Recommended
Recommended
More Related Content
What's hot
What's hot (19)
Viewers also liked
Viewers also liked (20)
Similar to The Monarch Initiative: An integrated genotype-phenotype platform for disease discovery
Similar to The Monarch Initiative: An integrated genotype-phenotype platform for disease discovery (19)
More from mhaendel
More from mhaendel (14)
Recently uploaded
Recently uploaded (20)
The Monarch Initiative: An integrated genotype-phenotype platform for disease discovery
- 1. An open science integrated genotype- phenotype platform for disease and model organism discovery Melissa Haendel, PhD 2015.10.10 ASHG 2015 “Opening Up Big Data” @monarchinit @ontowonka
- 3. monarchinitiative.org There are 3,462 Mendelian or suspected Mendelian diseases with no genetic basis identified in OMIM There are 47,964 variants of unknown significance in ClinVar What can we do about that?
- 4. monarchinitiative.org Phenotypic coverage of human coding genes
- 5. monarchinitiative.org Model organisms supply ~50% phenotypic knowledge to human genes
- 6. monarchinitiative.org We have a common language for sequence data…. ….not so much for phenotypes
- 8. monarchinitiative.org Diversity of disease and phenotype vocabularies
- 10. HPO is part of integrated ontology suite Hyposmia Abnormality of globe location eyeball of camera-type eye sensory perception of smell Abnormal eye morphology Motor neuron atrophyDeeply set eyes motor neuronCL 34571 annotations in 22 species 157534 phenotype annotations 2150 phenotype annotations
- 11. Which phenotypic profile is most similar? Model X Patient Disease Y
- 12. Model X Patient Disease Ywww.owlsim.org OWLSim enables fuzzy phenotype matches
- 13. Combining genotype and phenotype data for variant prioritization Whole exome Remove off-target and common variants Variant score from allele freq and pathogenicity Phenotype score from phenotypic similarity PHIVE score to give final candidates Mendelian filters https://www.sanger.ac.uk/reso urces/software/exomiser/
- 14. monarchinitiative.org PhenoGrid phenotype comparison widget Patient phenotypes Compare patients with: Other patients Known diseases Models http://monarchinitiative. org/page/phenogrid
- 15. monarchinitiative.org Exomiser uses model phenotypes to reveal new STIM1 disease York platelet syndrome is a CRAC channelopathy due to gain-of-function mutations in STIM1
- 16. monarchinitiative.org Image credit: Viljoen and Beighton, J Med Genet. 1992 Schwartz-Jampel Syndrome, Type I Hspg2 mutation, a proteoglycan ~100 phenotype annotations How much phenotyping is a enough?
- 17. Phenotype annotation sufficiency metric http://monarchini tiative.org/page/s ervices
- 18. monarchinitiative.org Monarch in Matchmaker Exchange www.monarchinitiative.org www.patientarchive.org Matchmake against known diseases and models Matchmake against patients
- 19. monarchinitiative.org PubMed Browser http://pubmed-browser.human-phenotype-ontology.org/ Search the literature for combinations of phenotype terms, using synonyms and HPO graph http://pubmed- browser.human-phenotype- ontology.org/
- 20. Monarch team Lawrence Berkeley Chris Mungall Nicole Washington Suzanna Lewis Jeremy Nguyen Seth Carbon Hieko Dietze Charité Peter Robinson Sebastian Kohler Max Schubach Tomasz Zemojtel U of Pittsburgh Harry Hochheiser Mike Davis Joe Zhou OHSU Melissa Haendel Nicole Vasilesky Matt Brush Kent Shefchek Julie McMurry Mark Engelstead Sanger Institute Damian Smedley Jules Jacobson Garvan Tudor Groza Craig McNamara Edwin Zhang Funding: NIH Office of Director: 1R24OD011883 NIH-UDP: HHSN268201300036C, HHSN268201400093P http://monarchinitiative.org
Editor's Notes
- Human: GWAS, OMIM, clinvar Orthology via PANTHER v9 When put together, they bring the phenotypic coverage of human genes (either directly or inferred via orthology) up to nearly 80%. That is A LOT of coverage. How can we better tap that?
- Human: GWAS, OMIM, clinvar Orthology via PANTHER v9 When put together, they bring the phenotypic coverage of human genes (either directly or inferred via orthology) up to nearly 80%. That is A LOT of coverage. How can we better tap that?
- If we include bridging ontologies, we can unify diseases across sources AND phenotypes across sources and organisms.
- Represent Human as a biological subject Represent diseases as collections of nodes in the graph 3. Interoperable with other bioinformatics resources and leverage modern semantic standards
- OWLsim algorithm About HPO 2: We want the vocabulary to be enable sophisticated phenotypic matching within and across species
- This was built specifically for the UDP Clinicians at NIH to provide quality human data to be able to compare to animal models…. What do you need from your clinicians to do something similar in cancer?
- There are a lot of people who have contributed to this work over many years.