Integrative genomic deconvolution of rheumatoid arthritis GWAS loci into gene and cell type associations. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Walsh, Alice; Whitaker, John; Huang, C.; Cherkas, Yauheniya; Lamberth, Sarah; Brodmerkel, Carrie; Curran, Mark; Dobrin, Radu
• Is Part Of: Genome biology. Volume 17:Issue 1(2016)
• Page Start: 1
• Page End: 16
• Publication Date: 2016-12
• Subjects: Genome-wide association studies (GWAS) -- Epigenomics -- Expression quantitative trait loci (eQTLs) -- Rheumatoid arthritis; Genomes -- Periodicals; Biology -- Periodicals; Molecular biology -- Periodicals; 572.8633
• DOI: 10.1186/s13059-016-0948-6
• Languages: English
• ISSNs: 1474-760X

Abstract

Abstract Background Although genome-wide association studies (GWAS) have identified over 100 genetic loci associated with rheumatoid arthritis (RA), our ability to translate these results into disease understanding and novel therapeutics is limited. Most RA GWAS loci reside outside of protein-coding regions and likely affect distal transcriptional enhancers. Furthermore, GWAS do not identify the cell types where the associated causal gene functions. Thus, mapping the transcriptional regulatory roles of GWAS hits and the relevant cell types will lead to better understanding of RA pathogenesis. Results We combine the whole-genome sequences and blood transcription profiles of 377 RA patients and identify over 6000 unique genes with expression quantitative trait loci (eQTLs). We demonstrate the quality of the identified eQTLs through comparison to non-RA individuals. We integrate the eQTLs with immune cell epigenome maps, RA GWAS risk loci, and adjustment for linkage disequilibrium to propose target genes of immune cell enhancers that overlap RA risk loci. We examine 20 immune cell epigenomes and perform a focused analysis on primary monocytes, B cells, and T cells. Conclusions We highlight cell-specific gene associations with relevance to RA pathogenesis including the identification of FCGR2B in B cells as possessing both intragenic and enhancer regulatory GWAS hits. We show that our RA patient cohort derived eQTL network is more informative for studying RA than that from a healthy cohort. While not experimentally validated here, the reported eQTLs and cell type-specific RA risk associations can prioritize future experiments with the goal of elucidating the regulatory mechanisms behind genetic risk associations.