1. Stanford Center for Biomedical Informatics Research An Ontology-Based Approach for Computational Phenomics: Application to Autism Spectrum Disorder Amar K. Das, MD, PhD Departments of Medicine and of Psychiatry and Behavioral Sciences

2. NCBO Webinar October 7, 2009 Outline Motivations NDAR project Phenologue project Future Directions

3. NCBO Webinar October 7, 2009 Motivation Psychiatric Genetics Phenotyping Terminology Ontology Logic

4. NCBO Webinar October 7, 2009 Hasler G,et al. Toward constructing an endophenotype strategy for bipolar disorders. Biological Psychiatry (2006) Represent findings and their links using structured knowledge

5. NCBO Webinar October 7, 2009 Phenomics “A primary task for the new field of phenomics will be to clarify what, in practical terms, constitutes a phenotype and then to delineate the different phenotypic components that compose the phenome.” Freimer & Sabatti, Nature Genetics (2003)

6. NCBO Webinar October 7, 2009 OMIM

7. NCBO Webinar October 7, 2009 dbGaP Mailman, M.D. Nature Genetics (2007)

8. NCBO Webinar October 7, 2009 PhenoWiki

9. NCBO Webinar October 7, 2009 PhenoWiki

10. NCBO Webinar October 7, 2009 Current Approaches Lack of standardization Lack of organization Lack of computability

11. NCBO Webinar October 7, 2009 Autism DSM-IV Diagnosis A total of six (or more) items from (1), (2), and (3), with at least two from (1), and one each from (2) and (3) (1) qualitative impairment in social interaction, as manifested by at least two of the following: a) marked impairments in the use of multiple nonverbal behaviors such as eye-to-eye gaze, facial expression, body posture, and gestures to regulate social interaction b) failure to develop peer relationships appropriate to developmental level c) a lack of spontaneous seeking to share enjoyment, interests, or achievements with other people, (e.g., by a lack of showing, bringing, or pointing out objects of interest to other people) d) lack of social or emotional reciprocity

12. NCBO Webinar October 7, 2009 Autism DSM-IV Diagnosis (2) qualitative impairments in communication as manifested by at least one of the following: a) delay in, or total lack of, the development of spoken language (not accompanied by an attempt to compensate through alternative modes of communication such as gesture or mime) b) in individuals with adequate speech, marked impairment in the ability to initiate or sustain a conversation with others c) stereotyped and repetitive use of language or idiosyncratic language d) lack of varied, spontaneous make-believe play or social imitative play appropriate to developmental level

13. NCBO Webinar October 7, 2009 Autism DSM-IV Diagnosis (3) restricted repetitive and stereotyped patterns of behavior, interests and activities, as manifested by at least two of the following: a) encompassing preoccupation with one or more stereotyped and restricted patterns of interest that is abnormal either in intensity or focus b) apparently inflexible adherence to specific, nonfunctional routines or rituals c) stereotyped and repetitive motor mannerisms (e.g hand or finger flapping or twisting, or complex whole body movements) d) persistent preoccupation with parts of objects Delays or abnormal functioning in at least one of the following areas, with onset prior to age 3 years:(1) social interaction(2) language as used

14. NCBO Webinar October 7, 2009 NDAR (ndar.nih.gov)

15. NCBO Webinar October 7, 2009 Goals of NDAR Develop standards to promote meta- analyses and cross site research data comparisons Provide researchers access to useful software tools and infrastructure Promote the sharing of research data relevant to ASD

16. NCBO Webinar October 7, 2009 NIH Research Support in Autism $100 million/year in funding Investigator-initiated grants (R01’s) Special initiatives, e.g. RFA for genetics Centers and networks Training grants (To institutions and individuals) New initiatives Intramural Research Program on Autism Autism Centers of Excellence (ACE) National Database for Autism Research (NDAR) ARRA stimulus program

17. NCBO Webinar October 7, 2009 BIRN Mediator

18. NCBO Webinar October 7, 2009 Query and Reporting BIRN Services & Resources NDAR System Security Portal Grid Computing Collaboration Data Storage Management Data Integration Tools AuditingUser Management Subject Tracking & Management Clinical Assessments (OpenClinica) Common Measures Study Management Neuroimaging Image Analysis Image Processing Image data access Genomics Genomics data access Data Integration

