The Pharmacogenetics and pharmacogenomics Knowledge Base (PharmGKB) is designed to catalyze research on how genetic variation contributes to variation in drug response. It provides information and analytical tools by means of a public website that is devoted to linking genotype and phenotype information in the post-genome era. The PharmGKB development team has five foci: user interface & functionality, data curation, outreach & dissemination, administration, and infrastructure. This proposal is based on four years of experience working with the scientific community to define and prioritize opportunities to support pharmacogenetics and pharmacogenomics. The PharmGKB has accumulated genotype data, phenotype data, curated literature annotations, and drug-related pathways-and currently attracts more than 23,000 unique visitors (IP addresses) each month. Our ? plan stresses 1) extending the PharmGKB to the entire scientific community by increasing data submissions, functionality, and ease of use, 2) focusing on acquiring high-quality drug-related pathways and using them as interfaces to, pharmacogenetic data and knowledge, 3) catalyzing the use of standards for information exchange within the field, and 4) participating in the public discussion of methods to protect the privacy and confidentiality of study subject data. ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project--Cooperative Agreements (U01)
Project #
3U01GM061374-09S1
Application #
7684456
Study Section
Special Emphasis Panel (ZRG1-GGG-B (51))
Program Officer
Long, Rochelle M
Project Start
2000-04-01
Project End
2010-06-30
Budget Start
2008-07-01
Budget End
2009-06-30
Support Year
9
Fiscal Year
2008
Total Cost
$65,643
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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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

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