Article | Published:

Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps

Nature Genetics (2018) | Download Citation

Abstract

We expanded GWAS discovery for type 2 diabetes (T2D) by combining data from 898,130 European-descent individuals (9% cases), after imputation to high-density reference panels. With these data, we (i) extend the inventory of T2D-risk variants (243 loci, 135 newly implicated in T2D predisposition, comprising 403 distinct association signals); (ii) enrich discovery of lower-frequency risk alleles (80 index variants with minor allele frequency <5%, 14 with estimated allelic odds ratio >2); (iii) substantially improve fine-mapping of causal variants (at 51 signals, one variant accounted for >80% posterior probability of association (PPA)); (iv) extend fine-mapping through integration of tissue-specific epigenomic information (islet regulatory annotations extend the number of variants with PPA >80% to 73); (v) highlight validated therapeutic targets (18 genes with associations attributable to coding variants); and (vi) demonstrate enhanced potential for clinical translation (genome-wide chip heritability explains 18% of T2D risk; individuals in the extremes of a T2D polygenic risk score differ more than ninefold in prevalence).

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Data availability

Summary-level data are available at the DIAGRAM consortium website http://diagram-consortium.org/ and Accelerating Medicines Partnership T2D portal http://www.type2diabetesgenetics.org/.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Acknowledgements

This work was supported primarily by the NIDDK as part of the Accelerating Medicines Partnership-T2D, funded by U01DK105535 (M.I.M.), U01DK062370 (M.B.), and U01DK078616 (J.M.) grants. Part of this work was conducted using the UK Biobank resource under application number 9161. A full list of acknowledgements appears in the Supplementary Note.

Author information

Author notes

  1. These authors contributed equally: Andrew P. Morris, Michael Boehnke, Mark I. McCarthy.

Affiliations

  1. Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK

    • Anubha Mahajan
    • , Matthias Thurner
    • , Neil R. Robertson
    • , Jason M. Torres
    • , N. William Rayner
    • , Anthony J. Payne
    • , Cecilia M. Lindgren
    • , Jonathan Marchini
    • , Anna L. Gloyn
    • , Andrew P. Morris
    •  & Mark I. McCarthy
  2. Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK

    • Anubha Mahajan
    • , Matthias Thurner
    • , Neil R. Robertson
    • , N. William Rayner
    • , Amanda J. Bennett
    • , Vibe Nylander
    • , Anna L. Gloyn
    •  & Mark I. McCarthy
  3. Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA

    • Daniel Taliun
    • , Ellen M. Schmidt
    • , Goncalo R. Abecasis
    •  & Michael Boehnke
  4. Department of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, UK

    • N. William Rayner
    • , Bram Peter Prins
    • , Sophie Hackinger
    •  & Eleftheria Zeggini
  5. deCODE Genetics, Amgen Inc., Reykjavik, Iceland

    • Valgerdur Steinthorsdottir
    • , Gudmar Thorleifsson
    • , Unnur Thorsteinsdottir
    •  & Kari Stefansson
  6. MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK

    • Robert A. Scott
    • , Jian’an Luan
    • , Claudia Langenberg
    •  & Nicholas J. Wareham
  7. Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

    • Niels Grarup
    • , Jette Bork-Jensen
    • , Oluf Pedersen
    •  & Torben Hansen
  8. Department of Biostatistics, University of Liverpool, Liverpool, UK

    • James P. Cook
    •  & Andrew P. Morris
  9. Institute of Genetic Epidemiology, Department of Biometry, Epidemiology, and Medical Bioinformatics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany

    • Matthias Wuttke
    •  & Anna Köttgen
  10. Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA

    • Chloé Sarnowski
    • , Ching-Ti Liu
    •  & Josée Dupuis
  11. Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia

    • Reedik Mägi
    • , Krista Fischer
    • , Kristi Läll
    • , Andres Metspalu
    •  & Andrew P. Morris
  12. Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands

    • Jana Nano
    • , Oscar H. Franco
    • , M. Arfan Ikram
    • , Symen Ligthart
    •  & Abbas Dehghan
  13. Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München Research Center for Environmental Health, Neuherberg, Germany

