Abstract
Most human traits are influenced by the interplay between genetic and environmental factors. Many statistical methods have been proposed to screen for gene-environment interaction (GxE) in the post genome-wide association study era. However, most of the existing methods assume a linear interaction between genetic and environmental factors toward phenotypic variations, which diminishes statistical power in the case of nonlinear GxE. In this paper, we present a flexible statistical procedure to detect GxE regardless of whether the underlying relationship is linear or not. By modeling the joint genetic and GxE effects as a varying-coefficient function of the environmental factor, the proposed model is able to capture dynamic trajectories of GxE. We employ a likelihood ratio test with a fast Monte Carlo algorithm for hypothesis testing. Simulations were conducted to evaluate validity and power of the proposed model in various settings. Real data analysis was performed to illustrate its power, in particular, in the case of nonlinear GxE.
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Data availability
Data sharing is not applicable to this article as no new data were created in this study. The program code of the proposed method is available from the R package GEVACO.
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Acknowledgements
The authors thank Dr. Helen Hobbs for granting permission to use the DHS data, and acknowledge the Texas Advanced Computing Center (https://www.tacc.utexas.edu) at The University of Texas at Austin for providing high performance computing resources that have contributed to the research results reported within this paper.
Funding
This work is supported by the National Institute of Environmental Health Sciences grant R03ES034138 to C.X. and Z.Z. Z.Z. is also supported in part by the National Institute on Minority Health and Health Disparities grant 5U54MD013376-8281 and the National Institute on Aging grant U19AG078109. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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Zhengyang Zhou, Hung‑Chih Ku, Sydney E. Manning, Ming Zhang and Chao Xing declare that they have no conflict of interest.
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Zhou, Z., Ku, HC., Manning, S.E. et al. A Varying Coefficient Model to Jointly Test Genetic and Gene–Environment Interaction Effects. Behav Genet 53, 374–382 (2023). https://doi.org/10.1007/s10519-022-10131-w
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DOI: https://doi.org/10.1007/s10519-022-10131-w