Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci (original) (raw)

References

  1. Kooner, J.S. et al. Genome-wide association study in individuals of South Asian ancestry identifies six new type 2 diabetes susceptibility loci. Nat. Genet. 43, 984–989 (2011).
    Article CAS PubMed PubMed Central Google Scholar
  2. Cho, Y.S. et al. Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in East Asians. Nat. Genet. 44, 67–72 (2012).
    Article CAS Google Scholar
  3. Voight, B.F. et al. Twelve type 2 diabetes susceptibility loci identified through large scale association analysis. Nat. Genet. 42, 579–589 (2010).
    Article CAS PubMed PubMed Central Google Scholar
  4. Morris, A.P. et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat. Genet. 44, 981–990 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  5. Mahajan, A. et al. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat. Genet. 46, 234–244 (2014).
    Article CAS PubMed Google Scholar
  6. Altshuler, D. et al. The common PPARγ Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes. Nat. Genet. 26, 76–80 (2000).
    Article CAS PubMed Google Scholar
  7. Gloyn, A.L. et al. Large-scale association studies of variants in genes encoding the pancreatic-cell KATP channel subunits Kir6.2 (KCNJ11) and SUR1 (ABCC8) conrm that the KCNJ11 E23K variant is associated with type 2 diabetes. Diabetes 52, 568–572 (2003).
    Article CAS PubMed Google Scholar
  8. Sladek, R. et al. A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 445, 881–885 (2007).
    Article CAS PubMed Google Scholar
  9. Dupuis, J. et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes. Nat. Genet. 42, 105–116 (2010).
    Article CAS PubMed PubMed Central Google Scholar
  10. ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).
  11. Elbein, S.C. et al. Genetic risk factors for type 2 diabetes: a _trans_-regulatory genetic architecture? Am. J. Hum. Genet. 91, 466–477 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  12. Trynka, G. et al. Chromatin marks identify critical cell types for fine-mapping complex trait variants. Nat. Genet. 45, 124–130 (2013).
    Article CAS PubMed Google Scholar
  13. Parker, S.C.J. et al. Chromatin stretch enhancer states drive cell-specific gene regulation and harbour human disease risk variants. Proc. Natl. Acad. Sci. USA 110, 17921–17926 (2013).
    Article PubMed PubMed Central Google Scholar
  14. Pasquali, L. et al. Pancreatic islet enhancer clusters enriched in type 2 diabetes risk-associated variants. Nat. Genet. 46, 136–143 (2014).
    Article CAS PubMed PubMed Central Google Scholar
  15. Voight, B.F. et al. The Metabochip, a custom genotyping array for genetic studies of metabolic, cardiovascular, and anthropometric traits. PLoS Genet. 8, e1002793 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  16. International HapMap Project Consortium. A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851–861 (2007).
  17. 1000 Genomes Project Consortium. A map of human genome variation from population-scale sequencing. Nature 467, 1061–1073 (2010).
  18. 1000 Genomes Project Consortium. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).
  19. Marchini, J. & Howie, B. Genotype imputation for genome-wide association studies. Nat. Rev. Genet. 11, 499–511 (2010).
    Article CAS PubMed Google Scholar
  20. Winkler, T.W. et al. Quality control and conduct of genome-wide association meta-analyses. Nat. Protoc. 9, 1192–1212 (2014).
    Article PubMed PubMed Central Google Scholar
  21. Howie, B.N., Donnelly, P. & Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009).
    Article CAS PubMed PubMed Central Google Scholar
  22. Howie, B., Fuchsberger, C., Stephens, M., Marchini, J. & Abecasis, G.R. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat. Genet. 44, 955–959 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  23. Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  24. Unoki, H. et al. SNPs in KCNQ1 are associated with susceptibility to type 2 diabetes in East Asian and European populations. Nat. Genet. 40, 1098–1102 (2008).
    Article CAS PubMed Google Scholar
  25. Fitzpatrick, G.V., Soloway, P.D. & Higgins, M.J. Regional loss of imprinting and growth deficiency in mice with a targeted deletion of KvDMR1. Nat. Genet. 32, 426–431 (2002).
    Article CAS PubMed Google Scholar
  26. Zeggini, E. et al. Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science 316, 1336–1341 (2007).
    Article CAS PubMed PubMed Central Google Scholar
  27. Shea, J. et al. Comparing strategies to fine-map the association of common SNPs at chromosome 9p21 with type 2 diabetes and myocardial infarction. Nat. Genet. 43, 801–805 (2011).
    Article CAS PubMed PubMed Central Google Scholar
  28. Maller, J.B. et al. Bayesian refinement of association signals for 14 loci in 3 common diseases. Nat. Genet. 44, 1294–1301 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  29. Jafar-Mohammadi, B. et al. A role for coding functional variants in HNF4A in type 2 diabetes susceptibility. Diabetologia 54, 111–119 (2011).
    Article CAS PubMed Google Scholar
  30. Teslovich, T.M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010).
    Article CAS PubMed PubMed Central Google Scholar
  31. Florez, J.C. et al. Haplotype structure and genotype-phenotype correlations of the sulfonylurea receptor and the islet ATP-sensitive potassium channel gene. Diabetes 53, 1360–1368 (2004).
    Article CAS PubMed Google Scholar
  32. Hamming, K.S. et al. Co-expression of the type 2 diabetes susceptibility gene variants KCNJ11 E23K and ABCC8 S1369A alter the ATP and sulfonylurea sensitivities of the ATP-sensitive K+ channel. Diabetes 58, 2419–2424 (2009).
    Article CAS PubMed PubMed Central Google Scholar
  33. Nicolson, T.J. et al. Insulin storage and glucose homeostasis in mice null for the granule zinc transporter ZnT8 and studies of the type 2 diabetes–associated variants. Diabetes 58, 2070–2083 (2009).
    Article CAS PubMed PubMed Central Google Scholar
  34. Beer, N.L. et al. The P446L variant in GCKR associated with fasting plasma glucose and triglyceride levels exerts its effect through increased glucokinase activity in liver. Hum. Mol. Genet. 18, 4081–4088 (2009).
    Article CAS PubMed PubMed Central Google Scholar
  35. Holmkvist, J. et al. Common variants in HNF-1α and risk of type 2 diabetes. Diabetologia 49, 2882–2891 (2006).
    Article CAS PubMed Google Scholar
  36. Yamagata, K. et al. Mutations in the hepatocyte nuclear factor-1α gene in maturity-onset diabetes of the young (MODY3). Nature 384, 455–458 (1996).
    Article CAS PubMed Google Scholar
  37. Yamagata, K. et al. Mutations in the hepatocyte nuclear factor-4α gene in maturity-onset diabetes of the young (MODY1). Nature 384, 458–460 (1996).
    Article CAS PubMed Google Scholar
  38. Gusev, A. et al. Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. Am. J. Hum. Genet. 95, 535–552 (2014).
    Article CAS PubMed PubMed Central Google Scholar
  39. Soccio, R.E. et al. Species-specific strategies underlying conserved functions of metabolic transcription factors. Mol. Endocrinol. 25, 694–706 (2011).
    Article CAS PubMed PubMed Central Google Scholar
