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Use of semantic phenotyping to aid disease diagnosis
Melissa HaendelJuly 10th, 2014
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Outline
Semantic Diagnosis of known diseases
Semantic similarity across species
Combining Exome analysis with cross-species semantic phenotyping
How much phenotyping is enough?
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The undiagnosed patient
Known disorders not recognized during prior evaluations?
Atypical presentation of known disorders?
Combinations of several disorders? Novel, unreported disorder?
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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
Searching for phenotypes usingtext alone is insufficient
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The Challenge: Interpretation of Disease Candidates
?
What’s in the box? How are
candidates identified?
How do they compare?
Prioritized Candidates, Models, functional validation
M1
M2
M3
M4
...
Phenotypes
P1
P2
P3
…
Genotype info
G1
G2
G3
G4
…
Pathogenicity, frequency, protein interactions, gene expression, gene networks, epigenomics, metabolomics….
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What is an ontology?A set of logically defined, inter-related terms used to annotate data
Use of common or logically related terms across databases enables integration
Relationships between terms allow annotations to be grouped in scientifically meaningful ways
Reasoning software enables computation of inferred knowledge
Groups of annotations can be compared using semantic similarity algorithms
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Human Phenotype Ontology10,158 terms used to annotate:• Patients• Disorders• Genotypes• Genes• Sequence variantsIn human
Köhler et al. The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data. Nucleic Acids Res. 2014 Jan 1;42(1):D966-74.
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A human phenotype example
Abnormality of the eye
Vitreous hemorrhage
Abnormal eye morphology
Abnormality of the cardiovascular system
Abnormal eye physiology
Hemorrhage of the eye
Internal hemorrhage
Abnormality of the globe
Abnormality of blood circulation
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➔Phenotype annotations are unevenly distributed across different anatomical systems
Survey of Annotations in Disease Corpus
7,401 diseases99,045 annotations
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exome analysis
Recessive, De novo filters
Remove off-target, common variants, and variants not in known disease causing genes
Zemojtelet al., manuscript in presshttp://compbio.charite.de/PhenIX/
Target panel of 2,742 known Mendelian disease genes
Compare phenotype profiles using data from:HGMD, Clinvar, OMIM, Orphanet
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PhenIX performance testing
Simulated datasets for a given disease and inheritance model created by spiking DAG panel generated VCF file with mutations from HGMD
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PhenIX helped diagnose 11/38 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)
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What to do when we can’t diagnose with a known disease?
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Outline
Semantic Diagnosis of known diseases
Semantic similarity across species
Combining Exome analysis with cross-species semantic phenotyping
How much phenotyping is enough?
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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 of disease
?
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How much phenotype data?• Human genes have poor phenotype coverage
GWAS+
ClinVar +
OMIM
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How much phenotype data?• Human genes have poor phenotype coverage
• What else can we leverage?
GWAS+
ClinVar +
OMIM
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How much phenotype data?• Human genes have poor phenotype coverage
• What else can we leverage? …animal models
Orthology via PANTHER v9
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How much phenotype data?• Combined, human and model phenotypes can be
linked to >75% human genes.
