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. Author manuscript; available in PMC: 2014 Dec 1.
Published in final edited form as: Arthritis Rheum. 2013 Dec;65(12):3239–3247. doi: 10.1002/art.38137

Genome-wide Association Study of Dermatomyositis Reveals Genetic Overlap with other Autoimmune Disorders

Frederick W Miller 1,*, Robert G Cooper 2,*, Jiri Vencovsky 3, Lisa G Rider 1, Katalin Danko 4, Lucy R Wedderburn 5, Ingrid E Lundberg 6, Lauren M Pachman 7, Ann M Reed 8, Steven R Ytterberg 8, Leonid Padyukov 6, Albert Selva-O’Callaghan 9, Timothy Radstake 10, David A Isenberg 5, Hector Chinoy 11, William E R Ollier 12, Terrance P O’Hanlon 1, Bo Peng 13, Annette Lee 14, Janine A Lamb 12, Wei Chen 13, Christopher I Amos 13,**, Peter K Gregersen 14,**; with the Myositis Genetics Consortium***
PMCID: PMC3934004  NIHMSID: NIHMS525308  PMID: 23983088

Abstract

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 = 1178) and controls (n = 4724). 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 < 5x10−8) at 80 genotyped SNPs. An analysis of 141 non-MHC SNPs previously associated with autoimmune diseases showed that three SNPs linked with three genes were associated with DM, with a false discovery rate (FDR) < 0.05. These genes were phospholipase C like 1 (PLCL1, rs6738825, FDR=0.00089), B lymphoid tyrosine kinase (BLK, rs2736340, FDR=0.00031), 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.

Keywords: dermatomyositis, adult, juvenile, shared autoimmunity genes

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 human leukocyte antigen 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 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

Study populations

Investigators with collections of DNA samples from myositis patients formed a collaboration called the Myositis Genetics Consortium (MYOGEN) with the goal of identifying new genetic factors associated with myositis. 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 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 per Bohan and Peter criteria [9]. Age at onset of less than 18 years defined juvenile DM. After excluding 241 cases due to low call rates (n = 123), outliers (n = 55), or related individuals (n = 48), 1178 Caucasian cases 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 three centers, including the National Institutes of Health (234 adult DM and 140 juvenile DM), the Mayo Clinic (53 adult DM and 36 juvenile DM), and the Children’s Memorial Research Center in Chicago (107 juvenile DM). The UK cases were obtained from the UK Adult Onset Myositis Immunogenetic Collaboration (149 adult DM) and the UK Juvenile Dermatomyositis Research Group (159 cases). Other European samples came from the Czech Republic (114 adult and 11 juvenile DM), Hungary (64 adult and 12 juvenile DM), Spain (43 adult and 4 juvenile DM), Sweden (37 adult and 4 juvenile DM), and the Netherlands (11 adult DM).

In order to optimize case-control matching, we utilized 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 the Illumina Human1M-Duo v3 BeadChip (n = 235: 166 Czechs and 69 Hungarians) or the Illumina Human660W-Quad v1 BeadChip (n = 21: all Hungarian). US controls were taken from previously available data from the North American Rheumatoid Arthritis Consortium [10]. UK controls were taken from the available data from The Wellcome Trust Case-Control Consortium (WTCCC 1958 birth cohort on the Illumina Human1M-Duo v3 BeadChip (n = 2415; http://www.wtccc.org.uk/ccc1). Swedish and Dutch controls (n = 642) were taken from previously published datasets [11], and Spanish controls (n = 259) were obtained from blood bank volunteers in Granada, Spain using data generated on the 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, New York, US. 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 the Illumina HumanHap550 BeadChip, 221 were genotyped using the Illumina HumanCNV370-Duo v1 BeadChip, 293 were genotyped using the Illumina Human610-Quad v1 BeadChip, and 578 were genotyped using the Illumina Human660W-Quad v1 BeadChip, according to the manufacturer’s protocols (Illumina Inc., San Diego, CA). 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=1372).

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 of minor allele frequency of ≤0.01 or Hardy-Weinberg equilibrium in controls with a P value ≤ 10−5. Merged data were separated into five 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 one member of each set of duplicated or related samples (n=48). Outliers identified in the clustering in PLINK (Z > 4 or < −4) were removed (n=15). Additional outliers (n=6) that deviated by more than 4 standard errors from the centroid were identified by principal component analysis in Eigenstrat (http://genepath.med.harvard.edu/~reich/Software.htm) using 16,819 SNPs that are in the linkage disequilibrium (LD)-pruned SNP set provided by The Gene, Environment Association Studies consortium (GENEVA) 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 five principal components for analysis of the US data, no principal components for analysis of the UK data, six principal components for analysis of the Dutch data, one principal component for analysis of Central European data, and one principal component for analysis of 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. Then meta-analysis using PLINK was done for all five groups. For the focused analysis of autoimmune-related SNPs, we adopted a Benjamini Hochberg false discovery rate (FDR) of <0.05.