    • Christian Gieger
    • , Jennifer Kriebel
    •  & Harald Grallert
  14. German Center for Diabetes Research (DZD), Neuherberg, Germany

    • Christian Gieger
    • , Christian Herder
    • , Jennifer Kriebel
    • , Annette Peters
    • , Barbara Thorand
    •  & Harald Grallert
  15. Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands

    • Stella Trompet
  16. Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands

    • Stella Trompet
    •  & J. Wouter Jukema
  17. CNRS-UMR8199, Lille University, Lille Pasteur Institute, Lille, France

    • Cécile Lecoeur
    • , Mickaël Canouil
    • , Loïc Yengo
    •  & Philippe Froguel
  18. Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    • Michael H. Preuss
    • , Claudia Schurmann
    • , Erwin P. Bottinger
    •  & Ruth J. F. Loos
  19. Department of Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor–UCLA Medical Center, Torrance, CA, USA

    • Xiuqing Guo
    • , Kent D. Taylor
    •  & Jerome I. Rotter
  20. Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA

    • Lawrence F. Bielak
    • , Sharon L. R. Kardia
    •  & Patricia A. Peyser
  21. Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA

    • Jennifer E. Below
    •  & Lauren E. Petty
  22. Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA

    • Donald W. Bowden
    •  & Maggie C. Y. Ng
  23. Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, NC, USA

    • Donald W. Bowden
    •  & Maggie C. Y. Ng
  24. Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA

    • Donald W. Bowden
    •  & Maggie C. Y. Ng
  25. Department of Epidemiology and Biostatistics, Imperial College London, London, UK

    • John Campbell Chambers
    • , Weihua Zhang
    •  & Abbas Dehghan
  26. Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, Middlesex, UK

    • John Campbell Chambers
    •  & Weihua Zhang
  27. Imperial College Healthcare NHS Trust, Imperial College London, London, UK

    • John Campbell Chambers
  28. Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore

    • John Campbell Chambers
  29. MRC–PHE Centre for Environment and Health, Imperial College London, London, UK

    • John Campbell Chambers
    •  & Abbas Dehghan
  30. Division of Genome Research, Center for Genome Science, Korea National Institute of Health, Chungcheongbuk-do, Republic of Korea

    • Young Jin Kim
  31. Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore

    • Xueling Sim
  32. Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, USA

    • Chad M. Brummett
  33. Department of Nephrology and Medical Intensive Care and German Chronic Kidney Disease Study, Charité, Universitätsmedizin Berlin, Berlin, Germany

    • Kai-Uwe Ec kardt
  34. Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Medical University of Innsbruck, Innsbruck, Austria

    • Florian Kronenberg
    •  & Sebastian Schönherr
  35. Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia

    • Kristi Läll
  36. McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA

    • Adam E. Locke
  37. Division of Genomics & Bioinformatics, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA

    • Adam E. Locke
  38. William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK

    • Ioanna Ntalla
  39. Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark

    • Ivan Brandslund
  40. Department of Clinical Biochemistry, Vejle Hospital, Vejle, Denmark

    • Ivan Brandslund
  41. Medical Department, Lillebælt Hospital Vejle, Vejle, Denmark

    • Cramer Christensen
  42. Department of Nutrition and Dietetics, Harokopio University of Athens, Athens, Greece

    • George Dedoussis
  43. Department of Medicine, Harvard Medical School, Boston, MA, USA

    • Jose C. Florez
    •  & James B. Meigs
  44. Diabetes Research Center (Diabetes Unit), Department of Medicine, Massachusetts General Hospital, Boston, MA, USA

    • Jose C. Florez
  45. Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA

    • Jose C. Florez
  46. Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA

    • Jose C. Florez
    •  & James B. Meigs
  47. Robertson Centre for Biostatistics, University of Glasgow, Glasgow, UK

    • Ian Ford
  48. Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK

    • Timothy M. Frayling
  49. Department of Public Health and Caring Sciences, Geriatrics, Uppsala University, Uppsala, Sweden

