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Patient A phenotypically matched to a mouse gene M, and via protein interactions to the ortholog of M to Disease gene D.
Two families were diagnosed with previously known diseases. (Kufor-Rakeb syndrome and HARP syndrome) One new disease-gene association was made STIM1 to York Platelet Syndrome (2 families) 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
The cross-species comparisons can be viewed for any phenotype profile in any species. We are also working on further user control to be able to compare any given set of phenotypes with any other, for example, a patient with a set of candidate variants against specific genotypes. We are also working on better hierarchical navigation of the phenotypes, and better display of the unmatched phenotypes (as these may be of interest for further phenotyping).
The monarch PhenoGrid widget is available for implementation into third party websites. The phenotype similarity data is available via services.
This widget is live on the Monarch website, if you go to a disease page and choose the “compare” tab you can play around with it. Note that fly and worm data are coming next, but aren’t yet in the display.
There are a lot of people who have contributed to this work over many years.
The GENO genotype ontology describes the relationships among the genetic, genomic, and parts of a genotype, and is organism agnostic. Monarch imports phenotypes annotated to any genetic component from multiple resources. Genotypes are decomposed into their atomic parts, and therefore phenotypes can be propagated to each genetic component via inferencing using the GENO model. Orthology links (red dashes above) allow for additional propagation of phenotypes. GVC= Genomic Variant Complement, e.g. the collection of variants VSLC= Variant Single Locus Complement, e.g. the zygosity of a variant at a single locus Effective Genotype- contains all variants, knockdowns, etc.
Since any computational methods rely heavily on the data, what does the available data look like?
The distribution of phenotype information per model genotype is different compared to human disease annotations. For mouse, there’s a much higher representation of metabolic, cardiovascular, blood, and endocrine phenotypes available to compare; For fish, there’s increased nervous, skeletal, head and neck, and cardiovascular, and connective tissue.
(Note that these do not include “normal” phenotypes for either diseases or genotypes.)
the message is, even then, the models tell you stuff the human data can't, because experiments, replication, controls, cutting up brains, etc.
Each model brings something different to the phenotype landscape.
Data from MGI, ZFIN, & HPO, reasoned over with cross-species phenotype ontology https://code.google.com/p/phenotype-ontologies/
Integrating clinical and model organism G2P data for disease discovery
Integrating clinical and model
organism genotype-phenotype data
for improved disease discovery
There are 47,964 variants of
unknown significance in
What are we gonna do about
The Human Phenotype Ontology
Each disease is associated with different phenotype nodes in the graph
Disease or Patient
HPO concepts are not well
represented in other vocabularies
Winnenburg and Bodenreider, ISMB PhenoDay, 2014
Phenotype “Blast”: Which phenotypic
profile is graphically most similar?
Finding the phenotype graph in
The Human Phenotype Ontology
perception of smell
atrophyDeeply set eyes
34571 annotations in
Standardizing Cross-species G2P
Data + Ontologies
SciGraph: A Neo4j-backed ontology store
All species ontologies and G2P data can be
stored in a graph together
Advantages: Semantics + Speed + Flexibility
Propagate provenance and evidence
Using to develop and evaluate GA4GH G2P
Combining genotype and
phenotype data for variant prioritization
Remove off-target and
Variant score from allele
freq and pathogenicity
Phenotype score from phenotypic similarity
PHIVE score to give final candidates
Cross-species phenotypic profile
comparison for disease discovery
U of Pitt
NIH Office of Director: 1R24OD011883
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