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Genome-wide association study identifies new multiple sclerosis susceptibility loci on chromosomes 12 and 20

Nature Genetics volume 41, pages 824–828 (2009)Cite this article

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Abstract

To identify multiple sclerosis (MS) susceptibility loci, we conducted a genome-wide association study (GWAS) in 1,618 cases and used shared data for 3,413 controls. We performed replication in an independent set of 2,256 cases and 2,310 controls, for a total of 3,874 cases and 5,723 controls. We identified risk-associated SNPs on chromosome 12q13–14 (rs703842, P = 5.4 × 10−11; rs10876994, P = 2.7 × 10−10; rs12368653, P = 1.0 × 10−7) and upstream of CD40 on chromosome 20q13 (rs6074022, P = 1.3 × 10−7; rs1569723, P = 2.9 × 10−7). Both loci are also associated with other autoimmune diseases1,2,3,4,5. We also replicated several known MS associations (HLA-DR15, P = 7.0 × 10−184; CD58, P = 9.6 × 10−8; EVI5-RPL5, P = 2.5 × 10−6; IL2RA, P = 7.4 × 10−6; CLEC16A, P = 1.1 × 10−4; IL7R, P = 1.3 × 10−3; TYK2, P = 3.5 × 10−3) and observed a statistical interaction between SNPs in EVI5-RPL5 and HLA-DR15 (P = 0.001).

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Figure 1: Chromosome 12q13–14 region and association with MS.
Figure 2: Chromosome 20q13 region and association with MS.

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Change history

  • 24 November 2009

    In the version of this article initially published, the 8th SNP in Table 3 was incorrectly listed as rs8118449. The correct identification number for this SNP is rs8112449. The error has been corrected in the HTML and PDF versions of the article.

References

  1. Jacobson, E.M. et al. A CD40 Kozak sequence polymorphism and susceptibility to antibody-mediated autoimmune conditions: the role of CD40 tissue-specific expression. Genes Immun. 8, 205–214 (2007).

    Article  CAS  Google Scholar 

  2. Raychaudhuri, S. et al. Common variants at CD40 and other loci confer risk of rheumatoid arthritis. Nat. Genet. 40, 1216–1223 (2008).

    Article  CAS  Google Scholar 

  3. Bailey, R. et al. Association of the vitamin D metabolism gene CYP27B1 with type 1 diabetes. Diabetes 56, 2616–2621 (2007).

    Article  CAS  Google Scholar 

  4. 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 (2008).

    Article  Google Scholar 

  5. Barton, A. et al. Rheumatoid arthritis susceptibility loci at chromosomes 10p15, 12q13 and 22q13. Nat. Genet. 40, 1156–1159 (2008).

    Article  CAS  Google Scholar 

  6. Hafler, D.A. et al. Risk alleles for multiple sclerosis identified by a genomewide study. N. Engl. J. Med. 357, 851–862 (2007).

    Article  CAS  Google Scholar 

  7. Rubio, J.P. et al. Replication of KIAA0350, IL2RA, RPL5 and CD58 as multiple sclerosis susceptibility genes in Australians. Genes Immun. 9, 624–630 (2008).

    Article  CAS  Google Scholar 

  8. International Multiple Sclerosis Genetics Consortium. The expanding genetic overlap between multiple sclerosis and type I diabetes. Genes Immun. 10, 11–14 (2009).

  9. Aulchenko, Y.S. et al. Genetic variation in the KIF1B locus influences susceptibility to multiple sclerosis. Nat. Genet. 40, 1402–1403 (2008).

    Article  CAS  Google Scholar 

  10. Ban, M. et al. Replication analysis identifies TYK2 as a multiple sclerosis susceptibility factor. Eur. J. Hum. Genet. advance online publication, doi: 10.1038/ejhg.2009.41 (18 March 2009).

    Article  CAS  Google Scholar 

  11. Adorini, L. & Penna, G. Control of autoimmune diseases by the vitamin D endocrine system. Nat. Clin. Pract. Rheumatol. 4, 404–412 (2008).

    Article  CAS  Google Scholar 

  12. Chen, S. et al. Modulatory effects of 1,25-dihydroxyvitamin D3 on human B cell differentiation. J. Immunol. 179, 1634–1647 (2007).

    Article  CAS  Google Scholar 

  13. Boonstra, A. et al. 1alpha,25-Dihydroxyvitamin d3 has a direct effect on naive CD4(+) T cells to enhance the development of Th2 cells. J. Immunol. 167, 4974–4980 (2001).