    • Vilmantas Giedraitis
    •  & Martin Ingelsson
  50. University of Exeter Medical School, University of Exeter, Exeter, UK

    • Andrew T. Hattersley
  51. Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany

    • Christian Herder
  52. Steno Diabetes Center Copenhagen, Gentofte, Denmark

    • Marit E. Jørgensen
  53. National Institute of Public Health, Southern Denmark University, Copenhagen, Denmark

    • Marit E. Jørgensen
  54. Research Centre for Prevention and Health, Capital Region of Denmark, Glostrup, Denmark

    • Torben Jørgensen
    •  & Allan Linneberg
  55. Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

    • Torben Jørgensen
  56. Faculty of Medicine, Aalborg University, Aalborg, Denmark

    • Torben Jørgensen
  57. Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland

    • Johanna Kuusisto
    • , Alena Stančáková
    •  & Markku Laakso
  58. Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA

    • Cecilia M. Lindgren
  59. Big Data Institute, Li Ka Shing Centre For Health Information and Discovery, University of Oxford, Oxford, UK

    • Cecilia M. Lindgren
  60. Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Frederiksberg, Denmark

    • Allan Linneberg
  61. Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

    • Allan Linneberg
  62. Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Malmö, Sweden

    • Valeriya Lyssenko
    •  & Leif Groop
  63. Department of Clinical Science, KG Jebsen Center for Diabetes Research, University of Bergen, Bergen, Norway

    • Valeriya Lyssenko
  64. Dromokaiteio Psychiatric Hospital, National and Kapodistrian University of Athens, Athens, Greece

    • Vasiliki Mamakou
  65. Institute of Human Genetics, Technische Universität München, Munich, Germany

    • Thomas Meitinger
  66. Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany

    • Thomas Meitinger
  67. DZHK (German Centre for Cardiovascular Research), Munich Heart Alliance partner site, Munich, Germany

    • Thomas Meitinger
    •  & Annette Peters
  68. Department of Genetics, University of North Carolina, Chapel Hill, NC, USA

    • Karen L. Mohlke
  69. Clinical Research Centre, Centre for Molecular Medicine, Ninewells Hospital and Medical School, Dundee, UK

    • Andrew D. Morris
  70. Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK

    • Andrew D. Morris
  71. Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    • Girish Nadkarni
  72. Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA

    • James S. Pankow
  73. Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany

    • Annette Peters
    •  & Barbara Thorand
  74. Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK

    • Naveed Sattar
  75. Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany

    • Konstantin Strauch
  76. Institute of Medical Informatics, Biometry, and Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany

    • Konstantin Strauch
  77. Faculty of Medicine, University of Iceland, Reykjavik, Iceland

    • Unnur Thorsteinsdottir
    •  & Kari Stefansson
  78. Department of Health, National Institute for Health and Welfare, Helsinki, Finland

    • Jaakko Tuomilehto
  79. Dasman Diabetes Institute, Dasman, Kuwait

    • Jaakko Tuomilehto
  80. Department of Neuroscience and Preventive Medicine, Danube-University Krems, Krems, Austria

    • Jaakko Tuomilehto
  81. Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia

    • Jaakko Tuomilehto
  82. Department of Public Health, Aarhus University, Aarhus, Denmark

    • Daniel R. Witte
  83. Danish Diabetes Academy, Odense, Denmark

    • Daniel R. Witte
  84. National Heart, Lung, and Blood Institute Framingham Heart Study, Framingham, MA, USA

    • Josée Dupuis
  85. Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    • Ruth J. F. Loos
  86. Department of Genomics of Common Disease, School of Public Health, Imperial College London, London, UK

    • Philippe Froguel
  87. Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA

    • Erik Ingelsson
  88. Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden

    • Erik Ingelsson
  89. Department of Medical Sciences, Uppsala University, Uppsala, Sweden

    • Lars Lind
  90. Finnish Institute for Molecular Medicine (FIMM), University of Helsinki, Helsinki, Finland

    • Leif Groop
  91. Genome Technology Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA

    • Francis S. Collins
  92. Pat Macpherson Centre for Pharmacogenetics and Pharmacogenomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK

