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

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/.

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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.

Authors and 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

Authors

1. Anubha Mahajan
2. Daniel Taliun
3. Matthias Thurner
4. Neil R. Robertson
5. Jason M. Torres
6. N. William Rayner
7. Anthony J. Payne
8. Valgerdur Steinthorsdottir
9. Robert A. Scott
10. Niels Grarup
11. James P. Cook
12. Ellen M. Schmidt
13. Matthias Wuttke
14. Chloé Sarnowski
15. Reedik Mägi
16. Jana Nano
17. Christian Gieger
18. Stella Trompet
19. Cécile Lecoeur
20. Michael H. Preuss
21. Bram Peter Prins
22. Xiuqing Guo
23. Lawrence F. Bielak
24. Jennifer E. Below
25. Donald W. Bowden
26. John Campbell Chambers
27. Young Jin Kim
28. Maggie C. Y. Ng
29. Lauren E. Petty
30. Xueling Sim
31. Weihua Zhang
32. Amanda J. Bennett
33. Jette Bork-Jensen
34. Chad M. Brummett
35. Mickaël Canouil
36. Kai-Uwe Ec kardt
37. Krista Fischer
38. Sharon L. R. Kardia
39. Florian Kronenberg
40. Kristi Läll
41. Ching-Ti Liu
42. Adam E. Locke
43. Jian’an Luan
44. Ioanna Ntalla
45. Vibe Nylander
46. Sebastian Schönherr
47. Claudia Schurmann
48. Loïc Yengo
49. Erwin P. Bottinger
50. Ivan Brandslund
51. Cramer Christensen
52. George Dedoussis
53. Jose C. Florez
54. Ian Ford
55. Oscar H. Franco
56. Timothy M. Frayling
57. Vilmantas Giedraitis
58. Sophie Hackinger
59. Andrew T. Hattersley
60. Christian Herder
61. M. Arfan Ikram
62. Martin Ingelsson
63. Marit E. Jørgensen
64. Torben Jørgensen
65. Jennifer Kriebel
66. Johanna Kuusisto
67. Symen Ligthart
68. Cecilia M. Lindgren
69. Allan Linneberg
70. Valeriya Lyssenko
71. Vasiliki Mamakou
72. Thomas Meitinger
73. Karen L. Mohlke
74. Andrew D. Morris
75. Girish Nadkarni
76. James S. Pankow
77. Annette Peters
78. Naveed Sattar
79. Alena Stančáková
80. Konstantin Strauch
81. Kent D. Taylor
82. Barbara Thorand
83. Gudmar Thorleifsson
84. Unnur Thorsteinsdottir
85. Jaakko Tuomilehto
86. Daniel R. Witte
87. Josée Dupuis
88. Patricia A. Peyser
89. Eleftheria Zeggini
90. Ruth J. F. Loos
91. Philippe Froguel
92. Erik Ingelsson
93. Lars Lind
94. Leif Groop
95. Markku Laakso
96. Francis S. Collins
97. J. Wouter Jukema
98. Colin N. A. Palmer
99. Harald Grallert
100. Andres Metspalu
101. Abbas Dehghan
102. Anna Köttgen
103. Goncalo R. Abecasis
104. James B. Meigs
105. Jerome I. Rotter
106. Jonathan Marchini
107. Oluf Pedersen
108. Torben Hansen
109. Claudia Langenberg
110. Nicholas J. Wareham
111. Kari Stefansson
112. Anna L. Gloyn
113. Andrew P. Morris
114. Michael Boehnke
115. Mark I. McCarthy

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.

Corresponding authors

Correspondence toAnubha Mahajan or Mark I. McCarthy.

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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.

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Integrated supplementary information

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.

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.

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.

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).

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.

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).

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).

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).

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.

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.

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.

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.

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Mahajan, A., Taliun, D., Thurner, M. et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps.Nat Genet 50, 1505–1513 (2018). https://doi.org/10.1038/s41588-018-0241-6

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• Received: 26 December 2017
• Accepted: 10 August 2018
• Published: 08 October 2018
• Version of record: 08 October 2018
• Issue date: November 2018
• DOI: https://doi.org/10.1038/s41588-018-0241-6