Drugs play a dual role in our understanding of neuropsychiatric disorders. First, they are used clinically to alleviate symptoms and (ideally) to reverse aberrant pathophysiology. Second, they provide a molecular """"""""challenge"""""""" to the CNS that responds and provides valuable information about the ways in which relevant phenotypes are modulated at the molecular level. Thus, they provide an excellent opportunity to link high level phenotypes and low-level molecular and cellular networks. Unfortunately, the relationships between drugs, genes, and phenotypes are disperse and not amenable to computational analysis. Integrating multiple data sources relevant to neuropsychiatric drugs, their associated indications and side effects, and their molecular targets in the context of genetic interactions will be a powerful way to both generate new hypotheses about how these drugs act and which genetic circuits they affect. Thus, the primary goal of this component project Is to develop and apply informatics methods for using drug-associated data to inform models of neuropsychiatric disease risk, occurrence and treatment.

Public Health Relevance

The project alms to deconvolute mathematically how prescription drugs affect patients with diverse genetic backgroundsespecially in relation to complex neuropsychiatric disorders, such as autism, schizophrenia and depression.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Specialized Center (P50)
Project #
3P50MH094267-04S1
Application #
8936053
Study Section
Special Emphasis Panel (ZMH1 (02))
Program Officer
Addington, Anjene M
Project Start
2011-09-22
Project End
2016-07-31
Budget Start
2014-08-01
Budget End
2015-07-31
Support Year
4
Fiscal Year
2014
Total Cost
$1
Indirect Cost
Name
University of Chicago
Department
Type
DUNS #
005421136
City
Chicago
State
IL
Country
United States
Zip Code
60637
Manrai, Arjun K; Funke, Birgit H; Rehm, Heidi L et al. (2016) Genetic Misdiagnoses and the Potential for Health Disparities. N Engl J Med 375:655-65
Nazeen, Sumaiya; Palmer, Nathan P; Berger, Bonnie et al. (2016) Integrative analysis of genetic data sets reveals a shared innate immune component in autism spectrum disorder and its co-morbidities. Genome Biol 17:228
Lykins, Joseph; Wang, Kanix; Wheeler, Kelsey et al. (2016) Understanding Toxoplasmosis in the United States Through ""Large Data"" Analyses. Clin Infect Dis 63:468-75
Somekh, Judith; Peleg, Mor; Eran, Alal et al. (2016) A model-driven methodology for exploring complex disease comorbidities applied to autism spectrum disorder and inflammatory bowel disease. J Biomed Inform 63:366-378
Mallory, Emily K; Zhang, Ce; Ré, Christopher et al. (2016) Large-scale extraction of gene interactions from full-text literature using DeepDive. Bioinformatics 32:106-13
Bagley, Steven C; Sirota, Marina; Chen, Richard et al. (2016) Constraints on Biological Mechanism from Disease Comorbidity Using Electronic Medical Records and Database of Genetic Variants. PLoS Comput Biol 12:e1004885
Li, Yong Fuga; Xin, Fuxiao; Altman, Russ B (2016) SEPARATING THE CAUSES AND CONSEQUENCES IN DISEASE TRANSCRIPTOME. Pac Symp Biocomput 21:381-92
Gamazon, Eric R; Wheeler, Heather E; Shah, Kaanan P et al. (2015) A gene-based association method for mapping traits using reference transcriptome data. Nat Genet 47:1091-8
Yu, Dongmei; Mathews, Carol A; Scharf, Jeremiah M et al. (2015) Cross-disorder genome-wide analyses suggest a complex genetic relationship between Tourette's syndrome and OCD. Am J Psychiatry 172:82-93
Kohane, Isaac S (2015) An autism case history to review the systematic analysis of large-scale data to refine the diagnosis and treatment of neuropsychiatric disorders. Biol Psychiatry 77:59-65

Showing the most recent 10 out of 65 publications