1.
???
Monarch is supported generously by:
a NIH Office of the Director Grant #5R24OD011883 as well as by
NCI/Leidos #15X143, BD2K U54HG007990-S2 (Haussler) &
BD2K PA-15-144-U01 (Kesselman)
info@monarchinitiative.org @monarchinit
The Problem: Human genome is poorly annotated
A better understanding of human gene function and disease
mechanisms is critical for diagnosis, precision medicine, and
targeted therapies
The Approach: Monarch cross-species
G2P Integration Pipeline
Ontologies Data Standards Curation and
Data Modeling
Algorithms Tools
The Solution: Leverage all the species data
Solve the cross-species language divide
www.monarchinitiative.org/sources
Acknowledgements and Contact Info
Palmoplantar
hyperkeratosis
Thick hand skin
Ulcerated
paws
MONARCH TEAM MAINTAINS
MONARCH TEAM CONTRIBUTES
LEGEND
Data source Ontology
Bridging
Ontology
PHENOTYPESDISEASES
MODEL
ORGNISMHUMAN
Community Ontology Term Phenotype
ANATOMY
ClinVar
Coriell
CTD
Elem of Morph
Gene Reviews
GWAS
HPOA
OMIMdb
Orphanet
KEGG
AnimalQTLDB
FlyBase
IMPC
MGI
MPD
OMIA
RGD
WormBase
ZFIN
MeSH
MedGen
OMIM
HP
EFO
ORDO
VT
FBcv
ZP
WP
MP
MONDO
UPheno
MA
ZFA
UBERON
FBbt
WA
CL
EMAPA
MODEL
ORGNISM
HUMAN
PROBLEM
Phenotypic language differs by organism
and also by community, thus impeding
integration
SOLUTION SOLUTION
Monarch integrates the data sources
through bridging ontologies
PROBLEM
SOLUTION
PROBLEM
SOLUTION
SOLUTION
SOLUTION
SOLUTION
SOLUTION
The phenotypes are
associated with very
different aspects of the
genotype in each data
source.
The Challenge: Fragmented, heterogeneous G2P data
Mus
mgdmgd
mmrrcmmrrc
mgimgi
animalqtldbanimalqtldb
Homo
cgdcgd
clinvarclinvar
gwascataloggwascatalog
hpoahpoa
keggkegg
omimomim
orphanetorphanet
coriellcoriell
omiaomia
monarchmonarch-curated
Canis
Macaca
Panthera
Equus
Ovis
Danio
zfinzfin
Gallus
Sula
Vulpes
Anas
Coturnix
Peromyscus
Tragelaphus
other
>100
SPECIES
Bos
Sus
0%
40%
60%
80%
100%
Human
only
Human +
other
20%
The phenotypic consequences of
mutation for the human coding
genome are <20%; inclusion of
orthologs from other species boosts
this number to over 80%
We learn about different
phenotypes from different
species, and want to use
all this data
Improve data quality and interoperability
Evidence and provenance for G2P associations is
incomplete, not computable, and frequently conflated.
This hampers integration and pathogenicity
determination.
Disentangle these concepts, and model data
to make it computable.
PROBLEMS SOLUTIONS
https://mme.monarchinitiative.org
github.com/ga4gh/schemas
Diagnosing rare diseases requires
identifying similar patients and
models Monarch integrated
cross-species data available on pa-
tient matchmaker exchange.
Data models for modeling any bio-
logical database source expecially
G2P sources are highly heterogene-
ous.
Data are insufficiently described to
understand what they are or how
they were produced.
Monarch integrated cross-
species data available on
patient matchmaker exchange
Monarch is contributing GA4GH
Schemas to bridge the heterogeneous
G2P sources
HCLS provides a guide to indicate what
are the essential metadata, and how to
express it. Monarch was a key contributor
toward this community effort and is testing
the model for all sources in its corpus
Compute over diseases, phenotypes, modes
to diagnose diseases
PhenoGrid
http://www.sanger.ac.uk/science/tools/exomiser
http://patientarchive.org/
Exomiser
https://www.npmjs.com/package/phenogrid
Whole exome
Remove off-target
and common
variants
Variant score from allele
freq and pathogenicity
Phenotype score from phenotypic
similarity
PHIVE score to give final candidates
Mendelianfilters
Combine genotype and phenotype data for variant prioritization
Visualize phenotype profile comparisons
Between patients and...
- Other patients
- Known diseases
- Models
Embeddable 3rd party widget for data resources
PhenoTua / Noctua
Uniquely identify a model or disease
Check organism/genotype nomenclature
Choose terms from any phenotype ontology
Provide evidence
Edit collaboratively, group sharing
View in two modalities:
- Ontology smart spreadsheet
- Graphical Causal Networks
HPO Pubmed Browser
Curate causal networks between genes, genotypes,
phenotypes, diseases, using organism-agnostic
standardized owl models
http://create.monarchinitiative.org/
Check Annotation Sufficiency
Automated extraction of Human
Phenotype Ontology concepts from
free text clinical summaries.
Intuitive visualization of patient
phenotype profiles and diagnoses.
Immediate visual feed-back on
phenotype profiles using the
Monarch annotation sufficiency score.
Fine-grained patient sharing access control.
Encrypted patient sensitive data - yet with
the possibility of searching over this data.
