Prioritisation of candidate genes for psychiatric disorders - Kalweit, Kerry (Health Sciences, 2016)
The application of genome-wide association studies and next-generation sequencing has had limited success in identifying causal genes for complex diseases. Bipolar disorder is one such disease whose aetiology has not been elucidated despite the application of these technologies. Candidate gene prioritisation offers a solution to limit the vast amount of possible candidate genes produced from the combination of data sources. Current prioritisation tools rely heavily on previous data and thus do not perform well for poorly characterised diseases such as bipolar disorder. Here we have developed Data Integrated Genetics, DIG, a new candidate gene prioritisation tool designed specifically for complex genetic diseases. Given a user-specified disease query, DIG initially data-mines literature, linkage, homolog and sequence data to create a pool of possible candidates. The tool filters out likely false positives by removing pseudogenes. A unique data integration method is used to rank the remaining list of genes. Additionally, ranking is validated by tissue expression and single nucleotide polymorphism annotation. DIG exhibited comparable performance to existing tools when evaluated with four complex diseases. Eight novel genes were identified when DIG was applied to bipolar disorder, of which the Huntingtin gene poses as an exciting avenue for new aetiology research. The ease of use and realistic number of possible candidates given in the DIG results make this tool highly useful for research application in the study of complex genetic diseases. DIG is freely available from http://www.cbio.uct.ac.za/DIG.