Using the ImmunoChip custom genotyping array, we analyzed 14,498 subjects with multiple sclerosis and 24,091 healthy controls for 161,311 autosomal variants and identified 135 potentially associated regions (P < 1.0 × 10−4). In a replication phase, we combined these data with previous genome-wide association study (GWAS) data from an independent 14,802 subjects with multiple sclerosis and 26,703 healthy controls. In these 80,094 individuals of European ancestry, we identified 48 new susceptibility variants (P < 5.0 × 10−8), 3 of which we found after conditioning on previously identified variants. Thus, there are now 110 established multiple sclerosis risk variants at 103 discrete loci outside of the major histocompatibility complex. With high-resolution Bayesian fine mapping, we identified five regions where one variant accounted for more than 50% of the posterior probability of association. This study enhances the catalog of multiple sclerosis risk variants and illustrates the value of fine mapping in the resolution of GWAS signals.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Causal relationship between PCSK9 inhibitor and autoimmune diseases: a drug target Mendelian randomization study
Arthritis Research & Therapy Open Access 14 August 2023
Clinical and Experimental Medicine Open Access 08 July 2023
The Use of Stem Cells as a Potential Treatment Method for Selected Neurodegenerative Diseases: Review
Cellular and Molecular Neurobiology Open Access 07 April 2023
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Rent or buy this article
Prices vary by article type
Prices may be subject to local taxes which are calculated during checkout
Gourraud, P.A., Harbo, H.F., Hauser, S.L. & Baranzini, S.E. The genetics of multiple sclerosis: an up-to-date review. Immunol. Rev. 248, 87–103 (2012).
Nylander, A. & Hafler, D.A. Multiple sclerosis. J. Clin. Invest. 122, 1180–1188 (2012).
Compston, A. et al. McAlpine's Multiple Sclerosis (Churchill Livingstone, London, 2006).
Dyment, D.A., Yee, I.M., Ebers, G.C. & Sadovnick, A.D. Multiple sclerosis in stepsiblings: recurrence risk and ascertainment. J. Neurol. Neurosurg. Psychiatry 77, 258–259 (2006).
Hemminki, K., Li, X., Sundquist, J., Hillert, J. & Sundquist, K. Risk for multiple sclerosis in relatives and spouses of patients diagnosed with autoimmune and related conditions. Neurogenetics 10, 5–11 (2009).
Jersild, C., Svejgaard, A. & Fog, T. HL-A antigens and multiple sclerosis. Lancet 1, 1240–1241 (1972).
International Multiple Sclerosis Genetics Consortium. Risk alleles for multiple sclerosis identified by a genomewide study. N. Engl. J. Med. 357, 851–862 (2007).
De Jager, P.L. et al. Meta-analysis of genome scans and replication identify CD6, IRF8 and TNFRSF1A as new multiple sclerosis susceptibility loci. Nat. Genet. 41, 776–782 (2009).
International Multiple Sclerosis Genetics Consortium & Wellcome Trust Case Control Consortium 2. Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature 476, 214–219 (2011).
Patsopoulos, N.A. et al. Genome-wide meta-analysis identifies novel multiple sclerosis susceptibility loci. Ann. Neurol. 70, 897–912 (2011).
International Multiple Sclerosis Genetics Consortium. Evidence for polygenic susceptibility to multiple sclerosis—the shape of things to come. Am. J. Hum. Genet. 86, 621–625 (2010).
Baranzini, S.E. The genetics of autoimmune diseases: a networked perspective. Curr. Opin. Immunol. 21, 596–605 (2009).
Cotsapas, C. et al. Pervasive sharing of genetic effects in autoimmune disease. PLoS Genet. 7, e1002254 (2011).
Cortes, A. & Brown, M.A. Promise and pitfalls of the Immunochip. Arthritis Res. Ther. 13, 101 (2011).
Jostins, L. et al. Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 491, 119–124 (2012).
