A MULTIFACETED APPROACH TO SELECT TARGET GENES FOR RESEQUENCING IN RHEUMATOID ARTHRITIS

Abstract

Background The common disease rare variant hypothesis states that disease etiology is caused collectively by multiple rare variants. Although many studies including genome-wide association study (GWAS) until recently have shown disease susceptibility genes associated with rheumatoid arthritis (RA), causal variants underlying these findings have not yet been identified.Objectives We used a comprehensive approach to select genes for targeted resequencing in rheumatoid arthritis (RA).Methods One of the critical components for success lies in the selection of target regions or genes, which are associated with common variants from GWAS for resequencing. We integrate multi-layered approaches for targeted resequencing in RA combining RA-related biological pathways, text-mining and animal model approach as well as experiment data such as GWAS, linkage study, and candidate study.Results In the experimental data approach, we selected 106 genes from previously known variants such as linkage study and candidate study, 155 genes from Korean RA/systemic lupus erythematosus (SLE) GWAS, and 519 genes from immunochip of Korean RA. In the pathway based approach, we selected 18 genes from sharing common pathways among the RA-related pathways. In the text mining approach, we obtained 65 genes using Gene Relationhips Across Implicated Loci (GRAIL). In the animal model approach, we obtained 8 homology genes from Mouse Genome Informatics (MGI) database (Fig 1).Conclusions When put together, 684 target genes were selected for resequencing. By applying targeted resequencing, we could extend the results of successful GWAS and pinpoint disease-causing genes in genomic regions initially identified by GWASs. Rare and common variants discovered by resequencing potential risk genes will help unravel the causality of RA disease risks.