Howey Richard, Cordell Heather J
Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE1 3BZ, UK.
Wellcome Open Res. 2022 Jul 5;7:180. doi: 10.12688/wellcomeopenres.17991.1. eCollection 2022.
Various methods exist that utilise information from genetic predictors to help identify potential causal relationships between measured biological or clinical traits. Here we conduct computer simulations to investigate the performance of a recently proposed causal Graphical Analysis Using Genetics (cGAUGE) pipeline, used as a precursor to Mendelian randomization analysis, in comparison to our previously proposed Bayesian Network approach for addressing this problem. We use the same simulation (and analysis) code as was used by the developers of cGAUGE, adding in a comparison with the Bayesian Network approach. Overall, we find the optimal method (in terms of giving high power and low false discovery rate) is the cGAUGE pipeline followed by subsequent analysis using the MR-PRESSO Mendelian randomization approach.
存在多种利用基因预测器信息来帮助识别测量的生物学或临床特征之间潜在因果关系的方法。在这里，我们进行计算机模拟，以研究最近提出的因果关系遗传图形分析（cGAUGE）流程（用作孟德尔随机化分析的前身）与我们之前提出的用于解决此问题的贝叶斯网络方法相比的性能。我们使用与cGAUGE开发者相同的模拟（和分析）代码，并加入了与贝叶斯网络方法的比较。总体而言，我们发现（在提供高功效和低错误发现率方面）最优方法是cGAUGE流程，随后使用MR-PRESSO孟德尔随机化方法进行后续分析。
