G/A
0.320
0.025
0.003
0.027%
322,061
6.61E-14
rs17024393
1:109,956,211
GNAT2
AMPD2(D)
C/T
0.040
0.066
0.009
0.033%
297,874
7.03E-14
rs12429545
13:53,000,207
OLFM4
OLFM4(B)
A/G
0.133
0.033
0.005
0.026%
312,934
1.09E-12
rs13107325
4:103,407,732
SLC39A8
SLC39A8(M,Q)
T/C
0.072
0.048
0.007
0.030%
321,461
1.83E-12
rs11165643
1:96,696,685
PTBP2
PTBP2(D)
T/C
0.583
0.022
0.003
0.023%
320,730
2.07E-12
rs17405819
8:76,969,139
HNF4G
HNF4G(B)
T/C
0.700
0.022
0.003
0.021%
322,085
2.07E-11
rs1016287
2:59,159,129
FLJ30838
-
T/C
0.287
0.023
0.003
0.021%
321,969
2.25E-11
rs4256980
11:8,630,515
TRIM66
TRIM66(M,D); TUB(B)
G/C
0.646
0.021
0.003
0.020%
320,028
2.90E-11
rs12401738
1:78,219,349
FUBP1
USP33(D)
A/G
0.352
0.021
0.003
0.020%
322,070
1.15E-10
rs205262
6:34,671,142
C6orf106
SNRPC(Q)
G/A
0.273
0.022
0.004
0.019%
315,542
1.75E-10
rs12016871
13:26,915,782
MTIF3
GTF3A(Q)
T/C
0.203
0.030
0.005
0.029%
233,803
2.29E-10
rs12940622
17:76,230,166
RPTOR
RPTOR(B)
G/A
0.575
0.018
0.003
0.016%
322,032
2.49E-09
rs11847697
14:29,584,863
PRKD1
-
T/C
0.042
0.049
0.008
0.019%
306,243
3.99E-09
rs2075650
19:50,087,459
TOMM40
TOMM40(B); APOE(B);
APOC1(B)
A/G
0.848
0.026
0.005
0.017%
308,408
1.25E-08
rs2121279
2:142,759,755
LRP1B
-
T/C
0.152
0.025
0.004
0.015%
322,065
2.31E-08
rs29941
19:39,001,372
KCTD15
-
G/A
0.669
0.018
0.003
0.015%
321,970
2.41E-08
rs1808579
18:19,358,886
C18orf8
NPC1(B,G,M,Q);
C18orf8(Q)
C/T
0.534
0.017
0.003
0.014%
322,032
4.17E-08
Page 48 of 68
SNP positions are reported according to Build 36 and their alleles are coded based on the
positive strand. Effect alleles, allele frequencies, betas (β), standard errors (SE), sample sizes (N),
and P values are based on the meta-analysis of GWAS I+II+Metabochip association data from
the European All dataset.
*Notable genes from biological relevance to obesity (B); GRAIL results (G); BMI-associated
variant is in strong LD (r2 ≥ 0.7) with a missense variant in the indicated gene (M); association and
eQTL data converge to affect gene expression (Q); DEPICT analyses (D); copy number variation
(C).
