Presented at Harvard CBMI to the Undiagnosed Disease Network 7.10.14

1. Use of semantic phenotyping to
aid disease diagnosis
Melissa Haendel
July 10th, 2014

2. Outline
 Semantic Diagnosis of known diseases
 Semantic similarity across species
 Combining Exome analysis with cross-
species semantic phenotyping
 How much phenotyping is enough?

3. The undiagnosed patient
 Known disorders not recognized during
prior evaluations?
 Atypical presentation of known
disorders?
 Combinations of several disorders?
 Novel, unreported disorder?

4. OMIM Query # Records
“large bone” 785
“enlarged bone” 156
“big bone” 16
“huge bones” 4
“massive bones” 28
“hyperplastic bones” 12
“hyperplastic bone” 40
“bone hyperplasia” 134
“increased bone growth” 612
Searching for phenotypes using
text alone is insufficient

5. The Challenge: Interpretation of
Disease Candidates
?
 What’s in the box?
 How are
candidates
identified?
 How do they
compare?
Prioritized
Candidates, Models,
functional validation
M1
M2
M3
M4
...
Phenotypes
P1
P2
P3
…
Genotype info
G1
G2
G3
G4
…
Pathogenicity, frequency,
protein interactions, gene
expression, gene
networks, epigenomics,
metabolomics….

6. What is an ontology?
A set of logically defined, inter-related terms
used to annotate data
Use of common or logically related terms across
databases enables integration
Relationships between terms allow annotations to
be grouped in scientifically meaningful ways
Reasoning software enables computation of inferred
knowledge
Groups of annotations can be compared using
semantic similarity algorithms

7. Human Phenotype Ontology
10,158 terms used to
annotate:
• Patients
• Disorders
• Genotypes
• Genes
• Sequence variants
In human
Reduced pancreatic
beta cells
Abnormality of
pancreatic islet
cells
Abnormality of endocrine
pancreas physiology
Pancreatic islet
cell adenoma
Pancreatic islet cell
adenoma
Insulinoma
Multiple pancreatic
beta-cell adenomas
Abnormality of exocrine
pancreas physiology
Köhler et al. The Human Phenotype Ontology project: linking molecular biology and
disease through phenotype data. Nucleic Acids Res. 2014 Jan 1;42(1):D966-74.

8. A human phenotype example
Abnormality
of the eye
Vitreous
hemorrhage
Abnormal
eye
morphology
Abnormality of the
cardiovascular system
Abnormal
eye
physiology
Hemorrhage
of the eye
Internal
hemorrhage
Abnormality
of the globe
Abnormality of
blood circulation

9. ➔Phenotype annotations are unevenly
distributed across different anatomical systems
Survey of Annotations in Disease Corpus
7,401 diseases
99,045 annotations

10. exome analysis
Recessive, De novo filters
Remove off-target, common variants,
and variants not in known disease
causing genes
Zemojtelet al., manuscript in presshttp://compbio.charite.de/PhenIX/
Target panel of 2,742 known
Mendelian disease genes
Compare
phenotype
profiles using
data from:
HGMD, Clinvar,
OMIM, Orphanet

11. PhenIX performance testing
Simulated datasets for a given disease and inheritance model created by spiking
DAG panel generated VCF file with mutations from HGMD

12. PhenIX helped diagnose 11/38 patients
global developmental delay (HP:0001263)
delayed speech and language development (HP:0000750)
motor delay (HP:0001270)
proportionate short stature (HP:0003508)
microcephaly (HP:0000252)
feeding difficulties (HP:0011968)
congenital megaloureter(HP:0008676)
cone-shaped epiphysis of the phalanges of the hand (HP:0010230)
sacral dimple (HP:0000960)
hyperpigmentated/hypopigmentated macules (HP:0007441)
hypertelorism (HP:0000316)
abnormality of the midface (HP:0000309)
flat nose (HP:0000457)
thick lower lip vermilion (HP:0000179)
thick upper lip vermilion (HP:0000215)
full cheeks (HP:0000293)
short neck (HP:0000470)

13. What to do when we can’t
diagnose with a known
disease?

14. Outline
 Semantic Diagnosis of known diseases
 Semantic similarity across species
 Combining Exome analysis with cross-
species semantic phenotyping
 How much phenotyping is enough?

