A key assumption in Mendelian randomisation is that the relationship between the genetic instruments and the outcome is fully mediated by the exposure, known as the exclusion restriction assumption. However, in epidemiological studies, the exposure is often a coarsened approximation to some latent continuous trait. For example, latent liability to schizophrenia can be thought of as underlying the binary diagnosis measure. Genetically driven variation in the outcome can exist within categories of the exposure measurement, thus violating this assumption. We propose a framework to clarify this violation, deriving a simple expression for the resulting bias and showing that it may inflate or deflate effect estimates but will not reverse their sign. We then characterise a set of assumptions and a straight‐forward method for estimating the effect of SD increases in the latent exposure. Our method relies on a sensitivity parameter which can be interpreted as the genetic variance of the latent exposure. We show that this method can be applied in both the one‐sample and two‐sample settings. We conclude by demonstrating our method in an applied example and reanalysing two papers which are likely to suffer from this type of bias, allowing meaningful interpretation of their effect sizes.

中文翻译：

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粗化暴露的孟德尔随机化

孟德尔随机化的一个关键假设是遗传工具与结果之间的关系完全由暴露介导，称为排除限制假设。然而，在流行病学研究中，暴露通常是对某些潜在连续性状的粗略近似。例如，对精神分裂症的潜在责任可以被认为是二元诊断措施的基础。结果的遗传驱动变异可能存在于暴露测量的类别中，因此违反了这一假设。我们提出了一个框架来澄清这种违规行为，为由此产生的偏差推导出一个简单的表达式，并表明它可能会夸大或缩小效果估计，但不会反转它们的符号。然后，我们描述了一组假设和一种直接的方法来估计SD在潜伏暴露中增加。我们的方法依赖于一个灵敏度参数，该参数可以解释为潜在暴露的遗传方差。我们表明，这种方法可以应用于单样本和双样本设置。最后，我们在一个应用示例中展示了我们的方法，并重新分析了两篇可能遭受此类偏见的论文，从而对它们的影响大小进行了有意义的解释。