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THU0002 Comparison of Pathways Implicated in Anti-Citrillunated Peptide Antibody Positive and Negative Rheumatoid Arthritis Patients
  1. P. Martin1,2,
  2. S. Viatte1,2,
  3. A. Brass3,
  4. S. Eyre1,2,
  5. Rheumatoid Arthritis Consortium for Immunochip (RACI)
  1. 1Arthritis Research UK Epidemiology Unit, Centre for Musculoskeletal Research, University of Manchester, Manchester Academic Health Science Centre
  2. 2National Institute for Health Research Manchester Musculoskeletal Biomedical Research Unit, Central Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Sciences Centre
  3. 3School of Computer Science, University of Manchester, Manchester, United Kingdom


Background Rheumatoid Arthritis (RA) is a chronic autoimmune disease characterised by inflammation and destruction of synovial joints affecting up to 1% of the adult population. GWAS have been successful in identifying SNPs associated with disease. However, previous studies have been under-powered to detect differences between anti-citrillunated peptide antibody (ACPA) positive and negative patients. Using a custom Illumina® Infinium® array, the RA Consortium for Immunochip (RACI) allows comprehensive analysis of disease associations in both ACPA positive and negative patients.

Pathway analyses test whether a molecular pathway is associated with disease by testing for enrichment of genes associated with disease. This involves mapping SNP associations to genes which can often be problematic or subjective.

Objectives The objectives of this study, therefore, were to develop a robust workflow based method to assign genes and to compare the pathways significantly enriched in ACPA positive and negative patients.

Methods Excluding HLA, the top 100 most associated SNPs from the immunochip analysis were selected for ACPA positive patients along with the top 100 most associated SNPs from the ACPA negative analysis. Genes were assigned to these SNPs using a Taverna workflow which defines an associated region using SNPs in linkage disequilibrium (LD) with the associated SNP. This prevents the bias introduced by researchers assigning SNPs to ‘biologically plausible’ genes and provides a robust, reproducible method for assigning genetic associations to genes. These genes were subsequently tested for enrichment in PANTHER pathways and Gene Ontologies (GO) using the Expression Analysis tool.

Results Overall 4 pathways were significantly (p<0.05) enriched in the ACPA positive gene list and 6 in the ACPA negative gene list, with 1 pathway significantly associated in both. The interleukin signalling pathway was associated in the ACPA positive (p=0.00322) group, the interferon-gamma signalling pathway in ACPA negative (p=0.0190) patients and the GO JAK/STAT signalling pathway in both (ACPA+ p=0.00234; ACPA- p=0.00243).

Conclusions Although results are preliminary, this study provides a promising pathway analysis of ACPA positive and negative RA patients. These findings not only suggest shared processes between the two disease sub-groups, such as JAK/STAT signalling, but also identify areas where possible differences exist helping to define patient groups and potentially leading to better targeting of therapies.

Disclosure of Interest None Declared

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