The major objective of this core is to provide wet-lab technological support and expertise to the investigators in the Center. We anticipate that results from network reverse engineering modeling in Core 1 will make predictions about not only the role of individual genes but also the state of particular suites of genes and of networks. To identify the relationship between multiple phenotypes, stimuli, and genotypes, high-throughput biochemical, proteomic, and genetic screening is essential. Simultaneous molecular profiling of large numbers of gene products to assess genetic status, expression level or protein modification state can rapidly provide an overview of the state of a cellular system.

Public Health Relevance

This core will provide an experimental infrastructure for functional validation of computational predictions about associations between the detected genetic variation in patient DNA and onset of complex neuropsychiatric disorders.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Specialized Center (P50)
Project #
5P50MH094267-04
Application #
8935612
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
$491,767
Indirect Cost
$80,172
Name
University of Chicago
Department
Type
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
Lahey, Benjamin B; Zald, David H; Hakes, Jahn K et al. (2014) Patterns of heterotypic continuity associated with the cross-sectional correlational structure of prevalent mental disorders in adults. JAMA Psychiatry 71:989-96
Melamed, Rachel D; Khiabanian, Hossein; Rabadan, Raul (2014) Data-driven discovery of seasonally linked diseases from an Electronic Health Records system. BMC Bioinformatics 15 Suppl 6:S3
Lee, In-Hee; Lee, Kyungjoon; Hsing, Michael et al. (2014) Prioritizing disease-linked variants, genes, and pathways with an interactive whole-genome analysis pipeline. Hum Mutat 35:537-47
Hart, Amy B; Gamazon, Eric R; Engelhardt, Barbara E et al. (2014) Genetic variation associated with euphorigenic effects of d-amphetamine is associated with diminished risk for schizophrenia and attention deficit hyperactivity disorder. Proc Natl Acad Sci U S A 111:5968-73
Kong, Sek Won; Sahin, Mustafa; Collins, Christin D et al. (2014) Divergent dysregulation of gene expression in murine models of fragile X syndrome and tuberous sclerosis. Mol Autism 5:16
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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