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Abstract

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgments
  8. REFERENCES
  9. Appendix: A: INVESTIGATORS, COLLABORATORS, RESEARCH COORDINATORS, AND CONTRIBUTORS
  10. Supporting Information

Objective

To identify new genetic associations with juvenile and adult dermatomyositis (DM).

Methods

We performed a genome-wide association study (GWAS) of adult and juvenile DM patients of European ancestry (n = 1,178) and controls (n = 4,724). To assess genetic overlap with other autoimmune disorders, we examined whether 141 single-nucleotide polymorphisms (SNPs) outside the major histocompatibility complex (MHC) locus, and previously associated with autoimmune diseases, predispose to DM.

Results

Compared to controls, patients with DM had a strong signal in the MHC region consisting of GWAS-level significance (P < 5 × 10–8) at 80 genotyped SNPs. An analysis of 141 non-MHC SNPs previously associated with autoimmune diseases showed that 3 SNPs linked with 3 genes were associated with DM, with a false discovery rate (FDR) of <0.05. These genes were phospholipase C–like 1 (PLCL1; rs6738825, FDR = 0.00089), B lymphoid tyrosine kinase (BLK; rs2736340, FDR = 0.0031), and chemokine (C-C motif) ligand 21 (CCL21; rs951005, FDR = 0.0076). None of these genes was previously reported to be associated with DM.

Conclusion

Our findings confirm the MHC as the major genetic region associated with DM and indicate that DM shares non-MHC genetic features with other autoimmune diseases, suggesting the presence of additional novel risk loci. This first identification of autoimmune disease genetic predispositions shared with DM may lead to enhanced understanding of pathogenesis and novel diagnostic and therapeutic approaches.

The idiopathic inflammatory myopathies, or myositis syndromes, are a heterogeneous group of systemic disorders that have been proposed to be autoimmune diseases based largely on the presence of unique autoantibodies and/or self-directed T or B lymphocyte responses in some subsets of patients ([1]). Myositis patients themselves can develop additional autoimmune diseases, and there is an elevated occurrence of other autoimmune diseases in close relatives ([2, 3]). Recent genome-wide association studies (GWAS) have identified many novel genes associated with several autoimmune diseases ([4]). However, outside of the HLA region, there is limited direct evidence supporting a genetic relationship between the idiopathic inflammatory myopathies and other autoimmune disorders ([5]). The idiopathic inflammatory myopathies are relatively rare, with a prevalence of 10–15 cases per 100,000, and this has hindered progress in genetic mapping studies ([6]).

We assembled a large international collection of samples from subjects with dermatomyositis (DM), the most frequent and readily identified phenotype of the idiopathic inflammatory myopathies, to identify new genetic associations with myositis. DM is defined by pathognomonic rashes and chronic muscle inflammation, consisting primarily of CD4+ T lymphocytes, B lymphocytes, dendritic cells, and macrophages ([1, 7]). DM in adults and children has similar clinical and pathologic features ([6, 8]) that likely share pathogenic mechanisms, including the involvement of type I interferon (IFN) pathways ([7]). To define the genetic architecture of DM, we performed the first GWAS of this disease, which confirmed a strong signal in the major histocompatibility complex (MHC) region and revealed enrichment of genetic loci that have been associated with a variety of other autoimmune disorders.

PATIENTS AND METHODS

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgments
  8. REFERENCES
  9. Appendix: A: INVESTIGATORS, COLLABORATORS, RESEARCH COORDINATORS, AND CONTRIBUTORS
  10. Supporting Information

Study populations

Investigators with collections of DNA samples from myositis patients formed a collaboration called the Myositis Genetics Consortium with the goal of identifying new genetic factors associated with myositis. A list of these investigators, in addition to the authors of this article, is provided in Appendix A. We focused our first study on DM because of its relatively higher frequency in children and adults and more homogeneous features compared to other myositis phenotypes ([6]). The criteria for inclusion of DM cases were predetermined to be probable or definite DM as defined by proximal weakness, myopathy on electromyography, muscle biopsy findings consistent with idiopathic inflammatory myopathy or elevated serum muscle enzymes, and the presence of Gottron's papules/sign or heliotrope rash, with exclusion of other causes of muscle disease in accordance with the Bohan and Peter criteria ([9]). Age <18 years at onset defined juvenile DM. After excluding 238 cases due to low call rates (n = 123), outliers (n = 55), or related individuals (n = 60), 1,178 Caucasian patients with either adult DM (n = 705) or juvenile DM (n = 473) from clinical centers in the US and Europe were analyzed.

The US cases were obtained from 3 centers, including the National Institutes of Health, Bethesda, MD (234 with adult DM and 140 with juvenile DM), the Mayo Clinic, Rochester, MN (53 with adult DM and 36 with juvenile DM), and the Children's Memorial Research Center, Chicago, IL (107 with juvenile DM). Members of the US Childhood Myositis Heterogeneity Study Group who contributed data and samples to this study are listed in Appendix A. The UK cases were obtained from the UK Adult Onset Myositis Immunogenetic Collaboration (149 with adult DM) and the UK Juvenile Dermatomyositis Research Group (159 cases); members of these 2 groups are listed in Appendix A. Other European samples came from the Czech Republic (114 with adult DM and 11 with juvenile DM), Hungary (64 with adult DM and 12 with juvenile DM), Spain (43 with adult DM and 4 with juvenile DM), Sweden (37 with adult DM and 4 with juvenile DM), and The Netherlands (11 with adult DM).

