Genome-wide physical activity interactions in adiposity - A meta-analysis of 200,452 adults

RESEARCH ARTICLE Genome-wide physical activity interactions in adiposity ―A meta-analysis of 200,452 adults Mariaelisa Graff 1☯ *, Robert A. Scott 2☯ , Anne E. Justice 1☯ , Kristin L. Young 1,3☯ , Mary F. Feitosa 4 , Llilda Barata 4 , Thomas W. Winkler 5 , Audrey Y. Chu 6,7 , Anubha Mahajan 8 , David Hadley 9 , Luting Xue 6,10 , Tsegaselassie Workalemahu 11 , Nancy L. Heard-Costa 6,12 , Marcel den Hoed 2,13 , Tarunveer S. Ahluwalia 14,15 , Qibin Qi 16 , Julius S. Ngwa 17 , Frida Renstro ¨m 18,19 , Lydia Quaye 20 , John D. Eicher 21 , James E. Hayes 22,23 , Marilyn Cornelis 11,24,25 , Zoltan Kutalik 26,27 , Elise Lim 10 , Jian’an Luan 2 , Jennifer E. Huffman 6,28 , Weihua Zhang 29,30 , Wei Zhao 31 , Paula J. Griffin 10 , Toomas Haller 32 , Shafqat Ahmad 18 , Pedro M. Marques-Vidal 33 , Stephanie Bien 34 , Loic Yengo 35 , Alexander Teumer 36,37 , Albert Vernon Smith 38,39 , Meena Kumari 40 , Marie Neergaard Harder 14 , Johanne Marie Justesen 14 , Marcus E. Kleber 41,42 , Mette Hollensted 14 , Kurt Lohman 43 , Natalia V. Rivera 44 , John B. Whitfield 45 , Jing Hua Zhao 2 , Heather M. Stringham 46 , Leo-Pekka Lyytika ¨inen 47,48 , Charlotte Huppertz 49,50,51 , Gonneke Willemsen 49,50 , Wouter J. Peyrot 52 , Ying Wu 53 , Kati Kristiansson 54,55 , Ayse Demirkan 56,57 , Myriam Fornage 58,59 , Maija Hassinen 60 , Lawrence F. Bielak 31 , Gemma Cadby 61 , Toshiko Tanaka 62 , Reedik Ma ¨gi 32 , Peter J. van der Most 63 , Anne U. Jackson 46 , Jennifer L. Bragg-Gresham 46 , Veronique Vitart 28 , Jonathan Marten 28 , Pau Navarro 28 , Claire Bellis 64,65 , Dorota Pasko 66 ,Åsa Johansson 67 , Søren Snitker 68 , YuChing Cheng 68,69 , Joel Eriksson 70 , Unhee Lim 71 , Mette Aadahl 72,73 , Linda S. Adair 74 , Najaf Amin 56 , Beverley Balkau 75 , Juha Auvinen 76,77 , John Beilby 78,79,80 , Richard N. Bergman 81 , Sven Bergmann 27,82 , Alain G. Bertoni 83,84 , John Blangero 85 , Ame ´lie Bonnefond 35 , Lori L. Bonnycastle 86 , Judith B. Borja 87,88 , Søren Brage 2 , Fabio Busonero 89 , Steve Buyske 90,91 , Harry Campbell 92 , Peter S. Chines 86 , Francis S. Collins 86 , Tanguy Corre 27,82 , George Davey Smith 93 , Graciela E. Delgado 41 , Nicole Dueker 94 , Marcus Do ¨rr 37,95 , Tapani Ebeling 96,97 , Gudny Eiriksdottir 38 , Tõnu Esko 32,98,99,100 , Jessica D. Faul 101 , Mao Fu 68 , Kristine Færch 15 , Christian Gieger 102,103,104 , Sven Gla ¨ser 95 , Jian Gong 34 , Penny Gordon-Larsen 3,74 , Harald Grallert 102,104,105 , Tanja B. Grammer 41 , Niels Grarup 14 , Gerard van Grootheest 52 , Kennet Harald 54 , Nicholas D. Hastie 28 , Aki S. Havulinna 54 , Dena Hernandez 106 , Lucia Hindorff 107 , Lynne J. Hocking 108,109 , Oddgeir L. Holmens 110 , Christina Holzapfel 102,111 , Jouke Jan Hottenga 49,112 , Jie Huang 113 , Tao Huang 11 , Jennie Hui 78,79,114 , Cornelia Huth 104,105 , Nina Hutri-Ka ¨ho ¨nen 115,116 , Alan L. James 78,117,118 , John-Olov Jansson 119 , Min A. Jhun 31 , Markus Juonala 120,121 , Leena Kinnunen 122 , Heikki A. Koistinen 122,123,124 , Ivana Kolcic 125 , Pirjo Komulainen 60 , Johanna Kuusisto 126 , Kirsti Kvaløy 127 , Mika Ka ¨ho ¨nen 128,129 , Timo A. Lakka 60,130 , Lenore J. Launer 131 , Benjamin Lehne 29 , Cecilia M. Lindgren 8,132,133 , Mattias Lorentzon 70,134 , Robert Luben 135 , Michel Marre 136,137 , Yuri Milaneschi 52 , Keri L. Monda 1,138 , Grant W. Montgomery 45 , Marleen H. M. De Moor 50,139 , Antonella Mulas 89,140 , Martina Mu ¨ller-Nurasyid 103,141,142 , A. W. Musk 78,114,143 , Reija Ma ¨nnikko ¨ 60 , Satu Ma ¨nnisto ¨ 54 , Narisu Narisu 86 , Matthias Nauck 37,144 , Jennifer A. Nettleton 59 , Ilja M. Nolte 63 , Albertine J. Oldehinkel 145 , Matthias Olden 5 , Ken K. Ong 2 , Sandosh Padmanabhan 109,146 , Lavinia Paternoster 93 , Jeremiah Perez 10 , Markus Perola 54,55,147 , Annette Peters 104,105,142 , Ulrike Peters 34 , Patricia A. Peyser 31 , Inga Prokopenko 148 , Hannu Puolijoki 149 , Olli T. Raitakari 150,151 , Tuomo Rankinen 152 , Laura J. Rasmussen-Torvik 24 , Rajesh Rawal 102,103,104 , Paul M. Ridker 7,153 , Lynda M. Rose 7 , Igor Rudan 92 , Cinzia Sarti 154 , Mark A. Sarzynski 152 , Kai Savonen 60 , William R. Scott 29 , Serena Sanna 89 , Alan R. Shuldiner 68,69 , Steve Sidney 155 , Gu ¨nther Silbernagel 156 , Blair H. Smith 109,157 , Jennifer A. Smith 31 , Harold Snieder 63 , Alena Stanča ´kova ´ 126 , Barbara Sternfeld 155 , Amy J. Swift 86 , Tuija Tammelin 158 , Sian-Tsung Tan 159 , Barbara Thorand 104,105 , Dorothe ´e Thuillier 35 , Liesbeth Vandenput 70 , Henrik Vestergaard 14,15 , Jana V. van Vliet-Ostaptchouk 160 , PLOS Genetics | https://doi.org/10.1371/journal.pgen.1006528 April 27, 2017 1 / 26 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Graff M, Scott RA, Justice AE, Young KL, Feitosa MF, Barata L, et al. (2017) Genome-wide physical activity interactions in adiposity ―A metaanalysis of 200,452 adults. PLoS Genet 13(4): e1006528. https://doi.org/10.1371/journal. pgen.1006528 Editor: Todd L. Edwards, Vanderbilt University, UNITED STATES Received: August 17, 2016 Accepted: December 7, 2016 Published: April 27, 2017 Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Data Availability Statement: All genome-wide association meta-analysis results files are available at the GIANT Consortium website: www. broadinstitute.org/collaboration/giant. Funding: The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services. Funding for this study was provided by the Aase and Ejner Danielsens Marie-Claude Vohl 161,162 , Uwe Vo ¨lker 37,163 , Ge ´rard Waeber 33 , Mark Walker 164 , Sarah Wild 165 , Andrew Wong 166 , Alan F. Wright 28 , M. Carola Zillikens 167 , Niha Zubair 34 , Christopher A. Haiman 168 , Loic Lemarchand 71 , Ulf Gyllensten 67 , Claes Ohlsson 70 , Albert Hofman 169,170 , Fernando Rivadeneira 167,169,170 , Andre ´G. Uitterlinden 167,169 , Louis Pe ´russe 161,171 , James F. Wilson 28,92 , Caroline Hayward 28 , Ozren Polasek 92,125 , Francesco Cucca 89,140 , Kristian Hveem 127 , Catharina A. Hartman 172 , Anke To ¨njes 173 , Stefania Bandinelli 174 , Lyle J. Palmer 175 , Sharon L. R. Kardia 31 , Rainer Rauramaa 60,176 , Thorkild I. A. Sørensen 14,73,93,177 , Jaakko Tuomilehto 122,178,179 , Veikko Salomaa 54 , Brenda W. J. H. Penninx 52 , Eco J. C. de Geus 49,50 , Dorret I. Boomsma 49,112 , Terho Lehtima ¨ki 47,48 , Massimo Mangino 20,180 , Markku Laakso 126 , Claude Bouchard 152 , Nicholas G. Martin 45 , Diana Kuh 166 , Yongmei Liu 83 , Allan Linneberg 72,181,182 , Winfried Ma ¨rz 41,183,184 , Konstantin Strauch 103,185 , Mika Kivima ¨ki 186 , Tamara B. Harris 187 , Vilmundur Gudnason 38,39 , Henry Vo ¨lzke 36,37 , Lu Qi 11 , Marjo-Riitta Ja ¨rvelin 29,76,77,188,189 , John C. Chambers 29,30,190 , Jaspal S. Kooner 30,159,190 , Philippe Froguel 35,191 , Charles Kooperberg 34 , Peter Vollenweider 33 , Go ¨ran Hallmans 19 , Torben Hansen 14 , Oluf Pedersen 14 , Andres Metspalu 32 , Nicholas J. Wareham 2 , Claudia Langenberg 2 , David R. Weir 101 , David J. Porteous 109,192 , Eric Boerwinkle 59 , Daniel I. Chasman 7,100,153 , CHARGE Consortium, EPIC-InterAct Consortium, PAGE Consortium ¶ , Gonc¸alo R. Abecasis 46 , Inês Barroso 193,194,195 , Mark I. McCarthy 8,196,197 , Timothy M. Frayling 66 , Jeffrey R. O’Connell 68 , Cornelia M. van Duijn 56,170,198 , Michael Boehnke 46 , Iris M. Heid 5 , Karen L. Mohlke 53 , David P. Strachan 199 , Caroline S. Fox 21 , Ching-Ti Liu 10 , Joel N. Hirschhorn 99,100,200 , Robert J. Klein 23 , Andrew D. Johnson 6,21 , Ingrid B. Borecki 4 , Paul W. Franks 11,18,201 , Kari E. North 202 , L. Adrienne Cupples 6,10 , Ruth J. F. Loos 2,203,204,205‡ *, Tuomas O. Kilpela ¨inen 2,14,205‡ * 1Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America, 2MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom, 3Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America, 4Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America, 5Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany, 6National Heart, Lung, and Blood Institute, Framingham Heart Study, Framingham, Massachusetts, United States of America, 7Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America, 8Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom, 9Division of Population Health Sciences and Education, St. George’s, University of London, London, United Kingdom, 10 Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America, 11 Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America, 12 Department of Neurology, Boston University School of Medicine, Boston, Massachusetts, United States of America, 13 Department of Immunology, Genetics and Pathology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden, 14 Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark, 15 Steno Diabetes Center, Gentofte, Denmark, 16 Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, United States of America, 17 Howard University, Department of Internal Medicine, Washington