International Mammalian Genome Society

logo18th International Mouse Genome Conference

17-22 October 2004, Seattle, USA


POSTER 109 -  IN SILICO POSITIONAL CANDIDATE GENE ANALYSIS IN THE MOUSE: NEW TOOLS FROM THE MOUSE GENOME INFORMATICS CONSORTIUM

Bult CJ, Ringwald M, Blake JA, Kadin JA, Richardson JE, Eppig JT, Sequences and Maps Group MGI, Phenotypes Group MGI, Expression Group MGI, Software Group MGI

The Jackson Laboratory, Bar Harbor, United States

The mouse genome sequence serves as a framework for the discovery of genes that underlie biological and disease processes. To leverage the genome sequence effectively depends on how well genetically defined mutant phenotypes are integrated with genome annotations and information about sequence variation (e.g. SNPs), allelic variation, gene expression, homology, and gene function annotations. Because of its long-standing focus on the integration of exactly these kinds of diverse biological data about mouse genes, the Mouse Genome Informatics database is a powerful platform to assist in the computational analysis of integrated biological data with the goal of identifying candidate genes associated with complex genetically defined phenotypes. Associating expression phenotypes with mutant phenotypes has proved to be a powerful approach to identifying candidate genes in regions of the mouse genome to which complex traits have been mapped. To facilitate such associations via MGI we have integrated probe sets from both Affymetrix and Agilent with genes in MGI. As a result, researchers can map expression data generated from these platforms to genes in MGI using both genomic and genetic coordinates. They can also combine expression data with other data including SNPs, phenotypic classifications, and functional annotations. We will show how the more than 1,800 Quantitative Trait Loci in MGI can be combined with genetic, expression, and variation data in support of in silico positional candidate gene analysis.

The MGD and GXD components of the Mouse Genome Informatics Consortium are supported by NIH 5 P41 HG000330-P1 and NIH HD33745, respectively.

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