Monarch Initiative Poster - Rare Disease Symposium 2015
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B B. If there is not a match, the patient's phenotype is compared to phenotypes associatarby genes in the protein- protein association network. T B. If there is not a match, the patient's phenotype is compared to phenotypes associated with nearby genes in the protein-protein association network. Nicole Vasilevsky, PhD 1 ; Mark Engelstad, MD 1,2 ; Melissa Haendel, PhD 1 1 Ontology Development Group, Oregon Health & Science University, Portland, OR 2 Department of Surgery, Oregon Health & Science University, Portland, OR Acknowledgements The Monarch Initiative is supported by NIH Grant 1 R24 OD011883 and 3 R24 OD011883 03S1, and NIH contracts HHSN268201400093P and HHSN 30220140 99P. The Monarch Initiative A community-based data integration platform that aims to make the most of known disease data and model systems data and reach deeply across the translational divide in support of precision medicine. Make phenotype data computationally tractable Integrate genotype and phenotype data across human and model species Use semantics for inferring relationships between genotypes, phenotypes, and diseases Provide graphical interactive tools to aid interpretation Cross-species phenotyping The Undiagnosed Disease Challenge Genotype + Environment ? Köhler et al. Nucleic Acids Res. 2014 Jan 1;42(1):D966-74. We don’t know the phentoypic consequences of mutation in >60% of the human coding genome. Model systems can be used to bridge this gap. Undiagnosed patient diagnoses Disease What is in the black box? How to prioritize candidate variants? Pathogenicity, frequency, protein interactions, gene expression, gene networks, epigenomics, metabolomics, treatment outcomes…. Ontologies are used to annotate: patients disorders/diseases genotypes genes sequence variants Phenotypic coverage of the human coding genome Exomizer for variant prioritization Exomiser combines variant data, phenotypic data, and interactome data with Mendelian filters to prioritize candidate variants. https://www.sanger.ac.uk/resources/software/exo miser/ Exomiser benchmarking A) Benchmarking using 10,000 simulated WESs injected with a single disease-causing variants (from 1000 Genomes). Percentage of exomes in which true variant was assigned as the best match are shown. Variant (V) score alone used allele frequency from the ESP and pathogenicity data; A. Patient's phenotyptic profiles annotated with HPO terms are compared all phenotypic profiles in human, mice, or zebrafish. A. B. A. B. The Monarch Initiative: Semantic Phenotyping for Disease Diagnosis and Discovery B. If there is not a match, the patient's phenotypic profile is compared to phenotypes associated with nearby genes in the protein-protein association network. www.monarchinitiative.org Human Phenotype Ontology Mammalian Phenotype Ontology 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 abnormal pancreatic beta cell mass abnormal pancreatic beta cell morphology abnormal pancreatic islet morphology abnormal endocrine pancreas morphology abnormal pancreatic beta cell number abnormal pancreatic alpha cell morphology abnormal pancreatic alpha cell differentiation abnormal pancreatic alpha cell number Why do we need another clinical vocabulary? HPO treats human as a phenotypic subject, SNOMED has <30% concepts that are in HPO HPO is being integrated into UMLS Top Candidates phenotypes and variant scores PhenomeCentral imperfect phenotype (iP) had some phenotypes removed, made more generic, or random ones added; Exomiser (E) score used a combination of variant, phenotype, and disease- gene associations. B) Variant prioritization of 11 diagnosed UDP patients based on variant, phenotype, Mendelian filtering, and disease-gene association and in various combinations. Bars show number of variants that were in the list of the top 1, 5, or 10 variants. 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 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
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Transcript of Monarch Initiative Poster - Rare Disease Symposium 2015
B
B. If there is not a match,
the patient's phenotype
is compared to
phenotypes associatarby
genes in the protein-
protein association network.
T
B. If there is not a match,
the patient's phenotype
is compared to
phenotypes associated
with nearby genes in the
protein-protein association network.
