MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
Department of Epidemiology and Biostatistics, Imperial College London, London, UK.
Biometrics. 2023 Dec;79(4):3458-3471. doi: 10.1111/biom.13888. Epub 2023 Jun 19.
Mendelian randomization (MR) is a widely used method to estimate the causal effect of an exposure on an outcome by using genetic variants as instrumental variables. MR analyses that use variants from only a single genetic region (cis-MR) encoding the protein target of a drug are able to provide supporting evidence for drug target validation. This paper proposes methods for cis-MR inference that use many correlated variants to make robust inferences even in situations, where those variants have only 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 for the setting where estimated factors are used as instruments. Second, 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.
孟德尔随机化（MR）是一种广泛使用的方法，通过将遗传变异作为工具变量来估计暴露对结果的因果效应。使用仅编码药物蛋白靶标的单个遗传区域（顺式-MR）中的变异进行的 MR 分析能够为药物靶标验证提供支持证据。本文提出了使用许多相关变异进行顺式-MR 推断的方法，即使在那些变异对暴露只有微弱影响的情况下，也能进行稳健推断。特别是，我们利用单基因区域中遗传相关性的高度结构化性质，使用因子分析来减少遗传变异的维度。然后，这些遗传因子被用作工具变量来构建对感兴趣的因果效应的检验。由于这些因子通常与暴露的相关性较弱，标准 t 检验的大小扭曲可能很严重。因此，我们考虑了两种基于条件检验的方法。首先，我们扩展了常用的识别稳健检验的结果，以适应估计因子被用作工具的情况。其次，我们提出了一种基于遗传因子相关性的第一阶段筛选的适当调整的检验方法。我们的实证结果提供了遗传证据，验证了旨在预防冠心病的降胆固醇药物靶标。