    • Colin N. A. Palmer
  93. Clinical Cooparation Group Type 2 Diabetes, Helmholtz Zentrum München, Ludwig-Maximilians-Universität, Munich, Germany

    • Harald Grallert
  94. Clinical Cooparation Group Nutrigenomics and Type 2 Diabetes, Helmholtz Zentrum München, Technical University, Munich, Germany

    • Harald Grallert
  95. Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA

    • James B. Meigs
  96. Departments of Medicine, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor–UCLA Medical Center, Torrance, CA, USA

    • Jerome I. Rotter
  97. Department of Statistics, University of Oxford, Oxford, UK

    • Jonathan Marchini
  98. Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark

    • Torben Hansen
  99. Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Trust, Oxford, UK

    • Anna L. Gloyn
    •  & Mark I. McCarthy

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Contributions

Project coordination: A. Mahajan, A.P.M., M.B., and M.I.M. Writing: A. Mahajan, D.T., A.P.M., M.B., and M.I.M. Core analyses: A. Mahajan, D.T., M.T., J.M.T., A.J.P., A.P.M., M.B., and M.I.M. DIAMANTE analysis group: A. Mahajan, J.E.B., D.W.B., J.C.C., Y.J.K., M.C.Y.N., L.E.P., X.S., W.Z., A.P.M., M.B., and M.I.M. Statistical analysis in individual studies: A. Mahajan, D.T., N.R.R., N.W.R., V.S., R.A.S., N.G., J.P.C., E.M.S., M.W., C. Sarnowski, J.N., S.T., C. Lecoeur, M.H.P., B.P.P., X.G., L.F.B., J.B.-J., M.C., K.L., C.-T.L., A.E.L., J’a.L., C. Schurmann, L.Y., G.T., and A.P.M. Genotyping and phenotyping: A. Mahajan, R.A.S., R.M., C.G., S.T., K.-U.E., K.F., S.L.R.K., F.K., I.N., C.M.B., C. Schurmann, E.P.B., I.B., C.C., G.D., I.F., V.G., M.I., M.E.J., S.L., A.L., V.L., V.M., A.D.M., G.N., N.S., A.S., D.R.W., S.S., E.P.B., S.H., C.H., J. Kriebel, T.M., A.P., B.T., A.D., A.K., G.R.A., C. Langenberg, N.J.W., A.P.M., M.B., and M.I.M. Islet annotations: M.T., J.M.T., A.J.B., V.N., A.L.G., and M.I.M. Individual study design and principal investigators: E.P.B., J.C.F., O.H.F., T.M.F., A.T.H., M.A.I., T.J., J. Kuusisto, C.M.L., K.L.M., J.S.P., K. Strauch, K.D.T., U.T., J.T., J.D., P.A.P., E.Z., R.J.F.L., P.F., E.I., L.L., L.G., M.L., F.S.C., J.W.J., C.N.A.P., H.G., A. Metspalu, A.D., A.K., G.R.A., J.B.M., J.I.R., J.M., O.P., T.H., C. Langenberg, N.J.W., K. Stefansson, A.P.M., M.B., and M.I.M.

Competing interests

J.C.F. has received consulting honoraria from Merck and from Boehringer-Ingelheim. O.H.F. works at ErasmusAGE, a center for aging research across the course of life, funded by Nestlé Nutrition (Nestec Ltd.), Metagenics Inc., and AXA. E.I. is a scientific advisor for Precision Wellness and Olink Proteomics for work unrelated to the present project. A.D. has received consultancy fees and research support from Metagenics Inc. (outside the scope of the present work). T.M.F. has consulted for Boeringer Ingelheim and Sanofi-Aventis on the genetics of diabetes and has an MRC CASE studentship with GSK. G.R.A. is a consultant for 23andMe, Regeneron, Merck, and Helix. R.A.S. is an employee of and shareholder in GlaxoSmithKline. N.S. is working with Boehringer-Ingelheim on a genetics project but has received no remuneration. M.I.M. has served on advisory panels for NovoNordisk and Pfizer, and has received honoraria from NovoNordisk, Pfizer, Sanofi-Aventis, and Eli Lilly. The companies named above had no role in the design or conduct of this study; collection, management, analysis, and interpretation of the data, or in the preparation, review, or approval of the manuscript. Authors affiliated with deCODE (V.S., G.T., U.T. and K.S.) are employed by deCODE Genetics/Amgen, Inc.