  40. Gaulton, K.J. et al. A map of open chromatin in human pancreatic islets. Nat. Genet. 42, 255–259 (2010).
    Article CAS PubMed PubMed Central Google Scholar
  41. Fogarty, M.P., Cannon, M.E., Vadlamudi, S., Gaulton, K.J. & Mohlke, K.L. Identification of a regulatory variant that binds FOXA1 and FOXA2 at the CDC123/CAMK1D type 2 diabetes GWAS locus. PLoS Genet. 10, e1004633 (2014).
    Article CAS PubMed PubMed Central Google Scholar
  42. Manning, A.K. et al. A genome-wide approach accounting for body-mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nat. Genet. 44, 659–669 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  43. Dimas, A.S. et al. Impact of type 2 diabetes susceptibility variants on quantitative glycemic traits reveals mechanistic heterogeneity. Diabetes 63, 2158–2171 (2014).
    Article CAS PubMed PubMed Central Google Scholar
  44. Ravassard, P. et al. A genetically engineered human pancreatic β cell line exhibiting glucose-inducible insulin secretion. J. Clin. Invest. 121, 3589–3597 (2011).
    Article CAS PubMed PubMed Central Google Scholar
  45. Fadista, J. et al. Global genomic and transcriptomic analysis of human pancreatic islets reveals novel genes influencing glucose metabolism. Proc. Natl. Acad. Sci. USA 111, 13924–13929 (2014).
    Article CAS PubMed PubMed Central Google Scholar
  46. Lyssenko, V. et al. Common variant in MTNR1B associated with increased risk of type 2 diabetes and impaired early insulin secretion. Nat. Genet. 41, 82–88 (2009).
    Article CAS PubMed Google Scholar
  47. Gao, N. et al. Foxa1 and Foxa2 maintain the metabolic and secretory features of the mature β-cell. Mol. Endocrinol. 24, 1594–1604 (2010).
    Article CAS PubMed PubMed Central Google Scholar
  48. Zhou, Y. et al. TCF7L2 is a master regulator of insulin production and processing. Hum. Mol. Genet. 23, 6419–6431 (2014).
    Article CAS PubMed PubMed Central Google Scholar
  49. Lyssenko, V. et al. Mechanisms by which common variants in the TCF7L2 gene increase risk of type 2 diabetes. J. Clin. Invest. 117, 2155–2163 (2007).
    Article CAS PubMed PubMed Central Google Scholar
  50. Gloyn, A.L. et al. Activating mutations in the gene encoding the ATP-sensitive potassium-channel subunit Kir6.2 and permanent neonatal diabetes. N. Engl. J. Med. 350, 1838–1849 (2004).
    Article CAS PubMed Google Scholar
  51. Flannick, J. et al. Loss-of-function mutations in SLC30A8 protect against type 2 diabetes. Nat. Genet. 46, 357–363 (2014).
    Article CAS PubMed PubMed Central Google Scholar
  52. Dickson, S.P., Wang, K., Krantz, I., Hakonarson, H. & Goldstein, D.B. Rare variants create synthetic genome-wide associations. PLoS Biol. 8, e1000294 (2010).
    Article CAS PubMed PubMed Central Google Scholar
  53. Bonnefond, A. et al. Rare MTNR1B variants impairing melatonin receptor 1B function contribute to type 2 diabetes. Nat. Genet. 44, 297–301 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  54. Zaret, K.S. & Carroll, J.S. Pioneer transcription factors: establishing a competence for gene expression. Genes Dev. 25, 2227–2241 (2011).
    Article CAS PubMed PubMed Central Google Scholar
  55. Gao, N. et al. Dynamic regulation of Pdx1 enhancers by Foxa1 and Foxa2 is essential for pancreas development. Genes Dev. 22, 3435–3448 (2008).
    Article CAS PubMed PubMed Central Google Scholar
  56. Lee, C.S., Friedman, J.R., Fulmer, J.T. & Kaestner, K.H. The initiation of liver development is dependent on Foxa transcription factors. Nature 435, 944–947 (2005).
    Article CAS PubMed Google Scholar
  57. Scott, R.A. et al. Large-scale association analyses identify new loci influencing glycaemic traits and provide insight into the underlying biological pathways. Nat. Genet. 44, 991–1005 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  58. Tabassum, R., Chavali, S., Dwivedi, O.P., Tandon, N. & Bharadwaj, D. Genetic variants of FOXA2: risk of type 2 diabetes and effect on metabolic traits in North Indians. J. Hum. Genet. 53, 957–965 (2008).
    Article CAS PubMed Google Scholar
  59. Johnson, M.E., Schug, J., Wells, A.D., Kaestner, K.H. & Grant, S.F. Genome-wide analyses of ChIP-Seq derived FOXA2 DNA occupancy in liver points to genetic networks underpinning multiple complex traits. J. Clin. Endocrinol. Metab. 99, E1580–E1585 (2014).
    Article CAS PubMed PubMed Central Google Scholar
  60. Devlin, B. & Roeder, K. Genomic control for association studies. Biometrics 55, 997–1004 (1999).
    Article CAS PubMed Google Scholar
  61. Wakefield, J. Bayesian measure of the probability of false discovery in genetic epidemiology studies. Am. J. Hum. Genet. 81, 208–227 (2007).
    Article CAS PubMed PubMed Central Google Scholar
  62. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
    CAS PubMed PubMed Central Google Scholar
  63. Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
    Article CAS PubMed PubMed Central Google Scholar
  64. Kharchenko, P.V., Tolstorukov, M.Y. & Park, P.J. Design and analysis of ChIP-seq experiments for DNA-binding proteins. Nat. Biotechnol. 26, 1351–1359 (2008).
    Article CAS PubMed PubMed Central Google Scholar
  65. Li, Q., Brown, J.B., Huang, H. & Bickel, P.J. Measuring reproducibility of high-throughput experiments. Ann. App. Stat. 5, 1752–1779 (2011).
    Article Google Scholar
  66. Mikkelsen, T.S. et al. Comparative epigenomic analysis of murine and human adipogenesis. Cell 143, 156–169 (2010).
    Article CAS PubMed PubMed Central Google Scholar
  67. Ernst, J. & Kellis, M. Discovery and characterization of chromatin states for systematic annotation of the human genome. Nat. Biotechnol. 28, 817–825 (2010).
    Article CAS PubMed PubMed Central Google Scholar
  68. Morán, I. et al. Human β cell transcriptome analysis uncovers lncRNAs that are tissue-specific, dynamically regulated, and abnormally expressed in type 2 diabetes. Cell Metab. 16, 435–448 (2012).
    Article CAS PubMed PubMed Central Google Scholar
  69. Quinlan, A.R. & Hall, I.M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
    CAS PubMed PubMed Central Google Scholar
  70. Machanick, P. & Bailey, T.L. MEME-ChiP. Motif analysis of large DNA datasets. Bioinformatics 27, 1696–1697 (2011).
    Article CAS PubMed PubMed Central Google Scholar
  71. Mathelier, A. et al. JASPAR 2014: an extensively expanded and updated open-access database of transcription factor binding profiles. Nucleic Acids Res. 42, D142–D147 (2014).
    Article CAS PubMed Google Scholar
  72. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B-cell identifies. Mol. Cell 38, 576–589 (2010).
    Article CAS PubMed PubMed Central Google Scholar
  73. Bailey, T.L. et al. MEME Suite: tools for motif discovery and searching. Nucleic Acids Res. 37, W202–W208 (2009).
    Article CAS PubMed PubMed Central Google Scholar
  74. Grant, C.E., Bailey, T.L. & Noble, W.S. FIMO: scanning for occurrences of a given motif. Bioinformatics 27, 1017–1018 (2011).
    Article CAS PubMed PubMed Central Google Scholar
  75. Pugh, C.W., Tan, C.C., Jones, R.W. & Ratcliffe, P.J. Functional analysis of an oxygen-regulated transcriptional enhancer lying 3′ to the mouse erythropoietin gene. Proc. Natl. Acad. Sci. USA 88, 10553–10557 (1991).
    Article CAS PubMed PubMed Central Google Scholar

