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Deep phenotyping for everyone

The Human Phenotype Ontology (HPO) was developed to describe phenotypic abnormalities, aka, “deep phenotyping”, whereby symptoms and characteristic phenotypic findings (a phenotypic profile) are captured. The HPO has been utilized to great success for assisting computational phenotype comparison against known diseases, other patients, and model organisms to support diagnosis of rare disease patients. Clinicians and geneticists create phenotypic profiles based on clinical evaluation, but this is time consuming and can miss important phenotypic features. Patients are sometimes the best source of information about their symptoms that might otherwise be missed in a clinical encounter. However, HPO primarily use medical terminology, which can be difficult for patients and their families to understand. To make the HPO accessible to patients, we systematically added non-expert terminology (i.e., layperson terms) synonyms. Using semantic similarity, patient-recorded phenotypic profiles can be evaluated against those created clinically for undiagnosed patients to determine the improvement gained from the patient-driven phenotyping, as well as how much the patient phenotyping narrows the diagnosis. This patient-centric HPO can be utilized by all: in patient-centered rare disease websites, in patient community platforms and registries, or even to post one’s hard-to-diagnosed phenotypic profile on the Web.

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Deep phenotyping for everyone

  1. 1. Deep phenotyping for everyone Melissa Haendel, PhD July 9th, 2016 Phenoday!! @monarchinit @ontowonka haendel@ohsu.edu
  2. 2. The genome is sequenced, but… …we still don’t know very much about what it does 3,435 OMIM Mendelian Diseases with no known genetic basis ? 66,396 ClinVar Variants with no known pathogenicity
  3. 3. Why we need all the organisms Model data can provide up to 80% phenotypic coverage of the human coding genome
  4. 4. The prevailing clinical diagnosis pipelines leverage only a tiny fraction of the available data PATIENT EXOME / GENOME PATIENT PHENOTYPES PATIENT ENVIRONMENT PUBLIC GENOMIC DATA PUBLIC HUMAN & MODEL PHENOTYPE, DISEASE DATA PUBLIC HUMAN & MODEL ENVIRONMENT, DISEASE DATA POSSIBLE DISEASES DIAGNOSIS & TREATMENT Under-utilized data
  5. 5. monarchinitiative.org PROBLEM  Diagnosis / treatment / prognosis on gestalt (Experience, intuition, and pattern recognition)  Things are not always what they first seem  Errors are common, and up to 35% of errors cause harm  It takes patients @ six years from noticing symptoms to being diagnosed with trips to eight physicians  25% of patients having to wait between 5 and 30 years HYPOTHESIS Diagnosis, treatment and prognosis may be informed and complemented by democratized deep phenotyping that is easier to compute, collect, and exchange
  6. 6. Ulcerated paws Palmoplantar hyperkeratosis Thick hand skin Image credits: "HandsEBS" by James Heilman, MD - Own work. Licensed under CC BY- SA 3.0 via Commons – https://commons.wikimedia.org/wiki/File:HandsEBS.JPG#/media/File :HandsEBS.JPG http://www.guinealynx.info/pododermatitis.html
  7. 7. Challenge: Each database uses their own vocabulary/ontology MP HP MGI HPOA
  8. 8. Challenge: Each database uses 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
  9. 9. Can we help machines understand phenotype terms? “Palmoplantar hyperkeratosis” Human phenotype I have absolutely no idea what that means
  10. 10. The Human Phenotype Ontology for deep phenotyping 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. 11. Decomposition of complex concepts allows interoperability Mungall, C. J., Gkoutos, G., Smith, C., Haendel, M., Lewis, S., & Ashburner, M. (2010). Integrating phenotype ontologies across multiple species. Genome Biology, 11(1), R2. doi:10.1186/gb-2010-11-1-r2 “Palmoplantar hyperkeratosis” increased Stratum corneum layer of skin = Human phenotype PATO Uberon Species neutral ontologies, homologous concepts Autopod keratinization GO
  12. 12. Harmonizing diseases, phenotypes, anatomy, and genotypes
  13. 13. We learn different phenotypes from different organisms
  14. 14. Diagnosing an undiagnosed disease
