Poster 39
Presenter: Howard Dene
Wednesday, 3:00 – 5:00pm
Howard Dene, Paul Hale, Steven Neuhauser, Jill Recla, Susan M. Bello, Cynthia L. Smith, Joel Richardson, Carol J. Bult, Janan T. Eppig. Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME 04609, USA
Analysis of the genetic basis of continuously varying and quantitative phenotypes including obesity, atherosclerosis, autoimmunity and susceptibility to infection, preferences for alcohol and drugs, and behavioral responses, is a continuing challenge. Quantitative trait loci (QTL) are identified by looking for genomic regions that contribute to phenotypic variation, frequently using populations of F2 crosses or recombinant inbred strains. The expanded BXD recombinant inbred strain set, Diversity Outcross (DO), and Collaborative Cross (CC) populations provide new resources to identify QTL and more exactly localize and identify these variants in the genome. The Mouse Genome Informatics Database (MGI, http://www.informatics.jax.org) has cataloged nearly 5000 QTL and curated mapping and phenotypic trait information on most of these. 8613 QTL variants distinguishing strain-specific locus differences have been integrated in MGI with data on genome sequence, gene expression, genetic polymorphisms and phenotypic consequences. Two graphical QTL browsers are implemented in MGI. The Mouse Genome Browser (Mouse GBrowse) provides a customizable graphic representation of those QTL that can be defined in the context of known genetic markers on a sequence-based map. This representation permits exploration of causative candidate genes and prioritization of these candidates for experimental analysis. Mouse GBrowse displays may suggest new strategies to help refine the region in which a particular QTL is located. A new Cancer QTL Viewer is available through the Mouse Tumor Database (MTB) and will be adapted for use in MGI as a whole. Examples of both Mouse GBrowse and the Cancer QTL viewer will be shown.
Supported by NIH grant HG000330.