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On the frontier of genotype-2-phenotype data integration
- 1. On the frontierof genotype-2-phenotype data integration Melissa Haendel, PhD March 22, 2016 AMIA TBI @monarchinit @ontowonka haendel@ohsu.edu
- 2. Filling the G2Pknowledge gap from other organisms Other= rat, fly, worm, mouse, zebrafish
- 3.
- 4. Challenge: Each databaseuses their own vocabulary/ontology MP HP MGI HPOA
- 5. Challenge: Each databaseuses their own phenotype vocabulary/ontology ZFA MP DPO WPO HP OMIA VT FYPO APO SNO MED … … … WB PB FB OMIA MGI RGD ZFIN SGD HPOA EHR IMPC OMIM … QTLdb
- 6. monarchinitiative.org Can we helpmachines understand phenotype terms? “Palmoplantar hyperkeratosis” Human phenotype I have absolutely no idea what that means
- 7. The Human PhenotypeOntology Hyposmia Abnormality of globe location eyeball of camera-type eye sensory perception of smell Abnormal eye morphology Motor neuron atrophyDeeply set eyes motor neuronCL 34571 annotations in 22 species 157534 phenotype annotations 2150 phenotype annotations
- 9. monarchinitiative.org Genotype-phenotype integration One source Twosources 3 or more 9% 91% of our 2.2 Million G2P associations required integrating 2 or more data sources (this number does not even include orthology (Panther) or any ontologies!) 91%
- 10. Diagnosing an undiagnoseddisease www.owlsim.org
- 11. Phenotype Exchange Standard Mechanisticdiscovery Improved searchability Integrated Data Landscape Tool/algorithm creation Cohort identification Patient registries Databases, Web tools, AlgorithmsPhenopacket Registry JournalsDiagnostic screening programs Clinical trials Phenopacket flow Primarybenefits tostakeholders Patients/ Families Physicians Patient matchmaking Diagnosis speed/accuracy Organismal biologist www.phenopackets.org
- 12. What’s in aPhenopacket? Ontology-based phenotypic descriptions for: Human patients, model organisms, or any organism Groupings of human patients or organisms What does it include? age of patient or organism sex of patient or organism disease (if named) age of onset of disease Positive and negative phenotype associations Reference to Genes, variants, or collections of variants Reference to environmental factors Multiple formats: TSV, JSON, YAML, JSON Validation tools Uses standardized publication citation mechanism for data sharing
- 13. brca-website.cloudapp.net 13501 variantsfrom ENIGMA, ClinVar, LOVD, exLOVD, BIC Merged by genomic coordinate and alternate allele string
- 14. Problems with evidenceand provenance of G2P Associations PROBLEMS: Variants have different pathogenicity calls due to annotation inconsistency AND different experimental evidence Incomplete, not computable, and frequently conflated Annotations are to different aspects of the genotype: allele, variant, gene, transcript, etc. A computable model would enable: context to evaluate credibility/confidence support filtering and analysis of data detailed history for attribution
- 15. Building a computablemodel for ACMG guidelines http://brcaexchange.org/ Provenance Evidence Claim - Materials & methods - Agent(s) of evidence - Agent(s) of claim - Time and place - Data (eg: images, sequences) - Evidence codes - Publications - Confidence (p-val, z-score) - Summary figures - Conclusions from previous studies - Domain expert’s knowledge Causal relationships, hypothesized relationships, correlations etc. https://github.com/monarch-initiative/SEPIO-ontology
- 16. Summary Ontologies canbe used to perform deep phenotyping integration across species An exchange standard is needed to facilitate distributed phenotype data sharing A computable G2P evidence model can aid variant interpretation
- 17. Acknowledgements Lawrence Berkeley Chris Mungall NicoleWashington Suzanna Lewis Jeremy Nguyen Seth Carbon Charité Peter Robinson Sebastian Kohler U of Pittsburgh Harry Hochheiser Mike Davis Joe Zhou OHSU Nicole Vasilesky Matt Brush Kent Shefchek Julie McMurry Tom Conlin Genomics England Damian Smedley Jules Jacobson UCSC David Haussler Benedict Paten Mark Diekhans Melissa Cline Garvan Tudor Groza Craig McNamara Edwin Zhang FUNDING: NIH Office of Director: 1R24OD011883; NIH-UDP: HHSN268201300036C, HHSN268201400093P; NCINCI/Leidos #15X143, BD2K U54HG007990-S2 (Haussler) & BD2K PA-15-144-U01 (Kesselman)
Editor's Notes
- #5 2 issues: database integration, vocabulary integration
- #6 Multiple databases
- #7 Our approach is to try and get the machine to understand the terms so that it can assist us intelligently.
- #8 Represent organism as a biological subject Represent diseases/genotypes as collections of nodes in the graph 3. Interoperable with other bioinformatics resources and leverage modern semantic standards
- #9 If we include bridging ontologies, we can unify diseases across sources AND phenotypes across sources and organisms.
- #16 To support downstream hypothesis testing and evaluation, “trust”, we need a computable model for evidence.
- #18 There are a lot of people who have contributed to this work over many years.