15. B6.Cg-Alms1foz/fox/J
increased weight,
adipose tissue volume,
glucose homeostasis altered
ALSM1(NM_015120.4)
[c.10775delC] + [-]
GENOTYPE
PHENOTYPE
obesity,
diabetes mellitus,
insulin resistance
increased food intake,
hyperglycemia,
insulin resistance
kcnj11c14/c14; insrt143/+(AB)
Models recapitulate various
phenotypic aspects of
disease
?

16. How much phenotype data?
• Human genes have poor phenotype coverage
GWAS
+
ClinVar
+
OMIM

17. How much phenotype data?
• Human genes have poor phenotype coverage
• What else can we leverage?
GWAS
+
ClinVar
+
OMIM

18. How much phenotype data?
• Human genes have poor phenotype coverage
• What else can we leverage? …animal models
Orthology via PANTHER v9

19. How much phenotype data?
• Combined, human and model phenotypes can be linked to
>75% human genes.
Orthology via PANTHER v9

20. Monarch phenotype data
Also in the system: Rat; IMPC; GO annotations; Coriell cell lines; OMIA; MPD;
Yeast; CTD; GWAS; Panther, Homologene orthologs; BioGrid interactions;
Drugbank; AutDB; Allen Brain …157 sources to date
Coming soon: Animal QTLs for pig, cattle, chicken, sheep, trout, dog, horse
Species Data source Genes Genotypes Variants Phenotype
annotations
Diseases
mouse MGI 13,433 59,087 34,895 271,621
fish ZFIN 7,612 25,588 17,244 81,406
fly Flybase 27,951 91,096 108,348 267,900
worm Wormbase 23,379 15,796 10,944 543,874
human HPOA 112,602 7,401
human OMIM 2,970 4,437 3,651
human ClinVar 3,215 100,523 445,241 4,056
human KEGG 2,509 3,927 1,159
human ORPHANET 3,113 5,690 3,064
human CTD 7,414 23,320 4,912

21. Survey of Annotations Disease/Model Corpus
Data from MGI, ZFIN, & HPO, reasoned over with cross-species phenotype ontology
https://code.google.com/p/phenotype-ontologies/
➔Models have a different phenotype distribution

22. Multiple ways to compare disease
to models
 Asserted models
 Inferred by orthology
 Inferred by gene enrichment
 Inferred by phenotypic similarity

23. Models based on phenotypic
similarity
Washington, N. L., Haendel, M. A., Mungall, C. J., Ashburner, M., Westerfield, M., & Lewis, S. E. (2009).
Linking Human Diseases to Animal Models Using Ontology-Based Phenotype Annotation. PLoS Biol,
7(11). doi:10.1371/journal.pbio.1000247

24. Problem: Clinical and model
phenotypes are described differently

25. lung
lung
lobular organ
parenchymatous
organ
solid organ
pleural sac
thoracic
cavity organ
thoracic
cavity
abnormal lung
morphology
abnormal respiratory
system morphology
Mammalian Phenotype
Mouse Anatomy
FMA
abnormal pulmonary
acinus morphology
abnormal pulmonary
alveolus morphology
lung
alveolus
organ system
respiratory
system
Lower
respiratory
tract
alveolar sac
pulmonary
acinus
organ system
respiratory
system
Human development
lung
lung bud
respiratory
primordium
pharyngeal region
Another Problem: Data silos
develops_from
part_of
is_a (SubClassOf)
surrounded_by

