With the increasing size and number of genome-wide association studies, individual single nucleotide polymorphisms are increasingly found to associate with multiple traits. Many different mechanisms could result in proposed genetic IVs for an exposure of interest being associated with multiple non-exposure traits, some of which could bias MR results. We describe and illustrate, through causal diagrams, a range of scenarios that could result in proposed IVs being related to non-exposure traits in MR studies. These associations could occur due to five scenarios: (i) confounding, (ii) vertical pleiotropy, (iii) horizontal pleiotropy, (iv) reverse causation and (v) selection bias. For each of these scenarios we outline steps that could be taken to explore the underlying mechanism and mitigate any resulting bias in the MR estimation. We recommend MR studies explore possible IV—non-exposure associations across a wider range of traits than is usually the case. We highlight the pros and cons of relying on sensitivity analyses without considering particular pleiotropic paths versus systematically exploring and controlling for potential pleiotropic or other biasing paths via known traits. We apply our recommendations to an illustrative example of the effect of maternal insomnia on offspring birthweight in UK Biobank.

随着全基因组关联研究的规模和数量不断增加，越来越多的个体单核苷酸多态性被发现与多种性状相关。许多不同的机制可能会导致针对与多种非暴露特征相关的感兴趣暴露提出的遗传 IV，其中一些特征可能会使 MR 结果产生偏差。我们通过因果图描述和说明了一系列可能导致建议的 IV 与 MR 研究中的非暴露特征相关的场景。这些关联可能由于五种情况而发生：（i）混杂，（ii）垂直多效性，（iii）水平多效性，（iv）反向因果关系和（v）选择偏差。对于每种情况，我们概述了可采取的步骤来探索潜在机制并减轻 MR 估计中产生的任何偏差。我们建议 MR 研究探索比通常情况更广泛的性状中可能存在的 IV-非暴露关联。我们强调了依赖敏感性分析而不考虑特定的多效性路径与通过已知性状系统地探索和控制潜在的多效性或其他偏差路径的优缺点。我们将我们的建议应用于英国生物银行中母亲失眠对后代出生体重影响的说明性示例。