This Research Plan is submitted in response to the RFA DK-06-504 for continuing the NIDDK Inflammatory Bowel Disease Genetics Consortium (IBDGC). This Master Application, submitted as a Data Coordinating Center application (DCC) from Yale University, describes prior research accomplishments, a prospective research plan, and the operational infrastructure of the entire IBDGC. This DCC application (Principal Investigator, PI: Judy H. Cho, M.D.) has been coordinated with Genetics Research Center (GRC) applications from the Cedars-Sinai Genetics Research Center (CSGRC), the Johns Hopkins Genetics Research Center (JHGRC), the University of Montreal Genetics Research Center (UMGRC), the University of Pittsburgh Genetics Research Center (UPGRC), the University of Toronto Genetics Research Center (UTGRC), and the Yale University Genetics Research Center (YUGRC). The central goal of this Consortium is to identify susceptibility genes contributing to the pathogenesis of IBD. This large challenge is optimally met through a Consortium structure due to synergy in a group with complementary skills, enhanced statistical power, ongoing NIH oversight and optimized quality control. The showpiece of the first funding period has been the identification of IL23R (interleukin-23 receptor) as a major susceptibility gene for IBD, highlighting the significant pathogenic role of genetic variation in IBD. Here, we propose to build on present progress to comprehensively define genetic contributions in IBD.
Three specific aims i nclude: 1) Expansion, Development and Management of Consortium Resources. Increased recruitment of key cohorts, including early-onset cases, will provide improved statistical power. 2) To employ a variety of approaches to identify genetic variation that contributes to IBD susceptibility. We provide a broad algorithm for follow-up of initial association signals. An ulcerative colitis genome-wide association study is proposed. 3) To build a risk model of IBD by understanding genetic influence on variations in phenotypic expressivity, gene pathway, and gene-gene (G x G) and gene-environmental (G x E) interactions. A basic model for identifying genetic predictors of phenotypic variability is provided. Specific stages of workup, optimally utilizing the strengths provided by uniform, well-powered, large Consortium-wide studies with the greater individualization and specialization of approaches achievable with GRC-led studies are described.
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