The goal of this application is to establish an animal model and accompanying database suitable for a systems genetic analysis of complex traits, specifically traits that represent genetic predisposing factors for alcohol use disorder (AUD). Systems genetic approaches require a global analysis of factors such as gene expression, protein and metabolite levels in multiple tissues of an organism, as well as an understanding of gene-gene and gene-environment interactions, and the interdependency of these factors in contributing to complex traits/disorders. As a result, a key requirement for an animal model is a genetically stable population that can be studied repeatedly, over many generations, to provide cumulative data that can eventually allow for a complete systems genetic analysis. During the past grant periods, we have progressed well with the development of a Hybrid Rat Diversity Panel (HRDP) that meets these criteria. We have chosen to focus on the rat, rather than the mouse, for studies of complex traits related to AUD, because of the greater size of the rat brain, the ease of training in operant tasks, and the rat?s higher cognitive ability. We have generated DNA sequence data, RNA sequence data and whole genome exon array data on four tissues (brain, liver [whole organ and cell-specific data], heart and brown adipose tissue) from rat strains of the HXB/BXH recombinant inbred (RI) panel and from classic inbred rat strains. We have mapped QTLs for behavioral/physiological traits (alcohol consumption, alcohol deprivation effect, alcohol metabolism including acetate levels after alcohol administration), as well as used transcriptome data to map expression QTLs, to generate transcript coexpression modules and map module eigengene (first principal component) QTLs. These data have been used to identify candidate genes and transcriptional networks that contribute to the measured biochemistry and behaviors. All of our raw, processed and analyzed data have been made available to the research community on our PhenoGen website (http://phenogen.ucdenver.edu). This website, that we developed, also includes several visualization tools to explore these data in a systems genetics framework and allows the user to observe genetic relationships between a complex phenotype of interest and networks of gene products that influence the phenotype. We are now proposing to complete the main core of transcriptional data for the 96- strain HRDP, adding data from another rat RI panel (FXLE/LEXF) and more inbred rat strains. We will obtain full transcriptome information of brain and liver of male and female rats from all strains, quantify the expression of transcript isoforms, including 3?UTR isoforms, and analyze the 3?UTR regions for alternative use of polyadenylation sites and miRNA binding sites. We will use our established and newly developed pipelines to disseminate integrated, systems level data (PhenoGen and Rat Genome Database). We will also expand our demonstration for applying the gathered information to the identification of genetic factors that are linked to the development of AUD by obtaining information on predisposition to ?depression? in the HXB/BXH RI panel, and we will continue to integrate the animal data with human GWAS data.
This project is designed to build a resource that will provide data leading to a ?systems genetic? understanding of complex disease-related traits. The resource is based on a large panel of genetically-defined inbred rat strains and will include their genome sequences and global levels of gene expression in two organs. These data will be integrated to identify genetic and molecular regulators of gene expression and to describe the interactions among genes as a system of inter-dependent components that work together to predispose an individual to complex disease. The data will be available to users of our website, https://Phenogen.ucdenver.edu, for integration with behavioral and clinical traits in animals, and with human genome-wide association studies, to shed light on the mechanisms that underlie the association of genetic variants with human disease.
| Lusk, Ryan; Saba, Laura M; Vanderlinden, Lauren A et al. (2018) Unsupervised, Statistically Based Systems Biology Approach for Unraveling the Genetics of Complex Traits: A Demonstration with Ethanol Metabolism. Alcohol Clin Exp Res 42:1177-1191 |
| Hoffman, Paula L; Saba, Laura M; Vanderlinden, Lauren A et al. (2018) Voluntary exposure to a toxin: the genetic influence on ethanol consumption. Mamm Genome 29:128-140 |
| Pravenec, Michal; Saba, Laura M; Zídek, Václav et al. (2018) Systems genetic analysis of brown adipose tissue function. Physiol Genomics 50:52-66 |
| Vestal, B; Russell, P; Radcliffe, R A et al. (2018) miRNA-regulated transcription associated with mouse strains predisposed to hypnotic effects of ethanol. Brain Behav 8:e00989 |
| Rudra, Pratyaydipta; Shi, Wen J; Russell, Pamela et al. (2018) Predictive modeling of miRNA-mediated predisposition to alcohol-related phenotypes in mouse. BMC Genomics 19:639 |
| Saba, Laura; Hoffman, Paula; Tabakoff, Boris (2017) Using Baseline Transcriptional Connectomes in Rat to Identify Genetic Pathways Associated with Predisposition to Complex Traits. Methods Mol Biol 1488:299-317 |
| Shimoyama, Mary; Smith, Jennifer R; Bryda, Elizabeth et al. (2017) Rat Genome and Model Resources. ILAR J 58:42-58 |
| Abdelmagid, Nada; Bereczky-Veress, Biborka; Atanur, Santosh et al. (2016) Von Willebrand Factor Gene Variants Associate with Herpes simplex Encephalitis. PLoS One 11:e0155832 |
| Harrall, Kylie K; Kechris, Katerina J; Tabakoff, Boris et al. (2016) Uncovering the liver's role in immunity through RNA co-expression networks. Mamm Genome 27:469-84 |
| Zuo, Lingjun; Tan, Yunlong; Zhang, Xiangyang et al. (2015) A New Genomewide Association Meta-Analysis of Alcohol Dependence. Alcohol Clin Exp Res 39:1388-95 |
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