Gene to Phenotype Networks for Alcohol & Drug Addiction

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DESCRIPTION (provided by applicant): Drug abuse and addiction are complex phenotypes. Typical of many human diseases, they are influenced by multiple genetic and environmental factors. Susceptibility to addiction is co-morbid with other behavioral disorders, which is evidence that the same genetic influences may be acting to affect multiple phenotypes, a phenomenon known as gene pleiotropy. The main purpose of this project is to systematically identify genes and gene networks that modulate pleiotropic responses to abused substances, behavioral variation, and susceptibility to abuse. This application exploits the unique mapping properties of Rl strains, a new, high power expanded set of Rl lines, advanced bioinformatics tools, extensive databases present in WebQTL, and the expertise of the TMGC high-throughput phenotyping resource to systematically identify upstream genes and molecular networks that ultimately modulate downstream pleiotropic drug and alcohol phenotypes. The powerful combination of QTL mapping and microarray transcript profiling will be applied to these systems level phenotypes by exploiting existing high-throughput molecular data resources in WebQTL. As part of this application, we have assembled a strong team of investigators with complementary expertise in several areas, most notably in complex trait analysis and gene mapping, behavioral and neural analysis, psychopharmacology and pharmacogenetics, transcriptome profiling and molecular genetics, drug abuse, alcoholism, mouse colony management and distribution and advanced bioinformatics and multivariate statistical methods of handling large data sets. This strong team will capitalize on the generous support offered by the Department of Energy's Oak Ridge National Lab. The data resources generated by this project will dramatically reduce the amount of phenotyping one needs to perform to discover the effects of any novel gene specific mutation. Candidate genes will be validated using a novel banked ENU resource at ORNL as well as publicly available mouse mutant resources. This will be invaluable for the development of realistic complex disease models and will provide data resources to suggest cost effective targeted phenotyping strategies for large scale single gene mutation efforts such as those proposed by the Comprehensive Knockout Mouse Project Consortium. By examining covariance of gene expression measures and known phenotypic measures in BXD Rl lines, we can rationally target phenotypes that are likely to be affected by particular gene mutations. More broadly, we will be able to identify the specific genetic basis of the pleiotropic and polygenic effects of genetic polymorphisms on drug abuse, addiction, and individual differences in brain and behavior.