Monarch Initiative: Deep Phenotyping for

Improved Diagnostics and Analysis

Melissa Haendel, PhD@ontowonka

haendel@ohsu.edu@monarchinit

Prevailing clinical diagnostic pipelines

leverage only a tiny fraction of the available

data

Under-utilized data Loss of discriminatory power

?

Can we help machines understand

phenotypes?

“Palmoplantar hyperkeratosis”

Human phenotype

I have absolutely no idea what that means

Ulcerated

paws

Palmoplantar

hyperkeratosis

Thick hand skin

"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.JPGhttp://www.guinealynx.info/pododermatitis.html

Different communities use different languages

The Human Phenotype Ontology

13,156phenotype terms

143,759 annotations for 7321 monogenic diseases

132,006 annotations for 3145 common diseases

bit.ly/hpo-paper

Peter Robinson, Sebastian Koehler, Chris Mungall

Defining disease and clinical pathogenicity:

A lumping and splitting problem

source IDs

split/merge

manage resolution &provenance

MONDO Unified Disease Ontology

SEPIOScientific Evidence and

Provenance Information

One disease or two? What does the evidence favor?

One disease or two? How do we manage identifiers, hierarchy?

http://bit.ly/Monarch-Disease

More species = more knowledge

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

“Fuzzy” phenotypic profile matching

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 sourceshttps://exomiser.github.io/Exomiser/

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

IMPC: Disease discovery from 3,328 gene

knockouts

Meehan et al, 2017, Nature Genetics, doi:10.1038/ng.3901

135 new candidate genes for Mendelian disorders

New model for Diamond–Blackfananemia • Phenotype profile similarity:

increased mean corpuscular hemoglobin and decreased erythrocyte cell numbers

• Differential expressionMay account for 46% of people with Diamond–Blackfan anemia with unknown genetic causes

Lay-person HPO for patient use

Layperson-HPO driven phenotyping tool

https://www.pcori.org/research-results/2017/realization-standard-care-rare-diseases-using-patient-engaged-phenotyping

Catherine Brownstein, Ingrid Holm

Matchmaker Exchange for patients, diseases, and model

organisms to aid diagnosis and mechanistic discovery

Computational matching of rare disease patients and model organisms across clinical & public sources

bit.ly/mme-matchboxpatientarchive.orgbit.ly/exomiser-2017

www.monarchinitiative.orgPIs: Melissa Haendel (OHSU), Chris Mungall (LBNL), Peter Robinson (JAX),

Damian Smedley (GEL), Tudor Groza (Garvan), David Osumi-Sutherland (EBI)

Funding:

NIH Office of Director: 1R24OD011883; NIH-UDP: HHSN268201300036C, HHSN268201400093P; NCINCI/Leidos #15X143)