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Acknowledgements

Funding for the research undertaken in this study has been received from the Academy of Finland (including grants 77299, 102318, 10493, 118065, 123885, 124243, 129293, 129680, 136895, 139635, 211119, 213506, 251217 and 263836); Agence National de la Recherche; Association de Langue Française pour l'Etude du Diabète et des Maladies Métaboliques; Association Diabèete Risque Vasculaire; Association Française des Diabétiques; the Association of Danish Pharmacies; the Augustinus Foundation; the Becket Foundation; the British Diabetes Association (BDA) Research; the British Heart Foundation; the Central Norway Health Authority; the Central Finland Hospital District; the Center for Inherited Disease Research (CIDR); the City of Kuopio; the City of Leutkirch; Copenhagen County; the Danish Centre for Evaluation and Health Technology Assessment; the Danish Council for Independent Research; the Danish Heart Foundation; the Danish Research Councils; Deutsche Forschungsgemeinschaft (including project ER 155/6-2); the Diabetes Research Foundation; Diabetes UK; the Doris Duke Charitable Foundation; Erasmus Medical Center; Erasmus University; the Estonian government (SF0180142s08); the European Commission (including ENGAGE HEALTH-F4-2007-201413, FP7-201413, FP7-245536, EXGENESIS LSHM-CT-2004-005272, FP6 LSHM_CT_2006_037197, LSHM-CT-2007-037273, Directorate C-Public Health 2004310, DG XII); the European Regional Development Fund; the Federal Ministry of Education and Research, Germany (including FKZ 01GI1128 and FKZ 01EO1001); the Federal Ministry of Health, Germany; the Finnish Diabetes Association; the Finnish Diabetes Research Foundation; the Finnish Foundation for Cardiovascular Research; the Finnish Medical Society; the Folkhalsan Research Foundation; the Foundation for Life and Health in Finland; the Foundation for Old Servants; the Fredrick och Ingrid Thuring Foundation; the French region of Nord-Pas-de-Calais (Contrat de Projets Etat-Région); the German Center for Diabetes Research; the German Research Council (including grant GRK1041); the German National Genome Research Network; Groupe d'Etude des Maladies Métaboliques et Systémiques; the Health Care Centers in Vasa, Närpes and Korsholm, Finland; the Health Foundation; the Heinz Nixdorf Foundation; Helmholtz Zentrum München; the Helsinki University Central Hospital Research Foundation; the Hospital District of Southwest Finland; the Ib Henriksens Foundation; IngaBritt and Arne Lundberg's Research Foundation (including grant 359); Karolinska Institutet; the Knut and Alice Wallenberg Foundation (including grant KAW 2009.0243); Kuopio University Hospital; the Lundbeck Foundation; the Magnus Bergvall Foundation; the Medical Faculty of University Duisburg-Essen; the Medical Research Council, UK (including grants G0000649 and G0601261); the Ministry for Health, Welfare and Sports, the Netherlands; the Ministry of Education and Culture, Finland (including grants 722 and 627; 2004-2011); the Ministry of Education, Culture and Science, the Netherlands; the Ministry of Health and Prevention, Denmark; the Ministry of Social Affairs and Health, Finland; the Ministry of Innovation, Science, Research and Technology of North Rhine-Westphalia, Germany; the Munich Center of Health Sciences; the Municipal Health Care Center and Hospital in Jakobstad, Finland; the municipality of Rotterdam, the Netherlands; the Närpes Health Care Foundation; the National Health Screening Service of Norway; the National Heart, Lung, and Blood Institute, USA (including grant numbers/contracts HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, HHSN268201100012C, N01HC25195, N02HL64278, R01HL087641, R01HL59367 and R01HL086694); the National Human Genome Research Institute, USA (including grant numbers/contracts U01HG004402 and N01HG65403); the National Institute for Diabetes and Digestive and Kidney Diseases, USA (including grants R01DK078616, U01DK085526, K24DK080140 and R01DK073490); the National Institute for Health and Welfare, Finland; the National Institutes of Health, USA (including grant numbers/contracts HHSN268200625226C, UL1RR025005, R01DK062370, R01DK072193, 1Z01HG000024, AG028555, AG08724, AG04563, AG10175, AG08861, U01HG004399, DK58845, CA055075, DK085545 and DK098032); the Netherlands Genomics Initiative; the Netherlands Organisation for Health Research and Development; the Netherlands Organisation of Scientific Research NOW Investments (including grants 175.010.2005.011, 911-03-012 and 050-060-810); the Nord-Trondelag County Council; the Nordic Center of Excellence in Disease Genetics; the Norwegian Institute of Public Health; the Norwegian Research Council; the Novo Nordisk Foundation; the Ollquist Foundation; the Oxford National Institute for Health Research (NIHR) Biomedical Research Centre; the Paavo Nurmi Foundation; the Paivikki and Sakari Sohlberg Foundation; the Perklen Foundation; the Pirkanmaa Hospital District, Finland; Programme Hospitalier de Recherche Clinique; Programme National de Recherche sur la Diabète; the Research Institute for Diseases in the Elderly (including grant 014-93-015); the Robert Dawson Evans Endowment, Department of Medicine, Boston University School of Medicine and Boston Medical Center; the Royal Swedish Academy of Sciences; Sarstedt, Germany; the Signe and Ane Gyllenberg Foundation; the Sigrid Juselius Foundation; the Slottery Machine Association, Finland; the Social Insurance Institution of Finland; the South OstroBothnia Hospital District; the state of Baden-Württemberg, Germany; the Stockholm County Council (including grant 560183); the Swedish Cultural Foundation, Finland; the Swedish Diabetes Foundation; the Swedish e-science Research Center; the Swedish Foundation for Strategic Research; the Swedish Heart-Lung Foundation; the Swedish Research Council (including grants SFO EXODIAB 2009-1039, 521-2010-3490, 521-2007-4037, 521-2008-2974, ANDIS 825-2010-5983, LUDC 349-2008-6589 and 8691); the Swedish Society of Medicine; the Tore Nilsson Foundation; the Torsten and Ragnar Soderbergs Stiftelser (including grant MT33/09); University Hospital Essen; University of Tromsø; the University College London NIHR Biomedical Research Centre; the UK NIHR Cambridge Biomedical Research Centre; Uppsala University; Uppsala University Hospital; the Vaasa Hospital District; the Velux Foundation; and the Wellcome Trust (including the Biomedical Collections Grant GR072960 and grants 076113, 083948, 090367, 090532, 083270, 086596, 098017, 095101, 098051 and 098381). We are grateful to R. Scharfmann (INSERM U1016, Cochin Institute Paris) for the gift of EndoC βH1 cells and for providing technical support with their maintenance. We thank P. Johnson and the Oxford NIHR Biomedical Research Centre–funded Islet Isolation facility for providing human islets for this study. Detailed acknowledgments are provided in the Supplementary Note.

Author information

Author notes

  1. Wen-Hong L Kao, Leena Peltonen and Steven Wiltshire: Deceased.
  2. Kyle J Gaulton, Teresa Ferreira, Yeji Lee, Anne Raimondo, Reedik Mägi and Michael E Reschen: These authors contributed equally to this work.
  3. Anna L Gloyn, David Altshuler, Michael Boehnke, Tanya M Teslovich, Mark I McCarthy and Andrew P Morris: These authors jointly supervised this work.

