This project two serves as the bookends of a hypothesis-test-verify cycle to define the shared and distinct components of neurodevelopmental diseases. For the first bookend: In the context of a large healthcare system, we use the processed electronic health records (through Natural Language ProcessingNLP) to determine the temporal course of multiple health/clinical attributes observed and/or obtained during the course of care delivery. These then are used, in collaboration with Project 4 to define temporal co-morbidity trajectories for each of these diseases and to determine the extent to which there is shared phenomenology particularly in time. For the second bookend: those genomic variants/sequences that are predicted/hypothesized to define subgroups previously characterized by clinical trajectories, or predict conventional biomarkers/clinical findings and/or (most preferably but most challenging) predict differential therapeutic responsiveness.

Public Health Relevance

The project aims to model succession of environmental factors and disease phenotypes in the life of patients, using data from large clinical databases. 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 #
3P50MH094267-04S1
Application #
8936054
Study Section
Special Emphasis Panel (ZMH1 (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
$1
Indirect Cost
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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