global phenotypic data sharing standards to maximize diagnostic discovery

Global Phenotypic Data Sharing Standards to Maximize Diagnostic Discovery

Melissa Haendel, PhD and Sebastian Köhler, PhD

RD-Action workshopApril 26th and 27th, Brussels

Talk outline

About HPO Semantic similarity Leveraging basic research data Exome analysis and disease discovery HPO-based tools Phenotype data standards for exchange

What do we mean by phenotype?

= Phenotypic abnormality = clinical feature

Constellation/Pattern clinical featuresdefines a disease:– [Disease X]... is a rare developmental disorder defined by

the combination of aplasia cutis congenita of the scalpvertex and terminal transverse limb defects. In addition, vascular anomalies such as cutis marmoratatelangiectatica ... are recurrently seen.

(Yes, this is a simplification)

Starting point: OMIM

Clinical Synopsis (CS) section

Free text phenotypic description Very expressive

Online Mendelian Inheritance in Man database

(Un)Controlled Vocabularies

Not designed to be easily machine interpretable

Spelling problems, acronyms, etc.

Homonyms:

... fibrillation ...

fibrillation ≠ fibrillation

= ventricular fibrillation= muscle fibrillation

Why you should care

OMIM Query Number of Results

large bones 264

large bone 785

enlarged bones 87

enlarged bone 156

big bones 16

huge bones 4

massive bones 28

hyperplastic bones 12

hyperplastic bone 40

bone hyperplasia 134

increased bone growth 612

Motivation

HPO started in 2008

Goal: computer-interpretable clinical features!

Reliable information extraction from databases based on clinicalfeatures

Compute similarity between diseases based on clinical features

Compute similarity between patients based on clinical features

Compute similarity between patients and diseases based on clinicalfeatures

Interoperability with basic research to improve diagnostic discovery

Easy to use

Freely available

The Human Phenotype Ontology

(HPO)

Description of phenotypic abnormalities (or clinical features) in

humans

abnormality of thenervous system

neurofibrillary tangles

cerebral inclusion bodies

gait ataxia

gait disturbance

ataxia

phenotypicabnormality

incoordination

abnormality of movement

abnormality of the central nervous

system

This is a term

CS of OMIM:0815

CS of OMIM:1234

Neurofibrillary tanglesmay be present

Paired helical filaments

The Human Phenotype Ontology (HPO)

Synonyms merged into one term

Textual definitions for each term

id: HP:0002185

name: Neurofibrillary tangles

def: Pathological protein

aggregates formed by

hyperphosphorylation of a

microtubule-associated protein

known as tau, causing it to

aggregate in an insoluble form.

[HPO:sdoelken]

synonym: Neurofibrillary tangles

may be present EXACT []

synonym: Paired helical filaments

EXACT []

abnormality of thenervous system


cerebral inclusion bodies

gait ataxia

gait disturbance

ataxia




system

incoordination

The Human Phenotype Ontology

(HPO)

Semantic relations

(’subclass of’, ‘is a’)

From top to bottom,

terms get more specific

abnormality of the nervous system


cerebral inclusionbodies

gait ataxia

gait disturbance

ataxia




system

is a

is ais a

is a

is a

is a

is ais a

is a

is a

is a

is a

is a

is a incoordination

Computable phenotype definitions of

disease

HPO Terms are used to annotate (describe) diseases

E.g. neurofibrillary tangles is used to annotate Alzheimer Disease:

Orphanet + Monarch:

~124,000 annotations of 7,700 rare diseases from OMIM, Orphanet, DECIPHER

~133,000 annotations of 3,145 common diseases

Köhler et al. https://doi.org/10.1093/nar/gkw1039

OMIM:0815 OMIM:1234

Neurofibrillary tanglesmay be present

Paired helical filaments

https://doi.org/10.1093/nar/gkw1039

Why HPO? Existing clinical vocabularies don’t

adequately cover phenotypic descriptions

Winnenburg and Bodenreider, 2014

0

10

20

30

40

50

60

70

80

90

100

HPO UMLS SNOMED CT CHV MedDRA MeSH NCIT ICD10 OMIM

Perc

ent

cove

rage

LDDB (✓)