In order to optimize case–control matching, we used separate control groups for each geographic collection of patients. For control samples, single-nucleotide polymorphism (SNP) genotyping of healthy Czech and Hungarian volunteers from the Institute of Rheumatology, Prague, Czech Republic or the University of Debrecen, Debrecen, Hungary was performed on either an Illumina Human1M-Duo v3 BeadChip (n = 235: 166 Czechs and 69 Hungarians) or an Illumina Human660W-Quad v1 BeadChip (n = 21: all Hungarians). US controls were obtained using previously available data from the North American Rheumatoid Arthritis Consortium ([10]). UK controls were obtained using available data from the Wellcome Trust Case Control Consortium (WTCCC2) (WTCCC2 1958 birth cohort on an Illumina Human1M-Duo v3 BeadChip; n = 2,415) (http://www.wtccc.org.uk/ccc2). Swedish and Dutch controls (n = 642) were obtained from previously published data sets ([11]), and Spanish controls (n = 259) were obtained from blood bank volunteers in Granada, Spain using data generated on an Illumina Human1M-Duo v3 BeadChip. All subjects consented to be enrolled in protocols approved by local ethics boards.

Genotyping and quality control

Genotyping of cases was carried out using various Illumina GWAS arrays at the Feinstein Institute for Medical Research, Manhasset, NY. Since the genotyping was done over several years, the specific Illumina chip used for analysis was upgraded as new platforms became available. Among the cases, 86 were genotyped using an Illumina HumanHap550 BeadChip, 221 were genotyped using an Illumina HumanCNV370-Duo v1 BeadChip, 293 were genotyped using an Illumina Human610-Quad v1 BeadChip, and 578 were genotyped using an Illumina Human660W-Quad v1 BeadChip, according to the manufacturer's protocols. Only SNPs that were present on all platforms were evaluated. SNPs that yielded P < 0.001 in association tests between cases genotyped on different chips within each geographic group were dropped in the final results (n = 1,372).

All data underwent quality control before merging and final statistical analyses. The following data were excluded: SNPs with a call rate of <95% on any platform, individuals with >10% missing rates in genotypes, and SNPs with minor allele frequency of ≤0.01 or with Hardy-Weinberg equilibrium in controls with a P value less than or equal to 10–5. Merged data were separated into 5 groups according to geographic region. Relatedness was checked by estimating the identity-by-descent coefficient in Plink (http://pngu.mgh.harvard.edu/purcell/plink/) ([12]).

A PI-HAT (representing the estimated identity-by-descent sharing among relatives, with 0 indicating unrelated and 1 indicating an identical twin) threshold >0.15 was used, and we retained only 1 member of each set of duplicated or related samples (n = 60). Outliers identified in the clustering in Plink (Z > 4 or < –4) were removed (n = 24). Additional outliers (n = 31) that deviated by more than 4 standard errors from the centroid were identified by principal components analysis in EigenStrat (http://genetics.med.harvard.edu/reich/Reich_Lab/Software.html) using 16,819 SNPs that are in the linkage disequilibrium (LD)–pruned SNP set provided by The Gene, Environment Association Studies consortium coordinating center ([13]). We included the principal components in which cases and controls had significantly different loadings for each site, and this analysis required that we adjust for the top 5 principal components for analysis of the US data, no principal components for analysis of the UK data, 6 principal components for analysis of the Dutch data, 1 principal component for analysis of the central European data, and 1 principal component for analysis of the Spanish data.

Statistical analysis

The additive model was used in the Plink logistic association test for each group separately, including the top principal components as covariates to remove residual population structure. Meta-analysis using Plink was then performed for all 5 groups. For the focused analysis of autoimmune-related SNPs, we adopted a Benjamini and Hochberg false discovery rate (FDR) of <0.05.

RESULTS

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgments
  8. REFERENCES
  9. Appendix: A: INVESTIGATORS, COLLABORATORS, RESEARCH COORDINATORS, AND CONTRIBUTORS
  10. Supporting Information

GWAS identifies the MHC locus as the strongest genetic risk region for DM

The GWAS of 1,178 cases and 4,724 control samples included in this study (Table 1) showed GWAS-level significance (P < 5 × 10–8) at 80 genotyped SNPs across the MHC region (Figure 1), which is consistent with prior targeted gene studies that associated this region with myositis phenotypes ([5]). No significant differences were noted between males and females or between adult and juvenile DM in these analyses.

Table 1. Characteristics of the dermatomyositis patients and healthy controls, and SNP data included in the study*
PopulationAdult dermatomyositis patientsJuvenile dermatomyositis patientsControlsNo. of successfully genotyped SNPsCovariateGenomic control inflation factor (λgc)
nFemale, %nFemale, %nFemale, %
  1. SNP = single-nucleotide polymorphism.

Czech/Hungarian17870.82378.325657.4242,530Population structure1.01
Spanish4381.4450.025965.6242,871Population structure1.009
Swedish/Dutch4868.8475.064272.4242,644Population structure1.021
UK14965.815970.42,41547.8236,039None1
US28776.028369.61,15270.8237,155Population structure1.073
Meta-analysis70572.447370.24,72458.2241,502None1.043
image

Figure 1. Results of genome-wide association analysis of dermatomyositis plotted on a genomic scale (Manhattan plot) showing P values for 242,876 successfully genotyped single-nucleotide polymorphisms. The horizontal line represents the genome-wide level of significance (P < 5 × 10–8). Chr = chromosome.

