Align human interactome with phenome to identify causative genes and networks underlying disease families

Abstract

Motivation: Understanding the complexity in gene–phenotype relationship is vital for revealing the genetic basis of common diseases. Recent studies on the basis of human interactome and phenome not only uncovers prevalent phenotypic overlap and genetic overlap between diseases, but also reveals a modular organization of the genetic landscape of human diseases, providing new opportunities to reduce the complexity in dissecting the gene–phenotype association. Results: We provide systematic and quantitative evidence that phenotypic overlap implies genetic overlap. With these results, we perform the first heterogeneous alignment of human interactome and phenome via a network alignment technique and identify 39 disease families with corresponding causative gene networks. Finally, we propose AlignPI, an alignment-based framework to predict disease genes, and identify plausible candidates for 70 diseases. Our method scales well to the whole genome, as demonstrated by prioritizing 6154 genes across 37 chromosome regions for Crohn's disease (CD). Results are consistent with a recent meta-analysis of genome-wide association studies for CD. Availability: Bi-modules and disease gene predictions are freely available at the URL http://bioinfo.au.tsinghua.edu.cn/alignpi/ Contact: ruijiang@tsinghua.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.

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

“…Since similarities between diseases showed overlap with similarities between genes that were associated with the diseases, the topological structures between the phenome and the interactome might also be similar. With this consideration, Wu et al proposed an approach called AlignPI to directly align the phenome network with the interactome network using the network alignment technology [21]. This method worked as follows.…”

“…There have been a few methods that use the phenomeinteractome network with different probabilistic models for the prioritization of candidate genes [12,[20][21][22][23][24]39]. In the following sections, we will briefly review these methods.…”

“…To overcome this limitation, a number of recent studies have suggested the "guilt-byindirect-association" principle, which resorts to the modular nature of human inherited diseases [13,[15][16][17][18][19] and utilizes similarities between disease phenotypes to infer genes that are truly associated with diseases [20][21][22][23][24].…”

Information theoretical methods for complex network structure reconstruction

“…We can mention, for instance the MARINa algorithm (Lefebvre et al 2010;Lefebvre et al 2007;Mani et al 2008) developed specifically for the assessment and reconstruction of gene regulatory networks based on statistical enrichment of certain signatures (Subramanian et al 1554), an approach close in philosophy of that of conditioning variables that, however requires for additional information (i.e. the signatures themselves) to be useful, hence is more restricted to its scope and applications as are approaches relying on additional phenotypic information (Wu et al 2009;Yu et al 2006). …”

RASopathies: Presentation at the Genome, Interactome, and Phenome Levels

“…New candidate genes for diseases and associations between previously unrelated diseases could be discovered by investigating connections between the genome and phenome of a disease, since genes that are linked by physical interactions among their protein products contribute to the same phenome, causing the same syndrome [Wu et al, 2009]. The final objective in this research area is the construction of a complex interactomic map, connecting all the diseases on interactome and genome levels and explaining their relationship [Oti and Brunner, 2007].…”