Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA.
Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA.
BMC Bioinformatics. 2022 Oct 13;23(1):420. doi: 10.1186/s12859-022-04977-4.
Observational studies and Mendelian randomization experiments have been used to identify many causal factors for complex traits in humans. Given a set of causal factors, it is important to understand the extent to which these causal factors explain some, all, or none of the genetic heritability, as measured by single-nucleotide polymorphisms (SNPs) that are associated with the trait. Using the mediation model framework with SNPs as the exposure, a trait of interest as the outcome, and the known causal factors as the mediators, we hypothesize that any unexplained association between the SNPs and the outcome trait is mediated by an additional unobserved, hidden causal factor.
We propose a method to infer the effect size of this hidden mediating causal factor on the outcome trait by utilizing the estimated associations between a continuous outcome trait, the known causal factors, and the SNPs. The proposed method consists of three steps and, in the end, implements Markov chain Monte Carlo to obtain a posterior distribution for the effect size of the hidden mediator. We evaluate our proposed method via extensive simulations and show that when model assumptions hold, our method estimates the effect size of the hidden mediator well and controls type I error rate if the hidden mediator does not exist. In addition, we apply the method to the UK Biobank data and estimate parameters for a potential hidden mediator for waist-hip ratio beyond body mass index (BMI), and find that the hidden mediator has a large effect size relatively to the effect size of the known mediator BMI.
We develop a framework to infer the effect of potential, hidden mediators influencing complex traits. This framework can be used to place boundaries on unexplained risk factors contributing to complex traits.
观察性研究和孟德尔随机化实验已被用于鉴定人类复杂特征的许多因果因素。给定一组因果因素，重要的是要了解这些因果因素在多大程度上可以解释由与特征相关的单核苷酸多态性（SNP）衡量的遗传表型的一部分、全部或无。我们使用中介模型框架，将 SNP 作为暴露，感兴趣的特征作为结果，已知的因果因素作为中介，假设 SNP 和结果特征之间任何未解释的关联都由另一个未观察到的、隐藏的因果因素介导。
我们提出了一种通过利用连续结果特征、已知因果因素和 SNP 之间的估计关联来推断隐藏中介因果因素对结果特征的效应大小的方法。所提出的方法包括三个步骤，最后通过马尔可夫链蒙特卡罗法获得隐藏中介的效应大小的后验分布。我们通过广泛的模拟评估了我们提出的方法，并表明在模型假设成立的情况下，如果不存在隐藏中介，我们的方法可以很好地估计隐藏中介的效应大小，并控制第一类错误率。此外，我们将该方法应用于英国生物银行数据，并估计了 BMI 以外的腰围臀围潜在隐藏中介的参数，发现隐藏中介对已知中介 BMI 的效应大小具有较大的效应大小。
我们开发了一种推断潜在隐藏中介影响复杂特征的效应的框架。该框架可用于限制对复杂特征有贡献的未解释风险因素。
