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High-Dimensional Gene-Environment Interaction Analysis
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DOI：10.1146/annurev-statistics-112723-034315.png)
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Abstract
En 中文
Beyond the main genetic and environmental effects, gene-environment (G-E) interactions have been demonstrated to significantly contribute to the development and progression of complex diseases. Published analyses of G-E interactions have primarily used a supervised framework to model both low-dimensional environmental factors and high-dimensional genetic factors in relation to disease outcomes. In this article, we aim to provide a selective review of methodological developments in G-E interaction analysis from a statistical perspective. The three main families of techniques are hypothesis testing, variable selection, and dimension reduction, which lead to three general frameworks: testing-based, estimation-based, and predictionbased. Linear- and nonlinear-effects analysis, fixed- and random-effects analysis, marginal and joint analysis, and Bayesian and frequentist analysis are reviewed to facilitate the conduct of interaction analysis in a wide range of situations with various assumptions and objectives. Statistical properties, computations, applications, and future directions are also discussed.
Keywords:
gene-environment interaction
hypothesis testing
variable selection
dimension reduction
marginal and joint analysis
Journal
IF:
8.7
Papers:
211
Citations:
2.4K