Candidate gene identification deals with associating genes to underlying biological phenomena, such as diseases and specific disorders. It has been shown that classes of diseases with similar phenotypes are caused by functionally related genes. Currently, a fair amount of knowledge about the functional characterization can be found across several public databases; however, functional descriptors can be ambiguous, domain specific, and context dependent. In order to cope with these issues, the Gene Ontology (GO) project developed a bio-ontology of broad scope and wide applicability. Thus, the structured and controlled vocabulary of terms provided by the GO project describing the biological roles of gene products can be very helpful in candidate gene identification approaches. The method presented here uses GO annotation data in order to identify the most meaningful functional aspects occurring in a given set of related gene products. The method measures this meaningfulness by calculating an e-value based on the frequency of annotation of each GO term in the set of gene products versus the total frequency of annotation. Then after selecting a GO term related to the underlying biological phenomena being studied, the method uses semantic similarity to rank the given gene products that are annotated to the term. This enables the user to further narrow down the list of gene products and identify those that are more likely of interest.