Unveiling challenges in Mendelian randomization for gene–environment interaction

Malka Gorfine, Conghui Qu, Ulrike Peters, Li Hsu

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)164-189
Number of pages26
JournalGenetic Epidemiology
Issue number4
Early online date29 Feb 2024
StatePublished - Jun 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2024 Wiley Periodicals LLC.


The work is supported in part by grants from the National Institutes of Health (R01CA189532, R01HL145806, U01CA164930, and R01CA20140), by the Israel Science Foundation (ISF) grant number 767/21, and by a grant from the Tel‐Aviv University Center for AI and Data Science (TAD). The authors thank the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO) and the participating studies, investigators, staff, and study participants for their dedication and contributions. A full list of these studies and funding is provided in the Supporting Information.

FundersFunder number
Tel‐Aviv University Center
National Institutes of HealthR01HL145806, R01CA189532, R01CA20140, U01CA164930
Israel Science Foundation767/21


    • GWAS
    • colorectal cancer
    • instrumental variable
    • interaction effect
    • linear regression
    • logistic regression
    • measurement error
    • polygenic risk score


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