An expanded genome-wide association study of type 2 diabetes in Europeans

Robert A Scott; Laura J Scott; Reedik Mägi; Letizia Marullo; Kyle J Gaulton; Marika Kaakinen; Natalia Pervjakova; Tune H Pers; Andrew D Johnson; John D Eicher; Anne U Jackson; Teresa Ferreira; Yeji Lee; Clement Ma; Valgerdur Steinthorsdottir; Gudmar Thorleifsson; Lu Qi; Natalie R Van Zuydam; Anubha Mahajan; Han Chen; Peter Almgren; Ben F Voight; Harald Grallert; Martina Müller-Nurasyid; Janina S Ried; William N Rayner; Neil Robertson; Lennart C Karssen; Elisabeth M van Leeuwen; Sara M Willems; Christian Fuchsberger; Phoenix Kwan; Tanya M Teslovich; Pritam Chanda; Man Li; Yingchang Lu; Christian Dina; Dorothee Thuillier; Loic Yengo; Longda Jiang; Thomas Sparso; Hans A Kestler; Himanshu Chheda; Lewin Eisele; Stefan Gustafsson; Mattias Frånberg; Rona J Strawbridge; Rafn Benediktsson; Astradur B Hreidarsson; Augustine Kong; Gunnar Sigurðsson; Nicola D Kerrison; Jian'an Luan; Liming Liang; Thomas Meitinger; Michael Roden; Barbara Thorand; Tõnu Esko; Evelin Mihailov; Caroline Fox; Ching-Ti Liu; Denis Rybin; Bo Isomaa; Valeriya Lyssenko; Tiinamaija Tuomi; David J Couper; James S Pankow; Niels Grarup; Christian T Have; Marit E Jørgensen; Torben Jørgensen; Allan Linneberg; Marilyn C Cornelis; Rob M van Dam; David J Hunter; Peter Kraft; Qi Sun; Sarah Edkins; Katharine R Owen; John RB Perry; Andrew R Wood; Eleftheria Zeggini; Juan Tajes-Fernandes; Goncalo R Abecasis; Lori L Bonnycastle; Peter S Chines; Heather M Stringham; Heikki A Koistinen; Leena Kinnunen; Bengt Sennblad; Thomas W Mühleisen; Markus M Nöthen; Sonali Pechlivanis; Damiano Baldassarre; Karl Gertow; Steve E Humphries; Elena Tremoli; Norman Klopp; Julia Meyer; Gerald Steinbach

doi:10.2337/db16-1253

. Author manuscript; available in PMC: 2018 May 1.

Published in final edited form as: Diabetes. 2017 May 31;66(11):2888–2902. doi: 10.2337/db16-1253

An expanded genome-wide association study of type 2 diabetes in Europeans

Robert A Scott ^1,^*, Laura J Scott ^2,^*, Reedik Mägi ^3,^*, Letizia Marullo ^4,^*, Kyle J Gaulton ^5,^6,^*, Marika Kaakinen ^7,^*, Natalia Pervjakova ³, Tune H Pers ^8,^9,^10,¹¹, Andrew D Johnson ¹², John D Eicher ¹², Anne U Jackson ², Teresa Ferreira ⁵, Yeji Lee ², Clement Ma ², Valgerdur Steinthorsdottir ¹³, Gudmar Thorleifsson ¹³, Lu Qi ^14,^15,¹⁶, Natalie R Van Zuydam ^5,¹⁷, Anubha Mahajan ⁵, Han Chen ^18,¹⁹, Peter Almgren ²⁰, Ben F Voight ^21,^22,²³, Harald Grallert ^24,^25,²⁶, Martina Müller-Nurasyid ^27,^28,^29,³⁰, Janina S Ried ²⁷, William N Rayner ^5,^31,³², Neil Robertson ^5,³¹, Lennart C Karssen ^33,³⁴, Elisabeth M van Leeuwen ³³, Sara M Willems ^1,³³, Christian Fuchsberger ², Phoenix Kwan ², Tanya M Teslovich ², Pritam Chanda ³⁵, Man Li ³⁶, Yingchang Lu ^37,³⁸, Christian Dina ³⁹, Dorothee Thuillier ^40,⁴¹, Loic Yengo ^40,⁴¹, Longda Jiang ⁷, Thomas Sparso ¹⁰, Hans A Kestler ^42,⁴³, Himanshu Chheda ⁴⁴, Lewin Eisele ⁴⁵, Stefan Gustafsson ⁴⁶, Mattias Frånberg ^47,^48,⁴⁹, Rona J Strawbridge ⁴⁷, Rafn Benediktsson ^50,⁵¹, Astradur B Hreidarsson ⁵¹, Augustine Kong ¹³, Gunnar Sigurðsson ^51,⁵², Nicola D Kerrison ¹, Jian'an Luan ¹, Liming Liang ^14,⁵³, Thomas Meitinger ^30,^54,⁵⁵, Michael Roden ^56,^57,⁵⁸, Barbara Thorand ^25,⁵⁷, Tõnu Esko ^3,^59,⁶⁰, Evelin Mihailov ³, Caroline Fox ^61,⁶², Ching-Ti Liu ⁶³, Denis Rybin ⁶⁴, Bo Isomaa ^65,⁶⁶, Valeriya Lyssenko ²⁰, Tiinamaija Tuomi ^65,⁶⁷, David J Couper ⁶⁸, James S Pankow ⁶⁹, Niels Grarup ¹⁰, Christian T Have ¹⁰, Marit E Jørgensen ⁷⁰, Torben Jørgensen ^71,^72,⁷³, Allan Linneberg ^71,^74,⁷⁵, Marilyn C Cornelis ⁷⁶, Rob M van Dam ^15,⁷⁷, David J Hunter ^14,^15,^78,⁷⁹, Peter Kraft ^14,^53,⁷⁹, Qi Sun ^15,⁷⁸, Sarah Edkins ³², Katharine R Owen ^31,⁸⁰, John RB Perry ¹, Andrew R Wood ⁸¹, Eleftheria Zeggini ³², Juan Tajes-Fernandes ⁵, Goncalo R Abecasis ², Lori L Bonnycastle ⁸², Peter S Chines ⁸², Heather M Stringham ², Heikki A Koistinen ^83,^84,⁸⁵, Leena Kinnunen ^83,^84,⁸⁵, Bengt Sennblad ^47,⁴⁸, Thomas W Mühleisen ^86,⁸⁷, Markus M Nöthen ^86,⁸⁷, Sonali Pechlivanis ⁴⁵, Damiano Baldassarre ^88,⁸⁹, Karl Gertow ⁴⁷, Steve E Humphries ⁹⁰, Elena Tremoli ^88,⁸⁹, Norman Klopp ^24,⁹¹, Julia Meyer ²⁷, Gerald Steinbach ⁹², Roman Wennauer ⁹³, Johan G Eriksson ^65,^94,^95,⁹⁶, Satu Männistö ⁹⁴, Leena Peltonen ^32,^44,^94,^97,¹⁴⁹, Emmi Tikkanen ^44,⁹⁸, Guillaume Charpentier ⁹⁹, Elodie Eury ⁴¹, Stéphane Lobbens ⁴¹, Bruna Gigante ¹⁰⁰, Karin Leander ¹⁰⁰, Olga McLeod ⁴⁷, Erwin P Bottinger ³⁷, Omri Gottesman ³⁷, Douglas Ruderfer ¹⁰¹, Matthias Blüher ^102,¹⁰³, Peter Kovacs ^102,¹⁰³, Anke Tonjes ^102,¹⁰³, Nisa M Maruthur ^36,^104,¹⁰⁵, Chiara Scapoli ⁴, Raimund Erbel ⁴⁵, Karl-Heinz Jöckel ⁴⁵, Susanne Moebus ⁴⁵, Ulf de Faire ¹⁰⁰, Anders Hamsten ⁴⁷, Michael Stumvoll ^102,¹⁰³, Panagiotis Deloukas ^32,¹⁰⁶, Peter J Donnelly ^5,¹⁰⁷, Timothy M Frayling ⁸¹, Andrew T Hattersley ¹⁰⁸, Samuli Ripatti ^32,^44,^98,¹⁰⁹, Veikko Salomaa ⁸³, Nancy L Pedersen ¹¹⁰, Bernhard O Boehm ^111,¹¹², Richard N Bergman ¹¹³, Francis S Collins ⁸², Karen L Mohlke ¹¹⁴, Jaakko Tuomilehto ^94,^115,^116,¹¹⁷, Torben Hansen ^10,¹¹⁸, Oluf Pedersen ¹⁰, Inês Barroso ^32,¹¹⁹, Lars Lannfelt ¹²⁰, Erik Ingelsson ^46,¹²¹, Lars Lind ¹²², Cecilia M Lindgren ^5,⁹⁷, Stephane Cauchi ⁴⁰, Philippe Froguel ^7,^40,⁴¹, Ruth JF Loos ^37,^38,¹²³, Beverley Balkau ^124,¹²⁵, Heiner Boeing ¹²⁶, Paul W Franks ^127,¹²⁸, Aurelio Barricarte Gurrea ^129,^130,¹³¹, Domenico Palli ¹³², Yvonne T van der Schouw ¹³³, David Altshuler ^97,^134,^135,^136,^137,¹³⁸, Leif C Groop ^20,⁴⁴, Claudia Langenberg ¹, Nicholas J Wareham ¹, Eric Sijbrands ⁹³, Cornelia M van Duijn ^33,¹³⁹, Jose C Florez ^60,^135,¹⁴⁰, James B Meigs ^8,^135,¹⁴¹, Eric Boerwinkle ^142,¹⁴³, Christian Gieger ^24,²⁵, Konstantin Strauch ^27,²⁹, Andres Metspalu ^3,¹⁴⁴, Andrew D Morris ¹⁴⁵, Colin NA Palmer ^17,¹⁴⁶, Frank B Hu ^14,^15,¹⁶, Unnur Thorsteinsdottir ^13,⁵⁰, Kari Stefansson ^13,⁵⁰, Josée Dupuis ^61,⁶³, Andrew P Morris ^3,^5,^147,^148,^#, Michael Boehnke ^2,^#, Mark I McCarthy ^5,^31,^80,^#, Inga Prokopenko, for the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium^5,^7,^31,^#

¹⁾MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom

²⁾Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA

³⁾Estonian Genome Center, University of Tartu, Tartu, Estonia

⁴⁾Department of Life Sciences and Biotechnology, University of Ferrara, Ferrara, Italy

⁵⁾Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK

⁶⁾Department of Genetics, Stanford University, Stanford, CA, USA

⁷⁾Department of Genomics of Common Disease, Imperial College London, London, UK

⁸⁾Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, 02142, USA

⁹⁾Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children’s Hospital, Boston, 02115, USA

¹⁰⁾The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

¹¹⁾Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark

¹²⁾National Heart, Lung and Blood Institute's The Framingham Heart Study, Population Sciences Branch, Division of Intramural Research, Framingham MA 01702 USA

¹³⁾deCODE Genetics, Amgen inc., Reykjavik, Iceland

¹⁴⁾Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA

¹⁵⁾Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA

¹⁶⁾Channing Laboratory, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA

¹⁷⁾Pharmacogenomics Centre, Biomedical Research Institute, University of Dundee, Ninewells Hospital, Dundee, UK

