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The aim of this study was to compare nonrandom associations between physically adjacent single methylation polymorphism loci among rheumatoid arthritis (RA) and normal subjects for investigating RA-risk methylation haplotypes (meplotype). With 354 ACPA-positive RA patients and 335 normal controls selected from a case-control study based on Swedish population, we conducted the first RA epigenome-wide meplotype association study using our software EWAS2.0, mainly including (i) converted the β value to methylation genotype (menotype) data, (ii) identified methylation disequilibrium (MD) block, (iii) calculated frequent of each meplotypes in MD block and performed case-control association test and (iv) screened for RA-risk meplotypes by odd ratio (OR) and p-values. Ultimately, 545 meplotypes on 334 MD blocks were identified significantly associated with RA (p-value < .05). These meplotypes were mapped to 329 candidate genes related to RA. Subsequently, combined with gene optimization, eight RA-risk meplotypes were identified on three risk genes: HLA-DRB1, HLA-DRB5 and HLA-DQB1. Our results reported the relationship between DNA methylation pattern on HLA-DQB1 and the risk of RA for the first time, demonstrating the co-demethylation of 'cg22984282' and 'cg13423887' on HLA-DQB1 gene (meplotype UU, p-value = 2.90E - 6, OR = 1.68, 95% CI = [1.35, 2.10]) may increase the risk of RA. Our results demonstrates the potential of methylation haplotype analysis to identify RA-related genes from a new perspective and its applicability to the study of other disease., (© 2023 John Wiley & Sons Ltd.)

Advances in sequencing technologies have led to the rapid growth of multi-omics data on rheumatoid arthritis (RA). However, a comprehensive database that systematically collects and classifies the scattered data is still lacking. Here, we developed the Rheumatoid Arthritis Bioinformatics Center (RABC, http://www.onethird-lab.com/RABC/), the first multi-omics data resource platform (data hub) for RA. There are four categories of data in RABC: (i) 175 multi-omics sample sets covering transcriptome, epigenome, genome, and proteome; (ii) 175 209 differentially expressed genes (DEGs), 105 differentially expressed microRNAs (DEMs), 18 464 differentially DNA methylated (DNAm) genes, 1 764 KEGG pathways, 30 488 GO terms, 74 334 SNPs, 242 779 eQTLs, 105 m6A-SNPs and 18 491 669 meta-mQTLs; (iii) prior knowledge on seven types of RA molecular markers from nine public and credible databases; (iv) 127 073 literature information from PubMed (from 1972 to March 2022). RABC provides a user-friendly interface for browsing, searching and downloading these data. In addition, a visualization module also supports users to generate graphs of analysis results by inputting personalized parameters. We believe that RABC will become a valuable resource and make a significant contribution to the study of RA., (© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The initiation and development of rheumatoid arthritis (RA) is closely related to mutual dysfunction of multiple pathways. Furthermore, some similar molecular mechanisms are shared between RA and other immune diseases. Therefore it is vital to reveal the molecular mechanism of RA through searching for subpathways of immune diseases and investigating the crosstalk effect among subpathways. Here we exploited an integrated approach combining both construction of a subpathway-subpathway interaction network and a random walk strategy to prioritize RA risk subpathways. Our research can be divided into three parts: (1) acquisition of risk genes and identification of risk subpathways of 85 immune diseases by using subpathway-lenient distance similarity (subpathway-LDS) method; (2) construction of a global immune subpathway interaction (GISI) network with subpathways identified by subpathway-LDS; (3) optimization of RA risk subpathways by random walk strategy based on GISI network. The results showed that our method could effectively identify RA risk subpathways, such as MAPK signaling pathway, prostate cancer pathway and chemokine signaling pathway. The integrated strategy considering crosstalk between immune subpathways significantly improved the effect of risk subpathway identification. With the development of GWAS, our method will provide insight into exploring molecular mechanisms of immune diseases and might be a promising approach for studying other diseases.

Rheumatoid arthritis (RA) is an autoimmune disease that has a complex genetic basis. Therefore, it is important to explore the genetic background of RA. The extensive recent application of polymorphic genetic markers, especially single nucleotide polymorphisms, has presented us with a large quantity of genetic data. In this study, we developed the Database of Rheumatoid Arthritis-related Polymorphisms (RADB), to integrate all the RA-related genetic polymorphisms and provide a useful resource for researchers. We manually extracted the RA-related polymorphisms from 686 published reports, including RA susceptibility loci, polymorphisms associated with particular clinical features of RA, polymorphisms associated with drug response in RA and polymorphisms associated with a higher risk of cardiovascular disease in RA. Currently, RADB V1.0 contains 3235 polymorphisms that are associated with 636 genes and refer to 68 countries. The detailed information extracted from the literature includes basic information about the articles (e.g., PubMed ID, title and abstract), population information (e.g., country, geographic area and sample size) and polymorphism information (e.g., polymorphism name, gene, genotype, odds ratio and 95% confidence interval, P-value and risk allele). Meanwhile, useful annotations, such as hyperlinks to dbSNP, GenBank, UCSC, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway, are included. In addition, a tool for meta-analysis was developed to summarize the results of multiple studies. The database is freely available at http://www.bioapp.org/RADB. Database URL: http://www.bioapp.org/RADB., (© The Author(s) 2014. Published by Oxford University Press.)

Objective: Candidate gene association studies and genome-wide association studies (GWAs) have identified a large number of single nucleotide polymorphisms (SNPs) loci affecting susceptibility to rheumatoid arthritis (RA). However, for the same locus, some studies have yielded inconsistent results. To assess all the available evidence for association, we performed a meta-analysis on previously published case-control studies investigating the association between SNPs and RA., Methods: Two hundred and sixteen studies, involving 125 SNPs, were reviewed. For each SNP, three genetic models were considered: the allele, dominant and recessive effects models. For each model, the effect summary odds ratio (OR) and 95% CIs were calculated. Cochran's Q-statistics were used to assess heterogeneity. If the heterogeneity was high, a random effects model was used for meta-analysis, otherwise a fixed effects model was used., Results: The meta-analysis results showed that: (1) 30, 28 and 26 SNPs were significantly associated with RA (P<0.01) for the allele, dominant, and recessive models, respectively. (2) rs2476601 (PTPN22) showed the strongest association for all the three models: OR = 1.605, 95% CI: 1.540-1.672, P<1.00E-15 for the T-allele; OR = 1.638, 95% CI: 1.565-1.714, P<1.00E-15 for the T/T+T/C genotype and OR = 2.544, 95% CI: 2.173-2.978, P<1.00E-15 for the T/T genotype. (3) Only 23 (18.4%), 13 (10.4%) and 15 (12.0%) SNPs had high heterogeneity (P<0.01) for the three models, respectively. (4) For some of the SNPs, there was no publication bias according to Funnel plots and Egger's regression tests (P<0.01). For the other SNPs, the associations were tested in only a few studies, and may have been subject to publication bias. More studies on these loci are required., Conclusion: Our meta-analysis provides a comprehensive evaluation of the RA association studies from the past two decades. The detailed meta-analysis results are available at: http://210.46.85.180/DRAP/index.php/Metaanalysis/index.