Objectives The idiopathic inflammatory myopathies (IIMs) are a heterogeneous group of rare autoimmune diseases characterised by muscle weakness and extramuscular manifestations such as skin rashes and interstitial lung disease. We genotyped 2566 IIM cases of Caucasian descent using the Immunochip; a custom array covering 186 established autoimmune susceptibility loci. The cohort was predominantly comprised of patients with dermatomyositis (DM, n=879), juvenile DM (JDM, n=481), polymyositis (PM, n=931) and inclusion body myositis (n=252) collected from 14 countries through the Myositis Genetics Consortium.
Results The human leucocyte antigen (HLA) and PTPN22 regions reached genome-wide significance (p<5×10−8). Nine regions were associated at a significance level of p<2.25×10−5, including UBE2L3, CD28 and TRAF6, with evidence of independent effects within STAT4. Analysis of clinical subgroups revealed distinct differences between PM, and DM and JDM. PTPN22 was associated at genome-wide significance with PM, but not DM and JDM, suggesting this effect is driven by PM. Additional suggestive associations including IL18R1 and RGS1 in PM and GSDMB in DM were identified. HLA imputation confirmed that alleles HLA-DRB1*03:01 and HLA-B*08:01 of the 8.1 ancestral haplotype (8.1AH) are most strongly associated with IIM, and provides evidence that amino acids within the HLA, such as HLA-DQB1 position 57 in DM, may explain part of the risk in this locus. Associations with alleles outside the 8.1AH reveal differences between PM, DM and JDM.
Conclusions This work represents the largest IIM genetic study to date, reveals new insights into the genetic architecture of these rare diseases and suggests different predominating pathophysiology in different clinical subgroups.
- Gene Polymorphism
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The idiopathic inflammatory myopathies (IIMs) are a heterogeneous group of rare autoimmune diseases, the major phenotypes of which are dermatomyositis (DM), polymyositis (PM), inclusion body myositis (IBM) and DM/PM overlapping with other connective tissue diseases.1 IIMs are primarily characterised by the presence of proximal muscle weakness, elevated levels of skeletal muscle enzymes and inflammatory infiltrates in skeletal muscle, but may also present with extramuscular manifestations including skin rashes, interstitial lung disease and malignancy that often correlate with serum antibody status.2
IIMs are thought to be complex genetic diseases, initiated by immune activation following specific environmental events in genetically predisposed individuals. The strongest genetic association in the IIM has been consistently within the major histocompatibility complex (MHC),3 specifically with the 8.1 ancestral haplotype (8.1 AH). A recent genome-wide association study in DM, and a candidate gene study, also indicate overlap of genes implicated in other autoimmune diseases.4 ,5 The Immunochip is a custom-designed array containing coverage of 186 established autoimmune susceptibility loci and extended coverage across the MHC.6 In this study, we report an Immunochip analysis of 2566 global IIM cases and 15 651 controls, representing the largest genetic association study to date in IIM.
Two thousand nine hundred and fifty-four samples from 14 countries were collected through the Myositis Genetics Consortium, and written informed consent was obtained from all cases. There is overlap between these samples and previous IIM genetic studies.3–5 IIM cases were included if they fulfilled probable or definite Bohan and Peter classification criteria for PM, juvenile PM, DM or juvenile DM (JDM),7 ,8 and Griggs or European Neuromuscular Centre or Medical Research Council criteria for IBM.9–11 Eleven samples met the criteria for antisynthetase syndrome,12 however available clinical data was not able to differentiate between PM or DM. These were included in the combined IIM analysis, but removed from the clinical subgroup analysis.
Immunochip control data from 12 countries was provided by four disease consortia (see online supplementary methods).
Genotyping and quality control
Genotyping was performed in accordance with Illumina’s protocols in the UK (Centre for Genetics and Genomics Arthritis Research UK, University of Manchester, UK) and the USA (Feinstein Institute, New York, New York, USA). Standard sample and SNP quality control (QC) was performed in PLINK V.1.07 (see online supplementary methods).
Statistical analysis was performed in PLINK V.1.07 using a logistic regression applying an additive model, including the top 10 principal components as covariates to control for population stratification. Evidence for additional independent effects was investigated using a stepwise logistic regression including the most associated variants as covariates in subsequent conditional analyses.
