Bio-ontologies in medicine: From bench to bedside and back again - PowerPoint PPT Presentation

Bio-ontologies in medicine: From bench to bedside and back again XXIII International Conference of the European Federation for Medical Informatics, Oslo, 28–31.08.2011 Peter N. Robinson Charit´ e Universit¨ atsmedizin Berlin

Desiderata for biomedical ontologies, terminologies, and classifications Bioinformatics for molecular biology Terms for annotating and searching experimental data ⋆ Integrative semantic analysis of data ⋆ ⋆ Overrepresentation analysis Clinical Informatics ⋆ Structured reporting of clinical data Enabling decision support ⋆ Billing systems ⋆ ◮ Bioinformatics and medical informatics have traditionally been separate disciplines ◮ Need for bridge between them in the coming era of genomic and personalized medicine.

Utility of clinical data for research ◮ Disease pathobiology is associated with breakdown of one or more cellular networks ◮ Comorbidity analysis of 13 million medicare records. ◮ from the Barab´ asi lab: Lee et al. (2008) The implications of human metabolic network topology for disease comorbidity PNAS 105 :9880–9885

Detecting Drug Interactions from EHR data ◮ Mine FDA’s Adverse Event Reporting System for side-effect profiles involving glucose homeostasis ◮ strong signal for comedication with pravastatin and paroxetine ◮ Validation by mining of EHR data ◮ From the Altman lab, Tatonetti et al. (2011) Detecting Drug Interactions From Adverse-Event Reports: Interaction Between Paroxetine and Pravastatin Increases Blood Glucose Levels Clin Pharmacol Ther 90 :133-42.

Computational Analysis of Human Phenotypes Costello-Syndrom Noonan-Syndrome LEOPARD-Syndrome Neurofibromatosis Type 1 CFC-Syndrome Unique challenges in the field of genetics and rare disease

(Un)controlled vocabularies generalized generalized amyotrophy muscle atrophy muscular atrophy, muscle atrophy, generalized generalized

The Human Phenotype Ontology ◮ Ontologies represent a powerful tool for annotating, extracting, and analyzing clinical data. organ general terms abnormality cardiovascular abnormality cardiac abnormality cardiac malformation abn. of the abn. of the cardiac septa cardiac atria abn. of the atrial septum atrial septal specific terms defect ◮ ∼ 10 . 000 terms, ∼ 55 . 000 annotations for 4804 monogenic diseases ◮ http://www.human-phenotype-ontology.org ◮ Robinson PN et al. (2008) The Human Phenotype Ontology: a tool for annotating and analyzing human hereditary disease. Am J Hum Genet . 83 :610–5.

Similarity Measures for the Human Phenome Muscular Disorder class Bone Skeletal/Bone/ Connective tissue/ Cancer Development Cardiovascular Neuro Connective Tissue Dermatological Developmental Ear, Nose, Throat Endocrine Gastrointestinal Hematological Heme/ Immunological Immuno Metabolic Endo/Renal Muscular Metab Neurological Nutritional Ophtamalogical CV Psychiatric Renal Respiratory Ophth Skeletal Multiple Cancer Derma c) 1 1 � � sim( d 1 , d 2 ) = max t ∈ d 2 sim( s , t ) + max t ∈ d 1 sim( s , t ) 2 · | d 1 | 2 · | d 2 | s ∈ d 1 s ∈ d 2

Ontological Diagnostics in Human Genetics Noonan Syndrome Opitz Syndrome a) b) abn. of abn. of abn. of the abn. of the the eye the eye ocular region ocular region abn. of the abn. of the abn. of globe abn. of globe eyelid eyelid localization or size localization or size telecanthus abn. of the abn. of the hypertelorism hypertelorism palpebral fissures palpebral fissures Syndrome term downward slanting downward slanting palpebral fissures palpebral fissures Query term Overlap between query and disease Noonan Syndrome 3.78 c) downward slanting palpebral fissures sim(Q,Noonan) = 3.78 + 3.05 2 = 3.42 3.05 Query (Q) hypertelorism downward slanting palpebral fissures hypertelorism Opitz Syndrome 3.05 sim(Q,Opitz) = 2.45 + 3.05 2 hypertelorism = 2.75 2.45 Q: Query terms ( IC of abn. of the eyelid) telecanthus d: Disease terms 1 � sim( Q → d ) = max t ∈ d sim( s , t ) | Q | s ∈ Q

The Phenomizer ◮ Search for diagnosis according to phenotypic features

The Phenomizer ◮ Now there is only one diagnosis with a significant P -value ◮ Sebastian K¨ ohler et al. (2009) Clinical Diagnostics with Semantic Similarity Searches in Ontologies. Am J Hum Genet , 85 :457–64. http://compbio.charite.de/Phenomizer