  15. 15. Putting all that data to use to diagnose a rare platelet syndrome http://bit.ly/stim1paper Phenotypic profile Genes Heterozygous, missense mutation STIM-1 MGI mouse N/A Heterozygous, missense mutation STIM-1 N/A Ranked STIM-1 variant maximally pathogenic based on cross-species G2P data, in the absence of traditional data sources http://bit.ly/exomiser Stim1Sax/Sax
  16. 16. What about patients? Can they phenotype themselves?
  17. 17. The GenomeConnect survey Eg. Machado-Joseph disease might present: With:  Ptosis, and Abnormalities of:  eye movement,  globe size,  vision,  optic nerve Without:  Myopia,  Hypermetropia, and Abnormalities of:  the retina,  the iris, and  the lens
  18. 18. Annotation sufficiency determination per disease
  19. 19. Patient self-reported HPO profile HPO reference profile Comparison Ensure that the survey is maximally diagnostic Disease 1 Disease 2 Disease 3 Disease 4 Disease 7500 HPO reference profile HPO reference profile HPO reference profile HPO reference profile Patient self-reported HPO profile Patient self-reported HPO profile Patient self-reported HPO profile Patient self-reported HPO profile
  20. 20. Simulated GenomeConnect survey HPO Profiles Monarch Initiative reference HPO Profiles Ensure that the survey is maximally diagnostic Patient Expert Phenotypic Profile overlap Compare phenotypic profiles For every known disease, fill the survey and ask: Does the profile match the disease best based on the survey mapping?
  21. 21. GC HPO profile HPO reference profile GC HPO profile GC HPO profile GC HPO profile HPO reference profile HPO reference profile HPO reference profile Comparisons Assess patient-derived profile generation HPO profile HPO profile HPO profile HPO profile HPO profile Disease 1 Disease 2 Disease 3 Disease 4 Disease 7500 HPO reference profileGC HPO profile
  22. 22. Assess patient-derived profile generation Patient ExpertPhenotypicProfile overlap Compare phenotypic profiles For every diagnosed patient: Can the patients utilize the survey and retrieve the correct disease?
  23. 23. GC HPO profile Clinical evaluation HPO profile GC HPO profile GC HPO profile GC HPO profile Clinical evaluation HPO profile Clinical evaluation HPO profile Clinical evaluation HPO profile GC HPO profile Clinical evaluation HPO profile Comparison Determine the contribution and sufficiency of patient self-phenotyping Patient 1 Patient 2 Patient 3 Patient 4 Patient 7500
  24. 24. Determine the contribution and sufficiency of patient self-phenotyping UDN patient generated GenomeConnect survey HPO profile UDN patient Clinical evaluation HPO profile Patient Expert Phenotypic Profile overlap Compare phenotypic profiles
  25. 25. Human Phenotype Ontology, now with 6,200 plain language synonyms for patients, families, and non-experts www.human-phenotype-ontology.org@HP_ontology
  26. 26. Almost half of the 14k synonyms are plain language All synonyms Plain language synonyms
  27. 27. Introducing PhenoPackets It’s exactly what you think it is: a packet of phenotype data to be used anywhere, written by anyone
  28. 28. Genes Environment Phenotypes+ = Biology central dogma Standards for encoding and exchanging data must be up to these challenges @ontowonka
  29. 29. The relationships too must be captured It is not just the bits… G-P or D (disease) causes contributes to is risk factor for protects against correlates with is marker for modulates involved in increases susceptibility to G-G (kind of) regulates negatively regulates (inhibits) positively regulates (activates) directly regulates interacts with co-localizes with co-expressed with P/D - P/D part of results in co-occurs with correlates with hallmark of (P->D) E-P contributes to (E->P) influences (E->P) exacerbates (E->P) manifest in (P->E) G-E (kind of) expressed in expressed during contains inactivated by
  30. 30. Genes Environment Phenotypes+ = Computable encodings are essential Base pairs Variant notation (eg. HGVS) Human Phenotype Ontology Mammalian Phenotype Ontology Medical procedure coding Environment Ontology @ontowonka
  31. 31. Genes Environment Phenotypes VCF PXFGFF Standard exchange formats exist for genes … but for phenotypes? Environment? BED @ontowonka