Page 49 of 68
Table 2 | Loci reaching genome-wide significance from analyses other than European sexcombined
SNP
Chr:position
(bp)
Nearest
gene
*Notable
gene(s)
CALCR
CALCR(B); hsamiR-653(Q)
Alleles
Effect
(Effect/
allele
Other) frequency
β
SE
N
P value
Most
significant
analysis
Variance
P Value explained
(European (European
All)
All)
Novel Loci
C/G
0.430
0.029
0.005
161,756
European
2.08E-10 Population
Based
5.00E-07
0.018%
LOC100287559 BBS4(B,M,Q)
T/C
0.671
0.019
0.003
338,384
3.92E-09 All Ancestries
6.83E-08
0.014%
PLCD4(B,Q);
CYP27A1(B);
TTLL4(M,Q);
STK36(B,M);
ZNF142(M);
RQCD1(Q)
C/T
0.424
0.024
0.004
152,153
6.78E-09 European Men
4.17E-07
0.012%
-
C/A
0.413
0.017
0.003
339,166
8.60E-09 All Ancestries
6.55E-08
0.014%
RIT2(B,D)
G/A
0.391
0.023
0.004
171,837
1.51E-08
1.63E-07
0.013%
-
C/T
0.599
0.017
0.003
337,300
1.61E-08 All Ancestries
4.16E-07
0.013%
G/C
0.229
0.020
0.004
338,177
2.07E-08 All Ancestries
8.99E-08
0.013%
T/G
0.403
0.017
0.003
339,152
2.18E-08 All Ancestries
8.65E-08
0.013%
9.67E-08
0.013%
rs9641123
7:93,035,668
rs7164727
15:70,881,044
rs492400
2:219,057,996
rs2080454
16:47,620,091
CBLN1
rs7239883
18:38,401,669
LOC284260
rs2836754
21:39,213,610
ETS2
USP37
rs9914578
17:1,951,886
SMG6
SMG6(D);
N29617(unsplic
ed EST)(Q)
rs977747
1:47,457,264
TAL1
-
rs9374842
6:120,227,364
European
Women
LOC285762
-
T/C
0.744
0.023
0.004
209,392
European
2.67E-08 Population
Based
MAPK3(D);
KCTD13(D);
TAOK2(D);
YPEL3(D);
DOC2A(D);
FAM57B(D)
G/A
0.510
0.022
0.004
179,613
European
2.70E-08 Population
Based
2.24E-06
0.013%
rs4787491
16:29,922,838
INO80E
rs1441264
13:78,478,920
MIR548A2
-
A/G
0.613
0.017
0.003
326,858
2.96E-08 All Ancestries
6.04E-08
0.015%
rs17203016
2:207,963,763
CREB1
CREB1(B);
KLF7(B)
G/A
0.195
0.021
0.004
333,383
3.41E-08 All Ancestries
8.15E-08
0.014%
rs16907751
8:81,538,012
ZBTB10
-
C/T
0.913
0.047
0.009
145,842
3.89E-08 European Men
1.26E-07
0.019%
rs13201877
6:137,717,234
IFNGR1
OLIG3(G)
G/A
0.140
0.024
0.004
339,026
4.29E-08 All Ancestries
2.35E-07
0.013%
1.42E-07
0.015%
rs9540493
13:65,103,705
MIR548X2
rs1460676
2:164,275,935
FIGN
rs6465468
7:95,007,450
ASB4
A/G
0.452
0.021
0.004
206,327
European
4.97E-08 Population
Based
-
C/T
0.179
0.021
0.004
339,157
4.98E-08 All Ancestries
8.98E-07
0.011%
ASB4(B)
T/G
0.306
0.025
0.005
166,136
4.98E-08
European
Women
2.32E-06
0.012%
European
Women
PCDH9(D)
Previously identified loci
rs6091540
20:50,521,269
ZFP64
-
C/T
0.721
0.030
0.004
171,875
2.15E-11
8.02E-08
0.014%
rs7715256
5:153,518,086
GALNT10
-
G/T
0.422
0.017
0.003
339,153
8.85E-09 All Ancestries
1.70E-07
0.013%
rs2176040
2:226,801,046
LOC646736
IRS1(B,Q)
A/G
0.365
0.024
0.004
152,818
9.99E-09 European Men
6.06E-06
0.009%
SNP positions are reported according to Build 36 and their alleles are coded based on the
positive strand. Effect alleles, allele frequencies, betas (β), standard errors (SE), sample sizes (N),
and P values are based on the meta-analysis of GWAS I+II+Metabochip association data from
the European All dataset.
*Notable genes from biological relevance to obesity (B); GRAIL results (G); BMI-associated
variant is in strong LD (r2 ≥ 0.7) with a missense variant in the indicated gene (M); association and
eQTL data converge to affect gene expression (Q); DEPICT analyses (D); copy number variation
(C).