    Article  CAS  Google Scholar 

  14. Penna, G. & Adorini, L. 1 Alpha,25-dihydroxyvitamin D3 inhibits differentiation, maturation, activation, and survival of dendritic cells leading to impaired alloreactive T cell activation. J. Immunol. 164, 2405–2411 (2000).

    Article  CAS  Google Scholar 

  15. Lemire, J.M. & Archer, D.C. 1,25-dihydroxyvitamin D3 prevents the in vivo induction of murine experimental autoimmune encephalomyelitis. J. Clin. Invest. 87, 1103–1107 (1991).

    Article  CAS  Google Scholar 

  16. Cantorna, M.T., Hayes, C.E. & DeLuca, H.F. 1,25-Dihydroxycholecalciferol inhibits the progression of arthritis in murine models of human arthritis. J. Nutr. 128, 68–72 (1998).

    Article  CAS  Google Scholar 

  17. Munger, K.L., Levin, L.I., Hollis, B.W., Howard, N.S. & Ascherio, A. Serum 25-hydroxyvitamin D levels and risk of multiple sclerosis. J. Am. Med. Assoc. 296, 2832–2838 (2006).

    Article  CAS  Google Scholar 

  18. Holick, M.F. Vitamin D deficiency. N. Engl. J. Med. 357, 266–281 (2007).

    Article  CAS  Google Scholar 

  19. van der Mei, I.A. et al. Vitamin D levels in people with multiple sclerosis and community controls in Tasmania, Australia. J. Neurol. 254, 581–590 (2007).

    Article  CAS  Google Scholar 

  20. Ishida, T. et al. CD40 signaling-mediated induction of Bcl-XL, Cdk4, and Cdk6. Implication of their cooperation in selective B cell growth. J. Immunol. 155, 5527–5535 (1995).

    CAS  PubMed  Google Scholar 

  21. Marzo, N. et al. Cyclin-dependent kinase 4 hyperactivity promotes autoreactivity in the immune system but protects pancreatic cell mass from autoimmune destruction in the nonobese diabetic mouse model. J. Immunol. 180, 1189–1198 (2008).

    Article  CAS  Google Scholar 

  22. Sekine, C. et al. Successful treatment of animal models of rheumatoid arthritis with small-molecule cyclin-dependent kinase inhibitors. J. Immunol. 180, 1954–1961 (2008).

    Article  CAS  Google Scholar 

  23. Satoh, J. et al. T cell gene expression profiling identifies distinct subgroups of Japanese multiple sclerosis patients. J. Neuroimmunol. 174, 108–118 (2006).

    Article  CAS  Google Scholar 

  24. Durie, F.H., Foy, T.M., Masters, S.R., Laman, J.D. & Noelle, R.J. The role of CD40 in the regulation of humoral and cell-mediated immunity. Immunol. Today 15, 406–411 (1994).

    Article  CAS  Google Scholar 

  25. Gerritse, K. et al. CD40–CD40 ligand interactions in experimental allergic encephalomyelitis and multiple sclerosis. Proc. Natl. Acad. Sci. USA 93, 2499–2504 (1996).

    Article  CAS  Google Scholar 

  26. Durie, F.H. et al. Prevention of collagen-induced arthritis with an antibody to gp39, the ligand for CD40. Science 261, 1328–1330 (1993).

    Article  CAS  Google Scholar 

  27. Jacobson, E.M., Concepcion, E., Oashi, T. & Tomer, Y.A. Graves' disease-associated Kozak sequence single-nucleotide polymorphism enhances the efficiency of CD40 gene translation: a case for translational pathophysiology. Endocrinology 146, 2684–2691 (2005).

    Article  CAS  Google Scholar 

  28. Barrett, J.C., Fry, B., Maller, J. & Daly, M.J. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21, 263–265 (2005).

    Article  CAS  Google Scholar 

  29. Poser, C.M. et al. New diagnostic criteria for multiple sclerosis: guidelines for research protocols. Ann. Neurol. 13, 227–231 (1983).

    Article  CAS  Google Scholar 

  30. McDonald, W.I. et al. Recommended diagnostic criteria for multiple sclerosis: guidelines from the International Panel on the diagnosis of multiple sclerosis. Ann. Neurol. 50, 121–127 (2001).

