Mendelian randomization is a widely-used method to estimate the unconfounded effect of an exposure on an outcome by using genetic variants as instrumental variables. Mendelian randomization analyses which use variants from a single genetic region (cis-MR) have gained popularity for being an economical way to provide supporting evidence for drug target validation. This paper proposes methods for cis-MR inference which use the explanatory power of many correlated variants to make valid inferences even in situations where those variants only have weak effects on the exposure. In particular, we exploit the highly structured nature of genetic correlations in single gene regions to reduce the dimension of genetic variants using factor analysis. These genetic factors are then used as instrumental variables to construct tests for the causal effect of interest. Since these factors may often be weakly associated with the exposure, size distortions of standard t-tests can be severe. Therefore, we consider two approaches based on conditional testing. First, we extend results of commonly-used identification-robust tests to account for the use of estimated factors as instruments. Secondly, we propose a test which appropriately adjusts for first-stage screening of genetic factors based on their relevance. Our empirical results provide genetic evidence to validate cholesterol-lowering drug targets aimed at preventing coronary heart disease.
翻译:使用单一遗传区域(Cis-MR)变量进行的门德尔随机分析越来越受欢迎,因为这种分析是提供药物目标验证支持证据的一种经济方法。本文件建议了Cis-MR推论方法,这种推论方法利用许多相关变量的解释力来作出有效的推论,即使这些变量对暴露影响不大。特别是,我们利用单一基因区域遗传关联高度结构化的性质,利用要素分析来减少遗传变量的维度。这些遗传因素随后被用作构建因果关系测试的工具变量。由于这些因素往往与暴露关系不大,标准测试的大小扭曲可能很严重。因此,我们考虑以有条件测试为基础的两种方法。首先,我们推广常用的识别-坏疽测试结果,以考虑将估计因素作为工具的使用。第二,我们提议进行一项测试,根据我们的实验结果,适当调整第一阶段对基因变异体的心脏目标进行先期筛选,目的是防止遗传病。