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Semantic Phenotyping for Disease Diagnosis and Discovery
- 1. CBIIT SPEAKER SERIES 1.21.15 SEMANTIC PHENOTYPING FOR DISEASE DIAGNOSIS AND DISCOVERY @monarchinit www.monarchinitiative.org Matchmaker Exchange Melissa Haendel @ontowonka
- 2. TODAY’S TALK The computable phenotypic profile Exome analysis for disease diagnosis Crossing the species divide What is GOOD phenotyping? Chronological considerations
- 4. BUT A COMPUTER DOES NOT “Phenotypic Profile”
- 5. Often free text or checkboxes Dysmorphic features • df • dysmorphic • dysmorphic faces • dysmorphic features Congenital malformation/anomaly: • congenital anomaly • congenital malformation • congenital anamoly • congenital anomly • congential anomaly • congentital anomaly • cong. m. • cong. Mal • cong. malfor • congenital malform • congenital m. • multiple congenital anomalies • multiple congenital abormalities • multiple congenital abnormalities Examples of lists: * dd. cong. malfor. behav. pro. * dd. mental retardation * df< delayed puberty * df< * dd df mr * mental retar.short stature CLINICAL PHENOTYPING
- 6. 6% OF THE GENERAL POPULATION SUFFERS FROM A RARE DISORDER 6% of patients contacting the NIH Office of Rare Disorders do not have a diagnosis
- 7. THE YET-TO-BE DIAGNOSED PATIENT Known disorders not recognized during prior evaluations? Atypical presentation of known disorders? Combinations of several disorders? Novel, unreported disorder?
- 8. THE CHALLENGE: INTERPRETATION OF DISEASE CANDIDATES ? What’s in the box? How are candidates identified? How do they compare? Prioritized Candidates, functional validation C1 C2 C3 C4 ... Phenotypes P1 P2 P3 … Genotype G1 G2 G3 G4 … Pathogenicity, frequency, protein interactions, gene expression, gene networks, epigenomics, metabolomics…. Environments E1, E2, E3, E4 …
- 9. MATCHING PATIENTS TO DISEASES Patient Disease X Differential diagnosis with similar but non-matching phenotypes is difficult Flat back of head Hypotonia Abnormal skull morphology Decreased muscle mass
- 10. SEARCHING FOR PHENOTYPES USING TEXT ALONE IS INSUFFICIENT OMIM Query # Records “large bone” 785 “enlarged bone” 156 “big bone” 16 “huge bones” 4 “massive bones” 28 “hyperplastic bones” 12 “hyperplastic bone” 40 “bone hyperplasia” 134 “increased bone growth” 612
- 11. Phenotypes.
- 12. THINK GRAPHICALLY Each node is a different phenotype, classified by anatomical system
- 13. DISEASE X IS A COLLECTION OF NODES Each disease is associated with different phenotype nodes in the graph Disease X
- 14. EACH DISEASE IS ANNOTATED WITH A PHENOTYPIC PROFILE Chromosome 21 Trisomy Failure to thrive Umbilical hernia Broad hands Abnormal ears Flat head Down’s Syndrome
- 15. PHENOTYPE “BLAST”: WHICH PHENTOYPIC PROFILE IS GRAPHICALLY MOST SIMILAR? Disease X Patient Disease Y
- 16. FINDING THE PHENOTYPE GRAPH IN COMMON Disease X Patient Disease Y
- 17. THE HUMAN PHENOTYPE ONTOLOGY Used to annotate: • Patients • Disorders/Diseases • Genotypes • Genes • Sequence variants In human Reduced pancreatic beta cells Abnormality of pancreatic islet cells Abnormality of endocrine pancreas physiology Pancreatic islet cell adenoma Pancreatic islet cell adenoma Insulinoma Multiple pancreatic beta-cell adenomas Abnormality of exocrine pancreas physiology Köhler et al. Nucleic Acids Res. 2014 Jan 1;42(1):D966-74.
- 18. WHY DO WE NEED THE HUMAN PHENOTYPE ONTOLOGY? Winnenburg and Bodenreider, ISMB PhenoDay, 2014 How does HPO relate to other clinical vocabularies?
