Causal inference via Mendelian randomization requires making strong assumptions about horizontal pleiotropy, where genetic instruments are connected to the outcome not only through the exposure. Here, we present causal Graphical Analysis Using Genetics (cGAUGE), a pipeline that overcomes these limitations using instrument filters with provable properties. This is achievable by identifying conditional independencies while examining multiple traits. cGAUGE also uses ExSep (Exposure-based Separation), a novel test for the existence of causal pathways that does not require selecting instruments. In simulated data we illustrate how cGAUGE can reduce the empirical false discovery rate by up to 30%, while retaining the majority of true discoveries. On 96 complex traits from 337,198 subjects from the UK Biobank, our results cover expected causal links and many new ones that were previously suggested by correlation-based observational studies. Notably, we identify multiple risk factors for cardiovascular disease, including red blood cell distribution width.

通过孟德尔随机性进行因果推断需要对水平多效性做出强有力的假设，在这种假设中，遗传手段不仅通过暴露与结果联系在一起。在这里，我们介绍了使用遗传学进行因果图形分析（cGAUGE），该管道使用具有可证明性质的仪器过滤器克服了这些限制。这可以通过在检查多个特征时确定条件独立性来实现。cGAUGE还使用ExSep（基于暴露的分离），这是一种新颖的因果关系测试方法，不需要选择仪器。在模拟数据中，我们说明了cGAUGE如何在保留大多数真实发现的同时，将经验错误发现率降低多达30％。来自英国生物库的337198名受试者的96个复杂性状，我们的结果涵盖了预期的因果联系以及以前基于相关性的观察研究所建议的许多新因果联系。值得注意的是，我们确定了心血管疾病的多种危险因素，包括红细胞分布宽度。