This document summarizes the development of an ontology for representing pharmacogenomic knowledge to enable personalized medicine and clinical decision support. The ontology aims to represent genetic variants and phenotypes, assign these to patients, and match patients to treatment guidelines. It represents over 300 genetic markers, 665 gene haplotypes, and over 300 dosage recommendations. Reasoning over the ontology is challenging due to its size, and completing reasoning requires significant computing resources. While the ontology achieves many goals, expressing all needed concepts and supporting critical applications remains difficult due to limitations of the underlying ontology language OWL.

1. An update on Genomic CDS
A complex ontology for pharmacogenomics /
personalized medicine and clinical decision
support
Matthias Samwald, José Antonio Minarro-Giménez
Medical University of Vienna
W3C Semantic Web for Healthcare and Life Science Interest Group

2. Drug efficacy and toxicity can vary drastically between
patients with different genetic profiles
Up to 100,000 deaths and 2 million hospitalizations are caused by adverse drug
reactions per year in the United States alone.

4. Goals of ontology development
• Providing a simple and concise formalism for
representing pharmacogenomic knowledge
• Finding errors and lacking definitions in
pharmacogenomic knowledge bases
• Automatically assigning alleles and phenotypes to
patients
• Matching patients to clinically appropriate
pharmacogenomic guidelines and clinical decision
support messages
• Being able to detect inconsistencies between
pharmacogenomics treatment guidelines from different
sources.

5. This is how it actually looks in the ontology
Class: rs1057911
SubClassOf:
polymorphism
Annotations:
rsid "rs1057911",
relevant_for CYP2C9,
can_be_tested_with 23andMe_v2,
can_be_tested_with 23andMe_v3,
can_be_tested_with Affymetrix_DMET_chip,
rdfs:seeAlso <http://bio2rdf.org/dbsnp:rs1057911>,
dbsnp_orientation_on_reference_genome "forward"
Class: rs1057911_A
SubClassOf:
rs1057911
Class: rs1057911_T
SubClassOf:
rs1057911
DisjointClasses: rs1057911_A, rs1057911_T

6. Examples of OWL axioms to represent humans with
homozygous or heterozygous genotypes. Humans
usually have two copies of each gene
(and hence each polymorphism occurs twice)
Class: human_with_genotype_rs1057911_variant_A_A
SubClassOf: has exactly 2 rs1057911_A
Class: human_with_genotype_rs1057911_variant_A_T
SubClassOf: has some rs1057911_A and has some rs1057911_T

7. An excerpt of a translational allele/haplotype table
for the gene CYP2C9

8. An excerpt of a translational allele/haplotype table
for the gene CYP2C9

9. An excerpt of a translational allele/haplotype table
for the gene CYP2C9

10. Examples of scenarios where automated scripts
helped in the curation of haplotype definitions

11. Dosing guideline from a US Food and Drug
Administration (FDA) drug label

12. An excerpt of a CDS rule derived from the warfarin
drug label
Class: 'human triggering CDS rule 9'
Annotations:
CDS_message "0.5-2 mg warfarin per day should be considered
as a starting dose range for a patient with this genotype
according to the warfarin drug label.”,
relevant_for Warfarin,
recommendation_importance "Important modification"
EquivalentTo:
human and
(has some 'CYP2C9 *1') and
(has some 'CYP2C9 *3') and
(has exactly 2 rs9923231_T)

13. An example of how pharmacogenomic findings about
an individual patient can be represented
Individual: ‘John Doe’
Types:
human,
(has some rs6025_C) and (has some rs6025_T),
(has some rs9934438_A) and (has some rs9934438_G),
has exactly 2 rs12979860_T,
has exactly 2 rs9923231_T,
(has some ‘CYP2C9*1’) and (has some ‘CYP2C9*3’),
has exactly 2 ‘CYP2D6*2’