- 19. EXOME ANALYSIS Recessive, de novo filters Remove off-target, common variants, and variants not in known disease causing genes http://compbio.charite.de/PhenIX/ Target panel of 2741 known Mendelian disease genes Compare phenotype profiles using data from: HGMD, Clinvar, OMIM, Orphanet Zemojtel et al. Sci Transl Med 3 September 2014: Vol. 6, Issue 252, p.252ra123
- 20. CONTROL PATIENTS WITH KNOWN MUTATIONS Inheritance Gene Average Rank AD ACVR1, ATL1, BRCA1, BRCA2, CHD7 (4), CLCN7, COL1A1, COL2A1, EXT1, FGFR2 (2), FGFR3, GDF5, KCNQ1, MLH1 (2), MLL2/KMT2D, MSH2, MSH6, MYBPC3, NF1 (6), P63, PTCH1, PTH1R (2), PTPN11 (2), SCN1A, SOS1, TRPS1, TSC1, WNT10A 1.7 AR ATM, ATP6V0A2, CLCN1 (2), LRP5, PYCR1, SLC39A4 5 X EFNB1, MECP2 (2), DMD, PHF6 1.8 52 patients with diagnosed rare diseases
- 21. PHENIX HELPED DIAGNOSE 11/40 PATIENTS global developmental delay (HP:0001263) delayed speech and language development (HP:0000750) motor delay (HP:0001270) proportionate short stature (HP:0003508) microcephaly (HP:0000252) feeding difficulties (HP:0011968) congenital megaloureter (HP:0008676) cone-shaped epiphysis of the phalanges of the hand (HP:0010230) sacral dimple (HP:0000960) hyperpigmentated/hypopigmentated macules (HP:0007441) hypertelorism (HP:0000316) abnormality of the midface (HP:0000309) flat nose (HP:0000457) thick lower lip vermilion (HP:0000179) thick upper lip vermilion (HP:0000215) full cheeks (HP:0000293) short neck (HP:0000470)
- 22. WHAT ABOUT THE PATIENTS WE CAN’T SOLVE? HOW DO WE UNDERSTAND RARE DISEASE ETIOLOGY AND DISCOVER TREATMENTS?
- 23. B6.Cg-Alms1foz/fox/J increased weight, adipose tissue volume, glucose homeostasis altered ALSM1(NM_015120.4) [c.10775delC] + [-] GENOTYPE PHENOTYPE obesity, diabetes mellitus, insulin resistance increased food intake, hyperglycemia, insulin resistance kcnj11c14/c14; insrt143/+(AB) MODELS RECAPITULATE VARIOUS PHENOTYPIC ASPECTS ???
- 24. HOW MUCH PHENOTYPE DATA? Human genes have poor phenotype coverage GWAS + ClinVar + OMIM
- 25. HOW MUCH PHENOTYPE DATA? Human genes have poor phenotype coverage What else can we leverage? GWAS + ClinVar + OMIM
- 26. HOW MUCH PHENOTYPE DATA? Human genes have poor phenotype coverage What else can we leverage? …animal models Orthology via PANTHER v9
- 27. WHY WE NEED ALL THE MODELS Combined, human and model phenotypes can be linked to >75% human genes. Orthology via PANTHER v9
- 28. PHENOTYPIC DIVERSITY ACROSS SPECIES => May need different models to recapitulate different aspects of the disease
- 29. PROBLEM: CLINICAL AND MODEL PHENOTYPES ARE DESCRIBED DIFFERENTLY
- 30. lung lung lobular organ parenchymatous organ solid organ pleural sac thoracic cavity organ thoracic cavity abnormal lung morphology abnormal respiratory system morphology Mammalian Phenotype Mouse Anatomy FMA abnormal pulmonary acinus morphology abnormal pulmonary alveolus morphology lung alveolus organ system respiratory system Lower respiratory tract alveolar sac pulmonary acinus organ system respiratory system Human development lung lung bud respiratory primordium pharyngeal region PROBLEM: EACH ORGANISM USES DIFFERENT VOCABULARIES develops_from part_of is_a (SubClassOf) surrounded_by
