title: The complete compositional epistasis detection in genome-wide association studies.
abstract: BACKGROUND
  The detection of epistasis among genetic markers is of great interest in genome-wide association studies (GWAS). In recent years, much research has been devoted to find disease-associated epistasis in GWAS. However, due to the high computational cost involved, most methods focus on specific epistasis models, making the potential loss of power when the underlying epistasis models are not examined in these analyses.
  
  
  RESULTS
  In this work, we propose a computational efficient approach based on complete enumeration of two-locus epistasis models. This approach uses a two-stage (screening and testing) search strategy and guarantees the enumeration of all epistasis patterns. The implementation is done on graphic processing units (GPU), which can finish the analysis on a GWAS data (with around 5,000 subjects and around 350,000 markers) within two hours. Source code is available at http://bioinformatics.ust.hk/BOOST.html#GBOOST.
  
  
  CONCLUSIONS
  This work demonstrates that the complete compositional epistasis detection is computationally feasible in GWAS.
authors: Department of Computer Science and Institute of Theoretical and Computational Study, Hong Kong Baptist University, Hong Kong, China. xwan@comp.hkbu.edu.hk
authors: Xiang
authors: X
authors: Wan
authors: Can
authors: C
authors: Yang
authors: Qiang
authors: Q
authors: Yang
authors: Hongyu
authors: H
authors: Zhao
authors: Weichuan
authors: W
authors: Yu
mesh_terms: D016022:Case-Control Studies; D019295:Computational Biology / Q000295:instrumentation / Q000379:methods*; D004194:Disease / Q000235:genetics*; D004843:Epistasis, Genetic; D055106:Genome-Wide Association Study; D006801:Humans; D020641:Polymorphism, Single Nucleotide