Krzywinski, M. et al. Circos: an information aesthetic for comparative genomics. Genome Res. 19, 1639–1645 (2009).
Willis, T.G. et al. Bcl10 is involved in t(1;14)(p22;q32) of MALT B cell lymphoma and mutated in multiple tumor types. Cell 96, 35–45 (1999).
Yan, J. & Greer, J.M. NF-κB, a potential therapeutic target for the treatment of multiple sclerosis. CNS Neurol. Disord. Drug Targets 7, 536–557 (2008).
Wegener, E. & Krappmann, D. CARD-Bcl10-Malt1 signalosomes: missing link to NF-κB. Sci. STKE 2007, pe21 (2007).
Fairfax, B.P. et al. Genetics of gene expression in primary immune cells identifies cell type–specific master regulators and roles of HLA alleles. Nat. Genet. 44, 502–510 (2012).
Lill, C.M. et al. Genome-wide significant association of ANKRD55 rs6859219 and multiple sclerosis risk. J. Med. Genet. 50, 140–143 (2013).
Maier, L.M. et al. IL2RA genetic heterogeneity in multiple sclerosis and type 1 diabetes susceptibility and soluble interleukin-2 receptor production. PLoS Genet. 5, e1000322 (2009).
Maller, J.B. et al. Bayesian refinement of association signals for 14 loci in 3 common diseases. Nat. Genet. 44, 1294–1301 (2012).
McLaren, W. et al. Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics 26, 2069–2070 (2010).
Gregory, A.P. et al. TNF receptor 1 genetic risk mirrors outcome of anti-TNF therapy in multiple sclerosis. Nature 488, 508–511 (2012).
De Jager, P.L. et al. The role of the CD58 locus in multiple sclerosis. Proc. Natl. Acad. Sci. USA 106, 5264–5269 (2009).
Malmeström, C. et al. Serum levels of LIGHT in MS. Mult. Scler. 19, 871–876 (2013).
ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).
Schaub, M.A., Boyle, A.P., Kundaje, A., Batzoglou, S. & Snyder, M. Linking disease associations with regulatory information in the human genome. Genome Res. 22, 1748–1759 (2012).
Davydov, E.V. et al. Identifying a high fraction of the human genome to be under selective constraint using GERP++. PLoS Comput. Biol. 6, e1001025 (2010).
Johnson, A.D. et al. SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap. Bioinformatics 24, 2938–2939 (2008).
Juran, B.D. et al. Immunochip analyses identify a novel risk locus for primary biliary cirrhosis at 13q14, multiple independent associations at four established risk loci and epistasis between 1p31 and 7q32 risk variants. Hum. Mol. Genet. 21, 5209–5221 (2012).
Liu, J.Z. et al. Dense fine-mapping study identifies new susceptibility loci for primary biliary cirrhosis. Nat. Genet. 44, 1137–1141 (2012).
Trynka, G. et al. Dense genotyping identifies and localizes multiple common and rare variant association signals in celiac disease. Nat. Genet. 43, 1193–1201 (2011).
Eyre, S. et al. High-density genetic mapping identifies new susceptibility loci for rheumatoid arthritis. Nat. Genet. 44, 1336–1340 (2012).
Tsoi, L.C. et al. Identification of 15 new psoriasis susceptibility loci highlights the role of innate immunity. Nat. Genet. 44, 1341–1348 (2012).
Cooper, J.D. et al. Seven newly identified loci for autoimmune thyroid disease. Hum. Mol. Genet. 21, 5202–5208 (2012).
Ban, M. et al. Replication analysis identifies TYK2 as a multiple sclerosis susceptibility factor. Eur. J. Hum. Genet. 17, 1309–1313 (2009).
Ban, M. et al. A non-synonymous SNP within membrane metalloendopeptidase–like 1 (MMEL1) is associated with multiple sclerosis. Genes Immun. 11, 660–664 (2010).
