Objective

We undertook this study to estimate changes in cell-specific DNA methylation (DNAm) associated with methotrexate (MTX) response using whole blood samples collected from rheumatoid arthritis (RA) patients before and after initiation of MTX treatment.

Methods

Patients included in this study were from the Rheumatoid Arthritis Medication Study (n = 66) and the University of California San Francisco Rheumatoid Arthritis study (n = 11). All patients met the American College of Rheumatology RA classification criteria. Blood samples were collected at baseline and following treatment. Disease Activity Scores in 28 joints using the C-reactive protein level were collected at baseline and after 3-6 months of treatment with MTX. Methylation profiles were generated using the Illumina Infinium HumanMethylation450 and MethylationEPIC v1.0 BeadChip arrays using DNA from whole blood. MTX response was defined using the EULAR response criteria (responders showed good/moderate response; nonresponders showed no response). Differentially methylated positions were identified using the Limma software package and Tensor Composition Analysis, which is a method for identifying cell-specific differential DNAm at the CpG level from tissue-level ("bulk") data. Differentially methylated regions were identified using Comb-p software.

Results

We found evidence of differential global methylation between treatment response groups. Further, we found patterns of cell-specific differential global methylation associated with MTX response. After correction for multiple testing, 1 differentially methylated position was associated with differential DNAm between responders and nonresponders at baseline in CD4+ T cells, CD8+ T cells, and natural killer cells. Thirty-nine cell-specific differentially methylated regions associated with MTX treatment response were identified. There were no significant findings in analyses of whole blood samples.

Conclusion

We identified cell-specific changes in DNAm that were associated with MTX treatment response in RA patients. Future studies of DNAm and MTX treatment response should include measurements of DNAm from sorted cells.

Background

Treatment strategies blocking tumour necrosis factor (anti-TNF) have proven very successful in patients with rheumatoid arthritis (RA). However, a significant subset of patients does not respond for unknown reasons. Currently, there are no means of identifying these patients before treatment. This study was aimed at identifying genetic factors predicting anti-TNF treatment outcome in patients with RA using a genome-wide association approach.

Methods

We conducted a multistage, genome-wide association study with a primary analysis of 2 557 253 single-nucleotide polymorphisms (SNPs) in 882 patients with RA receiving anti-TNF therapy included through the Dutch Rheumatoid Arthritis Monitoring (DREAM) registry and the database of Apotheekzorg. Linear regression analysis of changes in the Disease Activity Score in 28 joints after 14 weeks of treatment was performed using an additive model. Markers with p<10(-3) were selected for replication in 1821 patients from three independent cohorts. Pathway analysis including all SNPs with p<10(-3) was performed using Ingenuity.

Results

772 markers showed evidence of association with treatment outcome in the initial stage. Eight genetic loci showed improved p value in the overall meta-analysis compared with the first stage, three of which (rs1568885, rs1813443 and rs4411591) showed directional consistency over all four cohorts studied. We were unable to replicate markers previously reported to be associated with anti-TNF outcome. Network analysis indicated strong involvement of biological processes underlying inflammatory response and cell morphology.

Conclusions

Using a multistage strategy, we have identified eight genetic loci associated with response to anti-TNF treatment. Further studies are required to validate these findings in additional patient collections.

A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological data sets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA). Here we performed a genome-wide association study meta-analysis in a total of >100,000 subjects of European and Asian ancestries (29,880 RA cases and 73,758 controls), by evaluating ∼10 million single-nucleotide polymorphisms. We discovered 42 novel RA risk loci at a genome-wide level of significance, bringing the total to 101 (refs 2 - 4). We devised an in silico pipeline using established bioinformatics methods based on functional annotation, cis-acting expression quantitative trait loci and pathway analyses--as well as novel methods based on genetic overlap with human primary immunodeficiency, haematological cancer somatic mutations and knockout mouse phenotypes--to identify 98 biological candidate genes at these 101 risk loci. We demonstrate that these genes are the targets of approved therapies for RA, and further suggest that drugs approved for other indications may be repurposed for the treatment of RA. Together, this comprehensive genetic study sheds light on fundamental genes, pathways and cell types that contribute to RA pathogenesis, and provides empirical evidence that the genetics of RA can provide important information for drug discovery.