论文标题
Mendelian随机化和不完整的暴露数据：贝叶斯方法
Mendelian Randomization with Incomplete Exposure Data: a Bayesian Approach
论文作者
论文摘要
我们扩展了Mendelian随机化（MR）方法论，以处理有关暴露或结果变量的随机数据，以及与非独立个体（例如家庭的组成部分）的数据。我们的方法取决于Berzuini等人（2018）提出的贝叶斯MR框架，我们将其应用于对多重多发性硬化症（MS）Sardinian家族的研究，以表征某些血浆蛋白在MS因果关系中的作用。该方法对于未知数量的仪器中的多效效应的存在是强大的，并且能够结合个体间的亲属关系信息。引入缺失的数据使我们能够克服（在MS情况下）对蛋白质水平的治疗（反向）影响引入的偏差。从实质的角度来看，我们的研究结果证实了最近怀疑循环IL12A和STAT4蛋白水平的增加不会导致MS风险增加，这表明这两种蛋白可能不是MS的合适药物靶标。
We expand Mendelian Randomization (MR) methodology to deal with randomly missing data on either the exposure or the outcome variable, and furthermore with data from nonindependent individuals (eg components of a family). Our method rests on the Bayesian MR framework proposed by Berzuini et al (2018), which we apply in a study of multiplex Multiple Sclerosis (MS) Sardinian families to characterise the role of certain plasma proteins in MS causation. The method is robust to presence of pleiotropic effects in an unknown number of instruments, and is able to incorporate inter-individual kinship information. Introduction of missing data allows us to overcome the bias introduced by the (reverse) effect of treatment (in MS cases) on level of protein. From a substantive point of view, our study results confirm recent suspicion that an increase in circulating IL12A and STAT4 protein levels does not cause an increase in MS risk, as originally believed, suggesting that these two proteins may not be suitable drug targets for MS.
