title: IGENT: efficient entropy based algorithm for genome-wide gene-gene interaction analysis.
abstract: BACKGROUND
  With the development of high-throughput genotyping and sequencing technology, there are growing evidences of association with genetic variants and complex traits. In spite of thousands of genetic variants discovered, such genetic markers have been shown to explain only a very small proportion of the underlying genetic variance of complex traits. Gene-gene interaction (GGI) analysis is expected to unveil a large portion of unexplained heritability of complex traits.
  
  
  METHODS
  In this work, we propose IGENT, Information theory-based GEnome-wide gene-gene iNTeraction method. IGENT is an efficient algorithm for identifying genome-wide gene-gene interactions (GGI) and gene-environment interaction (GEI). For detecting significant GGIs in genome-wide scale, it is important to reduce computational burden significantly. Our method uses information gain (IG) and evaluates its significance without resampling.
  
  
  RESULTS
  Through our simulation studies, the power of the IGENT is shown to be better than or equivalent to that of that of BOOST. The proposed method successfully detected GGI for bipolar disorder in the Wellcome Trust Case Control Consortium (WTCCC) and age-related macular degeneration (AMD).
  
  
  CONCLUSIONS
  The proposed method is implemented by C++ and available on Windows, Linux and MacOSX.
authors: Min-Seok
authors: MS
authors: Kwon
authors: Mira
authors: M
authors: Park
authors: Taesung
authors: T
authors: Park
mesh_terms: D000465:Algorithms; D001714:Bipolar Disorder / Q000235:genetics; D019277:Entropy; D004843:Epistasis, Genetic; D023281:Genomics / Q000379:methods*; D006801:Humans; D008268:Macular Degeneration / Q000235:genetics; D008957:Models, Genetic; D013997:Time Factors