Genome-wide association analyses on large patient cohorts are generating large 
sets of candidate disease genes. This is coupled with the availability of 
ever-increasing genomic databases and a rapidly expanding repository of 
biomedical literature. Computational approaches to disease-gene association 
attempt to harness these data sources to identify the most likely disease gene 
candidates for further empirical analysis by translational researchers, 
resulting in efficient identification of genes of diagnostic, prognostic and 
therapeutic value. Existing computational methods analyze gene structure and 
sequence, functional annotation of candidate genes, characteristics of known 
disease genes, gene regulatory networks, protein-protein interactions, data from 
animal models and disease phenotype. To date, a few studies have successfully 
applied computational analysis of clinical phenotype data for specific diseases 
and shown genetic associations. In the near future, computational strategies 
will be facilitated by improved integration of clinical and computational 
research, and by increased availability of clinical phenotype data in a format 
accessible to computational approaches.