W042
The Collaborative Cross Multiparental Population: Applications for Rapid Gene Identification, Genetic Correlations and Novel Disease Models
The Collaborative Cross Multiparental Population: Applications for Rapid Gene Identification, Genetic Correlations and Novel Disease Models
Date: Monday, July 13, 2015
Time: 1:00 PM
Time: 1:00 PM
Room: Riverfront 1
The Collaborative Cross (CC) was planned as the first population of multiparental recombinant inbred strains; after a dozen years of production, this mouse-based resource has now reached maturity. The “Southern Cross” resource was produced in Australia and constitutes the world’s largest collection of CC strains. An international consortium of researchers has been investigating these strains for many traits across a range of fields, including cancer (melanoma; nevi); immunology (lymphocyte populations, disease resistance, autoantibody production); behavior (activity); physiology (regeneration; diabetes related traits; muscle growth; wound healing); neurology (resistance to dementia; metals in brain sub-regions), etc. These data have been used to demonstrate the efficacy of the CC both for gene mapping and identification of causal variants, and to develop new animal models of disease. We developed software suitable for analyzing the complex genetics of the CC, successfully mapping genes mediating these traits with sufficient resolution to identify several causative variants. We also developed strategies to detect interacting genes mediating complex traits. With phenotypic data accumulating at an increasing rate, we searched for correlations between over 100 traits. This allows serendipitous discoveries from work done by different teams of investigators and opens up new opportunities for collaboration. Our strains can be used to investigate several diseases, ranging from the common (e.g. diabetes) to the infrequent (e.g. ulcerative colitis) to the rare (e.g. achalasia). Our results illustrate the power and diversity of the CC and provide strong evidence that it can be applied for robust detection of both qualitative and quantitative trait loci, and for the production of novel models of disease.