Associated loci were interrogated for potentially causative variants using expression quantitative trait loci (eQTL) databases, and the functional prediction tools PolyPhen-2,13 SIFT14 and phastCons17-way15 (see online supplementary methods).
MHC imputation and association analysis
Classical human leucocyte antigen (HLA) alleles and corresponding amino acid sequences were imputed using SNP2HLA. A logistic regression assuming an additive model was used to test for association, and forward stepwise logistic regression was used to test for independent effects (see online supplementary methods). Classical four-digit HLA alleles were preferentially reported, unless an amino acid association explained more risk than HLA alleles alone.
Genotyping quality control
After stringent SNP and sample quality control we analysed 90 536 genetic variants in 2566 IIM cases and 15 651 controls of Caucasian descent (table 1). A breakdown of this cohort by clinical subgroup is reported in online supplementary table S1. Australia, Denmark and Switzerland did not have an ethnically matched control group; however, these were adequately matched by existing cohorts (UK, Sweden and Germany, respectively). By including the top 10 principal components as covariates and calculating the genomic inflation on a set of null SNPs (from a study investigating the genetic basis for reading and writing ability)16 on the Immunochip gave a λGC1000=1.05, indicating that cases and controls are well matched for ethnicity (see online supplementary figure S1).
HLA and PTPN22 are the most strongly associated regions in IIM
Two regions in this study reached genome-wide significance (p<5×10−8) (figure 1A and table 2). As expected, the most strongly associated region was within the MHC (p=9×10−133) (see online supplementary figure S2). HLA-imputation was performed separately on this locus.
The other region reaching genome-wide significance was within the PTPN22 locus (rs2476601; p=7.22×10−9), an established autoimmune risk locus. This SNP/locus has been previously associated in an IIM candidate gene study,3 but was not associated in a genome-wide association study in DM.4
A further nine regions were associated at a suggestive level of significance
We next investigated associations reaching our suggestive tier of association (p=2.25×10−5) calculated using the genetic Type 1 error calculator.31 This estimates the effective number of independent tests based on the linkage disequilibrium (LD) between SNPs contained on the genotyping array. Here, we found evidence of a further nine associated loci (table 2).
The third most strongly associated SNP in this analysis was in the YDJC gene (rs5754467) on chromosome 22 (4.67×10−7). This SNP tags a large haplotype block containing UBE2L3, an established autoimmune risk locus.
STAT4 is a susceptibility locus for many autoimmune diseases. The lead SNP in this region was protective, which is a novel finding in IIM (rs4853540, p=1.57×10−6). Stepwise logistic regression analysis in this region suggested an independent risk effect of rs10174238 (p=1.08×10−5, OR=1.17, 95% CI 1.09 to 1.26) (see online supplementary figure S3) and a further potential independent effect was seen at rs932169 (p=2.88×10−5, OR=1.25, 95% CI 1.13 to 1.39).
Further variants reaching our suggestive significance threshold reveal loci of interest that have been previously associated with autoimmune disease, including DGKQ, EOMES, CD28 and PRR5L/TRAF6. Associated SNPs that tag risk haplotypes (r2>0.7) in other autoimmune diseases are reported in table 2, and the direction of effect is reported in online supplementary table S2.
Subgroup analysis reveals unique associations within PM and DM
We stratified our cohort by the two largest subgroups within IIM, consisting of 931 adult PM cases (figure 1B and table 2) and 1360 DM cases (figure 1C and table 2). JDM cases were included in the DM analysis to increase power, and on previous evidence that there is not extensive genetic heterogeneity between the subgroups.4 The only non-HLA region to reach genome-wide significance in either subgroup was PTPN22 in PM (rs2476601, p=7.9×10−11). Interestingly, with a smaller sample size, the association in PM with PTPN22 was stronger than in the combined IIM analysis. There was no evidence of association in DM (p=0.19), therefore the stronger effect in PM appears to be driving the association in the combined IIM analysis. Other interesting regions reaching a suggestive level of significance were SL26A1/IDUA and RGS1 in PM, and GSDMB in DM (see online supplementary table S3).