¹⁸⁾Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA

¹⁹⁾Center for Precision Health, Schools of Public Health and Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA

²⁰⁾Lund University Diabetes Centre, Department of Clincal Science Malmö, University Hospital Scania, Lund University, 20502 Malmö, Sweden

²¹⁾University of Pennsylvania - Perelman School of Medicine, Department of Pharmacology, Philadelphia PA, 19104, USA

²²⁾University of Pennsylvania - Perelman School of Medicine, Department of Genetics, Philadelphia PA, 19104, USA

²³⁾Institute of Translational Medicine and Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia PA 19104, USA

²⁴⁾Research Unit of Molecular Epidemiology, Helmholtz Zentrum Muenchen, German Research Centre for Environmental Health, Neuherberg, Germany

²⁵⁾Institute of Epidemiology II, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Neuherberg, Germany

²⁶⁾German Center for Diabetes Research, Neuherberg, Germany

²⁷⁾Institute of Genetic Epidemiology, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Neuherberg, Germany

²⁸⁾Department of Medicine I, University Hospital Grosshadern, Ludwig-Maximilians-Universität, Munich, Germany

²⁹⁾Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany

³⁰⁾German Center for Cardiovascular Disease (DZHK), Munich Heart Alliance, Munich, Germany

³¹⁾Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK

³²⁾Wellcome Trust Sanger Institute, Hinxton, UK

³³⁾Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands

³⁴⁾PolyOmica, 's-Hertogenbosch, The Netherlands

³⁵⁾High Throughput Biology Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA

³⁶⁾Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

³⁷⁾The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

³⁸⁾The Genetics of Obesity and Related Metabolic Traits Program, The Icahn School of Medicine at Mount Sinai, New York, NY, USA

³⁹⁾l'Institut du thorax, INSERM, CNRS, Université de Nantes, Centre Hospitalier Universitaire (CHU) Nantes, Nantes, France

⁴⁰⁾Lille Institute of Biology, European Genomics Institute of Diabetes, Lille, France

⁴¹⁾CNRS-UMR-8199, Institute of Biology and Lille 2 University, Pasteur Institute, Lille, France

⁴²⁾Leibniz Institute on Aging, Fritz Lipmann Institute, Jena, Germany

⁴³⁾Institute of Medical Systems Biology, Ulm University, Germany

⁴⁴⁾Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland

⁴⁵⁾Institute for Medical Informatics, Biometry and Epidemiology, University Hospital of Essen, Essen, Germany

⁴⁶⁾Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden

⁴⁷⁾Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden

⁴⁸⁾Science for Life Laboratory, Stockholm, Sweden

⁴⁹⁾Department for Numerical Analysis and Computer Science, Stockholm University, Stockholm, Sweden

⁵⁰⁾Faculty of Medicine, University of Iceland, Reykjavik, Iceland

⁵¹⁾Landspitali University Hospital, Reykjavik, Iceland

⁵²⁾Icelandic Heart Association, Kopavogur, Iceland

⁵³⁾Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

⁵⁴⁾Institute of Human Genetics, Helmholtz Zentrum München, Neuherberg, Germany

⁵⁵⁾Institute of Human Genetics, Technische Universität München, Munich, Germany

⁵⁶⁾Department of Endocrinology and Diabetology, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany

⁵⁷⁾German Center for Diabetes Research (DZD e.V.), München-Neuherberg, Germany

⁵⁸⁾Institute for Clinical Diabetology, German Diabetes Center, Leibniz Institute for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany

⁵⁹⁾Division of Genetics and Endocrinology, Children's Hospital, Boston, MA, USA

⁶⁰⁾Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA

⁶¹⁾National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, USA

⁶²⁾Division of Endocrinology and Metabolism, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

⁶³⁾Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA

⁶⁴⁾Boston University Data Coordinating Center, Boston, MA, USA

⁶⁵⁾Folkhälsan Research Center, FIN-00014 Helsinki, Finland

⁶⁶⁾Department of Social Services and Health Care, 68601 Jakobstad, Finland

⁶⁷⁾Department of Medicine, Helsinki University Hospital, University of Helsinki, 000290 HUS Helsinki, Finland

⁶⁸⁾Collaborative Studies Coordinating Center, Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

⁶⁹⁾Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA

⁷⁰⁾Steno Diabetes Center, Gentofte, Denmark

⁷¹⁾Research Centre for Prevention and Health, Capital Region of Denmark, Copenhagen, Denmark

⁷²⁾Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

⁷³⁾Faculty of Medicine, University of Aalborg, Aalborg, Denmark

⁷⁴⁾Copenhagen University Hospital, Rigshospitalet, Denmark

⁷⁵⁾Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark

⁷⁶⁾Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

⁷⁷⁾Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore

⁷⁸⁾Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA

⁷⁹⁾Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

⁸⁰⁾Oxford National Institute for Health Research Biomedical Research Centre, Churchill Hospital, Oxford, UK

⁸¹⁾Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK

⁸²⁾National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA

⁸³⁾Department of Health, National Institute for Health and Welfare, Helsinki, Finland

⁸⁴⁾University of Helsinki and Helsinki University Central Hospital: Department of Medicine and Abdominal Center: Endocrinology, Helsinki, Finland

⁸⁵⁾Minerva Foundation Institute for Medical Research, Biomedicum 2U, Helsinki, Finland

⁸⁶⁾Institute of Human Genetics, University of Bonn, Bonn, Germany

⁸⁷⁾Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany

⁸⁸⁾Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy

⁸⁹⁾Dipartimento di Scienze Farmacologiche e Biomolecolari, Università di Milano, Milan, Italy

⁹⁰⁾Cardiovascular Genetics, BHF Laboratories, Institute Cardiovascular Sciences, UCL, London, UK

⁹¹⁾Hannover Unified Biobank, Hannover Medical School, Hannover, Germany

⁹²⁾Department of Clinical Chemistry and Central Laboratory, University of Ulm, Ulm, Germany

⁹³⁾Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands

⁹⁴⁾Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland

⁹⁵⁾Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland

⁹⁶⁾Unit of General Practice, Helsinki University Hospital, Helsinki, Finland

⁹⁷⁾Broad Institute of Harvard and Massachusetts Institute of Technology (MIT), Cambridge, MA 02142, USA

⁹⁸⁾Department of Public Health, Hjelt Institute, University of Helsinki, Helsinki, Finland

⁹⁹⁾Endocrinology-Diabetology Unit, Corbeil-Essonnes Hospital, Corbeil-Essonnes, France

¹⁰⁰⁾Division of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden

¹⁰¹⁾Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA

¹⁰²⁾IFB Adiposity Diseases, University of Leipzig, Leipzig, Germany

¹⁰³⁾Department of Medicine, University of Leipzig, Leipzig, Germany

¹⁰⁴⁾Department of Medicine, Division of General Internal Medicine, The Johns Hopkins Bloomberg School of Medicine, Baltimore, MD, USA

¹⁰⁵⁾The Welch Center for Prevention, Epidemiology, and Clinical Research, The Johns Hopkins University, Baltimore, MD, USA

¹⁰⁶⁾William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University London, London, UK

¹⁰⁷⁾Department of Statistics, University of Oxford, Oxford, UK

¹⁰⁸⁾Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK

¹⁰⁹⁾Public Health Genomics Unit, National Institute for Health and Welfare, Helsinki, Finland

¹¹⁰⁾Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

¹¹¹⁾Division of Endocrinology and Diabetes, Department of Internal Medicine, University Medical Centre Ulm, Ulm, Germany

¹¹²⁾Lee Kong Chian School of Medicine, Imperial College London and Nanyang Technological University, Singapore, Singapore

¹¹³⁾Diabetes and Obesity Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA

¹¹⁴⁾Department of Genetics, University of North Carolina, Chapel Hill, NC, USA

¹¹⁵⁾Dasman Diabetes Institute, Dasman, Kuwait

¹¹⁶⁾Centre for Vascular Prevention, Danube-University Krems, Krems, Austria

¹¹⁷⁾Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia

¹¹⁸⁾Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark

¹¹⁹⁾University of Cambridge Metabolic Research Laboratories and NIHR Cambridge Biomedical Research Centre, Level 4, WT-MRC Institute of Metabolic Science Box 289 Addenbrooke’s Hospital Cambridge, Cambridge, UK

¹²⁰⁾Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden

¹²¹⁾Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, USA

¹²²⁾Department of Medical Sciences, Uppsala University Hospital, Uppsala, Sweden

¹²³⁾The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

¹²⁴⁾Inserm, CESP, U1018, Villejuif, France

¹²⁵⁾Univ Paris-Sud, UMRS 1018, Villejuif, France

¹²⁶⁾German Institute of Human Nutrition Potsdam-Rehbruecke, Germany

¹²⁷⁾Lund University, Malmö, Sweden

¹²⁸⁾Umeå University, Umeå, Sweden

¹²⁹⁾Navarra Public Health Institute, Pamplona, Spain

¹³⁰⁾Navarra Institute for Health Research (IdiSNA), Pamplona, Spain

¹³¹⁾CIBER Epidemiology and Public Health (CIBERESP), Madrid, Spain

¹³²⁾Cancer Research and Prevention Institute (ISPO), Florence, Italy

¹³³⁾University Medical Center Utrecht, Utrecht, the Netherlands

¹³⁴⁾Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA 02114, USA

¹³⁵⁾Department of Medicine, Harvard Medical School, Boston, MA 02115, USA

¹³⁶⁾Department of Genetics, Harvard Medical School, Boston, MA 02115, USA

¹³⁷⁾Department of Molecular Biology, Harvard Medical School, Boston, Massachusetts 02115, USA

¹³⁸⁾Diabetes Unit, Massachusetts General Hospital, Boston, MA 02144, USA

¹³⁹⁾Netherlands Genomics Initiative, Netherlands Consortium for Healthy Ageing and Center for Medical Systems Biology, Rotterdam, The Netherlands

¹⁴⁰⁾Diabetes Unit and Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA

¹⁴¹⁾General Medicine Division, Massachusetts General Hospital, Boston, MA, USA

¹⁴²⁾Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, USA

¹⁴³⁾Human Genome Sequencing Center at Baylor College of Medicine, Houston, TX, USA

¹⁴⁴⁾Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia

¹⁴⁵⁾Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK

¹⁴⁶⁾Cardiovascular and Diabetes Medicine, Biomedical Research Institute, University of Dundee, Ninewells Hospital, Dundee, UK

¹⁴⁷⁾Department of Biostatistics, University of Liverpool, Liverpool, UK

¹⁴⁸⁾Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool, UK

^✉

Correspondence should be addressed to: Dr. Inga Prokopenko, Department of Genomics of Common Disease, School of Public Health, Imperial College London, Burlington Danes Building, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK, Phone: +4420 759 46501, i.prokopenko@imperial.ac.uk; Prof. Mark I. McCarthy, OCDEM, Churchill Hospital, University of Oxford, Old Road, Headington, OX3 7LJ, UK, mark.mccarthy@drl.ox.ac.uk; Prof. Michael Boehnke, Department of Biostatistics and Center for Statistical Genetics, University of Michigan, School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109-2029, boehnke@umich.edu

¹⁴⁹

Deceased.

These authors contributed equally to this research.

These authors jointly directed this research.

