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.

Public Health Relevance

Mice are a powerful tool for understanding the genetic basis behavioral and physiological traits;this proposal will use cutting-edge statistical and molecular techniques for this purpose. The results will provide insights into several medically important phenotypes and will also develop new methods for mouse genetics.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM097737-02
Application #
8248807
Study Section
Genetics of Health and Disease Study Section (GHD)
Program Officer
Krasnewich, Donna M
Project Start
2011-04-01
Project End
2015-03-31
Budget Start
2012-04-01
Budget End
2013-03-31
Support Year
2
Fiscal Year
2012
Total Cost
$552,902
Indirect Cost
$198,085
Name
University of Chicago
Department
Genetics
Type
Schools of Medicine
DUNS #
005421136
City
Chicago
State
IL
Country
United States
Zip Code
60637
Wang, T; Han, W; Wang, B et al. (2014) Propensity for social interaction predicts nicotine-reinforced behaviors in outbred rats. Genes Brain Behav 13:202-12
Parker, Clarissa C; Chen, Hao; Flagel, Shelly B et al. (2014) Rats are the smart choice: Rationale for a renewed focus on rats in behavioral genetics. Neuropharmacology 76 Pt B:250-8
Cheng, Riyan; Parker, Clarissa C; Abney, Mark et al. (2013) Practical considerations regarding the use of genotype and pedigree data to model relatedness in the context of genome-wide association studies. G3 (Bethesda) 3:1861-7
Fitzpatrick, Christopher J; Gopalakrishnan, Shyam; Cogan, Elizabeth S et al. (2013) Variation in the form of Pavlovian conditioned approach behavior among outbred male Sprague-Dawley rats from different vendors and colonies: sign-tracking vs. goal-tracking. PLoS One 8:e75042
Bartnikas, Thomas B; Parker, Clarissa C; Cheng, Riyan et al. (2012) QTLs for murine red blood cell parameters in LG/J and SM/J F(2) and advanced intercross lines. Mamm Genome 23:356-66
Parker, Clarissa C; Sokoloff, Greta; Cheng, Riyan et al. (2012) Genome-wide association for fear conditioning in an advanced intercross mouse line. Behav Genet 42:437-48
Palmer, Abraham A; de Wit, Harriet (2012) Translational genetic approaches to substance use disorders: bridging the gap between mice and humans. Hum Genet 131:931-9
Johnson, Luke R; McGuire, Jennifer; Lazarus, Rachel et al. (2012) Pavlovian fear memory circuits and phenotype models of PTSD. Neuropharmacology 62:638-46
Bryant, Camron D; Parker, Clarissa C; Zhou, Lili et al. (2012) Csnk1e is a genetic regulator of sensitivity to psychostimulants and opioids. Neuropsychopharmacology 37:1026-35
Philip, Vivek M; Sokoloff, Greta; Ackert-Bicknell, Cheryl L et al. (2011) Genetic analysis in the Collaborative Cross breeding population. Genome Res 21:1223-38

Showing the most recent 10 out of 13 publications