Ejima Keisuke, Liu Nianjun, Mestre Luis Miguel, de Los Campos Gustavo, Allison David B
Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, IN, United States.
Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
Front Genet. 2023 Mar 6;14:1014014. doi: 10.3389/fgene.2023.1014014. eCollection 2023.
Mendelian randomization (MR) has become a common tool used in epidemiological studies. However, when confounding variables are correlated with the instrumental variable (in this case, a genetic/variant/marker), the estimation can remain biased even with MR. We propose conditioning on parental mating types (a function of parental genotypes) in MR to eliminate the need for one set of assumptions, thereby plausibly reducing such bias. We illustrate a situation in which the instrumental variable and confounding variables are correlated using two unlinked diallelic genetic loci: one, an instrumental variable and the other, a confounding variable. Assortative mating or population admixture can create an association between the two unlinked loci, which can violate one of the necessary assumptions for MR. We simulated datasets involving assortative mating and population admixture and analyzed them using three different methods: 1) conventional MR, 2) MR conditioning on parental genotypes, and 3) MR conditioning on parental mating types. We demonstrated that conventional MR leads to type I error rate inflation and biased estimates for cases with assortative mating or population admixtures. In the presence of non-additive effects, MR with an adjustment for parental genotypes only partially reduced the type I error rate inflation and bias. In contrast, conditioning on parental mating types in MR eliminated the type I error inflation and bias under these circumstances. Conditioning on parental mating types is a useful strategy to reduce the burden of assumptions and the potential bias in MR when the correlation between the instrument variable and confounders is due to assortative mating or population stratification but not linkage.
孟德尔随机化（MR）已成为流行病学研究中常用的工具。然而，当混杂变量与工具变量（在这种情况下，是一种基因/变体/标记）相关时，即使使用MR估计仍可能存在偏差。我们建议在MR中以亲本交配类型（亲本基因型的函数）为条件，从而无需一组假设，进而有可能减少此类偏差。我们使用两个不连锁的双等位基因遗传位点说明了工具变量和混杂变量相关的情况：一个是工具变量，另一个是混杂变量。选型交配或群体混合可在两个不连锁的位点之间产生关联，这可能会违反MR的必要假设之一。我们模拟了涉及选型交配和群体混合的数据集，并使用三种不同方法进行分析：1）传统MR，2）以亲本基因型为条件的MR，3）以亲本交配类型为条件的MR。我们证明，对于存在选型交配或群体混合的情况，传统MR会导致I型错误率膨胀和估计偏差。在存在非加性效应的情况下，仅对亲本基因型进行调整的MR只能部分降低I型错误率膨胀和偏差。相比之下，在这些情况下，以亲本交配类型为条件的MR消除了I型错误膨胀和偏差。当工具变量与混杂因素之间的相关性是由于选型交配或群体分层而非连锁时，以亲本交配类型为条件是一种有用的策略，可减轻MR中假设的负担并减少潜在偏差。
