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Tao J, Helali AE, Ho JC, Lam WWT, Pang H. An Evaluation of Gene-Diet Interaction Statistical Methods and Discovery of rs7175421-Whole Grain Interaction in Lung Cancer. Nutr Cancer 2022; 75:219-227. [PMID: 35930377 DOI: 10.1080/01635581.2022.2104878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Dietary factors show different effects on genetically diverse populations. Scientific research uses gene-environment interaction models to study the effects of dietary factors on genetically diverse populations for lung cancer risk. However, previous study designs have not investigated the degree of type I error inflation and, in some instances, have not corrected for multiple testing. Using a motivating investigation of diet-gene interaction and lung cancer risk, we propose a training and testing strategy and perform real-world simulations to select the appropriate statistical methods to reduce false-positive discoveries. The simulation results show that the unconstrained maximum likelihood (UML) method controls the type I error better than the constrained maximum likelihood (CML). The empirical Bayesian (EB) method can compete with the UML method in achieving statistical power and controlling type I error. We observed a significant interaction between SNP rs7175421 with dietary whole grain in lung cancer prevention, with an effect size (standard error) of -0.312 (0.112) for EB estimate. SNP rs7175421 may interact with dietary whole grains in modulating lung cancer risk. Evaluating statistical methods for gene-diet interaction analysis can help balance the statistical power and type I error.
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Affiliation(s)
- Jun Tao
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong, China
| | - Aya El Helali
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong, China
| | - James C Ho
- Department of Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong, China
| | - Wendy W T Lam
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong, China.,Jockey Club Institute of Cancer Care, The University of Hong Kong, Pok Fu Lam, Hong Kong, China
| | - Herbert Pang
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong, China.,Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina, USA
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Sanyal N, Napolioni V, de Rochemonteix M, Belloy ME, Caporaso NE, Landi MT, Greicius MD, Chatterjee N, Han SS. A Robust Test for Additive Gene-Environment Interaction Under the Trend Effect of Genotype Using an Empirical Bayes-Type Shrinkage Estimator. Am J Epidemiol 2021; 190:1948-1960. [PMID: 33942053 DOI: 10.1093/aje/kwab124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 04/19/2021] [Accepted: 04/21/2021] [Indexed: 11/12/2022] Open
Abstract
Evaluating gene by environment (G × E) interaction under an additive risk model (i.e., additive interaction) has gained wider attention. Recently, statistical tests have been proposed for detecting additive interaction, utilizing an assumption on gene-environment (G-E) independence to boost power, that do not rely on restrictive genetic models such as dominant or recessive models. However, a major limitation of these methods is a sharp increase in type I error when this assumption is violated. Our goal was to develop a robust test for additive G × E interaction under the trend effect of genotype, applying an empirical Bayes-type shrinkage estimator of the relative excess risk due to interaction. The proposed method uses a set of constraints to impose the trend effect of genotype and builds an estimator that data-adaptively shrinks an estimator of relative excess risk due to interaction obtained under a general model for G-E dependence using a retrospective likelihood framework. Numerical study under varying levels of departures from G-E independence shows that the proposed method is robust against the violation of the independence assumption while providing an adequate balance between bias and efficiency compared with existing methods. We applied the proposed method to the genetic data of Alzheimer disease and lung cancer.
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