Department of Psychiatry, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
Centre for PanorOmic Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
Psychol Med. 2024 Jun;54(8):1461-1474. doi: 10.1017/S0033291724000321. Epub 2024 Apr 19.
Mendelian randomization (MR) leverages genetic information to examine the causal relationship between phenotypes allowing for the presence of unmeasured confounders. MR has been widely applied to unresolved questions in epidemiology, making use of summary statistics from genome-wide association studies on an increasing number of human traits. However, an understanding of essential concepts is necessary for the appropriate application and interpretation of MR. This review aims to provide a non-technical overview of MR and demonstrate its relevance to psychiatric research. We begin with the origins of MR and the reasons for its recent expansion, followed by an overview of its statistical methodology. We then describe the limitations of MR, and how these are being addressed by recent methodological advances. We showcase the practical use of MR in psychiatry through three illustrative examples - the connection between cannabis use and psychosis, the link between intelligence and schizophrenia, and the search for modifiable risk factors for depression. The review concludes with a discussion of the prospects of MR, focusing on the integration of multi-omics data and its extension to delineating complex causal networks.
孟德尔随机化（MR）利用遗传信息来检验表型之间的因果关系，从而可以考虑到未测量的混杂因素。MR 已经广泛应用于流行病学中的未解决问题，利用越来越多的人类特征的全基因组关联研究的汇总统计数据。然而，为了正确应用和解释 MR，理解基本概念是必要的。本综述旨在提供一个非技术性的 MR 概述，并展示其与精神科研究的相关性。我们首先介绍 MR 的起源及其最近扩展的原因，然后概述其统计方法。接下来，我们描述了 MR 的局限性，以及最近的方法学进展如何解决这些局限性。我们通过三个示例展示了 MR 在精神病学中的实际应用——大麻使用与精神病之间的联系、智力与精神分裂症之间的联系以及寻找可修改的抑郁症风险因素。最后，我们讨论了 MR 的前景，重点是多组学数据的整合及其对复杂因果网络的扩展。
