Identification of novel meQTLs strongly associated with rheumatoid arthritis by large‐scale epigenome‐wide analysis

Abstract

Rheumatoid arthritis (RA) is highly heritable, and previous studies have suggested that genetic variation may affect susceptibility to RA by altering epigenetic modifications (e.g. DNA methylation). Here we examined how genetic variation influences DNA methylation (DNAm) in RA by integrating individual genetic variation and DNAm data. Epigenome‐wide meQTL (methylation quantitative trait loci) analysis was performed on 354 RA patients and 335 controls, scanning 30,101,744 relationships between 62 SNPs and 485,512 DNA methylation sites. Two regulatory relationship pairs (FDR < 0.05) showed very strong associations with RA risk. One was rs10796216‐cg00475509, and the DNAm decreased by 0.0168 per addition of allele rs10796216‐A. The other was rs6546473‐cg13358873, for which a 0.0365 reduction of DNAm at cg13358873 was observed for each addition of allele rs6546473‐A, and lower DNAm was found to be significantly associated with RA risk (P = 2.0407e‐28). Moreover, both pairs of meQTL showed a strong regulatory relationship only in RA samples, so they can be subsequently considered as risk markers for RA. In conclusion, our integrated analysis of genetic and epigenetic variation suggests that genetic variation may affect the risk of RA by regulating DNA methylation levels. Alterations of DNAm at cg00475509 and cg13358873 loci conferred by rs10796216‐A and rs6546473‐A allele may suggest a potential risk for RA. Our results deepen our understanding of the genetic and epigenetic mechanisms of RA and provide novel associations that can be further investigated in future studies.

Global research trends in DNA methylation in rheumatoid arthritis: A bibliometric analysis and visual analysis

“…[ 45 , 46 ] EWAS can mitigate the risk of false positives often encountered in studies and reveal significant regulatory relationships between the epigenome and RA through the analysis of large-scale methylation quantitative trait loci. [ 47 ] Furthermore, EWAS can identify common apparent mutations in disease by detecting the distribution of methyl groups. For instance, changes in the pathogenic activity of B cells attributed to DNA methylation are strongly associated with RA.…”