Challenges Representing Phenotype in Pharmacogenomics Tina HernandezBoussard Pharm

Challenges Representing Phenotype in Pharmacogenomics Tina Hernandez-Boussard Pharm. GKB www. pharm. Gk. B. org boussard@stanford. edu

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

Pharmacogenomics Flow

Pharm. GKB: Capturing knowledge to to catalyze pharmacogenomics research

Pharm. GKB Core Contents Mission: aggregate, integrate & annotate pharmacogenomic data and knowledge

Pharm. GKB Knowledge n VIPs – Structured textual summaries of Very Important Pharmacogenes and their key variants n Pathways – Graphical pathway representations built by consensus, associated with literature evidence and links to Pharm. GKB genes, drugs, phenotypes. n Literature Annotations – Pharm. GKB 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 n “Gene” exists in the genome n “Gene variations” specify the existence of polymorphism: n – E. g. “There is A/C SNP at Golden Path X. ” – Haplotype variations = collection of simple variations n “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” n ASSOCIATIONS can be described to all of these n

Genotype-Phenotype Relations n 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 n Limited curatorial staff has many duties n Need methods to quickly identify important knowledge and capture it in computable form ONCE for multiple uses n With computable knowledge, can generate displays appropriate for user interests: pathways, VIP summaries, literature summaries.

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

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

Knowledge Templates n Ingredients – Controlled vocabulary of objects – Logical representation of relationships – Statement of key “slots” to be filled using objects, according to logic. n 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 n Pharm. GKB integrates, aggregates and annotates data and knowledge to serve the PGx research community n Deep, high quality genotype data n Phenotype data--mostly small studies, some large ones in the pipeline. n Knowledge services include literature curations, pathways, VIP gene summaries n Research efforts focus on creating pipeline to improve efficiency and precision of curated information

Pharm. GKB Team

Questions? Thanks. boussard@stanford. edu