title: Does accounting for gene-environment interactions help uncover association between rare variants and complex diseases?
abstract: OBJECTIVE
  To determine whether accounting for gene-environment (G×E) interactions improves the power to detect associations between rare variants and a disease, we have extended three statistical methods and compared their power under various simulated disease models.
  
  
  METHODS
  To test for association of a group of rare variants with a disease, Min-P uses the lowest p value within the group of variants, CAST (Cohort Allelic Sums Test) uses an indicator variable to quantify the rare alleles within the group of variants, and SKAT (Sequence Kernel Association Test) uses a logistic regression based on kernel machine. For each method, we incorporate a term for the G×E interaction and test for association and interaction jointly.
  
  
  RESULTS
  When testing for disease association with a set of rare variants, accounting for G×E interactions can improve power in specific situations (pure interaction or high proportion of causal variants interacting with the environment). However, the power of this approach can decrease, in particular in the presence of main genetic or environmental effects. Among the methods compared, the optimized and weighted SKAT performed best, whether to test for genetic association or to test it jointly with G×E interactions.
  
  
  CONCLUSIONS
  This approach can be used in specific situations but is not appropriate for a primary analysis.
authors.affiliation: Department of Epidemiology and Biostatistics and Institute for Human Genetics, University of California, San Francisco, CA, USA.
authors.forename: Rémi
authors.initials: R
authors.lastname: Kazma
authors.forename: Niall J
authors.initials: NJ
authors.lastname: Cardin
authors.forename: John S
authors.initials: JS
authors.lastname: Witte
mesh_terms: D059647:Gene-Environment Interaction; D020022:Genetic Predisposition to Disease; D014644:Genetic Variation; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical