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Data Translator: an Open Science Data Platform
for Mechanistic Disease Discovery
Melissa Haendel, PhD
@ontowonka
Prevailing clinical genomic pipelines
leverage only a tiny fraction of the available
data
Prevailing clinical genomic pipelines
leverage only a tiny fraction of the available
data
Under-utilized data 
Loss of discriminatory power
?
More species = more coverage
Number of human protein-coding genes in ExAC DB as per Lek et al. Nature 2016
19,008
9,739
51%
More species = more coverage
19,008
78%
14,779
Number of human protein-coding genes in ExAC DB as per Lek et al. Nature 2016
19,008
9,739
51%
Mungall et al Nucleic Acids Research bit.ly/monarch-nar-2016
More species = more coverage
Even inclusion of just four species boosts phenotypic coverage of genes by 38%
(5189%)
Combined = 89%
19,008
2,195 7,544 7,235 = 16,974
(union of coverage in any species)
Mungall et al Nucleic Acids Research bit.ly/monarch-nar-2016
Polydactyly
Triphalangeal
thumb
Extra thumb
bone
https://radiopaedia.org/cases/triphalangeal-thumb-in-fanconi-anemia
Pajni-Underwood, 2007, http://dev.biologists.org/content/134/12/2359
Different communities use different languages
Challenge: Each data source uses their own
vocabulary/ontology
MP
HP
MGI
HPOA
Challenge: Each data source uses their own
vocabulary/ontology
ZFA
MP
DPO
WPO
HP
OMIA
VT
FYPO
APO
SNOMED
…
…
…
WB
PB
FB
OMIA
MGI
RGD
ZFIN
SGD
IMPC
OMIM
…
QTLdb
HPOA
EHR
Can we help machines understand
phenotypes?
“Triphalangeal
thumb”
Human phenotype
I have absolutely
no idea what
that means
Decomposition of complex concepts allows
interoperability
“Triphalangeal
thumb”
Phalanx of manual
digit
=
Human phenotype PATO
Uberon
Species neutral ontologies, homologous concepts
Autopod
GO
=
duplicated
embryonic skeletal
system
morphogenesis
Decomposition of complex concepts allows
interoperability
“Triphalangeal
thumb”
Phalanx of manual
digit
=
Human phenotype PATO
Uberon
Species neutral ontologies, homologous concepts
Autopod
GO
“Polydactyly”
Mouse phenotype
=
duplicated
embryonic skeletal
system
morphogenesis
Example case solved by Exomiser
Phenotypic
profile
Genes
Heterozygous,
missense mutation
STIM-1
N/A
Heterozygous,
missense mutation
STIM-1
N/A
Stim1Sax/Sax
Ranked STIM-1 variant maximally pathogenic
based on cross-species G2P data,
in the absence of traditional data sources
https://exomiser.github.io/Exomiser/
bit.ly/stim1paper
Example case solved by Exomiser
Phenotypic
profile
Genes
Heterozygous,
missense mutation
STIM-1
N/A
Heterozygous,
missense mutation
STIM-1
N/A
Stim1Sax/Sax
Ranked STIM-1 variant maximally pathogenic
based on cross-species G2P data,
in the absence of traditional data sources
bit.ly/stim1paper
In Genomics England 100K Genomes, of first 1936 diagnosed
patients, 82% are in the top 5 Exomiser hits across a range
of rare diseases and family structures
Harmonizing diseases, phenotypes, anatomy, and genotypes
91% of our 2.2 Million G2P associations require integrating
2 or more data sources
Enabling phenotype comparisons across
diseases and species
Plain language synonyms for computable
phenotypes
Translational applicability for FA
 Tools can support more rapid diagnostics for FA
patients
 Integration of data enables mechanistic discovery
and new candidate gene targets
 Identification of models for FA hypothesis
validation
 Helping patients contribute data and participate
in their ongoing evaluation, care, and science
Acknowledgements
Lawrence Berkeley
Chris Mungall
Suzanna Lewis
Jeremy Nguyen
Seth Carbon
Nicole Washington
Charite
Sebastian Kohler
Garvan
Tudor Groza
Craig McNamara
RTI
Jim Balhoff
Boston Children’s
Ingrid Holm
Catherine Brownstein
John Brownstein
ClinGen
Heidi Rehm
Larry Babb
Harindra Arachchi
OHSU
Matt Brush
Kent Shefchek
Julie McMurry
Tom Conlin
Nicole Vasilevsky
Dan Keith
Maureen Hoatlin
Genomics England/Queen
Mary
Damian Smedley
Jules Jacobson
Tomasz Konopka
Pilar Cacheiro
Jackson Laboratory
Peter Robinson
Leigh Carmody
Hannah Blau
EBI
Helen Parkinson
David Osumi-Sutherland
With special thanks to Julie McMurry for excellent graphic design
Johns Hopkins
Chris Chute
Casey Overby
Ada Hamosh
Mayo
Hongfang Liu
Ravi Komandur
UCSC
David Haussler
Benedict Paten
Mark Deikhans
Scripps
Andrew Su
Ben Good
Chunlei Wu
Gregg Stupp
Sanford Health Imagenetics
Neal Boerkoel
Kayli Rageth
Murat Sincan
www.monarchinitiative.org
Chris Mungall, Peter Robinson, Damian Smedley
Funding:
NIH Office of Director: 1R24OD011883; NIH-UDP: HHSN268201300036C, HHSN268201400093P;
NCINCI/Leidos #15X143, BD2K U54HG007990-S2 (Haussler) & BD2K PA-15-144-U01 (Kesselman)
extra
Layperson-HPO driven phenotyping tool
https://www.pcori.org/research-results/2017/realization-standard-care-rare-
diseases-using-patient-engaged-phenotyping
Genes Environment Phenotypes
VCF PXFGFF
Standard exchange formats exist for genes … but
for phenotypes? Environment?
BED
What does a phenopacket look like?
 Alacrima
 Sleep Apnea
 Microcephaly
phenotype_profile:
- entity: ”patient16"
phenotype:
types:
- id: "HP:0000522"
label: ”Alacrima"
onset:
description: “at birth”
types:
- id: "HP:0003577"
label: "Congenital onset"
evidence:
- types:
- id: "ECO:0000033"
label: ”Traceable Author Statement"
source:
- id: ”PMID:"
 Clinical labs
 Public databases
 Journals
Layperson HPO + Phenopackets
 Dry eyes
 Stops breathing during sleep
 Small head
phenotype_profile:
- entity: “Grace”
phenotype:
types:
- id: "HP:0000522"
label: “Alacrima"
onset:
description: “at birth"
types:
- id: "HP:0003577"
label: "Congenital onset"
evidence:
- types:
- id: “ECO:0000033”
label: “Traceable Author Statement"
source:
- id: “
https://twitter.com/examplepatient/status/1
23456789”
• Patient registries
• Social media

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Data Translator: an Open Science Data Platform for Mechanistic Disease Discovery

  • 1. Data Translator: an Open Science Data Platform for Mechanistic Disease Discovery Melissa Haendel, PhD @ontowonka
  • 2. Prevailing clinical genomic pipelines leverage only a tiny fraction of the available data
  • 3. Prevailing clinical genomic pipelines leverage only a tiny fraction of the available data Under-utilized data  Loss of discriminatory power ?
