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

Abstract

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.

Inside the atoms

“…Compared with the CROSSRANK method, whose time complexity is O(T * (m + gn)), where T * is the total number of iterations, Algorithm 2 is more efficient since in practice b f << m + gn, h << n and T << T * . Algorithm 2: CROSSQUERY-BASIC (adopted from [6]) while |S (t) | > k do 4 Increase the iteration number t = t + 1;…”

“…The heterogeneous network consists of three components: a disease similarity network, a generic protein interaction network, and known disease-gene associations connecting the two networks. Based on the "guilt-by-association" principle, these methods utilizes similarities between diseases to infer genes that are associated with diseases [4].…”

A conceptual review on systems biology in health and diseases: from biological networks to modern therapeutics

“…The network based approach is being used extensively to identify the candidate genes responsible for various diseases and syndromes (Chen et al 2012). In a outstanding work by Lage et al (2007), the authors applied such kind of analysis to construct the interactions network of various genes and proteins for human diseases.…”

A visual and curatorial approach to clinical variant prioritization and disease gene discovery in genome-wide diagnostics

“…Despite the promise of available data, the scale of variation presents an interpretive challenge: an individual patient’s genome can have hundreds of rare and putatively deleterious candidate causal variants [ 11 ]. Although in some instances diagnostic conclusions can be made without extensive interpretation (e.g., aneuploidies or nonsense variants in disease genes), the presence of numerous potentially deleterious variants typically requires substantial curation to identify the candidate deleterious variant(s) that best matches the clinical phenotypes of the patient in question [ 1 – 6 , 12 , 13 ]. The goal of integrated diagnostic approaches is to bring together variant knowledge with clinically ascertained patient phenotype characteristics to reach the best-informed diagnostic conclusions (Fig.…”