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. Our Pilot core is catalyzing new collaborations among early career investigators in the field of addiction research. In sum the Center is a national resource for 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.

Agency
National Institute of Health (NIH)
Institute
National Institute on Drug Abuse (NIDA)
Type
Center Core Grants (P30)
Project #
1P30DA044223-01
Application #
9360452
Study Section
Special Emphasis Panel (ZDA1)
Project Start
Project End
Budget Start
2017-07-01
Budget End
2018-06-30
Support Year
1
Fiscal Year
2017
Total Cost
Indirect Cost
Name
University of Tennessee Health Science Center
Department
Type
DUNS #
941884009
City
Memphis
State
TN
Country
United States
Zip Code
38103
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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
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