PMCID: PMC5652602 EMSID: EMS73893 PMID: 28566273

Abstract

To characterise type 2 diabetes (T2D) associated variation across the allele frequency spectrum, we conducted a meta-analysis of genome-wide association data from 26,676 T2D cases and 132,532 controls of European ancestry after imputation using the 1000 Genomes multi-ethnic reference panel. Promising association signals were followed-up in additional data sets (of 14,545 or 7,397 T2D cases and 38,994 or 71,604 controls). We identified 13 novel T2D-associated loci (p<5×10^-8), including variants near the GLP2R, GIP, and HLA-DQA1 genes. Our analysis brought the total number of independent T2D associations to 128 distinct signals at 113 loci. Despite substantially increased sample size and more complete coverage of low-frequency variation, all novel associations were driven by common SNVs. Credible sets of potentially causal variants were generally larger than those based on imputation with earlier reference panels, consistent with resolution of causal signals to common risk haplotypes. Stratification of T2D-associated loci based on T2D-related quantitative trait associations revealed tissue-specific enrichment of regulatory annotations in pancreatic islet enhancers for loci influencing insulin secretion, and in adipocytes, monocytes and hepatocytes for insulin action-associated loci. These findings highlight the predominant role played by common variants of modest effect and the diversity of biological mechanisms influencing T2D pathophysiology.

Type 2 diabetes (T2D) has rapidly increased in prevalence in recent years and represents a major component of the global disease burden (1). Previous efforts to use genome-wide association studies (GWAS) to characterise the genetic component of T2D risk have largely focused on common variants (minor allele frequency [MAF]>5%). These studies have identified close to 100 loci, almost all of them currently defined by common alleles associated with modest (typically 5-20%) increases in T2D risk (2–6). Direct sequencing of whole genomes or exomes offers the most comprehensive approach for extending discovery efforts to the detection of low-frequency (0.5%<MAF<5%) and rare (MAF<0.5%) risk and protective alleles, some of which might have greater impact on individual predisposition. However, extensive sequencing has, thus far, been limited to relatively small sample sizes (at most, a few thousand cases), restricting power to detect rarer risk alleles, even if they are of large effect (7–9). Whilst evidence of rare variant associations has been detected in some candidate gene studies (10,11), the largest study to date, involving exome sequencing in ~13,000 subjects, found little trace of rare variant association effects (9).

Here, we implement a complementary strategy that makes use of imputation into existing GWAS samples from the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium with sequence-based reference panels (12). This strategy allows the detection of common and low-frequency (but not rare) variant associations in extremely large samples (13), and facilitates the fine-mapping of causal variants. We performed a European ancestry meta-analysis of GWAS with 26,676 T2D cases and 132,532 controls, and followed up our findings in additional independent European ancestry studies of 14,545 T2D cases and 38,994 controls genotyped using the Metabochip (4). All contributing studies were imputed against the March 2012 multi-ethnic 1000 Genomes Project (1000G) reference panel of 1,092 whole-genome sequenced individuals (12). Our study provides near-complete evaluation of common variants with much improved coverage of low-frequency variants, and the combined sample size considerably exceeds that of the largest previous T2D GWAS meta-analyses in individuals of European ancestry (4). In addition to genetic discovery, we fine-map novel and established T2D-associated loci to identify regulatory motifs and cell types enriched for potential causal variants, and pathways through which T2D-associated loci increase disease susceptibility.

Research Design and Methods

Research participants

The DIAGRAM stage 1 meta-analyses is comprised of 26,676 T2D cases and 132,532 controls (effective sample size, N_eff=72,143 individuals, defined as 4/[(1/N_cases)+(1/N_controls)]) from 18 studies genotyped using commercial genome-wide single-nucleotide variant (SNV) arrays (Supplementary Table 1). The Metabochip stage 2 follow up is comprised of 14,545 T2D cases and 38,994 controls (N_eff=38,645) from 16 non-overlapping stage 1 studies (4,14). We performed additional follow-up in 2,796 T2D cases and 4,601 controls from the EPIC-InterAct (15) and 9,747 T2D cases and 61,857 controls from the GERA study (16) (Supplementary Material).

Statistical analyses

We imputed autosomal and X chromosome SNVs using the all ancestries 1000G reference panel (1,092 individuals from Africa, Asia, Europe, and the Americas [March, 2012 release]) using miniMAC (17) or IMPUTE2 (18). After imputation, from each study we removed monomorphic variants or those with imputation quality r²-hat<0.3 (miniMAC) or proper-info<0.4 (IMPUTE2, SNPTEST). Each study performed T2D association analysis using logistic regression, adjusting for age, sex, and principal components for ancestry, under an additive genetic model. We performed inverse-variance weighted fixed-effect meta-analyses of the 18 stage 1 GWAS (Supplementary Table 1). Fifteen of the 18 studies repeated analyses also adjusting for body mass index (BMI). SNVs reaching suggestive significance p<10^-5 in the stage 1 meta-analysis were followed-up. Novel loci were selected using the threshold for genome-wide significance (p<5×10^-8) in the combined stage 1 and stage 2 meta-analysis. For the 23 variants with no proxy (r²≥0.6) available in Metabochip with 1000G imputation in the fine-mapping regions, the stage 1 result was followed-up in EPIC-InterAct and GERA (N_eff=40,637), both imputed to 1000G variant density (Supplementary Material).

Approximate conditional analysis with GCTA

We performed approximate conditional analysis in the stage 1 sample using GCTA v1.24 (19,20). We analysed SNVs in the 1Mb-window around each lead variant, conditioning on the lead SNV at each locus (Supplementary Material) (21). We considered loci to contain multiple distinct signals if multiple SNVs reached locus-wide significance (p<10^-5), accounting for the approximate number of variants in each 1Mb window (14).

Fine-mapping analyses using credible set mapping

To identify 99% credible sets of causal variants for each distinct association signal, we performed fine-mapping for loci at which the lead independent SNV reached p<5×10^-4 in the stage 1 meta-analysis. We performed credible set mapping using the T2D stage 1 meta-analysis results to obtain the minimal set of SNVs with cumulative posterior probability>0.99 (Supplementary Material).

Type 1 diabetes (T1D)/T2D discrimination analysis

Given the overlap between loci previously associated with T1D and the associated T2D loci, we used an inverse variance weighted Mendelian randomisation approach (22) to test whether this was likely to reflect misclassification of T1D cases as individuals with T2D in the current study (Supplementary Material).

Expression quantitative trait locus (eQTL) analysis

To look for potential biological overlap of T2D lead variants and eQTL variants, we extracted the lead (most significantly associated) eQTL for each tested gene from existing datasets for a range of tissues (Supplementary Material). We concluded that a lead T2D SNV showed evidence of association with gene expression if it was in high LD (r²>0.8) with the lead eQTL SNV (p<5×10^-6).

Hierarchical clustering of T2D-related metabolic phenotypes

Starting with the T2D associated SNVs, we obtained T2D-related quantitative trait Z-scores from published HapMap-based GWAS meta-analysis for: fasting glucose, fasting insulin adjusted for BMI, homeostasis model assessment for beta-cell function (HOMA-B), homeostasis model assessment for insulin resistance (HOMA-IR) (23); 2-hour glucose adjusted for BMI (24); proinsulin (25); corrected insulin response (CIR) (26); BMI (27); high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), total cholesterol, and triglycerides (28). When an association result for a SNV was not available, we used the results for the variant in highest LD and only for variants with r²>0.6. We performed clustering of phenotypic effects using Z-scores for association with T2D risk alleles and standard methods (Supplementary Material) (29).

Functional annotation and enrichment analysis

We tested for enrichment of genomic and epigenomic annotations using chromatin states for 93 cell types (after excluding cancer cell lines) from the NIH Epigenome Roadmap project, and binding sites for 165 transcription factors (TF) from ENCODE (30) and Pasquali et al. (31). Using fractional logistic regression, we then tested for the effect of variants with each cell type and TF annotation on the variant posterior probabilities (π_c) using all variants within 1Mb of the lead SNV for each distinct association signal from the fine-mapping analyses (Supplementary Material). In each analysis, we considered an annotation significant if it reached a Bonferroni-corrected p<1.9×10^-4 (i.e. 0.05/258 annotations).

Pathway analyses with DEPICT

We used the Data-driven Expression Prioritized Integration for Complex Traits (DEPICT) tool (32) to (i) prioritize genes that may represent promising candidates for T2D pathophysiology, and (ii) identify reconstituted gene sets that are enriched in genes from associated regions and might be related to T2D biological pathways. As input, we used independent SNVs from the stage 1 meta-analysis SNVs with p<10^-5 and lead variants at established loci (Supplementary Material). For the calculation of empirical enrichment p values, we used 200 sets of SNVs randomly drawn from entire genome within regions matching by gene density; we performed 20 replications for false discovery rate (FDR) estimation.

Results

Novel loci detected in T2D GWAS and Metabochip-based follow-up

The stage 1 GWAS meta-analysis included 26,676 T2D cases and 132,532 controls and evaluated 12.1M SNVs, of which 11.8M were autosomal and 260k mapped to the X chromosome. Of these, 3.9M variants had MAF between 0.5% and 5%, a near fifteen-fold increase in the number of low-frequency variants tested for association compared to previous array-based T2D GWAS meta-analyses (2,4) (Supplementary Table 2). Of the 52 signals showing promising evidence of association (p<10^-5) in stage 1, 29 could be followed up in the stage 2 Metabochip data. In combined stage 1 and stage 2 data, 13 novel loci were detected at genome-wide significance (Table 1, Figure 1, Supplementary Figure 1A-D, Supplementary Table 3).

Table 1. Novel loci associated with T2D from the combination of 1000G-imputed GWAS meta-analysis (stage 1) and Metabochip follow-up (stage 2).