    Article  CAS  Google Scholar 

  31. Paty, D.W. et al. MRI in the diagnosis of MS: a prospective study with comparison of clinical evaluation, evoked potentials, oligoclonal banding, and CT. Neurology 38, 180–185 (1988).

    Article  CAS  Google Scholar 

  32. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    Article  CAS  Google Scholar 

  33. Price, A.L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

    Article  CAS  Google Scholar 

  34. Browning, B.L. & Browning, S.R. A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. Am. J. Hum. Genet. 84, 210–223 (2009).

    Article  CAS  Google Scholar 

  35. Browning, S.R. & Browning, B.L. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am. J. Hum. Genet. 81, 1084–1097 (2007).

    Article  CAS  Google Scholar 

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Acknowledgements

We thank individuals with MS in Australia and New Zealand for supporting this research. We are grateful to M. Tanner for database management and J. Wright and C. Remediakis from Multiple Sclerosis Research Australia (MSRA) for expediting this research. J.P.R. and M.B. are supported by Career Development Awards from The National Health and Medical Research Council of Australia (NHMRC). M.A.B. is an NHMRC Principal Research Fellow. H.B. is an NHMRC Peter Doherty Post-doctoral Fellow. J.F. is an MSRA Post-doctoral Fellow. M.B.C. is supported by a grant from the John Hunter Hospital Charitable Trust Fund and a special grant from Macquarie Bank. Replication genotyping was conducted at the Murdoch Children's Research Institute Sequenom Platform Facility. This work was supported by MSRA, John T. Reid Charitable Trusts, Trish MS Research Foundation and the Australian Research Council, under the Linkage Projects Scheme (LP0776744).

Author information

Authors and Affiliations

  1. The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia

    Melanie Bahlo & Melanie Bahlo

  2. The Westmead Millenium Institute, Westmead, New South Wales, Australia

    David R Booth, Graeme J Stewart, David R Booth, Graeme J Stewart & Robert N Heard

  3. School of Medicine, Griffith University, Queensland, Australia

    Simon A Broadley & Simon Broadley

  4. Department of Neurology, Gold Coast Hospital, Queensland, Australia

    Simon A Broadley & Simon Broadley

  5. Diamantina Institute of Cancer, Immunology and Metabolic Medicine, Princess Alexandra Hospital, University of Queensland, Brisbane, Queensland, Australia

    Matthew A Brown, Matthew A Brown, Patrick Danoy, Johanna Hadler, Karen Pryce & Matthew A Brown

  6. Nuffield Department of Orthopaedic Surgery, Botnar Research Centre, University of Oxford, Oxford, UK

    Matthew A Brown, Matthew A Brown & Matthew A Brown

  7. Menzies Research Institute, University of Tasmania, Hobart, Tasmania

    Simon J Foote, Jim Stankovich, Bruce V Taylor, Karen Drysdale, Simon J Foote, Preethi Guru, Brendan J McMorran, Devindri Perera,  Jim Stankovich & Bruce V Taylor

  8. Genomics Research Centre, Griffith University, Queensland, Australia

    Lyn R Griffiths, Lyn R Griffiths & Lotfi Tajouri

  9. Centre for Neuroscience, University of Melbourne, Victoria, Australia

    Trevor J Kilpatrick, Victoria M Perreau &  Trevor J Kilpatrick

  10. The Howard Florey Institute, University of Melbourne, Victoria, Australia

    Trevor J Kilpatrick, Justin P Rubio, Judith Field, Cathy J Jensen, Laura J Johnson, Ella J Wilkins,  Justin P Rubio, Justin P Rubio, Helmut Butzkueven &  Trevor J Kilpatrick

  11. Royal Melbourne Hospital, Parkville, Victoria, Australia

    Trevor J Kilpatrick, Mark Marriott &  Trevor J Kilpatrick

  12. John Hunter Hospital, Hunter New England Health Service, Newcastle, New South Wales, Australia

    Jeanette Lechner-Scott, Rodney J Scott, Rodney J Scott & Jeanette Lechner-Scott

  13. Hunter Medical Research Institute, Newcastle, New South Wales, Australia

    Jeanette Lechner-Scott, Pablo Moscato, Rodney J Scott, Mathew B Cox, Rodney J Scott & Jeanette Lechner-Scott

  14. Centre for Bioinformatics, Biomarker Discovery and Information-based Medicine, University of Newcastle, New South Wales, Australia

