Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes (original) (raw)

References

  1. Bell, G.I., Horita, S. & Karam, J.H. A polymorphic locus near the human insulin gene is associated with insulin-dependent diabetes mellitus. Diabetes 33, 176–183 (1984).
    Article CAS Google Scholar
  2. Bottini, N. et al. A functional variant of lymphoid tyrosine phosphatase is associated with type I diabetes. Nat. Genet. 36, 337–338 (2004).
    Article CAS Google Scholar
  3. Nistico, L. et al. The CTLA-4 gene region of chromosome 2q33 is linked to, and associated with, type 1 diabetes. Hum. Mol. Genet. 5, 1075–1080 (1996).
    Article CAS Google Scholar
  4. Lowe, C.E. et al. Large-scale genetic fine mapping and genotype-phenotype associations implicate polymorphism in the IL2RA region in type 1 diabetes. Nat. Genet. 39, 1074–1082 (2007).
    Article CAS Google Scholar
  5. Smyth, D.J. et al. A genome-wide association study of nonsynonymous SNPs identifies a type 1 diabetes locus in the interferon-induced helicase (IFIH1) region. Nat. Genet. 38, 617–619 (2006).
    Article CAS Google Scholar
  6. Concannon, P. et al. A human type 1 diabetes susceptibility locus maps to chromosome 21q22.3. Diabetes 57, 2858–2861 (2008).
    Article CAS Google Scholar
  7. Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678 (2007).
  8. Hakonarson, H. et al. A genome-wide association study identifies KIAA0350 as a type 1 diabetes gene. Nature 448, 591–594 (2007).
    Article CAS Google Scholar
  9. Todd, J.A. et al. Robust associations of four new chromosome regions from genome-wide analyses of type 1 diabetes. Nat. Genet. 39, 857–864 (2007).
    Article CAS Google Scholar
  10. Smyth, D.J. et al. Shared and distinct genetic variants in type 1 diabetes and celiac disease. N. Engl. J. Med. 359, 2767–2777 (2008).
    Article CAS Google Scholar
  11. Fung, E. et al. Analysis of 17 autoimmune disease-associated variants in type 1 diabetes identifies 6q23/TNFAIP3 as a susceptibility locus. Genes Immun. 10, 188–191 (2009).
    Article CAS Google Scholar
  12. Cooper, J.D. et al. Meta-analysis of genome-wide association study data identifies additional type 1 diabetes risk loci. Nat. Genet. 40, 1399–1401 (2008).
    Article CAS Google Scholar
  13. Cooper, J.D. et al. Analysis of 55 autoimmune disease and type 2 diabetes loci: further confirmation of chromosomes 4q27, 12q13.2 and 12q24.13 as a type 1 diabetes loci, and support for a new locus in 12q13.3-q14.1. Genes Immun (in the press).
  14. Mueller, P.W. et al. Genetics of Kidneys in Diabetes (GoKinD) study: a genetics collection available for identifying genetic susceptibility factors for diabetic nephropathy in type 1 diabetes. J. Am. Soc. Nephrol. 17, 1782–1790 (2006).
    Article CAS Google Scholar
  15. Baum, A.E. et al. A genome-wide association study implicates diacylglycerol kinase eta (DGKH) and several other genes in the etiology of bipolar disorder. Mol. Psychiatry 13, 197–207 (2008).
    Article CAS Google Scholar
  16. Nejentsev, S. et al. Localization of type 1 diabetes susceptibility to the MHC class I genes HLA-B and HLA-A. Nature 450, 887–892 (2007).
    Article CAS Google Scholar
  17. Smyth, D.J. et al. PTPN22 Trp620 explains the association of chromosome 1p13 with type 1 diabetes and shows a statistical interaction with HLA class II genotypes. Diabetes 57, 1730–1737 (2008).
    Article CAS Google Scholar
  18. Bjornvold, M. et al. Joint effects of HLA, INS, PTPN22 and CTLA4 genes on the risk of type 1 diabetes. Diabetologia 51, 589–596 (2008).
    Article CAS Google Scholar
  19. Senee, V. et al. Mutations in GLIS3 are responsible for a rare syndrome with neonatal diabetes mellitus and congenital hypothyroidism. Nat. Genet. 38, 682–687 (2006).
    Article CAS Google Scholar
  20. Shiow, L.R. et al. CD69 acts downstream of interferon-alpha/beta to inhibit S1P1 and lymphocyte egress from lymphoid organs. Nature 440, 540–544 (2006).
    Article CAS Google Scholar
  21. Power, C. & Elliott, J. Cohort profile: 1958 British birth cohort (National Child Development Study). Int. J. Epidemiol. 35, 34–41 (2006).
    Article Google Scholar
  22. Glumer, C., Jorgensen, T. & Borch-Johnsen, K. Prevalences of diabetes and impaired glucose regulation in a Danish population: the Inter99 study. Diabetes Care 26, 2335–2340 (2003).
    Article Google Scholar
  23. Teo, Y.Y. et al. A genotype calling algorithm for the Illumina BeadArray platform. Bioinformatics 23, 2741–2746 (2007).
    Article CAS Google Scholar
  24. Mantel, N. Chi-square tests with one degree of freedom: extension of the Mantel-Haenszel procedure. J. Am. Stat. Assoc. 58, 690–700 (1963).
    Google Scholar
  25. Clayton, D. Testing for association on the X chromosome. Biostatistics 9, 593–600 (2008).
    Article Google Scholar
  26. Chapman, J.M., Cooper, J.D., Todd, J.A. & Clayton, D.G. Detecting disease associations due to linkage disequilibrium using haplotype tags: a class of tests and the determinants of statistical power. Hum. Hered. 56, 18–31 (2003).
    Article Google Scholar
  27. Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Statist. Soc. B. 58, 267–288 (1996).
    Google Scholar
  28. Gentleman, R.C. et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5, R80 (2004).
    Article Google Scholar
  29. Hakonarson, H. et al. A novel susceptibility locus for type 1 diabetes on Chr12q13 identified by a genome-wide association study. Diabetes 57, 1143–1146 (2008).
    Article CAS Google Scholar

