Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Molecular and Clinical Sciences Institute, St. George's, University of London, London, United Kingdom.
Physiol Genomics. 2020 Sep 1;52(9):369-378. doi: 10.1152/physiolgenomics.00115.2019. Epub 2020 Jul 20.
The increasing availability of genetic cohort data has led to many genome-wide association studies (GWAS) successfully identifying genetic associations with an ever-expanding list of phenotypic traits. Association, however, does not imply causation, and therefore methods have been developed to study the issue of causality. Under additional assumptions, Mendelian randomization (MR) studies have proved popular in identifying causal effects between two phenotypes, often using GWAS summary statistics. Given the widespread use of these methods, it is more important than ever to understand, and communicate, the causal assumptions upon which they are based, so that methods are transparent, and findings are clinically relevant. Causal graphs can be used to represent causal assumptions graphically and provide insights into the limitations associated with different analysis methods. Here we review GWAS and MR from a causal perspective, to build up intuition for causal diagrams in genetic problems. We also examine issues of confounding by ancestry and comment on approaches for dealing with such confounding, as well as discussing approaches for dealing with selection biases arising from study design.
遗传队列数据的可用性不断增加，导致许多全基因组关联研究（GWAS）成功确定了与不断扩展的表型特征的遗传关联。然而，关联并不意味着因果关系，因此已经开发了方法来研究因果关系问题。在附加假设下，孟德尔随机化（MR）研究已被证明在识别两种表型之间的因果效应方面很受欢迎，通常使用 GWAS 汇总统计数据。鉴于这些方法的广泛使用，比以往任何时候都更重要的是要理解和传达它们所基于的因果假设，以便方法具有透明度，并且发现具有临床相关性。因果图可用于以图形方式表示因果假设，并深入了解与不同分析方法相关的局限性。在这里，我们从因果的角度回顾 GWAS 和 MR，为遗传问题中的因果图建立直觉。我们还检查了祖先混杂的问题，并评论了处理这种混杂的方法，以及讨论了处理由于研究设计引起的选择偏差的方法。
