Bias from heritable confounding in Mendelian randomization studies

Eleanor Sanderson, Dan Rosoff, Tom Palmer, Kate Tilling, George Davey Smith, Gibran Hemani
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Abstract

Mendelian randomization (MR) is an approach to causal inference that utilises genetic variants to obtain estimates of the causal effect of an exposure on an outcome in the presence of unobserved confounding. MR relies on a set of assumptions to obtain unbiased effect estimates, one of these assumptions is that there is no pathway from the genetic variants to the outcome that does not act through the exposure. Increasing genome-wide association study (GWAS) sample sizes for the exposure enables discovery of instrumental variables with smaller effect sizes. We illustrate through simulations how smaller effect sizes could arise from genetic variants that act through traits that have greater liability to confound an exposure-outcome relationship. When such genetic variants are selected as instruments this can bias the MR effect estimate obtained from that instrument in the same direction as the confounded observational association but with larger magnitude. Through simulation we illustrate how the total bias of the MR estimates increases across a range of standard MR estimation methods increases as the proportion of the genetic instruments that are associated with the confounder increases. However, if such heritable confounders are known and can be instrumented, the confounder free effect estimate can be obtained through applying a pre-estimation filtering to standard MR methods, removing instruments that explain more variation in that confounder than the exposure, or by estimating effects through multivariable MR. We highlight the potential for SNPs identified in GWAS to be associated with potential confounders through examination of a recent GWAS of C-Reactive Protein. Finally, we illustrate our approach through estimation of the causal effect of age at menarche on type 2 diabetes, hypothesising that the MR effect estimate may be biased by confounding due to the inclusion of genetic variants associated with early life adiposity as instruments.
孟德尔随机研究中的遗传混杂偏差
孟德尔随机化(Mendelian randomization,MR)是一种因果推断方法,它利用基因变异获得暴露对结果的因果效应估计值,但存在未观察到的混杂因素。MR 依靠一系列假设来获得无偏的效应估计值,其中一个假设是,从遗传变异到结果之间不存在不通过暴露作用的途径。增加暴露的全基因组关联研究(GWAS)样本量可以发现效应较小的工具变量。我们通过模拟来说明,如果基因变异是通过更容易混淆暴露-结果关系的性状起作用,那么就会产生较小的效应量。当这类基因变异被选作工具时,会使从该工具中获得的 MR 效应估计值偏向与混淆观察关联相同的方向,但幅度更大。通过模拟,我们说明了在一系列标准 MR 估计方法中,随着与混杂因素相关的遗传工具比例的增加,MR 估计的总偏差是如何增加的。但是,如果这类遗传混杂因素是已知的,并且可以通过工具进行分析,那么就可以通过对标准磁共振方法进行预估过滤,去除能解释混杂因素变异大于暴露变异的工具,或者通过多变量磁共振估算效应,从而获得无混杂因素效应估算值。我们通过研究最近一项关于 C 反应蛋白的基因组研究,强调了在基因组研究中发现的 SNPs 与潜在混杂因素相关的可能性。最后,我们通过估计初潮年龄对 2 型糖尿病的因果效应来说明我们的方法,并假设由于将与生命早期肥胖相关的遗传变异作为工具纳入,MR 效应估计可能会因混杂因素而产生偏差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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