Orphanet (✓) (Use HPO directly)

MedDRA (✓)

UMLS (completely incorporated)

Community contribution

Multiple HPO-specific workshopsConstant discussions via Tracker-System and E-Mail

We try our best to acknowledge contributors:

+ microattributions

Contributing to and extending HPO

HPO language translations

We need your help! http://bit.ly/hpo-translations

Translation of labels, synonyms, and text definitions

Italian Spanish Russian French

German English layperson Japanese Chinese

100%11%

12%

100%

19%19%

near 100%

20%

Adoption of HPO

Public facing databases using HPO toannotate patients

Tools ingesting HPO-annotated data:

Köhler et al. https://doi.org/10.1093/nar/gkw1039

https://doi.org/10.1093/nar/gkw1039

Why HPO is a successful standard

One language shared by “all“ Synonyms “map“ to one concept (HPO term) Contains terms that no other ontology has Comes with disease annotations! (Not just “Yet another clinical

terminology“) Simple, qualitative phenotyping, deviation (abnormal, abnormal

increase, abnormal decrease, ...) to ease analysis Documented, traceable editing Open science community project with diverse contributors Constantly improved and extended, examples:

Layperson version for patients Language translations Opposite-relations between terms

Talk outline


A disease can be described

algorithmically as a collection of

phenotypes

Patient

Disease X

Differential diagnosis with matching phenotype concepts is already good

SplenomegalyNasal speech

Increased spleen size Nasal voice

These are synonyms in HPO, i.e. map to the same term

These are synonyms in HPO, i.e. map to the same term

A disease can be described

algorithmically as a collection of

phenotypes

Patient

Disease X

Differential diagnosis with similar but non-matching phenotypes is difficult

Splenomegaly Oral motor hypotonia

Ruptured spleen Decreased muscle mass

Similarity between two terms

Oral motor

hypotonia

Muscular

hypotonia of the

trunk

Abnormal muscle

tone

Oral motor

hypotonia

Abnormality of

calvarial

morphology

Phenotypic

abnormality

High scoring match

Very low scoring match

Medium scoring match

Score: Measured by Information Content

Comparing phenotype profiles

E.g. Patient-to-Disease

comparison

Patient‘s phenotypesmore similar to Disease A

Orphamizer would rank Disease A before DiseaseB

Disease BPatientPatient Disease A

High scoring match

Very low scoring match

Medium scoring match

Score: Measured by Information Content

Orphamizer

Talk outline

About HPO Semantic similarity Leveraging basic research data Exome analysis and disease discovery HPO-based visualization tools Phenotype data standards for exchange

The genome is sequenced, but...

3,398 OMIM

Mendelian Diseases with

no known genetic basis

?

At least 120,000* ClinVar

Variants with no known

pathogenicity

…we still don’t know very much about what it does

*This is > twice what it was in 2016!

Adding other species’ data

helps fill knowledge gaps in human genome

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

Even inclusion of just four species boosts

phenotypic coverage of genes by 38%

(5189%)Combined = 89%

19,008

2,195 7,544 7,235 = 16,974 (union of coverage in any species)

9,739

51%

Mungall et al Nucleic Acids Research bit.ly/monarch-nar-2016

Ulcerated

paws

Palmoplantar

hyperkeratosis

Thick hand skin

Image credits:

"HandsEBS" by James Heilman, MD - Own work. Licensed under CC BY-SA 3.0 via Commons –https://commons.wikimedia.org/wiki/File:HandsEBS.JPG#/media/File:HandsEBS.JPG

http://www.guinealynx.info/pododermatitis.html

Challenge: Each database uses their own

vocabulary/ontology

MP

HP

MGIHPOA

Challenge: Each database uses their own

phenotype vocabulary/ontology

ZFA

MPDPO

WPO

HP

OMIA

VT

FYPOAPO

SNOMED

…NCIT

…

WB

PB

FB

OMIA

MGI

RGD

ZFIN

SGD

HPOA

EHR

IMPCOMIM

…

QTLdb

Can we help machines understand

phenotype terms?

