The expanding genetic overlap between multiple sclerosis and type I diabetes

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Abstract

Familial clustering of autoimmune disease is well recognized and raises the possibility that some susceptibility genes may predispose to autoimmunity in general. In light of this observation, it might be expected that some of the variants of established relevance in one autoimmune disease may also be relevant in other related conditions. On the basis of this hypothesis, we tested seven single nucleotide polymorphisms (SNPs) that are known to be associated with type I diabetes in a large multiple sclerosis data set consisting of 2369 trio families, 5737 cases and 10 296 unrelated controls. Two of these seven SNPs showed evidence of association with multiple sclerosis; that is rs12708716 from the CLEC16A gene (P=1.6 × 10−16) and rs763361 from the CD226 gene (P=5.4 × 10−8). These findings thereby identify two additional multiple sclerosis susceptibility genes and lend support to the notion of autoimmune susceptibility genes.

Introduction

The increased risk of a second autoimmune disorder seen in the families of individuals who develop multiple sclerosis is well recognized.1, 2 Such findings imply that certain genetic variants may increase susceptibility to autoimmune disease in general as opposed to influencing the development of one specific condition. Under this hypothesis, the loci associated with one autoimmune disorder are reasonable candidates for other autoimmune disorders. To explore this rationale and possibly expand our understanding of genetically determined susceptibility to multiple sclerosis, we examined seven single nucleotide polymorphisms (SNPs) shown to be unequivocally associated with type I diabetes (T1D).3 These SNPs were typed in a large multiple sclerosis data set consisting of 2369 trio families, 5737 cases and 10 296 unrelated controls. Samples were recruited from across six countries, namely Australia, Belgium, Norway, Sweden, the United Kingdom and the United States. A population-specific breakdown of the samples considered is shown in Table 1, whereas basic marker performance characteristics for the seven SNPs analysed are shown in Supplementary Table S1.

Table 1 Demographic feature of affected individuals

Results and discussion

Each marker was tested for evidence of association in the case–control samples using the Cochran–Mantel–Haenszel test (treating each population-specific set of samples as a separate stratum) and in the trio family using the transmission disequilibrium test.7 The results of these analyses are shown in Table 2. In a combined analysis using all available case–control and trio family data (23 140 individuals), the strongest evidence of association was identified for rs12708716 from the C-type lectin domain family 16, member A (CLEC16A) gene (P=1.6 × 10−16) followed by rs763361 from the CD226 gene (P=5.4 × 10−8). For both genes, the allele associated with increased risk for multiple sclerosis was identical to that showing evidence of association with T1D.3 Using the Breslow–Day test, we found no evidence of any heterogeneity of effect between the populations for any of the tested markers. These data establish two further multiple sclerosis susceptibility loci in addition to those recently identified.8, 9, 10 For a third SNP, rs3184504, located in exon 3 of the SH2B3 gene, evidence of association (P=4.4 × 10−06) was also identified in the same direction as in T1D.3 This SNP leads to an amino acid change (Arg262Trp) in the pleckstrin homology domain. It is notable that the C allele (Arg) is conserved within closely related species, and that the risk allele T (Trp) is common in Europeans (46–52% in the populations investigated here) but virtually absent in African or Asian populations (<1% in HapMap data). For each of the other SNPs, there was no significant evidence of association with multiple sclerosis.

Table 2 Results from association testing

Only two of the seven T1D-associated SNPs tested here (rs12708716 and rs2292239) were included on the Affymetrix 500 k chip, which was utilized in our recently completed genome-wide association screen in multiple sclerosis.11 Of these two SNPs, only rs2292239 passed the stringent quality control thresholds employed in the screen and, in accordance with the data presented here, showed no evidence of association in the screen.11 Although rs12708716 failed to generate reliable data and was therefore not assessed in the screen, a second SNP from within the CLEC16A gene (rs6498169) was analysed and emerged as the fourth most strongly associated locus identified in the extended analysis of our genome-wide association screen (P=3.83 × 10−6).11 Not surprisingly, HapMap data show that there is extensive linkage disequilibrium between rs6498169 and rs12708716 (D′=1.0 and r2=0.259).