Page 50 of 68
Table 3 | Secondary signals reaching genome-wide significance by joint conditional
association analysis
Alleles
Effect/Other
Estimated
effect allele
frequency
FLJ30838
T/C
0.294
0.023
0.003
0.021%
356,608
2.62E-11
Lead
FLJ30838
T/G
0.457
0.021
0.004
0.021%
248,759
2.73E-08
Second signal
16:3567359
NLRC3
T/C
0.241
0.022
0.004
0.018%
316,090
2.00E-09
Lead
16:3955730
ADCY9
T/C
0.620
0.024
0.004
0.027%
222,610
2.17E-09
Second signal
rs12446632
16:19842890
GPRC5B
G/A
0.860
0.036
0.005
0.031%
341,146
1.06E-14
Lead
rs11074446
16:20162624
GP2
T/C
0.867
0.029
0.005
0.019%
338,145
1.71E-10
Second signal
rs6567160
18:55980115
MC4R
C/T
0.233
0.048
0.004
0.084%
360,901
3.52E-38
Lead
rs17066842
18:56191604
MC4R
G/A
0.960
0.051
0.008
0.020%
312,679
6.99E-10
Second signal
rs9944545
18:56109224
MC4R
T/C
0.296
0.020
0.004
0.017%
349,842
1.01E-08
Second signal
rs11030104
11:27641093
BDNF
A/G
0.791
0.051
0.004
0.087%
354,703
1.26E-34
Lead
rs10835210
11:27652486
BDNF
C/A
0.570
0.020
0.004
0.020%
334,430
1.25E-08
Second signal
Chr:position
(bp)
Nearest gene
rs1016287
2:59159129
rs4671328
2:58788786
rs758747
rs879620
SNP
Estimated β Estimated SE
Estimated
variance
explained
Estimated N
Estimated P
value
Lead SNP or
second signal?
SNP positions are reported according to Build 36 and their alleles are coded based on the
positive strand. Effect allele frequencies, betas (β), standard errors (SE), variances explained,
sample sizes (N), and P values from GCTA.
Page 51 of 68
1819
METHODS
1820
Main Analyses
1821
Study design
1822
We conducted a two-stage meta-analysis to identify BMI-associated loci in
1823
European adults (Extended Data Fig. 1, Extended Data Table 1). In stage 1
1824
we performed meta-analysis of 80 GWAS (N=234,069); and stage 2 incorporated
1825
data from 34 additional studies (N=88,137) genotyped using the Metabochip25
1826
(Supplementary Tables 1-3). Secondary meta-analyses were also conducted
1827
for: 1) all ancestries, 2) European men, 3) European women, and 4) European
1828
population-based studies. The total number of subjects and SNPs included in
1829
each stage for all analyses is shown in Extended Data Table 1.
1830
1831
Phenotype
1832
BMI, measured or self-reported weight in kg / height in meters squared
1833
(Supplementary Tables 1 & 3) was adjusted for age, age2, and any necessary
1834
study-specific covariates (e.g., genotype-derived principal components) in a
1835
linear regression model. The resulting residuals were transformed to approximate
1836
normality using inverse normal scores. For studies with no known related
1837
individuals, residuals were calculated separately by sex and case/control status.
1838
For family-based studies, residuals were calculated with men and women
1839
together, adding sex as an additional covariate in the linear regression model.
1840
Relatedness was accounted for in a study-specific manner (Supplementary
1841
Table 2).
Page 52 of 68
1842
1843
Sample QC, imputation, and association
1844
Following study-specific quality control (QC) measures (Supplementary Table
1845
2), all contributing GWAS common SNPs were imputed using the HapMap Phase
1846
II
1847
CEU+YRI+CHB+JPT HapMap Release 22 for the African American and Hispanic
1848
GWAS. Directly genotyped (GWAS and Metabochip) and imputed variants
1849
(GWAS only) were then tested for association with the inverse normally
1850
transformed BMI residuals using linear regression assuming an additive genetic
1851
model. QC following study level analyses was conducted following procedures
1852
outlined elsewhere67.
CEU
reference
panel
for
European-descent
studies54,
and
1853
1854
Meta-analysis
1855
Fixed effects meta-analyses were conducted using the inverse variance-
1856
weighted method implemented in METAL52. Study-specific GWAS results as well
1857
as GWAS meta-analysis results were corrected for genomic control (GC) using
1858
all SNPs68. Study-specific Metabochip results as well as Metabochip meta-
1859
analysis results were GC-corrected using 4,425 SNPs included on the
1860
Metabochip for replication of associations with QT-interval, a phenotype not
1861
correlated with BMI, after pruning of SNPs within 500 kb of an anthropometry
1862
replication SNP. The final meta-analysis combined the GC-corrected GWAS and
1863
Metabochip meta-analysis results.
