Presented at AMIA TBI CRI 2018.
Rare disease patients are expert in their medical history and these patients not only are some of the most engaged, but also they can themselves provision data for use in clinical evaluation. We therefore created a lay-person version of our clinical deep phenotyping instrument, the Human Phenotype Ontology. Here, we evaluate the diagnostic utility of this lay-HPO, and debut a new software tool for patient-led deep phenotyping.
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Patient-led deep phenotyping using a lay-friendly version of the Human Phenotype Ontology
1. Patient-led deep phenotyping using a lay-friendly
version of the Human Phenotype Ontology
Melissa Haendel@ontowonka
@monarchinit
2. The Human Phenotype Ontology
11,813
phenotype
terms
127,125 rare
disease -
phenotype
annotations
136,268
common
disease -
phenotype
annotations
bit.ly/hpo-paper
Peter Robinson, Sebastian Koehler, Chris Mungall
3. Exomiser: Free, secure, and better than ever
Validated for
the most
difficult
diagnoses; top
candidate
correct in 67%
of cases and
executes in
under 1 minute.
Exomiser
V 10.0
March 2018
bit.ly/exomiser-10
5. 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
8. Layperson disease coverage
4,555 terms in the HPO are annotated with at least
one lay person synonym (35.4% coverage)
7,607 number of lay person synonyms total
60% of all disease annotations (73,932 of 122,120)
are referring to HPO terms with lay translations
11. Groups
Analysis
How well can we describe a disease using plain language
synonyms only?
• 4555 HPO terms with a lay person synonym
• 216 terms in Genome Connect Survey
Analysis
• Semantic similarity to 7667 rare diseases
• What is the drop in annotation sufficiency?
• What diseases are enriched?
13. Similarity of Lay person and GC Subsets to Gold
Standard Annotations
0
500
1000
1500
2000
2500
3000
3500
4000 Layperson
Genome
Connect
NumberofDiseases
Phenotypic similarity range
(in deciles)
6944
402
321
HP outperforms GC
HP and GC equivalent
GC outperforms HP
HPO layperson subset
outperforms the Genome
Connect subset in more than 95%
of diseases.
Why the gaps?
1) existing HPO terms that should be layperson eligible (like
schizophrenia) but are not designated in this way yet
2) GC terms which aren't really layperson or borderline, such as
"abnormal EEG”
3) GC phenotypic descriptions that don’t distill into short lay-friendly
labels
Number of diseases by
performance category
14. Disease Enrichment
Analysis of Terms with Lay
Synonyms
2.35E-36 rare developmental defect during embryogenesis
1.29E-35 disorder of development or morphogenesis
2.84E-35 rare genetic developmental defect during embryogenesis
1.43E-25 bone disease
9.06E-25 connective tissue disease
4.47E-20 multiple congenital anomalies/dysmorphic syndrome
4.14E-19 dysostosis
1.72E-18 musculoskeletal system disease
4.09E-18 skeletal system disease
6.11E-18 congenital limb malformation
*2,917 of 4,555 phenotypes with direct
Disease annotations
16. Matchmaker Exchange:
for patients, diseases, and model organisms
Patients can assist in matching themselves to other n-of-1 patients globally
bit.ly/mme-matchbox
patientarchive.org
bit.ly/exomiser-2017
bit.ly/exomiser-10
17. Conclusions
For over 95% of the 7,000+ diseases, layperson
phenotype subsets were more diagnostically
useful than Genome Connect phenotype subsets
Patients are a key source of phenotypic information
and both patients and clinicians need to participate
(we think!) to maximize diagnostic capabilities
Some diseases have low coverage and require
diagnosis by a physician. (eg. glutathione synthetase
deficiency, beta-thalassemia, epithelial basement
membrane dystrophy)
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Next steps: Test with a disease search/classifier
o How confident are we in the diagnosis?
o Is the disease the top hit
o What is the confidence of the follow up hits
o Add noise
18. www.monarchinitiative.org
Funding:
PCORI: PCORI ME-1511-33184
NIH Office of Director: 2R24OD011883
Nicole Vasilevsky
Kent Shefchek
Erin Foster
Mark Englestad
Peter Robinson
Sebastian Koeller
Chris Mungall
Jim Balhoff
Ingrid Holm
Catherine Brownstein
Kayli Rageth
Julie McMurry
Of 250 very difficult-to-diagnose rare disease cases, Exomiser, when compared to manual diagnosis, correctly identified the top candidate in 67% of cases; that figure rises to 81% if it extends to the top 5 candidates.
not same variant, but same disease and same gene KMT2A
http://stm.sciencemag.org/content/scitransmed/suppl/2014/08/29/6.252.252ra123.DC1/6-252ra123_SM.pdf (paywalled) DOI: 10.1126/scitranslmed.3009262
Knowing what the normal distribution and clustering of phenotypes is helps us know that blue skin is rare and can reliably distinguish between phenotype profiles. Likewise to know that if the first phenotype entered is enlarged lip, the next one to ask for would be enlarged ears. The combination of 3 non-unique phenotypes offers a perfect match.
A) Coverage of HPO terms with plain language synonyms (terms broken down by anatomical system). B) A physician and a patient describe a patient’s phenotype profile in different ways but with the same meaning. This constellation of diverse phenotypes is common in Marfan Syndrome; each has a plain-language equivalent. C) A sub-branch of eye phenotypes within the HPO. Terms are structured rigorously, not only in terms of hierarchy (as shown) but also in terms of logical definitions (not shown).
7667
Chart Title:
Y axis: Number of Diseases
X axis: Phenotypic similarity range (in deciles)
This shows that the full set of layperson terms had greater phenotypic overlap for more diseases than did the GC-covered terms; this suggests that significantly more diseases may be diagnosable with the full complement of lay synonyms as compared with using the GC survey alone.
Fully translational – from bench to bedside – group of stakeholders, contributors and partners
