2012 — 2016 |
Hoffman, Paula Saba, Laura Maren Tabakoff, Boris [⬀] |
R24Activity Code Description: Undocumented code - click on the grant title for more information. |
Rgap: the Heritable Transcriptome and Alcoholism @ University of Colorado Denver
DESCRIPTION (provided by applicant): 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, http://Phenogen.ucdenver.edu, 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.
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1 |
2017 — 2021 |
Saba, Laura Maren Williams, Robert W. [⬀] Williams, Robert W. [⬀] |
P30Activity Code Description: To support shared resources and facilities for categorical research by a number of investigators from different disciplines who provide a multidisciplinary approach to a joint research effort or from the same discipline who focus on a common research problem. The core grant is integrated with the center's component projects or program projects, though funded independently from them. This support, by providing more accessible resources, is expected to assure a greater productivity than from the separate projects and program projects. |
Overall Nida Core 'Center of Excellence' in Transcriptomics, Systems Genetics and the Addictome @ University of Tennessee Health Sci Ctr
Addiction is a highly complex disease with risk factors that include genetic variants and differences in development, sex, and environment. The long term potential of precision medicine to improve drug treatment and prevention depends on gaining a much better understanding how genetics, drugs, brain cells, and neuronal circuitry interact to influence behavior. There are serious technical barriers that prevent researchers and clinicians from incorporating more powerful computational and predictive methods in addiction research. The purpose of the NIDA P30 Core Center of Excellence in Omics, Systems Genetics, and the Addictome is to empower and train researchers supported by NIH, NIDA, NIAAA, and other federal and state institutions to use more quantitative and testable ways to analyze genetic, epigenetic, and the environmental factors that influence drug abuse risk and treatment. In the Transcriptome Informatics and Mechanisms research core we assemble and upgrade hundreds of large genome (DNA) and transcriptome (RNA) datasets for experimental rodent (rat) models of addiction. In the Systems Analytics and Modeling research core, we are using innovative systems genetics methods (gene mapping) to understand the linkage between DNA differences, environmental risks such as stress, and the differential risk of drug abuse and relapse. Our Pilot core is catalyzing new collaborations among young investigator in the field of addiction research. In sum the Center is a national resource for more reproducible research in addiction. We are centralizing, archiving, distributing, analyzing and integrating high quality data, metadata, using open software systems in collaboration with many other teams of researchers. Our goal is to help build toward an NIDA Addictome Portal that will include all genomic research relevant to addiction research.
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0.933 |
2017 — 2021 |
Hoffman, Paula Saba, Laura Maren Tabakoff, Boris [⬀] |
R24Activity Code Description: Undocumented code - click on the grant title for more information. |
The Heritable Transcriptome and Alcoholism @ University of Colorado Denver
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.
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1 |
2017 — 2021 |
Saba, Laura Maren |
P30Activity Code Description: To support shared resources and facilities for categorical research by a number of investigators from different disciplines who provide a multidisciplinary approach to a joint research effort or from the same discipline who focus on a common research problem. The core grant is integrated with the center's component projects or program projects, though funded independently from them. This support, by providing more accessible resources, is expected to assure a greater productivity than from the separate projects and program projects. |
Tim Project Nida P30 Center @ University of Tennessee Health Sci Ctr
Addiction is a highly complex disease with risk factors that include genetic variants and differences in development, sex, and environment. The long term potential of precision medicine to improve drug treatment and prevention depends on gaining a much better understanding how genetics, drugs, brain cells, and neuronal circuitry interact to influence behavior. There are serious technical barriers that prevent researchers and clinicians from incorporating more powerful computational and predictive methods in addiction research. The purpose of the NIDA P30 Core Center of Excellence in Omics, Systems Genetics, and the Addictome is to empower and train researchers supported by NIH, NIDA, NIAAA, and other federal and state institutions to use more quantitative and testable ways to analyze genetic, epigenetic, and the environmental factors that influence drug abuse risk and treatment. The Administrative Core manages relations among research cores, groups of users, trainees, and pilot program participants. The Transcriptome Informatics and Mechanisms research core assembles and analyzes hundreds of large genome (DNA) and transcriptome (RNA) datasets for experimental rodent (rat) models of addiction. The Systems Analytics and Modeling research core is using innovative systems genetics methods to understand the linkage between DNA differences, environmental risks such as stress, and the differential risk of drug abuse and relapse. The Pilot core is catalyzing new collaborations among young investigator in the field of addiction research. In sum the Center is a national resource for more reproducible research in addiction. We are centralizing, archiving, distributing, analyzing and integrating high quality data, metadata, using open software systems in collaboration with many other teams of researchers. Our goal is to help build toward an NIDA Addictome Portal that will include all genomic research relevant to addiction research.
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0.933 |
2021 |
Bachtell, Ryan K (co-PI) [⬀] Ehringer, Marissa A [⬀] Saba, Laura Maren |
U01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Identification of Genes and Genetic Networks Contributing to Opioid Use Disorder Traits in the Hybrid Rat Diversity Panel
PROJECT SUMMARY Over the past 5-10 years, the opioid epidemic has become a national crisis in the United States. Currently, few good treatment options exist, and little is known about the underlying mechanisms contributing to risk for addiction and to drug effects on the brain. This project addresses both of these issues using a rat genetic model to identify genetic contributions to phenotypes associated with the development of opioid use disorders. We will identify oxycodone-related phenotypic, genotypic, and RNA expression differences within the HXB/BXH RI strains and 15 additional inbred rat strains for which genetic data are available, drawn from the Hybrid Rat Diversity Panel (HRDP). Our preliminary phenotypic data suggest that the founder strains SHR/OlaIpcv and BN-Lx/Cub, along with the ACI strain, differ on many of the phenotypic traits assessed including the self-administration of oxycodone. In Aim 1, 48 inbred rat strains will be assessed for multiple oxycodone-related behavioral phenotypes, including measures of analgesia. Quantitative trait loci (QTL) associated with these behaviors will be identified using existing genetic data. In Aim 2, we will perform RNA sequencing using tissue from the nucleus accumbens and amygdala in naïve animals and in rats following oxycodone self-administration. This will identify genes that differ by strain, which will be informative about baseline risk by genotype, and also identify genes that differ in response to oxycodone (shared and unshared across strains). Because genes do not operate independently, but work in networks and pathways, Aim 3 will employ a systems genetics approach to identify genetic networks involved in baseline differences across strains and in the response to oxycodone self-administration. Across all aims, we will compare the QTL regions, RNA expression differences, and gene network pathways to those found by others in the field using complementary rodent models and/or human studies (including our collaborator Dr. Olivier George) in order to narrow focus on priority genes and pathways.
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