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Global Phenotypic Data Sharing Standards to Maximize Diagnostics and Mechanism Discovery

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Presented at the IRDiRC 2017 conference in Paris, Feb 9th, 2017 (http://irdirc-conference.org/). This talk reviews use of the Human Phenotype Ontology for phenotype comparisons against other patients, known diseases, and animal models for diagnostic discovery. It also discusses the new Phenopackets Exchange mechanism for open phenotypic data sharing.
www.monarchinitiative.org
www.phenopackets.org
www.human-phenotype-ontology.org

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Global Phenotypic Data Sharing Standards to Maximize Diagnostics and Mechanism Discovery

  1. 1. Global Phenotypic Data Sharing Standards to Maximize Diagnostics and Mechanism Discovery Melissa Haendel, PhD @ontowonka
  2. 2. Prevailing clinical genomic pipelines leverage only a tiny fraction of the available data PATIENT EXOME / GENOME PATIENT CLINICAL PHENOTYPES PUBLIC GENOMIC DATA PUBLIC CLINICAL PHENOTYPE, DISEASE DATA POSSIBLE DISEASES DIAGNOSIS & TREATMENT PATIENT ENVIRONMENT PUBLIC ENVIRONMENT, DISEASE DATA PATIENT OMICS PHENOTYPES PUBLIC OMICS PHENOTYPES, CORRELATIONS Under-utilized data
  3. 3. Genes Environment Phenotypes+ = Computable encodings are essential Base pairs Variant notation (eg. HGVS) SNOMED-CTMedical procedure coding Environment Ontology @ontowonka
  4. 4. The Human Phenotype Ontology 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  11,813 phenotype terms  127,125 rare disease - phenotype annotations  136,268 common disease - phenotype annotations http://bit.ly/hpo-paper
  5. 5. Existing clinical vocabularies don’t adequately cover phenotypic descriptions Winnenburg and Bodenreider, 2014 0 10 20 30 40 50 60 70 80 90 100 HPO UMLS SNOMED CT CHV MedDRA MeSH NCIT ICD10 OMIM Percentcoverage => HPO is now in the UMLS
  6. 6. monarchinitiative.org Why model organisms matter to patients Model data can provide up to 80% phenotypic coverage of the human coding genome
  7. 7. Fuzzy phenotype matching for diagnosis
  8. 8. Deep phenotyping and “fuzzy” matching algorithms improve diagnostics Bone et al. Computational evaluation of exome sequence data using human and model organism phenotypes improves diagnostic efficiency Genetics in Medicine (2015) doi:10.1038/gim.2015.137 Phenotypic profile Genes Heterozygous, missense mutation STIM-1 Heterozygous, missense mutation STIM-1 Stim1Sax/Sax 4.9% exomes w dual molecular diagnoses, differentiated w deep phenotyping
  9. 9. Matchmaker Exchange for patients, diseases, and model organisms to aid diagnosis and mechanistic discovery www.monarchinitiative.org http://bit.ly/Monarch-MME Goal: Get clinical sites & public databases to provide standardized phenotype data
  10. 10. Journals are now requiring HPO terms Robinson, P. N., Mungall, C. J., & Haendel, M. (2015). Capturing phenotypes for precision medicine. Molecular Case Studies, 1(1), a000372. doi:10.1101/mcs.a000372
  11. 11. HPO language translations We need your help! http://bit.ly/hpo-translations Translation of labels, synonyms, and text definitions Italian Spanish Russian French German English layperson Japanese Chinese 100%11% 12% 100% 19%19% near 100% 20%
  12. 12. monarchinitiative.org How much phenotyping is enough? Enlarged ears (2)Dark hair (6) Female (4) Male (4) Blue skin (1) Pointy ears (1) Hair absent on head (1) Horns present (1) Hair present on head (7) Enlarged lip (2) Increased skin pigmentation (3) bit.ly/annotationsufficiency
  13. 13. Genes Environment Phenotypes+ = Biology central dogma Standards for exchanging data must be up to these challenges. @ontowonka
  14. 14. Genes Environment Phenotypes VCF PXFGFF Standard exchange mechanisms exist for genes … but for phenotypes? Environment? BED @ontowonka
  15. 15. Introducing PhenoPackets A packet of phenotype data to be used anywhere, written by anyone http://phenopackets.org
  16. 16. What does a phenopacket look like?  Alacrima  Sleep Apnea  Microcephaly phenotype_profile: - entity: ”patient16" phenotype: types: - id: "HP:0000522" label: ”Alacrima" onset: description: “at birth” types: - id: "HP:0003577" label: "Congenital onset" evidence: - types: - id: "ECO:0000033" label: ”Traceable Author Statement" source: - id: ”PMID:"  Clinical labs  Public databases  Journals
  17. 17. Layperson HPO + Phenopackets  Dry eyes  Stops breathing during sleep  Small head phenotype_profile: - entity: “Grace” phenotype: types: - id: "HP:0000522" label: “Alacrima" onset: description: “at birth" types: - id: "HP:0003577" label: "Congenital onset" evidence: - types: - id: “ECO:0000033” label: “Traceable Author Statement" source: - id: “ https://twitter.com/examplepatient/status/1 23456789” • Patient registries • Social media
  18. 18. Standards are vital to realize a mechanistic classification of disease
  19. 19. www.monarchinitiative.org Leadership: Melissa Haendel, Chris Mungall, Peter Robinson, Tudor Groza, Damian Smedley, Sebastian Köhler, Julie McMurry Funding: NIH Office of Director: 2R24OD011883; NHGRI UDP: HHSN268201300036C, HHSN268201400093P;

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