  • 4. More species = more coverage Number of human protein-coding genes in ExAC DB as per Lek et al. Nature 2016 19,008 9,739 51%
  • 5. More species = more coverage 19,008 78% 14,779 Number of human protein-coding genes in ExAC DB as per Lek et al. Nature 2016 19,008 9,739 51% Mungall et al Nucleic Acids Research bit.ly/monarch-nar-2016
  • 6. More species = more coverage Even inclusion of just four species boosts phenotypic coverage of genes by 38% (5189%) Combined = 89% 19,008 2,195 7,544 7,235 = 16,974 (union of coverage in any species) Mungall et al Nucleic Acids Research bit.ly/monarch-nar-2016
  • 7. Polydactyly Triphalangeal thumb Extra thumb bone https://radiopaedia.org/cases/triphalangeal-thumb-in-fanconi-anemia Pajni-Underwood, 2007, http://dev.biologists.org/content/134/12/2359 Different communities use different languages
  • 8. Challenge: Each data source uses their own vocabulary/ontology MP HP MGI HPOA
  • 9. Challenge: Each data source uses their own vocabulary/ontology ZFA MP DPO WPO HP OMIA VT FYPO APO SNOMED … … … WB PB FB OMIA MGI RGD ZFIN SGD IMPC OMIM … QTLdb HPOA EHR
  • 10. Can we help machines understand phenotypes? “Triphalangeal thumb” Human phenotype I have absolutely no idea what that means
  • 11. Decomposition of complex concepts allows interoperability “Triphalangeal thumb” Phalanx of manual digit = Human phenotype PATO Uberon Species neutral ontologies, homologous concepts Autopod GO = duplicated embryonic skeletal system morphogenesis
  • 12. Decomposition of complex concepts allows interoperability “Triphalangeal thumb” Phalanx of manual digit = Human phenotype PATO Uberon Species neutral ontologies, homologous concepts Autopod GO “Polydactyly” Mouse phenotype = duplicated embryonic skeletal system morphogenesis
  • 13. Example case solved by Exomiser Phenotypic profile Genes Heterozygous, missense mutation STIM-1 N/A Heterozygous, missense mutation STIM-1 N/A Stim1Sax/Sax Ranked STIM-1 variant maximally pathogenic based on cross-species G2P data, in the absence of traditional data sources https://exomiser.github.io/Exomiser/ bit.ly/stim1paper
  • 14. Example case solved by Exomiser Phenotypic profile Genes Heterozygous, missense mutation STIM-1 N/A Heterozygous, missense mutation STIM-1 N/A Stim1Sax/Sax Ranked STIM-1 variant maximally pathogenic based on cross-species G2P data, in the absence of traditional data sources bit.ly/stim1paper In Genomics England 100K Genomes, of first 1936 diagnosed patients, 82% are in the top 5 Exomiser hits across a range of rare diseases and family structures
  • 15.
  • 16. Harmonizing diseases, phenotypes, anatomy, and genotypes 91% of our 2.2 Million G2P associations require integrating 2 or more data sources
  • 17. Enabling phenotype comparisons across diseases and species
  • 18. Plain language synonyms for computable phenotypes
  • 19. Translational applicability for FA  Tools can support more rapid diagnostics for FA patients  Integration of data enables mechanistic discovery and new candidate gene targets  Identification of models for FA hypothesis validation  Helping patients contribute data and participate in their ongoing evaluation, care, and science
  • 20. Acknowledgements Lawrence Berkeley Chris Mungall Suzanna Lewis Jeremy Nguyen Seth Carbon Nicole Washington Charite Sebastian Kohler Garvan Tudor Groza Craig McNamara RTI Jim Balhoff Boston Children’s Ingrid Holm Catherine Brownstein John Brownstein ClinGen Heidi Rehm Larry Babb Harindra Arachchi OHSU Matt Brush Kent Shefchek Julie McMurry Tom Conlin Nicole Vasilevsky Dan Keith Maureen Hoatlin Genomics England/Queen Mary Damian Smedley Jules Jacobson Tomasz Konopka Pilar Cacheiro Jackson Laboratory Peter Robinson Leigh Carmody Hannah Blau EBI Helen Parkinson David Osumi-Sutherland With special thanks to Julie McMurry for excellent graphic design Johns Hopkins Chris Chute Casey Overby Ada Hamosh Mayo Hongfang Liu Ravi Komandur UCSC David Haussler Benedict Paten Mark Deikhans Scripps Andrew Su Ben Good Chunlei Wu Gregg Stupp Sanford Health Imagenetics Neal Boerkoel Kayli Rageth Murat Sincan
