Prioritizing Crohn's disease genes by integrating association signals with gene expression implicates monocyte subsets

• Type of Material: Article
• Authors: Kyle Gettler, Yale University; Mamta Giri, Icahn School of Medicine at Mount Sinai; Ephraim Kenigsberg, Icahn School of Medicine at Mount Sinai; Jerome Martin, Icahn School of Medicine at Mount Sinai; Ling-Shiang Chuang, Icahn School of Medicine at Mount Sinai; Nai-Yun Hsu, Icahn School of Medicine at Mount Sinai; Lee A. Denson, Cincinnati Childrens Hospital Medical Center; Jeffrey S. Hyams, Connecticut Childrens Medical Center; Anne Griffiths, University of Toronto; Joshua D. Noe, Medical College of Wisconsin; Wallace V. Crandall, Ohio State University; David R. Mack, University of Ottawa; Richard Kellermayer, Texas Childrens Hospital; Clara Abraham, Yale University; Gabriel Hoffman, Icahn School of Medicine at Mount Sinai; Subramaniam Kugathasan, Emory University; Judy H. Cho, Icahn School of Medicine at Mount Sinai
• Language: English
• Date: 2019-09-01
• Publisher: Nature Publishing Group
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © Springer Nature Limited 2019.
• Final Published Version (URL): http://dx.doi.org/10.1038/s41435-019-0059-y
• Title of Journal or Parent Work: Genes and Immunity
• Volume: 20
• Issue: 7
• Start Page: 577
• End Page: 588
• Grant/Funding Information: NIH research grants (U01 DK62429, U01 DK62422, R01 DK106593, and P30 DK078392) as well as the Crohn’s and Colitis Foundation and the Sanford Grossman Charitable Trust.
• Keywords: Transcriptome; RNA-SEQ; Increases; Life Sciences & Biomedicine; Science & Technology; Frameshift; Susceptibility loci; Immunology; Colitis; Genetics & Heredity; Infliximab; Variants; Genome wide association; Inflammatory bowel disease
• Research Categories: Biology, Genetics; Health Sciences, Immunology; Health Sciences, Nutrition

Abstract

Genome-wide association studies have identified ~170 loci associated with Crohn’s disease (CD) and defining which genes drive these association signals is a major challenge. The primary aim of this study was to define which CD locus genes are most likely to be disease related. We developed a gene prioritization regression model (GPRM) by integrating complementary mRNA expression datasets, including bulk RNA-Seq from the terminal ileum of 302 newly diagnosed, untreated CD patients and controls, and in stimulated monocytes. Transcriptome-wide association and co-expression network analyses were performed on the ileal RNA-Seq datasets, identifying 40 genome-wide significant genes. Co-expression network analysis identified a single gene module, which was substantially enriched for CD locus genes and most highly expressed in monocytes. By including expression-based and epigenetic information, we refined likely CD genes to 2.5 prioritized genes per locus from an average of 7.8 total genes. We validated our model structure using cross-validation and our prioritization results by protein-association network analyses, which demonstrated significantly higher CD gene interactions for prioritized compared with non-prioritized genes. Although individual datasets cannot convey all of the information relevant to a disease, combining data from multiple relevant expression-based datasets improves prediction of disease genes and helps to further understanding of disease pathogenesis.