Identification of novel expression quantitative trait loci in CD4+ T cells of untreated early arthritis patients

Background Rheumatoid arthritis (RA) is a CD4 + T-cell-mediated disease of immune dysregulation. Genome-wide association scans (GWAS) have shown common variants at approximately 100 genetic loci to be associated with RA. In many cases, however, the associated single nucleotide polymorphisms (SNPs) lie in intergenic regions and the gene upon which they act has not yet been defined. Examining how RA-associated SNPs influence gene expression in relevant biological contexts will begin to address this. Methods Patients attending the Newcastle Early Arthritis Clinic, naive to immunomodulatory therapy, donated high integrity RNA and DNA extracted from 98% pure CD4 + T cells within 4 h of blood draw. Detailed baseline and longitudinal clinical data were recorded for all participants, who were followed up for ≥1 year. Genotyping and global gene expression measurement was carried out using the Illumina Human CoreExome array, and either HT12v4 or WG6v3 BeadChip arrays, respectively, and expression data was batch-corrected and normalised using standard algorithms. Data were analysed using the R package. Variants in linkage disequilibrium (LD) with 100 confirmed RA- SNPs (r 2 >0.8), and probes within 4MB of these LD blocks, were included in the analysis, seeking evidence of cis-eQTLs. Results Data from 249 white Caucasian early arthritis patients were available for analysis; diagnoses (confirmed at follow-up) were RA (n = 88), spondyloarthropathy/other inflammatory arthritis (n = 86), osteoarthritis/other non-inflammatory arthralgia (n = 70) and undifferentiated arthritis. Analysis of 1,247 SNPs and 8,023 probes identified strong evidence for cis-eQTLs at a number of RA risk loci in this population overall (experiment-wide p + T cells replicated by our study include BLK , FADS1 and FADS2 . Genes whose expression specifically in these cells has, to our knowledge, been associated with RA risk variants for the first time include RPS26 , RNF67 and IKZF3 . Conclusions We present the first detailed eQTL analysis in the pathophysiologically relevant setting of early arthritis CD4 + T cells, free of confounding immunomodulatory treatment. Interactions of clinical parameters (including disease phenotype) with eQTL effects remains the subject of ongoing analyses, to be presented. Our findings have the potential to modify the biological candidate gene landscape of RA.