1864
Page 53 of 68
1865
Identification of novel loci
1866
We used a distance criterion of ±500 kb surrounding each GWS peak (P < 5×10-8)
1867
to define independent loci and to place our results in the context of previous
1868
studies, including our previous GIANT meta-analyses. Of several locus models
1869
tested, this definition most closely reflected the loci defined by approximate
1870
conditional analysis using GCTA. (Tables 1 & 2, respectively). Current index
1871
SNPs falling within 500 kb of a SNP previously associated with BMI, weight,
1872
extreme obesity, or body fat percentage11,20,22,24,26 were considered previously
1873
identified.
1874
1875
Characterization of BMI associated SNP effects
1876
Heterogeneity among studies
1877
To investigate potential sources of heterogeneity between groups we compared
1878
the effect estimates of our 97 GWS SNPs for men vs. women of European
1879
ancestry and Europeans vs. non-Europeans. To address the effects of studies
1880
ascertained on a specific disease or phenotype on our results we also compare
1881
effect estimates of European ancestry studies of population-based studies with
1882
the following European-descent subsets of studies:
1883
studies (i.e., those ascertained on a specific disease or phenotype); 2) T2D
1884
cases; 3) T2D controls; 4) combined T2D cases and controls; 5) CAD cases; 6)
1885
CAD controls; and 7) combined CAD cases and controls (Supplementary
1886
Tables 10 & 11). We also tested for heterogeneity of effect estimates between
1887
our European sex-combined meta-analysis and results from recent GWAS meta-
Page 54 of 68
1) non-population-based
1888
analyses for BMI in individuals of African or East Asian ancestry11,69
1889
(Supplementary Table 9). Heterogeneity was assessed as described in Randall
1890
et al.15. A Bonferroni-corrected P value < 5×10-4 (corrected for 97 tests) was used
1891
to assess significance.
1892
1893
Fine-mapping
1894
We compared the meta-analysis results and credible sets of SNPs likely to
1895
contain the causal variant, based on the method of Maller et al. 27, across the
1896
European-only, non-European, and all ancestries sex-combined meta-analyses.
1897
For each index SNP falling within a Metabochip fine-mapping region (27 for BMI),
1898
all SNPs available within 500 kb on either side of the index SNP were selected.
1899
Effect size estimates and standard errors for each SNP were converted to
1900
approximate Bayes’ Factors (ABFs) according to the method described by
1901
Wakefield28. All ABFs were then summed across the 1 MB region and the
1902
proportion of the posterior odds of being the causal variant was calculated for
1903
each variant (ABF for SNPi/Sum of ABFs for the region). The set of SNPs that
1904
accounts for 99% of posterior odds of association in the region denotes the set
1905
most likely to contain the causal variant for that association region
1906
(Supplementary Table 12).
1907
1908
Cumulative effects, risk prediction, and variance explained
1909
We assessed the cumulative effects of the 97 GWS loci on mean BMI and on
1910
their ability to predict obesity (BMI ≥30 kg/m2) using the c statistic from logistic
Page 55 of 68
1911
regression models in the Health and Retirement Study30, a longitudinal study of
1912
26,000 European Americans 50 years or older. The variance explained by each
1913
SNP was calculated using the effect allele frequency (f) and beta (β) from the
1914
meta-analyses using the formula VarExp = β2(1-f)2f.
1915
1916
For polygene analyses, the approximate conditional analysis from GCTA32,33,
1917
was used to select SNPs using a range of P value thresholds (i.e., 5×10-8, 5×10-7,
1918
…, 5×10-3) based on summary data from the European sex-combined meta-
1919
analysis excluding TwinGene and QIMR studies. We performed a within-family
1920
prediction analysis using full-sib pairs selected from independent families (1,622
1921
pairs from the QIMR cohort and 2,758 pairs from the TwinGene cohort) and then
1922
SNPs at each threshold were used to calculate the percent of phenotypic
1923
variance explained and predict risk (Extended Data Figs. 2 & 3). We then
1924
confirmed the results from population-based prediction and estimation analyses
1925
in independent sample of unrelated individuals from the TwinGene (N = 5,668)
1926
and QIMR (N = 3,953) studies ((Extended Data Fig. 3, Fig. 1C). The SNP-
1927
derived predictor was calculated using the profile scoring approach implemented
1928
in PLINK and estimation analyses were performed using the all-SNP estimation
1929
approach implemented in GCTA.
