Sensitivity Analyses for Robust Causal Inference from Mendelian Randomization Analyses with Multiple Genetic Variants
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Mendelian randomization investigations are becoming more powerful and simpler to perform, due to the increasing size and coverage of genome-wide association studies and the increasing availability of summarized data on genetic associations with risk factors and disease outcomes. However, when using multiple genetic variants from different gene regions in a Mendelian randomization analysis, it is highly implausible that all the genetic variants satisfy the instrumental variable assumptions. This means that a simple instrumental variable analysis alone should not be relied on to give a causal conclusion. In this article, we discuss a range of sensitivity analyses that will either support or question the validity of causal inference from a Mendelian randomization analysis with multiple genetic variants. We focus on sensitivity analyses of greatest practical relevance for ensuring robust causal inferences, and those that can be undertaken using summarized data. Aside from cases in which the justification of the instrumental variable assumptions is supported by strong biological understanding, a Mendelian randomization analysis in which no assessment of the robustness of the findings to violations of the instrumental variable assumptions has been made should be viewed as speculative and incomplete. In particular, Mendelian randomization investigations with large numbers of genetic variants without such sensitivity analyses should be treated with skepticism.
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Heterogeneity in MR estimates generated by different instrument SNPs beyond chance can indicate the presence of pleiotropy.
“Heterogeneity in the MR estimates generated by different instrument SNPs beyond that expected by chance can be used to indicate the presence of such pleiotropy [28]”
From Associations of genetically determined iron status across the phenome: A mendelian randomization study (2019) · cites this paper as [28]
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- Authors
- Burgess, S., Bowden, J., Fall, T., Ingelsson, E., & Thompson, S. G.
- Affiliations
- Uppsala University (SE)
- Corresponding author
- Burgess, Stephen
- Venue
- Epidemiology
- Published
- 2017-01
- Volume
- 28
- Issue
- 1
- Pages
- 30-42
- ISSN
- 1044-3983 (ISSN-L)
- PMC
- PMC5133381
- OpenAlex
- W2528302205
- Cited by
- 2,017
- References
- 66
- Citations by year
- 2026: 165 · 2025: 435 · 2024: 630 · 2023: 281 · 2022: 174 · 2021: 112 · 2020: 83 · 2019: 69 · 2018: 41 · 2017: 19 · 2016: 7
- FWCI
- 36.26
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Epidemiology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- KTH Publication Database DiVA (KTH Royal Institute of Technology) (submittedVersion, other-oa)
- Explore Bristol Research (submittedVersion, cc-by)
- Bristol Research (University of Bristol) (submittedVersion, cc-by)
- Funders
- Wellcome Trust (100114)
- National Institute for Health and Care Research (NF-SI-0512-10165)
- British Heart Foundation (CH/12/2/29428, RG/13/13/30194, RG/08/014/24067)
- Medical Research Council (MC_EX_MR/L012286/1, MR/N501906/1, MR/L003120/1)
- National Heart, Lung, and Blood Institute (R01HL135313)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- Mendelian randomization, sensitivity analysis, genetic association studies
- MeSH terms
- C-Reactive Protein, Coronary Artery Disease, Humans, Genetic Variation, Reproducibility of Results, Causality, Odds Ratio, Genome-Wide Association Study, Mendelian Randomization Analysis
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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