With the human genome now nearly completely sequenced, attention is focusing on learning the medical significance of this genetic information. Large-scale studies in pharmacogenetics are being done to find variations in genotype in order to understand the variability in drug response among individuals. But to make sense of this information, computational tools capable of efficiently accessing and analyzing these data are needed. Genetic data are complex, and simply storing raw sequences in a relational database will be inadequate to answer the complex queries that are needed to discover the links between genotype to phenotype. We need to represent the varied features of genetic sequences and their genomic structure to allow a broad range of queries that are needed to analyze pharmacogenetics data. Ontologies specify the concepts and relationships in a given field, and they provide a means of modeling a complex domain. In this study, we developed an ontology for genetic information to represent genes, alleles, sequences, genomic structure, polymorphisms, and their relationships. We implemented this ontology in Prot�g�-2000, an environment for developing ontologies and knowledge bases. We tested our model by representing genetic data obtained from a research center that is actively collecting genetic data for a pharmacogenetics study. We were able to store all the data collected for a single gene, and we could reconstruct various views of the data, similar to those the study center currently constructs by hand. We believe our ontology is a rich yet flexible model of genetic information, and may be suitable for storing data and supporting queries in pharmacogenetics studies.
