1. Marginal interaction test for detecting interactions between genetic marker sets and environment in genome-wide studies.
- Author
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Shen, Linchuan, Amei, Amei, Liu, Bowen, Xu, Gang, Liu, Yunqing, Oh, Edwin C, Zhou, Xin, and Wang, Zuoheng
- Subjects
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FALSE positive error , *SYSTOLIC blood pressure , *FIXED effects model , *ECOLOGICAL genetics , *NATURE & nurture - Abstract
As human complex diseases are influenced by the interaction between genetics and the environment, identifying gene–environment interactions ( G × E ) is crucial for understanding disease mechanisms and predicting risk. Developing robust quantitative tools for G × E analysis can enhance the study of complex diseases. However, many existing methods that explore G × E focus on the interplay between an environmental factor and genetic variants, exclusively for common or rare variants. In this study, we developed MAGEIT_RAN and MAGEIT_FIX to identify interactions between an environmental factor and a set of genetic markers, including both rare and common variants, based on the MinQue for Summary statistics. The genetic main effects in MAGEIT_RAN and MAGEIT_FIX are modeled as random and fixed effects, respectively. Simulation studies showed that both tests had type I error under control, with MAGEIT_RAN being the most powerful test. Applying MAGEIT to a genome-wide analysis of gene–alcohol interactions on hypertension and seated systolic blood pressure in the Multiethnic Study of Atherosclerosis revealed genes like EIF2AK2 , CCNDBP1 , and EPB42 influencing blood pressure through alcohol interaction. Pathway analysis identified 1 apoptosis and survival pathway involving PKR and 2 signal transduction pathways associated with hypertension and alcohol intake, demonstrating MAGEIT_RAN's ability to detect biologically relevant gene–environment interactions. [ABSTRACT FROM AUTHOR]
- Published
- 2025
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