Authors and Affiliations

  1. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
    Kyle J Gaulton, Teresa Ferreira, Anubha Mahajan, N William Rayner, Neil Robertson, Nicola L Beer, Martijn van de Bunt, Cecilia M Lindgren, Joseph Trakalo, Steven Wiltshire, Erik Ingelsson, Peter J Donnelly, Anna L Gloyn, Mark I McCarthy & Andrew P Morris
  2. Department of Genetics, Stanford University, Stanford, California, USA
    Kyle J Gaulton
  3. Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA
    Yeji Lee, Adam Locke, Laura J Scott, Christian Fuchsberger, Phoenix Kwan, Clement Ma, Hyun Min Kang, Gonçalo R Abecasis, Anne U Jackson, Heather M Stringham, Michael Boehnke & Tanya M Teslovich
  4. Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
    Anne Raimondo, N William Rayner, Neil Robertson, Soren K Thomsen, Jana K Rundle, Nicola L Beer, Martijn van de Bunt, Christopher J Groves, Katharine R Owen, Anna L Gloyn & Mark I McCarthy
  5. Estonian Genome Center, University of Tartu, Tartu, Estonia
    Reedik Mägi, Tonu Esko, Evelin Mihailov, Andres Metspalu & Andrew P Morris
  6. Nuffield Department of Medicine, Centre for Cellular and Molecular Physiology, University of Oxford, Oxford, UK
    Michael E Reschen, Anil Chalisey & Christopher A O'Callaghan
  7. Wellcome Trust Sanger Institute, Hinxton, UK
    N William Rayner, Sarah Edkins, Cordelia Langford, Leena Peltonen, Eleftheria Zeggini, Samuli Ripatti, Panagiotis Deloukas & Inês Barroso
  8. Medical Research Council (MRC) Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
    Robert A Scott, Sara M Willems, Nicola D Kerrison, Jian'an Luan, John R B Perry, Ruth J F Loos, Claudia Langenberg & Nicholas J Wareham
  9. Genomics of Common Disease, Imperial College London, London, UK
    Inga Prokopenko & Philippe Froguel
  10. Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
    Todd Green, Noël P Burtt, Jason Carey, Andrew T Crenshaw, Jason Flannick, Pierre Fontanillas, George B Grant, Cecilia M Lindgren, Leena Peltonen, Sekar Kathiresan & David Altshuler
  11. Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
    Thomas Sparso, Niels Grarup, Christian T Have, Torben Hansen & Oluf Pedersen
  12. Lille Institute of Biology, European Genomics Institute of Diabetes, Lille, France
    Dorothee Thuillier, Loic Yengo, Stephane Cauchi & Philippe Froguel
  13. Research Unit of Molecular Epidemiology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
    Harald Grallert, Simone Wahl, Christian Gieger, Norman Klopp & Thomas Illig
  14. Institute of Epidemiology II, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
    Harald Grallert, Simone Wahl, Christian Gieger, Barbara Thorand & Annette Peters
  15. German Center for Diabetes Research, Neuherberg, Germany
    Harald Grallert, Simone Wahl, Barbara Thorand & Annette Peters
  16. Department of Medicine Solna, Atherosclerosis Research Unit, Karolinska Institutet, Stockholm, Sweden
    Mattias Frånberg, Rona J Strawbridge, Karl Gertow, Olga McLeod, Bengt Sennblad & Anders Hamsten
  17. Science for Life Laboratory, Stockholm, Sweden
    Mattias Frånberg & Bengt Sennblad
  18. Department for Numerical Analysis and Computer Science, Stockholm University, Stockholm, Sweden
    Mattias Frånberg
  19. Leibniz Institute for Age Research, Fritz Lipmann Institute, Jena, Germany
    Hans Kestler
  20. Medical Systems Biology, Ulm University, Ulm, Germany
    Hans Kestler
  21. Finnish Institute for Molecular Medicine (FIMM), Helsinki, Finland
    Himanshu Chheda, Leena Peltonen, Emmi Tikkanen, Tiinamaija Tuomi, Samuli Ripatti & Leif C Groop
  22. Institute for Medical Informatics, Biometry and Epidemiology, University Hospital of Essen, Essen, Germany
    Lewin Eisele, Sonali Pechlivanis & Susanne Moebus
  23. Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
    Stefan Gustafsson & Erik Ingelsson
  24. deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
    Valgerdur Steinthorsdottir, Gudmar Thorleifsson, Augustine Kong, Unnur Thorsteinsdottir & Kari Stefansson
  25. Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA
    Lu Qi, David J Hunter, Peter Kraft, Liming Liang & Frank B Hu
  26. Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, USA
    Lu Qi, Rob M van Dam, Paul W Franks, David J Hunter, Qi Sun & Frank B Hu
  27. Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
    Lu Qi, David J Hunter, Qi Sun & Frank B Hu
  28. Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, USA
    Lu Qi
  29. Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
    Lennart C Karssen, Elisabeth M van Leeuwen, Sara M Willems & Cornelia van Duijn
  30. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
    Man Li & Wen-Hong L Kao
  31. Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
    Han Chen, Ching-Ti Liu & Josée Dupuis
  32. Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA
    Han Chen, Peter Kraft & Liming Liang
  33. Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
    Michael Linderman
  34. Genetics of Obesity and Related Metabolic Traits Program, Icahn School of Medicine at Mount Sinai, New York,, New York, USA
    Yingchang Lu & Ruth J F Loos
  35. Department of Pharmacology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
    Benjamin F Voight
  36. Department of Clinical Science Malmo, Lund University Diabetes Centre, Scania University Hospital, Lund University, Malmo, Sweden
    Peter Almgren, João Fadista, Paul W Franks, Anna Jonsson, Jasmina Kravic, Eero Lindholm, Valeriya Lyssenko, Nikolay N Oskolkov, Petter Storm, Olle Melander, Peter M Nilsson & Leif C Groop
  37. Centro Cardiologico Monzino, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
    Damiano Baldassarre & Elena Tremoli
  38. Dipartimento di Scienze Farmacologiche e Biomolecolari, Università di Milano, Milan, Italy
    Damiano Baldassarre & Elena Tremoli
  39. INSERM Centre de Recherche Epidémiologie et Santé des Populations (CESP) U1018, Villejuif, France
    Beverley Balkau
  40. University Paris Sud 11, UMRS 1018, Villejuif, France
    Beverley Balkau
  41. Faculty of Medicine, University of Iceland, Reykjavik, Iceland
    Rafn Benediktsson, Unnur Thorsteinsdottir & Kari Stefansson
  42. Landspitali University Hospital, Reykjavik, Iceland
    Rafn Benediktsson, Astradur B Hreidarsson & Gunnar Sigurðsson
  43. Integrated Treatment and Research (IFB) Center for Adiposity Diseases, University of Leipzig, Leipzig, Germany
    Matthias Blüher, Peter Kovacs, Anke Tonjes & Michael Stumvoll
  44. Department of Medicine, University of Leipzig, Leipzig, Germany
    Matthias Blüher, Peter Kovacs, Anke Tonjes & Michael Stumvoll
  45. German Institute of Human Nutrition, Potsdam-Rehbruecke, Germany
    Heiner Boeing
  46. National Human Genome Research Institute, US National Institutes of Health, Bethesda, Maryland, USA
    Lori L Bonnycastle, Peter S Chines & Francis S Collins
  47. Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
    Erwin P Bottinger, Omri Gottesman & Ruth J F Loos
  48. Endocrinology-Diabetology Unit, Corbeil-Essonnes Hospital, Corbeil-Essonnes, France
    Guillaume Charpentier
  49. Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
    Marilyn C Cornelis
  50. Department of Biostatistics, Collaborative Studies Coordinating Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
    David J Couper
  51. Saw Swee Hock School of Public Health, National University of Singapore, Singapore
    Rob M van Dam
  52. Diabetes Research Centre, Biomedical Research Institute, University of Dundee, Ninewells Hospital, Dundee, UK
    Alex S F Doney & Collin N A Palmer
  53. Pharmacogenomics Centre, Biomedical Research Institute, University of Dundee, Ninewells Hospital, Dundee, UK
    Alex S F Doney & Collin N A Palmer
  54. Department of Clinical Science Malmo, Lund University Diabetes Centre, Novo Nordisk Scandinavia, Malmo, Sweden
    Mozhgan Dorkhan
  55. Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland
    Johan G Eriksson, Leena Kinnunen, Satu Männistö, Leena Peltonen, Veikko Salomaa, Heikki Koistinen & Jaakko Tuomilehto
  56. Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland
    Johan G Eriksson
  57. Unit of General Practice, Helsinki University General Hospital, Helsinki, Finland
    Johan G Eriksson
  58. Folkhalsan Research Center, Helsinki, Finland
    Johan G Eriksson & Tiinamaija Tuomi
  59. Division of Endocrinology, Children's Hospital, Boston, Massachusetts, USA
    Tonu Esko
  60. Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
    Tonu Esko & Jose C Florez
  61. CNRS UMR 8199, Institute of Biology and Lille 2 University, Pasteur Institute, Lille, France
    Elodie Eury, Stéphane Lobbens & Philippe Froguel
  62. National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts, USA
    Caroline Fox & Josée Dupuis
  63. Division of Endocrinology and Metabolism, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
    Caroline Fox
  64. Department of Clinical Sciences, Lund University, Malmo, Sweden
    Paul W Franks
  65. Department of Public Health and Clinical Medicine, Umea University, Umea, Sweden
    Paul W Franks
  66. Division of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
    Bruna Gigante, Karin Leander & Ulf de Faire
  67. Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
    Maija Hassinen, Timo A Lakka & Rainer Rauramaa
  68. Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Dusseldorf, Dusseldorf, Germany
    Christian Herder & Michael Roden
  69. German Center for Diabetes Research, partner site Dusseldorf, Dusseldorf, Germany
    Christian Herder & Michael Roden
  70. Department of Public Health and General Practice, Nord-Trøndelag Health Study (HUNT) Research Center, Norwegian University of Science and Technology, Levanger, Norway