Download references

Acknowledgements

This research utilizes resources provided by the Type 1 Diabetes Genetics Consortium, a collaborative clinical study sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute of Allergy and Infectious Diseases (NIAID), National Human Genome Research Institute (NHGRI), National Institute of Child Health and Human Development (NICHD) and Juvenile Diabetes Research Foundation International (JDRF) and supported by U01 DK062418. Further support was provided by a grant from the NIDDK (DK46635) to P.C. and a joint JDRF and Wellcome Trust grant to the Diabetes and Inflammation Laboratory at Cambridge, which also received support from the National Institute for Health Research Cambridge Biomedical Research Centre. D.C. is the recipient of a Wellcome Trust Principal Research Fellowship.

We acknowledge the contributions of the following individuals: J. Alipaz, A. Simpson, J. Brown and J. Garsetti for assistance with project management; M. Hardy and K. Downes for genotyping; M. Maisuria for DNA sample coordination and quality control; J. Hilner and J. Pierce for managing T1DGC Data and DNA resources; J. Allen, N. Ovington, V. Everett, G. Dolman and M. Brown for data services and computing; and L. Smink, O. Burren, J. Mychaleckyj and N. Goodman for bioinformatics support.

We gratefully acknowledge the following groups and individuals who provided biological samples or data for this study. We obtained DNA samples from the British 1958 Birth Cohort collection, funded by the Medical Research Council and the Wellcome Trust. We thank The Avon Longitudinal Study of Parents and Children laboratory in Bristol and the British 1958 Birth Cohort team, including S. Ring, R. Jones, M. Pembrey, W. McArdle, D. Strachan and P. Burton for preparing and providing the control DNA samples. We thank the Human Biological Data Interchange and Diabetes UK for providing DNA samples from USA and UK multiplex families, respectively. Danish subjects were from the Danish Society of Childhood Diabetes (DSBD) and control DNA samples were provided by T. Hansen, O. Pedersen, K. Borch-Johnsen and T. Joergensen. This study makes use of data generated by the Wellcome Trust Case Control Consortium, funded by Wellcome Trust award 076113, and a full list of the investigators who contributed to the generation of the data are available from http://www.wtccc.org.uk.

We gratefully acknowledge the Genetics of Kidneys in Diabetes (GoKinD) study for generously allowing the use of their sample SNP allele intensity and genotype data, which was obtained from the Genetic Association Information Network (GAIN) database (dbGAP, phs000018.v1.p1)12.

