Meta‐analysis of gene‐environment interaction exploiting gene‐environment independence across multiple case‐control studies
Year: 2017
Type: article
Study design: Systematic Review
Abstract: Multiple papers have studied the use of gene-environment (G-E) independence to enhance power for testing gene-environment interaction in case-control studies. However, studies that evaluate the role of G-E independence in a meta-analysis framework are limited. In this paper, we extend the single-study empirical Bayes type shrinkage estimators proposed by Mukherjee and Chatterjee (2008) to a meta-analysis setting that adjusts for uncertainty regarding the assumption of G-E independence across studies. We use the retrospective likelihood framework to derive an adaptive combination of estimators obtained under the constrained model (assuming G-E independence) and unconstrained model (without assumptions of G-E independence) with weights determined by measures of G-E association derived from multiple studies. Our simulation studies indicate that this newly proposed estimator has improved average performance across different simulation scenarios than the standard alternative of using inverse variance (covariance) weighted estimators that combines study-specific constrained, unconstrained, or empirical Bayes estimators. The results are illustrated by meta-analyzing 6 different studies of type 2 diabetes investigating interactions between genetic markers on the obesity related FTO gene and environmental factors body mass index and age.
Source: Statistics in Medicine
DOI: 10.1002/sim.7398
Institutions: University of Michigan, Genentech
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: bronze
Awards: Set based tests for genetic association and gene-environment interaction in longitudinal studies, Identifying Genes for Type 2 Diabetes: FUSION, Identifying Genes for Type 2 Diabetes: FUSION, G7083808364, Efficient Design and Analytic Strategies for Enhancing the Power of Detecting G X, Michigan Diabetes Research Center (MDRC), Identifying Genes for Type 2 Diabetes: FUSION, Michigan Center on Lifestage Environmental Exposures and Disease (M-LEEaD)
OpenAlex ID: W2737169039