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. 2021 Jun 21;13:104. doi: 10.1186/s13073-021-00914-x

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© The Author(s) 2021

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 4 — Multivariable MR and mediation analysis. First entry in each plot is the inverse variance-weighted causal estimate as given in Fig. 3. This estimate represents the direct effect of the risk factor on the outcome. Subsequent lines are adjusted for one risk factor each representing the total effect. Full model has all risk factors as covariate in the model. Differences in effect sizes resulting from attenuation can be interpreted as mediated by the exposure added to the model, if there is a causal connection between the mediator and the exposure as well as the mediator and the outcome