DC, United States of America, 18 Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Malmo ¨, Sweden, 19 Department of Biobank Research, UmeåUniversity, Umeå, Sweden, 20 Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom, 21 Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, The Framingham Heart Study, Framingham, Massachusetts, United States of America, 22 Cell and Developmental Biology Graduate Program, Weill Cornell Graduate School of Medical Sciences, Cornell University, New York, New York, United States of America, 23 Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America, 24 Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America, 25 Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America, 26 Institute of Social and Preventive Medicine, Lausanne University Hospital, Lausanne, Switzerland, 27 Swiss Institute of Bioinformatics, Lausanne, Switzerland, 28 MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, United Kingdom, 29 Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom, 30 Department of Cardiology, Ealing Hospital HNS Trust, Middlesex, United Kingdom, 31 Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Genome-wide physical activity interactions in adiposity PLOS Genetics | https://doi.org/10.1371/journal.pgen.1006528 April 27, 2017 2 / 26 Foundation; Academy of Finland (102318; 104781, 120315, 123885, 129619, 286284, 134309, 126925, 121584, 124282, 129378, 117787, 250207, 258753, 41071, 77299, 124243, 1114194, 24300796); Accare Center for Child and Adolescent Psychiatry; Action on Hearing Loss (G51); Agence Nationale de la Recherche; Agency for Health Care Policy Research (HS06516); Age UK Research into Ageing Fund; Åke Wiberg Foundation; ALF/LUA Research Grant in Gothenburg; ALFEDIAM; ALK-Abello´ A/S (Hørsholm, Denmark); American Heart Association (13POST16500011, 10SDG269004); Ardix Medical; Arthritis Research UK; Association Diabète Risque Vasculaire; AstraZeneca; Australian Associated Brewers; Australian National Health and Medical Research Council (241944, 339462, 389927, 389875, 389891, 389892, 389938, 442915, 442981, 496739, 552485, 552498); Avera Research Institute; Bayer Diagnostics; Becton Dickinson; Biobanking and Biomolecular Resources Research Infrastructure (BBMRI –NL, 184.021.007); Biocentrum Helsinki; Boston Obesity Nutrition Research Center (DK46200); British Heart Foundation (RG/10/12/28456, SP/04/002); Canada Foundation for Innovation; Canadian Institutes of Health Research (FRN-CCT-83028); Cancer Research UK; Cardionics; Center for Medical Systems Biology; Center of Excellence in Complex Disease Genetics and SALVECenter of Excellence in Genomics (EXCEGEN); Chief Scientist Office of the Scottish Government; City of Kuopio; Cohortes Sante ´TGIR; Contrat de Projets E ´tat-Re ´gion; Croatian Science Foundation (8875); Danish Agency for Science, Technology and Innovation; Danish Council for Independent Research (DFF– 1333-00124, DFF–1331-007308); Danish Diabetes Academy; Danish Medical Research Council; Department of Psychology and Education of the VU University Amsterdam; Diabetes Hilfsund Forschungsfonds Deutschland; Dutch Brain Foundation; Dutch Ministry of Justice; Emil Aaltonen Foundation; Erasmus Medical Center; Erasmus University; Estonian Government (IUT2060, IUT24-6); Estonian Ministry of Education and Research (3.2.0304.11-0312); European Commission (230374, 284167, 323195, 692145, FP7 EurHEALTHAgeing-277849, FP7 BBMRI-LPC 313010, nr 602633, HEALTH-F2-2008-201865GEFOS, HEALTH-F4-2007-201413, FP6 LSHM-CT2004-005272, FP5 QLG2-CT-2002-01254, FP6 LSHG-CT-2006-01947, FP7 HEALTH-F4-2007201413, FP7 279143, FP7 201668, FP7 305739, FP6 LSHG-CT-2006-018947, HEALTH-F4-2007201413, QLG1-CT-2001-01252); European Regional Development Fund; European Science Foundation (EuroSTRESS project FP-006, ESF, EU/ Michigan, United States of America, 32 Estonian Genome Center, University of Tartu, Tartu, Estonia, 33 Department of Internal Medicine, Internal Medicine, Lausanne University Hospital, Lausanne, Switzerland, 34 Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America, 35 University of Lille, CNRS, Institut Pasteur de Lille, UMR 8199 - EGID, Lille, France, 36 Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany, 37 DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany, 38 Icelandic Heart Association, Kopavogur, Iceland, 39 Faculty of Medicine, University of Iceland, Reykjavik, Iceland, 40 ISER, University of Essex, Colchester, Essex, United Kingdom, 41 Vth Department of Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany, 42 Institute of Nutrition, Friedrich Schiller University Jena, Jena, Germany, 43 Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America, 44 Karolinska Institutet, Respiratory Unit, Department of Medicine Solna, Stockholm, Sweden, 45 Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Australia, 46 Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America, 47 Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland, 48 Department of Clinical Chemistry, University of Tampere School of Medicine, Tampere, Finland, 49 Department of Biological Psychology, Vrije Universiteit, Amsterdam, The Netherlands, 50 EMGO+ Institute, Vrije Universiteit & VU University Medical Center, Amsterdam, The Netherlands, 51 Department of Public and Occupational Health, VU University Medical Center, Amsterdam, The Netherlands, 52 Department of Psychiatry, EMGO Institute for Health and Care Research and Neuroscience Campus Amsterdam, VU University Medical Center/GGZ InGeest, Amsterdam, The Netherlands, 53 Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America, 54 National Institute for Health and Welfare, Department of Health, Helsinki, Finland, 55 Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland, 56 Genetic Epidemiology Unit, Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands, 57 Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands, 58 Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, Texas, United States of America, 59 Division of Epidemiology, Human Genetics, and Environmental Sciences, University of Texas Health Science Center at Houston, Houston, Texas, United States of America, 60 Kuopio Research Institute of Exercise Medicine, Kuopio, Finland, 61 Centre for Genetic Origins of Health and Disease, University of Western Australia, Crawley, Western Australia, Australia, 62 Translational Gerontology Branch, National Institute on Aging, Baltimore, Maryland, United States of America, 63 Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands, 64 Human Genetics, Genome Institute of Singapore, Agency for Science, Technology and Research of Singapore, Singapore, 65 Genomics Research Centre, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia, 66 Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, United Kingdom, 67 Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden, 68 Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, Maryland, United States of America, 69 Veterans Affairs Maryland Health Care System, University of Maryland, Baltimore, Maryland, United States of America, 70 Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden, 71 Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii, United States of America, 72 Research Centre for Prevention and Health, Glostrup University Hospital, Glostrup, Denmark, 73 Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark, 74 Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America, 75 INSERM U-1018, CESP, Renal and Cardiovascular Epidemiology, UVSQ-UPS, Villejuif, France, 76 Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland, 77 Unit of Primary Care, Oulu University Hospital, Oulu, Finland, 78 Busselton Population Medical Research Institute, Nedlands, Western Australia, Australia, 79 PathWest Laboratory Medicine of WA, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia, 80 School of Pathology and Laboratory Medicine, The University of Western Australia, Crawley, Western Australia, Australia, 81 Diabetes and Obesity Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States of America, 82 Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland, 83 Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America, 84 Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America, 85 Texas Biomedical Research Institute, San Antonio, Texas, United States of America, 86 Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, NIH, Bethesda, Maryland, United States of America, 87 USC-Office of Population Studies Foundation, Inc., University of San Carlos, Cebu City, Philippines, 88 Department of Nutrition and Dietetics, University of San Carlos, Cebu City, Philippines, 89 Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale Delle Ricerche (CNR), Cittadella Universitaria di Monserrato, Monserrato, Italy, 90 Department of Genetics, Rutgers University, Piscataway, New Jersey, United States of America, Genome-wide