The identification of disease genes (genes that when mutated cause human diseases) is an important and challenging problem. Proper diagnosis, prevention, as well as care for patients require an understanding of disease pathophysiology, which is best understood when the underlying causative gene(s) or genetic element(s) are identified. While the availability of the sequenced human genome helped to lead to the discovery of more than 1,900 disease genes, the rate of disease gene discovery is still occurring at a slow pace. The use of genetic linkage methods have successfully led to the identification of numerous disease genes. However, linkage studies are ultimately restricted by available meioses (clinical samples) which result in numerous candidate disease genes. This thesis addresses candidate gene prioritizations in disease gene discovery as applied toward a genetically heterogeneous disease known as Bardet-Biedl Syndrome (BBS). Specifically, the integration of various functional information and the development of a novel comparative genomic approach (Computational Orthologous Prioritization - COP) that led to the identification of BBS3 and BBS11. Functional data integration and application of the COP method may be helpful toward the identification of other disease genes.