1930
1931
Enrichment analysis of Metabochip SNPs selected for replication
1932
The 5,055 SNPs that were included for BMI replication on the Metabochip
1933
included 1,909 independent SNPs (r2 < 0.1 and >500 kb apart), of which 1,458
Page 56 of 68
1934
displayed directionally consistent effect estimates with those reported in
1935
Speliotes et al.24. To estimate the number of Metabochip SNPs truly associated
1936
with BMI, we counted the number of SNPs with directional consistency (DC)
1937
between Speliotes et al.24 and a meta-analysis of non-overlapping samples for
1938
these 1,909 SNPs. We then calculated DC in the presence of a mixture of
1939
associated and non-associated SNPs assuming P(DC | associated) = 1 and
1940
P(DC | not associated) = 0.5. In this formulation, DC = R/2 + S, meaning that S =
1941
2DC – T, where T equals the total number of SNPs, R equals the number of
1942
SNPs not associated with BMI, and S equals the number of SNPs associated
1943
with BMI. With DC = 1,458 and T = 1,909, we estimate S to be 2DC – T = 2 ×
1944
1,458 – 1,909 = 1,007.
1945
1946
Joint and conditional multiple SNP association analysis
1947
In order to identify additional signals in regions of association, we used GCTA 32,
1948
an approach that uses meta-analysis summary statistics and an LD matrix
1949
derived from a reference sample, to perform approximate joint and conditional
1950
SNP association analysis. We used 6,654 unrelated individuals of European
1951
ancestry from the ARIC cohort as the reference sample to approximate
1952
conditional P values.
1953
Page 57 of 68
1954
Identifying potential biological pathways, genes, tissues, and functional
1955
variants at associated loci
1956
Manual gene annotation and biological description
1957
All genes within 500 kb of an index SNP were annotated for molecular function,
1958
cellular function, and for evidence of association with BMI-related traits in human
1959
or animal model experiments (Supplementary Table 22). We used several
1960
avenues
1961
(http://csg.sph.umich.edu/boehnke/spotter/),
1962
(http://www.ncbi.nlm.nih.gov/pubmed/),
1963
UNIPROT (http://www.uniprot.org/). When no genes mapped to this interval the
1964
nearest gene on each side of the index SNP was annotated. In examining
1965
possible functions of genes in the region, we excluded any references to GWAS
1966
or other genetic association studies. We analyzed 405 genes in the 97 GWS loci
1967
and manually curated them into 25 biological categories containing more than
1968
three genes.
for
annotation,
OMIM
including
Spotter
PubMed
(http:/www.omim.org),
and
1969
1970
Functional variants
1971
All variants within 500 kb (HapMap release 22/1000 Genomes CEU) and in LD
1972
(r2 > 0.7) with an index SNP were annotated for functional effects based on
1973
RefSeq transcripts using Annovar70 (http://www.openbioinformatics.org/annovar/).
1974
PhastCon, Grantham, GERP, and PolyPhen71 predictions were accessed via the
1975
Exome Variant Server72 (http://evs.gs.washington.edu/EVS), and from SIFT73
1976
(http://sift.jcvi.org/) (Extended Data Table 3).
Page 58 of 68
1977
1978
CNVs correlated with BMI index SNPs
1979
To study common copy number variations (CNVs), we used a list of CNV-tagging
1980
SNPs in high LD (r2 > 0.8) with deletions in European populations from Phase 1
1981
release of the 1000 Genomes55.
1982
1983
eQTLs
1984
We examined the cis associations between the 97 GWS SNPs and expression of
1985
nearby genes in whole blood, lymphocytes, skin, liver, omental fat, subcutaneous
1986
fat, and brain tissue56-63 (Supplementary Table 23). Conditional analyses were
1987
performed by including both the BMI-associated SNP and the most significant
1988
cis-associated SNP for the given transcript. Conditional analyses were conducted
1989
for all datasets, except the brain tissue dataset due to limited power. To minimize
1990
the potential for false-positives, only cis associations below a study-specific FDR
1991
of 5% (or 1% for some datasets), in LD with the peak SNP (r2 > 0.7) for the
1992
transcript, and with conditional P value >0.05 for the peak SNP, are reported
1993
(Extended Data Table 2).