RESULTS

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

The GWAS of 1178 cases and 4724 control samples included in this study (Table 1) showed GWAS-level significance (P < 5x10−8) 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 cases, controls and SNP data included in the study

Population Cases
 Controls
Adult dermatomyositis Juvenile
dermatomyositis		 No. of
successfully
genotyped
SNPs		 Covariate Genomic inflation factor (λ)
Sample size Female
(%)		 Sample size Female
(%)		 Sample
size		 Female
(%)
Czech/Hungarian 178 70.8% 23 78.3% 256 57.4% 242530 Population structure 1.01
Spanish 43 81.4% 4 50.0% 259 65.6% 242871 Population structure 1.009
Swedish/Dutch 48 68.8% 4 75.0% 642 72.4% 242644 Population structure 1.021
UK 149 65.8% 159 70.4% 2415 47.8% 236039 None 1
USA 287 76.0% 283 69.6% 1152 70.8% 237155 Population structure 1.073
Meta-analysis 705 72.4% 473 70.2% 4724 58.2% 241502 None 1.043
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Figure 1.

Figure 1

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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 orange line represents the genome-wide level of significance (P = 5x10−8). Chr = chromosome.

We used quantile-quantile (Q-Q) plots, which is a method for comparing two probability distributions by plotting quantiles against each other, to evaluate the comparability of tests we conducted to their expected distributions. We included any significant principal components as covariates to remove the residual population structure in 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 tests, we found no overall systematic inflation of the number of positive tests (Figure 2A), as the ratio of the median chi-square test to the expected value gave a lambda ratio 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 (lambda = 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).

Figure 2.

Figure 2

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(A) Quantile-quantile (Q-Q) plot of the genome-wide meta-analysis (lambda = 1.043). (B) Q-Q plot of the genome-wide meta-analysis without the major histocompatibility complex region (lambda = 1.037).

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 that have been found to be associated with first-degree relatives of DM patients [2; 3]. Therefore, we selected 269 SNPs that had been associated with rheumatoid arthritis (RA) [14; 15], systemic lupus erythematosus (SLE) [16; 17], type 1 diabetes [18; 19] [20], Crohn’s disease [21; 22] [23], thyroid disease [24], gluten-sensitive enteropathy [25], or multiple sclerosis [26], and 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 CEU (see data in Supplementary Table S1 for all 141 SNPs). Of these 141 SNPs, SNPs related to three genes, which had not been previously associated with DM, were found to have significant (FDR < 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 S1 for all 141 SNPs). 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 published genome-wide association study single nucleotide polymorphisms for autoimmune diseases with those for dermatomyositis*

Gene
Name		 SNP Marker Original SNP/LD Chr: Position OR (CI) P FDR SNP
Disease
Source [ref]
PLCL1 rs7572733 rs6738825/0.979 2: 198929806 0.80 (0.72–0.88) 6.18E-06 0.00089 SLE [17]
PLCL1 rs1518364 rs6738825/0.958 2: 198809975 1.22 (1.11–1.35) 5.11E-05 0.0037 SLE [17]
PLCL1 rs938929 rs6738825/0.958 2: 198780860 1.22 (1.10–1.34) 0.00008322 0.0030 SLE [17]
BLK rs2736340 8: 11343973 1.25 (1.12–1.40) 0.0000653 0.0031 RA [14]
CCL21 rs2492358 rs951005/1.0 9: 34737828 0.77 (0.67–0.88) 0.0002093 0.0060 RA [14]
CCL21 rs951005 9: 34743681 0.77 (0.67–0.89) 0.000317 0.0076 RA [14]
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*

Only SNPs with FDR < 0.05 are listed; SNP marker = directly genotyped single nucleotide polymorphism (SNP) 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 directly genotyped SNP on Illumina arrays; Chr = chromosome; Position = base pair in hg19/build37 coordinate; OR = odds ratio; CI = 95% confidence interval; P = fixed effect P value in meta-analysis; FDR = false discovery rate; SLE = systemic lupus erythematosus; RA = rheumatoid arthritis.

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 (Figure 3, lambda = 2.59). The current study had a low value of lambda in the entire population of SNPs that had been genotyped.

Figure 3.

Figure 3

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Quantile-quantile (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 (lambda = 2.59).

DISCUSSION

This work, which to our knowledge is the first GWAS of any form of myositis, is consistent with 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 myositis samples 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 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 have been 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 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.

The strongest non-MHC association of SNPs with DM that are seen in other autoimmune diseases 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 three typed SNPs (rs7572733, rs1518364, and rs938929) were in strong LD with a PLCL1 SNP (rs6738825), which was previously associated with SLE. PLCL1 is involved not only in the inositol phospholipid-based intracellular signaling cascade, but also regulates the turnover of receptors, and thus it contributes to the maintenance of muscle tone and of gamma-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 to be 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 biopsies [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 for 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 mRNA and protein levels in cell lines. Nonetheless, the rs922483 allele in the BLK gene, which is in LD with rs2736340, is reported to downregulate both BLK mRNA and protein expression in primary human transitional and naïve B cells from cord blood but not from adult B cell subsets, suggesting that involvement of BLK in the risk for 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 chemokine receptor 7 (CCR7) that 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 for the interferon 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 on the pathogenic mechanisms in DM, as well as 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], which should all 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.

