MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
Int J Epidemiol. 2021 Aug 30;50(4):1350-1361. doi: 10.1093/ije/dyaa288.
A key assumption of Mendelian randomization (MR) analysis is that there is no association between the genetic variants used as instruments and the outcome other than through the exposure of interest. One way in which this assumption can be violated is through population stratification, which can introduce confounding of the relationship between the genetic variants and the outcome and so induce an association between them. Negative control outcomes are increasingly used to detect unobserved confounding in observational epidemiological studies. Here we consider the use of negative control outcomes in MR studies to detect confounding of the genetic variants and the exposure or outcome. As a negative control outcome in an MR study, we propose the use of phenotypes which are determined before the exposure and outcome but which are likely to be subject to the same confounding as the exposure or outcome of interest. We illustrate our method with a two-sample MR analysis of a preselected set of exposures on self-reported tanning ability and hair colour. Our results show that, of the 33 exposures considered, genome-wide association studies (GWAS) of adiposity and education-related traits are likely to be subject to population stratification that is not controlled for through adjustment, and so any MR study including these traits may be subject to bias that cannot be identified through standard pleiotropy robust methods. Negative control outcomes should therefore be used regularly in MR studies to detect potential population stratification in the data used.
孟德尔随机化（MR）分析的一个关键假设是，除了通过感兴趣的暴露之外，用于作为工具的遗传变异与结果之间没有关联。违反这一假设的一种方式是通过群体分层，这可能会混淆遗传变异与结果之间的关系，并因此诱导它们之间的关联。负对照结果越来越多地用于检测观察性流行病学研究中未观察到的混杂。在这里，我们考虑在 MR 研究中使用负对照结果来检测遗传变异与暴露或结果之间的混杂。作为 MR 研究中的负对照结果，我们建议使用在暴露和结果之前确定的表型，但这些表型可能会受到与感兴趣的暴露或结果相同的混杂影响。我们用一组预先选择的暴露于自我报告的晒黑能力和头发颜色的研究进行了两样本 MR 分析来说明我们的方法。我们的结果表明，在考虑的 33 种暴露中，肥胖和与教育相关的特征的全基因组关联研究（GWAS）可能受到群体分层的影响，而通过调整无法控制这种分层，因此任何包括这些特征的 MR 研究都可能受到无法通过标准多效性稳健方法识别的偏差的影响。因此，负对照结果应在 MR 研究中定期使用，以检测数据中潜在的群体分层。
