Swerdlow Daniel I, Kuchenbaecker Karoline B, Shah Sonia, Sofat Reecha, Holmes Michael V, White Jon, Mindell Jennifer S, Kivimaki Mika, Brunner Eric J, Whittaker John C, Casas Juan P, Hingorani Aroon D
Institute of Cardiovascular Science, University College London, London, UK
Department of Medicine, Imperial College London, London, UK.
Int J Epidemiol. 2016 Oct;45(5):1600-1616. doi: 10.1093/ije/dyw088. Epub 2016 Jun 24.
Mendelian randomization (MR) studies typically assess the pathogenic relevance of environmental exposures or disease biomarkers, using genetic variants that instrument these exposures. The approach is gaining popularity-our systematic review reveals a greater than 10-fold increase in MR studies published between 2004 and 2015. When the MR paradigm was first proposed, few biomarker- or exposure-related genetic variants were known, most having been identified by candidate gene studies. However, genome-wide association studies (GWAS) are now providing a rich source of potential instruments for MR analysis. Many early reviews covering the concept, applications and analytical aspects of the MR technique preceded the surge in GWAS, and thus the question of how best to select instruments for MR studies from the now extensive pool of available variants has received insufficient attention. Here we focus on the most common category of MR studies-those concerning disease biomarkers. We consider how the selection of instruments for MR analysis from GWAS requires consideration of: the assumptions underlying the MR approach; the biology of the biomarker; the genome-wide distribution, frequency and effect size of biomarker-associated variants (the genetic architecture); and the specificity of the genetic associations. Based on this, we develop guidance that may help investigators to plan and readers interpret MR studies.
孟德尔随机化（MR）研究通常利用作为这些暴露因素代理的基因变异，来评估环境暴露因素或疾病生物标志物的致病相关性。这种方法越来越受欢迎——我们的系统评价显示，2004年至2015年间发表的MR研究增加了10倍以上。当MR范式首次被提出时，已知的与生物标志物或暴露相关的基因变异很少，大多数是通过候选基因研究确定的。然而，全基因组关联研究（GWAS）现在为MR分析提供了丰富的潜在代理来源。许多早期涵盖MR技术概念、应用和分析方面的综述在GWAS激增之前就已发表，因此，如何从现有的大量可用变异中为MR研究选择最佳代理的问题尚未得到足够的关注。在这里，我们聚焦于最常见的MR研究类别——那些关于疾病生物标志物的研究。我们考虑从GWAS中为MR分析选择代理时需要考虑的因素：MR方法的潜在假设；生物标志物的生物学特性；与生物标志物相关的变异的全基因组分布、频率和效应大小（遗传结构）；以及基因关联的特异性。基于此，我们制定了相关指南，可能有助于研究人员进行研究规划，并帮助读者解读MR研究。