Shah, T.S. et al. optiCall: a robust genotype-calling algorithm for rare, low-frequency andcommon variants. Bioinformatics 28, 1598–1603 (2012).
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).
Browning, B.L. & Yu, Z. Simultaneous genotype calling and haplotype phasing improves genotype accuracy and reduces false-positive associations for genome-wide association studies. Am. J. Hum. Genet. 85, 847–861 (2009).
Marchini, J., Howie, B., Myers, S., McVean, G. & Donnelly, P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat. Genet. 39, 906–913 (2007).
Howie, B., Marchini, J. & Stephens, M. Genotype imputation with thousands of genomes. G3 1, 457–470 (2011).
Marchini, J. & Howie, B. Genotype imputation for genome-wide association studies. Nat. Rev. Genet. 11, 499–511 (2010).
Liu, J.Z. et al. Meta-analysis and imputation refines the association of 15q25 with smoking quantity. Nat. Genet. 42, 436–440 (2010).
Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).
We thank the participants, the referring nurses, the physicians and the funders. Funding was provided by the US National Institutes of Health, the Wellcome Trust, the UK MS Society, the UK Medical Research Council, the US National MS Society, the Cambridge National Institute for Health Research (NIHR) Biomedical Research Centre, DeNDRon, the Bibbi and Niels Jensens Foundation, the Swedish Brain Foundation, the Swedish Research Council, the Knut and Alice Wallenberg Foundation, the Swedish Heart-Lung Foundation, the Foundation for Strategic Research, the Stockholm County Council, Karolinska Institutet, INSERM, Fondation d'Aide pour la Recherche sur la Sclérose en Plaques, Association Française contre les Myopathies, Infrastrutures en Biologie Santé et Agronomie (GIS-IBISA), the German Ministry for Education and Research, the German Competence Network MS, Deutsche Forschungsgemeinschaft, Munich Biotec Cluster M4, the Fidelity Biosciences Research Initiative, Research Foundation Flanders, Research Fund KU Leuven, the Belgian Charcot Foundation, Gemeinnützige Hertie Stiftung, University Zurich, the Danish MS Society, the Danish Council for Strategic Research, the Academy of Finland, the Sigrid Juselius Foundation, Helsinki University, the Italian MS Foundation, Fondazione Cariplo, the Italian Ministry of University and Research, the Torino Savings Bank Foundation, the Italian Ministry of Health, the Italian Institute of Experimental Neurology, the MS Association of Oslo, the Norwegian Research Council, the South-Eastern Norwegian Health Authorities, the Australian National Health and Medical Research Council, the Dutch MS Foundation and Kaiser Permanente. We acknowledge the British 1958 Birth Cohort, the UK National Blood Service, Vanderbilt University Medical Center's BioVU DNA Resources Core, Centre de Ressources Biologiques du Réseau Français d'Etude Génétique de la Sclérose en Plaques, the Norwegian Bone Marrow Registry, the Norwegian MS Registry and Biobank, the North American Research Committee on MS Registry, the Brigham and Women's Hospital PhenoGenetic Project and DILGOM, funded by the Academy of Finland. See the Supplementary Note for details.
The author declare no competing financial interests.
About this article
Cite this article
International Multiple Sclerosis Genetics Consortium (IMSGC). Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis. Nat Genet 45, 1353–1360 (2013). https://doi.org/10.1038/ng.2770
This article is cited by
Causal relationship between PCSK9 inhibitor and autoimmune diseases: a drug target Mendelian randomization study
Arthritis Research & Therapy (2023)
Cell Research (2023)
Nature Reviews Immunology (2023)
Genetic variation in cis-regulatory domains suggests cell type-specific regulatory mechanisms in immunity
Communications Biology (2023)
Multi-ancestry and multi-trait genome-wide association meta-analyses inform clinical risk prediction for systemic lupus erythematosus
Nature Communications (2023)