    Oddgeir L Holmen, Carl G P Platou & Kristian Hveem
  71. Cardiovascular Genetics, British Heart Foundation (BHF) Laboratories, Institute of Cardiovascular Sciences, University College London, London, UK
    Steve E Humphries
  72. Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, Massachusetts, USA
    David J Hunter & Peter Kraft
  73. Steno Diabetes Center, Gentofte, Denmark
    Marit E Jørgensen & Valeriya Lyssenko
  74. Research Centre for Prevention and Health, Capital Region of Denmark, Copenhagen, Denmark
    Torben Jørgensen & Allan Linneberg
  75. Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
    Torben Jørgensen
  76. Faculty of Medicine, University of Aalborg, Aalborg, Denmark
    Torben Jørgensen
  77. Hannover Unified Biobank, Hannover Medical School, Hannover, Germany
    Norman Klopp & Thomas Illig
  78. Institute of Human Genetics, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
    Peter Lichtner
  79. Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
    Allan Linneberg
  80. Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
    Allan Linneberg
  81. Institute of Genetic Epidemiology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
    Julia Meyer, Martina Müller-Nurasyid & Konstantin Strauch
  82. Biomedical Research Centre Genomics Core Facility, Guy's and St Thomas' National Health Service (NHS) Foundation Trust, Guy's & St Thomas' Hospital, London, UK
    Ghazala Mirza
  83. Institute of Human Genetics, University of Bonn, Bonn, Germany
    Thomas W Mühleisen & Markus M Nöthen
  84. Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany
    Thomas W Mühleisen & Markus M Nöthen
  85. Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
    Thomas W Mühleisen
  86. Department of Medicine I, University Hospital Grosshadern, Ludwig Maximilians Universität, Munich, Germany
    Martina Müller-Nurasyid
  87. Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig Maximilians Universität, Neuherberg, Germany
    Martina Müller-Nurasyid & Konstantin Strauch
  88. DZHK (German Center for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany
    Martina Müller-Nurasyid & Annette Peters
  89. Department of Epidemiology, Murcia Regional Health Council, Instituto Murciano de Investigación Biosanitaria Virgen de la Arrixaca (IMIB-Arrixaca), Murcia, Spain
    Carmen Navarro
  90. Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
    Carmen Navarro
  91. Department of Health and Social Sciences, Universidad de Murcia, Murcia, Spain
    Carmen Navarro
  92. Oxford National Institute for Health Research Biomedical Research Centre, Churchill Hospital, Oxford, UK
    Katharine R Owen, Anna L Gloyn & Mark I McCarthy
  93. Cancer Research and Prevention Institute (ISPO), Florence, Italy
    Domenico Palli
  94. Department of Internal Medicine, Levanger Hospital, Nord-Trondelag Health Trust, Levanger, Norway
    Carl G P Platou
  95. Department of Endocrinology and Diabetology, University Hospital Dusseldorf, Dusseldorf, Germany
    Michael Roden
  96. Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
    Douglas Ruderfer
  97. Boston University Data Coordinating Center, Boston, Massachusetts, USA
    Denis Rybin
  98. University Medical Center Utrecht, Utrecht, the Netherlands
    Yvonne T van der Schouw
  99. Icelandic Heart Association, Kopavogur, Iceland
    Gunnar Sigurðsson
  100. Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
    Alena Stančáková, Johanna Kuusisto & Markku Laakso
  101. Department of Clinical Chemistry and Central Laboratory, University of Ulm, Ulm, Germany
    Gerald Steinbach
  102. Department of Public Health, Hjelt Institute, University of Helsinki, Helsinki, Finland
    Emmi Tikkanen & Samuli Ripatti
  103. Department of Endocrinology, Abdominal Center, Helsinki University Hospital, Helsinki, Finland
    Tiinamaija Tuomi
  104. Research Program for Diabetes and Obesity, University of Helsinki, Helsinki, Finland
    Tiinamaija Tuomi
  105. Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
    Roman Wennauer & Eric Sijbrands
  106. Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
    Andrew R Wood & Timothy M Frayling
  107. European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
    Ian Dunham & Ewan Birney
  108. Division of Endocrinology, Germans Trias i Pujol University Hospital and Research Institute, Badalona, Spain
    Lorenzo Pasquali
  109. Josep Carreras Leukaemia Research Institute, Badalona, Spain
    Lorenzo Pasquali
  110. CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Barcelona, Spain
    Lorenzo Pasquali
  111. Department of Medicine, Imperial College London, London, UK
    Jorge Ferrer
  112. Institut d'Investigacions Biomèdiques August Pi i Sunyer, Centre Esther Koplowitz, Barcelona, Spain
    Jorge Ferrer
  113. Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
    Ruth J F Loos
  114. Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
    Jose C Florez, James B Meigs & David Altshuler
  115. Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, USA
    Jose C Florez, Sekar Kathiresan & David Altshuler
  116. Diabetes Research Center, Diabetes Unit, Massachusetts General Hospital, Boston, Massachusetts, USA
    Jose C Florez & David Altshuler
  117. Human Genetics Center, University of Texas Health Science Center at Houston, Houston, Texas, USA
    Eric Boerwinkle
  118. Human Genome Sequencing Center at Baylor College of Medicine, Houston, Texas, USA
    Eric Boerwinkle
  119. Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, Minnesota, USA
    James S Pankow
  120. Netherlands Genomics Initiative, Netherlands Consortium for Healthy Ageing and Center for Medical Systems Biology, Rotterdam, the Netherlands
    Cornelia van Duijn
  121. General Medicine Division, Massachusetts General Hospital, Boston, Massachusetts, USA
    James B Meigs
  122. Institute of Biomedicine/Physiology, University of Eastern Finland, Kuopio, Finland
    Timo A Lakka & Karl-Heinz Jöcke
  123. Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
    Timo A Lakka & Rainer Rauramaa
  124. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
    Nancy L Pedersen
  125. Department of Medical Sciences, Uppsala University Hospital, Uppsala, Sweden
    Lars Lind
  126. Faculty of Medicine, Institute of Health Sciences, University of Oulu, Oulu, Finland
    Sirkka M Keinanen-Kiukaanniemi
  127. Unit of General Practice, Oulu University Hospital, Oulu, Finland
    Sirkka M Keinanen-Kiukaanniemi
  128. South Ostrobothnia Central Hospital, Seinajoki, Finland
    Eeva Korpi-Hyövälti
  129. Finnish Diabetes Association, Tampere, Finland
    Timo E Saaristo
  130. Pirkanmaa District Hospital, Tampere, Finland
    Timo E Saaristo
  131. Department of Medicine, Central Finland Central Hospital, Jyvasklya, Finland
    Juha Saltevo
  132. Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
    Andres Metspalu
  133. Clinic of Cardiology, West German Heart Centre, University Hospital of Essen, University Duisdurg-Essen, Essen, Germany
    Raimund Erbel
  134. Public Health Genomics Unit, National Institute for Health and Welfare, Helsinki, Finland
    Samuli Ripatti
  135. Division of Endocrinology and Diabetes, Department of Internal Medicine, University Medical Centre Ulm, Ulm, Germany
    Bernhard O Boehm
  136. Lee Kong Chian School of Medicine, Imperial College London and Nanyang Technological University, Singapore
    Bernhard O Boehm
  137. Diabetes and Obesity Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
    Richard N Bergman
  138. Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
    Karen L Mohlke
  139. Division of Endocrinology, Department of Medicine, Helsinki University Central Hospital, Helsinki, Finland
    Heikki Koistinen
  140. Minerva Foundation Institute for Medical Research, Helsinki, Finland
    Heikki Koistinen
  141. Instituto de Investigación Sanitaria del Hospital Universitario La Paz, Madrid, Spain
    Jaakko Tuomilehto
  142. Centre for Vascular Prevention, Danube University Krems, Krems, Austria
    Jaakko Tuomilehto
  143. Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
    Jaakko Tuomilehto
  144. Department of Community Medicine, Faculty of Health Sciences, University of Tromso, Tromso, Norway
    Inger Njølstad
  145. William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
    Panagiotis Deloukas
  146. Department of Statistics, University of Oxford, Oxford, UK
    Peter J Donnelly
  147. Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK
    Andrew T Hattersley
  148. Montreal Diabetes Research Center, Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, Quebec, Canada
    Rob Sladek
  149. McGill University and Génome Québec Innovation Centre, Montreal, Quebec, Canada
    Rob Sladek
  150. Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
    Torben Hansen
  151. Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
    Andrew D Morris
  152. Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
    Sekar Kathiresan
  153. University of Cambridge Metabolic Research Laboratories, Wellcome Trust–MRC Institute of Metabolic Science, Cambridge, UK
    Inês Barroso
  154. National Institute for Health Research Cambridge Biomedical Research Centre, Cambridge, UK
    Inês Barroso
  155. Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA
    David Altshuler
  156. Department of Molecular Biology, Harvard Medical School, Boston, Massachusetts, USA
    David Altshuler
  157. Department of Biostatistics, University of Liverpool, Liverpool, UK
    Andrew P Morris
  158. Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool, UK
    Andrew P Morris