“Palmoplantar hyperkeratosis”

Human phenotype

I have absolutely no idea what that means

Decomposition of complex concepts

using species neutral terms

Mungall, C. J., Gkoutos, G., Smith, C., Haendel, M., Lewis, S., & Ashburner, M. (2010). Integrating phenotype ontologies across multiple species. Genome Biology, 11(1), R2. doi:10.1186/gb-2010-11-1-r2

“Palmoplantar

hyperkeratosis”

increased

Stratum corneumlayer of skin

=

Human phenotypePATO

Uberon

Species neutral ontologies, homologous concepts

Autopod

keratinization

GO

How can anatomy be “species-

neutral”?

HPO Interoperability and

annotations

Hyposmia

Abnormality of

globe location

eyeball of camera-type eye

sensory perception of smell

Abnormal eye

morphology

Motor neuron

atrophyDeeply set eyes

motor neuronCL

34571 annotations in

22 species

157534 phenotype

annotations

2150 phenotype

annotations

11,813phenotype terms

127,125 rare disease -phenotype annotations

136,268common disease -phenotype annotations

http://bit.ly/hpo-paper

Which phenotypic profile is most

similar?

Model X

Patient

Disease Y

Model X

Patient

Disease Ywww.owlsim.org

Fuzzy-phenotype matching

But what about the diseases? How to choose

which ones? What is their provenance?

A dynamic nosology

Challenge: can we rapidly synergize multiple knowledge sources into a dynamic ontology?

classic clinical phenotype-oriented disease classification and molecular sources

Knowledge-based approaches

Logical Definition OWL Ontology Merging

Bayesian OWL Ontology Merging

Data driven

Phenotype and functional ontology networks

Mungall, C. J.,. (2016). k-BOOM: bioRxiv, 048843. doi:10.1101/048843

DOID(blue)

OMIM(brown)

MESH(grey)

ORDO/Orphanet(yellow)

SubClassOf(solid line)

Xref(dashed grey line)

4 disease resourcesplus mappings:Hemolytic anemia

Coherent disease classification =>

Orphanet

https://github.com/monarch-initiative/monarch-disease-ontology

“Ontology” Classes (before, after merge)

SubClass axioms Xrefs

Inputs:

DOID 6878 6012 7082 36656

MESH (D) 11314 4152 19036

OMIM (D) 7783 7783 0 31242

Orphanet (D) 8740 4683 15182 20326

OMIA 4833 4833 3120 355

DC 209 208 310 316

Medic 0 8630 3435

Output:

Merged 39757 27617 44837

https://github.com/monarch-initiative/monarch-disease-ontology

Talk outline


Prevailing clinical genomic pipelines

leverage only a tiny fraction of the available

data

PATIENT EXOME/ GENOME

PATIENT CLINICAL PHENOTYPES

PUBLIC GENOMIC DATA

PUBLIC CLINICAL PHENOTYPE, DISEASE DATA

POSSIBLE DISEASES

DIAGNOSIS & TREATMENT

PATIENT ENVIRONMENTPUBLIC ENVIRONMENT,

DISEASE DATA

PATIENT OMICS PHENOTYPES PUBLIC OMICS PHENOTYPES,CORRELATIONS

Under-utilized data

Phenotypic profile matching

Combining G2P data for variant

prioritization

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

Mendelian filters

Exomiser results for UDP diagnosed

patients

Inclusion of phenotype data improves variant prioritization

In 60% of first 1000 genomes at GEL, Exomiserpredicts top candidateIn 86% of cases, Exomiser predicts within top 5

Example case solved by ExomiserP

he

no

typ

ic

pro

file

Ge

ne

s Heterozygous, missense mutation

STIM-1

N/A

Heterozygous, missense mutation

STIM-1N/A

Stim1Sax/Sax

Ranked STIM-1 variant maximally pathogenic based on cross-species G2P data,

in the absence of traditional data sourceshttp://bit.ly/exomiser

Deep phenotyping and “fuzzy” matching

algorithms improve diagnostics

4.9% exomes with dual molecular diagnoses, differentiated with deep phenotyping

Talk outline


How much phenotyping is enough?

