title: Leveraging Methylome-Environment Interaction to Detect Genetic Determinants of Disease.
abstract: The association between DNA methylation and a trait of interest may depend on an environmental exposure, and incorrectly accounting for this dependence can lead to a reduction in power of the standard tests used in epigenome-wide association studies. We present the M-ME test to jointly test for the main effect of DNA methylation and methylation-environment interaction.
  
  
  
  Through simulation, we compare the power and type 1 error of the M-ME test to a standard marginal test (M test) and a standard interaction test (ME test) under 1,800 different underlying models. These models allow for methylation-environment correlation and measurement error in the exposure.
  
  
  
  In many true underlying models, either the M test or the ME test has very low power, but the M-ME test has optimal or nearly optimal power to detect a DNA methylation effect in all models considered, including those with methylation- environment dependence and measurement error in the exposure. Type 1 error inflation occurs in the tests when the exposure is measured with error and correlated with DNA methylation.
  
  
  
  The M-ME test is an attractive choice for studies aiming to detect any DNA methylation association when little is known about the epigenetic associations a priori.
authors.affiliation: Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Mass., USA.
authors.forename: Emily
authors.initials: E
authors.lastname: Slade
authors.forename: Peter
authors.initials: P
authors.lastname: Kraft
mesh_terms: D003198:Computer Simulation; D019175:DNA Methylation; D044127:Epigenesis, Genetic; D059647:Gene-Environment Interaction; D020022:Genetic Predisposition to Disease; D008957:Models, Genetic