Exonic and eQTL SNPs suggest potential causal variants
Potential functionally relevant variants were investigated for non-synonymous SNPs (table 3) or eQTLs (table 4) that are tagged by the lead SNP (r2>0.9). Two variants within the GSDMB gene, suggestively associated in DM, are ‘potentially damaging’ as predicted by PolyPhen-2. The PTPN22 variant is confirmed to be conserved across vertebrates, as well as an SNP in UBE3B. Evidence for eQTLs in cells with immune function (lymphoblastoid cell lines and monocytes) was found in six loci and may help annotate our associations, for example, the association with NAB1 in PM may be due to an eQTL affecting the expression of STAT1, 275Kb upstream.
HLA imputation confirms alleles of the 8.1 AH as the strongest association in IIM
Due to the complex linkage disequilibrium/haplotype structure in the MHC, interpretation of causal associations and independent effects using SNPs may be inadequate. We used SNP2HLA to impute classical HLA alleles and amino acids from SNP genotyping information. For each analysis, all variants reaching statistical significance (p<6.8×10−6) after each round of conditioning are included in online supplementary tables S4–S15. For many associations, amino acids unique to classical HLA risk alleles were associated at similar levels of significance to the HLA allele. For consistency, four-digit HLA alleles are reported, unless an amino acid is significantly more associated than individual HLA alleles. In the combined IIM analysis (n=2566), the most associated variants were classical HLA alleles, with HLA-DRB1*03:01 being the most significant four-digit allele (p=2.58×10−135, OR=1.88, 95% CI 1.68 to 2.11). HLA-DRB1*03:01 forms part of the 8.1 AH which has been consistently associated with IIM. After conditioning on the effects of HLA-DRB1*03:01, a strong association was found with HLA-B*08:01 (p=3.23×10−14, OR=1.58, 95% CI 1.41 to 1.78) suggesting that there is an independent effect within this locus. Further residual associations highlight the heterogeneity within this cohort, so analysis was then conducted on clinical subgroups. Similar associations were found with PM (n=931), HLA-DRB1*03:01 being the most significant four-digit allele (p=6.11×10−80, OR=1.99, 95% CI 1.67 to 2.36) and an independent effect with HLA-B*08:01 (p=4.17×10−9, OR=1.71, 95% CI 1.43 to 2.05). As the effect size of the HLA is strong in IIM, we hypothesised that we may be able to detect any potential differences between adult DM and JDM, even with a reduced sample size. In adult DM (n=879), HLA-B*08:01 was the most significant allele (p=2.46×10−42, OR=1.90, 95% CI 1.66 to 2.17). Conditioning on HLA-B*08:01, there was evidence of multiple independent effects within the HLA-DQB1 locus, therefore we analysed imputed amino acid residues. Amino acid position 57 of HLA-DQB1 was more significantly associated with DM than individual HLA-DQB1 alleles (p=8.95×10−14), with alanine (p=1.29×10−12 OR=1.62, 95% CI 1.44 to 1.83) and serine (p=9.28×10−7, OR=2.15, 95% CI 1.60 to 2.84) conferring the greatest risk. Further association with HLA-DQB1 remains after conditioning, notably an independent effect of HLA-DQB1*04:02 (p=2.01×10−6, OR=1.99, 95% CI 1.52 to 2.58). In the JDM subgroup (n=481), HLA-DRB1*03:01 was the most associated allele (p=7.91×1014, OR=1.90, 95% CI 1.61 to 2.22) and an independent association was observed with HLA-C*02:02 (p=3.28×10−7, OR=1.99, 95% CI 1.55 to 2.52) which is not a part of the 8.1 AH.
This is the largest genetic study to date in IIM, and has revealed several novel suggestive associations in adult and juvenile IIM emphasising the autoimmune architecture of these diseases. We have confirmed HLA and PTPN22 as the most strongly associated regions in IIM, and identified nine additional associations at a suggestive level of significance. Subgroup analysis has identified distinct differences between PM and DM. Identification of exonic and eQTL SNPs has localised association signals to several potential causal variants.