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We used Q–Q plots, which is a method for comparing 2 probability distributions by plotting quantiles against each other, to evaluate the comparability of observed and expected distributions of results of tests that we conducted. We included any significant principal components as covariates to remove the residual population structure in the GWAS for each geographic group before the meta-analysis (Table 1); therefore, we did not adjust population structure again in the meta-analysis. For the fixed-effect P values of genotyped SNPs in the GWAS meta-analysis, when comparing the observed versus the expected distribution of test results, we found no overall systematic inflation of the number of positive tests (Figure 2A), as the ratio of the observed median chi-square value to the expected value gave a lambda value of 1.043, which is close to the expected value of 1.0 (Table 1). These findings were essentially unchanged after eliminating the MHC region (λgc = 1.037) (Figure 2B). The random-effect P values of genotyped SNPs in the GWAS meta-analysis were essentially the same as or very similar to the fixed-effect P values (data not shown).

image

Figure 2. A, Q–Q plot of the genome-wide meta-analysis (λgc = 1.043). B, Q–Q plot of the genome-wide meta-analysis without the major histocompatibility complex region (λgc = 1.037).

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GWAS of DM reveals genetic overlap with other autoimmune disorders

Given the familial aggregation of DM with several common autoimmune diseases, we tested the hypothesis that DM has a genetic architecture similar to that of other autoimmune diseases found to occur with increased frequency in first-degree relatives of DM patients ([2, 3]). To do this, we selected 269 SNPs that had been associated with rheumatoid arthritis (RA) ([14, 15]), systemic lupus erythematosus (SLE) ([16, 17]), type 1 diabetes mellitus ([18-20]), Crohn's disease ([21-23]), thyroid disease ([24]), gluten-sensitive enteropathy ([25]), or multiple sclerosis ([26]), and we assessed their association with DM.

Of these 269 SNPs, 141 were genotyped or were in LD (r2 > 0.9) with genotyped SNPs in DM, based on publicly accessible LD data from HapMap 3 Utah residents with ancestry from northern and western Europe (see data in Supplementary Table 1 for all 141 SNPs, available on the Arthritis & Rheumatism web site at http://onlinelibrary.wiley.com/doi/10.1002/art.38137/abstract). Of these 141 SNPs, SNPs related to 3 genes, which had not been previously associated with DM, were found to have significant (FDR of <0.05) associations with DM (Table 2). These SNPs were related to phospholipase C–like 1 (PLCL1: rs6738825 in LD with rs7572733, FDR = 0.00089, also in LD with rs1518364, FDR = 0.0037, and in LD with rs938929, FDR = 0.0030); B lymphoid tyrosine kinase (BLK: rs2736340, FDR = 0.0031); and chemokine (C-C motif) ligand 21 (CCL21: rs951005, FDR = 0.0076, and in LD with rs2492358, FDR = 0.0060) (see data in Supplementary Table 1 for all 141 SNPs, available on the Arthritis & Rheumatism web site at http://onlinelibrary/wiley.com/doi/10.1002/art.38137/abstract). None of these SNPs was in LD with SNPs from the other genes. Minor variations were noted in the SNP associations between the adult and juvenile DM cohorts, but no significant differences were seen.

Table 2. Overlap of the published genome-wide association study SNPs for autoimmune diseases with the SNPs for dermatomyositis*
GeneSNP markerOriginal SNP/LDChromosome positionOR (95% CI)PaFDRSNP disease source (ref.)
  1. Only single-nucleotide polymorphisms (SNPs) with a false discovery rate (FDR) of <0.05 are listed. SNP marker = SNP directly genotyped by genome-wide association studies; original SNP = original SNPs among 141 SNPs associated with autoimmune diseases, if not directly genotyped; LD = linkage disequilibrium in r2 with the SNP directly genotyped on Illumina arrays; position = basepair in hg19/build37 coordinate; OR = odds ratio; 95% CI = 95% confidence interval; SLE = systemic lupus erythematosus; RA = rheumatoid arthritis.

  2. a

    Fixed-effect P value in meta-analysis.

PLCL1rs7572733rs6738825/0.9792: 1989298060.80 (0.72–0.88)6.18 × 10–60.00089SLE (17)
PLCL1rs1518364rs6738825/0.9582: 1988099751.22 (1.11–1.35)5.11 × 10–50.0037SLE (17)
PLCL1rs938929rs6738825/0.9582: 1987808601.22 (1.10–1.34)0.000083220.0030SLE (17)
BLKrs27363408: 113439731.25 (1.12–1.40)0.00006530.0031RA (14)
CCL21rs2492358rs951005/1.09: 347378280.77 (0.67–0.88)0.00020930.0060RA (14)
CCL21rs9510059: 347436810.77 (0.67–0.89)0.0003170.0076RA (14)

To assess the relevance of these autoimmune-related SNPs to DM, we evaluated Q–Q plots of these SNPs in DM and found a marked excess of positive associations of these SNPs with DM across the range of variants (λgc = 2.59) (Figure 3). The current study had a low value of lambda in the entire population of SNPs that had been genotyped.

image

Figure 3. Q–Q plot showing an excess of positive associations of published genome-wide association study non–major histocompatibility complex single-nucleotide polymorphisms for autoimmune diseases with those for dermatomyositis (λgc = 2.59).