physical activity interactions in adiposity PLOS Genetics | https://doi.org/10.1371/journal.pgen.1006528 April 27, 2017 3 / 26 QLRT-2001-01254); Faculty of Biology and Medicine of Lausanne; Federal Ministry of Education and Research (01ZZ9603, 01ZZ0103, 01ZZ0403, 03ZIK012, 03IS2061A); Federal State of Mecklenburg - West Pomerania; Fe ´de ´ration Franc¸aise de Cardiologie; Finnish Cultural Foundation; Finnish Diabetes Association; Finnish Foundation of Cardiovascular Research; Finnish Heart Association; Food Standards Agency; Fondation de France; Fonds Sante ´; Genetic Association Information Network of the Foundation for the National Institutes of Health; German Diabetes Association; German Federal Ministry of Education and Research (BMBF, 01ER1206, 01ER1507); German Research Council (SFB-1052, SPP 1629 TO 718/2-1); GlaxoSmithKline; Go¨ran Gustafssons Foundation; Go¨teborg Medical Society; Health and Safety Executive; Heart Foundation of Northern Sweden; Icelandic Heart Association; Icelandic Parliament; Imperial College Healthcare NHS Trust; INSERM, Re ´seaux en Sante ´ Publique, Interactions entre les de ´terminants de la sante ´; Interreg IV Oberrhein Program (A28); Italian Ministry of Economy and Finance; Italian Ministry of Health (ICS110.1/RF97.71); John D and Catherine T MacArthur Foundation; Juho Vainio Foundation; King’s College London; Kjell och Ma¨rta Beijers Foundation; Kuopio University Hospital; Kuopio, Tampere and Turku University Hospital Medical Funds (X51001); Leiden University Medical Center; Lilly; LMUinnovativ; Lundbeck Foundation; Lundberg Foundation; Medical Research Council of Canada; MEKOS Laboratories (Denmark); Merck Sante ´; Mid-Atlantic Nutrition Obesity Research Center (P30 DK72488); Ministère de l’E ´conomie, de l’Innovation et des Exportations; Ministry for Health, Welfare and Sports of the Netherlands; Ministry of Cultural Affairs of the Federal State of Mecklenburg-West Pomerania; Ministry of Education and Culture of Finland (627;2004-2011); Ministry of Education, Culture and Science of the Netherlands; MRC Human Genetics Unit; MRC-GlaxoSmithKline Pilot Programme Grant (G0701863); Municipality of Rotterdam; Netherlands Bioinformatics Centre (2008.024); Netherlands Consortium for Healthy Aging (050-060-810); Netherlands Genomics Initiative; Netherlands Organisation for Health Research and Development (904-61-090, 985-10002, 904-61-193, 480-04-004, 400-05-717, Addiction-31160008, Middelgroot-911-09-032, Spinozapremie 56-464-14192); Netherlands Organisation for Health Research and Development (2010/31471/ZONMW); Netherlands Organisation for Scientific Research (10-000-1002, GB-MW 940-38-011, 100-001-004, 60-60600-97-118, 26198-710, GB-MaGW 480-01-006, GB-MaGW 480- 91 Department of Statistics and Biostatistics, Rutgers University, Piscataway, New Jersey, United States of America, 92 Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, Edinburgh, Scotland, 93 MRC Integrative Epidemiology Unit & School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom, 94 University of Maryland School of Medicine, Department of Epidemiology & Public Health, Baltimore, Maryland, United States of America, 95 Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany, 96 Department of Medicine, Oulu University Hospital, Oulu, Finland, 97 Institute of Clinical Medicine, Faculty of Medicine, University of Oulu, Oulu, Finland, 98 Division of Endocrinology, Boston Children’s Hospital, Boston, Massachusetts, United States of America, 99 Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America, 100 Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts, United States of America, 101 Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, Michigan, United States of America, 102 Research Unit of Molecular Epidemiology, Helmholtz Zentrum Mu¨nchen - German Research Center for Environmental Health, Neuherberg, Germany, 103 Institute of Genetic Epidemiology, Helmholtz Zentrum Mu¨nchen, German Research Center for Environmental Health, Neuherberg, Germany, 104 Institute of Epidemiology II, Helmholtz Zentrum Mu¨nchen-German Research Center for Environmental Health, Neuherberg, Germany, 105 German Center for Diabetes Research (DZD), Mu¨nchen-Neuherberg, Germany, 106 Laboratory of Neurogenetics, National Institute on Aging, Bethesda, Maryland, United States of America, 107 Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America, 108 Musculoskeletal Research Programme, Division of Applied Medicine, University of Aberdeen, Foresterhill, Aberdeen, United Kingdom, 109 Generation Scotland, Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, United Kingdom, 110 St. Olav Hospital, Trondheim University Hospital, Trondheim, Norway, 111 Institute for Nutritional Medicine, Klinikum Rechts der Isar, Technische Universita ¨t Mu¨nchen, Munich, Germany, 112 NCA Institute, VU University & VU Medical Center, Amsterdam, The Netherlands, 113 Department of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridge, United Kingdom, 114 School of Population Health, The University of Western Australia, Crawley, Western Australia, Australia, 115 Department of Pediatrics, Tampere University Hospital, Tampere, Finland, 116 Department of Pediatrics, University of Tampere School of Medicine, Tampere, Finland, 117 Department of Pulmonary Physiology and Sleep Medicine, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia, 118 School of Medicine and Pharmacology, The University of Western Australia, Crawley, Western Australia, Australia, 119 Department of Physiology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden, 120 Department of Medicine, University of Turku, Turku, Finland, 121 Division of Medicine, Turku University Hospital, Turku, Finland, 122 National Institute for Health and Welfare, Department of Health, Helsinki, Finland, 123 Department of Medicine and Abdominal Center: Endocrinology, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland, 124 Minerva Foundation Institute for Medical Research, Helsinki, Finland, 125 Department of Public Health, Faculty of Medicine, University of Split, Split, Croatia, 126 Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland, 127 HUNT Research Centre, Department of Public Health and General Practice, Norwegian University of Science and Technology, Levanger, Norway, 128 Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland, 129 Department of Clinical Physiology, University of Tampere School of Medicine, Tampere, Finland, 130 Institute of Biomedicine, Physiology, University of Eastern Finland, Kuopio Campus, Finland, 131 Neuroepidemiology Section, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, United States of America, 132 Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, United States of America, 133 The Big Data Institute, University of Oxford, Oxford, United Kingdom, 134 Geriatric Medicine, Sahlgrenska University Hospital, Mo ¨lndal, Sweden, 135 Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom, 136 INSERM U-1138, E ´quipe 2: Pathophysiology and Therapeutics of Vascular and Renal diseases Related to Diabetes, Centre de Recherche des Cordeliers, Paris, France, 137 Department of Endocrinology, Diabetology, Nutrition, and Metabolic Diseases, Bichat Claude Bernard Hospital, Paris, France, 138 Center for Observational Research, Amgen Inc., Thousand Oaks, California, United States of America, 139 Section of Clinical Child and Family Studies, Department of Educational and Family Studies, Vrije Universiteit, Amsterdam, The Netherlands, 140 Dipartimento di Scienze Biomediche, Universitàdegli Studi di Sassari, Sassari, Italy, 141 Department of Medicine I, Ludwig-Maximilians-Universita ¨t, Munich, Germany, 142 DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany, 143 Department of Respiratory Medicine, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia, 144 Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany, 145 Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands, 146 Institute of Cardiovascular and Medical Sciences, BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, United Kingdom, 147 University of Tartu, Estonian Genome Centre, Tartu, Estonia, 148 Genomics of Common Disease, Imperial College London, London, United Kingdom, 149 South Ostrobothnia Central Hospital, Seina ¨joki, Finland, 150 Department of Clinical Physiology and Nuclear Genome-wide physical activity interactions in adiposity PLOS Genetics | https://doi.org/10.1371/journal.pgen.1006528 April 27, 2017 4 / 26 07-001, GB-MaGW 452-04-314, GB-MaGW 45206-004, 175.010.2003.005, 175.010.2005.011, 481-08-013, 480-05-003, 911-03-012); Neuroscience Campus Amsterdam; NHS Foundation Trust; Novartis Pharmaceuticals; Novo Nordisk; Office National Interprofessionel des Vins; Paavo Nurmi Foundation; Påhlssons Foundation; Pa¨ivikki and Sakari Sohlberg Foundation; Pierre Fabre; Republic of Croatia Ministry of Science, Education and Sport (108-1080315-0302); Research Centre for Prevention and Health, the Capital Region for Denmark; Research Institute for Diseases in the Elderly (014-93-015, RIDE2); Roche; Russian Foundation for Basic Research (NWO-RFBR 047.017.043); Rutgers University Cell and DNA Repository (NIMH U24 MH068457-06); Sanofi-Aventis; Scottish Executive Health Department (CZD/16/6); Siemens Healthcare; Social Insurance Institution of Finland (4/26/2010); Social Ministry of the Federal State of Mecklenburg-West Pomerania; Socie ´te ´ Francophone du Diabète; State of Bavaria; Stroke Association; Swedish Diabetes Association; Swedish Foundation for Strategic Research; Swedish Heart-Lung Foundation (20140543); Swedish Research Council (2015-03657); Swedish Medical Research Council (K2007-66X-20270-013, 2011-2354); Swedish Society for Medical Research; Swiss National Science Foundation (33CSCO-122661, 33CS30-139468, 33CS30148401); Tampere Tuberculosis Foundation; The Marcus Borgstro¨m Foundation; The Royal Society; The Wellcome Trust (084723/Z/08/Z, 088869/B/09/ Z); Timber Merchant Vilhelm Bangs Foundation; Topcon; Torsten and Ragnar So¨derberg’s Foundation; UK Department of Health; UK Diabetes Association; UK Medical Research Council (MC_U106179471, G0500539, G0600705, G0601966, G0700931, G1002319, K013351, MC_UU_12019/1); UK National Institute for Health Research BioResource Clinical Research Facility and Biomedical Research Centre; UK National Institute for Health Research (NIHR) Comprehensive Biomedical Research Centre; UK National Institute for Health Research (RP-PG0407-10371); UmeåUniversity Career Development Award; United States – Israel Binational Science Foundation Grant (2011036); University Hospital Oulu (75617); University Medical Center Groningen; University of Tartu (SP1GVARENG); National Institutes of Health (AG13196, CA047988, HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, HHSC271201100004C, HHSN268200900041C, HHSN268201300025C, HHSN268201300026C, HHSN268201300027C, HHSN268201300028C, Medicine, Turku University Hospital, Turku, Finland, 151 Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland, 152 Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, Louisiana, United States of America, 153 Harvard Medical School, Boston, Massachusetts, United States of America, 154 Social Services and Health Care Department, City of Helsinki, Helsinki, Finland, 155 Division of Research, Kaiser Permanente Northern California, Oakland, California, United States of America, 156 Division of Angiology, Department of Internal Medicine, Medical University Graz, Austria, 157 School of Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, Scotland, 158 LIKES Research Center for Sport and Health Sciences, Jyva ¨skyla ¨, Finland, 159 National Heart and Lung Institute, Imperial College London, United Kingdom, 160 Department of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands, 161 Institute of Nutrition and Functional Foods, Quebec, Canada, 162 School of Nutrition, Laval University, Quebec, Canada, 163 Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Germany, 164 Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, United Kingdom, 165 Centre for Population Health Sciences, Usher Institute for Population Health Sciences and Informatics, Teviot Place, Edinburgh, Scotland, 166 MRC Unit for Lifelong Health and Ageing at UCL, London, United Kingdom, 167 Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands, 168 Department of Preventive Medicine, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America, 169 Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands, 170 Netherlands Consortium for Healthy Aging, Leiden University Medical Center, Leiden, The Netherlands, 171 Department of Kinesiology, Laval University, Quebec, Canada, 172 Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands, 173 University of Leipzig, Medical Department, Leipzig, Germany, 174 Geriatric Unit, Azienda Sanitaria Firenze, Florence, Italy, 175 School of Public Health, University of Adelaide, Adelaide, South Australia, Australia, 176 Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland, 177 Department of Clinical Epidemiology, Bispebjerg and Frederiksberg Hospitals, The Capital Region, Copenhagen, Denmark, 178 Centre for Vascular Prevention, Danube-University Krems, Krems, Austria, 179 Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia, 180 National Institute for Health Research Biomedical Research Centre at Guy’s and St. Thomas’ Foundation Trust, London, United Kingdom, 181 Department of Clinical Experimental Research, Rigshospitalet, Glostrup, Denmark, 182 Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark, 183 Synlab Academy, Synlab Services LLC, Mannheim, Germany, 184 Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria, 185 Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-MaximiliansUniversita ¨t, Munich, Germany, 186 Department of Epidemiology and Public Health, University College London, London, United Kingdom, 187 Laboratory of Epidemiology and Population Science, National Institute on Aging, Bethesda, Maryland, United States of America, 188 Biocenter Oulu, University of Oulu, Oulu, Finland, 189 MRC-PHE Centre for Environment and Health, Imperial College London, London, United Kingdom, 190 Imperial College Healthcare NHS Trust, London, United Kingdom, 191 Hammersmith Hospital, London, United Kingdom, 192 Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom, 193 Wellcome Trust Sanger Institute, Hinxton, United Kingdom, 194 NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, United Kingdom, 195 The University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Cambridge, United Kingdom, 196 Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, United Kingdom, 197 Oxford NIHR Biomedical Research Centre, Oxford, United Kingdom, 198 Center of Medical Systems Biology, Leiden, The Netherlands, 199 Population Health Research Institute, St. George’s University of London, London, United Kingdom, 200 Divisions of Endocrinology and Genetics and Center for Basic and Translational Obesity Research, Boston Children’s Hospital, Boston, Massachusetts, United States of America, 201 Department of Public Health & Clinical Medicine, Umeå University, Umeå, Sweden, 202 Carolina Center for Genome Sciences, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America, 203 Genetics of Obesity and Related Metabolic Traits Program, Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America, 204 The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America, 205 The Department of Preventive Medicine, The Icahn School of Medicine at Mount Sinai, New York, New York, United States of America ☯These authors contributed equally to this work. ‡ These authors jointly supervised this work. ¶ Membership is listed in the Supporting Information. *[email protected]u (MG); [email protected] (RJFL); [email protected] (TOK) Genome-wide physical activity interactions in adiposity PLOS Genetics | https://doi.org/10.1371/journal.pgen.1006528 April 27, 2017 5 / 26 HHSN268201300029C, HHSN268201500001I, HL36310, HG002651, HL034594, HL054457, HL054481, HL071981, HL084729, HL119443, HL126024, N01-AG12100, N01-AG12109, N01HC25195, N01-HC55015, N01-HC55016, N01HC55018, N01-HC55019, N01-HC55020, N01HC55021, N01-HC55022, N01-HD95159, N01HD95160, N01-HD95161, N01-HD95162, N01HD95163, N01-HD95164, N01-HD95165, N01HD95166, N01-HD95167, N01-HD95168, N01HD95169, N01-HG65403, N02-HL64278, R01HD057194, R01-HL087641, R01-HL59367, R01HL-086694, R01-HL088451, R24-HD050924, U01-HG-004402, HHSN268200625226C, UL1RR025005, UL1-RR025005, UL1-TR-001079, UL1-TR-00040, AA07535, AA10248, AA11998, AA13320, AA13321, AA13326, AA14041, AA17688, DA12854, MH081802, MH66206, R01D004215701A, R01-DK075787, R01-DK089256, R01-DK8925601, R01-HL088451, R01-HL117078, R01-DK062370, R01-DK072193, DK091718, DK100383, DK078616, 1Z01-HG000024, HL087660, HL100245, R01DK089256, 2T32HL007055-36, U01-HL072515-06, U01HL84756, NIA-U01AG009740, RC2-AG036495, RC4-AG039029, R03 AG046389, 263-MA-410953, 263-MD-9164, 263-MD-821336, U01-HG004802, R37CA54281, R01CA63, P01CA33619, U01CA136792, U01-CA98758, RC2-MH089951, MH085520, R01-D0042157-01A, MH081802, 1RC2-MH089951, 1RC2-MH089995, 1RL1MH08326801, U01-HG007376, 5R01HL08767902, 5R01MH63706:02, HG004790, N01WH22110, U01-HG007033, UM1CA182913, 24152, 32100-2, 32105-6, 32108-9, 32111-13, 32115, 32118-32119, 32122, 42107-26, 4212932, 44221); USDA National Institute of Food and Agriculture (2007-35205-17883); Va¨stra Go¨taland Foundation; Velux Foundation; Veterans Affairs (1 IK2 BX001823); Vleugels Foundation; VU University’s Institute for Health and Care Research (EMGO+, HEALTH-F4-2007-201413) and Neuroscience Campus Amsterdam; Wellcome Trust (090532, 091551, 098051, 098381); Wissenschaftsoffensive TMO; and Yrjo¨Jahnsson Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: We have read the journal’s policy and the authors of this