CLEC16A is located 20 kb centromeric to the major histocompatibility complex, class II, Transactivator (MHC2TA) on chromosome 16p13.3. The MHC2TA gene which we (TO, JH, JRO and JLH) and others have investigated earlier as a potential candidate in multiple sclerosis shows only modest linkage disequilibrium with rs12708716 and has yielded conflicting results in smaller data sets.12, 13, 14

Little is known regarding the function of the CLEC16A protein; however, it is shown to be highly expressed on B lymphocytes, natural killer and dendritic cells. Other members of the C-type lectin family such as CLEC4A2 (also known as DCIR) have been linked to autoimmunity in rodent models,15, 16 raising the possibility that such proteins may be involved in maintaining immune homeostasis through their carbohydrate-binding function. However, despite its name, CLEC16A is probably not functionally active through this mechanism because functionally active carbohydrate-binding C-type lectin domains are usually >200 amino acids long, whereas the C-type lectin domain on CLEC16A is only 22 amino acids long and lacks a carbohydrate-binding site.3 Todd et al.3 have suggested that the immunoreceptor tyrosine-based activation motif on CLEC16A is a more convincing functional element. Further work needs to be undertaken to understand the function of this gene and subsequently what possible role it may play in multiple sclerosis pathogenesis.

CD226 (also known as DNAX accessory molecule 1, DNAM1) is a transmembrane receptor of the immunoglobulin receptor family and functions as a potent-activating receptor on natural killer cells.17 CD226 is also upregulated on activated T cells, and thus may function as a co-stimulatory molecule.18 CD226 recognizes poliovirus receptor and nectin-2, which are both widely expressed on most cell types, including neurons, endothelial cells and fibroblasts. Interaction of CD226 with poliovirus receptor on endothelial cells is involved in the endothelial transmigration of leukocytes.19 It has been proposed that the SNP studied here is part of an exonic splicing silencer sequence that results in decreased splicing of exons 6 and 7.3 Anti-CD226 treatment in the experimental autoimmune encephalomyelitis model results in delayed onset and reduced severity of disease.18 The mechanism for this effect of CD226 blockade on experimental autoimmune encephalomyelitis could be at the level of altered T-cell activation,18 altered monocyte extravasation19 or altered natural killer cell responsiveness towards target cells.17

Most, if not all, autoimmune diseases show association with common variation in the human leukocyte antigen (HLA) genes from the major histocompatibility complex (MHC) on chromosome 6p21. In the case of multiple sclerosis, risk is substantially increased by carriage of DRB1*1501,20 an MHC haplotype that exerts a marked protective effect against the development of T1D. It is not surprising, therefore, that a familial clustering of these two diseases is strongest in populations such as in Sardinia, where DRB1*1501 is uncommon in the general population.21

The IL2RA (CD25) region, which we have recently replicated as associated with multiple sclerosis,10 is also implicated in susceptibility to T1D22 and rheumatoid arthritis,23 although it is not yet clear whether this actually corresponds to the same effect. Among the additional T1D susceptibility genes investigated here, CLEC16A is associated with autoimmune Addison's disease (primary adrenal insufficiency)24 and SH2B3 with celiac disease,25 and possibly rheumatoid arthritis and Crohn's disease.23 Our data add to this emerging picture that the T1D susceptibility genes CLEC16A, CD226 and possibly SH2B3 also influence susceptibility to multiple sclerosis and quite likely to autoimmunity in general. For the remaining three genes, PTPN2, ERBB3 or IFIH1, the lack of evidence of association with multiple sclerosis may indicate that the tested variants are susceptibility factors specific to only a few autoimmune diseases. However, we cannot rule out that other untested variants within these genes may have an effect in susceptibility to multiple sclerosis.

This study contributes further evidence to the concept that genetic susceptibility to autoimmune disease may be a mosaic of common sets of pleiotropic alleles as well as effects specific to one or a few diseases. Mapping of the commonalities of and differences between autoimmune diseases in studies like ours may provide essential clues to a better understanding of their pathogenesis.26 It is important to point out that although the data presented here establish these associations in multiple sclerosis, they do not confirm these particular SNPs as causative variants. Extensive fine mapping and functional studies will be required to establish these important additional details.

