PharmGKB: The Pharmacogenomics Knowledgebase

Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
Methods in Molecular Biology (Impact Factor: 1.29). 02/2005; 311:179-91. DOI: 10.1385/1-59259-957-5:179
Source: PubMed


The Pharmacogenetics and Pharmacogenomics Knowledge Base (PharmGKB) is an interactive tool for researchers investigating how genetic variation effects drug response. The PharmGKB web site,, displays genotype, molecular, and clinical primary data integrated with literature, pathway representations, protocol information, and links to additional external resources. Users can search and browse the knowledge base by genes, drugs, diseases, and pathways. Registration is free to the entire research community but subject to an agreement to respect the rights and privacy of the individuals whose information is contained within the database. Registered users can access and download primary data to aid in the design of future pharmacogenetics and pharmacogenomics studies.

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    • "In the absence of formal evidence-based reviews—which will emerge slowly, given an enormous number of potential gene–drug associations—stakeholders will largely be tasked with making initial implementation decisions. Clinical annotations for candidate pharmacogenetic tests [58] and models for evidence-based evaluation of genomic applications [59] are increasingly available to guide these decisions. "
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    ABSTRACT: Despite advances in characterizing genetic influences on addiction liability and treatment response, clinical applications of these efforts have been slow to evolve. Although challenges to clinical translation remain, stakeholders already face decisions about evidentiary thresholds for the uptake of pharmacogenetic tests in practice. There is optimism about potential pharmacogenetic applications for the treatment of alcohol use disorders, with particular interest in the OPRM1 A118G polymorphism as a moderator of naltrexone response. Findings from human and animal studies suggest preliminary evidence for the clinical validity of this association; on this basis, arguments for clinical implementation can be made in accordance with existing frameworks for the uptake of genomic applications. However, generating evidence-based guidelines requires evaluating the clinical utility of pharmacogenetic tests. This goal will remain challenging, largely due to minimal data to inform clinical utility estimates. The pace of genomic discovery highlights the need for clinical utility and implementation research to inform future translation efforts. Near-term implementation of promising pharmacogenetic tests can help expedite this goal, generating an evidence base to enable efficient translation as additional gene-drug associations are discovered.
    Addiction science & clinical practice 09/2014; 9(1):20. DOI:10.1186/1940-0640-9-20
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    • "Studies on the role of genetic variation in cis-regulatory regions in determining inter-individual splicing differences , including tissue-specific variability (GTex Consortium 2013), are poised to advance our understanding of genome function, disease pathogenesis and therapeutic applications. at the same time, uncovering the mechanistic basis for disease associations, and increasingly pharmacogenomic trait associations (Gamazon et al. 2010; Thorn et al. 2005), emerging from genome-wide association studies is likely to benefit from studies of the effect of genetic polymorphisms regulating differential transcript isoform Table 1 Databases and tools relevant to alternative splicing "
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    ABSTRACT: Alternative splicing is a major cellular mechanism in metazoans for generating proteomic diversity. A large proportion of protein-coding genes in multicellular organisms undergo alternative splicing, and in humans, it has been estimated that nearly 90 % of protein-coding genes-much larger than expected-are subject to alternative splicing. Genomic analyses of alternative splicing have illuminated its universal role in shaping the evolution of genomes, in the control of developmental processes, and in the dynamic regulation of the transcriptome to influence phenotype. Disruption of the splicing machinery has been found to drive pathophysiology, and indeed reprogramming of aberrant splicing can provide novel approaches to the development of molecular therapy. This review focuses on the recent progress in our understanding of alternative splicing brought about by the unprecedented explosive growth of genomic data and highlights the relevance of human splicing variation on disease and therapy.
    Human Genetics 06/2014; 133(6). DOI:10.1007/s00439-013-1411-3 · 4.82 Impact Factor
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    • "Recent advances in pharmacogenetics and pharmacogenomics have identified genetic variants in several hundreds genes, notably drug metabolizing enzymes (e.g., CYP450), transporters, and drug targets [188]. The knowledge derived from such data has already resulted in individualized therapy. "
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    ABSTRACT: Genome-Wide Association Studies (GWAS), whole genome sequencing, and high-throughput omics techniques have generated vast amounts of genotypic and molecular phenotypic data. However, these data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery, which continues along a one-drug-one-target-one-disease paradigm. As a partial consequence, both the cost to launch a new drug and the attrition rate are increasing. Systems pharmacology and pharmacogenomics are emerging to exploit the available data and potentially reverse this trend, but, as we argue here, more is needed. To understand the impact of genetic, epigenetic, and environmental factors on drug action, we must study the structural energetics and dynamics of molecular interactions in the context of the whole human genome and interactome. Such an approach requires an integrative modeling framework for drug action that leverages advances in data-driven statistical modeling and mechanism-based multiscale modeling and transforms heterogeneous data from GWAS, high-throughput sequencing, structural genomics, functional genomics, and chemical genomics into unified knowledge. This is not a small task, but, as reviewed here, progress is being made towards the final goal of personalized medicines for the treatment of complex diseases.
    PLoS Computational Biology 05/2014; 10(5):e1003554. DOI:10.1371/journal.pcbi.1003554 · 4.62 Impact Factor
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