Authors

  1. Kyle J Gaulton
  2. Teresa Ferreira
  3. Yeji Lee
  4. Anne Raimondo
  5. Reedik Mägi
  6. Michael E Reschen
  7. Anubha Mahajan
  8. Adam Locke
  9. N William Rayner
  10. Neil Robertson
  11. Robert A Scott
  12. Inga Prokopenko
  13. Laura J Scott
  14. Todd Green
  15. Thomas Sparso
  16. Dorothee Thuillier
  17. Loic Yengo
  18. Harald Grallert
  19. Simone Wahl
  20. Mattias Frånberg
  21. Rona J Strawbridge
  22. Hans Kestler
  23. Himanshu Chheda
  24. Lewin Eisele
  25. Stefan Gustafsson
  26. Valgerdur Steinthorsdottir
  27. Gudmar Thorleifsson
  28. Lu Qi
  29. Elisabeth M van Leeuwen
  30. Sara M Willems
  31. Man Li
  32. Han Chen
  33. Christian Fuchsberger
  34. Phoenix Kwan
  35. Clement Ma
  36. Michael Linderman
  37. Yingchang Lu
  38. Soren K Thomsen
  39. Jana K Rundle
  40. Nicola L Beer
  41. Martijn van de Bunt
  42. Anil Chalisey
  43. Hyun Min Kang
  44. Benjamin F Voight
  45. Gonçalo R Abecasis
  46. Peter Almgren
  47. Damiano Baldassarre
  48. Beverley Balkau
  49. Rafn Benediktsson
  50. Matthias Blüher
  51. Heiner Boeing
  52. Lori L Bonnycastle
  53. Erwin P Bottinger
  54. Noël P Burtt
  55. Jason Carey
  56. Guillaume Charpentier
  57. Peter S Chines
  58. Marilyn C Cornelis
  59. David J Couper
  60. Andrew T Crenshaw
  61. Rob M van Dam
  62. Alex S F Doney
  63. Mozhgan Dorkhan
  64. Sarah Edkins
  65. Johan G Eriksson
  66. Tonu Esko
  67. Elodie Eury
  68. João Fadista
  69. Jason Flannick
  70. Pierre Fontanillas
  71. Caroline Fox
  72. Paul W Franks
  73. Karl Gertow
  74. Christian Gieger
  75. Bruna Gigante
  76. Omri Gottesman
  77. George B Grant
  78. Niels Grarup
  79. Christopher J Groves
  80. Maija Hassinen
  81. Christian T Have
  82. Christian Herder
  83. Oddgeir L Holmen
  84. Steve E Humphries
  85. David J Hunter
  86. Anne U Jackson
  87. Anna Jonsson
  88. Marit E Jørgensen
  89. Torben Jørgensen
  90. Wen-Hong L Kao
  91. Nicola D Kerrison
  92. Leena Kinnunen
  93. Norman Klopp
  94. Augustine Kong
  95. Peter Kovacs
  96. Peter Kraft
  97. Jasmina Kravic
  98. Cordelia Langford
  99. Karin Leander
  100. Liming Liang
  101. Peter Lichtner
  102. Cecilia M Lindgren
  103. Eero Lindholm
  104. Allan Linneberg
  105. Ching-Ti Liu
  106. Stéphane Lobbens
  107. Jian'an Luan
  108. Valeriya Lyssenko
  109. Satu Männistö
  110. Olga McLeod
  111. Julia Meyer
  112. Evelin Mihailov
  113. Ghazala Mirza
  114. Thomas W Mühleisen
  115. Martina Müller-Nurasyid
  116. Carmen Navarro
  117. Markus M Nöthen
  118. Nikolay N Oskolkov
  119. Katharine R Owen
  120. Domenico Palli
  121. Sonali Pechlivanis
  122. Leena Peltonen
  123. John R B Perry
  124. Carl G P Platou
  125. Michael Roden
  126. Douglas Ruderfer
  127. Denis Rybin
  128. Yvonne T van der Schouw
  129. Bengt Sennblad
  130. Gunnar Sigurðsson
  131. Alena Stančáková
  132. Gerald Steinbach
  133. Petter Storm
  134. Konstantin Strauch
  135. Heather M Stringham
  136. Qi Sun
  137. Barbara Thorand
  138. Emmi Tikkanen
  139. Anke Tonjes
  140. Joseph Trakalo
  141. Elena Tremoli
  142. Tiinamaija Tuomi
  143. Roman Wennauer
  144. Steven Wiltshire
  145. Andrew R Wood
  146. Eleftheria Zeggini
  147. Ian Dunham
  148. Ewan Birney
  149. Lorenzo Pasquali
  150. Jorge Ferrer
  151. Ruth J F Loos
  152. Josée Dupuis
  153. Jose C Florez
  154. Eric Boerwinkle
  155. James S Pankow
  156. Cornelia van Duijn
  157. Eric Sijbrands
  158. James B Meigs
  159. Frank B Hu
  160. Unnur Thorsteinsdottir
  161. Kari Stefansson
  162. Timo A Lakka
  163. Rainer Rauramaa
  164. Michael Stumvoll
  165. Nancy L Pedersen
  166. Lars Lind
  167. Sirkka M Keinanen-Kiukaanniemi
  168. Eeva Korpi-Hyövälti
  169. Timo E Saaristo
  170. Juha Saltevo
  171. Johanna Kuusisto
  172. Markku Laakso
  173. Andres Metspalu
  174. Raimund Erbel
  175. Karl-Heinz Jöcke
  176. Susanne Moebus
  177. Samuli Ripatti
  178. Veikko Salomaa
  179. Erik Ingelsson
  180. Bernhard O Boehm
  181. Richard N Bergman
  182. Francis S Collins
  183. Karen L Mohlke
  184. Heikki Koistinen
  185. Jaakko Tuomilehto
  186. Kristian Hveem
  187. Inger Njølstad
  188. Panagiotis Deloukas
  189. Peter J Donnelly
  190. Timothy M Frayling
  191. Andrew T Hattersley
  192. Ulf de Faire
  193. Anders Hamsten
  194. Thomas Illig
  195. Annette Peters
  196. Stephane Cauchi
  197. Rob Sladek
  198. Philippe Froguel
  199. Torben Hansen
  200. Oluf Pedersen
  201. Andrew D Morris
  202. Collin N A Palmer
  203. Sekar Kathiresan
  204. Olle Melander
  205. Peter M Nilsson
  206. Leif C Groop
  207. Inês Barroso
  208. Claudia Langenberg
  209. Nicholas J Wareham
  210. Christopher A O'Callaghan
  211. Anna L Gloyn
  212. David Altshuler
  213. Michael Boehnke
  214. Tanya M Teslovich
  215. Mark I McCarthy
  216. Andrew P Morris

