Mice offer a powerful tool for elucidating the genetic basis of behavioral and physiological traits. Reverse genetic approaches such as the creation of knock-out and transgenic mice have been extremely successful in testing hypotheses about the function of specific genes. Forward genetic strategies, which seek to identify the relationship between genes and phenotypes based on standing variation in a heterogeneous population, have also provided insights, but almost never identify the causal genes. This is because traditional approaches to the analysis of quantitative traits in mice are analogous to family-based linkage designs in human, and typically identify large genomic regions that contain many genes. The goal of this proposal is to implement a forward genetic strategy that is similar to human genome-wide association studies (GWAS) and will be able to identify small regions and thus specific genes that are associated with phenotypic variability. We will utilize outbred CD-1 mice because linkage disequilibrium breaks down over short physical distances in these mice compared to other commonly studied mouse populations. Moreover, CD-1 mice are descendants of the same laboratory mice that gave rise to other laboratory strains and are therefore easy to handle and are likely to segregate many of the same alleles. We will exhaustively phenotype 1,008 CD-1 mice for a battery of behavioral and physiological traits. Mice will be genotyped at ~600,000 markers using the new Affymetrix Mouse Diversity Array; we will then perform GWAS to identify QTLs for all phenotypes measured. The traits that we will study are related to psychiatric disease and build on our prior experience in the area of behavioral genetics. The physiological traits are of interest to the diverse array of collaborators that we have assembled. In addition to providing information about these medically important traits, we will demonstrate the broad applicability of this method. We will also employ next-generation sequencing of mRNA (RNASeq) obtained from key brain regions to identify gene expression differences in a subset of these mice. These data will be used to map expression QTLs (eQTLs) and to identify coding polymorphisms. By identifying SNPs that are associated with both behavioral and gene-expression traits we can rapidly identify plausible biological explanations for how these SNPs influence behavior. Such hypotheses are directly testable in mice, which is a major advantage of performing GWAS in mice versus humans. This component of the project will be managed by Dr. Jonathan Pritchard's group, which has conducted similar analyses in human cell lines. In the final phase of this project we will synthesize data about QTLs, eQTLs and coding SNPs. We will implement a Bayesian approach that uses information about eQTLs and coding SNPs as priors for finding QTLs. In addition, we will examine the correlation between gene expression and complex traits in an effort to identify correlations that may not be attributable to a specific genomic locus. The methods proposed in this application are generally applicable to any quantitative trait and have the potential vastly accelerate the process of gene identification, which will aid in the identification of common and rare alleles that contribute to human disease.