Characterization of candidate genes in inflammatory bowel disease–associated risk loci
Joanna M. Peloquin, … , Sergio A. Lira, Ramnik J. Xavier
Joanna M. Peloquin, … , Sergio A. Lira, Ramnik J. Xavier
Published August 18, 2016
Citation Information: JCI Insight. 2016;1(13):e87899. https://doi.org/10.1172/jci.insight.87899.
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Categories: Research Article Gastroenterology Inflammation

Characterization of candidate genes in inflammatory bowel disease–associated risk loci

Abstract

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.

Authors

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

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Figure 1

Study design and analysis.

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Study design and analysis. The Sinai-Helmsley Alliance for Research Exce...
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 Supplemental Table 1. The analysis pipeline for RNA expression data included quality control, per-batch normalization, and cross-batch calibration. One hundred twenty-seven samples (11%) failed quality control. Eight hundred eighty-six samples, from 590 subjects who were genotyped, were selected for analysis. The final analysis cohort included 37 samples from healthy controls, 564 samples from patients with Crohn’s disease, and 285 samples from patients with ulcerative colitis. Five distinct analyses were carried out with genotype and gene expression data. Location-specific expression patterns identified a gene signature that can discriminate between healthy colon and terminal ileum. Variance analysis examined the change in coefficient of variation as inflammation increases from healthy to disease state. Coexpression analysis identified both conserved and differentially coexpressed modules of genes using weighted gene coexpression network analysis. Differential expression and expression quantitative trait loci (eQTL) analysis used linear mixed-effects models to identify genes with significant regulatory patterns in inflammation of disease-specific tissue samples.