title: Testing gene-gene interactions in genome wide association studies.
abstract: Detection of gene-gene interaction has become increasingly popular over the past decade in genome wide association studies (GWAS). Besides traditional logistic regression analysis for detecting interactions between two markers, new methods have been developed in recent years such as comparing linkage disequilibrium (LD) in case and control groups. All these methods form the building blocks of most screening strategies for disease susceptibility loci in GWAS. In this paper, we are interested in comparing the competing methods and providing practical guidelines for selecting appropriate testing methods for interaction in GWAS. We first review a series of existing statistical methods to detect interactions, and then examine different definitions of interactions to gain insight into the theoretical relationship between the existing testing methods. Lastly, we perform extensive simulations to compare powers of various methods to detect either interaction between two markers at two unlinked loci or the overall association allowing for both interaction and main effects. This investigation reveals informative characteristics of various methods that are helpful to GWAS investigators.
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, Washington, United States of America.
authors.forename: Jie Kate
authors.initials: JK
authors.lastname: Hu
authors.forename: Xianlong
authors.initials: X
authors.lastname: Wang
authors.forename: Pei
authors.initials: P
authors.lastname: Wang
mesh_terms: D004194:Disease / Q000235:genetics; D004843:Epistasis, Genetic; D005799:Genes, Dominant; D005808:Genes, Recessive; D005819:Genetic Markers; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D015810:Linkage Disequilibrium; D016015:Logistic Models; D008957:Models, Genetic; D019683:Penetrance; D020641:Polymorphism, Single Nucleotide
chemical_list: D005819:Genetic Markers
keywords: GWAS; gene-gene interaction; linkage disequilibrium; logistic regression; penetrance