We gratefully acknowledge the National Institute of Mental Health for generously allowing the use of their control CEL and genotype data. Control subjects from the National Institute of Mental Health Schizophrenia Genetics Initiative (NIMH-GI), data and biomaterials are being collected by the “Molecular Genetics of Schizophrenia II” (MGS-2) collaboration. The investigators and coinvestigators are as follows: ENH/Northwestern University, MH059571, P.V. Gejman (collaboration coordinator: PI) and A.R. Sanders; Emory University School of Medicine, MH59587, F. Amin (PI); Louisiana State University Health Sciences Center, MH067257, N. Buccola (PI); University of California-Irvine, MH60870, W. Byerley (PI); Washington University, St. Louis, U01, MH060879, C.R. Cloninger (PI); University of Iowa, MH59566, R. Crowe, (PI) and D. Black; University of Colorado, Denver, MH059565, R. Freedman (PI); University of Pennsylvania, MH061675, D. Levinson (PI); University of Queensland, MH059588, B. Mowry (PI); Mt. Sinai School of Medicine, MH59586, J. Silverman (PI). The samples were collected by V.L. Nimgaonkar's group at the University of Pittsburgh, as part of a multi-institutional collaborative research project with J. Smoller and P. Sklar (Massachusetts General Hospital) (grant MH 63420).

Author information

Authors and Affiliations

  1. Department of Medical Genetics, Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
    Jeffrey C Barrett, David G Clayton, Jason D Cooper, Vincent Plagnol, Helen Schuilenburg, Deborah J Smyth, Helen Stevens, John A Todd & Neil M Walker
  2. Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia, USA
    Patrick Concannon
  3. Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
    Patrick Concannon & Stephen S Rich
  4. Division of Diabetes, Endocrinology, and Metabolic Diseases, The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institutes of Health, Bethesda, Maryland, USA
    Beena Akolkar
  5. Roche Molecular Systems, Pleasanton, California, USA
    Henry A Erlich
  6. INSERM U958, Centre National de Génotypage, Evry, France
    Cécile Julier
  7. Centre for Diabetes Research, The Western Australian Institute for Medical Research, and Centre for Medical Research, University of Western Australia, Perth, WA, Australia
    Grant Morahan
  8. Steno Diabetes Center and Hagedorn Research Institute, Gentofte, Denmark
    Jørn Nerup & Flemming Pociot
  9. Juvenile Diabetes Research Foundation, New York, New York, USA
    Concepcion Nierras
  10. Department of Public Health Sciences, Division of Biostatistics and Epidemiology, University of Virginia, Charlottesville, Virginia, USA
    Stephen S Rich

Authors

  1. Jeffrey C Barrett
    You can also search for this author inPubMed Google Scholar
  2. David G Clayton
    You can also search for this author inPubMed Google Scholar
  3. Patrick Concannon
    You can also search for this author inPubMed Google Scholar
  4. Beena Akolkar
    You can also search for this author inPubMed Google Scholar
  5. Jason D Cooper
    You can also search for this author inPubMed Google Scholar
  6. Henry A Erlich
    You can also search for this author inPubMed Google Scholar
  7. Cécile Julier
    You can also search for this author inPubMed Google Scholar
  8. Grant Morahan
    You can also search for this author inPubMed Google Scholar
  9. Jørn Nerup
    You can also search for this author inPubMed Google Scholar
  10. Concepcion Nierras
    You can also search for this author inPubMed Google Scholar
  11. Vincent Plagnol
    You can also search for this author inPubMed Google Scholar
  12. Flemming Pociot
    You can also search for this author inPubMed Google Scholar
  13. Helen Schuilenburg
    You can also search for this author inPubMed Google Scholar
  14. Deborah J Smyth
    You can also search for this author inPubMed Google Scholar
  15. Helen Stevens
    You can also search for this author inPubMed Google Scholar
  16. John A Todd
    You can also search for this author inPubMed Google Scholar
  17. Neil M Walker
    You can also search for this author inPubMed Google Scholar
  18. Stephen S Rich
    You can also search for this author inPubMed Google Scholar

Consortia

The Type 1 Diabetes Genetics Consortium

Contributions

J.C.B. and D.G.C. helped design the study, and carried out the statistical analyses. J.C.B., D.G.C. and P.C. drafted the manuscript. P.C. managed the editing of the manuscript. F.P., C.J., J.A.T., B.A., H.A.E., G.M., J.N., C.N., P.C. and S.S.R. (Chair) are members of the T1DGC Steering Committee, and contributed to the general planning and design of the study, and to the writing of the manuscript. F.P. and J.N. also coordinated the inclusion of the Danish case-control samples. N.M.W. and H. Schuilenburg provided data services. J.D.C. and V.P. assisted with data analyses. D.J.S. carried out genotyping, and H. Stevens provided DNA sample coordination and quality control. The T1DGC provided biospecimens and data from families with T1D.

Corresponding author

Correspondence toPatrick Concannon.

Additional information

A full list of members appears in the Supplementary Note.

Supplementary information

Rights and permissions

About this article

Cite this article

Barrett, J., Clayton, D., Concannon, P. et al. Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes.Nat Genet 41, 703–707 (2009). https://doi.org/10.1038/ng.381

Download citation