Kraft Peter, Chen Hongjie, Lindström Sara
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA.
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA.
Curr Epidemiol Rep. 2020 Jun;7(2):104-112. doi: 10.1007/s40471-020-00233-6. Epub 2020 May 16.
Increasing access to large-scale genetic datasets in population-based studies allows for genetic association studies as a means to examine previously known and novel relationships among complex traits. In this review, we discuss two widely used approaches to leverage genetic data to study the links between traits: Genome-wide genetic correlation and Mendelian Randomization (MR) studies.
Both genetic correlation and MR studies have provided important novel insights. However, although they are less sensitive to many sources of bias present in traditional, observational epidemiology, they still rely on assumptions that in practice might be difficult to assess. To overcome this, development of novel methods less sensitive to these assumptions is an active area of research.
We believe that as population-based genetic datasets grow larger and novel methods allowing for weaker forms of current assumptions become available, genetic correlation and MR studies will become an integral part of genetic epidemiology studies.
在基于人群的研究中，获取大规模遗传数据集的机会越来越多，这使得基因关联研究成为检验复杂性状之间已知和新关系的一种手段。在本综述中，我们讨论两种广泛用于利用遗传数据研究性状之间联系的方法：全基因组遗传相关性研究和孟德尔随机化（MR）研究。
遗传相关性研究和MR研究都提供了重要的新见解。然而，尽管它们对传统观察性流行病学中存在的许多偏倚来源不太敏感，但它们仍然依赖于在实际中可能难以评估的假设。为了克服这一问题，开发对这些假设不太敏感的新方法是一个活跃的研究领域。
我们相信，随着基于人群的遗传数据集越来越大，以及允许采用当前假设较弱形式的新方法出现，遗传相关性研究和MR研究将成为遗传流行病学研究的一个组成部分。