manuscript have the following competing interests: Genotyping in the Ely and Fenland studies was supported in part by an MRC-GlaxoSmithKline pilot programme grant (G0701863). The RISC Study was supported in part by AstraZeneca. The D.E.S.I.R. study has been supported in part by INSERM contracts with Lilly, Novartis Pharma, Sanofi-Aventis, Ardix Medical, Abstract Physical activity (PA) may modify the genetic effects that give rise to increased risk of obesity. To identify adiposity loci whose effects are modified by PA, we performed genomewide interaction meta-analyses of BMI and BMI-adjusted waist circumference and waist-hip ratio from up to 200,452 adults of European (n = 180,423) or other ancestry (n = 20,029). We standardized PA by categorizing it into a dichotomous variable where, on average, 23% of participants were categorized as inactive and 77% as physically active. While we replicate the interaction with PA for the strongest known obesity-risk locus in the FTO gene, of which the effect is attenuated by ~30% in physically active individuals compared to inactive individuals, we do not identify additional loci that are sensitive to PA. In additional genome-wide meta-analyses adjusting for PA and interaction with PA, we identify 11 novel adiposity loci, suggesting that accounting for PA or other environmental factors that contribute to variation in adiposity may facilitate gene discovery. Author summary Decline in daily physical activity is thought to be a key contributor to the global obesity epidemic. However, the impact of sedentariness on adiposity may be in part determined by a person’s genetic constitution. The specific genetic variants that are sensitive to physical activity and regulate adiposity remain largely unknown. Here, we aimed to identify genetic variants whose effects on adiposity are modified by physical activity by examining ~2.5 million genetic variants in up to 200,452 individuals. We also tested whether adjusting for physical activity as a covariate could lead to the identification of novel adiposity variants. We find robust evidence of interaction with physical activity for the strongest known obesity risk-locus in the FTO gene, of which the body mass index-increasing effect is attenuated by ~30% in physically active individuals compared to inactive individuals. Our analyses indicate that other similar gene-physical activity interactions may exist, but better measurement of physical activity, larger sample sizes, and/or improved analytical methods will be required to identify them. Adjusting for physical activity, we identify 11 novel adiposity variants, suggesting that accounting for physical activity or other environmental factors that contribute to variation in adiposity may facilitate gene discovery. Introduction In recent decades, we have witnessed a global obesity epidemic that may be driven by changes in lifestyle such as easier access to energy-dense foods and decreased physical activity (PA) [1]. However, not everyone becomes obese in obesogenic environments. Twin studies suggest that changes in body weight in response to lifestyle interventions are in part determined by a person’s genetic constitution [2–4]. Nevertheless, the genes that are sensitive to environmental influences remain largely unknown. Previous studies suggest that genetic susceptibility to obesity, assessed by a genetic risk score for BMI, may be attenuated by PA [5,6]. A large-scale meta-analysis of the FTOobesity locus in 218,166 adults showed that being physically active attenuates the BMI-increasing effect of this locus by ~30% [7]. While these findings suggest that FTO, and potentially other Genome-wide physical activity interactions in adiposity PLOS Genetics | https://doi.org/10.1371/journal.pgen.1006528 April 27, 2017 6 / 26 Bayer Diagnostics, Becton Dickinson, Cardionics, Merck Sante ´, Novo Nordisk, Pierre Fabre, Roche, and Topcon. In SHIP, genome-wide data have been supported in part by a joint grant from Siemens Healthcare, Erlangen, Germany. previously established BMI loci, may interact with PA, it has been hypothesized that loci showing the strongest main effect associations in genome-wide association studies (GWAS) may be the least sensitive to environmental and lifestyle influences, and may therefore not make the best candidates for interactions [8]. Yet no genome-wide search for novel loci exhibiting SNP×PA interaction has been performed. A genome-wide meta-analysis of genotype-dependent phenotypic variance of BMI, a marker of sensitivity to environmental exposures, in ~170,000 participants identified FTO, but did not show robust evidence of environmental sensitivity for other loci [9]. Recent genome-wide meta-analyses of adiposity traits in >320,000 individuals uncovered loci interacting with age and sex, but also suggested that very large sample sizes are required for interaction studies to be successful [10]. Here, we report results from a large-scale genome-wide meta-analysis of SNP×PA interactions in adiposity in up to 200,452 adults. As part of these interaction analyses, we also examine whether adjusting for PA or jointly testing for SNP’s main effect and interaction with PA may identify novel adiposity loci. Results Identification of loci interacting with PA We performed meta-analyses of results from 60 studies, including up to 180,423 adults of European descent and 20,029 adults of other ancestries to assess interactions between ~2.5 million genotyped or HapMap-imputed SNPs and PA on BMI and BMI-adjusted waist circumference (WC adjBMI ) and waist-hip ratio (WHR adjBMI ) (S1–S5 Tables). Similar to a previous metaanalysis of the interaction between FTO and PA [7], we standardized PA by categorizing it into a dichotomous variable where on average ~23% of participants were categorized as inactive and ~77% as physically active (see Methods and S6 Table). On average, inactive individuals had 0.99 kg/m 2 higher BMI, 3.46 cm higher WC, and 0.018 higher WHR than active individuals (S4 and S5 Tables). Each study first performed genome-wide association analyses for each SNP’s effect on BMI in the inactive and active groups separately. Corresponding summary statistics from each cohort were subsequently meta-analyzed, and the SNP×PA interaction effect was estimated by calculating the difference in the SNP’s effect between the inactive and active groups. To identify sex-specific SNP×PA interactions, we performed the meta-analyses separately in men and women, as well as in the combined sample. In addition, we carried out meta-analyses in European-ancestry studies only and in European and other-ancestry studies combined. We used two approaches to identify loci whose effects are modified by PA. In the first approach, we searched for genome-wide significant SNP×PA interaction effects (P INT <5x10 -8 ). As shown in Fig 1, this approach yielded the highest power to identify cross-over interaction effects where the SNP’s effect is directionally opposite between the inactive and active groups. However, this approach has low power to identify interaction effects where the SNP’s effect is directionally concordant between the inactive and active groups (Fig 1). We identified a genome-wide significant interaction between rs986732 in cadherin 12 (CDH12)and PA on BMI in European-ancestry studies (beta INT = -0.076 SD/allele, P INT = 3.1x10 -8 , n = 134,767) (S7 Table). The interaction effect was directionally consistent but did not replicate in an independent sample of 31,097 individuals (beta INT = -0.019 SD/allele, P INT = 0.52), and the pooled association P value for the discovery and replication stages combined did not reach genome-wide significance (N TOTAL = 165,864; P INT-TOTAL = 3x10 -7 ) (S1 Fig). No loci showed genome-wide significant interactions with PA on WC adjBMI or WHR adjBMI .CDH12encodes an integral membrane protein mediating calcium-dependent cell-cell adhesion in the brain, where it may play a role in neurogenesis [11]. While CDH12 rs4701252 and rs268972 SNPs have shown suggestive Genome-wide physical activity interactions in adiposity PLOS Genetics | https://doi.org/10.1371/journal.pgen.1006528 April 27, 2017 7 / 26 Fig 1. Power to identify PA-adjusted main, joint or GxPA interaction effects in 200,000 individuals (45,000 inactive, 155,000 active). The plots compare power to identify genome-wide significant main effects (P adjPA <5x10 -8 , dashed black), joint effects (P JOINT <5x10 -8 , dotted green) or GxPA interaction effects (P INT <5x10 -8 , solid magenta) as well as the power to identify Bonferroni-corrected interaction effects (P INT <0.05/ number of loci, solid orange) for the SNPs that reached a genome-wide significant PA-adjusted main effect association (P adjPA <5x10 -8 ). The power computations were based on analytical power formulae provided elsewhere [50] and were conducted a-priori based on various types of Genome-wide physical activity