Nicole Vasilevsky, PhD1; Mark Engelstad, MD1,2; Melissa Haendel, PhD1
1Ontology Development Group, Oregon Health & Science University, Portland, OR2Department of Surgery, Oregon Health & Science University, Portland, OR
AcknowledgementsThe Monarch Initiative is supported by NIH Grant
1 R24 OD011883 and 3 R24 OD011883 03S1, and NIH
contracts HHSN268201400093P and HHSN 30220140
99P.
The Monarch InitiativeA community-based data integration platform that
aims to make the most of known disease data and
model systems data and reach deeply across the
translational divide in support of precision medicine.
Make phenotype data computationally tractable
Integrate genotype and phenotype data across
human and model species
Use semantics for inferring relationships
between genotypes, phenotypes, and diseases
Provide graphical interactive tools to aid
interpretation
Cross-species phenotypingThe Undiagnosed Disease
ChallengeGenotype
+ Environment
?
Köhler et al. Nucleic Acids Res. 2014 Jan 1;42(1):D966-74.
We don’t know the phentoypic consequences of mutation in >60% of the human coding genome. Model systems can be used to bridge this gap.
Undiagnosed patient diagnoses
Disease
What is in the black box? How to prioritize
candidate variants? Pathogenicity,
frequency, protein interactions, gene
expression, gene networks, epigenomics,
metabolomics, treatment outcomes….
Ontologies are used
to annotate:
patients
disorders/diseases
genotypes
genes
sequence variants
Phenotypic coverage of the
human coding genome
Exomizer for variant prioritization
Exomiser combines variant data, phenotypic data, and interactome data with Mendelianfilters to prioritize candidate variants. https://www.sanger.ac.uk/resources/software/exomiser/
Exomiser benchmarking
A) Benchmarking using 10,000 simulated WESs injected with a single disease-causing variants (from 1000 Genomes). Percentage of exomes in which true variant was assigned as the best match are shown. Variant (V) score alone used allele frequency from the ESP and pathogenicity data;
A. Patient's phenotypticprofiles annotated with HPO terms are compared all phenotypic profiles in human, mice, or zebrafish.
A. B.
A. B.
The Monarch Initiative: Semantic Phenotyping for Disease
Diagnosis and Discovery
B. If there is not a match, the patient's phenotypic profile is compared to phenotypes associated with nearby genes in the protein-protein association network.
www.monarchinitiative.org
Human
Phenotype
Ontology
Mammalian
Phenotype
Ontology
Reduced pancreaticbeta cells
Abnormality ofpancreatic islet
cells
Abnormality of endocrinepancreas physiology
Pancreatic islet cell adenoma
Pancreatic islet celladenoma
Insulinoma
Multiple pancreaticbeta-cell adenomas
Abnormality of exocrinepancreas physiology
abnormalpancreaticbeta cell
mass
abnormalpancreaticbeta cell
morphology
abnormalpancreatic islet
morphology
abnormalendocrinepancreas
morphology
abnormalpancreaticbeta cellnumber
abnormalpancreaticalpha cell
morphology
abnormalpancreaticalpha cell
differentiationabnormal
pancreaticalpha cellnumber
Why do we need another
clinical vocabulary?
HPO treats human as a
phenotypic subject,
SNOMED has <30%
concepts that are in HPO
HPO is being integrated
into UMLS
Top Candidates phenotypes and variant scores PhenomeCentral
imperfect phenotype (iP) had some phenotypes removed, made more generic, or random ones added; Exomiser (E) score used a combination of variant, phenotype, and disease-gene associations. B) Variant prioritization of 11 diagnosed UDP patients based on variant, phenotype, Mendelianfiltering, and disease-gene association and in various combinations. Bars show number of variants that were in the list of the top 1, 5, or 10 variants.
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
phenotypesSms
tm1a(EUCOMM)Wtsi
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
NIPBLMED23
?
CCNC
Contractures of the joints of the
lower limbs
Hypertonicity
CDK8
