Genome-wide imputation identifies novel associations and localises signals in idiopathic inflammatory myopathies.

OBJECTIVES The idiopathic inflammatory myopathies (IIM) are heterogeneous diseases, thought to be initiated by immune activation in genetically predisposed individuals. In this study we imputed variants from the Immunochip array using a large reference panel to fine-map associations and identify novel associations in IIM. METHODS We analysed 2,565 Caucasian IIM samples collected through the Myositis Genetics Consortium (MYOGEN) and 10,260 ethnically-matched controls. We imputed 1,648,116 variants from the Immunochip array using the Haplotype Reference Consortium panel and conducted association analysis on IIM, and clinical and serological subgroups. RESULTS The human leukocyte antigen (HLA) locus was consistently the most significantly associated region. Four non-HLA regions reached genome-wide significance, three in the whole IIM cohort (SDK2 and LINC00924 - both novel, and STAT4), with evidence of independent variants in STAT4, and NAB1 in the polymyositis (PM) subgroup. We also found suggestive evidence of association with loci previously associated with other autoimmune rheumatic diseases (TEC and LTBR). We identified more significant associations than those previously reported in IIM, for STAT4 and DGKQ in the total cohort, for NAB1 and FAM167A-BLK loci in PM, and CCR5 in inclusion body myositis. We found enrichment of variants among DNase I hypersensitivity sites and histone marks associated with active transcription within blood cells. CONCLUSIONS We report novel and strong associations in IIM and PM, and localise signals to single genes and immune cell types. This article is protected by copyright. All rights reserved

Identification of Novel Associations and Localization of Signals in Idiopathic Inflammatory Myopathies Using Genome-Wide Imputation

OBJECTIVES: The idiopathic inflammatory myopathies (IIM) are heterogeneous diseases, thought to be initiated by immune activation in genetically predisposed individuals. In this study we imputed variants from the Immunochip array using a large reference panel to fine-map associations and identify novel associations in IIM. METHODS: We analysed 2,565 Caucasian IIM samples collected through the Myositis Genetics Consortium (MYOGEN) and 10,260 ethnically-matched controls. We imputed 1,648,116 variants from the Immunochip array using the Haplotype Reference Consortium panel and conducted association analysis on IIM, and clinical and serological subgroups. RESULTS: The human leukocyte antigen (HLA) locus was consistently the most significantly associated region. Four non-HLA regions reached genome-wide significance, three in the whole IIM cohort (SDK2 and LINC00924 - both novel, and STAT4), with evidence of independent variants in STAT4, and NAB1 in the polymyositis (PM) subgroup. We also found suggestive evidence of association with loci previously associated with other autoimmune rheumatic diseases (TEC and LTBR). We identified more significant associations than those previously reported in IIM, for STAT4 and DGKQ in the total cohort, for NAB1 and FAM167A-BLK loci in PM, and CCR5 in inclusion body myositis. We found enrichment of variants among DNase I hypersensitivity sites and histone marks associated with active transcription within blood cells. CONCLUSIONS: We report novel and strong associations in IIM and PM, and localise signals to single genes and immune cell types

Focused HLA analysis in Caucasians with myositis identifies significant associations with autoantibody subgroups

Objectives: Idiopathic inflammatory myopathies (IIM) are a spectrum of rare autoimmune diseases characterised clinically by muscle weakness and heterogeneous systemic organ involvement. The strongest genetic risk is within the major histocompatibility complex (MHC). Since autoantibody presence defines specific clinical subgroups of IIM, we aimed to correlate serotype and genotype, to identify novel risk variants in the MHC region that co-occur with IIM autoantibodies. Methods: We collected available autoantibody data in our cohort of 2582 Caucasian patients with IIM. High resolution human leucocyte antigen (HLA) alleles and corresponding amino acid sequences were imputed using SNP2HLA from existing genotyping data and tested for association with 12 autoantibody subgroups. Results: We report associations with eight autoantibodies reaching our study-wide significance level of p<2.9x10(-5). Associations with the 8.1 ancestral haplotype were found with anti-Jo-1 (HLA-B*08:01, p=2.28x10(-53) and HLA-DRB1*03:01, p=3.25x10(-9)), anti-PM/Scl (HLA-DQB1*02:01, p=1.47x10(-26)) and anti-cN1A autoantibodies (HLA-DRB1*03:01, p=1.40x10(-11)). Associations independent of this haplotype were found with anti-Mi-2 (HLA-DRB1*07:01, p=4.92x10(-13)) and anti-HMGCR autoantibodies (HLA-DRB1*11, p=5.09x10(-6)). Amino acid positions may be more strongly associated than classical HLA associations; for example with anti-Jo-1 autoantibodies and position 74 of HLA-DRB1 (p=3.47x10(-64)) and position 9 of HLA-B (p=7.03x10(-11)). We report novel genetic associations with HLA-DQB1 anti-TIF1 autoantibodies and identify haplotypes that may differ between adult-onset and juvenile-onset patients with these autoantibodies. Conclusions: These findings provide new insights regarding the functional consequences of genetic polymorphisms within the MHC. As autoantibodies in IIM correlate with specific clinical features of disease, understanding genetic risk underlying development of autoantibody profiles has implications for future research

Development of a consensus core dataset in juvenile dermatomyositis for clinical use to inform research

Objectives This study aimed to develop consensus on an internationally agreed dataset for juvenile dermatomyositis (JDM), designed for clinical use, to enhance collaborative research and allow integration of data between centres. Methods A prototype dataset was developed through a formal process that included analysing items within existing databases of patients with idiopathic inflammatory myopathies. This template was used to aid a structured multistage consensus process. Exploiting Delphi methodology, two web-based questionnaires were distributed to healthcare professionals caring for patients with JDM identified through email distribution lists of international paediatric rheumatology and myositis research groups. A separate questionnaire was sent to parents of children with JDM and patients with JDM, identified through established research networks and patient support groups. The results of these parallel processes informed a face-to-face nominal group consensus meeting of international myositis experts, tasked with defining the content of the dataset. This developed dataset was tested in routine clinical practice before review and finalisation. Results A dataset containing 123 items was formulated with an accompanying glossary. Demographic and diagnostic data are contained within form A collected at baseline visit only, disease activity measures are included within form B collected at every visit and disease damage items within form C collected at baseline and annual visits thereafter. Conclusions Through a robust international process, a consensus dataset for JDM has been formulated that can capture disease activity and damage over time. This dataset can be incorporated into national and international collaborative efforts, including existing clinical research databases

Autoantibodies Against High Mobility Group Box 1 (HMGB1) in Patients with SLE

Abstract 2999For a searchable version of these abstracts, please visit www.acrabstracts.org.Dinesh Khanna, Veronica Berrocal, Edward Giannini, Maureen Mayes, Peter A. Merkel, Jeffrey Siegel, James R. Seibold, Murray Baron, Philip J. Clements, Yannick Allanore, Virginia D. Steen, Christopher P. Denton, Oliver Distler, Sindhu R. Johnson, Marco Matucci-Cerinic, Lazlo Czirjak, Janet E. Pope, Susanna Proudman, Weng Kee Wong, Athol U. Wells, and Daniel E. Furs