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Cited by 1,215Advanced Causal Inference TechniquesGenetic Associations and EpidemiologyStatistical Methods in Epidemiology

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Mendelian randomisation for mediation analysis: current methods and challenges for implementation

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Carter, Alice R., Sanderson, Eleanor, Hammerton, Gemma, Richmond, Rebecca C., Davey Smith, George, Heron, Jon, Taylor, Amy E., Davies, Neil M., Howe, Laura D.·2021·European Journal of Epidemiology
DOI 10.1007/s10654-021-00757-1PMC PMC8159796

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Abstract

Mediation analysis seeks to explain the pathway(s) through which an exposure affects an outcome. Traditional, non-instrumental variable methods for mediation analysis experience a number of methodological difficulties, including bias due to confounding between an exposure, mediator and outcome and measurement error. Mendelian randomisation (MR) can be used to improve causal inference for mediation analysis. We describe two approaches that can be used for estimating mediation analysis with MR: multivariable MR (MVMR) and two-step MR. We outline the approaches and provide code to demonstrate how they can be used in mediation analysis. We review issues that can affect analyses, including confounding, measurement error, weak instrument bias, interactions between exposures and mediators and analysis of multiple mediators. Description of the methods is supplemented by simulated and real data examples. Although MR relies on large sample sizes and strong assumptions, such as having strong instruments and no horizontally pleiotropic pathways, our simulations demonstrate that these methods are unaffected by confounders of the exposure or mediator and the outcome and non-differential measurement error of the exposure or mediator. Both MVMR and two-step MR can be implemented in both individual-level MR and summary data MR. MR mediation methods require different assumptions to be made, compared with non-instrumental variable mediation methods. Where these assumptions are more plausible, MR can be used to improve causal inference in mediation analysis.

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Fig. 1 The decomposed effects in a non-IV regression-based mediation analysis where c represents the total effect, c' represents the direct effect and the indirect effect can be calculated by subtracting c’ from c (difference method) or multiplying A times B (product of coefficients method) b multivariable Mendelian randomisation, using a combined genetic instrument for both the exposure and mediator of interest, to estimate the direct effect c' of the exposure and c two-step Mendelian randomisation, where the effect of the exposure on the mediator (A) and mediator on the outcome b are estimated separately, using separate genetic instrumental variables for both the exposure and mediator. These estimates are then multiplied together to estimate the indirect effect of the mediator (A*B)
Fig. 1 The decomposed effects in a non-IV regression-based mediation analysis where c represents the total effect, c' represents the direct effect and the indirect effect can be calculated by subtracting c’ from c (difference method) or multiplying A times B (product of coefficients method) b multivariable Mendelian randomisation, using a combined genetic instrument for both the exposure and mediator of interest, to estimate the direct effect c' of the exposure and c two-step Mendelian randomisation, where the effect of the exposure on the mediator (A) and mediator on the outcome b are estimated separately, using separate genetic instrumental variables for both the exposure and mediator. These estimates are then multiplied together to estimate the indirect effect of the mediator (A*B)CC BY · PubMed Central
Fig. 2 Schematic diagram illustrating the causal assumptions (dashed lines) in a non-IV regression-based mediation methods and b Mendelian randomisation mediation analysis with the measured associations in solid black lines. Additional assumptions: in non-IV mediation there is no measurement error in the exposure or mediator; in Mendelian randomisation mediation there is no exposure-mediator interaction. In Mendelian randomisation, the exclusion restriction criteria mean there are no alternative pathways from the instrument to the outcome other than via the exposure (or mediator) of interest
Fig. 2 Schematic diagram illustrating the causal assumptions (dashed lines) in a non-IV regression-based mediation methods and b Mendelian randomisation mediation analysis with the measured associations in solid black lines. Additional assumptions: in non-IV mediation there is no measurement error in the exposure or mediator; in Mendelian randomisation mediation there is no exposure-mediator interaction. In Mendelian randomisation, the exclusion restriction criteria mean there are no alternative pathways from the instrument to the outcome other than via the exposure (or mediator) of interestCC BY · PubMed Central
Fig. 3 Size of absolute bias for the indirect effect of an exposure on a continuous outcome, rare binary outcome and common binary outcome through a continuous mediator, for a range of fixed true total effect sizes (0.2, 0.5 and 1.0) and range of true indirect effect sizes using non-IV regression based mediation methods or Mendelian randomisation, on the relative scale (simulated N = 5000). In all scenarios, unmeasured confounding is simulated
Fig. 3 Size of absolute bias for the indirect effect of an exposure on a continuous outcome, rare binary outcome and common binary outcome through a continuous mediator, for a range of fixed true total effect sizes (0.2, 0.5 and 1.0) and range of true indirect effect sizes using non-IV regression based mediation methods or Mendelian randomisation, on the relative scale (simulated N = 5000). In all scenarios, unmeasured confounding is simulatedCC BY · PubMed Central
Fig. 4 Flow chart for analytical processes when carrying out mediation analyses using individual level Mendelian randomisation
Fig. 4 Flow chart for analytical processes when carrying out mediation analyses using individual level Mendelian randomisationCC BY · PubMed Central

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Research works printed but not linked: 1

  1. Richardson TG, Sanderson E, Elsworth B, Tilling K, Davey SG. Use of genetic variation to separate the effects of early and later life adiposity on disease risk: mendelian randomisation study. BMJ. 2020;369:m1203. https://doi.org/10.1136/bmj.m1203. · DOI · Paper page

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Metadata

Authors
Carter, A. R., Sanderson, E., Hammerton, G., Richmond, R. C., Davey Smith, G., Heron, J., Taylor, A. E., Davies, N. M., & Howe, L. D.
Affiliations
  • University of Bristol (GB)
  • University Hospitals Bristol NHS Foundation Trust (GB)
  • Norwegian University of Science and Technology (NO)
Corresponding author
Carter, Alice R.
ORCID
Venue
European Journal of Epidemiology
Publisher
Springer Science and Business Media LLC
Published
2021-05
Volume
36
Issue
5
Pages
465-478
ISSN
  • 0393-2990 (ISSN-L)
  • 1573-7284
DOI
10.1007/s10654-021-00757-1
PMC
PMC8159796
OpenAlex
W3159102563
Cited by
1,215
References
67
Citations by year
2026: 196 · 2025: 407 · 2024: 386 · 2023: 143 · 2022: 54 · 2021: 18
FWCI
117.42
Type
journal article
Language
en
Open access
Licence
Locations
  • European Journal of Epidemiology (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Bristol Research (University of Bristol) (submittedVersion, cc-by)
Funders
  • UK Medical Research Council (MC_UU_00011/1, MR/M020894)
  • UK Economic and Social Research Council (ES/N000757/1)
  • Wellcome Trust (209138/Z/17/Z)
  • Norwegian Research Council (295989)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Keywords
Mendelian randomization, mediation analysis, causal inference, confounding, measurement error, weak instrument bias, pleiotropy, multiple mediators
MeSH terms
Mediation Analysis, Humans, Genetic Variation, Bias, Causality, Genome-Wide Association Study, Mendelian Randomization Analysis, Genetic Pleiotropy

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