Defining Disease Across OrganismsBuffalo PRO-PO-GOMay 2013

Judith BlakeJackson Laboratory

Why do we need a formal disease classifications?

To search tagged dataTo aggregate datasetsTo mine data sources; eg literature, EHRsTo search coded data for sub- and superclassesTo discover novel relationships between diseases within a speciesTo discover the relationships between diseases and pathwaysTo search for related diseases between speciesTo allow stratification of disease populations

Modified from Paul Schofield 2012 ISB presentation

Classification systems

1. those that belong to the Emperor 2. embalmed ones 3. those that are trained 4. suckling pigs 5. mermaids 6. fabulous ones 7. stray dogs 8. those included in the present

classification 9. those that tremble as if they were mad 10. innumerable ones 11. those drawn with a very fine camelhair

brush 12. others 13. those that have just broken a flower

vase 14. those that from a long way off look like

flies.

The Celestial Emporium of Benevolent Knowledge Jorge-Luis Borges

Modified from Barry Smith 2011 DO meeting

“Defining Disease in the Genomics Era”

• A disease is: • a state that places individuals at increased risk of

adverse consequences.

• Where is the threshold for ‘adverse’ consequences (a) along the intensity dimension; (b) along the time dimension?

Science 293 (5531) I3 Aug 2001, pp. 807-808.

Adapted from Schriml and Kibbe: ICBO submission 2013

DiseaseCellAnatomy

What are axis and intersections for cross-organism disease representations?

• Diseases and Phenotypes – Phenoscape, Monarch, MGI, PhenomeNet

• Diseases and Taxonomic Distribution– Taxonomies, Anatomies (Uberon)

• Diseases and Genotypes– Inheritances, Complex Genotypes, Penetrance and

Susceptibility

• Diseases and Exposures– Chemicals, Pathogens, ENV, EXO, CTD

Epidemiology Metadata

Define and Standardize: Pathogen, Host, Reservoir, Mode of Transmission, Portal of Entry, Vector,

Disease, Symptom, Geographic LocationLynn Schriml 2013

Diseases and Phenotypes

• Diseases are (traditionally) described by signs and symptoms– Signs – things you can measure– Symptoms – things the patient notices

• Signs are phenotypes

• Diseases are characterized by phenotypes, the order, severity and duration with which they occur. A full model of disease takes into account dimensions of anatomy, time, severity, therapeutic responsiveness, outcomes etc. There is also a probabilistic element to an instance of the disease and a probabilistic association between phenotypic elements in one instance.

• Diseases are not phenotypes ( although predisposition may be considered as such) but single phenotype diseases may be viewed as phenotypes, eg. Osteoarthritis or plant rust diseases.

Human Phenotype Ontology

• ≈ 10,500 terms• ≈ 60,000 annotations formainly Mendelian disease

Broad uptake in human genetics community

http://www.human-phenotype-ontology.orgSlide courtesy of Peter Robinson

Marfan Syndrome: caused by mutations in fibrillin 1 gene

(top) wild-type littermates(bottom) Fbn1tm2Rmz/ Fbn1tm2Rmz

• systemic disorder of connective tissue• aortic aneurysm• partial loss of microfibrillar function

dolichostenomelia arachnodactyly micrognathia abnormal chest/rib overgrowth aortic aneurysm decreased muscle mass kyphosis premature death

Comparative Disease Phenotypes

Formalisation and logical definitions

• PATO, EQ syntax• Phenomenet• Mousefinder

Querying across species and time

NCBO

NESCent(Vision, Lapp,

Balhoff, Kothari)

OBO(host of TAO, PATO, taxonomy ontology)

Applications(Phenote, OBO-Edit)

Phenotype Ontologies for Evolutionary Biology

Workshops

Database

Curator interface

Public interface

U. South DakotaAcad. Natural Sciences

(Mabee,Lundberg, Dahdul)

U. Oregon(Westerfield)

Liason to ZFIN

Liason to NCBO

Usability testing

Working groups

Morphologycollaborators

(Arratia, Coburn,Hilton, Mayden)

Ichthyology community(DeepFin, Fishbase)

Ostariophysanphenotypic

data

Kansas(Midford)