19. NCBO Webinar October 7, 2009 NDAR Codebook

20. NCBO Webinar October 7, 2009 Phenotypes in Psychiatry ‘The observable structural and functional characteristics of an organism determined by its genotype and modulated by its environment’ Diagnostic component Intermediate phenotype Quantitative phenotype Covariates

21. NCBO Webinar October 7, 2009 Example Query #1 Find all subject who are verbal (ADIR A14). Then look at their IQ (Cognitive Total IQ > 70) and whether or not they have seizures (Medical History Q10). Also find out if they have an abnormal MRI or any genetic abnormalities.

22. NCBO Webinar October 7, 2009 Example Query #2 Use head circumference to categorize macroencephaly. Then see if the subjects differ in their ADOS, ADI-R, cognitive, and language profiles, and combine this with genetic data.

23. NCBO Webinar October 7, 2009 NDAR Project Systematic Review Ontology Development Database Infrastructure

24. NCBO Webinar October 7, 2009 Systematic Review “(ADI-R or ADOS or Vineland) and (genes or genetics) and autism” 26/43 papers relevant Mean # phenotypes 4.1, range 1-13 Three basic types (1:1, sum, cutoff score) Tu, S. W. AMIA Annual Proceedings (2008)

25. NCBO Webinar October 7, 2009 Systematic Review Different terms e.g., ‘age of first phrases’ and ‘age of onset of phrase speech’ Different cutoff scores e.g., ‘delayed word’ Different definitions e.g., ‘regression’ e.g., use of different instruments

26. NCBO Webinar October 7, 2009 Clinical Research Study Clinical Trial Study Case Study Controlled Case Study Study Arms Ontology A taxonomy with multiple link types, each with precise meaning

27. NCBO Webinar October 7, 2009 Perspectives on ‘Ontology’ Philosophy: The study of what entities and what types of entities exist in reality Computer Science: A schema that represents a domain and is used to reason about the objects in that domain and the relations between them

28. NCBO Webinar October 7, 2009 Critical to the ‘Semantic Web’ Shared research and development plan to Provide explicit semantic meaning to data and knowledge shared on the Web Bring structure to Web content Advance the current state-of-the-art in Web information retrieval, which is keyword searching Distributed applications will be able to process data and knowledge automatically through the use of ontologies

29. NCBO Webinar October 7, 2009 OWL: Web Ontology Language Advances current Semantic Web standards by using ontologies to represent knowledge OWL can be used to build ontologies of high- level descriptions, based on three concepts: Classes (e.g., Subject, Phenotype, Genotype) Properties (e.g., isBearerOf, hasResults) Individuals (e.g., “Macroencephaly”)

30. NCBO Webinar October 7, 2009 Subject Genotype Phenotype mutIn- RELN Macro- encephaly 011451 hasResult isBearerOf OWL: Web Ontology Language

31. NCBO Webinar October 7, 2009 BIRNLex A controlled terminology for annotation of BIRN data sources, focusing on imaging data from human subjects and mouse models Terms cover neuroanatomy, molecular species, behavioral and cognitive processes, subject information, experimental practice and design

32. NCBO Webinar October 7, 2009 Basic Formal Ontology An upper ontology which can be used to support the development of domain ontologies used in scientific research All concepts are subclasses of Continuants: exists in full at any time in which it exists at all Occurants: has temporal parts and that happens, unfolds or develops through time

33. NCBO Webinar October 7, 2009 OBO Foundry Ontologies should be orthogonal Minimize overlap Each distinct entity type (universal) should only be represented once Partition efforts in the OBO Foundry rationally to help organize and coordinate the ontology development

34. NCBO Webinar October 7, 2009 Chris Mungall, PATO

35. NCBO Webinar October 7, 2009 SWRL: Semantic Web Rule Language W3C specification for expressing logical rules that can be formulated in terms of OWL concepts Rules in SWRL can be used to deduce new knowledge about an existing OWL ontology Specification can be extended through the use of built ins

