title: A survey about methods dedicated to epistasis detection.
abstract: During the past decade, findings of genome-wide association studies (GWAS) improved our knowledge and understanding of disease genetics. To date, thousands of SNPs have been associated with diseases and other complex traits. Statistical analysis typically looks for association between a phenotype and a SNP taken individually via single-locus tests. However, geneticists admit this is an oversimplified approach to tackle the complexity of underlying biological mechanisms. Interaction between SNPs, namely epistasis, must be considered. Unfortunately, epistasis detection gives rise to analytic challenges since analyzing every SNP combination is at present impractical at a genome-wide scale. In this review, we will present the main strategies recently proposed to detect epistatic interactions, along with their operating principle. Some of these methods are exhaustive, such as multifactor dimensionality reduction, likelihood ratio-based tests or receiver operating characteristic curve analysis; some are non-exhaustive, such as machine learning techniques (random forests, Bayesian networks) or combinatorial optimization approaches (ant colony optimization, computational evolution system).
authors.affiliation: Computer Science Institute of Nantes-Atlantic (Lina), Centre National de la Recherche Scientifique UMR 6241, Ecole Polytechnique de l'Université de Nantes Nantes, France.
authors.forename: Clément
authors.initials: C
authors.lastname: Niel
authors.affiliation: Computer Science Institute of Nantes-Atlantic (Lina), Centre National de la Recherche Scientifique UMR 6241, University of Nantes Nantes, France.
authors.forename: Christine
authors.initials: C
authors.lastname: Sinoquet
authors.affiliation: Institut du Thorax, Institut National de la Santé et de la Recherche Médicale UMR 1087, Centre National de la Recherche Scientifique UMR 6291, University of Nantes Nantes, France.
authors.forename: Christian
authors.initials: C
authors.lastname: Dina
authors.affiliation: European Genomic Institute for Diabetes FR3508, Centre National de la Recherche Scientifique UMR 8199, Lille 2 University Lille, France.
authors.forename: Ghislain
authors.initials: G
authors.lastname: Rocheleau
keywords: biological data mining; complex disease; epistasis detection; feature selection; genome-wide association study