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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Nature. Author manuscript; available in PMC Apr 14, 2011.
Published in final edited form as:
PMCID: PMC2955183
NIHMSID: NIHMS225625
Hundreds of variants clustered in genomic loci and biological pathways affect human height
Hana Lango Allen,1* Karol Estrada,2,3,4* Guillaume Lettre,5,6* Sonja I. Berndt,7* Michael N. Weedon,1* Fernando Rivadeneira,2,3,4* Cristen J. Willer,8 Anne U. Jackson,8 Sailaja Vedantam,9,10 Soumya Raychaudhuri,11,12 Teresa Ferreira,13 Andrew R. Wood,1 Robert J. Weyant,8 Ayellet V. Segrè,11,14,15 Elizabeth K. Speliotes,10,16 Eleanor Wheeler,17 Nicole Soranzo,17,18 Ju-Hyun Park,7 Jian Yang,19 Daniel Gudbjartsson,20 Nancy L. Heard-Costa,21 Joshua C. Randall,13 Lu Qi,22,23 Albert Vernon Smith,24,25 Reedik Mägi,13 Tomi Pastinen,26,27,28 Liming Liang,29 Iris M. Heid,30,31 Jian'an Luan,32 Gudmar Thorleifsson,20 Thomas W. Winkler,30 Michael E. Goddard,33,34 Ken Sin Lo,5 Cameron Palmer,9,10 Tsegaselassie Workalemahu,22 Yurii S. Aulchenko,2,4 Åsa Johansson,35,36 M.Carola Zillikens,3 Mary F. Feitosa,37 Tõnu Esko,38,39,40 Toby Johnson,41,42,43,44 Shamika Ketkar,37 Peter Kraft,45,46 Massimo Mangino,18 Inga Prokopenko,13,47 Devin Absher,48 Eva Albrecht,31 Florian Ernst,49 Nicole L. Glazer,50 Caroline Hayward,51 Jouke-Jan Hottenga,52 Kevin B. Jacobs,53 Joshua W. Knowles,54 Zoltán Kutalik,41,42 Keri L. Monda,55 Ozren Polasek,56,57 Michael Preuss,58 Nigel W. Rayner,13,47 Neil R. Robertson,13,47 Valgerdur Steinthorsdottir,20 Jonathan P. Tyrer,59 Benjamin F. Voight,11,14,15 Fredrik Wiklund,60 Jianfeng Xu,61 Jing Hua Zhao,32 Dale R. Nyholt,62 Niina Pellikka,63,64 Markus Perola,63,64 John R.B. Perry,1 Ida Surakka,63,64 Mari-Liis Tammesoo,38 Elizabeth L. Altmaier,9,10 Najaf Amin,2 Thor Aspelund,24,25 Tushar Bhangale,65 Gabrielle Boucher,5 Daniel I. Chasman,66,67 Constance Chen,68 Lachlan Coin,69 Matthew N. Cooper,70 Anna L. Dixon,71 Quince Gibson,72 Elin Grundberg,17,26,27 Ke Hao,73 M. Juhani Junttila,74 Lee M. Kaplan,16,67,75 Johannes Kettunen,63,64 Inke R. König,58 Tony Kwan,26,27 Robert W. Lawrence,70 Douglas F. Levinson,76 Mattias Lorentzon,77 Barbara McKnight,78 Andrew P. Morris,13 Martina Müller,31,79,80 Julius Suh Ngwa,81 Shaun Purcell,14,82,83 Suzanne Rafelt,84 Rany M. Salem,9,10 Erika Salvi,85,86 Serena Sanna,87 Jianxin Shi,7 Ulla Sovio,69 John R. Thompson,88,89 Michael C. Turchin,9,10 Liesbeth Vandenput,77 Dominique J. Verlaan,26,27 Veronique Vitart,51 Charles C. White,81 Andreas Ziegler,90 Peter Almgren,91 Anthony J. Balmforth,92 Harry Campbell,93 Lorena Citterio,94 Alessandro De Grandi,95 Anna Dominiczak,96 Jubao Duan,97 Paul Elliott,69 Roberto Elosua,98 Johan G. Eriksson,99,100,101,102,103 Nelson B. Freimer,104 Eco J.C. Geus,52 Nicola Glorioso,105 Shen Haiqing,72 Anna-Liisa Hartikainen,106 Aki S. Havulinna,107 Andrew A. Hicks,95 Jennie Hui,70,108,109 Wilmar Igl,35 Thomas Illig,31 Antti Jula,110 Eero Kajantie,100 Tuomas O. Kilpeläinen,32 Markku Koiranen,111 Ivana Kolcic,56 Seppo Koskinen,107 Peter Kovacs,112 Jaana Laitinen,113 Jianjun Liu,114 Marja-Liisa Lokki,115 Ana Marusic,116 Andrea Maschio,87 Thomas Meitinger,117,118 Antonella Mulas,87 Guillaume Paré,119 Alex N. Parker,120 John F. Peden,13,121 Astrid Petersmann,122 Irene Pichler,95 Kirsi H. Pietiläinen,123,124 Anneli Pouta,106,125 Martin Ridderstråle,126 Jerome I. Rotter,127 Jennifer G. Sambrook,128,129 Alan R. Sanders,97 Carsten Oliver Schmidt,130 Juha Sinisalo,131 Jan H. Smit,132 Heather M. Stringham,8 G.Bragi Walters,20 Elisabeth Widen,63 Sarah H. Wild,93 Gonneke Willemsen,52 Laura Zagato,94 Lina Zgaga,56 Paavo Zitting,133 Helene Alavere,38 Martin Farrall,13,121,134 Wendy L. McArdle,135 Mari Nelis,38,39,40 Marjolein J. Peters,3,4 Samuli Ripatti,63,64 Joyce B.J. van Meurs,2,3,4 Katja K. Aben,136 Kristin G Ardlie,11 Jacques S. Beckmann,41,137 John P. Beilby,108,109,138 Richard N. Bergman,139 Sven Bergmann,41,42 Francis S. Collins,140 Daniele Cusi,85 Martin den Heijer,141 Gudny Eiriksdottir,24 Pablo V. Gejman,97 Alistair S. Hall,92 Anders Hamsten,142 Heikki V. Huikuri,74,74 Carlos Iribarren,143,144 Mika Kähönen,145 Jaakko Kaprio,63,123,146 Sekar Kathiresan,11,14,147,148,149 Lambertus Kiemeney,136,150,151 Thomas Kocher,152 Lenore J. Launer,153 Terho Lehtimäki,154 Olle Melander,126 Tom H. Mosley, Jr,155 Arthur W. Musk,109,156 Markku S. Nieminen,131,131 Christopher J. O'Donnell,148,157 Claes Ohlsson,77 Ben Oostra,158 Lyle J. Palmer,70,109 Olli Raitakari,159 Paul M. Ridker,66,67 John D. Rioux,5,6 Aila Rissanen,124 Carlo Rivolta,41 Heribert Schunkert,160 Alan R. Shuldiner,72,161 David S. Siscovick,162,163 Michael Stumvoll,164,165 Anke Tönjes,164,166 Jaakko Tuomilehto,167,168,169 Gert-Jan van Ommen,170 Jorma Viikari,171 Andrew C. Heath,172 Nicholas G. Martin,173 Grant W. Montgomery,174 Michael A. Province,37,175 Manfred Kayser,176 Alice M. Arnold,78,177 Larry D. Atwood,21 Eric Boerwinkle,178 Stephen J. Chanock,7 Panos Deloukas,17 Christian Gieger,31 Henrik Grönberg,60 Per Hall,60 Andrew T. Hattersley,1 Christian Hengstenberg,179,180 Wolfgang Hoffman,130 G.Mark Lathrop,181 Veikko Salomaa,107 Stefan Schreiber,182 Manuela Uda,87 Dawn Waterworth,183 Alan F. Wright,51 Themistocles L. Assimes,54 Inês Barroso,17,184 Albert Hofman,2,4 Karen L. Mohlke,185 Dorret I. Boomsma,52 Mark J. Caulfield,44 L.Adrienne Cupples,81 Jeanette Erdmann,160 Caroline S. Fox,186 Vilmundur Gudnason,24,25 Ulf Gyllensten,35 Tamara B. Harris,153 Richard B. Hayes,187 Marjo-Riitta Jarvelin,69,111,125,188 Vincent Mooser,183 Patricia B. Munroe,44 Willem H. Ouwehand,17,128,129 Brenda W. Penninx,132,189,190 Peter P. Pramstaller,95,191,192 Thomas Quertermous,54 Igor Rudan,51,116 Nilesh J. Samani,84,88 Timothy D. Spector,18 Henry Völzke,130 Hugh Watkins,13,121, Procardis Consortium James F. Wilson,93 Leif C. Groop,91 Talin Haritunians,127 Frank B. Hu,22,23,45 Robert C. Kaplan,193 Andres Metspalu,38,39,40 Kari E. North,55,194 David Schlessinger,195 Nicholas J. Wareham,32 David J. Hunter,22,23,45 Jeffrey R. O'Connell,72 David P. Strachan,196 H.-Erich Wichmann,31,80,197 Ingrid B. Borecki,37,175 Cornelia M. van Duijn,2,4 Eric E. Schadt,198,199 Unnur Thorsteinsdottir,20,200 Leena Peltonen,17,63,64,82,201 André Uitterlinden,2,3,4 Peter M. Visscher,19 Nilanjan Chatterjee,7 Ruth J.F. Loos,32 Michael Boehnke,8 Mark I. McCarthy,13,47,202 Erik Ingelsson,60 Cecilia M. Lindgren,13,47 Gonçalo R. Abecasis,8* Kari Stefansson,20,200* Timothy M. Frayling,1* and Joel N Hirschhorn9,10,203*, for the GIANT Consortium
