Disentangling function from topology to infer the network properties of disease genes

Dario Ghersi, Mona Singh

Research output: Contribution to journalArticle

12 Scopus citations

Abstract

Background: The topological features of disease genes within interaction networks are the subject of intense study, as they shed light on common mechanisms of pathology and are useful for uncovering additional disease genes. Computational analyses typically try to uncover whether disease genes exhibit distinct network features, as compared to all genes.Results: We demonstrate that the functional composition of disease gene sets is an important confounding factor in these types of analyses. We consider five disease sets and show that while they indeed have distinct topological features, they are also enriched in functions that a priori exhibit distinct network properties. To address this, we develop a computational framework to assess the network properties of disease genes based on a sampling algorithm that generates control gene sets that are functionally similar to the disease set. Using our function-constrained sampling approach, we demonstrate that for most of the topological properties studied, disease genes are more similar to sets of genes with similar functional make-up than they are to randomly selected genes; this suggests that these observed differences in topological properties reflect not only the distinguishing network features of disease genes but also their functional composition. Nevertheless, we also highlight many cases where disease genes have distinct topological properties even when accounting for function.Conclusions: Our approach is an important first step in extracting the residual topological differences in disease genes when accounting for function, and leads to new insights into the network properties of disease genes.

Original languageEnglish (US)
Article number5
JournalBMC systems biology
Volume7
DOIs
StatePublished - Jan 16 2013

Keywords

  • Disease genes
  • Functional bias
  • Gene ontology
  • Networks

ASJC Scopus subject areas

  • Structural Biology
  • Modeling and Simulation
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

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