interactions in adiposity PLOS Genetics | https://doi.org/10.1371/journal.pgen.1006528 April 27, 2017 8 / 26 associations with waist circumference (P = 2x10 -6 ) and BMI (P = 5x10 -5 ) in previous GWAS [12,13], the SNPs are not in LD with rs986732 (r 2 <0.1). In our second approach, we tested interaction for loci showing a genome-wide significant main effect on BMI, WC adjBMI or WHR adjBMI (S7–S12 Tables). We adjusted the significance threshold for SNP×PA interaction by Bonferroni correction (P = 0.05/number of SNPs tested). As shown in Fig 1, this approach enhanced our power to identify interaction effects where there is a difference in the magnitude of the SNP’s effect between inactive and active groups when the SNP’s effect is directionally concordant between the groups. We identified a significant SNP×PA interaction of the FTO rs9941349 SNP on BMI in the meta-analysis of European-ancestry individuals; the BMI-increasing effect was 33% smaller in active individuals (beta ACTIVE = 0.072 SD/allele) than in inactive individuals (beta INACTIVE = 0.106 SD/allele, P INT = 4x10 -5 ). The rs9941349 SNP is in strong LD (r 2 = 0.87) with FTO rs9939609 for which interaction with PA has been previously established in a meta-analysis of 218,166 adults [7]. We identified no loci interacting with PA for WC adjBMI or WHR adjBMI . In a previously published meta-analysis [7], the FTO locus showed a geographic difference for the interaction effect where the interaction was more pronounced in studies from North America than in those from Europe. To test for geographic differences in the present study, we performed additional meta-analyses for the FTO rs9941349 SNP, stratified by geographic origin (North America vs. Europe). While the interaction effect was more pronounced in studies from North America (beta INT = 0.052 SD/allele, P = 5x10 -4 , N = 63,896) than in those from Europe (beta INT = 0.028 SD/allele, P = 0.006, N = 109,806), we did not find a statistically significant difference between the regions (P = 0.14). Explained phenotypic variance in inactive and active individuals. We tested whether the variance explained by ~1.1 million common variants (MAF1%) differed between the inactive and active groups for BMI, WC adjBMI , and WHR adjBMI [14]. In the physically active individuals, the variants explained ~20% less of variance in BMI than in inactive individuals (12.4% vs. 15.7%, respectively; P difference = 0.046), suggesting that PA may reduce the impact of genetic predisposition to adiposity overall. There was no significant difference in the variance explained between active and inactive groups for WC adjBMI (8.6% for active, 9.3% for inactive; P difference = 0.70) or WHR adjBMI (6.9% for active, 8.0% for inactive; P difference = 0.59). To further investigate differences in explained variance between the inactive and active groups, we calculated variance explained by subsets of SNPs selected based on significance thresholds (ranging from P = 5x10 -8 to P = 0.05) of PA-adjusted SNP association with BMI, WC adjBMI or WHR adjBMI [15] (S13 Table). We found 17–26% smaller explained variance for BMI in the active group than in the inactive group at all P value thresholds (S13 Table). Identification of novel loci when adjusting for PA or when jointly testing for SNP main effect and interaction with PA Physical activity contributes to variation in BMI, WC adjBMI , and WHR adjBMI , hence, adjusting for PA as a covariate may enhance power to identify novel adiposity loci. To that extent, each study performed genome-wide analyses for association with BMI, WC adjBMI , and WHR adjBMI while adjusting for PA. Subsequently, we performed meta-analyses of the study-specific known realistic BMI effect sizes [51]. Panels A, C, E: Assuming an effect in inactive individuals similar to a small (R2 INACT ¼0:01%, comparable to the known BMI effect of the NUDT3 locus), medium (R2 INACT ¼0:07%, comparable to the known BMI effect of the BDNF locus) and large (R2 INACT ¼0:34%, comparable to the known BMI effect of the FTO locus) realistic effect on BMI and for various effects in physically active individuals (varied on the x axis); Panels B,D,F: Assuming an effect in physically active individuals similar to the small, medium and large realistic effects of the NUDT3,BDNF and FTO loci on BMI and for various effects in inactive individuals (varied on x axis). https://doi.org/10.1371/journal.pgen.1006528.g001 Genome-wide physical activity interactions in adiposity PLOS Genetics | https://doi.org/10.1371/journal.pgen.1006528 April 27, 2017 9 / 26 Methods Main analyses Ethics statement. All studies were conducted according to the Declaration of Helsinki. The studies were approved by the local ethical review boards and all study participants provided written informed consent for the collection of samples and subsequent analyses. Outcome traits—BMI, WC adjBMI and WHR adjBMI .We examined three anthropometric traits related to overall adiposity (BMI) or body fat distribution (WC adjBMI and WHR adjBMI ) [36] that were available from a large number of studies. Before the association analyses, we calculated sex-specific residuals by adjusting for age, age 2 , BMI (for WC adjBMI and WHR adjBMI traits only), and other necessary study-specific covariates, such as genotype-derived principal components. Subsequently, we normalized the distributions of sex-specific trait residuals using inverse normal transformation. Physical activity. Physical activity was assessed and quantified in various ways in the participating studies of the meta-analysis (S1 and S6 Tables). Aiming to amass as large a sample size as possible, we harmonized PA by categorizing it into a simple dichotomous variable— physically inactive vs. active—that could be derived in a relatively consistent way in all participating studies, and that would be consistent with previous findings on gene-physical activity interactions and the relationship between activity levels and health outcomes. In studies with categorical PA data, individuals were defined inactive if they reported having a sedentary occupation and being sedentary during transport and leisure-time (<1 h of moderate intensity leisure-time or commuting PA per week). All other individuals were defined physically active. Previous studies in large-scale individual cohorts have demonstrated that the interaction between FTO, or a BMI-increasing genetic risk score, with physical activity, is most pronounced approximately at this activity level [6,37,38]. In studies with continuous PA data, PA variables were standardized by defining individuals belonging to the lowest sexand ageadjusted quintile of PA levels as inactive, and all other individuals as active. The study-specific coding of the dichotomous PA variable in each study is described in S6 Table. Study-specific association analyses. We included 42 studies with genome-wide data, 10 studies with Metabochip data, and eight studies with both genome-wide and Metabochip data. If both genome-wide and Metabochip data were available for the same individual, we only included the genome-wide data (S1 Table). Studies with genome-wide genotyped data used either Affymetrix or Illumina arrays (S2 Table). Following study-specific quality control measures, the genotype data were imputed using the HapMap phase II reference panel (S2 Table). Studies with Metabochip data used the custom Illumina HumanCardio-Metabo BeadChip containing ~195K SNPs designed to support large-scale follow-up of known associations with metabolic and cardiovascular traits [39]. Each study ran autosomal SNP association analyses with BMI, WC adjBMI and WHR adjBMI across their array of genetic data using the following linear regression models in men and women separately: 1) active individuals only; 2) inactive individuals only; and 3) active and inactive individuals combined, adjusting for the PA stratum. In studies that included families or closely related individuals, regression coefficients were estimated using a variance component model that modeled relatedness in men and women combined, with sex as a covariate, in addition to the sex-specific analyses. The additive genetic effect for each SNP and phenotype association was estimated using linear regression. For studies with a case-control design (S1 Table), cases and controls were analyzed separately. Quality control of study-specific association results. All study-specific files for the three regression models listed above were processed through a standardized quality control protocol using the EasyQC software [40]. The study-specific quality control measures included checks on file completeness, range of test statistics, allele frequencies, trait transformation, population Genome-wide physical activity interactions in adiposity PLOS Genetics | https://doi.org/10.1371/journal.pgen.1006528 April 27, 2017 16 / 26 stratification, and filtering out of low