- 31. SOLUTION: BRIDGING SEMANTICS Mungall et al. (2012). Genome Biology, 13(1), R5. doi:10.1186/gb-2012-13-1-r5 anatomical structure endoderm of forgut lung bud lung respiration organ organ foregut alveolus alveolus of lung organ part FMA:lung MA:lung endoderm GO: respiratory gaseous exchange MA:lung alveolus FMA: pulmonary alveolus is_a (taxon equivalent) develops_from part_of is_a (SubClassOf) capable_of NCBITaxon: Mammalia EHDAA: lung bud only_in_taxon pulmonary acinus alveolar sac lung primordium swim bladder respiratory primordium NCBITaxon: Actinopterygii Köhler et al. (2014) F1000Research 2:30 Haendel et al. (2014) JBMS 5:21 doi:10.1186/2041-1480-5-21
- 32. => Web application for model phenotyping and G2P validation PROBLEM: EACH SPECIES MAKES DIFFERENT G2P ASSOCIATIONS
- 33. INTEGRATED GENTOYPE-2- PHENOTYPE DATA IN MONARCH Also in the system: Rat; IMPC; GO annotations; Coriell cell lines; OMIA; MPD; Yeast; CTD; GWAS; Panther, Homologene orthologs; BioGrid interactions; Drugbank; AutDB; Allen Brain …157 sources Coming soon: Animal QTLs for pig, cattle, chicken, sheep, trout, dog, horse Species Data source Genes Genotypes Variants Phenotype annotations Diseases mouse MGI 13,433 59,087 34,895 271,621 fish ZFIN 7,612 25,588 17,244 81,406 fly Flybase 27,951 91,096 108,348 267,900 worm Wormbase 23,379 15,796 10,944 543,874 human HPOA 112,602 7,401 human OMIM 2,970 4,437 3,651 human ClinVar 19,694 111,294 252,838 4,056 human KEGG 2,509 3,927 1,159 human ORPHANET 3,113 5,690 3,064 human CTD 7,414 23,320 4,912
- 34. EXOMISER: DIAGNOSING UDP_930 USING A PHENOTYPICALLY SIMILAR MOUSE Chronic acidosis Neonatal hypoglycemia Ostopenia Short stature decreased circulating potassium level Decreased circulating glucose level Decreased bone mineral density decreased body length abnormal ion homeostasis Decreased circulating glucose level Decreased bone mineral density Short stature UDP_930/29 phenotypes Sms tm1a(EUCOMM)Wtsi Robinson et al. (2013). Genome Res, doi:10.1101/gr.160325.113
- 35. EXOMISER: COMBINING PHENOTYPIC SIMILARITY WITH OTHER DATA MED21 MAU2 MED8 MED26 Recurrent otitis media Spasticity Esotropia Cerebral palsy Conductive hearing impairment Limitation of joint mobility Strabismus Hypertonia Abnormality of the middle ear Abnormal joint mobility Strabismus Abnormality of central motor function UDP_2146/56 phenotypes Brachmann-de Lange syndrome NIPBL MED23 ? CCNC Contractures of the joints of the lower limbs Hypertonicity CDK8
- 36. UDP CASES ANALYZED WITH EXOMISER => Use of genotype, phenotype, PPI, and inheritance together provide best prioritization
- 37. ANALYSIS OF UNSOLVED UDP CASES 4 families now have a diagnosis including, one novel disease-gene association discovered: York Platelet syndrome and STIM1 Strong candidates identified for 19 families that are now undergoing functional validation through mouse and zebrafish modeling Several hundred UDP cases now being analyzed using Exomiser and cross-species phenotype data
- 38. HOW DOES THE CLINICIAN KNOW THEY’VE PROVIDED ENOUGH PHENOTYPING? How many annotations…? How many different categories? How many within each?