14. An example of how pharmacogenomic findings about
an individual patient can be represented
Individual: ‘John Doe’
Types:
human,
(has some rs6025_C) and (has some rs6025_T),
(has some rs9934438_A) and (has some rs9934438_G),
has exactly 2 rs12979860_T,
has exactly 2 rs9923231_T,
(has some ‘CYP2C9*1’) and (has some ‘CYP2C9*3’),
has exactly 2 ‘CYP2D6*2’
"0.5 - 2 mg warfarin per day
should be considered as a
starting dose range for a patient
with this genotype according to
the warfarin drug label."
OWL Reasoner

15. Some basic statistics
The ontology currently represents
• 336 SNPs with 707 variants
• 665 haplotypes related to 43 genes
• 22 rules related to human phenotypes
• 308 dosage recommendations rules
It is made up of approximately
• 22.000 axioms
• 7.700 logical axioms
• 4.100 classes

16. Time taken by different reasoners for classifying and
realising the demo ontology.
Ontologies have ALCQ expressivity.
System specifications: Windows 7 Professional, java version 1.6.0_29-b11 and 64 bit platform
running on an Intel Core i5-2430M and 4GB of memory

17. Ontology development and application was
characterized by cycling through 3 emotional stages

18. Ontology development and application was
characterized by cycling through 3 emotional stages

19. Ontology development and application was
characterized by cycling through 3 emotional stages

20. The good
• Majority of primary goals of ontology development have
largely been met
o But devil is in the details, and there are roadblocks for practical
application
• Helped to find concise formalisation and identify pitfalls
that might have been overlooked with another
approach, at least initially
• Manchester Syntax is easily readable with this ontology
o Some decision support axioms were curated by medical student
who wrote them down in Manchester Syntax with minimal
training

21. The challenging
• TrOWL still performs best among freely available
reasoners by a wide margin, but still might only
provide partial results
o Seems complete, but hard to tell for sure
o Bad for critical applications such as health care
o Predictable incompleteness would be better than unpredictable
incompleteness
• Konclude also worked and is complete, need to evaluate
further (as well as other commercial reasoners)

22. The challenging
• OWL approach pushed everything firmly into ‘research
prototype’ mode
o Still feels quite adventerous and somewhat burdensome when
used for mission-critical applications
o We re-implemented part of the reasoning process with our own
code to get rid of OWL for mission critical inferences (this also
helped to make decision support algorithms run on Android)

23. The challenging
• Awkward moment when starting reasoner after
extending/modifying the ontology: will it still
terminate within an acceptable timespan?
o Quite unpredictable, shrouds development process in doubt
o It would be great if all reasoners would ship with end-user
friendly heuristics describing ontology features known to
significantly decrease performance

24. The challenging
• After implementing 80% of the needed features in an
elegant OWL 2 DL ontology, I found that the missing 20%
cannot be expressed in OWL…
o There should be more end-user friendly documentation
describing patterns that might seem as if they could be handled
by a specific reasoner, but cannot actually be handled.
o For me: realizing that I would need cardinality restrictions on
transitive properties / property paths, but that is a no-go. Sigh.

25. The bad
• TrOWL did not alert us about some errors while other
reasoners did. Some of the time.
• But those other reasoners often could not explain the
errors either (waiting forever), so not very helpful with
the complex ontology we are working with.
• When explanations were available, it was often very
tricky to spot the actual mistake
o Need (even) better explanation summaries
o A few times the error reports seemed to be errors by
the reasoners, since explanations did not make sense
and we were unable to find a cause ourselves

26. The bad
• If reasoning takes long / forever, no easy means for profiling
to find out what is causing performance problems, therefore
difficult to fix

27. All this is part of a larger project

28. Thanks
W3C collaborators:
Michel Dumontier (Carleton University)
Robert R. Freimuth (Mayo Clinic)
Richard Boyce (University of Pittsburgh)
Simon Lin (Marshfield Clinic)
Robert L. Powers (Predictive Medicine, Inc.)
Joanne S. Luciano (Rensselaer Polytechnic Institute)
Eric Prud’hommeaux (W3C)
M. Scott Marshall (MAASTRO Clinic)
Funding:
Austrian Science Fund (FWF): [PP 25608-N15]
http://www.genomic-cds.org/
http://safety-code.org/