1. Melissa Haendel@ontowonka
@monarchinit
Phenotype Ontologies (and a bit of G2P) task team

2. Genes Environment Phenotypes
Determinants of Health are Diverse
 Physical environment
 Chemical exposures
 Treatments
 Smoking, alcohol
 Education
 Health services
 Income
 Social status
 Stress
 Employment
 Working conditions
 Microbiome
 Pathogens
 Clinical observations
 Laboratory tests
 Patient reported outcomes
 Child development
 Biometrics
 Behaviors
 Sleep
 Exercise
 Screen time
 Diet
 Genomic endowment
 Epigenetics
 Gene expression
 Gene regulation
Ontologies can help make (some of) this
computable

3. Genes Environment Phenotypes+ =
But its not just the types of things

4. …the relationships and their evidence must also be
captured
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

5. The Human Phenotype Ontology
 11,813
phenotype
terms
 127,125 rare
disease -
phenotype
annotations
 136,268
common
disease -
phenotype
annotations
bit.ly/hpo-paper
Peter Robinson, Sebastian Koehler, Chris Mungall

6. Other clinical vocabularies don’t adequately
cover phenotypic descriptions
Winnenburg and Bodenreider, 2014
Percentcoverage
=> HPO is now in the UMLS
0
20
40
60
80
100
HPO
UMLS
SNOMEDCT
CHV
MedDRA
MeSH
NCIT
ICD10
OMIM

7. Precision fuzzy phenotype matching
DOI: 10.1126/scitranslmed.3009262

8. 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

9. Matchmaker Exchange for patients, diseases, and model
organisms
Computational matching of rare disease patients across clinical & public sources
Find models and experts for functional validation
bit.ly/mme-matchbox
patientarchive.org
bit.ly/exomiser-2017

10. Plain language synonyms for computable
phenotypes

11. Layperson-HPO driven phenotyping tool
https://www.pcori.org/research-results/2017/realization-standard-care-rare-
diseases-using-patient-engaged-phenotyping
Catherine Brownstein, Ingrid Holm

12. NCI Thesaurus is the de facto cancer
vocabulary standard
Required for drug trials by FDA, but not interoperable with other vocabulary standards

13. Sequence
Ontology
Uberon
Anatomy
Ontology
Genotype
Ontology
MONDO
Disease
Ontology
Human
Phenotype
Ontology NCBIGene
Reactome
NCBITaxon
Protein
Ontology
ChEBI chemical
entities ontology
UNII chemical
substance registry
Cell
Ontology
Cell
Ontology
Ontology of
Biomedical
Investigations
Gene
Ontology
(GO-BP)
Uberon
Anatomy
Ontology
Gene
Ontology
(GO-CC)
UniProt
Tailoring the NCIt for computational
interoperability
https://github.com/NCI-Thesaurus
ICD-O and Oncotree slims available too:
https://github.com/NCI-Thesaurus/thesaurus-obo-edition/wiki/Downloads

14. Sequence
Ontology
Uberon
Anatomy
Ontology
Genotype
Ontology
MONDO
Disease
Ontology
Human
Phenotype
Ontology NCBIGene
Reactome
NCBITaxon
Protein
Ontology
ChEBI chemical
entities ontology
UNII chemical
substance registry
Cell
Ontology
Cell
Ontology
Ontology of
Biomedical
Investigations
Gene
Ontology
(GO-BP)
Uberon
Anatomy
Ontology
Gene
Ontology
(GO-CC)
UniProt
Lobular Breast Carcinoma =
'Breast Adenocarcinoma'
and (Disease_Has_Normal_Tissue_Origin some 'Terminal Ductal Lobular Unit')
and (Disease_Has_Normal_Cell_Origin some 'Terminal Ductal Lobular Unit Cell')
and (Disease_Has_Abnormal_Cell some 'Lobular Carcinoma Cell')
and (Disease_May_Have_Cytogenetic_Abnormality some 'Loss of Chromosome 16q')
and (Disease_Excludes_Abnormal_Cell some 'Ductal Carcinoma Cell')
and (Disease_Excludes_Finding some 'Mixed Cellular Population')
and (Disease_Mapped_To_Gene some 'CDH1 Gene')
and (Disease_May_Have_Molecular_Abnormality some 'Loss of E-cadherin Expression')
and (Disease_May_Have_Molecular_Abnormality some 'CDH1 Gene Inactivation')
Tailoring the NCIt for computational
interoperability
Jim Balhoff, Sherri DeCorronado, Giberto Fragoso, Nicole Vasilevsky,
Paula Carrio Caro, Matt Brush, Chris Mungall

