High-specificity bioinformatics framework for epigenomic profiling of discordant twins reveals specific and shared markers for ACPA and ACPA-positive rheumatoid arthritis

Abstract

Abstract Background Twin studies are powerful models to elucidate epigenetic modifications resulting from gene–environment interactions. Yet, commonly a limited number of clinical twin samples are available, leading to an underpowered situation afflicted with false positives and hampered by low sensitivity. We investigated genome-wide DNA methylation data from two small sets of monozygotic twins representing different phases during the progression of rheumatoid arthritis (RA) to find novel genes for further research. Methods We implemented a robust statistical methodology aimed at investigating a small number of samples to identify differential methylation utilizing the comprehensive CHARM platform with whole blood cell DNA from two sets of twin pairs discordant either for ACPA (antibodies to citrullinated protein antigens)-positive RA versus ACPA-negative healthy or for ACPA-positive healthy (a pre-RA stage) versus ACPA-negative healthy. To deconvolute cell type-dependent differential methylation, we assayed the methylation patterns of sorted cells and used computational algorithms to resolve the relative contributions of different cell types and used them as covariates. Results To identify methylation biomarkers, five healthy twin pairs discordant for ACPAs were profiled, revealing a single differentially methylated region (DMR). Seven twin pairs discordant for ACPA-positive RA revealed six significant DMRs. After deconvolution of cell type proportions, profiling of the healthy ACPA discordant twin-set revealed 17 genome-wide significant DMRs. When methylation profiles of ACPA-positive RA twin pairs were adjusted for cell type, the analysis disclosed one significant DMR, associated with the EXOSC1 gene. Additionally, the results from our methodology suggest a temporal connection of the protocadherine beta-14 gene to ACPA-positivity with clinical RA. Conclusions Our biostatistical methodology, optimized for a low-sample twin design, revealed non-genetically linked genes associated with two distinct phases of RA. Functional evidence is still lacking but the results reinforce further study of epigenetic modifications influencing the progression of RA. Our study design and methodology may prove generally useful in twin studies.

Keywords

• Rheumatoid arthritis
• ACPA
• DNA methylation
• Epigenetics
• Bioinformatics