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Lin, S., Woon, M., Rubin, D., Oliver, D., Hewett, M, Zhou, T., Conroy, J., Fergerson, R., Altman, R., Klein, T.
Department of Genetics, Stanford Medical Informatics
Contact slin@smi.stanford.edu
The Pharmacogenetics and Pharmacogenomics Knowledge Base (PharmGKB; http://www.pharmgkb.org/) is a public resource that promotes research into the relationship between drug response and genetic variation by linking genomic, phenotype and clinical information collected from ongoing research. As a framework for indexing drug-gene relationships, we have developed a set of categories that characterize the data that we collect. We have defined an XML format for exchanging genotypic data, a relational database schema for data storage, and a flexible mechanism for submitting phenotypic data. In addition, we created a community project, which encourages the general public to informally submit pharmacogenomic knowledge. The PharmGKB project was initiated in April 2000 and first went online in February 2001. An updated version appeared in March 2002.
INTRODUCTION The field of pharmacogenomics studies how inherited genetic variation results in differences in drug response. Such studies are of significant value to medicine, since a better understanding of a patient’s genetic makeup should allow physicians to avoid drugs with deleterious effects while prescribing drugs with the highest efficacy.(1) The Pharmacogenetics and Pharmacogenomics Knowledge Base (PharmGKB, http://www.pharmgkb.org), provides a centralized site for submitting, updating, and querying data collected by research groups. CATEGORIES OF EVIDENCE To establish a link between a genetic variation and variation in a phenotype, many different types of experiments can be performed, ranging from genotypic studies to cellular phenotype assays to clinical studies. It is problematic to search a database with such diverse data types. We have attempted, therefore, to associate all experimental results with a category of evidence, which provides the context for its pharmacogenomic significance. We have begun with five such categories, and all data entering PharmGKB must be labeled with one of them. If a study shows a clinically significant difference in a clinical outcome (death, disability, pain, days of work missed) based on a genetic difference, then the data falls under the Clinical Outcome category. The Pharmacodynamics and Drug Response category consists of studies that have shown variation in a drug response that can be measured clinically, but is not a direct outcome . The Pharmacokinetics category establishes that drug metabolism changes based on genetic changes, and Molecular and Cellular Functional Assays shows associations between genetic changes and changes in functional assay results. Finally, the Genotype category contains data showing basic variability in gene sequences. We try to show the Category of Evidence for all data being displayed. We hope that users noticing unfilled categories will be encouraged to direct research into those areas. PHENOTYPE DATA While much work has been done to establish create an XML and associated database schema for describing information about genetic variation, the PharmGKB is also focused on collecting phenotypic data associated with genetic variation. There are inherent difficulties of attempting to create a database schema for phenotypic data because of the vast range of clinical studies, each with specialized methods of reporting results. Therefore, PharmGKB is initially taking a library approach to accepting phenotypic data. We accept annotated spreadsheet data from researchers, who also provide us with the Category of Evidence of the data, the gene-drug relationships, and keywords for finding the data. We store the data in the database and link to related research information. We present a unified view with the Categories of Evidence, such that users can easily access all information related to a drug-gene relationship. Users searching our site can then view and download phenotype data. As we receive larger volumes of information in certain classes, we will build a more structured data model to support these classes of data. DATABASE UPGRADE Qualitative analysis of our system has shown that having a frame-based data storage facility suffered in retrieval performance. While the use of a frame-base system provided a flexible means for early data modeling, a database backend has shown better performance in storing and retrieving information.(2) Data is now stored centrally in a relational database, and we have developed tools to push this data into frame-based for specialized functions such as inferencing and other reasoning tasks with hierarchical data at which frame-based systems excel.(3) COMMUNITY PROJECT In an effort to build up a repository of drug-gene relationships, we have created the Community-Based Pharmacogenetic Information Project, which allows the scientific community to submit information about gene-drug relationships while specifying a Category of Evidence and citing a source for the information. We expect that by encouraging the community to submit pharmacogenomic associations that they deem important, we can capture relationships and solicit data sets that may have been overlooked. . Users can also search through this repository to discover relationships between genes or drugs of interest, and direct research into those areas. ADDITIONAL INFORMATION In the coming months, we are making our application software available as open source, in order to encourage other informatics groups to benefit from our code, and to allow them to contribute improvements to the code base.
PharmGKB is financially supported by grants from the National Institute of General Medical Sciences (NIGMS), Human Genome Research Institute (NHGRI) and National Library of Medicine (NLM) within the National Institutes of Health (NIH) and the Pharmacogenetics Research Network and Stanford University’s Children’s Health Initiative (Russ Altman, PI). This work is supported by the NIH/NIGMS Pharmacogenetics Research Network and Database (U01GM61374; Russ Altman, PI).
1 Evans,W.E. and Relling,M.V. (1999) Pharmacogenomics: translating functional genomics into rational therapeutics. Science, 286, 487–491.[Abstract/Full Text]
2 Rubin DL, Shafa F, Oliver DE, Hewett M, Altman RB. (2002) Representing genetic sequence data for pharmacogenomics: an evolutionary approach using ontological and relational database models. Bioinformatics, 18 Suppl 1, S207-15
Hewett M, Oliver DE, Rubin DL, Easton KL, Stuart JM, Altman RB, Klein TE. (2001) PharmGKB: the Pharmacogenomics Knowledge Base. Nucleic Acids Research, 30, 163-5
Category Varied Biomedical Content
Go to the abstract in the NAR 2002 Database Issue.