title: An integrative approach for causal gene identification and gene regulatory pathway inference.
abstract: BACKGROUND
  Gene expression variation can often be linked to certain chromosomal regions and are tightly associated with phenotypic variation such as disease conditions. Inferring the causal genes for the expression variation is of great importance but rather challenging as the linked region generally contains multiple genes. Even when a single candidate gene is proposed, the underlying biological mechanism by which the regulation is enforced remains unknown. Novel approaches are needed to both infer the causal genes and generate hypothesis on the underlying regulatory mechanisms.
  
  
  RESULTS
  We propose a new approach which aims at achieving the above objectives by integrating genotype information, gene expression, protein-protein interaction, protein phosphorylation, and transcription factor (TF)-DNA binding information. A network based stochastic algorithm is designed to infer the causal genes and identify the underlying regulatory pathways. We first quantitatively verified our method by a test using data generated by yeast knock-out experiments. Over 40% of inferred causal genes are correct, which is significantly better than 10% by random guess. We then applied our method to a recent genome-wide expression variation study in yeast. We show that our method can correctly identify the causal genes and effectively output experimentally verified pathways. New potential gene regulatory pathways are generated and presented as a global network.
  
  
  BACKGROUND
  Source code is available upon request.
authors.affiliation: Molecular and Computational Biology Program, University of Southern California, Los Angeles, USA.
authors.forename: Zhidong
authors.initials: Z
authors.lastname: Tu
authors.forename: Li
authors.initials: L
authors.lastname: Wang
authors.forename: Michelle N
authors.initials: MN
authors.lastname: Arbeitman
authors.forename: Ting
authors.initials: T
authors.lastname: Chen
authors.forename: Fengzhu
authors.initials: F
authors.lastname: Sun
mesh_terms: D002874:Chromosome Mapping / Q000379:methods; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D018390:Gene Targeting / Q000379:methods*; D008954:Models, Biological; D025941:Protein Interaction Mapping / Q000379:methods; D050436:Regulatory Elements, Transcriptional / Q000502:physiology*; D015398:Signal Transduction / Q000502:physiology*; D018511:Systems Integration; D014157:Transcription Factors / Q000502:physiology*
chemical_list: D014157:Transcription Factors