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Human complex diseases are affected by both genetic and environmental factors. When multiple environmental risk factors are present, the interaction effect between a gene and the environmental mixture can be larger than the addition of individual interactions, resulting in the so-called synergistic gene–environment (GxE) interactions. Existing literature has shown the power of synergistic gene-environment interaction analysis with cross-sectional traits. In this work, we propose a functional varying index coefficient model for longitudinal traits together with multiple longitudinal environmental risk factors and assess how the genetic effects on a longitudinal disease trait are nonlinearly modified by a mixture of environmental influences. We derive an estimation procedure for the nonparametric functional varying index coefficients under the quadratic inference function and penalized spline framework. We evaluate some theoretical properties such as estimation consistency and asymptotic normality of the estimates. We further propose a hypothesis testing procedure to assess the significance of the synergistic GxE effect. The performance of the estimation and testing procedure is evaluated through Monte Carlo simulation studies. Finally, the utility of the method is illustrated by a real dataset from a pain sensitivity study in which SNP effects are nonlinearly modulated by a mixture of drug dosages and other environmental variables to affect patients’ blood pressure and heart rate.
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High-Dimensional Gene–Environment Interaction Analysis
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 prediction-based. 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.
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- Award ID(s):
- 2209685
- PAR ID:
- 10598510
- Publisher / Repository:
- Annual Reviews
- Date Published:
- Journal Name:
- Annual Review of Statistics and Its Application
- ISSN:
- 2326-8298
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1146/annurev-statistics-112723-034315
