Querying phenotype-genotype relationships on patient datasets using semantic web technology: the example of cerebrotendinous xanthomatosis

Abstract

BackgroundSemantic Web technology can considerably catalyze translational genetics and genomics research in medicine, where the interchange of information between basic research and clinical levels becomes crucial. This exchange involves mapping abstract phenotype descriptions from research resources, such as knowledge databases and catalogs, to unstructured datasets produced through experimental methods and clinical practice. This is especially true for the construction of mutation databases. This paper presents a way of harmonizing abstract phenotype descriptions with patient data from clinical practice, and querying this dataset about relationships between phenotypes and genetic variants, at different levels of abstraction.MethodsDue to the current availability of ontological and terminological resources that have already reached some consensus in biomedicine, a reuse-based ontology engineering approach was followed. The proposed approach uses the Ontology Web Language (OWL) to represent the phenotype ontology and the patient model, the Semantic Web Rule Language (SWRL) to bridge the gap between phenotype descriptions and clinical data, and the Semantic Query Web Rule Language (SQWRL) to query relevant phenotype-genotype bidirectional relationships. The work tests the use of semantic web technology in the biomedical research domain named cerebrotendinous xanthomatosis (CTX), using a real dataset and ontologies.ResultsA framework to query relevant phenotype-genotype bidirectional relationships is provided. Phenotype descriptions and patient data were harmonized by defining 28 Horn-like rules in terms of the OWL concepts. In total, 24 patterns of SWQRL queries were designed following the initial list of competency questions. As the approach is based on OWL, the semantic of the framework adapts the standard logical model of an open world assumption.ConclusionsThis work demonstrates how semantic web technologies can be used to support flexible representation and computational inference mechanisms required to query patient datasets at different levels of abstraction. The open world assumption is especially good for describing only partially known phenotype-genotype relationships, in a way that is easily extensible. In future, this type of approach could offer researchers a valuable resource to infer new data from patient data for statistical analysis in translational research. In conclusion, phenotype description formalization and mapping to clinical data are two key elements for interchanging knowledge between basic and clinical research.

Concept mapping One-Carbon Metabolism to model future ontologies for nutrient–gene–phenotype interactions

“…As resources develop, with the incorporation of genotype information into the electronic medical record, CMAPs can act as a tool to guide clinicians and scientists through evidence-based nutrient gene interactions, guiding them toward resolution of higher-order questions about genotype-phenotype associations and potential dietary interventions. Continued development and refinement of information resources such as these will provide clinicians with the ability to evaluate patient health based on current evolving evidence-based knowledge about specific SNPs or groups of SNPs, environments, and lifestyles (Taboada et al 2012). The conceptual maps presented here highlight the varied ways individuals possess genetic risk factors for nutrient metabolism.…”

“…While several studies investigate nutrientgene interactions and their phenotypic consequences, a comprehensive ontology for annotating these interactions remains to be developed. Taboada et al (2012) provide a disease-based ontological model of genotype-phenotype associations that outlines a preliminary approach for such an ontology. In their model, patient data are used to create a queryable database that documents concepts related to the rare disease cerebrotendinous xanthomatosis (Taboada et al 2012).…”

“…Taboada et al (2012) provide a disease-based ontological model of genotype-phenotype associations that outlines a preliminary approach for such an ontology. In their model, patient data are used to create a queryable database that documents concepts related to the rare disease cerebrotendinous xanthomatosis (Taboada et al 2012). The authors suggest that semantic web technologies are essential for translating genomic and genetic research into actionable phenotype information.…”

“…The principles and applications envisioned in the model described by Taboada et al (2012) can be broadly extended to ontologically enriched nutrient-gene interaction databases that provide a platform enabling users to query nutrient-gene-phenotype associations. Information generated from such databases will be useful for defining, characterizing, and ultimately personalizing nutrition.…”

“…Mapping phenotype data from literature is one method of channeling information from basic research to a clinical setting (Taboada et al 2012). Others have presented methodologies for building nutrigenomics ontologies using CMAPs (Castro et al 2006).…”