摘要：Genome-wide association study (GWAS) aims to find genetic factors underlying complex phenotypic traits, for which epistasis or gene-gene interaction detection is often preferred over single-locus approach. However, the computational burden has been a major hurdle to apply epistasis test in the genome-wide scale due to a large number of single nucleotide polymorphism (SNP) pairs to be tested. We have developed a set of three efficient programs, FastANOVA, COE and TEAM, that support epistasis test in a variety of problem settings in GWAS. These programs utilize permutation test to properly control error rate such as family-wise error rate (FWER) and false discovery rate (FDR). They guarantee to find the optimal solutions, and significantly speed up the process of epistasis detection in GWAS. A web server with user interface and source codes are available at the website http://www.csbio.unc.edu/epistasis/ . The source codes are also available at SourceForge http://sourceforge.net/projects/epistasis/ .
关键词：False Discovery Rate ; Minimum Span Tree ; Brute Force Approach ; False Discovery Rate Control ; Binary Phenotype
