Inbred mouse strains are a very powerful and well studied human
disease and complex trait model. A tremendous amount of information
is available for various inbred strains including phenotypic
information stored in the Mouse Phenome Database (MPD) and
high-throughput genomic data such as expression microarray data.
Recently, several high density SNP maps have also been developed for
inbred mouse stains. These resources combined with what is already
known about mouse genetics in terms of quantitative trait loci (QTLs)
and known pathways, make inbred mouse strains an ideal model system.
We present results of our analyses which combines multiple types of
data in order to understand the genetic basis of complex traits. We
perform whole genome association analysis of the mouse SNP maps over
the phenotypes in the MPD. We describe how we augment our association
analysis results with information from expression data, known pathways
and QTLs. We demonstrate the how our approach is able to discover
many regions in the mouse genome associated with phenotypes and how
many of our predictions are consistent with genes known to influence
specific traits.
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Joel Bader Seminar
Andrew Smith Seminar
Gene Regulatory Networks Special Feature (PNAS)
Abstract
disease and complex trait model. A tremendous amount of information
is available for various inbred strains including phenotypic
information stored in the Mouse Phenome Database (MPD) and
high-throughput genomic data such as expression microarray data.
Recently, several high density SNP maps have also been developed for
inbred mouse stains. These resources combined with what is already
known about mouse genetics in terms of quantitative trait loci (QTLs)
and known pathways, make inbred mouse strains an ideal model system.
We present results of our analyses which combines multiple types of
data in order to understand the genetic basis of complex traits. We
perform whole genome association analysis of the mouse SNP maps over
the phenotypes in the MPD. We describe how we augment our association
analysis results with information from expression data, known pathways
and QTLs. We demonstrate the how our approach is able to discover
many regions in the mouse genome associated with phenotypes and how
many of our predictions are consistent with genes known to influence
specific traits.