<![CDATA[Rheumatoid arthritis (RA) is an autoimmune disease involving the interaction of genetic and immunological factors. Genome-wide association studies (GWAS) have identified many RA risk loci, but the biological mechanisms linking genetic variation to disease pathogenesis are not yet fully understood. This study aims to prioritize RA candidate genes through a multi-database bioinformatics approach. SNPs significantly associated with RA were obtained from the GWAS catalog, followed by linkage disequilibrium (LD) screening and functional annotation to identify missense variants. The data were integrated with cis-expression quantitative trait loci (cis-eQTL) information, gene ontology (GO) annotation, and Kyoto Encyclopedia of Genes and Genomes (KEGG) molecular pathway mapping. Genes with a total score ≥2 were classified as RA risk genes. A total of 3.145 RA-significant SNPs were identified, of which 58 were missense variants that could potentially affect protein function. The integration of cis-eQTL and functional annotation resulted in a number of candidate genes with the highest scores (score = 4), where TYK2, IL23R, and IRAK1 were identified as priority RA genes in the main immune pathways, namely JAK-STAT signaling, IL-23/Th17 axis, and Toll-like receptor-NF-κB signaling. These findings demonstrate that this multi-database-based bioinformatics approach successfully identifies RA candidate genes with strong biological relevance.]]>
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