Systems biology and the availability of genomic data have revolutionized our ability to identify the underlying biological mechanisms of diverse diseases and disorders of behavior. Indeed it is becoming within our reach to reclassify diseases based on biological substrate, rather than external manifestation. Using combinatorial, graph-centered approaches, we are able to infer relations among behavioral disorders, addiction-related phenotypes and causative pathways based on gene-phenotype associations. We integrate knowledge of biological functions based on associated molecular networks using The Ontological Discovery Environment, our web based software system designed to enable biologists to perform integrated analysis of phenotype centered genomic data. We make use of advances in database design, algorithms, and advanced mouse resources. In this proposal, we address all three of these areas, and propose to demonstrate our tools for the study of relations among stress, anxiety, psychological disorders and the sensitivity toward and use of drugs and alcohol. Our findings will be validated through the use of mouse models. The three aims of the project are to develop a large data repository for behavioral neuroscience and data structures that more efficiently enable the use of databases to represent graphical network data, to develop algorithms for the analysis of integrated gene centered data across species and experimental platforms to incorporate these developments into a Web-based software system, and to use this tool to find genes underlying relationships between multiple abused substances and behavior.

Public Health Relevance

A wealth of data is being generated that associates multiple genes to multiple behavioral disorders and addiction-related phenotypes. The Ontological Discovery Environment project enables the integration of this data across species and experimental systems based on gene-phenotype associations. The methods and software we develop will enable enhanced understanding of the relationships among behavioral traits and disorders of addiction and of the underlying molecular mechanisms underlying classes of diseases. We will identify and test the role of specific genes in drug addiction. Our methods will also facilitate the mapping of research in non-human model organisms onto human disease. A major component of this effort is software development and training to ensure that the technical advances are translated into deliverable tools for the research community to perform similar analyses.

Agency
National Institute of Health (NIH)
Institute
National Institute on Alcohol Abuse and Alcoholism (NIAAA)
Type
Research Project (R01)
Project #
5R01AA018776-02
Application #
8147797
Study Section
Neurotechnology Study Section (NT)
Program Officer
Grandison, Lindsey
Project Start
2010-09-25
Project End
2014-08-31
Budget Start
2011-09-01
Budget End
2012-08-31
Support Year
2
Fiscal Year
2011
Total Cost
$511,090
Indirect Cost
Name
Jackson Laboratory
Department
Type
DUNS #
042140483
City
Bar Harbor
State
ME
Country
United States
Zip Code
04609
Macartney-Coxson, Donia; Benton, Miles C; Blick, Ray et al. (2017) Genome-wide DNA methylation analysis reveals loci that distinguish different types of adipose tissue in obese individuals. Clin Epigenetics 9:48
Cong, Yingnan; Chan, Yao-Ban; Phillips, Charles A et al. (2017) Robust Inference of Genetic Exchange Communities from Microbial Genomes Using TF-IDF. Front Microbiol 8:21
Gittner, LisaAnn S; Kilbourne, Barbara J; Vadapalli, Ravi et al. (2017) A multifactorial obesity model developed from nationwide public health exposome data and modern computational analyses. Obes Res Clin Pract 11:522-533
Parker, Clarissa C; Dickson, Price E; Philip, Vivek M et al. (2017) Systems Genetic Analysis in GeneNetwork.org. Curr Protoc Neurosci 79:8.39.1-8.39.20
Juarez, Paul D; Hood, Darryl B; Rogers, Gary L et al. (2017) A novel approach to analyzing lung cancer mortality disparities: Using the exposome and a graph-theoretical toolchain. Environ Dis 2:33-44
Bubier, Jason A; Langston, Michael A; Baker, Erich J et al. (2017) Integrative Functional Genomics for Systems Genetics in GeneWeaver.org. Methods Mol Biol 1488:131-152
Chesler, Elissa J; Gatti, Daniel M; Morgan, Andrew P et al. (2016) Diversity Outbred Mice at 21: Maintaining Allelic Variation in the Face of Selection. G3 (Bethesda) 6:3893-3902
Bubier, Jason A; Wilcox, Troy D; Jay, Jeremy J et al. (2016) Cross-Species Integrative Functional Genomics in GeneWeaver Reveals a Role for Pafah1b1 in Altered Response to Alcohol. Front Behav Neurosci 10:1
Baker, Erich; Bubier, Jason A; Reynolds, Timothy et al. (2016) GeneWeaver: data driven alignment of cross-species genomics in biology and disease. Nucleic Acids Res 44:D555-9
Young, E E; Bryant, C D; Lee, S E et al. (2016) Systems genetic and pharmacological analysis identifies candidate genes underlying mechanosensation in the von Frey test. Genes Brain Behav 15:604-15

Showing the most recent 10 out of 39 publications