Orthology via PANTHER v9
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Monarch phenotype data
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 to date
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 3,215 100,523 445,241 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
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Survey of Annotations Disease/Model Corpus
Data from MGI, ZFIN, & HPO, reasoned over with cross-species phenotype ontology https://code.google.com/p/phenotype-ontologies/
➔Models have a different phenotype distribution
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Multiple ways to compare disease to models
Asserted models Inferred by orthology Inferred by gene enrichment Inferred by phenotypic similarity
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Models based on phenotypic similarity
Washington, N. L., Haendel, M. A., Mungall, C. J., Ashburner, M., Westerfield, M., & Lewis, S. E. (2009). Linking Human Diseases to Animal Models Using Ontology-Based Phenotype Annotation. PLoS Biol, 7(11). doi:10.1371/journal.pbio.1000247
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Problem: Clinical and model phenotypes are described differently
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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
lungalveolus
organ system
respiratory system
Lower respiratory
tract
alveolar sac
pulmonary acinus
organ system
respiratory system
Human development
lung
lung bud
respiratory primordium
pharyngeal region
Another Problem: Data silos
develops_frompart_of
is_a (SubClassOf)
surrounded_by
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Solution: bridging semantics
Mungall, C. J., Torniai, C., Gkoutos, G. V., Lewis, S. E., & Haendel, M. A. (2012). Uberon, an integrative multi-species anatomy ontology. 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_frompart_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
Haendel, M. A. et al. (2014). Unification of multi-species vertebrate anatomy ontologies for comparative biology in Uberon. Journal of Biomedical Semantics 2014, 5:21. doi:10.1186/2041-1480-5-21
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Modular phenotype description
Entity (Anatomy, Spatial, Gene Ontology)BSPO: anterior region part_of ZFA:head
ZFA:heartZFA:ventral mandibular arch
GO:swim bladder inflation
Quality (PATO)Small sizeEdematousThickArrested
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Mammalian Phenotype Ontology
Smith et al. (2005). The Mammalian Phenotype Ontology as a tool for annotating, analyzing and comparing phenotypic information. Genome Biol, 6(1). doi:10.1186/gb-2004-6-1-r7
10,097 terms used to annotate and query:• Genotypes• Alleles• GenesIn mice
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Phenotype representation requires more than “phenotype ontologies”
glucose metabolism (GO:00060
06)
Gene/protein function
data
glucose(CHEBI:17
234)
Metabolomics,
toxicogenomics
data
Disease & phenotyp
e data
type II diabetes mellitus
(DOID:9352)
pyruvate(CHEBI:15
361)
Disease Gene Ontology Chemical
pancreatic beta cell
(CL:0000169)
transcriptomic data
Cell
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Uberpheno – building a cross-species semantic framework
Köhler et al. (2014) Construction and accessibility of a cross-species phenotype ontology along with gene annotations for biomedical research F1000Research 2014, 2:30
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Uberpheno construction
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Uberpheno construction
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Uberpheno construction
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Uberpheno construction
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OWLsim: Phenotype similarity across patients or organisms
https://code.google.com/p/owltools/wiki/OwlSim
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Visualizing phenotypic similarity
➔Each model recapitulates some of the disease phenotypes
Holoprosencephaly I (unknown gene, mapped to 21q22.3)compared to most similar mouse models
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Models of disease based on phenotypic similarity
Holoprosencephaly I (unknown gene, mapped to 21q22.3)compared to most similar mouse models
➔The ontologies enable comparison across species
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Outline
Semantic Diagnosis of known diseases
Semantic similarity across species
Combining Exome analysis with cross-species semantic phenotyping
How much phenotyping is enough?
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https://www.sanger.ac.uk/resources/databases/exomiser/query/exomiser2
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Exomiser results for the Undiagnosed Disease Program 11 previously diagnosed families Exomiser 2.0 identified the causative variants with a rank of at least 7/408 potential variants
23 families without identified disorders
We have now prioritized variants in STIM1, ATP13A2, PANK2, and CSF1R in 5 different families (2 STIM1 families)
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Exomiser performance on solved UDP cases
Exo Variant Exo Pheno Exo Exo no Mendelian Exo Novel0
1
2
3
4
5
6
7
8
9
10
11
top10top 5top candidate
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UDP_2731 candidatesChromosome Position Reference Allele Variant Allele GENE Phenotype score Variant Score Exomiser ScorechrX 19554576T C SH3KBP1 0.5051473 0.995576 0.7503617chr2 179658310T C TTN 0.64627105 0.79311335 0.71969223chr2 179632598C T TTN 0.64627105 0.79311335 0.71969223chr2 179567340G A TTN 0.64627105 0.79311335 0.71969223chr2 179553542G T TTN 0.64627105 0.79311335 0.71969223chr2 179549131C T TTN 0.64627105 0.79311335 0.71969223chr18 67836115G T RTTN 0.7629328 0.25979215 0.51136243chr18 67721492G C RTTN 0.7629328 0.25979215 0.51136243chr18 67673764T C RTTN 0.7629328 0.25979215 0.51136243
chrX 140993905-
GCTCCTTCTCCTCCACTTTATTGAGTATTTTCCAGAGTTCCCCTGAGAGAAGTCAGAGAACTTCTGAGGGTTTTGCACAGTCTCCTCTCCAGATTCCTGTGAGCT MAGEC1 0.5416666 0.85 0.6958333
chr6 30858858G A DDR1 0.37619072 1 0.68809533
chr3 129308149AGCCTCCCACCCCCACCCCCTCCCCACATCCCCAACCATACCTACCTTGAGA - PLXND1 0.34432834 0.95 0.64716417
chr5 37245866G A C5orf42 0.7855199 0.5 0.6427599chr5 37169169T C C5orf42 0.7855199 0.5 0.6427599chr6 42946264G A PEX6 0.7187602 0.5 0.6093801chr6 42931861G A PEX6 0.7187602 0.5 0.6093801chrX 53113897G C TSPYL2 0.59999996 0.4906897 0.5453448chr13 75911097T C TBC1D4 0.23643239 0.7895149 0.51297367chr13 75900510G A TBC1D4 0.23643239 0.7895149 0.51297367chr13 75861174- A TBC1D4 0.23643239 0.7895149 0.51297367chr18 67836115G T RTTN 0.7629328 0.25979215 0.51136243chr18 67721492G C RTTN 0.7629328 0.25979215 0.51136243chr18 67673764T C RTTN 0.7629328 0.25979215 0.51136243
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UDP_2731
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What if there aren’t any similar diseases or models?