  • 21. www.monarchinitiative.org Chris Mungall, Peter Robinson, Damian Smedley Funding: NIH Office of Director: 1R24OD011883; NIH-UDP: HHSN268201300036C, HHSN268201400093P; NCINCI/Leidos #15X143, BD2K U54HG007990-S2 (Haussler) & BD2K PA-15-144-U01 (Kesselman)
  • 22. extra
  • 23. Layperson-HPO driven phenotyping tool https://www.pcori.org/research-results/2017/realization-standard-care-rare- diseases-using-patient-engaged-phenotyping
  • 24. Genes Environment Phenotypes VCF PXFGFF Standard exchange formats exist for genes … but for phenotypes? Environment? BED
  • 25. What does a phenopacket look like?  Alacrima  Sleep Apnea  Microcephaly phenotype_profile: - entity: ”patient16" phenotype: types: - id: "HP:0000522" label: ”Alacrima" onset: description: “at birth” types: - id: "HP:0003577" label: "Congenital onset" evidence: - types: - id: "ECO:0000033" label: ”Traceable Author Statement" source: - id: ”PMID:"  Clinical labs  Public databases  Journals
  • 26. Layperson HPO + Phenopackets  Dry eyes  Stops breathing during sleep  Small head phenotype_profile: - entity: “Grace” phenotype: types: - id: "HP:0000522" label: “Alacrima" onset: description: “at birth" types: - id: "HP:0003577" label: "Congenital onset" evidence: - types: - id: “ECO:0000033” label: “Traceable Author Statement" source: - id: “ https://twitter.com/examplepatient/status/1 23456789” • Patient registries • Social media

Editor's Notes

  1. Geospatial social determinants of health
  2. Geospatial social determinants of health
  3. If clinvar + omim 20  80%
  4. If clinvar + omim 20  80%
  5. If clinvar + omim 20  80%
  6. 2 issues: database integration, vocabulary integration
  7. Multiple databases
  8. Our approach is to try and get the machine to understand the terms so that it can assist us intelligently.
  9. We make things digestible. Complex concepts into simpler parts. We use ontologies that are comparative by design.
  10. This was the novel case we solved. The UDP patient had a number of signs and symptoms including various platelet abnormalities. The same heterozygous, missense mutation was seen in 2 patients and ranked top by Exomiser. It had never been seen in any of the SNP databases and was predicted maximally pathogenic. Finally a mouse curated by MGI involving a heterozygous, missense point mutation introduced by chemical mutagenesis exhibited strikingly similar platelet abnormalities. In thefirst 1936 patients, 82% are in the top 5 Exomiser hits. This is across a whole range of different rare diseases and family structures ie. 34% cases are just simple singletons.
  11. This was the novel case we solved. The UDP patient had a number of signs and symptoms including various platelet abnormalities. The same heterozygous, missense mutation was seen in 2 patients and ranked top by Exomiser. It had never been seen in any of the SNP databases and was predicted maximally pathogenic. Finally a mouse curated by MGI involving a heterozygous, missense point mutation introduced by chemical mutagenesis exhibited strikingly similar platelet abnormalities. In thefirst 1936 patients, 82% are in the top 5 Exomiser hits. This is across a whole range of different rare diseases and family structures ie. 34% cases are just simple singletons.
  12. If we include bridging ontologies, we can unify diseases across sources AND phenotypes across sources and organisms.
  13. If we include bridging ontologies, we can unify diseases across sources AND phenotypes across sources and organisms.
  14. There are a lot of people who have contributed to this work over many years. 
  15. Fully translational – from bench to bedside – group of stakeholders, contributors and partners
  16. The classic G+E=P. But the = has a lot that can be applied to aid the linking.
  17. Phenopackets for clinicians
  18. Phenopackets to assist patient data sharing.