1 Genetics of Complex Traits, Peninsula College of Medicine and Dentistry, University of Exeter, Exeter, EX1 2LU, UK
2 Department of Epidemiology, Erasmus MC, Rotterdam, 3015GE, The Netherlands
3 Department of Internal Medicine, Erasmus MC, Rotterdam, 3015GE, The Netherlands
4 Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA)
5 Montreal Heart Institute, Montreal, Quebec, H1T 1C8, Canada
6 Department of Medicine, Université de Montréal, Montreal, Quebec, H3T 1J4, Canada
7 Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland 20892, USA
8 Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA
9 Divisions of Genetics and Endocrinology and Program in Genomics, Children's Hospital, Boston, Massachusetts 02115, USA
10 Metabolism Initiative and Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts 02142, USA
11 Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA
12 Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115 USA
13 Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
14 Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
15 Department of Molecular Biology, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
16 Division of Gastroenterology, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
17 Wellcome Trust Sanger Institute, Hinxton, Cambridge, CB10 1SA, UK
18 Department of Twin Research and Genetic Epidemiology, King's College London, Lambeth Palace Rd, London, SE1 7EH, UK
19 Queensland Statistical Genetics Laboratory, Queensland Institute of Medical Research, Queensland 4006, Australia
20 deCODE Genetics, 101 Reykjavik, Iceland
21 Department of Neurology, Boston University School of Medicine, Boston, Massachusetts 02118, USA
22 Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts 02115, USA
23 Channing Laboratory, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA
24 Icelandic Heart Association, Kopavogur, Iceland
25 University of Iceland, Reykjavik, Iceland
26 McGill University and Genome Québec Innovation Centre, Montréal, Québec H3A 1A4, Canada
27 Department of Human Genetics, McGill University Health Centre, McGill University, Montréal, Québec H3G 1A4, Canada
28 Department of Medical Genetics, McGill University Health Centre, McGill University, Montréal, Québec H3G 1A4, Canada
29 Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Cambridge, Massachusetts 02138, USA
30 Regensburg University Medical Center, Department of Epidemiology and Preventive Medicine, 93053 Regensburg, Germany
31 Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany
32 MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, CB2 0QQ, UK
33 University of Melbourne, Parkville 3010, Australia
34 Department of Primary Industries, Melbourne, Victoria 3001, Australia
35 Department of Genetics and Pathology, Rudbeck Laboratory, University of Uppsala, SE-75185 Uppsala, Sweden
36 Department of Cancer Research and Molecular Medicine, Faculty of Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, N-7489, Norway
37 Department of Genetics, Washington University School of Medicine, St Louis, Missouri 63110, USA
38 Estonian Genome Center, University of Tartu, Tartu 50410, Estonia
39 Estonian Biocenter, Tartu 51010, Estonia
40 Institute of Molecular and Cell Biology, University of Tartu, Tartu 51010, Estonia
41 Department of Medical Genetics, University of Lausanne, 1005 Lausanne, Switzerland
42 Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
43 Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary, University of London, London, UK
44 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
45 Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts 02115, USA
46 Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA
47 Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, OX3 7LJ, UK
48 Hudson Alpha Institute for Biotechnology, Huntsville, Alabama 35806, USA
49 Interfaculty Institute for Genetics and Functional Genomics, Ernst-Moritz-Arndt-University Greifswald, 17487 Greifswald, Germany
50 Cardiovascular Health Resarch Unit and Department of Medicine, University of Washington, Seattle, Washington 98101, USA
51 MRC Human Genetics Unit, Institute for Genetics and Molecular Medicine, Western General Hospital, Edinburgh, EH4 2XU, Scotland, UK
52 Department of Biological Psychology, VU University Amsterdam, 1081 BT Amsterdam, The Netherlands
53 Core Genotyping Facility, SAIC-Frederick, Inc., NCI-Frederick, Frederick, Maryland 21702, USA
54 Department of Medicine, Stanford University School of Medicine, Stanford, California 94305, USA
55 Department of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514 USA
56 Andrija Stampar School of Public Health, Medical School, University of Zagreb, 10000 Zagreb, Croatia
57 Gen-Info Ltd, 10000 Zagreb, Croatia
58 Universität zu Lübeck, Institut für Medizinische Biometrie und Statistik, 23562 Lübeck, Germany
59 Department of Oncology, University of Cambridge, Cambridge, CB1 8RN, UK
60 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77 Stockholm, Sweden
61 Center for Human Genomics, Wake Forest University, Winston-Salem, North Carolina 27157, USA
62 Neurogenetics Laboratory, Queensland Institute of Medical Research, Queensland 4006, Australia
63 Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00014, Helsinki, Finland
64 National Institute for Health and Welfare, Department of Chronic Disease Prevention, Unit of Public Health Genomics, 00014, Helsinki, Finland
65 Department of Genome Sciences, University of Washington, Seattle, 98195 Washington, USA
66 Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02215, USA
67 Harvard Medical School, Boston, Massachusetts 02115, USA
68 Program in Molecular and Genetic Epidemiology, Harvard School of Public Health, Boston, Massachusetts 02115, USA
69 Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, W2 1PG, UK
70 Centre for Genetic Epidemiology and Biostatistics, University of Western Australia, Crawley, Western Australia 6009, Australia
71 Royal National Hospital for Rheumatic Diseases and University of Bath, Bath, BA1 1RL, UK
72 Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland 21201, USA
73 Genetics Department, Rosetta Inpharmatics, a Wholly Owned Subsidiary of Merck & Co. Inc., Seattle, Washington 98109, USA
74 Department of Internal Medicine, University of Oulu, 90014 Oulu, Finland