quality data. Checks on file completeness included screening for missing alleles, effect estimates, allele frequencies, and other missing data. Checks on range of test statistics included screening for invalid statistics such as P-values >1 or <0, negative standard errors, or SNPs with low minor allele count (MAC, calculated as MAFN, where MAF is the minor allele frequency and N is the sample size) and where SNPs with MAC<5 in the inactive or the active group were removed. The correctness of trait transformation to inverse normal was examined by plotting 2/median of the standard error with the square root of the sample size. Population stratification was examined by calculating the study specific genomic control inflation factor (λ GC ) [41]. If a study had λ GC >1.1, the study analyst was contacted and asked to revise the analyses by adjusting for principal components. The allele frequencies in each study were examined for strand issues and miscoded alleles by plotting effect allele frequencies against the corresponding allele frequencies from the HapMap2 reference panel. Finally, low quality data were filtered out by removing monomorphic SNPs, imputed SNPs with poor imputation quality (r2_hat <0.3 in MACH [42], observed/ expected dosage variance <0.3 in BIMBAM [43], proper_info <0.4 in IMPUTE [44]), and genotyped SNPs with a low call-rate (<95%) or that were out of Hardy-Weinberg equilibrium (P<10 −6 ). Meta-analyses. Beta-coefficients and standard errors were combined by an inverse-variance weighted fixed effect method, implemented using the METAL software [45]. We performed meta-analyses for each of the three models (active, inactive, active + inactive adjusted for PA) in men only, in women only, and in men and women combined. Study-specific GWAS results were corrected for genomic control using all SNPs. Study-specific Metabochip results as well as the meta-analysis results for GWAS and Metabochip combined were corrected for genomic control using 4,425 SNPs included on the Metabochip for replication of associations with QT-interval, a phenotype not correlated with BMI, WC adjBMI or WHR adjBMI , after pruning of SNPs within 500 kb of an anthropometry replication SNP. We excluded SNPs that 1) were not available in at least half of the maximum sample size in each stratum; 2) had a heterogeneity I 2 >75%, or 3) were missing chromosomal and base position annotation in dbSNP. Calculation of the significance of SNP×PA interaction and of the joint significance of SNP main effect and SNP×PA interaction. To identify SNP×PA interactions, we used the EasyStrata R package [46] to test for the difference in meta-analyzed beta-coefficients between the active and inactive groups for the association of each SNP with BMI, WC adjBMI and WHR adjBMI . Easystrata tests for differences in effect estimates between the active and inactive strata by subtracting one beta from the other (β active −β inactive ,) and dividing by the overall standard error of the difference as follows: Zdiff ¼bactive binactive ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi SE2 active SE2 inactive 2rSE2 active SE2 inactive p where ris the Spearman rank correlation coefficient between β active and β inactive for all genome-wide SNPs. The joint significance of the SNP main and SNP×PA interaction effects was estimated using the method by Aschard et al. [16] which is a joint test for genetic main effects and gene-environment interaction effects where gene-environment interaction is calculated as the difference in effect estimates between two exposure strata, accounting for 2 degrees of freedom. Testing for secondary signals. Approximate conditional analyses were conducted using GCTA version 1.24 [19]. In the analyses for SNPs identified in our meta-analyses of Europeanancestry individuals only, LD correlations between SNPs were estimated using a reference Genome-wide physical activity interactions in adiposity PLOS Genetics | https://doi.org/10.1371/journal.pgen.1006528 April 27, 2017 17 / 26 sample comprised of European-ancestry participants of the Atherosclerosis Risk in Communities (ARIC) study. In the analyses for SNPs identified in our meta-analyses of all ancestries combined, the reference sample comprised 93% of European-ancestry individuals and 6% of African ancestry participants from ARIC, as well as 1% of CHB and JPT samples from the HapMap2 panel, to approximate the ancestry mixture in our all ancestry meta-analyses. To test if our identified SNPs were independent secondary signals that fell within 1 Mbp of a previously established signal, we used the GCTA—cojo-cond command to condition our lead SNPs on each previously established SNP in the same locus. Replication analysis for the CDH12 locus. The replication analysis for the CDH12 locus included participants from the EPIC-Norfolk (N INACTIVE = 4,755, N ACTIVE = 11,526) and Fenland studies (N INACTIVE = 1,213, N ACTIVE = 4,817), and from the random subcohort of the EPIC-InterAct Consortium (N INACTIVE = 2,154, N ACTIVE = 6,632). PA stratum-specific estimates of the association of CDH12 with BMI were assessed and meta-analyzed by fixed effects meta-analyses, and the differences between the PA-strata were determined as described above. Examining the influence of BMI, WC adjBMI and WHR adjBMI -associated loci on other complex traits and their potential functional roles NHGRI-EBI GWAS catalog lookups. To identify associations of the novel BMI, WC adjBMI or WHR adjBMI loci with other complex traits in published GWAS, we extracted previously reported GWAS associations within 500 kb and r 2 >0.6 with any of the lead SNPs, from the GWAS Catalog of the National Human Genome Research Institute and European Bioinformatics Institute [47] (S14 Table). eQTLs. We examined the cis-associations of the novel BMI, WC adjBMI or WHR adjBMI loci with the expression of nearby genes from various tissues by performing a look-up in a library of >100 published expression datasets, as described previously by Zhang et al [48]. In addition, we examined cis-associations using gene expression data derived from fasting peripheral whole blood in the Framingham Heart Study [49] (n = 5,206), adjusting for PA, age, age 2 , sex and cohort. For each novel locus, we evaluated the association of all transcripts ±1 Mb from the lead SNP. To minimize the potential for false positives, we only considered associations where our lead SNP or its proxy (r 2 >0.8) was either the peak SNP associated with the expression of a gene transcript in the region, or in strong LD (r 2 >0.8) with the peak SNP. Overlap with functional regulatory elements. We used the Uncovering Enrichment Through Simulation method to combine the genetic association data with the Roadmap Epigenomics Project segmentation data [22]. First, 10,000 sets of random SNPs were selected among HapMap2 SNPs with a MAF >0.05 that matched the original input SNPs based on proximity to a transcription start site and the number of LD partners (r 2 >0.8 in individuals of European ancestry in the 1000 Genomes Project). The LD partners were combined with their original lead SNPs to create 10,000 sets of matched random SNPs and their respective LD partners. These sets were intersected with the 15-state ChromHMM data from the Roadmap Epigenomics Project and resultant co-localizations were collapsed from total SNPs down to loci, which were then used to calculate an empirical P value when comparing the original SNPs to the random sets. We examined the enrichment for all loci reaching P<10 −5 for SNP×PA interaction combined, and for all loci reaching P<5x10 -8 in the PA-adjusted SNP main effect model combined. In addition, we examined the variant-specific overlap with regulatory elements for each of the index SNPs of the novel BMI, WC adjBMI and WHR adjBMI loci and variants in strong LD (r 2 >0.8). Estimation of variance explained in inactive and active groups. We compared variance explained for BMI, WC adjBMI and WHR adjBMI between the active and inactive groups using Genome-wide physical activity interactions in adiposity PLOS Genetics | https://doi.org/10.1371/journal.pgen.1006528 April 27, 2017 18 / 26 two approaches. First, we used a method previously reported by Kutalik et al [15], and selected subsets of SNPs based on varying P value thresholds (ranging from 5x10 -8 to 0.05) from the SNP main effect model adjusted for PA. Each subset of SNPs was clumped into independent regions using a physical distance criterion of <500kb, and the most significant lead SNP within the respective region was selected. For each lead SNP, the explained variance was calculated as: r2¼1 1þN F1P 2 ð Þð Þ21 N in the active and inactive groups separately, where Nis the sample size and Pis the P value for SNP main effect in active or inactive strata. Finally, the variance explained by each subset of SNPs in the active and inactive strata was estimated by summing up the variance