Background: The availability of genetic data has increased dramatically in recent years. The greatest value of this data is its potential for personalized medicine. Many new associations are reported every day from Genome Wide Association Studies (GWAS). However, robust, reproducible associations are elusive for some complex diseases. Ontologies present a potential way to distinguish between spurious associations and those with a potential influence on the phenotype. Such an approach would be based on finding associations of the same genetic variant with closely related, but distinct, phenotypes. This approach can be accomplished with a phenotype ontology that also holds genetic association data. Results: Here, we report a structured knowledge application to navigate and to facilitate the discovery of relationships between different phenotypes and their genetic associations. Conclusions: OGA allows users to (1) find the intersecting set of genes for phenotypes of interest, (2) find empirical p values for such observations and (3) OGA outperforms similar applications in number of total concepts and genes mapped.
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)