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.

中文翻译：

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整合基于家庭和孟德尔的随机设计

迄今为止，文献中发表的大多数孟德尔随机（MR）研究都涉及对不相关，假定独立的个体集的分析。但是，从这些类型的研究中获得的估计值会受到一系列偏差的影响，包括动态影响，分类交配，残留种群分层和水平多效性。在MR研究中纳入相关个人可以帮助控制并在某些情况下估计这些偏见对因果参数的影响。在这篇综述中，我们讨论了这些偏见，它们如何影响MR研究，并描述了三种可以用来控制它们的基于家庭的研究设计。我们得出的结论是，鉴于世界上现有的双胞胎，出生和大规模人群，不仅可能包含相关个人的家庭信息，