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DISCUSSION

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgments
  8. REFERENCES
  9. Appendix: A: INVESTIGATORS, COLLABORATORS, RESEARCH COORDINATORS, AND CONTRIBUTORS
  10. Supporting Information

The findings of our study, which to our knowledge is the first GWAS of any form of myositis, are consistent with those of previous targeted studies suggesting that the MHC is the major genetic region associated with DM ([5]). In addition, we have provided initial evidence that a number of non-MHC genes that were previously associated with other autoimmune diseases are also associated with DM. None of these new associations, which require replication for confirmation, has been previously reported for any form of myositis. Sufficient numbers of samples from myositis patients are not yet available to allow independent consideration of other myositis phenotypes, and these should be addressed in future investigations.

Although this GWAS had a sample size comparable to that in similar studies of other autoimmune diseases that did identify significant non-MHC signals, no genetic signals with a genome-wide level of significance were observed outside of the MHC. This may be due to a relatively weaker genetic influence and stronger environmental influence on DM susceptibility compared to other autoimmune diseases, or it could be a reflection of disease heterogeneity ([1]).

By focusing our analysis on a subset of SNPs that are known to be associated with various forms of autoimmunity, we were able to evaluate these associations in DM without the statistical implications of multiple testing that are associated with a full GWAS analysis. Thus, we have provided evidence for associations between DM and a number of genes previously identified as risk factors for other forms of autoimmunity. These data are consistent with the familial clustering of multiple autoimmune diseases ([27]), as well as with the higher frequencies of certain autoimmune diseases in close relatives of myositis patients ([2, 3]). The direction and strength of association with these risk alleles were consistent with published findings in other autoimmune diseases ([14, 16, 19, 21, 26]). Nonetheless, we do not believe that our current findings allow us to effectively compare genetic risk scores for DM and other autoimmune diseases at this time.

Of the non-MHC associations of SNPs with DM that are seen in other autoimmune diseases, the strongest was a suggestive signal on chromosome 2q that was observed in a region containing PLCL1, which is involved in an inositol phospholipid-based intracellular signaling cascade (http://www.omim.org/entry/600597). In this case, 3 typed SNPs (rs7572733, rs1518364, and rs938929) were in strong LD with a PLCL1 SNP (rs6738825) that was previously associated with SLE. Not only is PLCL1 involved in the inositol phospholipid-based intracellular signaling cascade, but it also regulates the turnover of receptors, and thus, it contributes to the maintenance of muscle tone and of γ-aminobutyric acid–mediated synaptic inhibition ([28]). Yet the exact mechanism by which PLCL1 could be associated with the pathogenesis of DM is not clear and will require additional study.

The other autoimmunity genes that are shared with DM encode proteins that current studies suggest are likely to play a role in the pathogenesis of DM. Among the genes found in common with other autoimmune diseases, BLK encodes a nonreceptor tyrosine kinase of the Src family of proto-oncogenes that are typically involved in cell proliferation and differentiation. The Blk protein has a role in B cell receptor signaling and B cell development, and B cells are prominent forms of mononuclear cells found in DM skin and muscle biopsy samples ([29]), as well as markers of disease activity ([8]). Further evidence for the role of B cells in DM comes from the growing list of disease-specific autoantibodies and from anecdotal reports of the efficacy of anti–B cell therapies ([1]). The BLK gene has been associated with SLE ([30]), systemic sclerosis ([31]), Sjögren's syndrome ([32]), and RA ([10]), diseases in which B cells are suspected to play important pathogenic roles and with which DM may occasionally form an overlap syndrome.

The function of Blk in human B cells and other hematopoietic cells is not well studied, so little information is available regarding the regulation of BLK at the messenger RNA (mRNA) and protein levels in cell lines. Nonetheless, the rs922483 allele in the BLK gene, which is in LD with rs2736340, is reported to down-regulate both BLK mRNA and protein expression in primary human transitional and naive B cells from cord blood but not from adult B cell subsets, suggesting that involvement of Blk in the risk of autoimmune disease occurs during the early stages of B cell development ([33]).

CCL21 is one of several chemokine genes clustered on the p-arm of chromosome 9. The protein encoded by this gene inhibits hematopoiesis and stimulates chemotaxis in vitro for thymocytes and activated T cells ([34]). The CCL21 protein may also play roles in mediating the homing of lymphocytes to secondary lymphoid organs in angiogenesis ([35]) and in B cell migration and proliferation ([36]) in RA. It is a high-affinity functional ligand for CCR7, which is expressed on T and B lymphocytes. CCR7 and CCL21 are both expressed on mononuclear cells in the muscles of myositis patients, and CCL21 is also expressed on plasmacytoid dendritic cells, which are important sources of the IFN signature seen in both adult and juvenile DM ([37]). CCL21 is also expressed in the extranodal lymphoid microstructures in muscle in juvenile DM ([38]). SNPs of CCL21 have been associated with RA, although the functional nature of these SNPs and their possible role in pathogenesis remain to be elucidated ([14]).

Given the limited information available about the pathogenic mechanisms in DM as well as about the specific functions of the alleles of genes associated with autoimmunity, more investigation is needed to understand the implications of these SNP associations. The limitations of this study include its moderate statistical power, use of multiple Illumina arrays, and possible heterogeneity from multiple autoantibody phenotypes whose genetic associations sometimes vary from the clinical phenotypes ([5]), all of which should be addressed in future larger confirmatory studies.

