GWAS have linked SNPs to risk of inflammatory bowel disease (IBD), but a systematic characterization of disease-associated genes has been lacking. Prior studies utilized microarrays that did not capture many genes encoded within risk loci or defined expression quantitative trait loci (eQTLs) using peripheral blood, which is not the target tissue in IBD. To address these gaps, we sought to characterize the expression of IBD-associated risk genes in disease-relevant tissues and in the setting of active IBD. Terminal ileal (TI) and colonic mucosal tissues were obtained from patients with Crohn’s disease or ulcerative colitis and from healthy controls. We developed a NanoString code set to profile 678 genes within IBD risk loci. A subset of patients and controls were genotyped for IBD-associated risk SNPs. Analyses included differential expression and variance analysis, weighted gene coexpression network analysis, and eQTL analysis. We identified 116 genes that discriminate between healthy TI and colon samples and uncovered patterns in variance of gene expression that highlight heterogeneity of disease. We identified 107 coexpressed gene pairs for which transcriptional regulation is either conserved or reversed in an inflammation-independent or -dependent manner. We demonstrate that on average approximately 60% of disease-associated genes are differentially expressed in inflamed tissue. Last, we identified eQTLs with either genotype-only effects on expression or an interaction effect between genotype and inflammation. Our data reinforce tissue specificity of expression in disease-associated candidate genes, highlight genes and gene pairs that are regulated in disease-relevant tissue and inflammation, and provide a foundation to advance the understanding of IBD pathogenesis.
Joanna M. Peloquin, Gautam Goel, Lingjia Kong, Hailiang Huang, Talin Haritunians, R. Balfour Sartor, Mark J. Daly, Rodney D. Newberry, Dermot P. McGovern, Vijay Yajnik, Sergio A. Lira, Ramnik J. Xavier
Study design and analysis.
The Sinai-Helmsley Alliance for Research Excellence (SHARE) consortium facilitated a multi-institutional collaboration for patient recruitment and sample collection, with centralized RNA expression profiling using NanoString technology, genotyping on the Immunochip array, and bioinformatics analysis. Samples were divided into 3 analysis groups stratified by tissue type, disease type, and inflammation status, as shown in