Unveiling challenges in Mendelian randomization for gene–environment interaction

IF 1.7 4区 医学 Q3 GENETICS & HEREDITY
Malka Gorfine, Conghui Qu, Ulrike Peters, Li Hsu
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引用次数: 0

Abstract

Gene–environment (GxE) interactions play a crucial role in understanding the complex etiology of various traits, but assessing them using observational data can be challenging due to unmeasured confounders for lifestyle and environmental risk factors. Mendelian randomization (MR) has emerged as a valuable method for assessing causal relationships based on observational data. This approach utilizes genetic variants as instrumental variables (IVs) with the aim of providing a valid statistical test and estimation of causal effects in the presence of unmeasured confounders. MR has gained substantial popularity in recent years largely due to the success of genome-wide association studies. Many methods have been developed for MR; however, limited work has been done on evaluating GxE interaction. In this paper, we focus on two primary IV approaches: the two-stage predictor substitution and the two-stage residual inclusion, and extend them to accommodate GxE interaction under both the linear and logistic regression models for continuous and binary outcomes, respectively. Comprehensive simulation study and analytical derivations reveal that resolving the linear regression model is relatively straightforward. In contrast, the logistic regression model presents a considerably more intricate challenge, which demands additional effort.

揭示基因与环境相互作用的孟德尔随机化所面临的挑战。
基因与环境(GxE)之间的相互作用在了解各种性状的复杂病因方面起着至关重要的作用,但由于生活方式和环境风险因素的混杂因素无法测量,因此利用观察数据评估这些相互作用具有挑战性。孟德尔随机化(MR)已成为一种基于观察数据评估因果关系的重要方法。这种方法利用遗传变异作为工具变量(IV),目的是在存在未测量混杂因素的情况下提供有效的统计检验和因果效应估计。近年来,主要由于全基因组关联研究的成功,MR 得到了广泛的推广。目前已开发出许多 MR 方法,但评估 GxE 相互作用的工作还很有限。在本文中,我们重点讨论了两种主要的 IV 方法:两阶段预测因子替换法和两阶段残差包含法,并将它们扩展到线性回归模型和逻辑回归模型下,分别用于连续结果和二元结果的 GxE 交互作用。综合模拟研究和分析推导表明,线性回归模型的解析相对简单。相比之下,逻辑回归模型面临的挑战要复杂得多,需要付出更多的努力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Genetic Epidemiology
Genetic Epidemiology 医学-公共卫生、环境卫生与职业卫生
CiteScore
4.40
自引率
9.50%
发文量
49
审稿时长
6-12 weeks
期刊介绍: Genetic Epidemiology is a peer-reviewed journal for discussion of research on the genetic causes of the distribution of human traits in families and populations. Emphasis is placed on the relative contribution of genetic and environmental factors to human disease as revealed by genetic, epidemiological, and biologic investigations. Genetic Epidemiology primarily publishes papers in statistical genetics, a research field that is primarily concerned with development of statistical, bioinformatical, and computational models for analyzing genetic data. Incorporation of underlying biology and population genetics into conceptual models is favored. The Journal seeks original articles comprising either applied research or innovative statistical, mathematical, computational, or genomic methodologies that advance studies in genetic epidemiology. Other types of reports are encouraged, such as letters to the editor, topic reviews, and perspectives from other fields of research that will likely enrich the field of genetic epidemiology.
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