It is reassuring that associations such as PTPN22, STAT4 and UBE2L3 follow a similar genetic profile as reported in other autoimmune diseases. The most significantly associated SNP in the PTPN22 region is the rs2476601 variant, a C>T polymorphism that results in a non-synonymous arginine (R) to tryptophan (W) amino acid change at position 620. Although this SNP has been extensively studied in the context of autoimmunity, there is no consensus regarding the functional consequences of this SNP. Some studies report a gain of function mutation by enhancing the inhibitory effect on T-cell receptor (TCR) signalling,39 while others report a loss of function by increased degradation of the protein and a diminished inhibitory effect on T cell activation.40
STAT4 is an important transcription factor for many genes involved in T cell differentiation and has previously been associated with DM in the Japanese population.41 Stepwise logistic regression analysis was conducted on all regions in this study; however STAT4 is the only locus with evidence of independent associations. The three independent SNPs are in LD with associations in different diseases. The lead SNP in STAT4 is protective, and in moderate LD with protective SNPs in STAT4 reported in inflammatory bowel disease, Crohn's disease and ulcerative colitis.16 The independent risk effect of rs10174238 is the same SNP reported in juvenile idiopathic arthritis,18 and is in strong LD with disease-associated SNPs in rheumatoid arthritis and systemic lupus erythematosus.19 ,42 A SNP in high LD with rs932169 has been reported to be associated with primary biliary cirrhosis.25
The most significantly associated SNP in the YDJC gene tags an established autoimmune susceptibility locus where UBE2L3 is thought to be the causal gene.43 This risk haplotype is thought to increase UBE2L3 expression in B cells and monocytes and amplify nuclear factor kappa-B (NF-κB) activation.43
Stratification by clinical subgroup revealed further novel suggestive associations. These distinct differences between PM and DM suggest different autoimmune pathways in these subsets of IIM. For example, when splitting the total IIM cohort into PM and DM, we have shown that the association with PTPN22 is predominantly driven by a strong association with PM. For all associations, we have stratified by clinical subgroup and reported the summary statistics in online supplementary table S3. Patients with IBM were included in the combined IIM analysis on the basis of their diagnosis as an inflammatory autoimmune myopathy, however we did not analyse this subgroup separately due to a lack of power (n=252). Removing this group from our analyses did not make any substantial difference in associated regions, however the strength of the signals were attenuated in most instances. With eight non-HLA loci reaching our level of suggestive significance in PM, and only three in DM, it may be that the Immunochip is explaining less of the genetic risk to DM. This may be due to lack of power, the selected content of the Immunochip, heterogeneity of phenotypes within DM or a weaker genetic influence compared with other autoimmune diseases. Some previous reported associations with DM failed to replicate in this study. We looked for evidence of association with loci that have previously been associated with IIM that did not reach our suggestive level of significance. For example, in the DM and JDM subgroup analysis, an association was found with rs2618476 (p=3.2×10−5, OR=1.2, 95% CI 1.1 to 1.32), an SNP in B lymphoid tyrosine kinase (BLK). rs2618476 is a proxy for an SNP that was associated with DM in the Japanese population,44 and is also highly correlated (r2>0.8) with associations found in systemic lupus erythematosus and rheumatoid arthritis.20 ,29 With this knowledge, this association becomes more convincing, whereas the single association in the FAM167A-BLK region in the PM subgroup (rs17799348) that is independent of the established risk haplotype, is less so.
It is important to note that this study was conducted on Caucasian IIM individuals. While there is evidence that risk loci may be shared across populations, such as STAT4 and BLK in the Japanese population, the association between PTPN22 and autoimmune disease is unique to Caucasians as the R620W variant is rarely seen in Asian populations. There is therefore a need to conduct further genetic studies on different IIM populations.
