title: Genome-wide meta-analysis of joint tests for genetic and gene-environment interaction effects.
abstract: BACKGROUND
  There is growing interest in the study of gene-environment interactions in the context of genome-wide association studies (GWASs). These studies will likely require meta-analytic approaches to have sufficient power.
  
  
  METHODS
  We describe an approach for meta-analysis of a joint test for genetic main effects and gene-environment interaction effects. Using simulation studies based on a meta-analysis of five studies (total n = 10,161), we compare the power of this test to the meta-analysis of marginal test of genetic association and the meta-analysis of standard 1 d.f. interaction tests across a broad range of genetic main effects and gene-environment interaction effects.
  
  
  RESULTS
  We show that the joint meta-analysis is valid and can be more powerful than classical meta-analytic approaches, with a potential gain of power over 50% compared to the marginal test. The standard interaction test had less than 1% power in almost all the situations we considered. We also show that regardless of the test used, sample sizes far exceeding those of a typical individual GWAS will be needed to reliably detect genes with subtle gene-environment interaction patterns.
  
  
  CONCLUSIONS
  The joint meta-analysis is an attractive approach to discover markers which may have been missed by initial GWASs focusing on marginal marker-trait associations.
authors.affiliation: Program in Molecular and Genetic Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA. haschard @ hsph.harvard.edu
authors.forename: Hugues
authors.initials: H
authors.lastname: Aschard
authors.forename: Dana B
authors.initials: DB
authors.lastname: Hancock
authors.forename: Stephanie J
authors.initials: SJ
authors.lastname: London
authors.forename: Peter
authors.initials: P
authors.lastname: Kraft
mesh_terms: D016022:Case-Control Studies; D003198:Computer Simulation; D059647:Gene-Environment Interaction; D055614:Genetic Phenomena; D055106:Genome-Wide Association Study; D006801:Humans; D015201:Meta-Analysis as Topic; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D018401:Sample Size