Department of Health Sciences, University of Leicester, Leicester LE1 7RH, United Kingdom.
Cold Spring Harb Perspect Med. 2021 Feb 1;11(2):a039586. doi: 10.1101/cshperspect.a039586.
Many exposures considered in Mendelian randomization (MR) studies are polygenic in that they are influenced by thousands of genetic variants. By using many single-nucleotide polymorphisms (SNPs) as instrumental variables, more variation in the exposure is explained, increasing the precision of MR. Furthermore, methods can be designed that relax the assumptions of MR, especially concerning direct pleiotropic effects on the outcome. This article reviews the concepts and assumptions underlying the commonly used polygenic MR methods. Using a polygenic score as an instrument is equivalent to a weighted mean of individual SNP results, and the other fundamental averages, median and mode, may also be used to estimate causal effects. Outlier detection is useful for identifying pleiotropic SNPs to be excluded from analysis. Bayesian approaches are available to incorporate prior beliefs about pleiotropy. These methods each entail different assumptions, and together provide a set of sensitivity analyses to help triangulate evidence about causality.
孟德尔随机化（MR）研究中考虑的许多暴露因素都是多基因的，因为它们受到数千个遗传变异的影响。通过使用许多单核苷酸多态性（SNP）作为工具变量，可以解释更多的暴露变异，从而提高 MR 的精度。此外，还可以设计方法来放宽 MR 的假设，特别是关于对结果的直接多效性影响。本文综述了常用多基因 MR 方法的概念和假设。使用多基因分数作为工具相当于个体 SNP 结果的加权平均值，其他基本平均值，如中位数和众数，也可用于估计因果效应。异常值检测有助于识别需要排除在分析之外的多效性 SNP。贝叶斯方法可用于纳入关于多效性的先验信念。这些方法各自需要不同的假设，共同提供了一组敏感性分析，有助于确定因果关系的证据。