26. Solution: bridging semantics
Mungall, C. J., Torniai, C., Gkoutos, G. V., Lewis, S. E., & Haendel, M. A. (2012). Uberon, an integrative
multi-species anatomy ontology. Genome Biology, 13(1), R5. doi:10.1186/gb-2012-13-1-r5
anatomical
structure
endoderm of
forgut
lung bud
lung
respiration organ
organ
foregut
alveolus
alveolus of lung
organ part
FMA:lung
MA:lung
endoderm
GO: respiratory
gaseous exchange
MA:lung
alveolus
FMA:
pulmonary
alveolus
is_a (taxon equivalent)
develops_from
part_of
is_a (SubClassOf)
capable_of
NCBITaxon: Mammalia
EHDAA:
lung bud
only_in_taxon
pulmonary acinus
alveolar sac
lung primordium
swim bladder
respiratory
primordium
NCBITaxon:
Actinopterygii
Haendel, M. A. et al. (2014). Unification of multi-species vertebrate anatomy ontologies for comparative
biology in Uberon. Journal of Biomedical Semantics 2014, 5:21. doi:10.1186/2041-1480-5-21

27. Modular phenotype description
Entity (Anatomy, Spatial, Gene Ontology)
BSPO: anterior region part_of ZFA:head
ZFA:heart
ZFA:ventral mandibular arch
GO:swim bladder inflation
Quality (PATO)
Small size
Edematous
Thick
Arrested

28. Mammalian Phenotype Ontology
Smith et al. (2005). The Mammalian Phenotype Ontology as a
tool for annotating, analyzing and comparing phenotypic
information. Genome Biol, 6(1). doi:10.1186/gb-2004-6-1-r7
10,097 terms used to
annotate and query:
• Genotypes
• Alleles
• Genes
In mice
abnormal
pancreatic
beta cell
mass
abnormal
pancreatic
beta cell
morphology
abnormal
pancreatic islet
morphology
abnormal
endocrine
pancreas
morphology
abnormal
pancreatic
beta cell
differentiation
abnormal
pancreatic
alpha cell
morphology
abnormal
pancreatic
alpha cell
differentiation
abnormal
pancreatic
alpha cell
number

29. Phenotype representation requires
more than “phenotype ontologies”
glucose
metabolism
(GO:0006006)
Gene/protein
function data
glucose
(CHEBI:172
34)
Metabolomics,
toxicogenomics
data
Disease &
phenotype
data
type II
diabetes
mellitus
(DOID:9352)
pyruvate
(CHEBI:153
61)
Disease Gene Ontology Chemical
pancreatic
beta cell
(CL:0000169)
transcriptomic
data
Cell

30. Uberpheno – building a cross-
species semantic framework
Köhler et al. (2014) Construction and accessibility of a cross-species phenotype ontology along with
gene annotations for biomedical research F1000Research 2014, 2:30

31. Uberpheno construction

32. Uberpheno construction

33. Uberpheno construction

34. Uberpheno construction

35. OWLsim: Phenotype similarity
across patients or organisms
Unstable
posture
Constipation
Neuronal loss in
Substantia Nigra
Shuffling gait
Resting tremors
REM disorder
Hyposmia
poor rotarod
performance
decreased gut
peristalsis
axon
degeneration
decreased
stride length
sterotypic
behavior
abnormal
EEG
failure to find
food
abnormal
coordination
abnormal
digestive
physiology
CNS neuron
degeneration
abnormal
locomotion
abnormal
motor function
sleep
disturbance
abnormal
olfaction
https://code.google.com/p/owltools/wiki/OwlSim

36. Visualizing phenotypic similarity
➔Each model recapitulates some of the disease
phenotypes
Holoprosencephaly I (unknown gene, mapped to 21q22.3)
compared to most similar mouse models

37. Models of disease based on
phenotypic similarity
Holoprosencephaly I (unknown gene, mapped to 21q22.3)
compared to most similar mouse models
➔The ontologies enable comparison across species

38. Outline
 Semantic Diagnosis of known diseases
 Semantic similarity across species
 Combining Exome analysis with cross-
species semantic phenotyping
 How much phenotyping is enough?

