Abstract
Tendinopathy is a multifactorial syndrome characterised by tendon pain and thickening, and impaired performance during activity. Candidate gene association studies have identified genetic factors that contribute to intrinsic risk of developing tendinopathy upon exposure to extrinsic factors. Bioinformatics approaches that data-mine existing knowledge for biological relationships may assist with the identification of candidate genes. The aim of this study was to data-mine functional annotation of human genes and identify candidate genes by ontology-seeded queries capturing the features of tendinopathy. Our BioOntological Relationship Graph database (BORG) integrates multiple sources of genomic and biomedical knowledge into an on-disk semantic network where human genes and their orthologs in mouse and rat are central concepts mapped to ontology terms. The BORG was used to screen all human genes for potential links to tendinopathy. Following further prioritisation, four strong candidate genes (COL11A2, ELN, ITGB3, LOX) were identified. These genes are differentially expressed in tendinopathy, functionally linked to features of tendinopathy and previously implicated in other connective tissue diseases. In conclusion, cross-domain semantic integration of multiple sources of biomedical knowledge, and interrogation of phenotypes and gene functions associated with disease, may significantly increase the probability of identifying strong and unobvious candidate genes in genetic association studies.