Consortia

the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium

Contributions

Writing group. K.J.G., T.F., Y. Lee, A.R., R.M., M.E.R., A.L.G., D.A., M. Boehnke, T.M.T., M.I.M., A.P.M. Central meta-analysis group. K.J.G., T.F., Y. Lee, R.M., A. Mahajan, A. Locke, N.W.R., N.R., T.M.T., M.I.M., A.P.M. Annotation and functional analysis group. K.J.G., A.R., M.E.R., S.K.T., J.K.R., N.L.B., M.v.d.B., A.C., I.D., E. Birney, L. Pasquali, J. Ferrer, C.A.O'C., A.L.G., M.I.M. Validation meta-analysis group. R.M., R.A.S., I.P., L.J.S., A.P.M. Metabochip cohort-level primary analysis. Y. Lee, T.G., T.S., D.T., L.Y., H.G., S. Wahl, M.F., R.J.S., H. Kestler, H. Chheda, L.E., S.G., T.M.T., A.P.M. Validation cohort-level primary analysis. V. Steinthorsdottir, G.T., L.Q., L.C.K., E.M.v.L., S.M.W., M. Li, H. Chen, C. Fuchsberger, P. Kwan, C.M., M. Linderman, Y. Lu. Metabochip design. H.M.K., B.F.V. Cohort sample collection, genotyping, phenotyping or additional analysis. B.F.V., G.R.A., P.A., D.B., B.B., R.B., M. Blüher, H.B., L.L.B., E.P.B., N.P.B., J.C., G.C., P.S.C., M.C.C., D.J.C., A.T.C., R.M.v.D., A.S.F.D., M.D., S.E., J.G.E., T.E., E.E., J. Fadista, J. Flannick, P. Fontanillas, C. Fox, P.W.F., K.G., C.G., B.G., O.G., G.B.G., N.G., C.J.G., M.H., C.T.H., C.H., O.L.H., A.B.H., S.E.H., D.J.H., A.U.J., A.J., M.E.J., T.J., W.-H.L.K., N.D.K., L.K., N.K., A.K., P. Kovacs, P. Kraft, J. Kravic, C. Langford, K.L., L. Liang, P.L., C.M.L., E.L., A. Linneberg, C.-T.L., S.L., J.L., V.L., S. Männistö, O. McLeod, J.M., E.M., G.M., T.W.M., M.M.-N., C.N., M.M.N., N.N.O., K.R.O., D.P., S.P., L. Peltonen, J.R.B.P., C.G.P.P., M.R., D. Ruderfer, D. Rybin, Y.T.v.d.S., B.S., G. Sigur∂sson, A.S., G. Steinbach, P.S., K. Strauch, H.M.S., Q.S., B.T., E. Tikkanen, A.T., J. Trakalo, E. Tremoli, T.T., R.W., S. Wiltshire, A.R.W., E.Z. Validation cohort principal investigators. R.J.F.L., J.D., J.C.F., E. Boerwinkle, J.S.P., C.v.D., E.S., J.B.M., F.B.H., U.T., K. Stefansson, P.D., P.J.D., T.M.F., A.T.H., I.B., C. Langenberg, N.J.W., M. Boehnke, M.I.M. Metabochip cohort principal investigators. T.A.L., R.R., M.S., N.L.P., L. Lind, S.M.K.-K., E.K.-H., T.E.S., J.S., J. Kuusisto, M. Laakso, A. Metspalu, R.E., K.-H.J., S. Moebus, S.R., V. Salomaa, E.I., B.O.B., R.N.B., F.S.C., K.L.M., H. Koistinen, J. Tuomilehto, K.H., I.N., P.D., P.J.D., T.M.F., A.T.H., U.d.F., A.H., T.I., A.P., S.C., R.S., P. Froguel, O.P., T.H., A.D.M., C.N.A.P., S.K., O. Melander, P.M.N., L.C.G., I.B., C. Langenberg, N.J.W., D.A., M. Boehnke, M.I.M. Project management. K.J.G., A.L.G., D.A., M. Boehnke, T.M.T., M.I.M., A.P.M. DIAGRAM Consortium management. D.A., M. Boehnke, M.I.M.

Corresponding authors

Correspondence toKyle J Gaulton, Mark I McCarthy or Andrew P Morris.

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Competing interests

V. Steinthorsdottir, G.T., A.K., U.T. and K. Stefansson are employed by deCODE Genetics/Amgen, Inc. I.B. and spouse own stock in GlaxoSmithKline and Incyte.

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Supplementary Figure 1 Signal plots for association signals achieving locus-wide significance at the KCNQ1 locus.

Association summary statistics are based on the meta-analysis of Metabochip studies in 27,206 cases and 57,574 controls of European ancestry. Results are presented from exact conditioning after adjusting for all other index variants at the locus. Each point represents a SNP passing quality control in the meta-analysis, plotted with its conditional P value (on a –log10 scale) as a function of genomic position (NCBI Build 37). In each plot, the index variant is represented by the purple symbol. The color coding of all other SNPs indicates LD with the index variant in European-ancestry haplotypes from the 1000 Genomes Project reference panel: red, _r_2 ≥ 0.8; gold, 0.6 ≤ _r_2 < 0.8; green, 0.4 ≤ _r_2 < 0.6; cyan, 0.2 ≤ _r_2 < 0.4; blue, _r_2 < 0.2; gray, _r_2 unknown. The shape of the symbol corresponds to the annotation of the variant: upward triangle, framestop or splice; downward triangle, nonsynonymous; square, synonymous or UTR; and circle, intronic or noncoding. Recombination rates are estimated from HapMap Phase 2, and gene annotations are taken from the UCSC Genome Browser.