explained by the SNPs. Second, we applied the LD Score regression tool developed by Bulik-Sullivan et al [14] to quantify the proportion of inflation due to polygenicity (heritability) rather than confounding (cryptic relatedness or population stratification) using meta-analysis summary results. LD Score regression leverages LD between causal and index variants to distinguish true signals by regressing meta-analysis summary results on an ‘LD Score’, i.e. the cumulative genetic variation that an index SNP tags. To obtain heritability estimates by PA strata, we regressed our summary results from the genome-wide meta-analyses of BMI, WC adjBMI and WHR adjBMI , stratified by PA status (active and inactive), on pre-calculated LD Scores available in HapMap3 reference samples of up to 1,061,094 variants with MAF1% and N>10 th percentile of the total sample size. Supporting information S1 Acknowledgements. A full list of acknowledgements. (DOCX) S1 Fig. Interaction between the CDH12 locus and physical activity on BMI in the discovery genome-wide meta-analysis (n = 134,767), in the independent replication sample (n = 31,097), and in the discovery and replication samples combined. (DOCX) S2 Fig. Quantile-Quantile and Manhattan plots for the genome-wide meta-analysis results of the SNP main effect adjusting for physical activity (SNPadjPA), interaction between SNP and physical activity, and the joint effect of SNP main effect and SNP×PA interaction (Joint2df) in men and women of European-ancestry combined. (DOCX) S3 Fig. Regional association plots for novel BMI, WCadjBMI or WHRadjBMI loci showing either a genome-wide significant SNP main effect when adjusting for physical activity as a covariate, or a genome-wide significant joint effect of physical activity-adjusted SNP main effect and SNP ×physical activity interaction. (DOCX) S4 Fig. Heatmap of P values for the physical activity-adjusted SNP main effect model (PadjPA), the joint model (Pjoint), and the SNPxPA interaction model (Pint). (DOCX) Genome-wide physical activity interactions in adiposity PLOS Genetics | https://doi.org/10.1371/journal.pgen.1006528 April 27, 2017 19 / 26 S1 Table. Basic study information and description of outcome assessment (BMI, WC, WHR) and Physical activity assessment. (XLSX) S2 Table. Genotyping and imputation platforms of the participating studies. (XLSX) S3 Table. Population characteristics for inactive and active individuals combined in the participating studies. (XLSX) S4 Table. Population characteristics for inactive individuals in the participating studies. (XLSX) S5 Table. Population characteristics for active individuals in the participating studies. (XLSX) S6 Table. Methods used for measuring physical activity and definitions of inactive for studies participating in the meta-analyses. (XLSX) S7 Table. All SNPs that met significance for BMI in the European only analyses for at least one of the approaches tested: interaction, adjusted for physical activity, or jointly accounting for the main and interaction effects. (XLSX) S8 Table. All SNPs that met significance for BMI in the all ancestry analyses for at least one of the approaches tested: interaction, adjusted for physical activity, or jointly accounting for the main and interaction effects. (XLSX) S9 Table. All SNPs that met significance for waist circumference adjusted for BMI in the European only analyses for at least one of the approaches tested: interaction, adjusted for physical activity, or jointly accounting for the main and interaction effects. (XLSX) S10 Table. All SNPs that met significance for waist circumference adjusted for BMI in the all ancestry analyses for at least one of the approaches tested: interaction, adjusted for physical activity, or jointly accounting for the main and interaction effects. (XLSX) S11 Table. All SNPs that met significance for waist-to-hip ratio adjusted for BMI in the European only analyses for at least one of the approaches tested: interaction, adjusted for physical activity, or jointly accounting for the main and interaction effects. (XLSX) S12 Table. All SNPs that met significance for waist-to-hip ratio adjusted for BMI in the all ancestry analyses for at least one of the approaches tested: interaction, adjusted for physical activity, or jointly accounting for the main and interaction effects. (XLSX) S13 Table. Variance explained using P value thresholds. (XLSX) S14 Table. GWAS catalog lookups for novel loci and new secondary signal in known loci. (XLSX) Genome-wide physical activity interactions in adiposity PLOS Genetics | https://doi.org/10.1371/journal.pgen.1006528 April 27, 2017 20 / 26 S15 Table. Association of the novel loci with cis gene expression (cis-eQTL). (XLSX) S16 Table. Association of loci identified for interaction with physical activity, for physical activity-adjusted SNP main effect, or for joint association of SNP main effect and physical activity interaction, with physical activity and sedentary behaviour. (XLSX) S17 Table. Results for approximate conditional analyses to identify secondary signals in the novel BMI, WC adjBMI or WHR adjBMI -associated loci a . (XLSX) S18 Table. Enrichment of loci interacting with PA (P int <10 −5 ) on the level of BMI with functional genomic elements in adipose, brain, and muscle tissue cell lines from the Roadmap Epigenomics Project. (XLSX) S19 Table. Enrichment of loci interacting with PA (P int <10 −5 ) on the level of WHRadjBMI with functional genomic elements in adipose, brain, and muscle tissue cell lines from the Roadmap Epigenomics Project. (XLSX) S20 Table. Enrichment of loci showing association with BMI (P adjPA <5x10 -8 ) with functional genomic elements in adipose, brain, and muscle tissue cell lines from the Roadmap Epigenomics Project. (XLSX) S21 Table. Enrichment of loci showing association with WHRadjBMI (P adjPA <5x10 -8 ) with functional genomic elements in adipose, brain, and muscle tissue cell lines from the Roadmap Epigenomics Project. (XLSX) S22 Table. Association of novel loci identified for interaction with physical activity, for physical activity-adjusted SNP main effect, or for joint association of the SNP main effect and physical activity interaction, in GIANT results not accounting for physical activity. (XLSX) Author Contributions Conceptualization: TOK RJFL. Data curation: TOK LAC RJFL KLMon. Formal analysis: RAS KLY AEJ TWW AMa DHa NLHC JSN TSA LQ TW FRe MdH TOK QQ MG TSA LX AYC LAC MC RJFL MFF KEN JDE ADJ JEHa RJK. Investigation: AVS TBH GE LJL VG KEN MG AJ KLMon KLY EB PGL JBW NGM GWM DPS GC LJP JHua AWM ALJ JB MFo CBe HMS TR SSn BS YW JBB LSA ZK PMMV TC SBi GWi PV KKr ASH KHa SM VS MP JM IR CHup VV IK OPo LY DT BB MMar AB PF RRaw TAL PK MHo KSa RMag THal TEb AMa JL RAS SBr NJW JHZ RL SBu AD NA CMvD IBB MFF LB RJFL LX NLHC EL JP PJG CSF CTL LAC MB FSC KLMol RNB JT LK TEs HP CS HAK LFB SLRK MAJ PAP SA FRe IB GH PWF JE CO MLo AEJ JOJ TSA LPa GDS TIAS JEHu DJP SP LJH BHS SSa YCC MFu JRO ARS MHa MA AL HVe LQu THu QQ JAS JDF WZhan DRW KHv OLH AUJ LLB AJS KKv TTan DHe SBa MNH JMJ KF Genome-wide physical activity interactions in adiposity PLOS Genetics | https://doi.org/10.1371/journal.pgen.1006528 April 27, 2017 21 / 26 NG OPe TWW IMH MO CG HG AP KSt CHo CHut RRau BT MMN WZhao BL WRS STT JCC JSK MEK GED TBG GS JHui WM UL CHa LL LH KL AGB LJRT JAN YL MLa JK AS PSC NN WJP GvG YM BWJHP AMu CML MIM TTam JA MRJ LQi THan DK KKO AW ÅJ UG EJCdG MHMdM JJH DIB GWa PN AFW NDH SW HC JFW CBo MCV LPe DP MW TMF NVR MCZ FRi AH AGU JLBG SSi FB AMe GRA FC ATe HVo UV MD SG MN RMan IP ATo PJvdM JVvVO IMN HS AJO CAH MMan DIC AYC LMR PMR SBi NZ JG UP CK MKu MKi CL LPL NHK MJ MKa¨OTR TL. Supervision: GRA IB MB IBB CSF TMF IMH RJFL MIM KLMon KEN JRO DPS CMvD JNH. Writing – original draft: MG RAS AEJ KLY MFF LB LQu PWF RJFL TOK. Writing – review & editing: MG RAS AEJ KLY MFF TWW LB AYC AMa DHa LX TW NLHC TSA MdH QQ JSN FRe LQu JDE JEHa MC ZK EL JL JEHu WZhan WZhao PJG THal SA PMMV SBi LY ATe AVS MKu MNH JMJ MEK MHo NVR JBW JHZ HS LL CHup GWi WJP YW KKr AD KL YL MFo MHa LFB GC TTan RMag PJvdM AUJ JLBG VV JM PN CBe DP ÅJ SSn YCC JE UL MA LSA NA BB JA JB RNB AGB JB AB LLB JBB SBr FB SBu HC PSC FSC TC GDS GED ND MD TEb GE TEs JDF MFu KF CG SG JG PGL HG TBG NG GvG THu KHa NDH ASH DHe LH LJH OLH CHo JJH JHua THan JHui CHut NHK ALJ JOJ MAH MJ LK HAK IK PK JK KKv MKa TAL LJL BL CML MLo RL MMar YM KLMon GWM MHMdM AMu AMe RMan SM NN MN JAN IMN AJO MO KKO SP LPa JP MP AP UP PAP IP HP OTR TR LJRT RRaw PMR LMR IR CS MAS KSa WRS SSa ARS SSi GS BHS JAS HS AS BS AJS TTam STT BT DT LV HVe JVvVO MCV UV GWa MW SW AW AFW MCZ NZ CAHai LL UG CO AH FRi AGU LPe JFW CHa OPo FC KHv CAHar ATo SBa LJP SLRK RRau TIAS JT VS BWJHP EJCdG DIB TL MMan MLa CBo NGM DK AL WM KSt MKi TBH VG HVo LQi MRJ JCC JSK PF CK PV GH OPe NJW CL DRW DJP EB DIC GRA IB MIM TMF JRO CMvD MB IMH KLMol DPS CSF CTL JNH RJK ADJ IBB PWF KEN LAC RJFL TOK. 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