Locus name^*	Stage 1						Stage 2							Stage1+Stage2
Locus name^*	Chr:Position	SNV^†	EA/NEA	EAF	OR (CI 95%)	P-value	Chr:Position	SNV^‡	r² with lead SNV	EA/NEA	EAF	OR (95% CI)	P-value	OR (95% CI) ^¢	P-value
ACSL1	4:185708807	rs60780116	T/C	0.84	1.09 (1.06-1.13)	7.38x10^-8	4:185714289	rs1996546	0.62	G/T	0.86	1.08 (1.03-1.13)	5.60x10^-4	1.09 (1.06-1.12)	1.98x10^-10
HLA-DQA1	6:32594309	rs9271774	C/A	0.74	1.10 (1.06-1.14)	3.30x10^-7	6:32594328	rs9271775	0.91	T/C	0.80	1.08 (1.03-1.13)	7.59x10^-4	1.09 (1.06-1.12)	1.11x10^-9
SLC35D3	6:137287702	rs6918311	A/G	0.53	1.07 (1.04-1.10)	6.67x10^-7	6:137299152	rs4407733	0.92	A/G	0.52	1.05 (1.02-1.08)	1.63x10^-3	1.06 (1.04-1.08)	6.78x10^-9
MNX1	7:157027753	rs1182436	C/T	0.80	1.08 (1.05-1.12)	8.30x10^-7	7:157031407	rs1182397	0.92	G/T	0.85	1.06 (1.02-1.11)	4.38x10^-3	1.08 (1.05-1.10)	1.71x10^-8
ABO	9:136155000	rs635634	T/C	0.18	1.08 (1.05-1.12)	3.59x10^-7	9:136154867	rs495828	0.83	T/G	0.20	1.06 (1.01-1.10)	1.23x10^-2	1.08 (1.05-1.10)	2.30x10^-8
PLEKHA1	10:124186714	rs2292626	C/T	0.50	1.09 (1.06-1.11)	1.75x10^-12	10:124167512	rs2421016	0.99	C/T	0.50	1.05 (1.02-1.08)	2.30x10^-3	1.07 (1.05-1.09)	1.51x10^-13
HSD17B12	11:43877934	rs1061810	A/C	0.28	1.08 (1.05-1.11)	5.29x10^-9	11:43876435	rs3736505	0.92	G/A	0.30	1.05 (1.01-1.08)	4.82x10^-3	1.07 (1.05-1.09)	3.95x10^-10
MAP3K11	11:65364385	rs111669836	A/T	0.25	1.07 (1.04-1.10)	7.43x10^-7	11:65365171	rs11227234	1.00	T/G	0.24	1.05 (1.01-1.08)	8.77x10^-3	1.06 (1.04-1.09)	4.12x10^-8
NRXN3	14:79945162	rs10146997	G/A	0.21	1.07 (1.04-1.10)	4.59x10^-6	14:79939993	rs17109256	0.98	A/G	0.21	1.07 (1.03-1.11)	1.27x10^-4	1.07 (1.05-1.09)	2.27x10^-9
CMIP	16:81534790	rs2925979	T/C	0.30	1.08 (1.05-1.10)	2.72x10^-8	16:81534790	rs2925979	1.00	T/C	0.31	1.05 (1.02-1.08)	3.06x10^-3	1.07 (1.04-1.09)	2.27x10^-9
ZZEF1	17:4014384	rs7224685	T/G	0.30	1.07 (1.04-1.10)	2.00x10^-7	17:3985864	rs8068804	0.95	A/G	0.31	1.07 (1.03-1.11)	4.11x10^-4	1.07 (1.05-1.09)	3.23x10^-10
GLP2R	17:9780387	rs78761021	G/A	0.34	1.07 (1.05-1.10)	5.49x10^-8	17:9791375	rs17676067	0.87	C/T	0.31	1.03 (1.00-1.07)	3.54x10^-2	1.06 (1.04-1.08)	3.04x10^-8
GIP	17:46967038	rs79349575	A/T	0.51	1.07 (1.04-1.09)	2.61x10^-7	17:47005193	rs15563	0.78	G/A	0.54	1.04 (1.01-1.07)	2.09x10^-2	1.06 (1.03-1.08)	4.43x10^-8

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The nearest gene is listed; this does not imply this is the biologically relevant gene;

^†

Lead SNV types: all map outside transcripts except rs429358 (missense variant) and rs1061810 (3’UTR);

^‡

Stage 2: proxy SNV (r²>0.6 with stage 1 lead SNV) was used when no stage 1 SNV was available.

^¢

The meta-analysis OR is aligned to the Stage 1 SNV risk allele. Abbreviations: Chr – chromosome, CI – confidence interval, EA - effect allele, EAF – effect allele frequency, OR – odds ratio, NEA – non-effect allele.

The effect sizes of the established (blue diamonds, N=69, p<5×10^-4, Supplementary Material), novel (red diamonds, N=13), and additional distinct (sky blue diamonds, N=13, Supplementary Table 7) signals according to their risk allele frequency (Supplementary Table 3). The additional distinct signals are based on approximate conditional analyses. The distinct signal at *TP53INP1* led by rs11786613 (Supplementary Table 7) is plotted (sky blue diamond). This signal did not reach locus-wide significance, but was selected for follow-up because of its low frequency and absence of LD with previously reported signal at this locus. The power curve shows the estimated effect size for which we had 80% power to detect associations. Established common variants with OR>1.12 are annotated.

Lead SNVs at all 13 novel loci were common. Although detected here using 1000G imputed data, all 13 were well captured by variants in the HapMap CEU reference panel (2 directly, 10 via proxies with r²>0.8, and one via proxy with r²=0.62) (Supplementary Materials). At all 13, lead variants defined through 1000G and those seen when the SNP density was restricted to HapMap content, had broadly similar evidence of association and were of similar frequency (Supplementary Figure 2; Supplementary Table 3). Throughout this manuscript, loci are named for the gene nearest to the lead SNV, unless otherwise specified (Table 1, Supplementary Materials: Biology box).

Adjustment for BMI revealed no additional genome-wide significant associations for T2D and, at most known and novel loci, there were only minimal differences in statistical significance and estimated T2D effect size between BMI-adjusted and unadjusted models. The four signals at which we observed a significant effect of BMI adjustment (p_{heterogeneity}<4.4×10^-4; based on 0.05/113 variants currently or previously reported to be associated with T2D at genome-wide significance) were FTO and MC4R (at which the T2D association is known to reflect a primary effect on BMI), and TCF7L2 and SLC30A8 (at which T2D associations were strengthened after BMI-adjustment) (Supplementary Figure 3; Supplementary Table 4).

Insights into genetic architecture of T2D

In this meta-analysis, we tested 3.9M low-frequency variants (r²≥0.3 or proper-info≥0.4; minor allele present in ≥3 studies) for T2D association, constituting 96.7% of the low-frequency variants ascertained by the 1000G European Panel (March 2012) (Supplementary Table 2). For variants with risk-allele frequencies (RAF) of 0.5%, 1%, or 5%, we had 80% power to detect association (p<5×10^-8) for allelic ORs of 1.80, 1.48, and 1.16, respectively, after accounting for imputation quality (Figure 1, Supplementary Table 5). Despite the increased coverage and sample size, we identified no novel low-frequency variants at genome-wide significance (Figure 1).

Since we had only been able to test 29 of the 52 promising stage 1 signals on the Metabochip, we investigated whether this failure to detect low-frequency variant associations with T2D could be a consequence of selective variant inclusion on the Metabochip. Amongst the remaining 23 variants, none reached genome-wide significance after aggregating with GWAS data available from EPIC-InterAct. Six of these 23 SNVs had MAF<5%, and for these we performed additional follow-up in the GERA study. However, none reached genome-wide significance in a combined analysis of stage 1, EPIC-InterAct and GERA (a total of 39,219 cases and 198,990 controls) (Supplementary Table 6). Therefore, despite substantially enlarged sample sizes that would have allowed us to detect low-frequency risk alleles with modest effect sizes, the overwhelming majority of variants for which T2D-association can be detected with these sample sizes are themselves common.

To identify loci containing multiple distinct signals, we performed approximate conditional analysis within the established and novel GWAS loci and detected two such novel common variant signals (Supplementary Table 7) (19,20). At the ANKRD55 locus, we identified a previously-unreported distinct (p_conditional<10^-5) association signal led by rs173964 (p_conditional=3.54×10^-7, MAF=26%) (Supplementary Table 7, Supplementary Figure 4). We also observed multiple signals of association at loci with previous reports of such signals (4,14), including CDKN2A/B (3 signals in total), DGKB, KCNQ1 (6 signals), HNF4A, and CCND2 (3 signals) (Supplementary Table 7, Supplementary Figure 4). At CCND2, in addition to the main signal with lead SNV rs4238013, we detected: (i) a novel distinct signal led by a common variant, rs11063018 (p_conditional=2.70×10^-7, MAF=19%); and (ii) a third distinct signal led by a low-frequency protective allele (rs188827514, MAF=0.6%; OR_conditional=0.60, p_conditional=1.24×10^-6) (Supplementary Figure 5A, Supplementary Table 7), which represents the same distinct signal as that at rs76895963 (p_conditional=1.0) reported in the Icelandic population (Supplementary Figure 5B) (7). At HNF4A, we confirm recent analyses (obtained in partially-overlapping data) (14) that a low-frequency missense variant (rs1800961, p.Thr139Ile, MAF=3.7%) is associated with T2D, and is distinct from the known common variant GWAS signal (which we map here to rs12625671).

We evaluated the trans-ethnic heterogeneity of allelic effects (i.e. discordance in the direction and/or magnitude of estimated odds ratios) at novel loci on the basis of Cochran’s Q statistics from the largest T2D trans-ancestry GWAS meta-analysis to date (2). Using reported summary statistics from that study, we observed no significant evidence of heterogeneity of effect size (Bonferroni correction p_{Cochran’s Q}<0.05/13=0.0038) between major ancestral groups at any of the 13 loci (Supplementary Table 8). These results are consistent with these loci being driven by common causal variants that are widely distributed across populations.

1000G variant density for identification of potentially causal genetic variants

We used credible set fine-mapping (33) to investigate whether 1000G imputation allowed us to better resolve the specific variants driving 95 distinct T2D association signals at 82 loci (Supplementary Material). 99% credible sets included between 1 and 7,636 SNVs; 25 included fewer than 20 SNVs, 16 fewer than 10 (Supplementary Tables 9 and 10). We compared 1000G-based credible sets with those constructed from HapMap SNVs alone (Figure 2B, Supplementary Table 9). At all but three of the association signals (two at KCNQ1 and rs1800961 at HNF4A), 1000G imputation resulted in larger credible sets (median increase of 34 variants) spanning wider genomic intervals (median interval size increase of 5kb) (Figure 2B, Supplementary Table 9). The 1000G-defined credible sets included >85% of the SNVs in the corresponding HapMap sets (Supplementary Table 9). Despite the overall larger credible sets, we asked whether 1000G imputation enabled an increase in the posterior probability afforded to the lead SNVs, but found no evidence to this effect (Figure 2C).

A) The number of SNVs included in 99% credible sets when performed on all SNVs compared to when analyses were restricted to those SNVs present in HapMap. B) The cumulative π_c of the top 3 SNVs among all 1000G SNVs and after restriction to HapMap SNVs is shown. While the low frequency SNV at *TP53INP1* (rs11786613) did not reach the threshold for a distinct signal in approximate conditional analyses, we fine-mapped both this variant and the previous common signal separately after reciprocal conditioning, which suggested they were independent. C) The minor allele frequency of the lead SNV identified in current analyses compared to that identified among SNVs present in HapMap. D) The association of the low frequency variant rs11786613 (blue) and that of the previous lead variant at this locus, rs7845219 (purple). The low frequency variant overlaps regulatory annotations active in pancreatic islets, among other tissues, and the sequence surrounding the A allele of this variant has a *in silico* recognition motif for a FOXA1:AR (androgen receptor) protein complex.

Within the 50 loci previously associated with T2D in Europeans (4) which had at least modest evidence of association in the current analyses (p<5x10^-4), we asked whether the lead SNV in 1000G-imputed analysis was of similar frequency to that observed in HapMap analyses. Only at TP53INP1, was the most strongly associated 1000G-imputed SNV (rs11786613, OR=1.21, p=1.6x10^-6, MAF=3.2%) of substantially lower frequency than the lead HapMap-imputed SNV (3) (rs7845219, MAF=47.7%, Figure 2A). rs11786613 was neither present in HapMap, nor on the Metabochip (Supplementary Figure 6). Reciprocal conditioning of this low-frequency SNV and the previously identified common lead SNV (rs7845219: OR=1.05, p=5.0x10^-5, MAF=47.5%) indicated that the two signals were likely to be distinct but the signal at rs11786613 did not meet our threshold (p_conditional<10^-5) for locus-wide significance (Supplementary Figure 4).

