LaPierre Nathan, Fu Boyang, Turnbull Steven, Eskin Eleazar, Sankararaman Sriram
Department of Computer Science, UCLA, Los Angeles CA.
Department of Statistics, UCLA, Los Angeles CA.
bioRxiv. 2023 Jan 6:2023.01.05.522936. doi: 10.1101/2023.01.05.522936.
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 due 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 demonstrate in simulations that our approach, MR-Twin, is robust to confounding from population stratification and is not affected by weak instrument bias, while standard MR methods yield inflated false positive rates. We applied MR-Twin to 121 trait pairs in the UK Biobank dataset and found that MR-Twin identifies likely causal trait pairs and does not identify trait pairs that are unlikely to be causal. Our results suggest that confounding from population stratification can lead to false positives for existing MR methods, while MR-Twin is immune to this type of confounding.
孟德尔随机化（MR）已成为一种强大的方法，可利用基因工具在观察性研究中推断成对性状之间的因果关系。然而，由于弱工具以及群体分层和水平多效性的混杂效应，此类研究的结果容易产生偏差。在这里，我们表明可以利用家庭数据来设计MR检验，这些检验被证明对群体分层、选型交配和王朝效应的混杂具有鲁棒性。我们在模拟中证明，我们的方法MR-Twin对群体分层的混杂具有鲁棒性，不受弱工具偏差的影响，而标准的MR方法会产生过高的假阳性率。我们将MR-Twin应用于英国生物银行数据集中的121对性状，发现MR-Twin能够识别可能的因果性状对，而不会识别不太可能是因果关系的性状对。我们的结果表明，群体分层的混杂会导致现有MR方法出现假阳性，而MR-Twin对此类混杂具有免疫力。
