Abstract

Mendelian randomization may give biased causal estimates if the instrument affects the outcome not solely via the exposure of interest (violating the exclusion restriction assumption). We demonstrate use of a global randomization test as a falsification test for the exclusion restriction assumption. Using simulations, we explored the statistical power of the randomization test to detect an association between a genetic instrument and a covariate set due to (a) selection bias or (b) horizontal pleiotropy, compared to three approaches examining associations with individual covariates: (i) Bonferroni correction for the number of covariates, (ii) correction for the effective number of independent covariates, and (iii) an r² permutation-based approach. We conducted proof-of-principle analyses in UK Biobank, using CRP as the exposure and coronary heart disease (CHD) as the outcome. In simulations, power of the randomization test was higher than the other approaches for detecting selection bias when the correlation between the covariates was low (r² < 0.1), and at least as powerful as the other approaches across all simulated horizontal pleiotropy scenarios. In our applied example, we found strong evidence of selection bias using all approaches (e.g., global randomization test p < 0.002). We identified 51 of the 58 CRP genetic variants as horizontally pleiotropic, and estimated effects of CRP on CHD attenuated somewhat to the null when excluding these from the genetic risk score (OR = 0.96 [95% CI: 0.92, 1.00] versus 0.97 [95% CI: 0.90, 1.05] per 1-unit higher log CRP levels). The global randomization test can be a useful addition to the MR researcher’s toolkit.

中文翻译：

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使用全局随机化检验作为排除限制假设的孟德尔随机化证伪检验

摘要

如果工具不仅仅通过感兴趣的暴露（违反排除限制假设）影响结果，则孟德尔随机化可能会给出有偏差的因果估计。我们演示了使用全局随机化测试作为排除限制假设的证伪测试。通过模拟，我们探索了随机化检验的统计功效，以检测遗传仪器和协变量集之间的关联，这是由于（a）选择偏差或（b）水平多效性，与检查与个体协变量关联的三种方法相比：（i ) 协变量数量的 Bonferroni 校正，(ii) 独立协变量有效数量的校正，以及 (iii) 基于 r ²排列的方法。我们在英国生物银行进行了原理验证分析，使用 CRP 作为暴露，以冠心病 (CHD) 作为结果。^{在模拟中，当协变量之间的相关性较低（r 2} < 0.1）时，随机化测试的功效高于检测选择偏差的其他方法 ，并且在所有模拟的水平多效性场景中至少与其他方法一样强大。在我们的应用示例中，我们发现使用所有方法都存在选择偏差的有力证据（例如，全局随机化检验p  < 0.002）。我们确定了 58 个 CRP 遗传变异中的 51 个为水平多效性，并且当从遗传风险评分中排除这些变异时，CRP 对 CHD 的估计影响有所减弱至零（OR = 0.96 [95% CI: 0.92, 1.00] 对比 0.97 [95] % CI：0.90，1.05]每升高 1 个单位的对数 CRP 水平）。全局随机化测试可以成为 MR 研究人员工具包的有用补充。