The Immunochip contains a dense set of SNPs covering 186 loci based on evidence of association with 12 different autoimmune and inflammatory diseases.18 IIM was not one of these diseases, so this study can be seen as an exploratory investigation to assess genetic overlap with other autoimmune diseases, rather than the identification of genes novel to IIM. With 2566 samples, Immunochip studies of similar size have revealed multiple non-HLA associations reaching genome-wide significance.18 ,45 The fact that only a single locus reached this threshold may due to low statistical power owing to phenotypical heterogeneity within IIM. A more conservative level of significance (p<2.25×10−5) revealed suggestive associations of interest. SNPs that are the same, or in high LD with established autoimmune variants, along with biological knowledge and/or evidence of functionality may lead us to pursue these associations with more confidence. Indeed, a recent Immunochip study in type 1 diabetes calculated a Bayesian posterior probability of disease association >0.9 of SNPs reaching a suggestive level of significance (p<1×10−5) when there is evidence of genome-wide significance in other Immunochip studies.30
Due to the extended haplotypes that are present in the HLA region, for many associations, alleles carried on the same haplotype reached an equivalent significance level. For consistency, the most associated allele was used in the stepwise conditional analysis; however, this is not to say that the allele is causative. Interestingly in PM, two alleles frequently inherited together on the 8.1 AH (HLA-DRB1*03:01 and HLA-B*08:01) show evidence of having independent effects. This may also be the case in DM, however, after conditioning on HLA-B*08:01, the association with other alleles of the 8.1 AH did not reach genome-wide significance. In DM, the independent association with amino acid position 57 in HLA-DQB1 may explain part of the risk within this gene. Indeed, this position is an established risk factor for type 1 diabetes.46 In this study, classical four-digit HLA alleles were preferentially reported, unless an amino acid association explained more risk than HLA alleles alone. However looking at amino acids may give insight into functionality. For example, the association with HLA-DRB1*03:01 may be explained by the presence of amino acids that are unique to that allele. An asparagine at position 77, and an arginine at position 74 also were highly associated with IIM (see online supplementary table S4), and these residues are predominantly found on DRB1*03 alleles. As there are multiple residues unique to this allele, it is hard to tease out which positions may be functionally important; however the location of these amino acids in the HLA-DRB1 molecule may give insight (see online supplementary figure S4). Amino acid position 74 of HLA-DRB1 lies within the peptide binding groove, and almost all of the risk at this position can be explained by the presence of an arginine (p=3.1×10−72, OR=2.83, 95% CI 2.53 to 3.17). The location of Arg-74 may change the structure of the peptide binding groove in such a way as to accommodate autoantigenic peptides. Indeed Arg-74 is an established risk factor for the development of autoimmune diseases.47 A similar phenomenon is seen with HLA-B*08:01 and the occurrence of Phe-67 and Asp-9 (see online supplementary figure S5).
Alleles of the 8.1 AH have frequently been associated with the presence of myositis autoantibodies such as anti-Jo-1 and anti-PM-Scl. It may be that the association with the 8.1 AH and IIM is due to the prevalence of these antibodies, and weak associations with other HLA-alleles may be due to associations with autoantibodies that are less frequent in the disease subgroup. Further work is planned to stratify patients by serotype to clarify these differences.
This study has revealed new suggestive associations with IIM in the Caucasian population, and independent associations with PM and DM, and has shown that subgrouping patients into clinical subgroups is important to expand our knowledge of IIM. This international collaboration has made it possible to perform the largest study to date in IIM and it has considerably expanded our knowledge about the genetic architecture of this rare disease.