Pathophysiological insights from novel T2D associations

Among the 13 novel T2D-associated loci, many (such as those near HLA-DQA1, NRXN3, GIP, ABO and CMIP) included variants previously implicated in predisposition to other diseases and traits (r²>0.6 with the lead SNV) (Supplementary Table 3, Supplementary Materials: Biology box). For example, the novel association at SNV rs1182436 lies ~120Kb upstream of MNX1, a gene implicated in pancreatic hypoplasia and neonatal diabetes (34–36).

The lead SNV rs78761021 at the GLP2R locus, encoding the receptor for glucagon-like peptide 2, is in strong LD (r²=0.87) with a common missense variant in GLP2R (rs17681684, D470N, p=3×10^-7). These signals were strongly dependent and mutually extinguished in reciprocal conditional analyses, consistent with the coding variant being causal and implicating GLP2R as the putative causal gene (Supplementary Figure 7). While previously suggested to regulate energy balance and glucose tolerance (37), GLP2R has primarily been implicated in gastrointestinal function (38,39). In contrast, GLP1R, encoding the GLP-1 receptor (the target for a major class of T2D therapies (40)) is more directly implicated in pancreatic islet function and variation at this gene has been associated with glucose levels and T2D risk (41).

We also observed associations with T2D centred on rs9271774 near HLA-DQA1 (Table 1), a region showing a particularly strong association with T1D (42). There is considerable heterogeneity within, and overlap between, the clinical presentations of T1D and T2D, but these can be partially resolved through measurement of islet cell autoantibodies (43). Such measures were not uniformly available across studies contributing to our meta-analysis (Supplementary Table 1). We therefore considered whether the adjacency between T1D- and T2D-risk loci was likely to reflect misclassification of individuals with autoimmune diabetes as cases in the present study.

Three lines of evidence make this unlikely. First, the lead T1D-associated SNV in the HLA region (rs6916742) was only weakly associated with T2D in the present study (p=0.01), and conditioning on this variant had only modest impact on the T2D-association signal at rs9271774 (p_{unconditional}=3.3x10^-7; p_conditional=9.1x10^-6). Second, of 52 published genome-wide significant T1D-association GWAS signals, 50 were included in the current analysis: only six of these reached even nominal association with T2D (p<0.05; Supplementary Figure 8), and at one of these six (BCAR1), the T1D risk-allele was protective for T2D. Third, in genetic risk score (GRS) analyses, the combined effect of these 50 T1D signals on T2D risk was of only nominal significance (OR =1.02[1.00, 1.03], p=0.026), and significance was eliminated when the 6 overlapping loci were excluded (OR =1.00[0.98, 1.02], p=0.73). In combination, these findings argue against substantial misclassification and indicate that the signal at HLA-DQA1 is likely to be a genuine T2D signal.

Potential genes and pathways underlying the T2D loci: eQTL and pathway analysis

Cis-eQTLs analyses highlighted four genes as possible effector transcripts: ABO (pancreatic islets), PLEKHA1 (whole blood), HSD17B12 (adipose, liver, muscle, whole blood) at the respective loci, and HLA-DRB5 expression (adipose, pancreatic islets, whole blood) at the HLA-DQA1 locus (Supplementary Table 11).

We next asked whether large-scale gene expression data, mouse phenotypes, and protein-protein interaction (PPI) networks could implicate specific gene candidates and gene sets in the aetiology of T2D. Using DEPICT (32), 29 genes were prioritised as driving observed associations (FDR<0.05), including ACSL1 and CMIP among the genes mapping to the novel loci (Supplementary Table 12). These analyses also identified 20 enriched reconstituted gene sets (FDR<5%) falling into 4 groups (Supplementary Figure 9; complete results, including gene prioritisation, can be downloaded from https://onedrive.live.com/redir?resid=7848F2AF5103AA1B!1505&authkey=!AIC31supgUwjZVU&ithint=file%2cxlsx). These included pathways related to mammalian target of rapamycin (mTOR), based on co-regulation of the IDE, TLE1, SPRY2, CMIP, and MTMR3 genes (44).

Overlap of associated variants with regulatory annotations

We observed significant enrichment for T2D-associated credible set variants in pancreatic islet active enhancers and/or promoters (log odds [β]=0.74, p=4.2x10^-8) and FOXA2 binding sites (β=1.40, p=4.1×10^-7), as previously reported (Supplementary Table 13) (14). We also observed enrichment for T2D-associated variants in coding exons (β=1.56, p=7.9x10^-5), in EZH2-binding sites across many tissues (β=1.35, p=5.3x10^-6), and in binding sites for NKX2.2 (β=1.73, p=4.1x10^-8) and PDX1 (β=1.46, p=7.4x10^-6) in pancreatic islets (Supplementary Figure 10).

Even though credible sets were generally larger, analyses performed on the 1000G imputed results produced stronger evidence of enrichment than equivalent analyses restricted to SNVs present in HapMap. This was most notably the case for variants within coding exons (β=1.56, p=7.9x10^-5 in 1000G compared to β=0.68, p=0.62 in HapMap), and likely reflects more complete capture of the true causal variants in the more densely imputed credible sets. Single lead SNVs overlapping an enriched annotation accounted for the majority of the total posterior probability (π_c>0.5) at seven loci. For example, the lead SNV (rs8056814) at BCAR1 (π_c=0.57) overlaps an islet enhancer (Supplementary Figure 11A), while the newly-identified low-frequency signal at TP53INP1 overlaps an islet promoter element (rs117866713; π_c=0.53) (Figure 2D) (31).

We applied hierarchical clustering to the results of diabetes-related quantitative trait associations for the set of T2D-associated loci from the present study, identifying three main clusters of association signals with differing impact on quantitative traits (Supplementary Table 9). The first, including GIPR, C2CDC4A, CDKAL1, GCK, TCF7L2, GLIS3, THADA, IGF2BP2, and DGKB involved loci with a primary impact on insulin secretion and processing (26,29). The second cluster captured loci (including PPARG, KLF14, and IRS1) disrupting insulin action. The third cluster, showing marked associations with BMI and lipid levels, included NRXN3, CMIP, APOE, and MC4R, but not FTO, which clustered alone.

In regulatory enhancement analyses, we observed strong tissue-specific enrichment patterns broadly consistent with the phenotypic characteristics of the physiologically-stratified locus subsets. The cluster of loci disrupting insulin secretion showed the most marked enrichment for pancreatic islet regulatory elements (β=0.91, p=9.5×10^-5). In contrast, the cluster of loci implicated in insulin action was enriched for annotations from adipocytes (β=1.3, p=2.7×10^-11) and monocytes (β=1.4, p=1.4×10^-12), and that characterised by associations with BMI and lipids showed preferential enrichment for hepatic annotations (β=1.15, p=5.8×10^-4) (Figure 3A-C). For example, at the novel T2D-associated CMIP locus, previously associated with adiposity and lipid levels (28,45), the lead SNV (rs2925979, π_c=0.91) overlaps an active enhancer element in both liver and adipose tissue, among others (Supplementary Figure 11B).

Type 2 diabetes loci stratified by patterns of quantitative trait (e.g. glycaemic, insulin, lipid, and anthropometric) effects show distinct cell-type annotation patterns. We hierarchically clustered loci based on endophenotype data and identified groups of T2D loci associated with measures of A) insulin secretion, B) insulin resistance, and C) BMI/lipids. We then tested the effect of variants in cell-type enhancer and promoter chromatin states on the posterior probabilities of credible sets for each group. We identified most significant effects among pancreatic islet chromatin for insulin secretion loci, CD14+ monocyte and adipose chromatin for insulin resistance loci, and liver chromatin for BMI/lipid loci.

Discussion

In this large-scale study of T2D genetics, in which individual variants were assayed in up to 238,209 subjects, we identify 13 novel T2D-associated loci at genome-wide significance and refine causal variant location for the 13 novel and 69 established T2D loci. We also provide evidence for enrichment in regulatory elements at associated loci in tissues relevant for T2D, and demonstrate tissue-specific enrichment in regulatory annotations when T2D loci were stratified according to inferred physiological mechanism.

Together with loci reported in other recent publications (9), we calculate that the present analysis brings the total number of independent T2D associations to 128 distinct signals at 113 loci (Supplementary Table 3). Lead SNVs at all 13 novel loci were common (MAF > 0.15) and of comparable effect size (1.07≤OR≤1.10) to previously-identified common variant associations (2,4). Associations at the novel loci showed homogeneous effects across diverse ethnicities, supporting the evidence for coincident common risk alleles across ancestry groups (2). Moreover, we conclude that misclassification of diabetes subtype is not a major concern for these analyses and that the HLA-DQA1 signal represents genuine association with T2D, independent of nearby signals that influence T1D.

We observed a general increase in the size of credible sets with 1000G imputation compared to HapMap imputation. This is likely due to improved enumeration of potential causal common variants on known risk haplotypes, rather than resolution towards low-frequency variants of larger effect driving common variant associations. These findings are consistent with the inference (arising also from the other analyses reported here) that the T2D-risk signals identified by GWAS are overwhelmingly driven by common causal variants. In such a setting, imputation with denser reference panels, at least in ethnically restricted samples, provides more complete elaboration of the allelic content of common risk haplotypes. Finer resolution of those haplotypes that would provide greater confidence in the location of causal variants will likely require further expansion of trans-ethnic fine-mapping efforts (2). The distinct signals at the established CCND2 and TP53INP1 loci point to contributions of low-frequency variant associations of modest effect, but indicate that even larger samples will be required to robustly detect association signals at low frequency. Such new large datasets might be used to expand the follow-up of suggestive signals from our analysis.

The discovery of novel genome-wide significant association signals in the current analysis is attributable primarily to increased sample size, rather than improved genomic coverage. Although we queried a large proportion of the low-frequency variants present in the 1000G European reference haplotypes, and had >80% power to detect genome-wide significant associations with OR>1.8 for the tested low-frequency risk variants, we found no such low-frequency variant associations in either established or novel loci. Whilst low-frequency variant coverage in the present study was not complete, this observation adds to the growing evidence (2,4,9,46) that few low-frequency T2D-risk variants with moderate to strong effect sizes exist in European ancestry samples, and is consistent with a primary role for common variants of modest effect in T2D risk. The present study reinforces the conclusions from a recent study which imputed from whole-genome sequencing data - from 2,657 European T2D cases and controls, rather than 1000G - into a set of GWAS studies partially overlapping with the present meta-analysis. We demonstrated that the failure to detect low frequency associations in that study is not overcome by a substantial increase in sample size (9). It is worth emphasising that we did not, in this study, have sufficient imputation quality to test for T2D associations with rare variants and we cannot evaluate the collective contribution of variants with MAF<0.5% to T2D risk.

