Challenges Representing Phenotype in Pharmacogenomics

Tina Hernandez-Boussard

PharmGKB

www.pharmGkB.org

PharmacogenomicsUnderstanding 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

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PharmGKB: Capturing knowledge to Capturing knowledge to to catalyze pharmacogenomics researchto 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 ASSUMESASSUMES:

– 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.

boussard@stanford.edu