  32. 32. If it is alive, it can be PhenoPackaged Some biodiversity images adapted from http://i.vimeocdn.com/video/417366050_1280x720.jpg Model Organisms Biodiversity Crops Domestic Animals Disease vectors Epidemiological Monitoring Drug discovery & Development Rare Disease Diagnosis Personalized Medicine Environmental Monitoring Patients & Cohorts Genetic Engineering Mechanistic Discovery
  33. 33. Phenopackets for organisms This is “Maru”, a 4-year-old, male cat of the Scottish Fold breed abnormal sheltering behavior [MP:0014039] (onset at birth) Biography Phenotypes &qualifiers youtube.com/user /mugumogu Weighs 6kg Measurements Source
  34. 34. title: "age of onset example" persons: - id: "#1" label: "Donald Trump" sex: "M" phenotype_profile: - entity: "person#1" phenotype: types: - id: "HP:0200055" label: "Small hands" onset: description: "during development" types: - id: "HP:0003577" label: "Congenital onset" evidence: - types: - id: "ECO:0000033" label: ”Traceable Author Statement" source: - id: "PMID:1" Image credits: upi.com Phenopackets for humans Canonical JSON format
  35. 35. Phenopackets for Patients Image credits: ngly1.org • Dry eyes • Developmental delay • Elevated liver function 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: ” https://twitter.com/examplepatient/status/1 • Patient registries • Social media
  36. 36. Phenopackets for journals Each article can be associated with a phenopacket 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 Each phenopacket can be shared via DOI in any repository outside paywall (eg. Figshare, Zenodo, etc)
  37. 37. PhenoPacket formats CSV JSON RDF OWL Export phenopacket to
  38. 38. The PhenoPackets ecosystem Mechanistic discovery 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 https://github.com/phenopackets/
  39. 39. So, do you expect us to put these together ourselves? Emerging tool: WebPhenote (based on Phenote) create.monarchinitiative.org
  40. 40. WebPhenote Form-based Graph-based Noctua / LEGO inside
  41. 41. Thank you! Deep Phenotype and have a magical day Community engagement survey bit.ly/monarchcommunity
  42. 42. Acknowledgements Lawrence Berkeley Chris Mungall Suzanna Lewis Jeremy Nguyen Seth Carbon Charité Peter Robinson Sebastian Kohler RTI Jim Balhoff Cyverse Ramona Walls U of Pittsburgh Harry Hochheiser OHSU Matt Brush Kent Shefchek Julie McMurry Tom Conlin Nicole Vasilevsky Queen Mary College London Damian Smedley Jules Jacobson Garvan Tudor Groza Alfred Wegener Pier Buttigieg GSoC Satwik Bhattamishra FUNDING: NIH Office of Director: 1R24OD011883; NIH-UDP: HHSN268201300036C, HHSN268201400093P, Phenotype Ontology Research Coordination Network (NSF-DEB-0956049) With special thanks to Julie McMurry for excellent graphic design

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  • WeitingLin8

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    Nov. 25, 2017
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    Aug. 6, 2018

The Human Phenotype Ontology (HPO) was developed to describe phenotypic abnormalities, aka, “deep phenotyping”, whereby symptoms and characteristic phenotypic findings (a phenotypic profile) are captured. The HPO has been utilized to great success for assisting computational phenotype comparison against known diseases, other patients, and model organisms to support diagnosis of rare disease patients. Clinicians and geneticists create phenotypic profiles based on clinical evaluation, but this is time consuming and can miss important phenotypic features. Patients are sometimes the best source of information about their symptoms that might otherwise be missed in a clinical encounter. However, HPO primarily use medical terminology, which can be difficult for patients and their families to understand. To make the HPO accessible to patients, we systematically added non-expert terminology (i.e., layperson terms) synonyms. Using semantic similarity, patient-recorded phenotypic profiles can be evaluated against those created clinically for undiagnosed patients to determine the improvement gained from the patient-driven phenotyping, as well as how much the patient phenotyping narrows the diagnosis. This patient-centric HPO can be utilized by all: in patient-centered rare disease websites, in patient community platforms and registries, or even to post one’s hard-to-diagnosed phenotypic profile on the Web.

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