The authors thank Mrs Fiona Marriage (Centre for Integrated Genomic Medical Research, University of Manchester) for technical support, Drs Elaine Remmers, Douglas Bell (National Institutes of Health) and Jonathan Massey (University of Manchester) for critical review of the manuscript, and Mr Paul New (Salford Royal Foundation Trust) for ethical and technical support. The authors thank all of the patients and their families who contributed to this study. This publication was supported by researchers at the National Institute for Health Research (NIHR) Biomedical Research Unit Funding Scheme and the University College London Hospitals Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the (UK) National Health Service (NHS), the NIHR or the (UK) Department of Health. Shared Immunochip Control Cohorts: The authors thank the Rheumatoid Arthritis Consortium International (RACI) for Netherlands, Spanish, Swedish, UK and US Immunochip control genotypes. This study makes use of data generated by the Wellcome Trust Case-Control Consortium. A full list of the investigators who contributed to the generation of the data is available from (http://www.wtccc.org.uk). Funding for the project was provided by the Wellcome Trust under award 076113 and 085475. Swedish control data was provided from the EIRA study, Professor Lars Alfredsson, Department of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden. Control data from the Netherlands was provided from the department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands. Polish control data was provided by the Celiac Disease Consortium and Hungarian control data collected with the help of the Hungarian Research Fund (OTKA) grant K101788 was provided by Prof Ilma. Korponay-Szabo, Celiac Disease Centre, Heim Pál Children's Hospital, Budapest and University of Debrecen, Debrecen Hungary. The authors acknowledge the International MS Genetics Consortium for providing access to control sample data from Belgium, France, Norway, Italy and Germany. The collection and genotyping of these samples was made possible by: the Norwegian MS society and the Norwegian Bone Marrow Registry (Norwegian samples); the French Biological Resource Center for MS Genetics, Genethon and INSERM (French samples); and a FISM (Italian Foundation for Multiple Sclerosis) grant (‘Progetto Speciale Immunochip’) for Italian samples. Italian samples were collected by Prof Sandra D'Alfonso (Interdisciplinary Research Center of Autoimmune Diseases IRCAD, University of Eastern Piedmont, Novara, Italy; PROGEMUS Consortium) and Dr Martinelli Boneschi (Laboratory of Genetics of Complex Neurological Disorders, Division of Neuroscience & INSPE, San Raffaele Scientific Institute, Milan, Italy; PROGRESSO Consortium); funding was provided by a FISM (Italian Foundation for Multiple Sclerosis) grant (‘Progetto Speciale Immunochip’). The KORA study was initiated and financed by the Helmholtz Zentrum München—German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research (BMBF) and by the State of Bavaria. Furthermore, KORA research was supported within the Munich Center of Health Sciences (MC-Health), Ludwig-Maximilians-Universität, as part of LMUinnovativ. The SweMyoNet: Maria Lidén (Uppsala University), Awat Jalal (Örebro University), Helena Hellström (Rheumatology Clinic Falun), Thomas Skogh (Linköping University), Aladdin Mohammad (Lund). UK Adult Onset Myositis Immunogenetic Collaboration (UKMYONET): 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: Dr Kate Armon, Mr Joe Ellis-Gage, Ms Holly Roper, Ms Vanja Briggs, and Ms Joanna Watts (Norfolk and Norwich University Hospitals); Dr Liza McCann, Mr Ian Roberts, Dr Eileen Baildam, Ms Louise Hanna, Ms Olivia Lloyd and Ms Susan Wadeson (The Royal Liverpool Children's Hospital, Alder Hey, Liverpool); Dr Phil Riley and Ms Ann McGovern (Royal Manchester Children's Hospital, Manchester); Dr Clive Ryder, Mrs Janis Scott, Mrs Beverley Thomas, Professor Taunton Southwood, Dr Eslam Al-Abadi and Lisa Charles (Birmingham Children's Hospital, Birmingham); Dr Sue Wyatt, Mrs Gillian Jackson, Dr Tania Amin, Dr Mark Wood, Dr Vanessa Van Rooyen and Ms Deborah Burton (Leeds General Infirmary, Leeds); Dr Joyce Davidson, Dr Janet Gardner-Medwin, Dr Neil Martin, Ms Sue Ferguson, Ms Liz Waxman, and Mr Michael Browne (The Royal Hospital for Sick Children, Yorkhill, Glasgow); Dr Mark Friswell, Professor Helen Foster, Mrs Alison Swift, Dr Sharmila Jandial, Ms Vicky Stevenson, Ms Debbie Wade, Dr Ethan Sen, Dr Eve Smith, Ms Lisa Qiao, Mr Stuart Watson and Claire Duong (Great North Children's