Title: Leveraging Methylome-Environment Interaction to Detect Genetic Determinants of Disease.
Authors: Slade, Emily; Kraft, Peter
Published In Hum Hered, (2016)
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.
PubMed ID: 27490128
MeSH Terms: Computer Simulation; DNA Methylation*; Epigenesis, Genetic; Gene-Environment Interaction*; Genetic Predisposition to Disease*; Models, Genetic