15. Variant Pathogenicity Interpretations
Pathogenic ?
Benign ?
"DSC2:c.631-2A>G
Right
Ventricular
Cardiomyopathy
Complications to variant interpretation:
 Pathogenicity evidence is complex, diverse, indirect, conflicting
 Siloed curation guidelines
 High stakes (Applied directly to care)

16. Improving Rigor and Consistency of
Variant Interpretation
2015 ACMG-AMP Variant Interpretation Guidelines
 28 ‘criteria’ re: evidence types, strength
 Framework for combining criteria outcomes
ClinGen Variant Curation Interface (VCI) and DMWG
 Data model and curation for variant evidence and provenance
SEPIO Scientific Evidence and Provenance Information Ontology
 Computable model for representation and analysis of evidence and provenance
Merged Disease Classification
• Harmonized disease classification for algorithmic use and pathogenicity assignment
SEPIOScientific Evidence and
Provenance Information
Matt Brush, Selina Dwight, Larry Babb, Chris Bizon, Bradford Powell, Tristan Nelson, Bob Freimuth, Chris Mungall

17. co-localization evidence
functional
complementation evidence
microscopy evidence
imaging evidence
co-immunoprecipitation
evidence
:e4
Algorithms can leverage semantics of SEPIO models to compute
quantitative metrics of evidence quality, quantity, diversity, and
concordance – supporting automated evaluation of claims.
:e5:e3:e1 :e2
:claim1
“pathogenic”
:claim2
“benign”
Evidence-Based Computational
Evaluation of Claims
https://github.com/monarch-initiative/SEPIO-ontology/wiki

18. Disease 1 Disease 2
Data Standards Ontologies Data Standards Ontologies Data Standards Ontologies
Genes Environment Phenotypes
How do all these ontologies fit into our
notion of disease?
FHIR

19. Disease 1 Disease 2
Data Standards Ontologies Data Standards Ontologies Data Standards Ontologies
Genes Environment Phenotypes
FHIR
METADATA, EVIDENCE

20. Defining disease and clinical pathogenicity:
A lumping and splitting problem
source IDs
split/merge
manage
resolution &
provenance
MONDO Unified
Disease OntologySEPIOScientific Evidence and
Provenance Information
One disease or two?
What does the evidence favor?
One disease or two?
How do we manage identifiers, hierarchy?

21. OMIM
(brown)
MESH
(grey)
ORDO/Orphanet
(yellow)
SubClassOf
(solid line)
Xref
(dashed grey line)
Hemolytic anemia
mappings across
resources
Each nosology is different, they inconsistently map to each
other, leading to poor interoperability and computability

22. New integrated nosology
http://bit.ly/Monarch-Disease
http://purl.obolibrary.org/obo/mondo/pre/mondo.owl

23. Genes Environment Phenotypes
VCF PXFGFF
Standard exchange formats exist for genes …
but for phenotypes? Environment?
BED
http://phenopackets.org New Funding: Forums for Phenomics!

24. 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

25. 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

26. What’s next? Challenges for this
workstream
Figure out how ontologies, metadata, eHealth and exchange
standards all fit together in this workstream
Further harmonize existing disease and phenotype ontologies and
standards
Define exchange of structured phenotype data in different contexts:
clinical, basic research, patients, databases, journals
Getting structured G2P data–that is about the biology of the patient -
into/out of the EHR
Demonstrate standardization success across the driver projects
Discuss!