Taken together, our findings suggest that DM shares genetic features with other autoimmune diseases, including major genetic contributions in the MHC region and several non-MHC genes that may interact in common functional pathways ([39]). This is the first systematic identification of genetic predispositions that are common to autoimmune diseases and that promote the development of DM. An enhanced appreciation of the autoimmune pathogenesis of DM and identification and confirmation of additional genetic risk factors should ultimately lead to molecular profiles that could catalyze novel diagnostic and therapeutic advances.

AUTHOR CONTRIBUTIONS

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgments
  8. REFERENCES
  9. Appendix: A: INVESTIGATORS, COLLABORATORS, RESEARCH COORDINATORS, AND CONTRIBUTORS
  10. Supporting Information

All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be published. Dr. Miller had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study conception and design. Miller, Cooper, Vencovský, Rider, Lundberg, Padyukov, Radstake, Ollier, O'Hanlon, Amos, Gregersen.

Acquisition of data. Miller, Cooper, Vencovský, Rider, Danko, Wedderburn, Lundberg, Pachman, Reed, Ytterberg, Padyukov, Selva-O'Callaghan, Radstake, Isenberg, Chinoy, Lee, Lamb, Gregersen.

Analysis and interpretation of data. Lundberg, Pachman, Padyukov, Radstake, Ollier, O'Hanlon, Peng, Lee, Lamb, Chen, Amos, Gregersen.

Acknowledgments

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgments
  8. REFERENCES
  9. Appendix: A: INVESTIGATORS, COLLABORATORS, RESEARCH COORDINATORS, AND CONTRIBUTORS
  10. Supporting Information

We are indebted to Dr. Javier Martin (Instituto de Parasitología y Biomedicina, Granada, Spain) for supplying Spanish control data and to Dr. Peter Novota (Institute of Rheumatology, Prague, Czech Republic) for supplying Czech controls. We thank Dr. Younghun Han (M. D. Anderson Cancer Center, Houston, TX) for statistical support, Miss Hazel Platt and Mrs. Fiona Marriage (Centre for Integrated Genomic Medical Research, University of Manchester, Manchester, UK) and Drs. Maryam Dastmalchi and Eva Jemseby (Karolinska Institutet, Stockholm, Sweden) for technical support, and Mr. Paul New (Salford Royal Foundation Trust, Salford, UK) for ethical and technical support. We thank Dr. Elaine Remmers (National Institute of Arthritis and Musculoskeletal and Skin Diseases, Bethesda, MD) and Dr. Douglas Bell (National Institute of Environmental Health Sciences, Research Triangle Park, NC) of the National Institutes of Health for their critical review of the manuscript. We used genome-wide association data generated by the Wellcome Trust Case Control Consortium 2 (WTCCC2 1958 birth cohort). Finally, we thank all of the patients and their families who contributed to this study.

