Identification of novel loci controlling inflammatory bowel disease susceptibility utilizing the genetic diversity of wild-derived mice.
Karolyn G LahueMontana K LaraAlisha A LintonBrigitte LavoieQian FangMahalia M McGillJessica W CrothersCory TeuscherGary M MaweAnna L TylerJ Matthew MahoneyDimitry N KrementsovPublished in: Genes and immunity (2020)
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.
Keyphrases
- genetic diversity
- endothelial cells
- genome wide
- ulcerative colitis
- machine learning
- high fat diet induced
- escherichia coli
- induced pluripotent stem cells
- healthcare
- pluripotent stem cells
- public health
- mental health
- type diabetes
- insulin resistance
- genome wide association study
- artificial intelligence
- big data
- risk factors
- gene expression
- wild type
- deep learning
- electronic health record
- intellectual disability
- autism spectrum disorder
- adipose tissue
- risk assessment
- rna seq
- bioinformatics analysis