Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands.
Department of Child and Adolescent Psychiatry, Erasmus MC, Rotterdam, the Netherlands.
Eur J Epidemiol. 2023 Sep;38(9):921-927. doi: 10.1007/s10654-023-01003-6. Epub 2023 May 31.
Mendelian randomization (MR) is an increasingly popular approach to estimating causal effects. Although the assumptions underlying MR cannot be verified, they imply certain constraints, the instrumental inequalities, which can be used to falsify the MR conditions. However, the instrumental inequalities are rarely applied in MR. We aimed to explore whether the instrumental inequalities could detect violations of the MR conditions in case studies analyzing the effect of commonly studied exposures on coronary artery disease risk.Using 1077 single nucleotide polymorphisms (SNPs), we applied the instrumental inequalities to MR models for the effects of vitamin D concentration, alcohol consumption, C-reactive protein (CRP), triglycerides, high-density lipoprotein (HDL) cholesterol, and low-density lipoprotein (LDL) cholesterol on coronary artery disease in the UK Biobank. For their relevant exposure, we applied the instrumental inequalities to MR models proposing each SNP as an instrument individually, and to MR models proposing unweighted allele scores as an instrument. We did not identify any violations of the MR assumptions when proposing each SNP as an instrument individually. When proposing allele scores as instruments, we detected violations of the MR assumptions for 5 of 6 exposures.Within our setting, this suggests the instrumental inequalities can be useful for identifying violations of the MR conditions when proposing multiple SNPs as instruments, but may be less useful in determining which SNPs are not instruments. This work demonstrates how incorporating the instrumental inequalities into MR analyses can help researchers to identify and mitigate potential bias.
孟德尔随机化(MR)是一种越来越受欢迎的估计因果效应的方法。尽管 MR 所基于的假设无法验证,但它们暗示了某些约束条件,即工具变量不等式,可以用来反驳 MR 条件。然而,工具变量不等式在 MR 中很少被应用。我们旨在探讨在分析常见暴露因素对冠心病风险的影响的案例研究中,工具变量不等式是否可以检测到 MR 条件的违反。
使用 1077 个单核苷酸多态性(SNP),我们将工具变量不等式应用于 MR 模型,以研究维生素 D 浓度、饮酒、C 反应蛋白(CRP)、甘油三酯、高密度脂蛋白(HDL)胆固醇和低密度脂蛋白(LDL)胆固醇对英国生物库中冠心病的影响。对于相关暴露,我们将工具变量不等式应用于 MR 模型,分别将每个 SNP 作为工具和未加权的等位基因评分作为工具。当我们将每个 SNP 单独作为工具时,我们没有发现违反 MR 假设的情况。当我们提出等位基因评分作为工具时,我们检测到 6 个暴露因素中有 5 个违反了 MR 假设。
在我们的研究中,这表明当提出多个 SNP 作为工具时,工具变量不等式可用于识别违反 MR 条件的情况,但在确定哪些 SNP 不是工具方面可能效果较差。这项工作展示了如何将工具变量不等式纳入 MR 分析中,以帮助研究人员识别和减轻潜在的偏差。