1994
1995
Pathway analyses
1996
MAGENTA
1997
We used the MAGENTA method (Meta-Analysis Gene-set Enrichment of variaNT
1998
Associations) to test predefined gene sets for enrichment at BMI-associated
1999
loci36. We used the GWAS+Metabochip data as input and applied default settings.
Page 59 of 68
2000
2001
GRAIL
2002
We used GRAIL, Gene Relationships Across Implicated Loci42, to identify genes
2003
near BMI-associated loci having similarities in the published scientific text using
2004
PubMed abstracts as of December 2006. The BMI loci were queried against
2005
HapMap release 22 for the European panel, and we controlled for gene size.
2006
2007
Data-driven Expression-Prioritized Integration for Complex Traits (DEPICT)
2008
We used DEPICT, Data-driven Expression-Prioritized Integration for Complex Traits, to
2009
identify the most likely causal gene at a given associated locus, reconstituted gene sets
2010
enriched for BMI associations, and tissues and cell types in which genes from
2011
associated loci are highly expressed (T. H. Pers et al., in preparation). To accomplish
2012
this, the method relies on publicly available gene sets (including molecular pathways)
2013
and uses gene expression data from 77,840 gene expression arrays to predict which
2014
other genes are likely to be part of these gene sets, thus combining known annotations
2015
with predicted annotations. For details and negative control analyses please refer to
2016
Supplementary Online Materials (T.H. Pers et al., in preparation).
2017
2018
We first clumped the European-only GWAS-based meta-analysis summary statistics
2019
using 500 kb flanking regions, LD r2 > 0.1 and excluded SNPs with P ≥ 5×10-4; which
2020
resulted in a list of 590 independent SNPs. HapMap Project Phase II CEU genotype
2021
data54 was used to compute LD and genomic coordinates were defined by genome build
2022
GRCh37. Because the GWAS meta-analysis was based on both GWAS and Metabochip
2023
studies, there were discrepancies in the index SNPs that are referenced in Table 1 of
2024
the paper and the ones used in DEPICT, which was run on the GWAS data only.
Page 60 of 68
2025
Therefore we forced in GWS index SNPs from the GWAS plus Metabochip GWA meta-
2026
analysis into the DEPICT GWAS-only based analysis. This enabled a more
2027
straightforward comparison of genes in DEPICT loci and genes in GWS loci highlighted
2028
by manual lookups, and did not lead to any significant bias towards SNPs on the
2029
Metabochip (data not shown). We forced in 62 of the GWS loci in Table 1, so all of 97
2030
SNPs were among the 590 SNPs. The 590 SNPs were further merged into 511 non-
2031
overlapping regions (FDR < 0.05) used in DEPICT analysis. For additional information
2032
on the analysis please refer to Supplementary Online Materials.
2033
2034
Cross-trait analyses
2035
Cross-trait lookups
2036
To carefully explore
2037
cardiometabolic traits and diseases, association results for the 97 BMI index
2038
SNPs were requested from 13 GWAS meta-analysis consortia:
2039
(T2D)74, CARDIoGRAM-C4D (CAD)75, ICBP (systolic and diastolic blood
2040
pressure (SBP, DBP))76, GIANT (waist-to-hip ratio, hip circumference, and waist
2041
circumference, each unadjusted and adjusted for BMI) (A.R.W. et al. and D.
2042
Shungin et al., submitted), GLGC (high density lipoprotein cholesterol (HDL), low
2043
density lipoprotein cholesterol (LDL), triglycerides (TG), and total cholesterol
2044
(TC)) [manuscript in preparation], MAGIC (fasting glucose, fasting insulin, fasting
2045
insulin adjusted for BMI, and two-hour glucose)40,77,78, ADIPOGen (BMI-adjusted
2046
adiponectin)64, CKDgen (urine albumin-to-creatinine ratio (UACR), estimated
2047
glomerular filtration rate (eGFR), and overall CKD)79,80, ReproGen (age at
the relationship between BMI and an array of
Page 61 of 68
DIAGRAM
2048
menarche, age at menopause)81,82, GENIE (diabetic nephropathy)83,84. Proxies
2049
(r2 > 0.8 in CEU) were used when an index SNP was unavailable.
