Powerful Cocktail Methods for Detecting Genome-wide Gene-Environment Interaction

摘要: Identifying gene and environment interaction (G × E) can provide insights into biological networks of com- plex diseases, identify novel genes that act synergistically with environmental factors, inform risk prediction. However, despite the fact hundreds disease-associated loci have been identified from genome-wide association studies (GWAS), few G ×Es discovered. One reason is most are underpowered for detecting these interactions. Several new methods proposed to improve power E analysis, but performance varies scenario. In this article, we present a module-based approach integrating various exploits each method's appealing aspects. There three modules in our approach: (1) screening module prioritizing Single Nucleotide Polymorphisms (SNPs); (2) multiple comparison testing E; (3) module. We combine all develop two "cocktail" methods. demonstrate cocktail maintain type I error, tracks well best existing methods, may be different un- der scenarios models. For GWAS, where true models unknown, like powerful under wide range situations particularly appealing. Broadly speaking, modular conceptually straightforward computationally simple. It builds on common test statistics easily implemented without additional computational efforts. also allows an easy incorporation as they developed. Our work provides comprehensive tool devising effective strategies detection gene-environment Genet. Epidemiol. 36:183-194, 2012. C � 2012 Wiley Periodicals, Inc.