title: Detecting genome-wide epistases based on the clustering of relatively frequent items.
abstract: BACKGROUND
  In genome-wide association studies (GWAS), up to millions of single nucleotide polymorphisms (SNPs) are genotyped for thousands of individuals. However, conventional single locus-based approaches are usually unable to detect gene-gene interactions underlying complex diseases. Due to the huge search space for complicated high order interactions, many existing multi-locus approaches are slow and may suffer from low detection power for GWAS.
  
  
  RESULTS
  In this article, we develop a simple, fast and effective algorithm to detect genome-wide multi-locus epistatic interactions based on the clustering of relatively frequent items. Extensive experiments on simulated data show that our algorithm is fast and more powerful in general than some recently proposed methods. On a real genome-wide case-control dataset for age-related macular degeneration (AMD), the algorithm has identified genotype combinations that are significantly enriched in the cases.
  
  
  BACKGROUND
  http://www.cs.ucr.edu/~minzhux/EDCF.zip
  
  
  BACKGROUND
  minzhux@cs.ucr.edu; jingli@cwru.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors: Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA.
authors: Minzhu
authors: M
authors: Xie
authors: Jing
authors: J
authors: Li
authors: Tao
authors: T
authors: Jiang
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D004843:Epistasis, Genetic; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study; D006801:Humans; D008268:Macular Degeneration / Q000235:genetics; D020641:Polymorphism, Single Nucleotide