Rheumatoid arthritis (RA) is an autoimmune disease of unknown pathologic mechanism. Extensive single-level analyses have been conducted, including genome-wide association studies (GWASs) and genome-wide copy number variation analyses, whole transcriptomics, and epigenetic analyses. These data are analyzed separately to identify RA-associated genetic components. Recently, it has become possible to integrate these analyses, as multi-omics studies, to obtain more accurate results to infer novel insights into disease causality. GWASs have enabled us to understand RA causal risks, but how these risks are functionally related to RA remains unclear. To date, more than 100 loci have been associated with RA, and 80% of these risk variants are located in non-coding regions. This suggests that polygenic diseases such as RA are likely to be substantiated by changes in the RNA expression of responsible genes, rather than structural or functional changes in proteins. These genetic variants would also affect promoter and enhancer activity, alternative splicing, chromatin configuration, and mRNA stability. Loci identified by GWASs that have no apparent connection to each other may also be controlled by common transcription factors. Statistical approaches such as gene enrichment analysis and polygenic analysis may clarify the key genetic contribution that cannot be identified by GWAS significant signals. These approaches could also clarify many of the missing links between genetic risk variants and causal genetic components, thus expanding our understanding of RA pathogenesis.
Keywords: Expression – quantitative trait loci (eQTL); Genetic variants; Genome wide association study (GWAS); Multi-omics analysis; Rheumatoid arthritis (RA).
Copyright © 2019. Published by Elsevier Inc.