This is a renewal application to continue and expand a resource for systems genetic analyses of the rodent (mouse and rat) transcriptome. We have created a website,, that makes available high quality genetic and whole genome microarray expression data (brain and other organs) from recombinant inbred and inbred strains of mice and rats. This website also provides an array of tools for gene expression analysis. The tools allow a user to identify candidate genes and transcriptional networks for complex physiological and behavioral traits based on the tenets of quantitative genetics, i.e., by combining transcript expression and phenotypic data with expression and behavioral QTL data. We now propose to expand our focus on the rat species, a favored model for human disease. We will breed additional strains of the HXB/BXH recombinant inbred rat panel, and add a panel of genetically diverse classical inbred rat strains, to form a hybrid, high-resolution association mapping panel of rats. Rat strains will be genotyped, and we will complete RNA-Seq analysis of total RNA from brain and liver of all rat strains. We will combine data on exons of protein-coding transcripts with already available exon microarray data, and we will identify, quantify and catalog both protein-coding and non-coding transcripts (including miRNAs, other small non-coding RNAs and long non-coding RNAs) from both organs. We will calculate the heritability of transcript expression levels as one measure of functionality. We will use quantitative data on the transcripts to identify organ-specific genetic locations of transcriptional control by performing association (eQTL) analysis using genetic marker information from each strain. The combined genotypic and transcript expression data will be incorporated into coexpression network modules and we will calculate module QTLs. The module analysis will also provide means to assign gene expression in a whole organ to cell types and anatomical regions of an organ. The annotated and curated data and systems genetic tools will be made available to investigators on the PhenoGen website. We will measure alcohol consumption and alcohol metabolism in the hybrid rat panel, and will integrate the genetic, transcriptome and transcriptional network data with information on these phenotypes to generate causal networks that elucidate the genetic, epigenetic and genomic contributions to predisposition to phenotypic variability.

Public Health Relevance

We are continuing to build a resource that will enable investigators to use high-throughput, carefully curated data and analytical tools to elucidate the genetic basis for predisposition to complex diseases. The integration of genetic, functional genomic and phenotypic data across organs and species that is exemplified here is an approach that will facilitate development of medications that target the polygenic pathways that predispose to disease and disease progression, and our approach can also be used to predict the response to medications.

National Institute of Health (NIH)
National Institute on Alcohol Abuse and Alcoholism (NIAAA)
Resource-Related Research Projects (R24)
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Special Emphasis Panel (ZAA1-DD (01))
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Murray, Gary
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University of Colorado Denver
Schools of Medicine
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