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.

Keywords: DNA methylation; SNP; bioinformatics; epigenetics; genetic risk; rheumatoid arthritis.

Fig. 1

Summary for all meQTLs. (A) Chessboard plot for all meQTLs identified. Each point is an SNP‐CpG pair. The axis‐x and axis‐y show the location of SNP and CpG on the genome. Points are colored according to the distance between SNP and CpG, red for cis meQTLs, blue for long cis meQTLs, and black for trans meQTLs. (B) The number of meQTLs of each type detected in normal, RA or both. (C) The regression coefficient calculated in RA and normal samples for each meQTL that significantly identified both in normal samples and RA samples (left panel), only in normal samples (middle panel), only in RA samples (right panel).

Fig. 2

meQTLs identified only in normal samples or RA samples. The inner circle and the outer circle are respectively the SNP site and the CpG site colored according to the chromosome. The height of the column of the outer circle is proportional to the negative logarithm of the T‐test P‐value for CpG sites. The connecting lines within the two circles represent SNP‐CpG relationship pairs, and each line is colored according to the type of meQTL. The solid/dashed blue line represents the cis/trans meQTL identified only in normal samples, and the solid/dashed yellow line represents the cis/trans meQTL identified only in the RA samples.

Fig. 3

Examples of meQTLs identified only in RA or normal samples. Boxplot of (A) rs10796216‐cg00475509, (B) rs6546473‐cg13358873, (C) rs951295‐cg06321045, and (D) rs951295‐cg24735489 relationship pairs. Relationships between SNP genotypes and DNA methylation level were tested by linear regression, with age included as a covariate, and the difference of DNA methylation level was identified by t‐test.