Tools for efficient epistasis detection in genome-wide association study

Xiang Zhang, Shunping Huang, Fei Zou, Wei Wang

Research output: Contribution to journalReview article

9 Scopus citations

Abstract

Background: 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.Results: 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.Conclusions: 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/.

Original languageEnglish (US)
Article number1
JournalSource Code for Biology and Medicine
Volume6
DOIs
StatePublished - Jan 4 2011

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Health Informatics
  • Information Systems and Management

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