Mendelian randomization in health research: Using appropriate genetic variants and avoiding biased estimates
Open accessAbstract
Mendelian randomization methods, which use genetic variants as instrumental variables for exposures of interest to overcome problems of confounding and reverse causality, are becoming widespread for assessing causal relationships in epidemiological studies. The main purpose of this paper is to demonstrate how results can be biased if researchers select genetic variants on the basis of their association with the exposure in their own dataset, as often happens in candidate gene analyses. This can lead to estimates that indicate apparent "causal" relationships, despite there being no true effect of the exposure. In addition, we discuss the potential bias in estimates of magnitudes of effect from Mendelian randomization analyses when the measured exposure is a poor proxy for the true underlying exposure. We illustrate these points with specific reference to tobacco research.
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Three genetic variants (rs1800562 and rs1799945 in HFE and rs855791 in TMPRSS6) associate with increased serum iron, ferritin, and transferrin saturation and decreased transferrin levels, serving as instruments for systemic iron status.
“3 genetic variants (rs1800562 and rs1799945 in the hemochromatosis gene [HFE] and rs855791 in the transmembrane protease serine 6 gene [TMPRSS6]) that associate with increased serum iron, ferritin, and transferrin saturation and decreased transferrin levels, thus serving as instruments for systemic iron status.”
From Associations of genetically determined iron status across the phenome: A mendelian randomization study (2019) · cites this paper as [13]
The Introduction’s ‘Assumptions of Mendelian randomization’ section explains when a SNP can serve as an instrument, but does not discuss systemic iron status. Searches for the three rs identifiers, HFE, iron biomarkers, and transferrin found no passage in our copy supporting the claimed associations or use of these variants as iron-status instruments. (LLM, conf=0.75)
Experimental — accuracy not yet measured'Winner's curse' is unlikely to happen in two-sample Mendelian randomization, whereas it can underestimate true causal effects in one-sample Mendelian randomization.
“In particular ‘winners’ curse’, which can underestimate true causal effects in one-sample MR, 10 is unlikely to happen in two-sample MR”
From Commentary: Two-sample Mendelian randomization: opportunities and challenges (2016) · cites this paper as [10]
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- Authors
- Taylor, A. E., Davies, N. M., Ware, J. J., VanderWeele, T., Smith, G. D., & Munafò, M. R.
- Affiliations
- University of Bristol (GB)
- Harvard University (US)
- Corresponding author
- Taylor, Amy E.
- ORCID
- Venue
- Economics & Human Biology
- Publisher
- Elsevier BV
- Published
- 2014-03
- Volume
- 13
- Pages
- 99-106
- ISSN
- 1570-677X (ISSN-L)
- PMC
- PMC3989031
- OpenAlex
- W2041215109
- Cited by
- 166
- References
- 56
- Citations by year
- 2026: 6 · 2025: 5 · 2024: 26 · 2023: 15 · 2022: 18 · 2021: 9 · 2020: 17 · 2019: 19 · 2018: 14 · 2017: 12 · 2016: 13 · 2015: 9 · 2014: 3
- FWCI
- 5.53
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Economics & Human Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Bristol Research (University of Bristol) (submittedVersion)
- Explore Bristol Research (submittedVersion)
- Funders
- Wellcome Trust (086684)
- Cancer Research UK
- British Heart Foundation
- National Institutes of Health (UL1TR001425)
- Medical Research Council (MR/K023195/1, MR/J01351X/1, MR/K023233/1)
- Economic and Social Research Council (ES/G007489/1)
- National Institute of Environmental Health Sciences (R01ES017876)
- National Center for Advancing Translational Sciences
- 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, genetic variants as instrumental variables, selection bias, candidate gene association, confounding, reverse causality, effect size bias, exposure measurement error, tobacco use, causal inference
- MeSH terms
- Humans, Research Design, Smoking, Bias, Causality, Confounding Factors, Epidemiologic, Genome-Wide Association Study, Mendelian Randomization Analysis
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