A Bayesian approach to Mendelian randomization with multiple pleiotropic variants.
A Bayesian approach to Mendelian randomization with multiple pleiotropic variants.
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DOI：
10.1093/biostatistics/kxy027
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发表时间：
2020-01-01
期刊：
影响因子：
--
通讯作者：
Bernardinelli L
中科院分区：
文献类型：
--
作者：
Berzuini C;Guo H;Burgess S;Bernardinelli L
我们提出了一种孟德尔随机化（MR）的贝叶斯方法，允许仪器对结果产生多效性（即不是由暴露介导）效应。通过在模型中用未知参数表示这些效应，并通过施加收缩先验分布（假设效应的未指定子集为零），我们获得了感兴趣的因果效应的正确后验分布。该后验可以通过马尔可夫链蒙特卡罗推理方法进行采样，以获得点和区间估计。模型先验需要用户最少的输入。我们通过模拟实验来探索我们方法的性能。我们的结果表明，该方法对于定向多效性和仪器之间的中等相关性的存在相当稳健。本文的一部分详细阐述了处理两次暴露的模型，并说明了使用 MR 来估计这种情况下的直接和间接影响的可能性。本文的主要目标是为 MR 的发展奠定基础，利用贝叶斯方法解决问题所提供的潜力，涉及在先验中纳入外部信息、处理多个不确定性来源以及灵活地阐述基本模型的可能性。
We propose a Bayesian approach to Mendelian randomization (MR), where instruments are allowed to exert pleiotropic (i.e. not mediated by the exposure) effects on the outcome. By having these effects represented in the model by unknown parameters, and by imposing a shrinkage prior distribution that assumes an unspecified subset of the effects to be zero, we obtain a proper posterior distribution for the causal effect of interest. This posterior can be sampled via Markov chain Monte Carlo methods of inference to obtain point and interval estimates. The model priors require a minimal input from the user. We explore the performance of our method by means of a simulation experiment. Our results show that the method is reasonably robust to the presence of directional pleiotropy and moderate correlation between the instruments. One section of the article elaborates the model to deal with two exposures, and illustrates the possibility of using MR to estimate direct and indirect effects in this situation. A main objective of the article is to create a basis for developments in MR that exploit the potential offered by a Bayesian approach to the problem, in relation with the possibility of incorporating external information in the prior, handling multiple sources of uncertainty, and flexibly elaborating the basic model.
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