We aim to develop, test, and apply a drastically new computational methodology for the analysis of more than one complex phenotype at a time, with the goal of generating novel biological results. Specifically, we propose to design and validate a battery of novel analytical tools for the inference of causal relationships among human genomic variations, environmental factors, and more than one mental health phenotype, explicitly exploiting the genetic and environmental non-independence of complex (multigenic) disorders.

Public Health Relevance

We attempt to consolidate in a single modeling framework a number of disparate approaches for analysis of complex neuropsychiatric disorders. The comprehensive modeling approach will produce experimentally testable predictions, a considerable number of which we will be able to validate within the proposed research. We will focus on several phenotypes with major impacts on the health of US populations, such as anxiety, schizophrenia and depression.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Specialized Center (P50)
Project #
5P50MH094267-03
Application #
8531353
Study Section
Special Emphasis Panel (ZMH1-ERB-S (02))
Program Officer
Senthil, Geetha
Project Start
2011-09-22
Project End
2016-07-31
Budget Start
2013-08-01
Budget End
2014-07-31
Support Year
3
Fiscal Year
2013
Total Cost
$1,970,132
Indirect Cost
$576,812
Name
University of Chicago
Department
Internal Medicine/Medicine
Type
Schools of Medicine
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

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