Literature DB >> 33386149

Integrative analysis identifies potential causal methylation-mRNA regulation chains for rheumatoid arthritis.

Xing-Bo Mo1, Yong-Hong Zhang2, Shu-Feng Lei3.   

Abstract

Genome-wide association studies have identified many genetic loci for rheumatoid arthritis (RA). However, causal factors underlying these loci were largely unknown. The aim of this study was to identify potential causal methylation-mRNA regulation chains for RA. We identified differentially expressed mRNAs and methylations and conducted summary statistic data-based Mendelian randomization (SMR) analysis to detect potential causal mRNAs and methylations for RA. Then causal inference test (CIT) was performed to determine if the methylation-mRNA pairs formed causal chains. We identified 11,170 mRNAs and 24,065 methylations that were nominally associated with RA. Among them, 197 mRNAs and 104 methylations passed the SMR test. According to physical positions, we defined 16 cis methylation-mRNA pairs and inferred 5 chains containing 4 methylations and 4 genes (BACH2, MBP, MX1 and SYNGR1) to be methylation→mRNA→RA causal chains. The effect of SYNGR1 expression in peripheral blood mononuclear cells on RA risk was found to be consistent in both the in-house and public data. The identified methylations located in CpG Islands that overlap promoters in the 5' region of the genes. The promoter regions showed long-range interactions with other enhancers and promoters, suggesting a regulatory potential of these methylations. Therefore, the present study provided a new integrative analysis strategy and highlighted potential causal methylation-mRNA chains for RA. Taking the evidences together, SYNGR1 promoter methylations most probably affect mRNA expressions and then affect RA risk.
Copyright © 2020 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Causal inference; Gene expression; Mendelian randomization; Methylation; Rheumatoid arthritis

Year:  2020        PMID: 33386149     DOI: 10.1016/j.molimm.2020.12.021

Source DB:  PubMed          Journal:  Mol Immunol        ISSN: 0161-5890            Impact factor:   4.407


  1 in total

1.  Machine learning to identify immune-related biomarkers of rheumatoid arthritis based on WGCNA network.

Authors:  Yulan Chen; Ruobing Liao; Yuxin Yao; Qiao Wang; Lingyu Fu
Journal:  Clin Rheumatol       Date:  2021-11-12       Impact factor: 2.980

  1 in total

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