UDP_1166
➔ Exomiser can utilize phenotypic similarity via the interactome
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Outline
Semantic Diagnosis of known diseases
Semantic similarity across species
Combining Exome analysis with cross-species semantic phenotyping
How much phenotyping is enough?
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How does the clinician know they’ve provided enough phenotyping?
How many annotations…? How many different categories? How many within each?
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Method Create a variety of “derived” diseases that
are less-specific
Assess the change in similarity between the derived disease and it’s parent.
Ask questions: Is the derived disease still considered
similar to the original disease? …or more similar to a different disease? Is it distinguishable beyond random?
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Image credit: Viljoen and Beighton, J Med Genet. 1992
Example: Schwartz-jampel Syndrome, Type I
Rare disease
Caused by Hspg2 mutation, a proteoglycan
~100 phenotype annotations
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Example: Schwartz-jampel Syndrome, Type Ito test influence of a single phenotypic category
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Schwartz-jampel Syndrome derivationsto test influence of a single phenotypic category
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Schwartz-jampel Syndrome derivations
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Example: Schwartz-jampel Syndrome, Type I
*
**
➔ When averaged over all diseases, the absence of a single phenotypic category has far less impact when there’s more breadth in annotations
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How much phenotyping is enough?
• How many annotations…? • How many different categories?• How many within each?
As much as is
reasonable
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Annotation Sufficiency Score• Measurement of breadth and depth of an
phenotype profile
• Uses human disease, mouse and fish* gene phenotype profiles to seed the individual phenotype scores
• Custom queries available via REST services• http://monarchinitiative.org/page/services
*soon to add more species
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Annotation Sufficiency Score
http://www.phenotips.orghttp://www.monarchinitiative.org
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Conclusions Semantic representation of patient
phenotypes can aid disease diagnosis
There exists a lot of phenotype data in model organisms that is complementary to known human data
Ontological integration and use of cross-species inferencing can aid prioritization of variants
The entire cross-species corpus can be utilized to support quality assurance processes for phenotype data capture
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NIH-UDPWilliam BoneMurat SincanDavid AdamsAmanda LinksDavid DraperJoie DavisNeal BoerkoelCyndi TifftBill Gahl
OHSUNicole VasileskyMatt BrushBryan LarawayShahim Essaid
Lawrence BerkeleyNicole WashingtonSuzanna LewisChris Mungall
UCSDAmarnath GuptaJeff GretheAnita BandrowskiMaryann Martone
U of PittChuck BoromeoJeremy EspinoBecky BoesHarry Hochheiser
AcknowledgmentsSanger
Anika OehlrichJules JacobsonDamian Smedley
TorontoMarta GirdeaSergiu DumitriuHeather TrangMike Brudno
JAXCynthia Smith
CharitéSebastian KohlerSandra DoelkenSebastian BauerPeter Robinson
Funding:NIH Office of Director: 1R24OD011883NIH-UDP: HHSN268201300036C
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Candidate gene prioritization
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Survey of Annotations in Disease Corpus*
➔Most diseases impact >1 system
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PhenoViz: Integrate all human, mouse, and fish data to understand CNVs
Desktop application for differential diagnostics in CNVs
Explain manifestations of CNV diseases based on genes contained in CNV
E.g., Supravalcular aortic stenosis in Williams syndrome can be explained by haploinsufficiency for elastin Double the number of explanations using model data
Doelken, Köhler, et al. (2013) Dis Model Mech 6:358-72