REFERENCES

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgments
  8. REFERENCES
  9. Appendix: A: INVESTIGATORS, COLLABORATORS, RESEARCH COORDINATORS, AND CONTRIBUTORS
  10. Supporting Information
  • 1
    Rider LG, Miller FW.Deciphering the clinical presentations, pathogenesis, and treatment of the idiopathic inflammatory myopathies.JAMA2011;305:18390.
  • 2
    Ginn LR, Lin JP, Plotz PH, Bale SJ, Wilder RL, Mbauya A, et al.Familial autoimmunity in pedigrees of idiopathic inflammatory myopathy patients suggests common genetic risk factors for many autoimmune diseases.Arthritis Rheum1998;41:4005.
  • 3
    Niewold TB, Wu SC, Smith M, Morgan GA, Pachman LM.Familial aggregation of autoimmune disease in juvenile dermatomyositis.Pediatrics2011;127:e123946.
  • 4
    Deitiker P, Atassi MZ.Non-MHC genes linked to autoimmune disease.Crit Rev Immunol2012;32:193285.
  • 5
    Chinoy H, Lamb JA, Ollier WE, Cooper RG.Recent advances in the immunogenetics of idiopathic inflammatory myopathy.Arthritis Res Ther2011;13:216.
  • 6
    Miller FW. Inflammatory myopathies: polymyositis, dermatomyositis, and related conditions. In: Koopman WJ, Moreland LW, editors.Arthritis and allied conditions: a textbook of rheumatology. 15th ed.Philadelphia:Lippincott Williams & Wilkins;2005. p.1593620.
  • 7
    Zong M, Lundberg IE.Pathogenesis, classification and treatment of inflammatory myopathies.Nat Rev Rheumatol2011;7:297306.
  • 8
    Feldman BM, Rider LG, Reed AM, Pachman LM.Juvenile dermatomyositis and other idiopathic inflammatory myopathies of childhood.Lancet2008;371:220112.
  • 9
    Bohan A, Peter JB, Bowman RL, Pearson CM.Computer-assisted analysis of 153 patients with polymyositis and dermatomyositis.Medicine (Baltimore)1977;56:25586.
  • 10
    Gregersen PK, Amos CI, Lee AT, Lu Y, Remmers EF, Kastner DL, et al.REL, encoding a member of the NF-κB family of transcription factors, is a newly defined risk locus for rheumatoid arthritis.Nat Genet2009;41:8203.
  • 11
    Padyukov L, Seielstad M, Ong RT, Ding B, Ronnelid J, Seddighzadeh M, et al.A genome-wide association study suggests contrasting associations in ACPA-positive versus ACPA-negative rheumatoid arthritis.Ann Rheum Dis2011;70:25965.
  • 12
    Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al.PLINK: a tool set for whole-genome association and population-based linkage analyses.Am J Hum Genet2007;81:55975.
  • 13
    Cornelis MC, Agrawal A, Cole JW, Hansel NN, Barnes KC, Beaty TH, et al, GENEVA Consortium.The Gene, Environment Association Studies consortium (GENEVA): maximizing the knowledge obtained from GWAS by collaboration across studies of multiple conditions.Genet Epidemiol2010;34:36472.
  • 14
    Stahl EA, Raychaudhuri S, Remmers EF, Xie G, Eyre S, Thomson BP, et al.Genome-wide association study meta-analysis identifies seven new rheumatoid arthritis risk loci.Nat Genet2010;42:50814.
  • 15
    Terao C, Yamada R, Ohmura K, Takahashi M, Kawaguchi T, Kochi Y, et al.The human AIRE gene at chromosome 21q22 is a genetic determinant for the predisposition to rheumatoid arthritis in Japanese population.Hum Mol Genet2011;20:26805.
  • 16
    Flesher DL, Sun X, Behrens TW, Graham RR, Criswell LA.Recent advances in the genetics of systemic lupus erythematosus.Expert Rev Clin Immunol2010;6:46179.
  • 17
    Ramos PS, Criswell LA, Moser KL, Comeau ME, Williams AH, Pajewski NM, et al, for the International Consortium on the Genetics of Systemic Lupus Erythematosus (SLEGEN).A comprehensive analysis of shared loci between systemic lupus erythematosus (SLE) and sixteen autoimmune diseases reveals limited genetic overlap.PLoS Genet2011;7:e1002406.
  • 18
    Cooper JD, Walker NM, Smyth DJ, Downes K, Healy BC, Todd JA, and Type I Diabetes Genetics Consortium.Follow-up of 1715 SNPs from the Wellcome Trust Case Control Consortium genome-wide association study in type I diabetes families.Genes Immun2009;10 Suppl 1:S8594.
  • 19
    Barrett JC, Clayton DG, Concannon P, Akolkar B, Cooper JD, Erlich HA, et al, and the Type 1 Diabetes Genetics Consortium.Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes.Nat Genet2009;41:7037.
  • 20
    Swafford AD, Howson JM, Davison LJ, Wallace C, Smyth DJ, Schuilenburg H, et al.An allele of IKZF1 (Ikaros) conferring susceptibility to childhood acute lymphoblastic leukemia protects against type 1 diabetes.Diabetes2011;60:10414.
  • 21
    Barrett JC, Hansoul S, Nicolae DL, Cho JH, Duerr RH, Rioux JD, et al.Genome-wide association defines more than 30 distinct susceptibility loci for Crohn's disease.Nat Genet2008;40:95562.
  • 22
    Franke A, McGovern DP, Barrett JC, Wang K, Radford-Smith GL, Ahmad T, et al.Genome-wide meta-analysis increases to 71 the number of confirmed Crohn's disease susceptibility loci.Nat Genet2010;42:111825.
  • 23
    Amre DK, Mack DR, Morgan K, Israel D, Deslandres C, Seidman EG, et al.Association between genome-wide association studies reported SNPs and pediatric-onset Crohn's disease in Canadian children.Hum Genet2010;128:1315.
  • 24
    Cooper JD, Simmonds MJ, Walker NM, Burren O, Brand OJ, Guo H, et al.Seven newly identified loci for autoimmune thyroid disease.Hum Mol Genet2012;21:52028.
  • 25
    Dubois PC, Trynka G, Franke L, Hunt KA, Romanos J, Curtotti A, et al.Multiple common variants for celiac disease influencing immune gene expression [published erratum appears in Nat Genet 2010;42:465].Nat Genet2010;42:295302.
  • 26
    De Jager PL, Jia X, Wang J, de Bakker PI, Ottoboni L, Aggarwal NT, et al.Meta-analysis of genome scans and replication identify CD6, IRF8 and TNFRSF1A as new multiple sclerosis susceptibility loci.Nat Genet2009;41:77682.
  • 27
    Zhernakova A, van Diemen CC, Wijmenga C.Detecting shared pathogenesis from the shared genetics of immune-related diseases.Nat Rev Genet2009;10:4355.
  • 28
    Watanabe M, Maemura K, Kanbara K, Tamayama T, Hayasaki H.GABA and GABA receptors in the central nervous system and other organs.Int Rev Cytol2002;213:147.
  • 29
    Nagaraju K, Lundberg IE.Polymyositis and dermatomyositis: pathophysiology.Rheum Dis Clin North Am2011;37:15971.
  • 30
    Deng Y, Tsao BP.Genetic susceptibility to systemic lupus erythematosus in the genomic era.Nat Rev Rheumatol2010;6:68392.
  • 31
    Coustet B, Dieude P, Guedj M, Bouaziz M, Avouac J, Ruiz B, et al.C8orf13–BLK is a genetic risk locus for systemic sclerosis and has additive effects with BANK1: results from a large French cohort and meta-analysis.Arthritis Rheum2011;63:20916.
  • 32
    Nordmark G, Kristjansdottir G, Theander E, Appel S, Eriksson P, Vasaitis L, et al.Association of EBF1, FAM167A(C8orf13)-BLK and TNFSF4 gene variants with primary Sjogren's syndrome.Genes Immun2011;12:1009.
  • 33
    Simpfendorfer KR, Olsson LM, Manjarrez ON, Khalili H, Simeone AM, Katz MS, et al.The autoimmunity-associated BLK haplotype exhibits cis-regulatory effects on mRNA and protein expression that are prominently observed in B cells early in development.Hum Mol Genet2012;21:391825.
  • 34
    Nandagopal S, Wu D, Lin F.Combinatorial guidance by CCR7 ligands for T lymphocytes migration in co-existing chemokine fields.PLoS One2011;6:e18183.
  • 35
    Pickens SR, Chamberlain ND, Volin MV, Pope RM, Talarico NE, Mandelin AM II, et al.Role of the CCL21 and CCR7 pathways in rheumatoid arthritis angiogenesis.Arthritis Rheum2012;64:247181.
  • 36
    Nanki T, Takada K, Komano Y, Morio T, Kanegane H, Nakajima A, et al.Chemokine receptor expression and functional effects of chemokines on B cells: implication in the pathogenesis of rheumatoid arthritis.Arthritis Res Ther2009;11:R149.
  • 37
    Khanna S, Reed AM.Immunopathogenesis of juvenile dermatomyositis.Muscle Nerve2010;41:58192.
  • 38
    Lopez de Padilla CM, Vallejo AN, Lacomis D, McNallan K, Reed AM.Extranodal lymphoid microstructures in inflamed muscle and disease severity of new-onset juvenile dermatomyositis.Arthritis Rheum2009;60:116072.
  • 39
    Eleftherohorinou H, Wright V, Hoggart C, Hartikainen AL, Jarvelin MR, Balding D, et al.Pathway analysis of GWAS provides new insights into genetic susceptibility to 3 inflammatory diseases.PLoS One2009;4:e8068.

