Exploring and mitigating potential bias when genetic instrumental variables are associated with multiple non-exposure traits in Mendelian randomization
Open accessAbstract
Background: Our aim is to produce guidance on exploring and mitigating possible bias when genetic instrumental variables (IVs) associate with traits other than the exposure of interest in Mendelian randomization (MR) studies. Methods: We use causal diagrams to illustrate scenarios that could result in IVs being related to (non-exposure) traits. We recommend that MR studies explore possible IV-non-exposure associations across a much wider range of traits than is usually the case. Where associations are found, confounding by population stratification should be assessed through adjusting for relevant population structure variables. To distinguish vertical from horizontal pleiotropy we suggest using bidirectional MR between the exposure and non-exposure traits and MR of the effect of the non-exposure traits on the outcome of interest. If vertical pleiotropy is plausible, standard MR methods should be unbiased. If horizontal pleiotropy is plausible, we recommend using multivariable MR to control for observed pleiotropic traits and conducting sensitivity analyses which do not require prior knowledge of specific invalid IVs or pleiotropic paths. Results: We applied our recommendations to an illustrative example of the effect of maternal insomnia on offspring birthweight in the UK Biobank. We found little evidence that unexpected IV-non-exposure associations were driven by population stratification. Three out of six observed non-exposure traits plausibly reflected horizontal pleiotropy. Multivariable MR and sensitivity analyses suggested an inverse association of insomnia with birthweight, but effects were imprecisely estimated in some of these analyses. Conclusions: We provide guidance for MR studies where genetic IVs associate with non-exposure traits. Key messages: Genetic variants are increasingly found to associate with more than one social, behavioural or biological trait at genome-wide significance, which is a challenge in Mendelian randomization (MR) studies. Four broad scenarios (i.e. population stratification, vertical pleiotropy, horizontal pleiotropy and reverse causality) could result in an IV-non-exposure trait association. Population stratification can be assessed through adjusting for population structure with individual data, while two-sample MR studies should check whether the original genome-wide association studies have used robust methods to properly account for it. We apply currently available MR methods for discriminating between vertical and horizontal pleiotropy and mitigating against horizontal pleiotropy to an example exploring the effect of maternal insomnia on offspring birthweight. Our study highlights the pros and cons of relying more on sensitivity analyses without considering particular pleiotropic paths versus systematically exploring and controlling for potential pleiotropic paths via known characteristics.
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One further method for identifying potential violations of the IV conditions when the exposure is binary or categorical is using IV inequality constraints.
“One further method for identifying potential violations of the IV conditions when the exposure is binary or categorical is using IV inequality constraints.29,67,68”
From Mendelian randomization (2022) · cites this paper as [68]
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- Authors
- Yang, Q., Sanderson, E., Tilling, K., Borges, M. C., & Lawlor, D. A.
- Affiliations
- University of Bristol (GB)
- University Hospitals Bristol NHS Foundation Trust (GB)
- Corresponding author
- Yang, Qian
- Venue
- European Journal of Epidemiology
- Publisher
- openRxiv
- Published
- 2019-10-18
- Volume
- 37
- Issue
- 7
- Pages
- 683-700
- ISSN
- 0393-2990 (ISSN-L)
- PMC
- PMC9329407
- OpenAlex
- W4281628584
- Cited by
- 85
- References
- 107
- Citations by year
- 2026: 8 · 2025: 21 · 2024: 19 · 2023: 15 · 2022: 15 · 2021: 4 · 2020: 2 · 2019: 1
- FWCI
- 10.70
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- European Journal of Epidemiology (publishedVersion, cc-by)
- medRxiv (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Explore Bristol Research (submittedVersion, cc-by)
- Bristol Research (University of Bristol) (submittedVersion, cc-by)
- Funders
- University Hospitals Bristol NHS Foundation Trust
- National Institute for Health and Care Research (NF‐0616‐10102, NF-SI-0616-10102, MC_UU_00011/1)
- British Heart Foundation (CH/F/20/90003, MC_UU_00011/1, NF-0616-10102, 669545, R01 DK10324, AA/18/7/34219, MR/P014054/1)
- University of Bristol (R01 DK10324, MC_UU_00011/1, CH/F/20/90003, AA/18/7/34219, MR/P014054/1)
- China Scholarship Council (CSC201808060273)
- National Institutes of Health (R01 DK10324)
- Medical Research Council (MR/P014054/1, MC_UU_00011/6, MC_PC_21038, NF-0616-10102, MC_UU_00011/2, 11/NW/0382, MM_UU_00011/3, AA/18/7/34219, MC_UU_00011/3, MM_UU_00011/2, MC_PC_17228, CH/F/20/90003, MC_UU_00011/1, MM_UU_00011/1, MM_UU_00011/6, MC_UU_00011)
- European Research Council (669545)
- H2020 European Research Council (669545)
- 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 instruments, pleiotropy, bias mitigation, confounding, horizontal pleiotropy, reverse causality, selection bias, causal graphs, sensitivity analysis, single nucleotide polymorphisms
- MeSH terms
- Humans, Phenotype, Genetic Variation, Polymorphism, Single Nucleotide, Genome-Wide Association Study, Mendelian Randomization Analysis, Genetic Pleiotropy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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