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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 – Making drug use effective and safe based on a person’s specific genotype Pharmacogenomics Flow QuickTime™ and a TIFF (Uncompressed) decompressor are needed to see this picture. PharmGKB: Capturing knowledge to to catalyze pharmacogenomics research PharmGKB Core Contents Mission: aggregate, integrate & annotate pharmacogenomic data and knowledge 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. 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 Genotype-Phenotype Relations Knowledge about gene-drug-pheno interactions comes at different levels of granularity: 1. Product of Gene X interacts with Drug Y (in pheno Z)--in a physical sense 2. 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) 3. Specific Allele of Variant of Gene X has a particular effect on pheno Z for Drug Y--also in an association sense 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. 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 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 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) Sample metabolism step Sample Drug Interaction Sample Phenotype Association 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 PharmGKB Team Questions? Thanks. [email protected]