Methodological Challenges in Mendelian Randomization
Open accessAbstract
We give critical attention to the assumptions underlying Mendelian randomization analysis and their biological plausibility. Several scenarios violating the Mendelian randomization assumptions are described, including settings with inadequate phenotype definition, the setting of time-varying exposures, the presence of gene-environment interaction, the existence of measurement error, the possibility of reverse causation, and the presence of linkage disequilibrium. Data analysis examples are given, illustrating that the inappropriate use of instrumental variable techniques when the Mendelian randomization assumptions are violated can lead to biases of enormous magnitude. To help address some of the strong assumptions being made, three possible approaches are suggested. First, the original proposal of Katan (Lancet. 1986;1:507-508) for Mendelian randomization was not to use instrumental variable techniques to obtain estimates but merely to examine genotype-outcome associations to test for the presence of an effect of the exposure on the outcome. We show that this more modest goal and approach can circumvent many, though not all, the potential biases described. Second, we discuss the use of sensitivity analysis in evaluating the consequences of violations in the assumptions and in attempting to correct for those violations. Third, we suggest that a focus on negative, rather than positive, Mendelian randomization results may turn out to be more reliable.
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The exclusion restriction is sometimes also referred to as an assumption of no horizontal pleiotropy, but it can be violated in several other ways, including by gene-exposure interaction, by having some form of time varying exposures, by measurement error in the exposure that is related to the instrument, or by a multi-component exposure.
“The exclusion restriction is sometimes also referred to as an assumption of no horizontal pleiotropy (box 3), but it can be violated in several other ways (eg, by gene-exposure interaction, by having some form of time varying exposures, by measurement error in the exposure that is related to the instrument or by a multi-component exposure).59”
From Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomisation (STROBE-MR): explanation and elaboration. (2021) · cites this paper as [59]
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Self-citation
- Authors
- VanderWeele, T. J., Tchetgen Tchetgen, E. J., Cornelis, M., & Kraft, P.
- Affiliations
- Harvard University (US)
- Corresponding author
- VanderWeele, Tyler J.
- Venue
- Epidemiology
- Published
- 2014-05
- Volume
- 25
- Issue
- 3
- Pages
- 427-435
- ISSN
- 1044-3983 (ISSN-L)
- PMC
- PMC3981897
- OpenAlex
- W2325761966
- Cited by
- 555
- References
- 44
- Citations by year
- 2026: 26 · 2025: 52 · 2024: 82 · 2023: 70 · 2022: 58 · 2021: 48 · 2020: 44 · 2019: 33 · 2018: 43 · 2017: 31 · 2016: 24 · 2015: 31 · 2014: 11
- FWCI
- 4.28
- Type
- journal article
- Language
- en
- Update notices
- correction (2025-03-01)
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Cancer Institute (P01CA134294, R35CA197449)
- National Institute of Allergy and Infectious Diseases (R01AI104459)
- National Institute of Environmental Health Sciences (ES017876, R01ES017876, R01ES020337, R56ES017876)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- Mendelian randomization, gene-environment interaction, reverse causality, measurement error, linkage disequilibrium, time-varying exposure, sensitivity analysis
- MeSH terms
- Genetic Markers, Genotype, Humans, Random Allocation, Sensitivity and Specificity, Bias, Mendelian Randomization Analysis, Gene-Environment Interaction
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