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Acknowledgements

This work was supported by the National Multiple Sclerosis Society (AP 3758-A-16, RG 2899 and FG-1718-A1), grants from the NINDS (NS049477, NS032830 and NS26799), AI067152, the NIAID (P01 AI039671), an NMSS Collaborative Research Award (CA 1001-A-14) and the Penates Foundation. We acknowledge use of DNA from the British 1958 Birth Cohort collection, funded by the Medical Research Council Grant G0000934 and the Wellcome Trust Grant 068545/Z/02. The 1958 Birth Cohort samples were typed by John Todd and his group, and the resulting data made available by Neil Walker, we thank all the members of this team for generously providing these data. The Norwegian Bone Marrow Donor Registry is acknowledged for collaboration in establishment of the Norwegian control material. The Swedish sample was collected and analysed with the help of grants from the European Union fp6 program—NeuroproMiSe (LSHM-CT-2005-018637) and the Bibbi and Nils Jensens foundation. The Australian National Health and Medical Research Council is acknowledged for funding the collection and analysis of the Australian samples. AG is a Postdoctora Fellow and BD a Clinical Investigator of the Research Foundation Flanders (FWO-Vlaanderen). ARL is supported by The Research Council of Norway (166005/V5) and Odd Fellow MS society. We also thank the many healthy controls, multiple sclerosis patients and their families who participated in this study.

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Contributors Australia David R Booth, Robert N Heard, Graeme J Stewart University of Sydney, Institute for Immunology and Allergy Research, Westmead Millennium Institute, Westmead Hospital, NSW 2145, Australia Belgium An Goris, Rita Dobosi, Bénédicte Dubois Section for Experimental Neurology, Katholieke Universiteit Leuven, Leuven 3000, Belgium. Norway Åslaug R Lorentzen Department of Neurology, Faculty Division Ullevål University Hospital, University of Oslo, Oslo, Norway, and Institute of Immunology, Rikshospitalet University Hospital, Oslo N-0027, Norway Elisabeth G Celius, Hanne F Harbo Department of Neurology, Ullevål University Hospital, Oslo N-0407, Norway Anne Spurkland Institute of Basal Medical Sciences, University of Oslo, Blindern, Oslo N-0317, Norway Sweden Tomas Olsson, Ingrid Kockum, Jenny Link, Jan Hillert Department of Clinical Neurosciences, Center for Molecular Medicine CMM, L8:04, Karolinska Hospital, Stockholm 171 76, Sweden United Kingdom Maria Ban, Amie Baker, Stephen Sawcer, Alastair Compston Department of Clinical Neuroscience, University of Cambridge, Addenbrooke's Hospital, Hills Road, Cambridge CB2 2QQ, UK Tania Mihalova, Richard Strange, Clive Hawkins Department of Neurology and Human Genomics, Keele University Medical School, Hartshill Campus, Stoke on Trent ST4 7LN, UK Gillian Ingram, Neil P Robertson Department of Neurology, University Hospital of Wales, Heath Park, Cardiff CF14 4XW, UK. United States Philip L De Jager, David A Hafler Division of Molecular Immunology, Center for Neurologic Diseases, Brigham and Women's Hospital, Boston, MA, USA, and Harvard Medical School/Partners Healthcare Center for Genetics and Genomics, Boston, MA, USA Lisa F Barcellos University of California at Berkeley, Berkeley, CA, USA Adrian J Ivinson Harvard NeuroDiscovery Center, Harvard Medical School, Boston, MA, USA. Margaret Pericak-Vance University of Miami School of Medicine, Miami, FL, USA Jorge R Oksenberg, Stephen L Hauser Department of Neurology, University of California San Francisco, San Francisco, CA, USA Jacob L McCauley, David Sexton, Jonathan Haines Center for Human Genetics Research, Vanderbilt University Medical Center, Nashville, TN, USA.

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