75 MGH Weight Center, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
76 Stanford University School of Medicine, Stanford, California 93405, USA
77 Department of Internal Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, 413 45 Gothenburg, Sweden
78 Departments of Biostatistics, University of Washington, Seattle, Washington 98195, USA
79 Ludwig-Maximilians-University, Department of Medicine I, University Hospital Grosshadern, 81377 Munich, Germany
80 Ludwig-Maximilians-Universität, Institute of Medical Informatics, Biometry and Epidemiology, Chair of Epidemiology, 81377 Munich, Germany
81 Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts 02118, USA
82 The Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA
83 Department of Psychiatry, Harvard Medical School, Boston, Massachusetts 02115, USA
84 Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Leicester, LE3 9QP, UK
85 University of Milan, Department of Medicine, Surgery and Dentistry, 20139 Milano, Italy
86 KOS Genetic Srl, 20123 Milan, Italy
87 Istituto di Neurogenetica e Neurofarmacologia del CNR, Monserrato, 09042, Cagliari, Italy
88 Leicester NIHR Biomedical Research Unit in Cardiovascular Disease, Glenfield Hospital, Leicester, LE3 9QP, UK
89 Department of Health Sciences, University of Leicester, University Road, Leicester, LE1 7RH, UK
90 Universität zu Lübeck, Institut für Medizinische Biometrie und Statistik, 23562 Lübeck, Germany
91 Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, 20502 Malmö, Sweden
92 Multidisciplinary Cardiovascular Research Centre (MCRC), Leeds Institute of Genetics, Health and Therapeutics (LIGHT), University of Leeds, Leeds LS2 9JT, UK
93 Centre for Population Health Sciences, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, Scotland
94 University Vita-Salute San Raffaele, Division of Nephrology and Dialysis, 20132 Milan, Italy
95 Institute of Genetic Medicine, European Academy Bozen/Bolzano (EURAC), Bolzano/Bozen, 39100, Italy. Affiliated Institute of the University of Lübeck, Lübeck, Germany
96 British Heart Foundation Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, G12 8TA, UK
97 Northshore University Healthsystem, Evanston, Ilinois 60201, USA
98 Cardiovascular Epidemiology and Genetics, Institut Municipal D'investigacio Medica and CIBER Epidemiología y Salud Pública, Barcelona, Spain
99 Department of General Practice and Primary health Care, University of Helsinki, Helsinki, Finland
100 National Institute for Health and Welfare, 00271 Helsinki, Finland
101 Helsinki University Central Hospital, Unit of General Practice, 00280 Helsinki, Finland
102 Folkhalsan Research Centre, 00250 Helsinki, Finland
103 Vasa Central Hospital, 65130 Vasa, Finland
104 Center for Neurobehavioral Genetics, University of California, Los Angeles, California 90095, USA
105 Hypertension and Cardiovascular Prevention Center, University of Sassari, 07100 Sassari, Italy
106 Department of Clinical Sciences/Obstetrics and Gynecology, University of Oulu, 90014 Oulu, Finland
107 National Institute for Health and Welfare, Department of Chronic Disease Prevention, Chronic Disease Epidemiology and Prevention Unit, 00014, Helsinki, Finland
108 PathWest Laboratory of Western Australia, Department of Molecular Genetics, J Block, QEII Medical Centre, Nedlands, Western Australia 6009, Australia
109 Busselton Population Medical Research Foundation Inc., Sir Charles Gairdner Hospital, Nedlands, Western Australia 6009, Australia
110 National Institute for Health and Welfare, Department of Chronic Disease Prevention, Population Studies Unit, 20720 Turku, Finland
111 Institute of Health Sciences, University of Oulu, 90014 Oulu, Finland
112 Interdisciplinary Centre for Clinical Research, University of Leipzig, 04103 Leipzig, Germany
113 Finnish Institute of Occupational Health, 90220 Oulu, Finland
114 Human Genetics, Genome Institute of Singapore, Singapore 138672, Singapore
115 Transplantation Laboratory, Haartman Institute, University of Helsinki, 00014, Helsinki, Finland
116 Croatian Centre for Global Health, School of Medicine, University of Split, Split 21000, Croatia
117 Institute of Human Genetics, Klinikum rechts der Isar der Technischen Universität München, 81675 Munich, Germany
118 Institute of Human Genetics, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany
119 Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario L8N3Z5, Canada
120 Amgen, Cambridge, Massachusetts 02139, USA
121 Department of Cardiovascular Medicine, University of Oxford, Level 6 West Wing, John Radcliffe Hospital, Headley Way, Headington, Oxford, OX3 9DU
122 Institut für Klinische Chemie und Laboratoriumsmedizin, Universität Greifswald, 17475 Greifswald, Germany
123 Finnish Twin Cohort Study, Department of Public Health, University of Helsinki, 00014, Helsinki, Finland
124 Obesity Research unit, Department of Psychiatry, Helsinki University Central Hospital, Helsinki, Finland
125 National Institute for Health and Welfare, 90101 Oulu, Finland
126 Department of Clinical Sciences, Lund University, 20502 Malmö, Sweden
127 Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, USA
128 Department of Haematology, University of Cambridge, Cambridge CB2 0PT, UK
129 NHS Blood and Transplant, Cambridge Centre, Cambridge, CB2 0PT, UK
130 Institut für Community Medicine, 17489 Greifswald, Germany
131 Division of Cardiology, Cardiovascular Laboratory, Helsinki University Central Hospital, 00029 Helsinki, Finland
132 Department of Psychiatry/EMGO Institute, VU University Medical Center, 1081 BT Amsterdam, The Netherlands
133 Department of Psychiatrics, Lapland Central Hospital, 96101 Rovaniemi, Finland
134 Cardiovascular Medicine, University of Oxford, Wellcome Trust Centre for Human Genetics, Oxford, OX3 7BN, UK
135 Avon Longitudinal Study of Parents and Children (ALSPAC) Laboratory, Department of Social Medicine, University of Bristol, Bristol, BS8 2BN, UK
136 Comprehensive Cancer Center East, 6501 BG Nijmegen, The Netherlands
137 Service of Medical Genetics, Centre Hospitalier Universitaire Vaudois (CHUV) University Hospital, 1011 Lausanne, Switzerland
138 School of Pathology and Laboratory Medicine, University of Western Australia, Nedlands, Western Australia 6009, Australia
139 Department of Physiology and Biophysics, Keck School of Medicine, University of Southern California, Los Angeles, California 90033, USA
