Population Health Sciences, University of Bristol, Bristol, UK.
Department of Economics, Binghamton University, State University of New York, Binghamton, NY, USA.
Int J Epidemiol. 2019 Jun 1;48(3):702-712. doi: 10.1093/ije/dyy204.
Mendelian randomization (MR) has developed into an established method for strengthening causal inference and estimating causal effects, largely due to the proliferation of genome-wide association studies. However, genetic instruments remain controversial, as horizontal pleiotropic effects can introduce bias into causal estimates. Recent work has highlighted the potential of gene-environment interactions in detecting and correcting for pleiotropic bias in MR analyses.
We introduce MR using Gene-by-Environment interactions (MRGxE) as a framework capable of identifying and correcting for pleiotropic bias. If an instrument-covariate interaction induces variation in the association between a genetic instrument and exposure, it is possible to identify and correct for pleiotropic effects. The interpretation of MRGxE is similar to conventional summary MR approaches, with a particular advantage of MRGxE being the ability to assess the validity of an individual instrument.
We investigate the effect of adiposity, measured using body mass index (BMI), upon systolic blood pressure (SBP) using data from the UK Biobank and a single weighted allelic score informed by data from the GIANT consortium. We find MRGxE produces findings in agreement with two-sample summary MR approaches. Further, we perform simulations highlighting the utility of the approach even when the MRGxE assumptions are violated.
By utilizing instrument-covariate interactions in MR analyses implemented within a linear-regression framework, it is possible to identify and correct for horizontal pleiotropic bias, provided the average magnitude of pleiotropy is constant across interaction-covariate subgroups.
孟德尔随机化（MR）已发展成为一种用于加强因果推断和估计因果效应的成熟方法，这主要得益于全基因组关联研究的普及。然而，遗传工具仍然存在争议，因为水平多效性效应可能会给因果估计带来偏差。最近的研究强调了基因-环境相互作用在检测和纠正 MR 分析中的多效性偏差的潜力。
我们引入了基于基因-环境相互作用的 MR（MRGxE）作为一种能够识别和纠正多效性偏差的框架。如果工具-协变量相互作用导致遗传工具与暴露之间的关联发生变化，则可以识别和纠正多效性效应。MRGxE 的解释类似于传统的汇总 MR 方法，MRGxE 的一个特别优势是能够评估单个工具的有效性。
我们使用 UK Biobank 中的数据和 GIANT 联盟中的单一加权等位基因分数来研究肥胖（用体重指数 BMI 衡量）对收缩压 SBP 的影响。我们发现 MRGxE 产生的结果与两样本汇总 MR 方法一致。此外，我们进行了模拟，强调了即使在违反 MRGxE 假设的情况下，该方法的实用性。
通过在线性回归框架内实施的 MR 分析中利用工具-协变量相互作用，可以识别和纠正水平多效性偏差，前提是多效性的平均大小在交互协变量亚组之间保持不变。
