Individual genetic data can be used to better predict the efficacy and safety of medications for individual patients. The Genomic Clinical Decision Support (Genomic CDS) ontology aims to utilize advanced Web Ontology Language 2 (OWL 2) reasoning for this task. The important, clear-cut medical use case, the complex axioms in the ontology and the heavy use of qualified cardinality restrictions make the ontology an interesting test object for new OWL 2 reasoners with improved performance.
Different patients can react drastically different to the same type of medication (Fig. 1). The goal of personalized medicine and pharmacogenetics is to predict an individual patient’s response by analyzing genetic markers that influence how medications are metabolized or able to bind to their targets.
To produce clinically valid and trustworthy predictions, no errors or ambiguities should arise in the process of inferring a patient’s likely response from raw genetic data. Current formalisms, data infrastructures and software applications leave many opportunities for introducing such errors and ambiguities. Ontologies formalized with the Web Ontology Language 2 (OWL 2) could be an excellent choice for tackling this problem, but the complexity and potentially large scale of ontologies in this domain also pose formidable challenges to currently available OWL 2 reasoners. 2
The Genomic Clinical Decision Support (Genomic CDS) ontology is an OWL 2 ontology aimed at representing pharmacogenetic knowledge and providing clinical decision support based on pharmacogenetic data. It is being developed by members of the Clinical Pharmacogenomics Task Force, which is part of the Health Care and Life Science Interest Group of the World Wide Web Consortium (W3C). The OWL files of the ontology, as well as ‘demo’ files containing example patient data can be downloaded from http://www.genomic-cds.org/ont/snapshot-june-2013 We also created a simplified version of the Genomic CDS ontology, called ‘Genomic CDS light’, which does not contain some of the axioms of the full ontology. Both versions of the ontology have ALCQ expressivity. They are characterized by extensive use of qualified cardinality restrictions.
The goals of developing the ontology are: Providing a simple and concise formalism for representing pharmacogenetic knowledge, Finding errors and lacking definitions in pharmacogenetic knowledge bases Automatically assigning alleles and phenotypes to patients Matching patients to clinically appropriate pharmacogenetic guidelines and clinical decision support messages In the most common scenario, genetic patient data in OWL format is combined with the axioms of the Genomic CDS ontology, and an OWL reasoner is used to infer matching pharmacogenetic treatment recommendations. Several inference steps are needed to derive matching treatment recommendations from raw data about genetic markers (Fig. 2). The raw data consists of small variants in the genetic code, which in most cases are so-called single nucleotide polymorphisms (SNPs), such as an ‘A’ instead of a ‘G’. Alleles are variants of a gene that are defined by containing sets of such small variants. Phenotypes are referring to the specific effects that certain small variants and alleles can have on the organism, e.g., how quickly a patient metabolizes a specific drug. Clinical guidelines can use small variants, alleles and/or phenotypes to match patients with treatment recommendations The human genome usually contains two copies of each gene (one from the father, one from the mother), with each copy potentially bearing multiple genetic variants. Because of this, the ontologies rely heavily on qualified cardinality restrictions with cardinalities of two, which seems to cause performance issues with most current OWL reasoners.
Fig. 2. : Through a series of inference steps, matching pharmacogenetic treatment guidelines are inferred from raw genetic patient data.
A simplified example of a rule for inferring an allele (CYP2C9*3) and its single nucleotide polymorphisms (SNPs) from a so-called ‘tagging SNP’ (a SNP that is necessary and sufficient for inferring the presence of the allele) looks like this in Manchester syntax: An example of an axiom for inferring an adequate clinical decision support message for the anticoagulant drug warfarin (based on a combination of alleles and SNPs according to an official recommendation in the drug label): Class: 'human triggering CDS rule 7'
EquivalentTo: (has some 'CYP2C9*1') and (has some 'CYP2C9*3')
and (has exactly 2 rs9923231_C) Annotations: label "human triggering CDS rule 7", CDS_message "3-4 mg warfarin per day should be considered as a starting dose range for a patient with this genotype according to the Warfarin drug label (Bristol-Myers
Squibb)."
We used two OWL 2 reasoners with our ontology: TrOWL1 [
These preliminary tests showed TrOWL to be significantly more performant than HermiT for classifying the ontologies ( Table 1). However, HermiT was able to identify biologically meaningful inconsistencies present in genomic-cds-demo.owl (but not present in the light version of the ontology). TrOWL did not recognize these inconsistencies, most likely because it only partially covers the OWL 2 DL ruleset. These results show that only TrOWL is performant enough to be used in realistic settings (e.g. for clinical decision support), but that HermiT could serve to test and validate the results from TrOWL during develop1 http://trowl.eu 2 http://www.hermit-reasoner.com/ 3 http://code.google.com/p/factplusplus/ 4 http://clarkparsia.com/pellet/ 5 http://aws.amazon.com/en/ec2/ ment (possibly comparing the results of the two reasoners for smaller ontology fragments).
HermiT 1.3.8
TrOWL 1.1
TrOWL 0.6 3 hours 48
minutes detected inconsistencies 1.5 seconds The Genomic CDS ontology is an example of an OWL 2 ontology for clinical genetics and decision support. Even though it is focused on a relatively small set of the most important pharmacogenetic markers, the ontology poses a significant challenge to currently available OWL 2 reasoners. There is great need for reasoners that are optimized for the kinds of OWL axioms encountered in ontologies dealing with clinical genomics.
The research leading to these results has received funding from the Austrian Science Fund (FWF): [PP 25608-N15].