Hospital, Newcastle); Dr Helen Venning, Dr Rangaraj Satyapal, Mrs Elizabeth Stretton, Ms Mary Jordan, Dr Ellen Mosley, Ms Anna Frost, Ms Lindsay Crate, Dr Kishore Warrier and Ms Stefanie Stafford (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, Ms Elli Enayat, Mrs Hemlata Varsani, Miss Laura Kassoumeri, Miss Laura Beard, Miss Katie Arnold, Mrs Yvonne Glackin, Miss Stephanie Simou, Dr Beverley Almeida, Dr Raquel Marques, Dr Claire Deakin and Ms Stefanie Dowle (Great Ormond Street Hospital, London); Dr Kevin Murray (Princess Margaret Hospital, Perth, Western Australia); Dr John Ioannou and Ms Linda Suffield (University College London Hospital); Dr Muthana Al-Obaidi, Ms Helen Lee, Ms Sam Leach, Ms Helen Smith, Dr Anne-Marie McMahon, Ms Heather Chisem and Ruth Kingshott (Sheffield's Children's Hospital); Dr Nick Wilkinson, Ms Emma Inness, Ms Eunice Kendall, Mr David Mayers, Ruth Etherton and Dr Kathryn Bailey (Oxford University Hospitals); Dr Jacqui Clinch, Ms Natalie Fineman and Ms Helen Pluess-Hall (Bristol Royal Hospital for Children, Bristol); Ms Lindsay Vallance (Royal Aberdeen Children's Hospital); Ms Louise Akeroyd (Bradford Teaching Hospitals). 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, Michigan, USA), Catherine A Bingham (Hershey Medical Center, Hershey, Pennsylvania, USA), Gail D Cawkwell (All Children's Hospital, St. Petersburg, Florida, USA), Terri H Finkel (Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA), Steven W George (Ellicott City, Maryland, USA), Harry L Gewanter (Richmond, Virginia, USA), Ellen A Goldmuntz (Children's National Medical Center, Washington DC, USA), Donald P Goldsmith (St. Christopher's Hospital for Children, Philadelphia, Pennsylvania, USA), Michael Henrickson (Children's Hospital, Madera, California, USA), Lisa Imundo (Columbia University, New York, New York, USA), Ildy M Katona (Uniformed Services University, Bethesda, Maryland, USA), Carol B Lindsley (University of Kansas, Kansas City), Chester P Oddis (University of Pittsburgh, Pittsburgh, Pennsylvania, USA), Judyann C Olson (Medical College of Wisconsin, Milwaukee), David Sherry (Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA), Scott A Vogelgesang (Walter Reed Army Medical Center, Washington DC, USA), Carol A Wallace (Children's Medical Center, Seattle, Washington, USA), Patience H White (George Washington University, Washington DC, USA), and Lawrence S Zemel (Connecticut Children's Hospital, Hartford).
This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.
- Data supplement 1 - Online supplement
Handling editor Tore K Kvien
Collaborators Further MYOGEN 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), Herman Mann (Institute of Rheumatology, Prague), 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, Maryland, USA), Kelly Rouster-Stevens (Emory University School of Medicine, Atlanta, Georgia, USA), Adam M Huber (Dalhousie University, Halifax, Nova Scotia, Canada), Galina Marder (North Shore Univeristy Hospital, Great Neck, New York, USA) and Mazen Dimachkie (University of Kansas Medical Center, Kansas City, Kansas, USA).
Contributors JAL, RGC, IEL, FWM, PKG and HC devised the study concept and design and obtained funding. SR, HC and JAL wrote the manuscript. SR, HP and AL generated genetic data. SR performed the statistical analysis. JB and JC provided statistical support. FWM, IEL, PKG, LGR, LMP, LRW, HC and JAL contributed to the interpretation of the findings. Other authors contributed samples and/or data and all authors contributed to and approved the manuscript.
Funding This study was supported in part by: Association Francaise Contre Les Myopathies (AFM),The European Union Sixth Framework Programme (project AutoCure; LSH-018661), European Science Foundation (ESF) in the framework of the Research Networking Programme European Myositis Network (EUMYONET), The Swedish Research Council and The regional agreement on medical training and clinical research (ALF) between Stockholm County Council and Karolinska Institutet, the intramural research programme of the National Institute of Environmental Health Sciences (NIEHS), the National Institutes of Health (NIH); 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 supported by Project for Conceptual Development of Research Organization 00023728 from Ministry of Health in the Czech Republic.
Competing interests None declared.
Ethics approval Approval from research ethics boards at each participating centre.
Provenance and peer review Not commissioned; externally peer reviewed.
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