Corresponding authors

Correspondence to Anubha Mahajan or Mark I. McCarthy.

Integrated supplementary information

  1. Supplementary Figure 1 Sex-differentiated analyses.

    (a) Manhattan plot (top panel) of genome-wide association results for T2D (without BMI adjustment) from female-specific meta-analysis of up to 30,053 cases and 434,336 controls. The association p-value (on -log10 scale) for each SNP (y-axis) is plotted against the genomic position (NCBI Build 37; x-axis). Association signals that reached genome-wide significance (p < 5×10−8) in sex-combined analysis are shown in purple or yellow, if novel. (b) Manhattan plot (bottom panel) of genome-wide association results for T2D without BMI adjustment from male-specific meta-analysis of up to 41,846 cases and 383,767 controls. (c) Z-score for each of the 403 distinct signals from male-specific analysis (y-axis) is plotted against the z-score from the female-specific analysis (y-axis). Colour of each point varies with –log10 gender heterogeneity p-value and diameter of the circle is proportional to sex-combined -log10 p-value.

  2. Supplementary Figure 2 Distributions of the allele frequency, imputation score, and posterior probability of association.

    Distribution of the risk allele frequencies for all variants having >1% posterior probability of association in genetic credible set (x-axis) plotted against average imputation quality (y-axis). Diameter varies with the posterior probability of association assigned to each variant.

  3. Supplementary Figure 3 Islet annotation overlap of the variant with the highest probability in genetic credible sets.

    Number of variants with posterior probability of association >1% (x-axis) plotted against the highest posterior probability of association (y-axis) assigned to a variant in the credible set. Points are colour coded according to (a) islet epigenome states and (b) overlap with transcription factor binding sites.

  4. Supplementary Figure 4 Enrichment of cross-tissue epigenetic states in T2D GWAS data.

    fGWAS log2 fold enrichment (based on joint model for each tissue) including 95% confidence intervals (x-axis) of all chromatin states (y-axis) genome-wide. Analyses are based on the Varshney et al.1 data which combined standard epigenomic annotations for the four principal tissues of interest. These analyses performed separately for each tissue show some enrichment for enhancers and/or promoters in all tissues with strongest and most consistent enrichment observed in islets. The universally enriched “transcript” category refers to coding sequence which is by definition represented by the same sequence in each “tissue-specific” analysis. 1Varshney, A. et al. Genetic regulatory signatures underlying islet gene expression and type 2 diabetes. Proc Natl Acad Sci U S A 114, 2301-2306 (2017).

  5. Supplementary Figure 5 Enrichment of islet epigenetic states in T2D GWAS data.

    fGWAS log2 fold enrichment including 95% confidence intervals (x-axis) of all chromatin states (y-axis) genome-wide.

  6. Supplementary Figure 6 Epigenome landscape of the ST6GAL1 locus.

    For variants included in 99% credible set (PPA>1%) of each distinct signal at ST6GAL1 locus, following information is shown: genomic position of each variant (colour coded for each distinct signal; variant with highest PPA in bold); whole genome bisulphite methylation data (black), 4 human islet ATAC-seq tracks (green, middle), islet chromatin states (from Thurner et al.1, Pasquali et al.2, and Varshney et al.3); and adipose, liver and skeletal muscle chromatin states from Varshney et al.3. 1 Thurner, M. et al. Integration of human pancreatic islet genomic data refines regulatory mechanisms at Type 2 Diabetes susceptibility loci. Elife 7(2018). 2 Pasquali, L. et al. Pancreatic islet enhancer clusters enriched in type 2 diabetes risk-associated variants. Nat Genet 46, 136-143 (2014). 3 Varshney, A. et al. Genetic regulatory signatures underlying islet gene expression and type 2 diabetes. Proc Natl Acad Sci U S A 114, 2301-2306 (2017).

