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Challenges Representing Phenotype in Pharmacogenomics

Challenges Representing Phenotype in Pharmacogenomics. Tina Hernandez-Boussard PharmGKB www.pharmGkB.org. Pharmacogenomics. Understanding how genetic variation leads to variation in responses to drugs A promise from the Genome Project Personalized Medicine

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Challenges Representing Phenotype in Pharmacogenomics

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  1. Challenges Representing Phenotype in Pharmacogenomics Tina Hernandez-Boussard PharmGKB www.pharmGkB.org

  2. Pharmacogenomics • Understanding how genetic variation leads to variation in responses to drugs • A promise from the Genome Project • Personalized Medicine • Making drug use effective and safe based on a person’s specific genotype

  3. Pharmacogenomics Flow

  4. PharmGKB: Capturing knowledge to to catalyze pharmacogenomics research

  5. PharmGKB Core Contents Mission: aggregate, integrate & annotate pharmacogenomic data and knowledge

  6. PharmGKB Knowledge • VIPs • Structured textual summaries of Very Important Pharmacogenes and their key variants • Pathways • Graphical pathway representations built by consensus, associated with literature evidence and links to PharmGKB genes, drugs, phenotypes. • Literature Annotations • PharmGKB curators create data entries that associate genes with drugs and phenotypes, based on an interpretation of the literature. They encode with controlled vocabularies.

  7. Genetic Variation Complexity • Genetic variation and its relation to proteins is complicated • “Gene” exists in the genome • “Gene variations” specify the existence of polymorphism: • E.g. “There is A/C SNP at Golden Path X.” • Haplotype variations = collection of simple variations • “Gene alleles” are specific variation options • E.g. “One allele of the A/C SNP is A at GP X…” • Haplotype alleles = collection of simple alleles • Genotypes are diploid alleles = “diplotypes” • ASSOCIATIONS can be described to all of these

  8. Genotype-Phenotype Relations • Knowledge about gene-drug-pheno interactions comes at different levels of granularity: • Product of Gene X interacts with Drug Y (in pheno Z)--in a physical sense • Variant of Gene X makes a difference in pheno Z for Drug Y--in an association sense (can also be a physical interaction, but that is with product) • Specific Allele of Variant of Gene X has a particular effect on pheno Z for Drug Y--also in an association sense

  9. Mosaic Challenge: Throughput & Redundancy • Limited curatorial staff has many duties • Need methods to quickly identify important knowledge and capture it in computable form ONCE for multiple uses • With computable knowledge, can generate displays appropriate for user interests: pathways, VIP summaries, literature summaries.

  10. Goals for Representing Knowledge in PharmGKB • Common platform for entering & curating Pharmacogenomic knowledge = Protégé-based • Pathways • Very important pharmacogenes + variants • Gene+variant-drug-phenotype associations • Structured entry for computability • Standard vocabularies • Automated linkages to existing data • Genes, drugs, external resources • Clear semantics • Extensible • Usable SOON • Expandable ALWAYS

  11. Vocabularies Currently Used • HGNC for genes • Gene families? • MEDDRA for adverse events • Medical dictionary • MESH for disease, symptoms • Vocabulary • Gene Ontology for cellular location, molecular function, cellular biological process • ASSUMES: • Cell type vocabulary (MESH for now) • chemical & drug vocabulary (MESH for now) • Switch to chEBI for chemicals? • Building drug dictionary @ PharmGKB

  12. Knowledge Templates • Ingredients • Controlled vocabulary of objects • Logical representation of relationships • Statement of key “slots” to be filled using objects, according to logic. • EXAMPLE: Pathway Knowledge • Pathway Overview template, points to “Steps” • Pathway Step templates for • Metabolism step (PK) • Transport step (PK) • Inhibition step (PD!) • Downstream phenotype step (PK & PD)

  13. Sample metabolism step

  14. Sample Drug Interaction

  15. Sample Phenotype Association

  16. Conclusions • PharmGKB integrates, aggregates and annotates data and knowledge to serve the PGx research community • Deep, high quality genotype data • Phenotype data--mostly small studies, some large ones in the pipeline. • Knowledge services include literature curations, pathways, VIP gene summaries • Research efforts focus on creating pipeline to improve efficiency and precision of curated information

  17. PharmGKB Team

  18. Questions? Thanks. boussard@stanford.edu

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