Visualize and Browse Relationships
Finding literature relevant to a set of phenotypes
should be easy.
http://pubmed-browser.human-phenotype-ontology.org/
Zemojtel, T. et al. Effective diagnosis of genetic disease by computation-
al phenotype analysis of the disease-associated genome. Science Trans-
lational Medicine Vol. 6, Issue 252, pp. 252ra123 (11 diagnosed fami-
lies)
Pippucci, T. et al. A novel null homozygous mutation confirms CAC-
NA2D2 as a gene mutated in epileptic encephalopathy. PLoS One 8,
e82154 (2013). (1 diagnosed family)
Requena, T. et al. Identification of two novel mutations in FAM136A and
DTNA genes in autosomal-dominant familial Meniereʼs disease. Human
Molecular Genetics. 24, 1119–26 (2015). (2 diagnosed families)
Bone, W. et al. Computational evaluation of exome sequence data using
human and model organism phenotypes improves diagnostic efficiency.
Genetics in Medicine. In press (2015). doi:10.1038/gim.2015.137 (4
diagnosed families)
18PublishedDiagnoses
www.monarchinitiative.org
www.owlsim.org
Patient X
Disease Y Model Z
Make causal relationships computable:
Improve modeling of evidence and provenance
owlsim
http://brcaexchange.org/
Providence Evidence Claim
- Data (eg: images, sequences)
- Evidence codes
- Publications
- Statistical confidence (p-val, z-score)
- Summary figures
- Conclusions from previous studies
- Tacit knowledge of a domain expert
- types of assay/technique/study or
instances thereof
- agent(s) who produced evidence
- agent(s) who asserted the claim
- time and place
- materials (e.g. models systems,
reagents, instruments)
Process history
Key participants in process Outputs of process
http://tinyurl.com/brca-g2p
http://tinyurl.com/acmg-guidelines
- Causal relationships, hypothesized
relationships, coorelations etc.
Fuzzy matching between patients, phenotypes, and diseases
Problem: It is difficult to prioritize candidate genes for
diagnosis, or identifying model that best capitulates a disease
Compute similarity of phenotypic profiles
Graph-based semantic similarity
PROBLEM SOLUTION
Researchers donʼt know when their
phenotyping is sufficient to be useful
beyond their specialized community
Clinicians donʼt know when their phe-
notyping is sufficient for diagnosis
Compare patient or organism phenotypic
profile against all known diesases and
genotypes. Get feedback in real time.
http://tinyurl.com/phenotypesufficiency
https://monarchinitiative.org/page/services
patient
archive
? ? ? ? ?
patient
archive
PROBLEMS SOLUTIONS
Problems with identifier design and provision
result in link rot and content drift therefore com-
promising the flow and integrity of information.
Identifiers must resolve, and when referenced in
the same context must not collide. Prefixes play a
critical role in these two goals; however, due to
confusion and inconsistency about prefixes, a
single identifier can be referenced multiple differ-
ent ways: 12345, MGI:12345, MGI:MGI:12345,
MGI:MGI_12345, thus complicating determina-
tions of equivalence and data integration.
Moreover prefixes used in the same context can
conflict (eg. GEO).
Monarch is a key contributor to
identifier standards for big data
integration
10 Simple Rules for Design and
Provision of Life Science Database
Identifiers for the Web
Monarch is leading a community
effort to coordinate prefixes
between the eight active prefix
registries
JDDCP
prefix commons
zenodo.org/record/31765
github.com/prefixcommons
health care &
life sciences
w3.org/TR/hcls-dataset/
MENDELIAN DISEASES
3,462
OMIM ?
47,964
VARIANTS
CLINVAR
with no known genetic basis with no known diseases
1 Oregon Health & Sciences University; Portland, OR • 2 Lawrence Berkeley National Lab, Berkeley, CA • 3 University of Pittsburgh, Pittsburgh, PA • 4 University of California San Diego, San Diego, CA • 5 Garvan Institute, Sydney, Australia • 6 Sanger Center, Hinxton, UK • 7 Charite
From Model Mechanism to Precision Medicine:
an Open Science Integrated Genotype-Phenotype Platform
Nicole Vasilevsky1, Nicole Washington2, Chuck Borromeo3, Matthew Brush1, Seth Carbon2, Michael Davis3, Nathan Dunn2, Mark Englestad1, Jeremy Espino3, Shahim Essaid1, Jeffrey Grethe4, Tudor Groza5, Harry Hochheiser3, Sebastian Köhler6, Suzanna Lewis2,
Julie McMurry1, Craig McNamara5, Chris Mungall2, Jeremy Nguyen Xuan2, Peter Robinson7, Kent Shefchek1, Damian Smedley6, Zhou Yuan3, Edwin Zhang5, Melissa Haendel1,
Human Disease:
HADZISELIMOVIC
SYNDROME
mouse model:
b2b1035Clo
(aka Blue Meanie)
tricuspid
valve atresia
MP:0006123
prenatal growth
retardation
MP:0010865
persistent truncus
arteriosis
MP:0002633
cleft palate
MP:0000111
1
Ventricular
hypertrophy
HP:0001714
High-arched
palate
HP:0000156
Failure to thrive
HP:0001508
Pulmonary
artery atresia
HP:0004935
Renal
hypoplasia
HP:0000089
abnormal
kidney
morphology
abnormal
palate
morphology
growth
deficiency
Malformation
of the heart
and great
vessels
abnormal
heart and
great artery
attachment
duplex kidney
MP:0004017
common
(UPheno)