The overall goal of this project is to model and make phenotype-gene-environment association predictions using multiple data types relevant to neuropsychiatric phenotypes. While numerous groups around the globe tackle the bigger problem of tracing environmental and genetic factors affecting manifestation of complex traits in humans, only a small fraction of these studies is relevant to neuropsychiatric phenotypes. To our knowledge, no holistic approach of modeling and computational scrutiny of multiple threads of experimental evidence exists. This project is designed to fill this gap;it is the focal point of all the modeling proposed hereit brings together all projects and all experts of the Center, to create a consensus on modeling assumptions and analytics, in approaches to normalize data, and in ways to evaluate results of the global inference.

Public Health Relevance

This project aims to provide a mathematical modeling framework for several data types (clinical patient records, genetic variation data, information about prescription drugs and biological background knowledge) to generate and experimentally test predictions about cause-effect relations between genetic variations in humans and disorders. This project focuses on complex neuropsychiatric disorders, such as autism, schizophrenia and depression.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Specialized Center (P50)
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Study Section
Special Emphasis Panel (ZMH1 (02))
Program Officer
Addington, Anjene M
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University of Chicago
United States
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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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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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