- 39. Image credit: Viljoen and Beighton, J Med Genet. 1992 Schwartz-jampel Syndrome, Type I Schwartz-jampel Syndrome, Type I Caused by Hspg2 mutation, a proteoglycan ~100 phenotype annotations
- 40. EVALUATION METHOD Create a variety of “derived” diseases More general (depth) Remove subset(s) (breadth) Introduce noise Assess the change in similarity between the derived disease and it’s parent. Ask questions: Is the derived disease considered similar to original? …or more similar to a different disease? Is it distinguishable beyond random? Are there any specific factors that influence similarity?
- 41. FINDING THE PHENOTYPE GRAPH IN COMMON The most specific phenotypic profile in common
- 42. METHOD: DERIVE BY CATEGORY REMOVAL Remove annotations that are subclasses of a single high-level node Repeat for each 1° subclass
- 43. Example: Schwartz-jampel Syndrome, Type I to test influence of a single phenotypic category
- 44. Example: Schwartz-jampel Syndrome derivations to test influence of a single phenotypic category
- 45. Example: Schwartz-jampel Syndrome derivations
- 46. SEMANTIC SIMILARITY ALGORITHMS ARE ROBUST IN THE FACE OF MISSING INFORMATION (avg) 92% of derived diseases are most-similar to original disease Severity of impact follows proportion of phenotype Similarity of Derived Disease to Original Derived Disease Profile Rank
- 47. METHOD: DERIVE BY LIFTING Iteratively map each class to their direct superclass(es) Keep only leaf nodes
- 48. SEMANTIC SIMILARITY ALGORITHMS ARE SENSITIVE TO SPECIFICITY OF INFORMATION Severity of impact increases with more-general phenotypes Similarity of Derived Disease to Original Derived Disease Profile Rank
- 51. CONSIDERING TIME
- 52. PATIENT 1 Lower back pain Motor weakness Unpleasant muscle twitching 40yrs old
- 53. PATIENT 2 Unpleasant muscle twitching 65 yrs old Stumbling Leg weakness
- 54. PATIENT 1 Diagnosis: Degenerative disc disease with L3 nerve root radiculopathy causing muscle weakness. More recent onset of benign fasciculation syndrome, a non- progressive disease.
- 55. PATIENT 2 Diagnosis: Amyotrophic lateral sclerosis (Lou Gehrig disease)
- 56. ADDING CHRONOLOGY TO THE ALGORITHM
- 57. ADDING EXPOSURE TO THE ALGORITHM Patient 1 Disease/condition Drug/chemical
- 58. ADDING NEGATION TO THE ALGORITHM Patient Disease X
- 59. CONCLUSIONS Phenotypic data can be represented using ontologies to support improved comparisons within and across species For known disease-gene associations comparison to human phenotype data is effective at variant prioritization. For unknown disease-gene associations the expansion of phenotypic coverage using model organisms greatly improves variant prioritization. Phenotype breadth is recommended to buffer lack of information, ALSO very specific phenotypes are necessary to ensure quality matches
- 60. FUTURE WORK Add additional variables to semantic similarity algorithm – e.g. negation, environment, chronology Validate existing animal models for recapitulation of disease Further characterization of organism-specific phenotypes Adding many more non-model organisms to the analysis
- 61. ACKNOWLEDGMENTS NIH-UDP William Bone Murat Sincan David Adams Amanda Links Joie Davis Neal Boerkoel Cyndi Tifft Bill Gahl OHSU Nicole Vasilesky Matt Brush Bryan Laraway Shahim Essaid Kent Shefchek Garvan Tudor Groza Lawrence Berkeley Nicole Washington Suzanna Lewis Chris Mungall UCSD Jeff Grethe Chris Condit Anita Bandrowski Maryann Martone U of Pitt Chuck Boromeo Vincent Agresti Becky Boes Harry Hochheiser Sanger Anika Oehlrich Jules Jacobson Damian Smedley Toronto Marta Girdea Sergiu Dumitriu Heather Trang Bailey Gallinger Orion Buske Mike Brudno JAX Cynthia Smith Charité Sebastian Kohler Sandra Doelken Sebastian Bauer Peter Robinson Funding: NIH Office of Director: 1R24OD011883 NIH-UDP: HHSN268201300036C, HHSN268201400093P