Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses
Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses
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DOI：
10.1038/s41467-020-17117-4
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发表时间：
2020-07-14
影响因子：
16.6
通讯作者：
Davies, Neil M.
中科院分区：
文献类型：
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作者：
Brumpton, Ben;Sanderson, Eleanor;Davies, Neil M.
来自无关个体的孟德尔随机化研究的估计值可能由于来自家族效应的不受控制的混杂而存在偏倚。在这里，我们描述了家庭内孟德尔随机化分析的方法，并使用模拟研究表明，以家庭为基础的分析可以减少这种偏见。我们使用来自Nord-TrOndelag健康研究和英国生物银行的61，008名兄弟姐妹的数据实证说明了家族效应如何影响估计值，并使用来自23andMe的222，368名兄弟姐妹复制了我们的研究结果。使用无关个体和家庭内方法的孟德尔随机化估计再现了较低BMI降低糖尿病和高血压风险的既定效应。然而，虽然孟德尔随机化估计无关个体的样本表明，较高的身高和较低的BMI增加教育程度，这些影响在家庭内孟德尔随机化分析中强烈减弱。我们的研究结果表明，在孟德尔随机化研究中控制人口结构和家族效应的必要性。以家系为基础的研究设计已被应用于解决混杂的人口分层，王朝效应和替代交配的遗传关联分析。在这里，Brumpton等人描述了通过家族内研究克服孟德尔随机化中的这种偏差的理论和模拟。
Estimates from Mendelian randomization studies of unrelated individuals can be biased due to uncontrolled confounding from familial effects. Here we describe methods for within-family Mendelian randomization analyses and use simulation studies to show that family-based analyses can reduce such biases. We illustrate empirically how familial effects can affect estimates using data from 61,008 siblings from the Nord-TrOndelag Health Study and UK Biobank and replicated our findings using 222,368 siblings from 23andMe. Both Mendelian randomization estimates using unrelated individuals and within family methods reproduced established effects of lower BMI reducing risk of diabetes and high blood pressure. However, while Mendelian randomization estimates from samples of unrelated individuals suggested that taller height and lower BMI increase educational attainment, these effects were strongly attenuated in within-family Mendelian randomization analyses. Our findings indicate the necessity of controlling for population structure and familial effects in Mendelian randomization studies. Family-based study designs have been applied to resolve confounding by population stratification, dynastic effects and assortative mating in genetic association analyses. Here, Brumpton et al. describe theory and simulations for overcoming such biases in Mendelian randomization through within-family studies.