39. https://www.sanger.ac.uk/resources/databases/exomiser/query/exomiser2

40. Exomiser results for the
Undiagnosed Disease Program
 11 previously diagnosed families
Exomiser 2.0 identified the causative variants
with a rank of at least 7/408 potential variants
 23 families without identified disorders
We have now prioritized variants in STIM1,
ATP13A2, PANK2, and CSF1R in 5 different
families (2 STIM1 families)

41. Exomiser performance on
solved UDP cases
0
1
2
3
4
5
6
7
8
9
10
11
Exo Variant Exo Pheno Exo Exo no Mendelian Exo Novel
top10
top 5
top candidate

42. UDP_2731 candidates
Chromosome Position Reference Allele Variant Allele GENE Phenotype score Variant Score Exomiser Score
chrX 19554576T C SH3KBP1 0.5051473 0.995576 0.7503617
chr2 179658310T C TTN 0.64627105 0.79311335 0.71969223
chr2 179632598C T TTN 0.64627105 0.79311335 0.71969223
chr2 179567340G A TTN 0.64627105 0.79311335 0.71969223
chr2 179553542G T TTN 0.64627105 0.79311335 0.71969223
chr2 179549131C T TTN 0.64627105 0.79311335 0.71969223
chr18 67836115G T RTTN 0.7629328 0.25979215 0.51136243
chr18 67721492G C RTTN 0.7629328 0.25979215 0.51136243
chr18 67673764T C RTTN 0.7629328 0.25979215 0.51136243
chrX 140993905-
GCTCCTTCTCCTCCACTTTATTGAG
TATTTTCCAGAGTTCCCCTGAGAG
AAGTCAGAGAACTTCTGAGGGTTT
TGCACAGTCTCCTCTCCAGATTCCT
GTGAGCT MAGEC1 0.5416666 0.85 0.6958333
chr6 30858858G A DDR1 0.37619072 1 0.68809533
chr3 129308149
AGCCTCCCACCCCCACCCCCT
CCCCACATCCCCAACCATACC
TACCTTGAGA - PLXND1 0.34432834 0.95 0.64716417
chr5 37245866G A C5orf42 0.7855199 0.5 0.6427599
chr5 37169169T C C5orf42 0.7855199 0.5 0.6427599
chr6 42946264G A PEX6 0.7187602 0.5 0.6093801
chr6 42931861G A PEX6 0.7187602 0.5 0.6093801
chrX 53113897G C TSPYL2 0.59999996 0.4906897 0.5453448
chr13 75911097T C TBC1D4 0.23643239 0.7895149 0.51297367
chr13 75900510G A TBC1D4 0.23643239 0.7895149 0.51297367
chr13 75861174- A TBC1D4 0.23643239 0.7895149 0.51297367
chr18 67836115G T RTTN 0.7629328 0.25979215 0.51136243
chr18 67721492G C RTTN 0.7629328 0.25979215 0.51136243
chr18 67673764T C RTTN 0.7629328 0.25979215 0.51136243

43. UDP_2731
Behavioural/
Psychiatric
Abnormality
Thyroid
stimulating
hormone excess
Gait apraxia
Spasticity
increased
exploration in new
environment
increased
dopamine level
hyperactivity
hyperactivity
Behavioral
abnormality
Abnormality of
the endocrine
system
abnormal
locomotor
behavior
Abnormal
voluntary
movement
Patient
phenotypes Sh3kbp1 tm1Ivdi -/-

44. What if there aren’t any similar
diseases or models?
YARS
MARS
IARSIL41L
AARSIARS2
Abnormal
stereopsis
Choreoathetosis
Microcephaly
Akinesia
Visual impairment
Myoclonus
Microcephaly
Myoclonus
abnormal visual
perception
Involuntary
movements
Microcephaly
musculoskeletal
movement
phenotype
Patient
phenotypes
Combined Oxidative
Phosphorylation
Deficiency 14
FARS2
WARS2
?
AIMP1
UDP_1166
➔ Exomiser can utilize phenotypic similarity via the
interactome

45. Outline
 Semantic Diagnosis of known diseases
 Semantic similarity across species
 Combining Exome analysis with cross-
species semantic phenotyping
 How much phenotyping is enough?

46. How does the clinician know they’ve
provided enough phenotyping?
 How many annotations…?
 How many different categories?
 How many within each?

47. Method
 Create a variety of “derived” diseases that are less-
specific
 Assess the change in similarity between the derived
disease and it’s parent.
 Ask questions:
 Is the derived disease still considered similar to
the original disease?
 …or more similar to a different disease?
 Is it distinguishable beyond random?

