Analysis of the robustness of network-based disease-gene prioritization methods reveals redundancy in the human interactome and functional diversity of disease-genes

PLoS One. 2014 Apr 14;9(4):e94686. doi: 10.1371/journal.pone.0094686. eCollection 2014.


Complex biological systems usually pose a trade-off between robustness and fragility where a small number of perturbations can substantially disrupt the system. Although biological systems are robust against changes in many external and internal conditions, even a single mutation can perturb the system substantially, giving rise to a pathophenotype. Recent advances in identifying and analyzing the sequential variations beneath human disorders help to comprehend a systemic view of the mechanisms underlying various disease phenotypes. Network-based disease-gene prioritization methods rank the relevance of genes in a disease under the hypothesis that genes whose proteins interact with each other tend to exhibit similar phenotypes. In this study, we have tested the robustness of several network-based disease-gene prioritization methods with respect to the perturbations of the system using various disease phenotypes from the Online Mendelian Inheritance in Man database. These perturbations have been introduced either in the protein-protein interaction network or in the set of known disease-gene associations. As the network-based disease-gene prioritization methods are based on the connectivity between known disease-gene associations, we have further used these methods to categorize the pathophenotypes with respect to the recoverability of hidden disease-genes. Our results have suggested that, in general, disease-genes are connected through multiple paths in the human interactome. Moreover, even when these paths are disturbed, network-based prioritization can reveal hidden disease-gene associations in some pathophenotypes such as breast cancer, cardiomyopathy, diabetes, leukemia, parkinson disease and obesity to a greater extend compared to the rest of the pathophenotypes tested in this study. Gene Ontology (GO) analysis highlighted the role of functional diversity for such diseases.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Area Under Curve
  • Computational Biology / methods
  • Databases, Genetic
  • Gene Expression Regulation*
  • Gene Regulatory Networks
  • Genetic Diseases, Inborn / genetics
  • Humans
  • Models, Genetic
  • Models, Statistical
  • Mutation*
  • Phenotype
  • Protein Interaction Mapping / methods*
  • Protein Interaction Maps*
  • Proteins / metabolism


  • Proteins

Grant support

Spanish Ministry of Science and Innovation (MICINN) FEDER BIO2011-22568 (; and by EU grant EraSysbio+ (SHIPREC Euroinvestigación (EUI2009-04018). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.