We propose to create the Stanford PharmacoGenetic Knowledge Base (PharmGKB), an integrated data resource to support the NIGMS Pharmacogenetic Research Network and Database Initiative. This initiative will focus on how genetic variation contributes to variation in the response to drugs, and will produce data from a wide range of sources. The PharmGKB will therefore interlink genomic, molecular, cellular and clinical information about gene systems important for modulating drug responses. The PharmGKB is based on a powerful hierarchical data representation system that allows the data model to change as new knowledge is learned, while ensuring the security and stability of the data with a relational database foundation. Our proposal defines an interactive process for defining a data model, creating automated systems for data submission, integrating the PharmGKB with other biological and clinical data resources, and creating a robust interface to the data and to the associated analytic tools. Finally, we outline a research plan that uses the PharmGKB to (1) address difficult data modeling challenges that arise in the course of building the resource, (2) study the user interface requirements of a database with such a wide range of information sources, and (3) model and analyze the structural variations of proteins to shed light on the molecular consequences of genetic variation. The PharmGKB will respect the absolute confidentiality of genetic information from individuals.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01GM061374-05
Application #
6736326
Study Section
Special Emphasis Panel (ZRG1-MGN (01))
Program Officer
Long, Rochelle M
Project Start
2000-04-01
Project End
2005-08-07
Budget Start
2004-04-01
Budget End
2005-08-07
Support Year
5
Fiscal Year
2004
Total Cost
$2,152,102
Indirect Cost
Name
Stanford University
Department
Genetics
Type
Schools of Medicine
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305
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Moriyama, B; Obeng, A Owusu; Barbarino, J et al. (2017) Clinical Pharmacogenetics Implementation Consortium (CPIC) Guidelines for CYP2C19 and Voriconazole Therapy. Clin Pharmacol Ther 102:45-51
Rensi, Stefano; Altman, Russ B (2017) Flexible Analog Search with Kernel PCA Embedded Molecule Vectors. Comput Struct Biotechnol J 15:320-327
Gong, Li; Giacomini, Marilyn M; Giacomini, Craig et al. (2017) PharmGKB summary: sorafenib pathways. Pharmacogenet Genomics 27:240-246
Rensi, Stefano E; Altman, Russ B (2017) Shallow Representation Learning via Kernel PCA Improves QSAR Modelability. J Chem Inf Model 57:1859-1867
Gottlieb, Assaf; Daneshjou, Roxana; DeGorter, Marianne et al. (2017) Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans. Genome Med 9:98
Thorn, Caroline F; Sharma, Manish R; Altman, Russ B et al. (2017) PharmGKB summary: pazopanib pathway, pharmacokinetics. Pharmacogenet Genomics 27:307-312

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