The development of T2D involves dysfunction of multiple mechanisms across several distinct tissues (9,29,31,47,48). When coupled with functional data, we saw larger effect estimates for enrichment of coding variants than observed with HapMap SNVs alone, consistent with more complete recovery of the causal variants through imputation using a denser reference panel. The functional annotation analyses also demonstrated that the stratification of T2D-risk loci according to primary physiological mechanism resulted in evidence for consistent and appropriate tissue-specific effects on transcriptional regulation. These analyses exemplify the use of a combination of human physiology and genomic annotation to position T2D GWAS loci with respect to the cardinal mechanistic components of T2D development. Extension of this approach is likely to provide a valuable in silico strategy to aid prioritisation of tissues for mechanistic characterisation of genetic associations. Using the hypothesis-free pathway analysis of T2D associations with DEPICT (32), we highlighted a causal role of mTOR signalling pathway in the aetiology of T2D not observed from individual loci associations. The mTOR pathway has previously been implicated in the link between obesity, insulin resistance, and T2D from cell and animal models (44,49).

The current results emphasize that progressively larger sample sizes, coupled with higher density sequence-based imputation (13), will continue to represent a powerful strategy for genetic discovery in T2D, and in complex diseases and traits more generally. At known T2D-associated loci, identification of the most plausible T2D causal variants will likely require large-scale multi-ethnic analyses, where more diverse haplotypes, reflecting different patterns of LD, in combination with functional (31,50,51) data allow refinement of association signals to smaller numbers of variants (2).

Supplementary Material

NIHMS73893-supplement-Supplementary_Material.pdf^{(2.3MB, pdf)}

Supplementary Tables

NIHMS73893-supplement-Supplementary_Tables.xlsx^{(1.3MB, xlsx)}

Acknowledgements

ARIC: The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C), R01HL087641, R01HL59367 and R01HL086694; National Human Genome Research Institute contract U01HG004402; and National Institutes of Health contract HHSN268200625226C. Infrastructure was partly supported by Grant Number UL1RR025005, a component of the National Institutes of Health and NIH Roadmap for Medical Research. We wish to acknowledge the many contributions of Dr. Linda Kao, who helped direct the diabetes genetics working group in the ARIC Study until her passing in 2014. We thank the staff and participants of the ARIC study for their important contributions.

BioMe: This work is funded by The Mount Sinai IPM Biobank Program is supported by The Andrea and Charles Bronfman Philanthropies.

D2D2007: The FIN-D2D study has been financially supported by the hospital districts of Pirkanmaa, South Ostrobothnia, and Central Finland, the Finnish National Public Health Institute (National Institute for Health and Welfare), the Finnish Diabetes Association, the Ministry of Social Affairs and Health in Finland, the Academy of Finland (grant number 129293), the European Commission (Directorate C-Public Health grant agreement number 2004310), and Finland’s Slottery Machine Association.

DANISH: The study was funded by the Lundbeck Foundation and produced by the Lundbeck Foundation Centre for Applied Medical Genomics in Personalised Disease Prediction, Prevention and Care (LuCamp, www.lucamp.org), and Danish Council for Independent Research. The Novo Nordisk Foundation Center for Basic Metabolic Research is an independent Research Center at the University of Copenhagen, partially funded by an unrestricted donation from the Novo Nordisk Foundation (www.metabol.ku.dk).

DGI: This work was supported by a grant from Novartis. The Botnia study was supported by grants from the Signe and Ane Gyllenberg Foundation, Swedish Cultural Foundation in Finland, Finnish Diabetes Research Society, the Sigrid Juselius Foundation, Folkhälsan Research Foundation, Foundation for Life and Health in Finland, Jakobstad Hospital, Medical Society of Finland, Närpes Research Foundation and the Vasa and Närpes Health centers, the European Community's Seventh Framework Programme (FP7/2007-2013), the European Network for Genetic and Genomic Epidemiology (ENGAGE), the Collarative European Effort to Develop Diabetes Diagnostics (CEED/2008-2012), and the Swedish Research Council, including a Linné grant (No.31475113580).

DGDG: This work was funded by Genome Canada, Génome Quebec, and the Canada Foundation for Innovation. Cohort recruitment was supported by the Association Française des Diabetiques, INSERM, CNAMTS, Centre Hospitalier Universitaire Poitiers, La Fondation de France and the Endocrinology-Diabetology Department of the Corbeil-Essonnes Hospital. C. Petit, J-P. Riveline and S. Franc were instrumental in recruitment and S. Brunet, F. Bacot, R. Frechette, V. Catudal, M. Deweirder, F. Allegaert, P. Laflamme, P. Lepage, W. Astle, M. Leboeuf and S. Leroux provided technical assistance. K. Shazand and N. Foisset provided organizational guidance. We thank all individuals who participated as cases or controls in this study.

deCODE: The study was funded by deCODE Genetics/Amgen inc. and partly supported by ENGAGE HEALTH-F4-2007-201413. We thank the Icelandic study participants and the staff of deCODE Genetics core facilities and recruitment center for their contributions to this work.

DILGOM: The DILGOM study was supported by the Academy of Finland (grant number 118065). V.Salomaa was supported by the Academy of Finland (grant number 139635) and the Finnish Foundation for Cardiovascular Research. S.Mannisto was supported by the Academy of Finland (grant numbers 136895 and 263836). S.R. was supported by the Academy of Finland Center of Excellence in Complex Disease Genetics (grant numbers 213506 and 129680), the Academy of Finland (grant number 251217), the Finnish Foundation for Cardiovascular Research, and the Sigrid Juselius Foundation.

DRsEXTRA: The DR's EXTRA Study was supported by the Ministry of Education and Culture of Finland (627;2004-2011), the Academy of Finland (grant numbers 102318 and 123885), Kuopio University Hospital, the Finnish Diabetes Association, the Finnish Heart Association, the Päivikki and Sakari Sohlberg Foundation, and by grants from European Commission FP6 Integrated Project (EXGENESIS, LSHM-CT-2004-005272), the City of Kuopio, and the Social Insurance Institution of Finland (4/26/2010).

EGCUT: EU grant through the European Regional Development Fund (Project No. 2014-2020.4.01.15-0012), PerMedI (TerVE EstRC), EU H2020 grants 692145, 676550, 654248, and Estonian Research Council, Grant IUT20-60.

EMIL-Ulm: The EMIL Study received support by the State of Baden-Württemberg, Germany, the City of Leutkirch, Germany, and the German Research Council to B.O.B. (GRK 1041). The Ulm Diabetes Study Group received support by the German Research Foundation (DFG-GRK 1041) and the State of Baden-Wuerttemberg Centre of Excellence Metabolic Disorders to B.O.B.

EPIC-InterAct : This work was funded by the EU FP6 programme (grant number LSHM_CT_2006_037197). We thank all EPIC participants and staff for their contribution to the EPIC-InterAct study. We thank the lab team at the MRC Epidemiology Unit for sample management. I.B. was supported by grant WT098051.

FHS: This research was conducted in part using data and resources from the Framingham Heart Study of the National Heart Lung and Blood Institute of the National Institutes of Health and Boston University School of Medicine. The analyses reflect intellectual input and resource development from the Framingham Heart Study investigators participating in the SNP Health Association Resource (SHARe) project. This work was partially supported by the National Heart, Lung and Blood Institute's Framingham Heart Study (contract number N01-HC-25195) and its contract with Affymetrix, Inc for genotyping services (contract number N02-HL-6-4278). A portion of this research utilized the Linux Cluster for Genetic Analysis (LinGA-II) funded by the Robert Dawson Evans Endowment of the Department of Medicine at Boston University School of Medicine and Boston Medical Center. The work is also supported by National Institute for Diabetes and Digestive and Kidney Diseases (NIDDK) R01 DK078616 to J.B.M., J.D. and J.C.F., NIDDK K24 DK080140 to J.B.M., NIDDK U01 DK085526 to H.C., J.D. and J.B.M., and a Massachusetts General Hospital Research Scholars Award to J.C.F..

FUSION: This work was funded by NIH grants U01 DK062370, R01-HG000376, R01-DK072193, and NIH intramural project number ZIA HG000024. Genome-wide genotyping was conducted by the Johns Hopkins University Genetic Resources Core. Facility SNP Center at the Center for Inherited Disease Research (CIDR), with support from CIDR NIH contract number N01-HG-65403.

GERA: Data came from a grant, the Resource for Genetic Epidemiology Research in Adult Health and Aging (RC2 AG033067; Schaefer and Risch, PIs) awarded to the Kaiser Permanente Research Program on Genes, Environment, and Health (RPGEH) and the UCSF Institute for Human Genetics. The RPGEH was supported by grants from the Robert Wood Johnson Foundation, the Wayne and Gladys Valley Foundation, the Ellison Medical Foundation, Kaiser Permanente Northern California, and the Kaiser Permanente National and Northern California Community Benefit Programs.

GoDARTS: This study was funded by the Wellcome Trust (084727/Z/08/Z, 085475/Z/08/Z, 085475/B/08/Z) and as part of the EU IMI-SUMMIT program. We acknowledge the support of the Health Informatics Centre, University of Dundee for managing and supplying the anonymised data and NHS Tayside, the original data owner. We are grateful to all the participants who took part in the Go-DARTS study, to the general practitioners, to the Scottish School of Primary Care for their help in recruiting the participants, and to the whole team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, and nurses.

HEINZ NIXDORF RECALL (HNR): We thank the Heinz Nixdorf Foundation [Chairman: M. Nixdorf; Past Chairman: G. Schmidt (deceased)], the German Ministry of Education and Science (BMBF) for the generous support of this study. An additional research grant was received from Imatron Inc., South San Francisco, CA, which produced the EBCT scanners, and GE-Imatron, South San Francisco, CA, after the acquisition of Imatron Inc. We acknowledge the support of the Sarstedt AG & Co. (Nümbrecht, Germany) concerning laboratory equipment. We received support of the Ministry of Innovation, Science and Research, Nordrhine Westfalia for the genotyping of the Heinz Nixdorf Recall study participants. Technical support for the imputation of the Heinz Nixdorf Recall Study data on the Supercomputer Cray XT6m was provided by the Center for Information and Media Services, University of Duisburg-Essen. We are indebted to all the study participants and to the dedicated personnel of both the study center of the Heinz Nixdorf Recall study and the EBT-scanner facilities D. Grönemeyer, Bochum, and R. Seibel, Mülheim, as well as to the investigative group, in particular to U. Roggenbuck, U. Slomiany, E. M. Beck, A. Öffner, S. Münkel, M. Bauer, S. Schrader, R. Peter, and H. Hirche.

HPFS: This work was funded by the NIH grants P30 DK46200, DK58845, U01HG004399, and UM1CA167552.

IMPROVE and SCARFSHEEP: The IMPROVE study was supported by the European Commission (LSHM-CT-2007-037273), the Swedish Heart-Lung Foundation, the Swedish Research Council (8691), the Knut and Alice Wallenberg Foundation, the Foundation for Strategic Research, the Torsten and Ragnar Söderberg Foundation, the Strategic Cardiovascular Programme of Karolinska Institutet, and the Stockholm County Council (560183). The SCARFSHEEP study was supported by the Swedish Heart-Lung Foundation, the Swedish Research Council, the Strategic Cardiovascular Programme of Karolinska Institutet, the Strategic Support for Epidemiological Research at Karolinska Institutet, and the Stockholm County Council. B.S. acknowledges funding from the Magnus Bergvall Foundation and the Foundation for Old Servants. M.F. acknowledges funding from the Swedish e-science Research Center (SeRC). R.J.S. is supported by the Swedish Heart-Lung Foundation, the Tore Nilsson Foundation, the Thuring Foundation, and the Foundation for Old Servants. S.E.H. is funded by the British Heart Foundation (PG08/008).