140 National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
141 Department of Endocrinology, Radboud University Nijmegen Medical Centre, 6500 HB Nijmegen, The Netherlands
142 Atherosclerosis Research Unit, Department of Medicine, Solna, Karolinska Institutet, Karolinska University Hospital, 171 76 Stockholm, Sweden
143 Division of Research, Kaiser Permanente Northern California, Oakland, California 94612, USA
144 Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California 94107, USA
145 Department of Clinical Physiology, University of Tampere and Tampere University Hospital, 33520 Tampere, Finland
146 National Institute for Health and Welfare, Department of Mental Health and Substance Abuse Services, Unit for Child and Adolescent Mental Health, 00271 Helsinki, Finland
147 Cardiovascular Research Center and Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
148 Framingham Heart Study of the National, Heart, Lung, and Blood Institute and Boston University, Framingham, Massachusetts 01702, USA
149 Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA
150 Department of Epidemiology, Biostatistics and HTA, Radboud University Nijmegen Medical Centre, 6500 HB Nijmegen, The Netherlands
151 Department of Urology, Radboud University Nijmegen Medical Centre, 6500 HB Nijmegen, The Netherlands
152 Zentrum für Zahn-, Mund- und Kieferheilkunde, 17489 Greifswald, Germany
153 Laboratory of Epidemiology, Demography, Biometry, National Institute on Aging, National Institutes of Health, Bethesda, Maryland 20892, USA
154 Department of Clinical Chemistry, University of Tampere and Tampere University Hospital, 33520 Tampere, Finland
155 Department of Medicine, Division of Geriatrics, University of Mississippi Medical Center, Jackson, Mississippi 39216, USA
156 School of Medicine and Pharmacology, University of Western Australia, Perth, Western Australia 6009, Australia
157 National, Lung, and Blood Institute, National Institutes of Health, Framingham, Massachusetts 01702, USA
158 Department of Clinical Genetics, Erasmus MC, Rotterdam, 3015GE, The Netherlands
159 Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku and the Department of Clinical Physiology, Turku University Hospital, 20520 Turku, Finland
160 Universität zu Lübeck, Medizinische Klinik II, 23562 Lübeck, Germany
161 Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, Maryland 21201, USA
162 Cardiovascular Health Research Unit, University of Washington, Seattle, Washington 98101, USA
163 Departments of Medicine and Epidemiology, University of Washington, Seattle, Washington 98195, USA
164 Department of Medicine, University of Leipzig, 04103 Leipzig, Germany
165 LIFE Study Centre, University of Leipzig, Leipzig, Germany
166 Coordination Centre for Clinical Trials, University of Leipzig, Härtelstr. 16-18, 04103 Leipzig, Germany
167 National Institute for Health and Welfare, Diabetes Prevention Unit, 00271 Helsinki, Finland
168 Hjelt Institute, Department of Public Health, University of Helsinki, 00014 Helsinki, Finland
169 South Ostrobothnia Central Hospital, 60220 Seinajoki, Finland
170 Department of Human Genetics and Center of Medical Systems Biology, Leiden University Medical Center, 2333 ZC Leiden, the Netherlands
171 Department of Medicine, University of Turku and Turku University Hospital, 20520 Turku, Finland
172 Department of Psychiatry and Midwest Alcoholism Research Center, Washington University School of Medicine, St Louis, Missouri 63108, USA
173 Genetic Epidemiology Laboratory, Queensland Institute of Medical Research, Queensland 4006, Australia
174 Molecular Epidemiology Laboratory, Queensland Institute of Medical Research, Queensland 4006, Australia
175 Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri 63110, USA
176 Department of Forensic Molecular Biology, Erasmus MC, Rotterdam, 3015GE, The Netherlands
177 Collaborative Health Studies Coordinating Center, Seattle, Washington 98115, USA
178 Human Genetics Center and Institute of Molecular Medicine and Division of Epidemiology, University of Texas Health Science Center, Houston, Texas 77030, USA
179 Klinik und Poliklinik für Innere Medizin II, Universität Regensburg, 93053 Regensburg, Germany
180 Regensburg University Medical Center, Innere Medizin II, 93053 Regensburg, Germany
181 Centre National de Genotypage, Evry, Paris 91057, France
182 Christian-Albrechts-University, University Hospital Schleswig-Holstein, Institute for Clinical Molecular Biology and Department of Internal Medicine I, Schittenhelmstrasse 12, 24105 Kiel
183 Genetics Division, GlaxoSmithKline, King of Prussia, Pennsylvania 19406, USA
184 University of Cambridge Metabolic Research Labs, Institute of Metabolic Science Addenbrooke's Hospital, CB2 OQQ, Cambridge, UK
185 Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599, USA
186 Division of Intramural Research, National Heart, Lung and Blood Institute, Framingham Heart Study, Framingham, Massachusetts 01702, USA
187 New York University Medical Center, New York, New York 10016, USA
188 Biocenter Oulu, University of Oulu, 90014 Oulu, Finland
189 Department of Psychiatry, Leiden University Medical Centre, 2300 RC Leiden, The Netherlands
190 Department of Psychiatry, University Medical Centre Groningen, 9713 GZ Groningen, The Netherlands
191 Department of Neurology, General Central Hospital, Bolzano, Italy
192 Department of Neurology, University of Lübeck, Lübeck, Germany
193 Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York 10461, USA
194 Carolina Center for Genome Sciences, School of Public Health, University of North Carolina Chapel Hill, Chapel Hill, North Carolina 27514, USA
195 Laboratory of Genetics, National Institute on Aging, Baltimore, Maryland 21224, USA
196 Division of Community Health Sciences, St George's, University of London, London, SW17 0RE, UK
197 Klinikum Grosshadern, 81377 Munich, Germany
198 Pacific Biosciences, Menlo Park, California 94025, USA
199 Sage Bionetworks, Seattle, Washington 98109, USA
200 Faculty of Medicine, University of Iceland, 101 Reykjavík, Iceland
201 Department of Medical Genetics, University of Helsinki, 00014 Helsinki, Finland
202 NIHR Oxford Biomedical Research Centre, Churchill Hospital, Oxford, OX3 7LJ, UK
203 Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA
