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Although remarkable success has been achieved by traditional gene-mapping methods in locating genes associated with inherited human diseases, the resulting chromosomal regions are usually large, containing tens or even hundreds of genes. Therefore, it is indispensable to develop computational methods for the identification of genes that are truly responsible for diseases from candidate genes. To tackle this problem, several methods have been proposed to use both a phenotype similarity profile (phenome) and a proteinprotein interaction network (interactome) for the prioritization of candidate genes. The use of the phenome broadens the scope of applications of these methods for identifying disease-associated genes, and the use of the interactome provides a reliable measure of functional similarities between genes. These two data sources, together with carefully designed computational models, result in computational methods with superior performance in the prioritization of candidate genes for a given query disease of interest. In this paper, we review recent achievements of such computational methods that rely on the integration of the phenome and the interactome to prioritize candidate genes. We also summarize how similar methods can be readily used in identifying microRNAs that are potentially involved in complex diseases and discovering drugs that may target on disease-associated proteins. Finally, we discuss future prospects and challenges for the integration of multiple genomic data sources to systematically discover genes that underlie human diseases.

Constructing human phenome-interactome networks for the prioritization of candidate genes