KORAgen: The KORA research platform (KORA, Cooperative Research in the Region of Augsburg) was initiated and financed by the Helmholtz Zentrum München - German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research (BMBF) and by the State of Bavaria. The KORA research was supported within the Munich Center of Health Sciences (MC Health), Ludwig-Maximilians-Universität, as part of LMUinnovativ. Part of this project was supported by the German Center for Diabetes Research (DZD).

METSIM: The METSIM study was funded by the Academy of Finland (grant numbers 77299 and 124243).

NHS: This work was funded by the NIH grants P30 DK46200, DK58845, U01HG004399, and UM1CA186107.

PPP-MALMO-BOTNIA (PMB): The PPP-Botnia study has been financially supported by grants from the Sigrid Juselius Foundation, the Folkhälsan Research Foundation, the Ministry of Education in Finland, the Nordic Center of Excellence in Disease Genetics, the European Commission (EXGENESIS), the Signe and Ane Gyllenberg Foundation, the Swedish Cultural Foundation in Finland, the Finnish Diabetes Research Foundation, the Foundation for Life and Health in Finland, the Finnish Medical Society, the Paavo Nurmi Foundation, the Helsinki University Central Hospital Research Foundation, the Perklén Foundation, the Ollqvist Foundation, and the Närpes Health Care Foundation. The study has also been supported by the Municipal Heath Care Center and Hospital in Jakobstad and Health Care Centers in Vasa, Närpes and Korsholm. Studies from Malmö were supported by grants from the Swedish Research Council (SFO EXODIAB 2009-1039, LUDC 349-2008-6589, 521-2010-3490, 521-2010-3490, 521-2010-3490, 521-2007-4037, 521-2008-2974, ANDIS 825-2010-5983), the Knut and Alice Wallenberg Foundation (KAW 2009.0243), the Torsten and Ragnar Söderbergs Stiftelser (MT33/09), the IngaBritt and Arne Lundberg’s Research Foundation (grant number 359), and the Heart-Lung Foundation.

PIVUS and ULSAM: This work was funded by the Swedish Research Council, Swedish Heart-Lung Foundation, Knut och Alice Wallenberg Foundation, and Swedish Diabetes Foundation. Genome-wide genotyping was funded by the Wellcome Trust and performed by the SNP&SEQ Technology Platform in Uppsala (www.genotyping.se). We thank Tomas Axelsson, Ann-Christine Wiman, and Caisa Pöntinen for their assistance with genotyping. The SNP Technology Platform is supported by Uppsala University, Uppsala University Hospital, and the Swedish Research Council for Infrastructures.

Rotterdam Study: This work is funded by Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. This study is funded by the Research Institute for Diseases in the Elderly (014-93-015; RIDE2), the Netherlands Genomics Initiative (NGI)/Netherlands Organisation for Scientific Research (NWO) project nr. 050-060-810. The generation and management of GWAS genotype data for the Rotterdam Study is supported by the Netherlands Organisation of Scientific Research NWO Investments (nr. 175.010.2005.011, 911-03-012). We thank Pascal Arp, Mila Jhamai, Marijn Verkerk, Lizbeth Herrera and Marjolein Peters for their help in creating the GWAS database. The authors thank the study participants, the staff from the Rotterdam Study and the participating general practitioners and pharmacists.

SWEDISH TWIN REGISTRY (STR): This work was supported by grants from the US National Institutes of Health (AG028555, AG08724, AG04563, AG10175, AG08861), the Swedish Research Council, the Swedish Heart-Lung Foundation, the Swedish Foundation for Strategic Research, the Royal Swedish Academy of Science, and ENGAGE (within the European Union FP7 HEALTH-F4-2007-201413). Genotyping was performed by the SNP&SEQ Technology Platform in Uppsala (www.genotyping.se). We thank Tomas Axelsson, Ann-Christine Wiman, and Caisa Pöntinen for their excellent assistance with genotyping. The SNP Technology Platform is supported by Uppsala University, Uppsala University Hospital, and the Swedish Research Council for Infrastructures.

WARREN 2/58BC and WELLCOME TRUST CASE CONTROL CONSORTIUM (WTCCC): Collection of the UK type 2 diabetes cases was supported by Diabetes UK, BDA Research, and the UK Medical Research Council (Biomedical Collections Strategic Grant G0000649). The UK Type 2 Diabetes Genetics Consortium collection was supported by the Wellcome Trust (Biomedical Collections Grant GR072960). Metabochip genotyping was supported by the Wellcome Trust (Strategic Awards 076113, 083948, and 090367, and core support for the Wellcome Trust Centre for Human Genetics 090532), and analysis by the European Commission (ENGAGE HEALTH-F4-2007-201413), MRC (Project Grant G0601261), NIDDK (DK073490, DK085545 and DK098032), and Wellcome Trust (083270 and 098381). WTCCC is funded by Wellcome 076113 and 085475.

Institutional support for study design and analysis: This work was funded by MRC (G0601261), NIDDK (RC2-DK088389, U01-DK105535, U01-DK085545, U01-DK105535), FP7 (ENGAGE HEALTH-F4-2007-201413) and the Wellcome Trust (090532, 098381, 106130, and 090367)

Individual funding for study design and analysis: J.T.-F. is a Marie-Curie Fellow (PIEF-GA-2012-329156). M.K. is supported by the European Commission under the Marie Curie Intra-European Fellowship (project MARVEL, PIEF-GA-2013-626461). C.Langenberg, R.A.S. and N.J.W. are funded by the Medical Research Council (MC_UU_12015/1). L.M. is partially supported by 2010-2011 PRIN funds of the University of Ferrara – Holder: Prof. Guido Barbujani – and in part sponsored by the European Foundation for the Study of Diabetes (EFSD) Albert Renold Travel Fellowships for Young Scientists, and by the fund promoting internationalisation efforts of the University of Ferrara – Holder: Prof. Chiara Scapoli. A.P.M. is a Wellcome Trust Senior Fellow in Basic Biomedical Science (grant number WT098017). M.I.M. is a Wellcome Trust Senior Investigator. J.R.B.P is supported by the Wellcome Trust (WT092447MA). T.H.P. is supported by The Danish Council for Independent Research Medical Sciences (FSS) The Lundbeck Foundation and The Alfred Benzon Foundation. I.P. was in part funded by the Elsie Widdowson Fellowship, the Wellcome Trust Seed Award in Science (205915/Z/17/Z) and the European Union’s Horizon 2020 research and innovation programme (DYNAhealth, project number 633595). B.F.V. is supported by the NIH/NIDDK (R01DK101478) and the American Heart Association (13SDG14330006). E. Z. is supported by the Wellcome Trust (098051). S.E.H. is funded by British Heart Foundation PG08/008 and UCL BRC. V.Salomaa was supported by the Academy of Finland (grant # 139635) and by the Finnish Foundation for Cardiovascular Research.

Footnotes

AUTHOR CONTRIBUTIONS:

Writing and co-ordination group: R.A.S., L.J.S., R.M., L.M., K.J.G., M.K., J.D., A.P.M., M.B., M.I.M., I.P.

Central analysis group: R.A.S., L.J.S., R.M., L.M., C.M., A.P.M., M.B., M.I.M., I.P.

Additional lead analysts: L.M., K.J.G., M.K., N.P., T.H.P., A.D.J., J.D.E., T.F., Y.Lee, J.R.B.P., L.J., A.U.J.

GWAS cohort-level primary analysts: R.A.S., L.J.S., R.M., K.J.G., V.S., G.T., L.Q., N.R.V., A.Mahajan, H.Chen, P.A., B.F.V., H.G., M.M., J.S.R., N.W.R., N.R., L.C.K., E.M.L., S.M.W., C.Fuchsberger, P.K., C.M., P.C., M.L., Y.L., C.D., D.T., L.Y., C.Langenberg, A.P.M., I.P.

Metabochip cohort-level primary analysts: T.S., H.K., H.C., L.E., S.G., T.M.T, M.F., R.J.S.

Cohort sample collection, phenotyping, genotyping or additional analysis: R.A.S., H.G., R.B., A.B.H., A.K., G.S., N.D.K., J.L., L.L., T.M., M.R., B.T., T.E., E.M., C.F., C.L., D.Rybin, B.I., V.L., T.T., D.J.C., J.S.P., N.G., C.T.H., M.E.J., T.J., A.L., M.C.C., R.M.D., D.J.H., P.Kraft, Q.S., S.E., K.R.O., J.R.B.P., A.R.W., E.Z., J.T.-F., G.R.A., L.L.B., P.S.C., H.M.S., H.A.K., L.K., B.S., T.W.M., M.M.N., S.P., D.B., K.G., S.E.H., E.Tremoli, N.K., J.M., G.Steinbach, R.W., J.G.E., S.M., L.P., E.T., G.C., E.E., S.L., B.G., K.L., O.M., E.P.B., O.G., D.R., M.Blüher, P.Kovacs, A.T., N.M.M., C.S., T.M.F., A.T.H., I.B., B.B., H.B., P.W.F., A.B.G., D.P., Y.T.v.d.S., C.Langenberg, N.J.W., K.Strauch, M.B., M.I.M.

Metabochip cohort principal investigators: R.E., K.J., S.Moebus, U.d.F., A.H., M.S., P.D., P.J.D., T.M.F., A.T.H., S.R., V.Salomaa, N.L.P., B.O.B., R.N.B., F.S.C., K.L.M., J.T., T.H., O.B.P., I.B., C.Langenberg, N.J.W.

GWAS cohort principal investigators: L.Lannfelt, E.I., L.Lind, C.M.L., S.C., P.F., R.J.F.L., B.B., H.B., P.W.F., A.B.G., D.P., Y.T.v.d.S., D.A., L.C.G., C.Langenberg, N.J.W., E.S., C.Duijn van, J.C.F., J.B.M., E.B., C.G., K.Strauch, A.M., A.D.M., C.N.A.P., F.B.H., U.T., K.S., J.D., M.B., M.I.M.

GUARANTOR’S STATEMENT

Dr. Inga Prokopenko is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

COMPETING FINANCIAL INTERESTS STATEMENT

Inês Barroso and spouse own stock in GlaxoSmithKline and Incyte.

Jose C Florez has received consulting honoraria from Pfizer and PanGenX.

Valgerdur Steinthorsdottir, Gudmar Thorleifsson, Augustine Kong, Unnur Thorsteinsdottir, and Kari Stefansson are employed by deCODE 4 Genetics/Amgen inc.

Erik Ingelsson is a scientific advisor for Precision Wellness, Cellink and Olink Proteomics for work unrelated to the present project.

Mark I McCarthy sits on Advisory Panels for Pfizer and NovoNordisk, has received honoraria from Pfizer NovoNordisk and EliLilly, and is also a recipient of research funding from Pfizer, NovoNordisk, EliLilly, Takeda, Sanofi-Aventis, Merck, Boehringer-Ingelheim, Astra Zeneca, Janssen, Roche, Servier and Abbvie.