Ontology Curation

Zebrafishphenotypic& genetic

data

Ontologies(taxonomy, TAO,PATO, homology)

Todd Vision 2013

Taxon phenotype annotations

Brachyplatystoma capapretum

round that

inheres_in some

ethmoid cartilage

exhibits some

Taxon ontology term Anatomy

ontology term

Phenotypic Quality ontology term

Links a quality to the entity that is its bearer

Todd Vision 2013

Brachyplatystoma capapretum

round that

inheres_in some

ethmoid cartilage

exhibits some

tfap2ats213/ts213split that

inheres_in some

ethmoid cartilage

influences some

round

split

ethmoid cartilageBrachyplatystoma

tfap2a

is_avariant_of

inheres_in

inheres_in

is_a

is_a

shape

chondrocranium cartilage

olfactory region

Pimelodidae

sequence-specific DNA binding transcription factor activity

is_a

has_function is_a part_ofis_a

is_a

Todd Vision 2013

Defining diseases and phenotypes

• Phenotypic definition of a disease permits sophisticated computational analysis within a species. However, disease classification necessary for community tagging and tracking.

• Formal definition of phenotypes allows cross-species data integration and analysis. Plant Anatomy Ontology essential to phenotype classifications

• The accuracy of the asserted hierarchy is essential for utility.

• Inaccurate structure, inappropriate relations or incomplete classes render a formal ontology worse than useless.

• For asserted structures the upper levels need to reflect the uses to which it will be put. Terminologies should be familiar to pathologists, plant biologists and other major users

IDO scales of granularity

• Host-pathogen interactions occurs at a variety of scales

– Ecosystem

– Organism

– Organ

– Cell

– Molecule

Surveillance

Pathogenesis

Richard Scheuermann 2011

Richard Scheuermann 2011

Infections Disease Ontology scales & branches

Scale Independent Continuant

Dependent Continuant Occurrent

Ecosystem specimen isolation source, environment

vector (role), carrier (role), population density, routes of migration (?), host (role), pathogen (role), source (?), temperature

transmission, migration, specimen isolation process, specimen isolation event

Organism NCBI taxonomy, vaccine preparation,

healthy, sick, animal model (role), viral load, latent (state), sterile eradication (state), symptoms (quality), immunogen (role), adjuvant (role), pathogen (disposition), resistant (disposition)

pathogenesis, reactivation, progression, immune response, vaccination

Organ FMA (mucosal organ - lung, secondary lymphoid organ - lymph node), pericavitary tissue, abssess

viral load (quality), inductive site (role), effective site, inflamed (quality), granuloma (quality), caseous necrosis (quality)

inflammation, tissue damage, necrosis

Cell T cell, dendritic cell Infected (quality), activated, susceptible (disposition)

antigen presentation, proliferation, phagocytosis, cytoxicity, apoptosis

Molecule Glyoxalase, catalase, recA detoxification (role), DNA lesion recognition, DNA repair, transport

catalysis, binding, transport

Tier 1 Controlled Vocabulary/Terminology

Tier 2 Ontology/DAG

Tier 3a Diseases defined with by Phenotype terms from an ontology

Tier 3b Diseases Defined by

Phenotype terms plus quantitative

and deep phenotyping data

Tier 4 Diseases Defined using formal

logical definitions

Tier 5Formal

disease model

Indexing and simple searching

Discovery

Paul Schofiield 2013

Tier 1 Controlled Vocabulary/Terminology

Tier 2 Ontology/DAG

Tier 3a Diseases defined with by Phenotype terms from an ontology

Tier 3b Diseases Defined by

Phenotype terms plus quantitative

and deep phenotyping data

Tier 4 Diseases Defined using formal

logical definitions

Tier 5Formal

disease modelWork

Paul Schofiield 2013

Start from here?

• Review existing classifications: investigate, possibly emulate - – IDO (formal disease classification)– MEDIC (disease classification built on OMIM base)– Phenoscape (phenotype integration across species, anatomy-centric)– PhenomeNet– Orphanet (genetic disease classification)– Plant disease classifications

• Need extensive involvement of pathologists and biologists as well as informaticians

• Recognize differences and requirements of disease vs phenotype components, support inter-ontology constructions

• Do we need to reinvent the wheel or just pump up the tires?