2050
2051
Enrichment of Concordant Effects
2052
We compared the effects for the 97 BMI index SNP across these related traits
2053
using a one-sided binomial test of the number of concordant effects versus a null
2054
expectation of P = 0.5. Concordant and nominally significant (P < 0.05) SNP
2055
effects were similarly tested using a one-sided binomial test with a null
2056
expectation of P = 0.05. We evaluated significance in either test with a
2057
Bonferroni-corrected threshold of P = 0.002 (0.05/23 traits tested).
2058
2059
Joint effects of cross-trait associations
2060
In order to determine the joint effect of all 97 BMI loci on other cardiometabolic
2061
phenotypes, we employed the meta-regression technique from Dastani et al.64 to
2062
correlate the effect estimates of the BMI increasing alleles with effect estimates
2063
from meta-analyses for each of the metabolic traits from other consortia
2064
(DIAGRAM, MAGIC, ICBP, GLGC, ADIPOgen, ReproGEN and CARDIoGRAM).
2065
2066
Cross-Traits Heatmap
2067
To explore observed concordance in effects of BMI loci on other cardiometabolic
2068
and anthropometric traits, we converted the effect estimates and standard errors
2069
(or P values) from meta-analysis to Z-scores oriented with respect to the BMI-
2070
increasing allele, for each of the 97 BMI index SNPs in the twenty-three traits.
Page 62 of 68
2071
We then classified each Z-score as follows to generate a vector of the Z-score of
2072
each trait at each locus:
2073
2074
0 (not significant) if -2 <= Z <= 2
2075
1 (significant positive) if Z > 2
2076
-1 (significant negative) if Z < -2,
2077
2078
Fig. 2 displays these locus-trait relationships in a heat map using Euclidean
2079
distance and complete linkage clustering to order both loci and traits.
2080
2081
Cross-Traits Bubble Plot (Extended Data Fig. 5)
2082
We also represent the genetic overlap between other cardiometabolic traits and
2083
BMI susceptibility loci with a bubble plot in which the size of each bubble is
2084
proportional to the fraction of BMI-associated loci for which there was a
2085
significant association (P < 5×10-4). Each pair of bubbles is connected by a line
2086
proportional to the number significant BMI-increasing loci overlapping between
2087
the traits.
2088
2089
NHGRI GWAS Catalog Lookups
2090
We extracted previously reported GWAS association within 500 kb of and r2 > 0.7
2091
with any BMI-index SNP from the National Human Genome Research Institute
2092
(NHGRI) GWAS Catalog65 (www.genome.gov/gwastudies; Supplementary
2093
Tables 17A & B). For studies reporting greater than 30 significant hits, additional
Page 63 of 68
2094
SNP-trait associations were pulled from the literature and compared to BMI index
2095
SNPs the same as with other GWAS Catalog studies.
2096
2097
Regulatory variation
2098
ENCODE/Roadmap
2099
To identify global enrichment of datasets at the BMI-associated loci we
2100
performed permutation-based tests in a subset of 41 open chromatin (DNase-
2101
seq), histone modification (H3K27ac, H3K4me1, H3K4me3, H3K9ac), and
2102
transcription factor binding datasets from the ENCODE Consortium66, Roadmap
2103
Epigenomics Project35, and when available the ENCODE Integrative Analysis85,86
2104
(Supplementary Table 19A, B, & C). We processed Roadmap Epigenomics
2105
sequencing data with multiple biological replicates using MACS2 87 and then
2106
applied same Irreproducible Discovery Rate pipeline used in the ENCODE
2107
Integrative Analysis85,86. Roadmap Epigenomics data with only a single replicate
2108
were analyzed using MACS2 alone. We examined variants in LD with 97 BMI
2109
index SNPs based on r2 > 0.7 from the 1000 Genomes Phase 1 version 2 EUR
2110
samples88. We matched the index SNP at each locus with 500 variants having no
2111
evidence of association (P > 0.5, ~1.2 million total variants) with a similar
2112
distance to the nearest gene (± 11,655 bp), number of variants in LD (±
2113
8 variants), and minor allele frequency. Using these pools, we created 10,000
2114
sets of control variants for each of the 97 loci and identified variants in LD (r2 >
2115
0.7) and within 1 Mb. For each SNP set, we calculated the number of loci with at
2116
least one variant located in a regulatory region under the assumption that one
Page 64 of 68
2117
regulatory variant is responsible for each association signal. We estimated the P
2118
value assuming a sum of binomial distributions to represent the number of index
2119
SNPs (or their LD proxies; r2 > 0.7) that overlap a regulatory dataset compared to
2120
the expectation observed in the 500 matched control sets. Datasets were
2121
considered significantly enriched if the P value was below a Bonferroni-corrected
2122
threshold of 1.2×10-3, adjusting for 41 tests.
