Mendelian randomization (MR) has emerged as a powerful approach to leverage genetic instruments to infer causality between pairs of traits in observational studies. However, the results of such studies are susceptible to biases owing to weak instruments, as well as the confounding effects of population stratification and horizontal pleiotropy. Here, we show that family data can be leveraged to design MR tests that are provably robust to confounding from population stratification, assortative mating, and dynastic effects. We show in simulations that our approach, MR-Twin, is robust to confounding from population stratification and is not affected by weak instrument bias, whereas standard MR methods yield inflated false positive rates. We then conduct an exploratory analysis of MR-Twin and other MR methods applied to 121 trait pairs in the UK Biobank data set. Our results suggest that confounding from population stratification can lead to false positives for existing MR methods, whereas MR-Twin is immune to this type of confounding, and that MR-Twin can help assess whether traditional approaches may be inflated owing to confounding from population stratification.

中文翻译：

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利用家庭数据设计孟德尔随机化，该随机化对人口分层具有鲁棒性


孟德尔随机化 (MR) 已成为一种利用遗传仪器推断观察研究中性状对之间因果关系的强大方法。然而，由于仪器薄弱，以及人口分层和水平多效性的混杂效应，此类研究的结果很容易出现偏差。在这里，我们表明，可以利用家庭数据来设计 MR 测试，这些测试对于人口分层、选型交配和王朝效应等混杂因素具有鲁棒性。我们在模拟中表明，我们的方法 MR-Twin 对于群体分层的混杂具有鲁棒性，并且不受弱仪器偏差的影响，而标准 MR 方法会产生夸大的假阳性率。然后，我们对应用于英国生物银行数据集中 121 个性状对的 MR-Twin 和其他 MR 方法进行探索性分析。我们的结果表明，人口分层的混杂因素可能会导致现有 MR 方法出现误报，而 MR-Twin 不受此类混杂因素的影响，并且 MR-Twin 可以帮助评估传统方法是否可能因人口分层的混杂因素而被夸大。