  7. Supplementary Figure 7 Epigenome landscape of the ANK1 locus.

    For variants included in 99% credible set (PPA>1%) of each distinct signal at ANK1 locus, following information is shown: genomic position of each variant (colour coded for each distinct signal; variant with highest PPA in bold); whole genome bisulphite methylation data (black), 4 human islet ATAC-seq tracks (green, middle), islet chromatin states (from Thurner et al.1, Pasquali et al.2, and Varshney et al.3); and adipose, liver and skeletal muscle chromatin states from Varshney et al.3. 1 Thurner, M. et al. Integration of human pancreatic islet genomic data refines regulatory mechanisms at Type 2 Diabetes susceptibility loci. Elife 7(2018). 2 Pasquali, L. et al. Pancreatic islet enhancer clusters enriched in type 2 diabetes risk-associated variants. Nat Genet 46, 136-143 (2014).3 Varshney, A. et al. Genetic regulatory signatures underlying islet gene expression and type 2 diabetes. Proc Natl Acad Sci U S A 114, 2301-2306 (2017).

  8. Supplementary Figure 8 Epigenome landscape of the TCF7L2 locus.

    For variants included in 99% credible set (PPA>1%) of each distinct signal at TCF7L2 locus, following information is shown: genomic position of each variant (colour coded for each distinct signal; variant with highest PPA in bold); whole genome bisulphite methylation data (black), 4 human islet ATAC-seq tracks (green, middle), islet chromatin states (from Thurner et al.1, Pasquali et al.2, and Varshney et al.3); and adipose, liver and skeletal muscle chromatin states from Varshney et al.3. 1 Thurner, M. et al. Integration of human pancreatic islet genomic data refines regulatory mechanisms at Type 2 Diabetes susceptibility loci. Elife 7(2018). 2 Pasquali, L. et al. Pancreatic islet enhancer clusters enriched in type 2 diabetes risk-associated variants. Nat Genet 46, 136-143 (2014). 3 Varshney, A. et al. Genetic regulatory signatures underlying islet gene expression and type 2 diabetes. Proc Natl Acad Sci U S A 114, 2301-2306 (2017).

  9. Supplementary Figure 9 Heritability estimates.

    Chip heritability estimates for T2D (on the liability scale) at different empirical estimates of population- and sample-level T2D prevalence.

  10. Supplementary Figure 10 Polygenic risk scores.

    Genome-wide polygenic risk score (PRS) identifies individuals with significantly increased risk of T2D. a) PRS in UK Biobank individuals is normally distributed with a shift towards right, observed for T2D cases. PRS is plotted on the x-axis, with values scaled to a mean of 0 and standard deviation of 1. b) Individuals were binned into 40 groups based on PRS, with each grouping representing 2.5% of population. c) BMI distribution in T2D cases, within each PRS bin.

  11. Supplementary Figure 11 Genetic correlations between T2D and biomedically relevant traits, estimated by LD-score regression implemented in LDHub.

    Genetic correlations (z-score) between T2D (y-axis) and range of metabolic and anthropometric traits (x-axis) as estimated using LD Score regression. The genetic correlation estimates are colour coded according to phenotypic area. Allelic direction of effect is aligned to increased T2D risk. Size of the circle denotes the significance level for the correlation.

  12. Supplementary Figure 12 Effect of BMI adjustment on genetic correlation estimates between various traits and T2D.

    Genetic correlations (z-score) between range of metabolic and anthropometric traits and T2D without BMI adjustment (x-axis) and T2D with BMI adjustment (y-axis) as estimated using LD Score regression. The genetic correlation estimates are colour coded according to phenotypic area. Allelic direction of effect is aligned to increased T2D risk. Size of the circle denotes the significance level for the correlation.

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–12 and Supplementary Note

  2. Reporting Summary

  3. Supplementary Tables 1–10

About this article

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DOI

https://doi.org/10.1038/s41588-018-0241-6

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