48. Image credit: Viljoen and Beighton, J Med Genet. 1992
Example: Schwartz-jampel Syndrome, Type I
 Rare disease
 Caused by Hspg2 mutation, a
proteoglycan
~100 phenotype annotations

49. Example: Schwartz-jampel Syndrome, Type I
to test influence of a single
phenotypic category

50. Schwartz-jampel Syndrome derivations
to test influence of a single
phenotypic category

51. Schwartz-jampel Syndrome derivations

52. Example: Schwartz-jampel Syndrome, Type I
*
*
*
➔When averaged over all diseases, the absence of a
single phenotypic category has far less impact when
there’s more breadth in annotations

53. How much phenotyping is
enough?
• How many annotations…?
• How many different categories?
• How many within each?

54. Annotation Sufficiency Score
• Measurement of breadth and depth of an phenotype
profile
• Uses human disease, mouse and fish* gene phenotype
profiles to seed the individual phenotype scores
• Custom queries available via REST services
• http://monarchinitiative.org/page/services
*soon to add more species

55. Annotation Sufficiency Score
http://www.phenotips.orghttp://www.monarchinitiative.org

56. Conclusions
 Semantic representation of patient phenotypes
can aid disease diagnosis
 There exists a lot of phenotype data in model
organisms that is complementary to known human
data
 Ontological integration and use of cross-species
inferencing can aid prioritization of variants
 The entire cross-species corpus can be utilized to
support quality assurance processes for
phenotype data capture

57. NIH-UDP
William Bone
Murat Sincan
David Adams
Amanda Links
David Draper
Joie Davis
Neal Boerkoel
Cyndi Tifft
Bill Gahl
OHSU
Nicole Vasilesky
Matt Brush
Bryan Laraway
Shahim Essaid
Lawrence Berkeley
Nicole Washington
Suzanna Lewis
Chris Mungall
UCSD
Amarnath Gupta
Jeff Grethe
Anita Bandrowski
Maryann Martone
U of Pitt
Chuck Boromeo
Jeremy Espino
Becky Boes
Harry Hochheiser
Acknowledgments
Sanger
Anika Oehlrich
Jules Jacobson
Damian Smedley
Toronto
Marta Girdea
Sergiu Dumitriu
Heather Trang
Mike Brudno
JAX
Cynthia Smith
Charité
Sebastian Kohler
Sandra Doelken
Sebastian Bauer
Peter Robinson
Funding:
NIH Office of Director: 1R24OD011883
NIH-UDP: HHSN268201300036C

59. Candidate gene prioritization
Phenot ypic inf or mat ionGenet ic inf or mat ion
gene/ gene pr oduct Inf o
Phenotypes
collected for
individual patients
Sequences from an
individual,family,or
related group
Candidate interpretation
Human sequence reference
sequences (e.g.reference
sequence,1K genome data,
genomic location)
Community phenotype data (e.g.
literature MODS,KOMP2,OMIM,
EHRs,GWAS,ClinVar,disease
specific repositories,etc.)
Pathway
Functional (GO)
Gene
expression,
OMICS data
Protein-Protein
Interactions
Enrichment analysis
(e.g.GATACA,Galaxy)
Combined variant +
phenotype candidate
reporting(e.g.Exomizer)
BiomedicalKnowledgeIndividual'sInformation
Phenotypic comparison
methods
Variant calling
(e.g.GATK)
Pathogenicity
/Impact
calling (e.g.
VAAST,SIFT)
Orthologs
Network module analysis

60. Survey of Annotations in Disease Corpus*
➔Most diseases impact >1 system

61. PhenoViz: Integrate all human, mouse, and
fish data to understand CNVs
Desktop application
for differential
diagnostics in CNVs
 Explain manifestations of CNV diseases based on genes
contained in CNV
E.g., Supravalcular aortic stenosis in Williams syndrome can be
explained by haploinsufficiency for elastin
 Double the number of explanations using model data
Doelken, Köhler, et al. (2013) Dis Model Mech 6:358-72