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.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Specialized Center (P50)
Project #
5P50MH094267-04
Application #
8935616
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
2014-08-01
Budget End
2015-07-31
Support Year
4
Fiscal Year
2014
Total Cost
$242,265
Indirect Cost
$57,147
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