title: Identifying disease associated genes by network propagation.
abstract: BACKGROUND
  Genome-wide association studies have identified many individual genes associated with complex traits. However, pathway and network information have not been fully exploited in searches for genetic determinants, and including this information may increase our understanding of the underlying biology of common diseases.
  
  
  RESULTS
  In this study, we propose a framework to address this problem in a principled way, with the underlying hypothesis that complex disease operates through multiple connected genes. Associations inferred from GWAS are translated into prior scores for vertices in a protein-protein interaction network, and these scores are propagated through the network. Permutation is used to select genes that are guilty-by-association and thus consistently obtain high scores after network propagation. We apply the approach to data of Crohn's disease and call candidate genes that have been reported by other independent GWAS, but not in the analysed data set. A prediction model based on these candidate genes show good predictive power as measured by Area Under the Receiver Operating Curve (AUC) in 10 fold cross-validations.
  
  
  CONCLUSIONS
  Our network propagation method applied to a genome-wide association study increases association findings over other approaches.
authors.forename: Yu
authors.initials: Y
authors.lastname: Qian
authors.forename: Søren
authors.initials: S
authors.lastname: Besenbacher
authors.forename: Thomas
authors.initials: T
authors.lastname: Mailund
authors.forename: Mikkel Heide
authors.initials: MH
authors.lastname: Schierup
mesh_terms: D019295:Computational Biology; D003424:Crohn Disease / Q000235:genetics* / Q000378:metabolism* / Q000473:pathology; D055106:Genome-Wide Association Study; D006801:Humans; D018664:Interleukin-12 / Q000378:metabolism; D060066:Protein Interaction Maps; D012372:ROC Curve; D015398:Signal Transduction / Q000235:genetics
chemical_list: D018664:Interleukin-12