title: An empirical comparison of several recent epistatic interaction detection methods.
abstract: BACKGROUND
  Many new methods have recently been proposed for detecting epistatic interactions in GWAS data. There is, however, no in-depth independent comparison of these methods yet.
  
  
  RESULTS
  Five recent methods-TEAM, BOOST, SNPHarvester, SNPRuler and Screen and Clean (SC)-are evaluated here in terms of power, type-1 error rate, scalability and completeness. In terms of power, TEAM performs best on data with main effect and BOOST performs best on data without main effect. In terms of type-1 error rate, TEAM and BOOST have higher type-1 error rates than SNPRuler and SNPHarvester. SC does not control type-1 error rate well. In terms of scalability, we tested the five methods using a dataset with 100 000 SNPs on a 64 bit Ubuntu system, with Intel (R) Xeon(R) CPU 2.66 GHz, 16 GB memory. TEAM takes ~36 days to finish and SNPRuler reports heap allocation problems. BOOST scales up to 100 000 SNPs and the cost is much lower than that of TEAM. SC and SNPHarvester are the most scalable. In terms of completeness, we study how frequently the pruning techniques employed by these methods incorrectly prune away the most significant epistatic interactions. We find that, on average, 20% of datasets without main effect and 60% of datasets with main effect are pruned incorrectly by BOOST, SNPRuler and SNPHarvester.
  
  
  BACKGROUND
  The software for the five methods tested are available from the URLs below. TEAM: http://csbio.unc.edu/epistasis/download.php BOOST: http://ihome.ust.hk/~eeyang/papers.html. SNPHarvester: http://bioinformatics.ust.hk/SNPHarvester.html. SNPRuler: http://bioinformatics.ust.hk/SNPRuler.zip. Screen and Clean: http://wpicr.wpic.pitt.edu/WPICCompGen/.
  
  
  BACKGROUND
  wangyue@nus.edu.sg.
authors: NUS Graduate School for Integrative Sciences and Engineering, Department of Computer Science, School of Computing, National University of Singapore and Data Mining Department, Institute for Infocomm Research, Singapore. wangyue@nus.edu.sg
authors: Yue
authors: Y
authors: Wang
authors: Guimei
authors: G
authors: Liu
authors: Mengling
authors: M
authors: Feng
authors: Limsoon
authors: L
authors: Wong
mesh_terms: D004843:Epistasis, Genetic; D055106:Genome-Wide Association Study; D020641:Polymorphism, Single Nucleotide; D012984:Software