Appendix: A: INVESTIGATORS, COLLABORATORS, RESEARCH COORDINATORS, AND CONTRIBUTORS

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgments
  8. REFERENCES
  9. Appendix: A: INVESTIGATORS, COLLABORATORS, RESEARCH COORDINATORS, AND CONTRIBUTORS
  10. Supporting Information

Other Myositis Genetics Consortium study investigators

Study investigators of the Myositis Genetics Consortium, in addition to the authors of this article, are as follows: Drs. Christopher Denton (Royal Free Hospital, London, UK), David Hilton-Jones (John Radcliffe Hospital, Oxford, UK), Patrick Kiely (St. George's Hospital, London, UK), Paul H. Plotz, Mark Gourley (National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD), Paul Scheet (M. D. Anderson Cancer Center, Houston, TX), and Hemlata Varsani (University College London, London, UK).

UK Adult Onset Myositis Immunogenetic Collaboration

Members of the UK Adult Onset Myositis Immunogenetic Collaboration who recruited and enrolled subjects are as follows: Drs. Yasmeen Ahmed (Llandudno General Hospital), Raymond Armstrong (Southampton General Hospital), Robert Bernstein (Manchester Royal Infirmary), Carol Black (Royal Free Hospital, London), Simon Bowman (University Hospital, Birmingham), Ian Bruce (Manchester Royal Infirmary), Robin Butler (Robert Jones & Agnes Hunt Orthopaedic Hospital, Oswestry), John Carty (Lincoln County Hospital), Chandra Chattopadhyay (Wrightington Hospital), Easwaradhas Chelliah (Wrightington Hospital), Fiona Clarke (James Cook University Hospital, Middlesborough), Peter Dawes (Staffordshire Rheumatology Centre, Stoke on Trent), Joseph Devlin (Pinderfields General Hospital, Wakefield), Christopher Edwards (Southampton General Hospital), Paul Emery (Academic Unit of Musculoskeletal Disease, Leeds), John Fordham (South Cleveland Hospital, Middlesborough), Alexander Fraser (Academic Unit of Musculoskeletal Disease, Leeds), Hill Gaston (Addenbrooke's Hospital, Cambridge), Patrick Gordon (King's College Hospital, London), Bridget Griffiths (Freeman Hospital, Newcastle), Harsha Gunawardena (Frenchay Hospital, Bristol), Frances Hall (Addenbrooke's Hospital, Cambridge), Beverley Harrison (North Manchester General Hospital), Elaine Hay (Staffordshire Rheumatology Centre, Stoke on Trent), Lesley Horden (Dewsbury District General Hospital), John Isaacs (Freeman Hospital, Newcastle), Adrian Jones (Nottingham University Hospital), Sanjeet Kamath (Staffordshire Rheumatology Centre, Stoke on Trent), Thomas Kennedy (Royal Liverpool Hospital), George Kitas (Dudley Group Hospitals Trust, Birmingham), Peter Klimiuk (Royal Oldham Hospital), Sally Knights (Yeovil District Hospital, Somerset), John Lambert (Doncaster Royal Infirmary), Peter Lanyon (Queen's Medical Centre, Nottingham), Ramasharan Laxminarayan (Queen's Hospital, Burton Upon Trent), Bryan Lecky (Walton Neuroscience Centre, Liverpool), Raashid Luqmani (Nuffield Orthopaedic Centre, Oxford), Jeffrey Marks (Steeping Hill Hospital, Stockport), Michael Martin (St. James University Hospital, Leeds), Dennis McGonagle (Academic Unit of Musculoskeletal Disease, Leeds), Neil McHugh (Royal National Hospital for Rheumatic Diseases, Bath), Francis McKenna (Trafford General Hospital, Manchester), John McLaren (Cameron Hospital, Fife), Michael McMahon (Dumfries & Galloway Royal Infirmary, Dumfries), Euan McRorie (Western General Hospital, Edinburgh), Peter Merry (Norfolk & Norwich University Hospital, Norwich), Sarah Miles (Dewsbury & District General Hospital, Dewsbury), James Miller (Royal Victoria Hospital, Newcastle), Anne Nicholls (West Suffolk Hospital, Bury St. Edmunds), Jennifer Nixon (Countess of Chester Hospital, Chester), Voon Ong (Royal Free Hospital, London), Katherine Over (Countess of Chester Hospital, Chester), John Packham (Staffordshire Rheumatology Centre, Stoke on Trent), Nicolo Pipitone (King's College Hospital, London), Michael Plant (South Cleveland Hospital, Middlesborough), Gillian Pountain (Hinchingbrooke Hospital, Huntington), Thomas Pullar (Ninewells Hospital, Dundee), Mark Roberts (Salford Royal Foundation Trust), Paul Sanders (Wythenshawe Hospital, Manchester), David Scott (King's College Hospital, London), David Scott (Norfolk & Norwich University Hospital, Norwich), Michael Shadforth (Staffordshire Rheumatology Centre, Stoke on Trent), Thomas Sheeran (Cannock Chase Hospital, Cannock, Staffordshire), Arul Srinivasan (Broomfield Hospital, Chelmsford), David Swinson (Wrightington Hospital), Lee-Suan Teh (Royal Blackburn Hospital, Blackburn), Michael Webley (Stoke Manderville Hospital, Aylesbury), Brian Williams (University Hospital of Wales, Cardiff), and Jonathan Winer (Queen Elizabeth Hospital, Birmingham).

