Most of the current computational disease candidate gene prioritization methods (1–10) rely on functional annotations, gene-expression data or sequence-based features. The coverage of the gene functional annotations, however, is a limiting factor. Currently, only a fraction of the genome is annotated with pathways and phenotypes (10). While two-thirds of all the genes are annotated by at least one functional annotation, the remaining one-third is yet to be annotated. Recent biotechnological advances such as the high-throughput yeast two-hybrid screen have facilitated building proteome-wide protein–protein interaction networks (PPINs) or ‘interactome’ maps in humans (18,19). The shift in focus to systems biology in the post-genomic era has generated further interest in PPINs and biological pathways. While protein–protein interactions (PPI) have been used widely to identify novel disease candidate genes (20–24), several recent studies (22,23,25–27) report also using them for candidate gene prioritization.