Corresponding Authors: Michael Weedon, Genetics of Complex Traits, Peninsula College of Medicine and Dentistry, University of Exeter, Exeter, EX1 2LU, UK, michael.weedon/at/pms.ac.uk, Gonçalo Abecasis, Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA, goncalo/at/umich.edu, Kari Stefansson, deCODE Genetics, 101 Reykjavik, Iceland, kstefans/at/decode.is, Timothy Frayling, Genetics of Complex Traits, Peninsula College of Medicine and Dentistry, University of Exeter, Exeter, EX1 2LU, UK, tim.frayling/at/pms.ac.uk, Joel Hirschhorn, Children's Hospital, Harvard Medical School, Broad Institute, Boston, Massachusetts 02115, USA, joelh/at/broadinstitute.org
*These authors contributed equally
Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence phenotype. Genome-wide association (GWA) studies have identified >600 variants associated with human traits1, but these typically explain small fractions of phenotypic variation, raising questions about the utility of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait2,3. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P=0.016), and that underlie skeletal growth defects (P<0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants, and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented amongst variants that alter amino acid structure of proteins and expression levels of nearby genes. Our data explain ~10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to ~16% of phenotypic variation (~20% of heritable variation). Although additional approaches are needed to fully dissect the genetic architecture of polygenic human traits, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.
In Stage 1 of our study, we performed a meta-analysis of GWA data from 46 studies, comprising 133,653 individuals of recent European ancestry, to identify common genetic variation associated with adult height. To enable meta-analysis of studies across different genotyping platforms, we performed imputation of 2,834,208 single nucleotide polymorphisms (SNPs) present in the HapMap Phase 2 European-American reference panel4. After applying quality control filters, each individual study tested the association of adult height with each SNP using an additive model (Supplementary Methods). The individual study statistics were corrected using the genomic control (GC) method5,6 and then combined in a fixed effects based meta-analysis. We then applied a second GC correction on the meta-analysis statistics, although this approach may be overly conservative when there are many real signals of association (Supplementary Methods). We detected 207 loci (defined as 1Mb on either side of the most strongly associated SNP) as potentially associated with adult height (P<5×10-6).
To identify loci robustly associated with adult height, we took forward at least one SNP (Supplementary Methods) from each of the 207 loci reaching P<5×10-6 into an additional 50,074 samples (Stage 2) that became available after completion of our initial meta-analysis. In the joint analysis of our Stage 1 and Stage 2 studies, SNPs representing 180 loci reached genome-wide significance (P<5×10-8; Supplementary Figures 1 and 2, Supplementary Table 1). Additional tests, including genotyping of a randomly-selected subset of 33 SNPs in an independent sample of individuals from the 5th-10th and 90th-95th percentiles of the height distribution (N=3,190)7, provided further validation of our results, with all but two SNPs showing consistent direction of effect (sign test P<7×10-8) (Supplementary Methods, Supplementary Table 2).
Genome wide association studies can be susceptible to false positive associations from population stratification7. We therefore performed a family-based analysis, which is immune to population stratification in 7,336 individuals from two cohorts with pedigree information. Alleles representing 150 of the 180 genome-wide significant loci were associated in the expected direction (sign test P<6×10-20; Supplementary Table 3). The estimated effects on height were essentially identical in the overall meta-analysis and the family-based sample. Together with several other lines of evidence (Supplementary Methods), this indicates that stratification is not substantially inflating the test statistics in our meta-analysis.
Common genetic variants have typically explained only a small proportion of the heritable component of phenotypic variation8. This is particularly true for height, where >80% of the variation within a given population is estimated to be attributable to additive genetic factors9, but over 40 previously published variants explain <5% of the variance10-17. One possible explanation is that many common variants of small effects contribute to phenotypic variation, and current GWA studies remain underpowered to detect the majority of common variants. Using five studies not included in Stage 1, we found that the 180 associated SNPs explained on average 10.5% (range 7.9-11.2%) of the variance in adult height (Supplementary Methods). Including SNPs associated with height at lower significance levels (0.05>P>5×10-8) increased the variance explained to 13.3% (range 9.7-16.8%) (Figure 1a)18. In addition, we found no evidence that non-additive effects including gene-gene interaction would increase the proportion of the phenotypic variance explained (Supplementary Methods, Supplementary Tables 5 and 6).
Figure 1
Figure 1
Figure 1
Phenotypic variance explained by common variants
As a separate approach, we used a recently developed method19 to estimate the total number of independent height-associated variants with effect sizes similar to the ones identified. We obtained this estimate using the distribution of effect sizes observed in Stage 2 and the power to detect an association in Stage 1, given these effect sizes (Supplementary Methods). The cumulative distribution of height loci, including those we identified and others as yet undetected, is shown in Figure 1b. We estimate that there are 697 loci (95% confidence interval (CI): 483-1040) with effects equal or greater than those identified, which together would explain approximately 15.7% of the phenotypic variation in height or 19.6% (95% CI: 16.2-25.6) of height heritability (Supplementary Table 4). We estimated that a sample size of 500,000 would detect 99.6% of these loci at P<5×10-8. This figure does not account for variants that have effect sizes smaller than those observed in the current study and, therefore, underestimates the contribution of undiscovered common loci to phenotypic variation.
