The data management approach of the Program Project is an integrated statistics, data management and data protection solution. The Data Management and Statistics Core receives, stores, catalogues, tracks and integrates all data generated by the Cores and Projects in this Program Project Grant and provides expert advice for the statistical analysis of data components or integrations. A new data management plan has been developed, and its components have been deployed, during the past 18 months. At present, in excess of 2,400,000 data points are warehoused for this Program Project. Other components will be developed and integrated on a continuing basis. Because the data warehouse is implemented across multiple projects department-wide, it provides for data access not only to data derived by the specific projects and cores within this Program Project, but also to previously acquired legacy data and to data being derived by all related projects within the Department of Psychiatry network. A concrete example is data derived from subjects who were antemortem assessed by the Clinical Diagnosis Core, the Brain Bank Core, and data from experiments performed on Brain Bank distributed tissue specimens. Using the resources of the data warehouse, investigators in the current Program Project will be able to draw on these results to integrate antemortem clinical and neuropsychological assessment data, results of neuropathological diagnosis and lesion density, ApoE genotyping, data relating to cardiovascular risk factors (CvRF) and data obtained by specific studies to formulate their future hypotheses and to compare and contrast results obtained from identically processed specimens across multiple disease states. All of the projects in this proposal will also require considerable statistical data analysis and a high level of statistical analytic sophistication. This statistical analysis advice and support is provided by Dr. Schmeidler who has been the statistical expert for this Program Project Grant for over 14 years. Dr. Schmeidler is not only familiar with all of the data collected and analyzed by members of the Program in the past, but he has been actively involved in the planning of the currently proposed studies and in their experimental design. In this revised application, Dr. Schmeidler has helped each proposed project to reformulate their statistical approaches to more fully address the concerns raised by the reviewers.
The specific aims of the Data Management and Statistics core are: 1) to continue development of an integrated data warehouse with normalized data warehousing from all projects and cores; 2) to integrate data resulting from the Program Project cores with data emerging from the specific projects into a centralized resource that enhances cross-fertilization of projects while at the same time providing for ready access to all relevant data to all investigators; 3) to provide for data security at the patient and donor level, as well as guarding against data loss and data corruption by centralized integrity checking and backup; and 4) to provide highly sophisticated statistical consultations that involve not only statistical analysis of specific data sets, but integrate data analysis from multiple studies to test hypotheses spanning across projects.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Research Program Projects (P01)
Project #
5P01AG002219-26
Application #
7224217
Study Section
Special Emphasis Panel (ZAG1)
Project Start
Project End
Budget Start
2006-04-01
Budget End
2007-03-31
Support Year
26
Fiscal Year
2006
Total Cost
$158,615
Indirect Cost
Name
Mount Sinai School of Medicine
Department
Type
DUNS #
078861598
City
New York
State
NY
Country
United States
Zip Code
10029
Gusev, Alexander; Mancuso, Nicholas; Won, Hyejung et al. (2018) Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nat Genet 50:538-548
Girdhar, Kiran; Hoffman, Gabriel E; Jiang, Yan et al. (2018) Cell-specific histone modification maps in the human frontal lobe link schizophrenia risk to the neuronal epigenome. Nat Neurosci 21:1126-1136
Hauberg, Mads E; Fullard, John F; Zhu, Lingxue et al. (2018) Differential activity of transcribed enhancers in the prefrontal cortex of 537 cases with schizophrenia and controls. Mol Psychiatry :
Agrawal, A; Chou, Y-L; Carey, C E et al. (2018) Genome-wide association study identifies a novel locus for cannabis dependence. Mol Psychiatry 23:1293-1302
Dobbyn, Amanda; Huckins, Laura M; Boocock, James et al. (2018) Landscape of Conditional eQTL in Dorsolateral Prefrontal Cortex and Co-localization with Schizophrenia GWAS. Am J Hum Genet 102:1169-1184
Gandal, Michael J; Haney, Jillian R; Parikshak, Neelroop N et al. (2018) Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Science 359:693-697
Khan, Atlas; Liu, Qian; Wang, Kai (2018) iMEGES: integrated mental-disorder GEnome score by deep neural network for prioritizing the susceptibility genes for mental disorders in personal genomes. BMC Bioinformatics 19:501
Giambartolomei, Claudia; Zhenli Liu, Jimmy; Zhang, Wen et al. (2018) A Bayesian framework for multiple trait colocalization from summary association statistics. Bioinformatics 34:2538-2545
Toker, Lilah; Mancarci, Burak Ogan; Tripathy, Shreejoy et al. (2018) Transcriptomic Evidence for Alterations in Astrocytes and Parvalbumin Interneurons in Subjects With Bipolar Disorder and Schizophrenia. Biol Psychiatry 84:787-796
Huckins, L M; Hatzikotoulas, K; Southam, L et al. (2018) Investigation of common, low-frequency and rare genome-wide variation in anorexia nervosa. Mol Psychiatry 23:1169-1180

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