HPO and PATO: A Semantic Web of the Human Phenotype Logical Definition of HPO Term Osteosclerosis Bone [Term] density id: HP:0002796 ! Osteosclerosis intersection_of: PATO:0001788 ! increased density intersection_of: has_quality PATO:0001869 ! pathological intersection_of: inheres_in FMA:30317 ! Bone Pathological ◮ Method for defining semantics of medical terms by linking HPO to other bio-ontologies: GO, FMA, MPATH, Cell Ontology, ChEBI, PRO, etc. ◮ Gkoutos GV, Mungall C, D¨ olken S, Ashburner M, Lewis S, Hancock J, Schofield P, K¨ ohler S, and Robinson PN (2009) Entity/Quality-Based Logical Definitions for the Human Skeletal Phenome using PATO. Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2009)

PATO [Term] id: HP:0001943 ! Hypoglycemia intersection_of: qualifier PATO:0000460 ! abnormal intersection_of: PATO:0001163 ! decreased concentration intersection_of: towards CHEBI:17234 ! glucose intersection_of: inheres_in FMA:9670 ! Blood ◮ Link HPO to other ontologies ⇒ Data integration to facilitate analyzing, mining, and querying biological knowledge Class: Hypoglycemia EquivalentTo: ’decreased concentration’ and inheres_in some ’Blood’ and towards some ’glucose’ and qualifier some ’abnormal’

Computational Reasoning over the Human Phenotype abnormal ion homeostasis abnormal copper homeostasis intersection_of: PATO:0000001 ! quality intersection_of: PATO:0000001 ! quality identical intersection_of: qualifier PATO:0000460 ! abnormal intersection_of: qualifier PATO:0000460 ! abnormal intersection_of: inheres_in GO:0050801 ! ion homeostasis intersection_of: inheres_in GO:0006878 ! copper ion homeostasis G e n e O n t o l GO:0050801 o g y ion homeostasis is_a is_a GO:0006878 copper ion homeostasis ◮ We can now exploit knowledge in ontologies such as GO, FMA, MPATH, etc. to make sure our representation of knowledge about the human phenotype is accurate ◮ The definitions are also a basis for integrative computational analysis ◮ K¨ ohler et al., Improving ontologies by automatically reasoning and evaluating logical definitions , 2011

Mouse Disease Models ◮ Semantic bridge between human and model organism phenotypes: ◮ The International Knockout Mouse Consortium (IKMC) ⇒ mutate all protein-coding genes in the mouse. ◮ Similar efforts for zebrafish ◮ How to harness this information for human health? Collaboration with Paul Schofield & George Gkoutos (U Cambridge), Damian Smedley (EBI), Cynthia Smith (JAX), Chris Mungall & Michael Ashburner & Suzi Lewis (GO), Monte Westefield & Barbara Ruef (ZFIN)

Interspecies comparisons Phenotype ontologies ◮ HPO (Human Phenotype Ontology) ◮ MPO (Mammalian Phenotype Ontology) ◮ ZFIN (Zebrafish E/Q definitions) ◮ Semantic bridge between 3 species

Building a bridge between phenotype and genes CNVs and Other Genomic Disorders ◮ Genomic Disorders: Deletions, Duplications, Rearrangements affecting multiple genes ◮ Phenotype results from dosage imbalance or regulatory effects of one or more affected genes ◮ Diagnostic problem: Distinguish pathogenic from neutral CNVs ◮ Scientific and medical problem: Decide which genes are responsible for the phenotype

Williams-Beuren Syndrome Semantic analysis of mouse phenotypes to find candidate genes

Williams-Beuren Syndrome Numerous previously unknown “explanations” for the Williams Phenotype

Analysis of CNV Syndromes 1779 Human genes 426 ◮ 518 candidate genes for 1011 53 individual phenotypic 289 features, 4262 522 608 ◮ 348 not previously reported 6170 Mouse genes 1598 Zebrafish genes in the literature. ◮ A basis for understanding ◮ Analysis of 24 CNV disorders genotype-phenotype ◮ Phenotypic mapping correlations in pathogenic CNVs between disorders caused by mutations in orthologous genes Doelken et al., manuscript submitted

Next Steps ◮ The HPO is still young (11/2008) but has been adopted by many databases ◮ Wellcome Trust Sanger Institute: DECIPHER and DDD databases ◮ NCBI: dbGAP, dbVAR (Whole exome sequencing data) ◮ International Standards for Cytogenomic Arrays (ISCA) Consortium ◮ GWAS Central ◮ Several research-oriented bioinformatics databases ◮ Collaboration with OMIM & Orphanet on rare disease classification ontology and links between the two projects

Thank you for your attention .... Institut f¨ ur Medizinische Genetik und Human Genetik Charit´ e Universit¨ atsmedizin Berlin Sebastian Bauer Sandra D¨ olken Bego˜ na Garc´ ıa Mu˜ noz Johannes Gr¨ unhagen Gao Guo Peter Hansen Sebastian K¨ ohler Syzmon Kie� lbasa Christian R¨ odelsperger (alumnus) Marten J¨ ager Peter Krawitz Claus-Eric Ott Angelika Pletschacher Peter N. Robinson http://compbio.charite.de K11656_Cover.indd 1 3/28/11 1:46 PM