Supplementary Figure 2 Signal plots for association signals achieving locus-wide significance at the HNF1A locus.

Association summary statistics are based on the meta-analysis of Metabochip studies in 27,206 cases and 57,574 controls of European ancestry. Results are presented from exact conditioning after adjusting for all other index variants at the locus. Each point represents a SNP passing quality control in the meta-analysis, plotted with its conditional P value (on a –log10 scale) as a function of genomic position (NCBI Build 37). In each plot, the index variant is represented by the purple symbol. The color coding of all other SNPs indicates LD with the index variant in European-ancestry haplotypes from the 1000 Genomes Project reference panel: red, _r_2 ≥ 0.8; gold, 0.6 ≤ _r_2 < 0.8; green, 0.4 ≤ _r_2 < 0.6; cyan, 0.2 ≤ _r_2 < 0.4; blue, _r_2 < 0.2; gray, _r_2 unknown. The shape of the symbol corresponds to the annotation of the variant: upward triangle, framestop or splice; downward triangle, nonsynonymous; square, synonymous or UTR; and circle, intronic or noncoding. Recombination rates are estimated from HapMap Phase 2, and gene annotations are taken from the UCSC Genome Browser.

Supplementary Figure 3 Signal plots for association signals achieving locus-wide significance at the DGKB, _CDKN2A_-CDKN2B, MC4R and GIPR loci.

Association summary statistics are based on the meta-analysis of Metabochip studies in 27,206 cases and 57,574 controls of European ancestry. Results are presented from exact conditioning after adjusting for all other index variants at the locus. Each point represents a SNP passing quality control in the meta-analysis, plotted with its conditional P value (on a –log10 scale) as a function of genomic position (NCBI Build 37). In each plot, the index variant is represented by the purple symbol. The color coding of all other SNPs indicates LD with the index variant in European-ancestry haplotypes from the 1000 Genomes Project reference panel: red, _r_2 ≥ 0.8; gold, 0.6 ≤ _r_2 < 0.8; green, 0.4 ≤ _r_2 < 0.6; cyan, 0.2 ≤ _r_2 < 0.4; blue, _r_2 < 0.2; gray, _r_2 unknown. The shape of the symbol corresponds to the annotation of the variant: upward triangle, framestop or splice; downward triangle, nonsynonymous; square, synonymous or UTR; and circle, intronic or noncoding. Recombination rates are estimated from HapMap Phase 2, and gene annotations are taken from the UCSC Genome Browser.

Supplementary Figure 4 Signal plots for association signals at the _CDKN2A_-CDKN2B locus.

Association summary statistics are based on the meta-analysis of Metabochip studies in 27,206 cases and 57,574 controls of European ancestry. (a) Unconditional. (b) After approximate conditioning on the two index SNPs, rs10811660 and rs10757283. Each point represents a SNP passing quality control in the meta-analysis, plotted with its conditional P value (on a –log10 scale) as a function of genomic position (NCBI Build 37). In each plot, the index variant is represented by the purple symbol. The color coding of all other SNPs indicates LD with the index variant in European-ancestry haplotypes from the 1000 Genomes Project reference panel: red, _r_2 ≥ 0.8; gold, 0.6 ≤ _r_2 < 0.8; green, 0.4 ≤ _r_2 < 0.6; cyan, 0.2 ≤ _r_2 < 0.4; blue, _r_2 < 0.2; gray, _r_2 unknown. Recombination rates are estimated from HapMap Phase 2, and gene annotations are taken from the UCSC Genome Browser.

Supplementary Figure 5 Characteristics of index variants for association signals achieving locus-wide significance for T2D susceptibility across fine-mapping regions.

Each association signal was represented by an index variant in the GCTA-COJO joint regression model on the basis of (i) summary statistics from the meta-analysis of Metabochip studies in 27,206 cases and 57,574 controls of European ancestry and (ii) reference genotype data from GoDARTS (3,298 cases and 3,708 controls of European ancestry from the UK) to approximate LD across fine-mapping regions. Each variant is plotted according to risk allele frequency on the x axis and allelic log(OR) on the y axis (with error bars representing the corresponding standard error).

Supplementary Figure 6 Characteristics of the 99% credible sets of variants for each association signal achieving locus-wide significance for T2D susceptibility across fine-mapping regions.

Each point corresponds to an association signal, plotted according to the highest posterior probability of causality of any variant in the credible set on the x axis and the total number of variants in the credible set on the y axis. Distinct association signals at loci are referenced according to their index variant.

Supplementary Figure 7 Enrichment in chromatin state and noncoding RNA elements.

Variants in regulatory enhancer, promoter and insulator elements from 12 cell types and noncoding RNA elements from 25 cell types were tested for enrichment of average posterior causal probability compared to variants in shifted elements. Variants in islet enhancer elements demonstrated significant enrichment (fold = 1.97; P = 0.00022), and variants in islet and HepG2 promoter elements demonstrated nominally significant enrichment (P < 0.01).

Supplementary Figure 8 Null distribution of enriched transcription factors.

Null distribution of mean posterior probabilities of driving association signals for variants in shifted annotations (blue bars) compared to observed mean probability (dashed line) for the most significantly enriched transcription factors.

Supplementary Figure 9 FOXA2 ChIP-seq sites overlap, genome wide, across cell types.

The number of FOXA2 sites identified in ChIP-seq assays from primary pancreatic islets, HepG2 cells and primary liver was obtained, and sites from each cell type were intersected.

Supplementary Figure 10 Electrophoretic mobility shift assay of rs10830963 in HepG2 cellular extracts.

Nuclear extract (NE) from HepG2 cells was treated with labeled probes of 25 bp of sequence containing each allele of rs10830963. We observed protein bands bound to both sequences (black) as well as bands unique to the non-risk (red) and risk (blue) sequences. We then introduced antibodies against HNF3B (FOXA2 alias) and four factors (YY1, TAL1, NEURDO1 and PTF1A) whose known binding motifs match the de novo motif at rs10830963. We observed no shifting of the allele-specific bands with any antibody. Several bands shared by both alleles were removed in the presence of antibody to YY1.

Supplementary Figure 11 Genes at FOXA2-enriched signals are downregulated in Foxa1/Foxa2 null mice.

Pancreatic islet gene expression data were obtained from wild-type and Foxa1/Foxa2 knockout mice (Gao et al.47). Genes within 500 kb of each FOXA2-enriched T2D signal and closest to each FOXA2-enriched signal (orange) were significantly downregulated in the Foxa1/Foxa2 knockout compared to all genes (gray).

Supplementary Figure 12 Comparison of summary statistics for association signals achieving locus-wide significance from the GCTA-COJO joint regression model using genotype data from two reference studies to approximate LD between variants across each fine-mapping region.

Association summary statistics are based on the meta-analysis of Metabochip studies in 27,206 cases and 57,574 controls of European ancestry. Association summary statistics were obtained using (i) 3,298 T2D cases and 3,708 controls of UK ancestry from GoDARTS (represented on the x axis) and (ii) 4,435 T2D cases and 5,757 controls of Scandinavian ancestry from FUSION (represented on the y axis). Left, comparison of P values (_P_J) from the GCTA-COJO joint regression model. Right, comparison of allelic log(OR) (blue diamond) and standard error (gray error bars) from the GCTA-COJO joint regression model.

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Gaulton, K., Ferreira, T., Lee, Y. et al. Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci.Nat Genet 47, 1415–1425 (2015). https://doi.org/10.1038/ng.3437

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