4. Obstacles to phenome-based
interpretation
• Building a comprehensive phenomic
database
– Multiple disparate sources:
• Human Genes, Variants, etc databases
• Orthologous genes in model organisms
• Phenotype Search and Matching
• How do utilize phenotypes in a variant filtering pipeline?
• How do we match phenotypes in different species?
• How much difference does phenotype make?
8. Monarch Portal: linking human
diseases to model systems
• One stop shop for
gene-phenotype data
and analysis:
• Humans
• Models
– Data:
• Genes
• Variants
• Complex genotypes
• Phenotypes
• Disease
http://monarchinitiative.org/
Mungall, C. J., Washington, N. L., Nguyen-Xuan, J., Condit, C.,
Smedley, D., Köhler, S., … Haendel, M. A. (2015). Use of Model
Organism and Disease Databases to Support Matchmaking for
Human Disease Gene Discovery. Human Mutation, 36(10), 979–
84. doi:10.1002/humu.22857
10. How do we search phenome
databases?
• Given a patient
phenotypic profile
• What are the relevant
genes implicated in…
– Humans?
– Model systems?
Patient
Phenome
Gene
<->
Phenotype
Database
Hyperkeratosis,
hearing impairment,
…
Candidate
genes
KRT2
GJB2
11. monarchinitiative.org
We have a common
computable language for
sequence data….
ATCTTAGCACGTTAC…
OR g.241T>c
….not so much for phenotypes
14. monarchinitiative.org
Ulcerated
paws
Palmoplantar
hyperkeratosis
Thick hand skin
Abnormal
autopod skin
id: HP:0000972
Synonyms: “Thick palms and soles”
Def: “Hyperkeratosis affecting the palm of
the hand and the sole of the foot”
Köhler, S., Doelken, S. C., Mungall, … Robinson, P. N. (2013). The
Human Phenotype Ontology project: linking molecular biology and
disease through phenotype data. Nucleic Acids Res., Kohler, S.(1),
gkt1026–. doi:10.1093/nar/gkt1026
OMIM:309560
OMIM:613989
…
MP:0000578
Ctsk
Ntrk1
Lamc2
18. monarchinitiative.org
Smedley, D., Oellrich, A., Köhler, S., Ruef, B., Westerfield, M., Robinson, P., … Mungall, C. (2013). PhenoDigm: analyzing curated
annotations to associate animal models with human diseases. Database : The Journal of Biological Databases and Curation,
2013, bat025. doi:10.1093/database/bat025
Multi-phenotype search
19. Smedley, D., Oellrich, A., Köhler, S., Ruef, B., Westerfield, M., Robinson, P., … Mungall, C. (2013). PhenoDigm: analyzing curated
annotations to associate animal models with human diseases. Database : The Journal of Biological Databases and Curation,
2013, bat025. doi:10.1093/database/bat025
21. PHenotypic Interpretation of
Variants in Exomes
Whole exome
Remove off-target and
common variants
Variant score from allele
freq and pathogenicity
Phenotype score from phenotypic similarity
(hi)PHIVE score to give final candidates
Mendelian filters
https://www.sanger.ac.uk/reso
urces/software/exomiser/
22. monarchinitiative.org
Adding phenotype improves variant
interpretation
Robinson, P., Kohler, S., Oellrich, A., Wang, K., Mungall, C., Lewis, S.
E., … Köhler, S. (2013). Improved exome prioritization of disease
genes through cross species phenotype comparison. Genome
Research. doi:10.1101/gr.160325.113
23. monarchinitiative.org
Patient diagnosis example
Deleteriousness Phenotype Score
P
ID
Gen
e
MT P2 S Clinical Pheno Matching Pheno gene P Var ES Ran
k
92
9
SMS 1.00 0.99 0.00 Ostopenia Decreased BMD Sms 0.4 1.00 0.89 1/25
Short stature Decreased body length
Neonatal hyoglycemia Decreased circulating glucose
levels
acidosis Decreased circulating
potassion levels
Decreased body weight Decreased body weight
Bone, W. P. et al. Computational evaluation of exome sequence
data using human and model organism phenotypes improves
diagnostic efficiency. Genet. Med. in press, (2015)
25. Building up a massive phenomic
database
• Initial efforts
• Manual curation of OMIM records
• Expert biocurators and clinicians
• Lag between publication and phenotype capture
• How are we scaling up?
• Phenotypes at time of publication
• Working with patient registries
• Natural Language Processing
• Integration with Gene Ontology curation
27. Beyond mendelian phenotypes
• First pass
• Mendelian or ‘rare’ diseases
• Can we include a broader definition of
‘phenotype’
• Quantitative traits, e.g. hippocampus volume
• Common disease phenotypes
• Cancer
28. monarchinitiative.org
Groza, T., … Robinson, P. N. (2015). The Human Phenotype Ontology: Semantic Unification of Common and Rare Disease. The
American Journal of Human Genetics, 1–14. doi:10.1016/j.ajhg.2015.05.020
Mining pubmed for phenotypes
F-Score: 45%
31. Conclusions
• Phenotypes are crucial for precision medicine
• Variant interpretation needs more than genome data
• Methods of incorporating phenotypes are evolving
• We need all the organisms
• The Monarch Portal integrates and organizes
gene-phenotype data
• Ontologies make phenotypes computable
• Depth and breadth of structured phenotype data is
growing
32. Monarch team
Lawrence Berkeley
Chris Mungall
Nicole Washington
Suzanna Lewis
Jeremy Nguyen
Seth Carbon
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
33.
34. From phenomes to exposomes
• Environmental context
• Microbiome
• Drugs
Buttigieg, P. L., Morrison, N., Smith, B., Mungall, C. J., & Lewis, S. E. (2013). The environment ontology: contextualising biological
and biomedical entities. Journal of Biomedical Semantics, 4(1), 43. doi:10.1186/2041-1480-4-43
Notes de l'éditeur
Phenotype-Gene Associations in Variant Interpretation
Even with increased genomic data e.g. EXAC, it can still be hard to pinpoint the causative variant, or to be sure the real variant hasn’t been filtered
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?
More molecular. Just as the exome is only an incomplete view of the genome, only part of the phenome is observed and measured
We can quantify distance due to shared ancestry. Draw trees.
Mention HPOA
Mention HPOA
Backbone ontology. Bridges anatomical and pathological levels
There are a lot of people who have contributed to this work over many years.
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