2123
Page 65 of 68
2124
Figure Legends
2125
Fig. 1. Genetic characterization of BMI-associated variants. A. Plot of the cumulative
2126
phenotypic variance explained by each locus ordered by decreasing effect size. B. The
2127
relationship between effect size and allele frequency. Previously identified loci are blue
2128
circles and novel loci are red triangles. C. The estimated variance in BMI explained by
2129
SNPs selected at a range of P values using unrelated individuals from the QIMR (N =
2130
3,924; purple) and TwinGene (N = 5,668; gold) studies, their weighted average (cyan),
2131
and the variance explained inferred from within-family prediction (red; Extended Data
2132
Fig. 2). In orange is the estimated variance explained by all HapMap Phase III SNPs in
2133
16,275 unrelated individuals from the QIMR, TwinGene, and ARIC studies. D. QQ plot of
2134
meta-analysis P values for all 1,909 BMI-replication SNPs (blue) and after removing
2135
SNPs near the 97 associated loci (green). E. Histogram of cumulative effect of BMI risk
2136
alleles. Mean BMI for each bin is shown by the black dots (with standard deviation) and
2137
corresponds to the right hand y-axis. F. Regional association plot of the region upstream
2138
of and including MC4R89. SNP associations from the European sex-combined meta-
2139
analysis are plotted with joint conditional P values indicated for each of the three GWS
2140
signals. SNPs are shaded and shaped based on the index SNP with which they are in
2141
strongest LD (rs6567160 in blue, rs994545 in yellow, and rs17066842 in green).
2142
Page 66 of 68
2143
Fig 2. Effects of BMI-associated loci on related metabolic traits. Unsupervised
2144
hierarchical clustering of the 97 BMI-associated loci (y-axis) on 23 related metabolic
2145
traits (x-axis). The top row shows the a priori expected relationship with BMI (green is
2146
concordant effect direction, purple is opposite). Loci with statistically significant
2147
concordant direction of effect are highlighted in green, and significant but opposing
2148
effects are in purple. Grey indicates a non-significant relationship and those with no
2149
information are in white. The key in the upper left corner also shows the count of gene-
2150
phenotype pairs in each category (cyan bars).
2151
Page 67 of 68
2152
Fig 3. Tissues and reconstituted gene sets significantly enriched for genes within
2153
BMI-associated loci. A. Genes within BMI-associated loci (P < 5×10-4) are enriched for
2154
expression in the central nervous system and brain tissues as predicted by DEPICT.
2155
Tissues are sorted by physiological system and significantly enriched tissues are in
2156
black. B. The gene sets most significantly enriched for BMI-associated loci as identified
2157
by DEPICT (P < 10-6, FDR < 4×10-4). Nodes represent reconstituted gene sets and are
2158
color-coded by P value. Edge thickness between nodes is proportional to degree of gene
2159
overlap as measured by the Jaccard index. Nodes with gene overlap greater than 25%
2160
were collapsed into a single “meta-node” (denoted with a blue border). C. The nodes
2161
contained within the most enriched gene set meta-node, “Clathrin-Coated Vesicle,”
2162
which shares genes with other gene sets relevant to glutamate signaling and synapse
2163
biology. D. The “Generation of a Signal Involved in Cell-Cell Signaling” meta-node
2164
represents several overlapping gene sets relevant to obesity and energy metabolism
2165
(gene sets with P < 4×10-3, FDR < 0.05 shown). For the complete list of enriched gene
2166
sets refer to Supplementary Table 21A.
Page 68 of 68
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