Robert A Scott, Laura J Scott, Reedik Mägi, Letizia Marullo, Kyle J Gaulton, Marika Kaakinen, Natalia Pervjakova, Tune H Pers, Andrew D Johnson, John D Eicher, Anne U Jackson, Teresa Ferreira, Yeji Lee, Clement Ma, Lu Qi, Natalie R Van Zuydam, Anubha Mahajan, Han Chen, Peter Almgren, Ben F Voight, Harald Grallert, Martina Müller-Nurasyid, Janina S Ried, N William Rayner, Neil Robertson, Lennart C Karssen, Elisabeth M van Leeuwen, Sara M Willems, Christian Fuchsberger, Phoenix Kwan, Tanya M Teslovich, Pritam Chanda, Man Li, Yingchang Lu, Christian Dina, Dorothee Thuillier, Loic Yengo, Longda Jiang, Thomas Sparso, Hans A Kestler, Himanshu Chheda, Lewin Eisele, Stefan Gustafsson, Mattias Frånberg, Rona J Strawbridge, Rafn Benediktsson, Astradur B Hreidarsson, Gunnar Sigurðsson, Nicola D Kerrison, Jian'an Luan, Liming Liang, Thomas Meitinger, Michael Roden, Barbara Thorand, Tõnu Esko, Evelin Mihailov, Caroline Fox, Ching-Ti Liu, Denis Rybin, Bo Isomaa, Valeriya Lyssenko, Tiinamaija Tuomi, David J Couper, James S Pankow, Niels Grarup, Christian T Have, Marit E Jørgensen, Torben Jørgensen, Allan Linneberg, Marilyn C Cornelis, Rob M van Dam, David J Hunter, Peter Kraft, Qi Sun, Sarah Edkins, Katharine R Owen, John RB Perry, Andrew R Wood, Eleftheria Zeggini, Juan Tajes-Fernandes, Goncalo R Abecasis, Lori L Bonnycastle, Peter S Chines, Heather M Stringham, Heikki A Koistinen, Leena Kinnunen, Bengt Sennblad, Thomas W Mühleisen, Markus M Nöthen, Sonali Pechlivanis, Damiano Baldassarre, Karl Gertow, Steve E Humphries, Elena Tremoli, Norman Klopp, Julia Meyer, Gerald Steinbach, Roman Wennauer, Johan G Eriksson, Satu Männistö, Leena Peltonen, Emmi Tikkanen, Guillaume Charpentier, Elodie Eury, Stéphane Lobbens, Bruna Gigante, Karin Leander, Olga McLeod, Erwin P Bottinger, Omri Gottesman, Douglas Ruderfer, Matthias Blüher, Peter Kovacs, Anke Tonjes, Nisa M Maruthur, Chiara Scapoli, Raimund Erbel, Karl-Heinz Jöckel, Susanne Moebus, Ulf de Faire, Anders Hamsten, Michael Stumvoll, Panagiotis Deloukas, Peter J Donnelly, Timothy M Frayling, Andrew T Hattersley, Samuli Ripatti, Veikko Salomaa, Nancy L Pedersen, Bernhard O Boehm, Richard N Bergman, Francis S Collins, Karen L Mohlke, Jaakko Tuomilehto, Torben Hansen, Oluf Pedersen, Lars Lannfelt, Lars Lind, Cecilia M Lindgren, Stephane Cauchi, Philippe Froguel, Ruth JF Loos, Beverley Balkau, Heiner Boeing, Paul W Franks, Aurelio Barricarte Gurrea, Domenico Palli, Yvonne T van der Schouw, David Altshuler, Leif C Groop, Claudia Langenberg, Nicholas J Wareham, Eric Sijbrands, Cornelia M van Duijn, James B Meigs, Eric Boerwinkle, Christian Gieger, Konstantin Strauch, Andres Metspalu, Andrew D Morris, Colin NA Palmer, Frank B Hu, Josée Dupuis, Andrew P Morris, Michael Boehnke, and Inga Prokopenko declare to have no competing financial interest.

References

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Supplementary Materials

Supplementary Material

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Supplementary Tables

NIHMS73893-supplement-Supplementary_Tables.xlsx^{(1.3MB, xlsx)}

[R1] 1.Global Burden of Disease Study 2013 Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013. 2015;386:743–800. doi: 10.1016/S0140-6736(15)60692-4. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R2] 2.DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Asian Genetic Epidemiology Network Type 2 Diabetes (AGEN-T2D) Consortium, South Asian Type 2 Diabetes (SAT2D) Consortium, Mexican American Type 2 Diabetes (MAT2D) Consortium, Type 2 Diabetes Genetic Exploration by Nex-generation sequencing in muylti-Ethnic Samples (T2D-GENES) Consortium. Mahajan A, et al. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat Genet. 2014;46:234–44. doi: 10.1038/ng.2897. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R3] 3.Voight BF, Scott LJ, Steinthorsdottir V, Morris ADP, Dina C, Welch RP, et al. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat Genet. 2010;42:579–89. doi: 10.1038/ng.609. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R4] 4.Morris AP, Voight BF, Teslovich TM, Ferreira T, Segrè AV, Steinthorsdottir V, et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet. 2012;44:981–90. doi: 10.1038/ng.2383. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R5] 5.Zeggini E, Scott LJ, Saxena R, Voight BF, Marchini JL, Hu T, et al. Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet. 2008;40:638–45. doi: 10.1038/ng.120. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R6] 6.Dupuis J, Langenberg C, Prokopenko I, Saxena R, Soranzo N, Jackson AU, et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet. 2010;42:105–16. doi: 10.1038/ng.520. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R7] 7.Steinthorsdottir V, Thorleifsson G, Sulem P, Helgason H, Grarup N, Sigurdsson A, et al. Identification of low-frequency and rare sequence variants associated with elevated or reduced risk of type 2 diabetes. Nat Genet. 2014;46:294–8. doi: 10.1038/ng.2882. [DOI] [PubMed] [Google Scholar]

[R8] 8.Estrada K, Aukrust I, Bjørkhaug L, Burtt NP, Mercader JM, García-Ortiz H, et al. Association of a low-frequency variant in HNF1A with type 2 diabetes in a Latino population. JAMA. 2014;311:2305–14. doi: 10.1001/jama.2014.6511. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R9] 9.Fuchsberger C, Flannick J, Teslovich TM, Mahajan A, Agarwala V, Gaulton KJ, et al. The genetic architecture of type 2 diabetes. Nature. 2016;536:41–7. doi: 10.1038/nature18642. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R10] 10.Majithia AR, Flannick J, Shahinian P, Guo M, Bray M-A, Fontanillas P, et al. Rare variants in PPARG with decreased activity in adipocyte differentiation are associated with increased risk of type 2 diabetes. Proc Natl Acad Sci U S A. 2014;111:13127–32. doi: 10.1073/pnas.1410428111. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R11] 11.Bonnefond A, Clement N, Fawcett K, Yengo L, Vaillant E, Guillaume J-L, et al. Rare MTNR1B variants impairing melatonin receptor 1B function contribute to type 2 diabetes. Nat Genet. 2012;44:297–301. doi: 10.1038/ng.1053. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R12] 12.Abecasis GR, Auton A, Brooks LD, DePristo Ma, Durbin RM, Handsaker RE, et al. An integrated map of genetic variation from 1,092 human genomes. Nature. 2012;491:56–65. doi: 10.1038/nature11632. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R13] 13.Yang J, Bakshi A, Zhu Z, Hemani G, Vinkhuyzen AAE, Lee SH, et al. Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nat Genet. 2015;47:1114–20. doi: 10.1038/ng.3390. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R14] 14.Gaulton KJ, Ferreira T, Lee Y, Raimondo A, Mägi R, Reschen ME, et al. Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci. Nat Genet. 2015;47:1415–25. doi: 10.1038/ng.3437. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R15] 15.Langenberg C, Sharp S, Forouhi NG, Franks PW, Schulze MB, Kerrison N, et al. Design and cohort description of the InterAct Project: an examination of the interaction of genetic and lifestyle factors on the incidence of type 2 diabetes in the EPIC Study. Diabetologia. 2011;54:2272–82. doi: 10.1007/s00125-011-2182-9. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R16] 16.Cook JP, Morris AP. Multi-ethnic genome-wide association study identifies novel locus for type 2 diabetes susceptibility. Eur J Hum Genet. 2016;24:1175–80. doi: 10.1038/ejhg.2016.17. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R17] 17.Howie B, Fuchsberger C, Stephens M, Marchini J, Abecasis GR. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat Genet. 2012;44:955–9. doi: 10.1038/ng.2354. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R18] 18.Howie BN, Donnelly P, Marchini J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 2009;5:e1000529. doi: 10.1371/journal.pgen.1000529. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R19] 19.Yang J, Ferreira T, Morris AP, Medland SE, Madden PAF, Heath AC, et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat Genet. 2012;44:369–75. doi: 10.1038/ng.2213. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R20] 20.Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet. 2011;88:76–82. doi: 10.1016/j.ajhg.2010.11.011. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R21] 21.UK10K Consortium, Writing group, Production group, Cohorts group, Neurodevelopmental disorders group, Obesity group et al. The UK10K project identifies rare variants in health and disease. Nature. 2015;526:82–90. doi: 10.1038/nature14962. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R22] 22.Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37:658–65. doi: 10.1002/gepi.21758. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R23] 23.Manning AK, Hivert M-F, Scott RA, Grimsby JL, Bouatia-Naji N, Chen H, et al. A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nat Genet. 2012;44:659–69. doi: 10.1038/ng.2274. [DOI] [PMC free article] [PubMed] [Google Scholar]

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PERMALINK

An expanded genome-wide association study of type 2 diabetes in Europeans

Robert A Scott

Laura J Scott

Reedik Mägi

Letizia Marullo

Kyle J Gaulton

Marika Kaakinen

Natalia Pervjakova

Tune H Pers

Andrew D Johnson

John D Eicher

Anne U Jackson

Teresa Ferreira

Yeji Lee

Clement Ma

Valgerdur Steinthorsdottir

Gudmar Thorleifsson

Lu Qi

Natalie R Van Zuydam

Anubha Mahajan

Han Chen

Peter Almgren

Ben F Voight

Harald Grallert

Martina Müller-Nurasyid

Janina S Ried

William N Rayner

Neil Robertson

Lennart C Karssen

Elisabeth M van Leeuwen

Sara M Willems

Christian Fuchsberger

Phoenix Kwan

Tanya M Teslovich

Pritam Chanda

Man Li

Yingchang Lu

Christian Dina

Dorothee Thuillier

Loic Yengo

Longda Jiang

Thomas Sparso

Hans A Kestler

Himanshu Chheda

Lewin Eisele

Stefan Gustafsson

Mattias Frånberg

Rona J Strawbridge

Rafn Benediktsson

Astradur B Hreidarsson

Augustine Kong

Gunnar Sigurðsson

Nicola D Kerrison

Jian'an Luan

Liming Liang

Thomas Meitinger

Michael Roden

Barbara Thorand

Tõnu Esko

Evelin Mihailov

Caroline Fox

Ching-Ti Liu

Denis Rybin

Bo Isomaa

Valeriya Lyssenko

Tiinamaija Tuomi

David J Couper

James S Pankow

Niels Grarup

Christian T Have

Marit E Jørgensen

Torben Jørgensen

Allan Linneberg

Marilyn C Cornelis

Rob M van Dam

David J Hunter

Peter Kraft

Qi Sun

Sarah Edkins