Evaluating gene by environment (G$\times$E) interaction under an additive risk model (i.e. additive interaction) has gained wider attention. Recently, statistical tests have been proposed for detecting additive interaction that utilize an assumption on G-E independence to boost power, which do not rely on restrictive genetic models such as dominant or recessive models. However, a major limitation of these methods is a sharp increase in type I error when this assumption is violated. Our goal is to develop a robust test for additive G$\times$E interaction under the trend effect of genotype, applying an empirical Bayes-type shrinkage estimator of the relative excess risk due to interaction. The proposed method uses a set of constraints to impose the trend effect of genotype and builds an estimator that data-adaptively shrinks a RERI estimator obtained under a general model for G-E dependence using a retrospective likelihood framework. Numerical study under varying levels of departures from G-E independence shows that the proposed method is robust against the violation of the independence assumption while providing an adequate balance between bias and efficiency compared to existing methods. We applied the proposed method to the genetic data of Alzheimer's disease and lung cancer.
View details for DOI 10.1093/aje/kwab124
View details for PubMedID 33942053