Extensive association analyses including genome-wide association studies (GWAS) and powerful metaanalysis studies have identified a long list of loci associated with rheumatoid arthritis (RA) in very large populations, but most of them established statistical associations of genetic markers and RA only at the DNA level, without supporting evidence of functional relevance. Our study serves as a trial to detect the functional mechanisms underlying associations for RA by searching publicly available datasets and results.
Based on publicly available datasets and results, we performed integrative analyses (gene relationships across implicated loci analysis, differential gene expression analysis, and functional annotation clustering analysis) and combined them with the expression quantitative trait locus (eQTL) results to dissect functional mechanisms underlying the associations for RA.
By searching 2 GWAS, Integrator and PheGenI, we selected 98 RA association results (p < 10(-5)). Among these associations, we found that 8 single-nucleotide polymorphisms (SNP; rs1600249, rs2736340, rs3093023, rs3093024, rs4810485, rs615672, rs660895, and rs9272219) serve as cis-effect regulators of the corresponding eQTL genes (BLK and CD4 in non-HLA region; CCR6, HLA-DQA1, and HLA-DQB1 in HLA region) that also were differentially expressed in RA-related cell groups. These 5 genes are closely related with immune response in function.
Our results showed the functional mechanisms underlying the associations of 8 SNP and the corresponding genes. This study is an example of mining publicly available datasets and results in validation of significant disease-association results. Using public data resources for integrative analyses may provide insights into the molecular genetic mechanisms underlying human diseases.