36. NCBO Webinar October 7, 2009 hasParent(?x, ?y) ^ hasBrother(?y, ?z) → hasUncle(?x, ?z) Example SWRL Rule: hasUncle

37. NCBO Webinar October 7, 2009 Example SWRL Rule: hasSister Person(Amar) ^ hasSibling(Amar, ?s) ^ Woman(?s) → hasSister(Amar, ?s)

38. NCBO Webinar October 7, 2009 Person(?p) ^ hasAge(?p,?age) ^ swrlb:lessThan(?age,17) → Child(?p) Example SWRL Rule: Child

39. NCBO Webinar October 7, 2009

40. NCBO Webinar October 7, 2009 Rule-Based Methods Extensions to SWRL Temporal Library of temporal built ins Query Extraction of results as a table MakeSet Support for set-based operations

41. NCBO Webinar October 7, 2009 Development Methods Extensions to BIRNLex Encoding of phenotypes Querying of NDAR database

42. NCBO Webinar October 7, 2009 Autism Assessment Result Figure 1. The representation of data collected through the ADI-2003 autism assessment instrument as part of the autism ontology.

43. NCBO Webinar October 7, 2009 Phenotype Representation Figure 2. The representation of the Status of age of words phentotype group as a OWL class partition by the possible statuses.

44. NCBO Webinar October 7, 2009 Phenotype Rule ADI_2003_result(?assessment) ^ acqorlossoflang_aword(?assessment,?wordage) ^ swrlb:greaterThan(?wordage, 24) ^ subject_id(?assessment, ?subjectId) ^ orgtax:Human(?subject) ^ subject_id(?subject, ?subjectId) → birn_obo_ubo:bearer_of(?subject, Delayed_word)

45. NCBO Webinar October 7, 2009 Phenotype Rules

46. NCBO Webinar October 7, 2009 Ontology-Driven Querying Young, L. IEEE CBMS (2009)

47. NCBO Webinar October 7, 2009 Phenologue Project Develop an ontology of endophenotypes that maps brain connectivity, neural deficits, and genetic markers into a subject domain theory Develop logic-based methods to encode and classify endophenotypes based on multi-scale measurements Create tools to acquire new endophenotypes and annotate phenotype-genotype findings in online resources such as published literature Develop query-elicitation methods that can evaluate hypotheses about the subject domain theory of endophenotypes using deductive inference

48. NCBO Webinar October 7, 2009 Phenologue Project Database Phenotype Definitions New Associations Query CatalogAnalysis

49. NCBO Webinar October 7, 2009 Rule Technologies Rule paraphrasing Rule elicitation Rulebase visualization Knowledge mining using rules

50. NCBO Webinar October 7, 2009 Rule Paraphrasing

51. NCBO Webinar October 7, 2009 Rule Elicitation

52. NCBO Webinar October 7, 2009 Rulebase Visualization

53. NCBO Webinar October 7, 2009 Computational Phenomics Informatics methods to support phenomics Apply machine learning methods to discover groups of rules with common semantics Use natural language processing method to discover phenotype rules in published text

54. NCBO Webinar October 7, 2009 Semantic Similarity

55. NCBO Webinar October 7, 2009 Future Directions Expand phenotype categories Use natural language processing method to discover phenotype rules in published text Apply machine learning methods to discover groups of rules with common semantics

56. NCBO Webinar October 7, 2009 Summary The development of a standardized, organized, and computable set of phenotype terms is central to etiologic studies of complex disorders The use of ontologies and rules to model phenotypes is feasible and can enable automated discovery of new phenotype-genotype relationships

57. NCBO Webinar October 7, 2009 Acknowledgments Stanford Group Martin O’Connor Saeed Hassanpour Duriel Hardy Ravi Shankar Lakshika Tennakoon Samson Tu National Center for Biomedical Ontology Mark Musen Daniel Rubin NDAR/NIMH Lynn Young Matthew McAuliffe Dan Hall Lisa Gilotty Biomedical Informatics Research Network Bill Bug Maryann Martone