整合基于家庭的和孟德尔随机化设计。
Integrating Family-Based and Mendelian Randomization Designs.
机构信息
The University of Queensland Diamantina Institute, University of Queensland, Brisbane, Queensland 4102, Australia.
Medical Research Council Integrative Epidemiology Unit.
出版信息
Cold Spring Harb Perspect Med. 2021 Mar 1;11(3):a039503. doi: 10.1101/cshperspect.a039503.
Most Mendelian randomization (MR) studies published in the literature to date have involved analyses of unrelated, putatively independent sets of individuals. However, estimates obtained from these sorts of studies are subject to a range of biases including dynastic effects, assortative mating, residual population stratification, and horizontal pleiotropy. The inclusion of related individuals in MR studies can help control for and, in some cases, estimate the effect of these biases on causal parameters. In this review, we discuss these biases, how they can affect MR studies, and describe three sorts of family-based study designs that can be used to control for them. We conclude that including family information from related individuals is not only possible given the world's existing twin, birth, and large-scale population-based cohorts, but likely to reap rich rewards in understanding the etiology of complex traits and diseases in the near future.
迄今为止，文献中发表的大多数孟德尔随机化 (MR) 研究都涉及对无关的、据称独立的个体数据集进行分析。然而，从这些类型的研究中获得的估计值受到一系列偏差的影响，包括家族性影响、选择性交配、残余群体分层和水平多效性。在 MR 研究中纳入相关个体有助于控制和在某些情况下估计这些偏差对因果参数的影响。在这篇综述中，我们讨论了这些偏差，它们如何影响 MR 研究，并描述了三种可用于控制这些偏差的基于家庭的研究设计。我们得出的结论是，考虑到世界上现有的双胞胎、出生和大规模基于人群的队列，不仅可以从相关个体中包含家庭信息，而且很可能在不久的将来在理解复杂特征和疾病的病因方面获得丰厚的回报。
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