    Pablo Moscato, Rodney J Scott, Mathew B Cox & Rodney J Scott

  15. Department of Medicine, Nepean Hospital, Penrith, New South Wales, Australia

    James Wiley

  16. Christchurch School of Medicine & Health Sciences, University of Otago, New Zealand

    Glynnis Clarke

  17. UQ Centre for Clinical Research, University of Queensland, Queensland, Australia

    Peter A Csurhes & Judith M Greer

  18. Department of Biochemistry, University of Otago, Dunedin, New Zealand

    Ruth McCallum, Marilyn Merriman & Tony Merriman

  19. Department of Statistics, The University of Auckland, Auckland, New Zealand

    Brian L Browning & Sharon R Browning

  20. Department of Neurology, Box Hill Hospital, Victoria, Australia

    Helmut Butzkueven

  21. Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia

    William M Carroll & Allan G Kermode

  22. Australian Neuromuscular Research Institute, Nedlands, West Australia, Australia

    William M Carroll & Allan G Kermode

  23. Barwon Health, Geelong, Victoria, Australia

    Caron Chapman

  24. Canterbury District Health Board, Christchurch, New Zealand

    Deborah Mason

  25. School of Medicine, University of Queensland, Queensland, Australia

    Michael P Pender

  26. Department of Neurology, Royal Brisbane and Women's Hospital, Queensland, Australia

    Michael P Pender

  27. Department of Neurology, School of Medicine, Flinders University, Bedford Park, Adelaide, South Australia, Australia

    Mark Slee

  28. Department of Neurology, St. Vincent's University Hospital, Dublin, Republic of Ireland

    Niall Tubridy

  29. Auckland District Health board, Auckland, New Zealand

    Ernest Willoughby

Consortia

The Australia and New Zealand Multiple Sclerosis Genetics Consortium (ANZgene)

  • Study design and management committee:

    • Melanie Bahlo
    • , David R Booth
    • , Simon A Broadley
    • , Matthew A Brown
    • , Simon J Foote
    • , Lyn R Griffiths
    • , Trevor J Kilpatrick
    • , Jeanette Lechner-Scott
    • , Pablo Moscato
    • , Victoria M Perreau
    • , Justin P Rubio
    • , Rodney J Scott
    • , Jim Stankovich
    • , Graeme J Stewart
    • , Bruce V Taylor
    •  &  James Wiley

  • Sample processing, data handling and genotyping:

    • Matthew A Brown
    • , David R Booth
    • , Glynnis Clarke
    • , Mathew B Cox
    • , Peter A Csurhes
    • , Patrick Danoy
    • , Karen Drysdale
    • , Judith Field
    • , Simon J Foote
    • , Judith M Greer
    • , Lyn R Griffiths
    • , Preethi Guru
    • , Johanna Hadler
    • , Brendan J McMorran
    • , Cathy J Jensen
    • , Laura J Johnson
    • , Ruth McCallum
    • , Marilyn Merriman
    • , Tony Merriman
    • , Karen Pryce
    • , Rodney J Scott
    • , Graeme J Stewart
    • , Lotfi Tajouri
    • , Ella J Wilkins
    •  &  Justin P Rubio

  • Data analysis:

    • Melanie Bahlo
    • , Matthew A Brown
    • , Brian L Browning
    • , Sharon R Browning
    • , Devindri Perera
    • , Justin P Rubio
    •  &  Jim Stankovich

  • Phenotyping:

    • Simon Broadley
    • , Helmut Butzkueven
    • , William M Carroll
    • , Caron Chapman
    • , Allan G Kermode
    • , Mark Marriott
    • , Deborah Mason
    • , Robert N Heard
    • , Michael P Pender
    • , Mark Slee
    • , Niall Tubridy
    • , Jeanette Lechner-Scott
    • , Bruce V Taylor
    • , Ernest Willoughby
    •  &  Trevor J Kilpatrick

Corresponding authors

Correspondence to Justin P Rubio, Justin P Rubio or Justin P Rubio.

Additional information

A complete list of authors and affiliations is provided at the end of the article.

Supplementary information

Supplementary Text and Figures

Supplementary Tables 1–5, Supplementary Figures 1–3 and Supplementary Note (PDF 754 kb)

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The Australia and New Zealand Multiple Sclerosis Genetics Consortium (ANZgene). Genome-wide association study identifies new multiple sclerosis susceptibility loci on chromosomes 12 and 20. Nat Genet 41, 824–828 (2009). https://doi.org/10.1038/ng.396

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