A further possible source of missing heritability is allelic heterogeneity – the presence of multiple, independent variants influencing a trait at the same locus. We performed genome-wide conditional analyses in a subset of Stage 1 studies, including a total of 106,336 individuals. Each study repeated the primary GWA analysis but additionally adjusted for SNPs representing the 180 loci associated at P<5×10-6 (Supplementary Methods). We then meta-analysed these studies in the same way as for the primary GWA study meta-analysis. Nineteen SNPs within the 180 loci were associated with height at P<3.3×10-7 (a Bonferroni-corrected significance threshold calculated from the ~15% of the genome covered by the conditioned 2Mb loci; Supplementary Methods, Table 1, Figure 2, Supplementary Figure 3). The distances of the second signals to the lead SNPs suggested that both are likely to be affecting the same gene, rather than being coincidentally in close proximity. At 17 of 17 loci (excluding two contiguous loci in the HMGA1 region), the second signal occurred within 500kb, rather than between 500kb and 1 Mb, of this lead SNP (binomial test P=2×10-5). Further analyses of allelic heterogeneity may identify additional variants that increase the proportion of variance explained. For example, within the 180 2Mb loci, a total of 45 independent SNPs reached P<1×10-5 when we would expect <2 by chance.
Table 1
Table 1
Secondary signals at associated loci after conditional analysis
Figure 2
Figure 2
Figure 2
Example of a locus with a secondary signal before (a) and after (b) conditioning
Whilst GWA studies have identified many variants robustly associated with common human diseases and traits, the biological significance of these variants, and the genes on which they act, is often unclear. We first tested the overlap between the 180 height-associated variants and two types of putatively functional variants, nonsynonymous (ns) SNPs and cis-eQTLs (variants strongly associated with expression of nearby genes). Height variants were 2.4-fold more likely to overlap with cis-eQTLs in lymphocytes than expected by chance (47 variants: P=4.7×10-11) (Supplementary Table 7) and 1.7-fold more likely to be closely correlated (r2≥0.8 in HapMap CEU) with nsSNPs (24 variants P=0.004) (Supplementary Methods, Supplementary Table 8). Although the presence of a correlated eQTL or nsSNP at an individual locus does not establish the causality of any particular variant, this enrichment shows that common functional variants contribute to the causal variants at height-associated loci. We also noted five loci where the height associated variant was strongly correlated (r2>0.8) with variants associated with other traits and diseases (P<5×10-8), including bone mineral density, rheumatoid arthritis, type 1 diabetes, psoriasis and obesity, suggesting that these variants have pleiotropic effects on human phenotypes (Supplementary Methods; Supplementary Table 9).
We next addressed the extent to which height variants cluster near biologically relevant genes; specifically, genes mutated in human syndromes characterized by abnormal skeletal growth. We limited this analysis to the 652 genes occurring within the recombination hotspot-bounded regions surrounding each of the 180 index SNPs. We showed that the 180 loci associated with variation in normal height contained 21 of 241 genes (8.7%) found to underlie such syndromes (Supplementary Table 10), compared to a median of 8 (range 1-19) genes identified in 1,000 matched control sets of regions (P<0.001: 0 observations of 21 or more skeletal growth genes among 1,000 sets of matched SNPs). In 13 of these 21 loci the closest gene to the most associated height SNP in the region is the growth disorder gene, and in 9 of these cases, the most strongly associated height SNP is located within the growth disorder gene itself (Supplementary Methods, Supplementary Table 11). These results suggest that GWA studies may provide more clues about the identity of the functional genes at each locus than previously suspected.
We also investigated whether significant and relevant biological connections exist between the genes within the 180 loci, using two different computational approaches. We used the GRAIL text-mining algorithm to search for connectivity between genes near the associated SNPs, based on existing literature20. Of the 180 loci, 42 contained genes that were connected by existing literature to genes in the other associated loci (the pair of connected genes appear in articles that share scientific terms more often than expected at P<0.01). For comparison, when we used GRAIL to score 1,000 sets of 180 SNPs not associated with height (but matched for number of nearby genes, gene proximity, and allele frequency), we only observed 16 sets with 42 or more loci with a connectivity P<0.01, thus providing strong statistical evidence that the height loci are functionally related (P=0.016) (Figure 3a). For the 42 regions with GRAIL connectivity P<0.01, the implicated genes and SNPs are highlighted in Figure 3b. The most strongly connected genes include those in the Hedgehog, TGF-beta, and growth hormone pathways.
Figure 3
Figure 3
Loci associated with height contain genes related to each other
As a second approach to find biological connections, we applied a novel implementation of gene set enrichment analysis (GSEA) (Meta-Analysis Gene-set Enrichment of variaNT Associations, MAGENTA21) to perform pathway analysis (Supplementary Methods). This analysis revealed 17 different biological pathways and 14 molecular functions nominally enriched (P<0.05) for associated genes, many of which lie within the validated height loci. These gene-sets include previously reported11,13 (e.g. Hedgehog signaling) and novel (e.g. TGF-beta signaling, histones, and growth and development-related) pathways and molecular functions (Supplementary Table 12). Several SNPs near genes in these pathways narrowly missed genome-wide significance, suggesting that these pathways likely contain additional associated variants. These results provide complementary evidence for some of the genes and pathways highlighted in the GRAIL analysis. For instance, genes such as TGFB2 and LTBP1-3 highlight a role for the TGF-beta signaling pathway in regulating human height, consistent with the implication of this pathway in Marfan syndrome22.
Finally, to examine the evidence for the potential involvement of specific genes at individual loci, we aggregated evidence from our data (eQTLs, proximity to the associated variant, pathway-based analyses), and human and mouse genetic databases (Supplementary Table 13). Of 32 genes with highly correlated (r2>0.8) nsSNPs, several are newly identified strong candidates for playing a role in human growth. Some are in pathways enriched in our study (such as ECM2, implicated in extracellular matrix), while others have similar functions to known growth-related genes, including FGFR4 (FGFR3 underlies several classic skeletal dysplasias23) and STAT2 (STAT5B mutations cause growth defects in humans24). Interestingly, Fgfr4-/- Fgfr3-/- mice show severe growth retardation not seen in either single mutant25, suggesting that the FGFR4 variant might modify FGFR3-mediated skeletal dysplasias. Other genes at associated loci, such as NPPC and NPR3 (encoding the C-type natriuretic peptide and its receptor), influence skeletal growth in mice and will likely also influence human growth17. Many of the remaining 180 loci have no genes with obvious connections to growth biology, but at some our data provide modest supporting evidence for particular genes, including C3orf63, PML, CCDC91, ZNFX1, ID4, RYBP, SEPT2, ANKRD13B, FOLH1, LRRC37B, MFAP2, SLBP, SOCS5, and ZBTB24 (Supplementary Table 13).
