The increasing availability of genetic cohort data has led to many Genome Wide Association Studies (GWASs) 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 Randomisation (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.

中文翻译：

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用于分析遗传队列数据的因果图。

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) 成功地确定了与不断扩大的表型特征列表之间的遗传关联。然而，关联并不意味着因果关系，因此已经开发了研究因果关系问题的方法。在额外的假设下，孟德尔随机化 (MR) 研究已证明在确定两种表型之间的因果关系方面很受欢迎，通常使用 GWAS 汇总统计。鉴于这些方法的广泛使用，理解和交流它们所基于的因果假设比以往任何时候都更加重要，以便方法是透明的，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，我们从因果关系的角度回顾 GWAS 和 MR，以建立对遗传问题因果图的直觉。我们还研究了血统混杂的问题，并评论了处理这种混杂的方法，以及讨论处理研究设计引起的选择偏倚的方法。