1. Defining Disease Across Organisms Buffalo PRO-PO-GO May 2013 Judith Blake Jackson Laboratory

2. Why do we need a formal disease classifications? To search tagged data To aggregate datasets To mine data sources; eg literature, EHRs To search coded data for sub- and superclasses To discover novel relationships between diseases within a species To discover the relationships between diseases and pathways To search for related diseases between species To allow stratification of disease populations Modified from Paul Schofield 2012 ISB presentation

3. 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

4. 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.

5. Adapted from Schriml and Kibbe: ICBO submission 2013 Disease Cell Anatomy

6. 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

7. Epidemiology Metadata Define and Standardize: Pathogen, Host, Reservoir, Mode of Transmission, Portal of Entry, Vector, Disease, Symptom, Geographic Location Lynn Schriml 2013

8. 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.

9. Human Phenotype Ontology ≈ 10,500 terms ≈ 60,000 annotations for mainly Mendelian disease Broad uptake in human genetics community http://www.human-phenotype-ontology.org Slide courtesy of Peter Robinson

10. Marfan Syndrome: caused by mutations in fibrillin 1 gene (top) wild-type littermates (bottom) Fbn1 tm2Rmz / Fbn1 tm2Rmz 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

11. Formalisation and logical definitions PATO, EQ syntax Phenomenet Mousefinder

12. Querying across species and time

13. 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 Dakota Acad. Natural Sciences (Mabee,Lundberg, Dahdul) U. Oregon (Westerfield) Liason to ZFIN Liason to NCBO Usability testing Working groups Morphology collaborators (Arratia, Coburn, Hilton, Mayden) Ichthyology community (DeepFin, Fishbase) Ostariophysan phenotypic data Kansas (Midford) Ontology Curation Zebrafish phenotypic & genetic data Ontologies (taxonomy, TAO, PATO, homology) Todd Vision 2013

14. Taxon phenotype annotations Brachyplatystoma capapretum round that inheres_in some ethmoid cartilage 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

15. Brachyplatystoma capapretum round that inheres_in some ethmoid cartilage round that inheres_in some ethmoid cartilage exhibits some tfap2a ts213/ts213 split that inheres_in some ethmoid cartilage split that inheres_in some ethmoid cartilage influences some round split ethmoid cartilage Brachyplatystoma tfap2a is_a variant_of inheres_in is_a shape chondrocranium cartilage olfactory region Pimelodidae sequence-specific DNA binding transcription factor activity is_a has_function is_a part_of is_a Todd Vision 2013

16. 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

17. IDO scales of granularity Host-pathogen interactions occurs at a variety of scales – Ecosystem – Organism – Organ – Cell – Molecule Surveillance Pathogenesis Richard Scheuermann 2011

18. Infections Disease Ontology scales & branches ScaleIndependent Continuant Dependent ContinuantOccurrent 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 cellInfected (quality), activated, susceptible (disposition) antigen presentation, proliferation, phagocytosis, cytoxicity, apoptosis Molecule Glyoxalase, catalase, recAdetoxification (role), DNA lesion recognition, DNA repair, transport catalysis, binding, transport

19. 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 5 Formal disease model Indexing and simple searching Discovery Paul Schofiield 2013

20. 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 5 Formal disease model Work Paul Schofiield 2013

21. 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?