Journal Article

Identification of novel loci controlling inflammatory bowel disease susceptibility utilizing the genetic diversity of wild-derived mice

Matthew, Mahoney J, McGill Mahalia M, Fang Qian, Linton, Alisha A, Lavoie, Brigitte, Lahue, Karolyn G, Lara Montana K, Teuscher Cory, Mawe, Gary M, Krementsov, Dimitry N, Crothers, Jessica W, Tyler, Anna L

2020

Overview

Inflammatory bowel disease (IBD) is a complex disorder that imposes a growing health burden. Multiple genetic associations have been identified in IBD, but the mechanisms underlying many of these associations are poorly understood. Animal models are needed to bridge this gap, but conventional laboratory mouse strains lack the genetic diversity of human populations. To more accurately model human genetic diversity, we utilized a panel of chromosome (Chr) substitution strains, carrying chromosomes from the wild-derived and genetically divergent PWD/PhJ (PWD) strain on the commonly used C57BL/6J (B6) background, as well as their parental B6 and PWD strains. Two models of IBD were used, TNBS- and DSS-induced colitis. Compared with B6 mice, PWD mice were highly susceptible to TNBS-induced colitis, but resistant to DSS-induced colitis. Using consomic mice, we identified several PWD-derived loci that exhibited profound effects on IBD susceptibility. The most pronounced of these were loci on Chr1 and Chr2, which yielded high susceptibility in both IBD models, each acting at distinct phases of the disease. Leveraging transcriptomic data from B6 and PWD immune cells, together with a machine learning approach incorporating human IBD genetic associations, we identified lead candidate genes, including Itga4, Pip4k2a, Lcn10, Lgmn, and Gpr65.

• Publisher: Nature Publishing Group
• Subject: Animal models / Chromosomes / Colitis / Genetic diversity / Inflammatory bowel disease / Inflammatory bowel diseases / Intestine / Learning algorithms / Machine learning / Population genetics / Rodents