A Robust Test for Additive Gene-Environment Interaction Under the Trend Effect of Genotype Using an Empirical Bayes-Type Shrinkage Estimator

• Activity Number: 81 - Contributed Poster Presentations: Section on Statistics in Epidemiology
• Type: Contributed
• Date/Time: Monday, August 3, 2020 : 10:00 AM to 2:00 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Nilotpal Sanyal* and Valerio Napolioni and Matthieu de Rochemonteix and Michaël E. Belloy and Michael D. Greicius and Nilanjan Chatterjee and Summer Han
• Companies: Stanford University School of Medicine and Stanford University School of Medicine and Stanford University School of Medicine and Stanford University School of Medicine and Stanford University School of Medicine and Johns Hopkins University and Stanford University
• Keywords: Additive interaction; Empirical Bayes; Gene-Environment Interaction; Gene-Environment Independence; Trend effect of genotypes; Robust test

Abstract

Evaluating gene x environment (G×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×E interaction under the trend effect of genotype, applying an empirical Bayes type shrinkage estimator of the relative excess risk due to interaction (RERI). The proposed method uses a set of constraints to impose the trend effect of genotype and builds an estimator that data-adaptively shrinks an RERI estimator obtained under a general model for G-E dependence to an estimator obtained under G-E independence. Numerical study shows the proposed method is robust against the violation of G-E independence while providing an adequate balance between bias and efficiency. We illustrate the proposed method using genetic data for Alzheimer’s disease and lung cancer.