We have identified >100 novel loci that influence the classic polygenic trait of normal variation in human height, bringing the total to 180. Our results have potential general implications for genetic studies of complex traits. We show that loci identified by GWA studies highlight relevant genes: the 180 loci associated with height are non-randomly clustered within biologically relevant pathways and are enriched for genes that are involved in growth-related processes, that underlie syndromes of abnormal skeletal growth, and that are directly relevant to growth-modulating therapies (GH1, IGF1R, CYP19A1, ESR1). The large number of loci with clearly relevant genes suggests that the remaining loci could provide potential clues to important and novel biology.
We provide the strongest evidence yet that the causal gene will often be located near the most strongly associated DNA sequence variant. At the 21 loci containing a known growth disorder gene, that gene was on average 81 kb from the associated variant, and in over half of the loci it was the closest gene to the associated variant. Despite recent doubts about the benefits of GWA studies26, this finding suggests that GWA studies are useful mapping tools to highlight genes that merit further study. The presence of multiple variants within associated loci could help localize the relevant genes within these loci.
By increasing our sample size to >100,000 individuals, we identified common variants that account for approximately 10% of phenotypic variation. Although larger than predicted by some models26, this figure suggests that GWA studies, as currently implemented, will not explain a majority of the estimated 80% contribution of genetic factors to variation in height. This conclusion supports the idea that biological insights, rather than predictive power, will be the main outcome of this initial wave of GWA studies, and that new approaches, which could include sequencing studies or GWA studies targeting variants of lower frequency, will be needed to account for more of the “missing” heritability. Our finding that many loci exhibit allelic heterogeneity suggests that many as yet unidentified causal variants, including common variants, will map to the loci already identified in GWA studies, and that the fraction of causal loci that have been identified could be substantially greater than the fraction of causal variants that have been identified.
In our study, many associated variants are tightly correlated with common nsSNPs, which would not be expected if these associated common variants were proxies for collections of rare causal variants, as has been proposed27. Although a substantial contribution to heritability by less common and/or quite rare variants may be more plausible, our data are not inconsistent with the recent suggestion28 that a large number of common variants of very small effect mostly explain the regulation of height.
In summary, our findings indicate that additional approaches, including those aimed at less common variants, will likely be needed to dissect more completely the genetic component to complex human traits. Our results also strongly demonstrate that GWA studies can identify large numbers of loci that together implicate biologically relevant pathways and mechanisms. We envision that thorough exploration of the genes at associated loci through additional genetic, functional, and computational studies will lead to novel insights into human height and other polygenic traits and diseases.
The primary meta-analysis (Stage 1) included 46 GWA studies of 133,653 individuals. The in-silico follow up (Stage 2) included 15 studies of 50,074 individuals. All individuals were of European ancestry and >99.8% were adults. Details of genotyping, quality control, and imputation methods of each study are given in Supplementary Methods Table 1-2. Each study provided summary results of a linear regression of age-adjusted, within-sex Z scores of height against the imputed SNPs, and an inverse-variance meta-analysis was performed in METAL (http://www.sph.umich.edu/csg/abecasis/METAL/). Validation of selected SNPs was performed through direct genotyping in an extreme height panel (N=3,190) using Sequenom iPLeX, and in 492 Stage 1 samples using the KASPar SNP System. Family-based testing was performed using QFAM, a linear regression-based approach that uses permutation to account for dependency between related individuals29, and FBAT, which uses a linear combination of offspring genotypes and traits to determine the test statistic30. We used a previously described method to estimate the amount of genetic variance explained by the nominally associated loci (using significance threshold increments from P<5×10-8 to P<0.05)18. To predict the number of height susceptibility loci, we took the height loci that reached a significance level of P<5×10-8 in Stage 1 and estimated the number of height loci that are likely to exist based on the distribution of their effect sizes observed in Stage 2 and the power to detect their association in Stage 1. Gene-by-gene interaction, dominant, recessive and conditional analyses are described in Supplementary Methods. Empirical assessment of enrichment for coding SNPs used permutations of random sets of SNPs matched to the 180 height-associated SNPs on the number of nearby genes, gene proximity, and minor allele frequency. GRAIL and GSEA methods have been described previously20,21. To assess possible enrichment for genes known to be mutated in severe growth defects, we identified such genes in the OMIM database (Supplementary Table 10), and evaluated the extent of their overlap with the 180 height-associated regions through comparisons with 1000 random sets of regions with similar gene content (±10%).
