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 #
5P50MH094267-02
Application #
8337330
Study Section
Special Emphasis Panel (ZMH1-ERB-S (02))
Program Officer
Addington, Anjene M
Project Start
2011-09-22
Project End
2016-07-31
Budget Start
2012-08-01
Budget End
2013-07-31
Support Year
2
Fiscal Year
2012
Total Cost
$2,078,313
Indirect Cost
$515,076
Name
University of Chicago
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
005421136
City
Chicago
State
IL
Country
United States
Zip Code
60637
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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Heath, Allison P; Greenway, Matthew; Powell, Raymond et al. (2014) Bionimbus: a cloud for managing, analyzing and sharing large genomics datasets. J Am Med Inform Assoc 21:969-75
Huang, Sandy H; LePendu, Paea; Iyer, Srinivasan V et al. (2014) Toward personalizing treatment for depression: predicting diagnosis and severity. J Am Med Inform Assoc 21:1069-75
Doshi-Velez, Finale; Ge, Yaorong; Kohane, Isaac (2014) Comorbidity clusters in autism spectrum disorders: an electronic health record time-series analysis. Pediatrics 133:e54-63
Grennan, Kay S; Chen, Chao; Gershon, Elliot S et al. (2014) Molecular network analysis enhances understanding of the biology of mental disorders. Bioessays 36:606-16

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