Supplementary Material

01

ACKNOWLEDGMENTS

We thank the members of the UK Adult Onset Myositis Immunogenetic Collaboration (AOMIC) for recruiting and enrolling subjects: Drs. Yasmeen Ahmed (Llandudno General Hospital, Wales), 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 (Addenbrookes Hospital, Cambridge), Patrick Gordon (Kings College Hospital, London), Bridget Griffiths (Freeman Hospital, Newcastle), Harsha Gunawardena (Frenchay Hospital, Bristol), Frances Hall (Addenbrookes 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 (Queens Medical Centre, Nottingham), Ramasharan Laxminarayan (Queens Hospital, Burton Upon Trent), Bryan Lecky (Walton Neuroscience Centre, Liverpool), Raashid Luqmani (Nuffield Orthopaedic Centre, Oxford), Jeffrey Marks (Steeping Hill Hospital, Stockport), Micheal 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 (Dunfries & Galloway Royal Infirmary, Scotland), Euan McRorie (Western General Hospital, Edinburgh), Peter Merry (Norfolk & Norwich University Hospital), Sarah Miles (Dewsbury & District General Hospital), James Miller (Royal Victoria Hospital, Newcastle), Anne Nicholls (West Suffolk Hospital, Bury St Edmunds), Jennifer Nixon (Countess of Chester Hospital), Voon Ong (Royal Free Hospital, London), Katherine Over (Countess of Chester Hospital), John Packham (Staffordshire Rheumatology Centre, Stoke on Trent), Nicolo Pipitone (Kings 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 (Norfolk & Norwich University Hospital), David Scott (Kings College Hospital, London), Michael Shadforth (Staffordshire Rheumatology Centre, Stoke on Trent), Thomas Sheeran (Cannock Chase Hospital), Arul Srinivasan (Boomfield Hospital, Chelmsford) David Swinson (Wrightington Hospital), Lee-Suan Teh (Royal Blackburn Hospital), Michael Webley (Stoke Manderville Hospital, Aylesbury), Brian Williams (University Hospital of Wales), and Jonathan Winer (Queen Elizabeth Hospital, Birmingham).

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

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

We are indebted to Dr. Javier Martin of Granada Spain for supplying Spanish control data and to Dr. Peter Novota at the Institute of Rheumatology, Prague, Czech Republic for supplying Czech controls. We thank Dr. Younghun Han (MD Anderson Cancer Center) for statistical support; Miss Hazel Platt (Centre for Integrated Genomic Medical Research, University of Manchester), Mrs. Fiona Marriage (Centre for Integrated Genomic Medical Research, University of Manchester), and Drs. Maryam Dastmalchi and Eva Jemseby (Karolinska Institutet, Stockholm) for technical support; and Mr. Paul New (Salford Royal Foundation Trust) for ethical and technical support.

We thank Elaine Remmers (National Institute of Arthritis and Musculoskeletal and Skin Diseases, Bethesda, MD) and 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.

This study was supported in part by: the Intramural Program of the NIH, National Institute of Environmental Health Sciences (NIEHS Z01ES101074); European Community’s FP6, AutoCure LSHB CT-2006-018661; The UK Myositis Support Group; Arthritis Research UK (18474); The Cure JM Foundation; the European Science Foundation; the Wellcome Trust; the Henry Smith Charity UK; Action Medical UK; and the Swedish Research Council. The Czech cohort was partially supported by the Ministry of Health, Czech Republic (No. 00023728).

Role of the funding sources. The sponsors of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. All members of the writing group had full access to all the data in the study, and authors had final responsibility for the decision to submit for publication.

Footnotes

Disclaimer. The findings and conclusions in this article are those of the authors and do not necessarily represent the official position of the institutions with which they are affiliated.

Financial disclosures: The authors have nothing to disclose.

AUTHOR CONTRIBUTIONS

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.

Study conception and design and funding. Miller, Cooper, Vencovsky, Rider, Lundberg, Padyukov, Amos, and Gregersen designed the study and obtained funding.

Acquisition of data. Miller, Cooper, Vencovsky, Rider, Danko, Wedderburn, Lundberg, Padyukov, Pachman, Reed, Ytterberg, Selva-O’Callaghan, Radstake, Isenberg, and Chinoy collected samples. O’Hanlon, Ollier, Lee, Lamb, Pachman, and Gregersen performed sample processing.

Analysis and interpretation of data. O’Hanlon, Peng, Lee, Lamb, Padyukov, Chen, Amos, and Gregersen did the data analysis, interpretation and data management.

Other Myositis Genetics Consortium Study Investigators: Drs. Christopher Denton (Royal Free Hospital, London, UK), David Hilton-Jones (John Radcliffe Hospital, Oxford, UK), Patrick Kiely (St. Georges Hospital, London, UK), Paul H. Plotz, Mark Gourley (National Institute of Arthritis, Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD), Paul Scheet (MD Anderson Cancer Center, Houston, TX), and Hemlata Varsani (University College London, London, UK).

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