Supplementary Material
Acknowledgments
A number of participating studies are members of CHARGE and ENGAGE consortia. We acknowledge funding from the Academy of Finland (104781, 117797, 120315, 121584, 126925, 129269, 129494, 129680, 213506); Affymetrix, Inc for genotyping services (N02-HL-6-4278); Agency for Science, Technology and Research of Singapore (A*STAR); ALF/LUA Gothenburg; Althingi (the Icelandic Parliament); Amgen; AstraZeneca AB; Australian National Health and Medical Research Council (241944, 389875, 389891, 389892, 389938, 442915, 442981, 496739, 496688, 552485, 613672); Australian Research Council (DP0770096); Biocentrum Helsinki; Boston Obesity Nutrition Research Center (DK46200); British Diabetes Association; British Heart Foundation (PG/02/128); British Heart Foundation Centre for Research Excellence, Oxford; CamStrad; Cancer Research UK; Centre for Neurogenomics and Cognitive Research (CNCR-VU); Chief Scientist Office of the Scottish Government (CSO) (CZB/4/279); Council of Health of the Academy of Finland; DIAB Core project of the German Network of Diabetes; Diabetes UK; Donald W. Reynolds Foundation; Emil and Vera Cornell Foundation; Erasmus MC; Estonian Government (SF0180142s08); European Commission (201413, ECOGENE:205419, BBMRI:212111, OPENGENE:245536, ENGAGE:HEALTH-F4-2007-201413, EURODIA:LSHG-CT-2004-518153, EU/WLRT-2001-01254, HEALTH-F2-2008-ENGAGE, HEALTH-F4-2007-201550, LSH-2006-037593, LSHG-CT-2006-018947, LSHG-CT-2006-01947, Procardis:LSHM-CT-2007-037273, POLYGENE:LSHC-CT-2005, QLG1-CT-2000-01643, QLG2-CT-2002-01254, DG XII, Marie Curie Intra-European Fellowship); Eve Appeal; Finish Ministry of Education; Finnish Diabetes Research Foundation; Finnish Diabetes Research Society; Finnish Foundation for Cardiovascular Research; Finnish Medical Society; Finska Läkaresällskapet; Folkhälsan Research Foundation; Fondation LeDucq; Foundation for Life and Health in Finland; Foundation for Strategic Research (SSF); GEN-AU-Programme “GOLD”; Genetic Association InformationNetwork (GAIN); German Bundesministerium fuer Forschung und Technology (01 AK 803 A-H, 01 IG 07015 G); German Federal Ministry of Education and Research (BMBF) (01GS0831); German Ministry for Health, Welfare and Sports; German Ministry of Cultural Affairs; German Ministry of Education, Culture and Science; German National Genome Research Net (NGFN2 and NGFNplus) (01GS0823, 01ZZ0103, 01ZZ0403, 01ZZ9603, 03ZIK012); German Research Council (KFO-152); GlaxoSmithKline; Göteborg Medical Society; Gyllenberg Foundation; Helmholtz Center Munich; Juvenile Diabetes Research Foundation International (JDRF) (U01 DK062418); Karolinska Institute; Knut and Alice Wallenberg Foundation; Lundberg Foundation; March of Dimes (6-FY-09-507); MC Health; Medical Research Council UK (G0000649, G0000934, G0500539, G0600331, G0601261, G9521010D, PrevMetSyn); Microarray Core Facility of the Interdisciplinary Centre for Clinical Research (IZKF) (B27); Mid-Atlantic Nutrition and Obesity Research Center of Maryland (P30 DK072488); Ministry of Health and Department of Educational Assistance (South Tyrol, Italy); Ministry of Science, Education and Sport of the Republic of Croatia (216-1080315-0302); Montreal Heart Institute Foundation; Närpes Health Care Foundation; National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre; NIHR Oxford Biomedical Research Centre; NIHR comprehensive Biomedical Research Centre; National Institutes of Health (263-MA-410953, AA014041, AA07535, AA10248, AA13320, AA13321, AA13326, CA047988, CA49449, CA50385, CA65725, CA67262, CA87969, DA12854, DK062370, DK063491, DK072193, DK079466, DK080145, DK58845, HG002651, HG005214, HG005581, HL043851, HL084729, HL69757, HL71981, K08-AR055688, K23-DK080145, K99-HL094535, M01-RR00425, MH084698, N01-AG12100, N01-AG12109, N01-HC15103, N01-HC25195, N01-HC35129, N01-HC45133, N01-HC55015, N01-HC55016, N01-HC55018 through N01-HC55022, N01-HC55222, N01-HC75150, N01-HC85079 through N01-HC85086, N01-HG65403, R01-AG031890, R01 CA104021, R01-DK068336, R01-DK073490, R01-DK075681, R01-DK075787, R01-HL086694, R01-HL087641, R01-HL087647, R01-HL087652, R01-HL087676, R01-HL087679, R01-HL087700, R01-HL088119, R01-HL59367, R01-MH059160, R01-MH59565, R01-MH59566, R01-MH59571, R01-MH59586, R01-MH59587, R01-MH59588, R01-MH60870, R01-MH60879, R01-MH61675, R01-MH63706, R01-MH67257, R01-MH79469, R01-MH81800, RL1-MH083268, T32-HG00040, U01-CA098233, U01-GM074518, U01-HG004399, U01-HG004402, U01-HL080295, U01-HL084756, U01-HL72515, U01-MH79469, U01-MH79470, U54-RR020278, UL1-RR025005, Z01-AG00675, Z01-AG007380, Z01-HG000024; contract HHSN268200625226C; ADA Mentor-Based Postdoctoral Fellowship; Pew Scholarship for the Biomedical Sciences); Netherlands Genomics Initiative (NGI)/Netherlands Consortium for Healthy Aging (NCHA) (050-060-810); Netherlands Organisation for Scientific Research (NWO) (Investments nr. 175.010.2005.011, 911-03-012); Netherlands Organization for the Health Research and Development (ZonMw) (10-000-1002); Netherlands Scientific Organization (904-61-090, 904-61-193, 480-04-004, 400-05-717, Center for Medical Systems Biology (NOW Genomics), SPI 56-464-1419); NIA Intramural Research Program; Nordic Center of Excellence in Disease Genetics; Novo Nordisk Foundation; Ollqvist Foundation; Oxford NIHR Biomedical Research Centre; Paavo Nurmi Foundation; Perklén Foundation; Petrus and Augusta Hedlunds Foundation; Queensland Institute of Medical Research; Radboud University Nijmegen Medical Centre; Research Institute for Diseases in the Elderly (014-93-015); Royal Swedish Academy of Science; Sahlgrenska Center for Cardiovascular and Metabolic Research (A305:188); Siemens Healthcare, Erlangen, Germany; Signe and Ane Gyllenberg Foundation; Sigrid Juselius Foundation; Social Insurance Institution of Finland; Social Ministry of the Federal State of Mecklenburg-West Pomerania; South Tyrolean Sparkasse Foundation; Stockholm County Council (560183); Support for Science Funding programme; Susan G. Komen Breast Cancer Foundation; Swedish Cancer Society; Swedish Cultural Foundation in Finland; Swedish Foundation for Strategic Research; Swedish Heart-Lung Foundation; Swedish Medical Research Council (K2007-66X-20270-01-3, 8691); Swedish National Cancer Institute; Swedish Research Council; Swedish Society of Medicine; Swiss National Science Foundation (33CSCO-122661); Torsten and Ragnar Söderberg's Foundation; Vandervell Foundation; Västra Götaland Foundation; Wellcome Trust (072960, 075491, 079557, 079895, 083270, 068545/Z/02, 076113/B/04/Z, 076113/C/04/Z, 076113/C/04/Z, 077016/Z/05/Z, 081682/Z/06/Z, 084183/Z/07/Z, 085301/Z/08/Z, 086596/Z/08/Z, 091746/Z/10/Z; WT Research Career Development Fellowship); Western Australian Genetic Epidemiology Resource and the Western Australian DNA Bank (both National Health and Medical Research Council of Australia Enabling Facilities). Detailed list of acknowledgments by study is given in the Supplementary Information.
Footnotes
Author Contributions: Full author contributions and roles are listed in the Supplementary Information.
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