Enlarged ears (2)Dark hair (6) Female (4)Male (4)

Blue skin (1)Pointy ears (1)

Hair absent on head (1)Horns present (1)

Hair present on head (7)

Enlarged lip (2)

Increased skin pigmentation (3)

bit.ly/annotationsufficiency

Phenotype matching visualization

widget

file:///.file/id=6571367.18966428

bit.ly/monarch-nar-2016

Matchmaker Exchange for patients, diseases, and model

organisms to aid diagnosis and mechanistic discovery

www.monarchinitiative.orghttp://bit.ly/Monarch-MME

Goal: Get clinical sites & public databases to provide standardized phenotype data

Talk outline


Genes Environment Phenotypes+ =

Biology central dogma

Standards for exchanging data

must be up to these challenges.

Genes Environment Phenotypes+ =

Computable encodings are essential

Base pairsVariant notation (eg. HGVS)

SNOMED-CTMedical procedure codingEnvironment Ontology

@ontowonka

Genes Environment Phenotypes

VCF PXFGFF

Standard exchange mechanisms exist for

genes … but for phenotypes? Environment?

BED

Introducing PhenoPackets

A packet of phenotype data to be used

anywhere, written by anyone

http://phenopackets.org

http://phenopackets.org/

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

What about patients? Can they phenotype

themselves?

HPO for Patients

http://bit.ly/hpo-biocuration

6,200 plain language terms for patients, families, and non-experts

New software application being developed for patients

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

Journals are now requiring HPO

terms

Robinson, P. N., Mungall, C. J., & Haendel, M. (2015). Capturing phenotypes for precision medicine. Molecular Case Studies, 1(1), a000372. doi:10.1101/mcs.a000372

Each phenopacketcan be shared via DOI

in any repository outside paywall (eg.

Figshare, Zenodo, etc)

Each article can beassociated with a

phenopacket

Community “curate-athons” for of HPO

Cardiovascular curate-athon at Stanford. @20 cardiologists (surgeons, pediatric, etc.), four ontologists, and three clinical curators met for two days.

Abnormal ComplexVoltage to be added to all waves-increased, decreased, fluctuating (alternans)Duration to be added to all waves-increased, decreasedP wave-notching-axisQRS-fractionation-axis (right/left/extreme)Q waveR waveS waveR’ waveS’ wave (abnormal only)J wave (can be normal variant)Epsilon wave (abnormal only)Osborne wave (abnormal only)Terminal slur wave (can be normal variant)Delta wave (abnormal only)

Added 100s of clinically relevant cardiophysiology phenotypes to HPO, new exome analysis possible

Summary

The Human Phenotype Ontology is a robust standard describing phenotypic abnormalities FOR the community, FROM the community for deep phenotyping rare disease patients

Model organism data can fill gaps in our knowledge and aid mechanistic exploration of disease candidates

Tools that leverage the Human Phenotype Ontology can be used to prioritize coding and noncoding variants for WES and WGS and CNVs

Patients can provide self-phenotyping information as partners in the deep phenotyping process

Phenopackets is a FAIR-based GA4GH exchange standard for facilitating distributed phenotype data sharing for clinics, labs, patients, and journals

Acknowledgements

Orphanet

Ana Rath

Annie Olry

Marc Hanauer

Halima Lourghi

Lawrence BerkeleyChris Mungall

Suzanna Lewis

Jeremy Nguyen

Seth Carbon

RENCIJim Balhoff

OHSUMatt Brush

Kent Shefchek

Julie McMurry

Tom Conlin

Nicole Vasilevsky

Dan Keith

Genomics England/Queen Mary

Damian Smedley

Jules Jacobson

Jackson LaboratoryPeter Robinson

Leigh Carmody

With special thanks to Julie McMurry for excellent graphic design

GarvanTudor Groza

Craig McNamara

Hipbi / NeuroCureDominik Seelow

Markus Schülke-Gerstenfeld

ChariteDominik Seelow

Tomasz Zemojtel

www.monarchinitiative.org

Funding: NIH Office of Director: 2R24OD011883; NHGRI UDP: HHSN268201300036C,

HSN268201400093P; NCATS: UDN U01TR001395,

Biomedical Data Translator: 1OT3TR002019; E-RARE 2015: Hipbi-RD 01GM1608

http://www.monarchinitiative.org/

global phenotypic data sharing standards to maximize diagnostic discovery

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