UK Juvenile Dermatomyositis Research Group

Members of the UK Juvenile Dermatomyositis Research Group (local research coordinators and principal investigators) who contributed to the UK Juvenile Dermatomyositis Cohort Study are as follows: Dr. Clive Ryder and Mrs. Janis Scott (Birmingham Children's Hospital, Birmingham); Professor Helen Foster, Dr. Mark Friswell, Dr. Sharmila Jandial, Ms Vicky Stevenson, and Mrs. Alison Swift (Great North Children's Hospital, Newcastle); Miss Laura Beard, Ms Virginia Brown, Dr. Elli Enayat, Ms Elizabeth Halkon, Dr. N. Hasson, Ms Audrey Juggins, Mrs. Sian Lunt, Mrs. Sue Maillard, Dr. Clarissa Pilkington, Dr. Sally Smith, Mrs. Hemlata Varsani, and Professor Lucy Wedderburn (Great Ormond Street Hospital, London); Mrs. Gillian Jackson and Dr. Sue Wyatt (Leeds General Infirmary, Leeds); Dr. Kevin Murray (Princess Margaret Hospital, Perth, Western Australia, Australia); Mrs. Elizabeth Stretton and Dr. Helen Venning (Queen's Medical Centre, Nottingham); Dr. Joyce Davidson, Ms Sue Ferguson, and Dr. Janet Gardner-Medwin (The Royal Hospital for Sick Children, Yorkhill, Glasgow); Ms Louise Hanna, Dr. Liza McCann, and Mr. Ian Roberts (The Royal Liverpool Children's Hospital, Alder Hey, Liverpool); and Dr. Eileen Baildam, Ms Ann McGovern, and Dr. Phil Riley (Royal Manchester Children's Hospital, Manchester).

US Childhood Myositis Heterogeneity Study Group

The following members of the US Childhood Myositis Heterogeneity Study Group contributed to this study: Drs. Barbara S. Adams (University of Michigan, Ann Arbor), Catherine A. Bingham (Hershey Medical Center, Hershey, PA), Gail D. Cawkwell (All Children's Hospital, St. Petersburg, FL), Terri H. Finkel (Children's Hospital of Philadelphia, Philadelphia, PA), Steven W. George (Ellicott City, MD), Harry L. Gewanter (Richmond, VA), Ellen A. Goldmuntz (Children's National Medical Center, Washington, DC), Donald P. Goldsmith (St. Christopher's Hospital for Children, Philadelphia, PA), Michael Henrickson (Children's Hospital, Madera, CA), Lisa Imundo (Columbia University, New York, NY), Ildy M. Katona (Uniformed Services University, Bethesda, MD), Carol B. Lindsley (University of Kansas, Kansas City), Chester P. Oddis (University of Pittsburgh, Pittsburgh, PA), Judyann C. Olson (Medical College of Wisconsin, Milwaukee), David Sherry (Children's Hospital of Philadelphia, Philadelphia, PA), Scott A. Vogelgesang (Walter Reed Army Medical Center, Washington, DC), Carol A. Wallace (Children's Medical Center, Seattle, WA), Patience H. White (George Washington University, Washington, DC), and Lawrence S. Zemel (Connecticut Children's Hospital, Hartford).

Supporting Information

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgments
  8. REFERENCES
  9. Appendix: A: INVESTIGATORS, COLLABORATORS, RESEARCH COORDINATORS, AND CONTRIBUTORS
  10. Supporting Information

Additional Supporting Information may be found in the online version of this article.

FilenameFormatSizeDescription
ART_38137_sm_SupplTable1.doc204KSupplementary Table

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