Mendelian randomisation (MR) analysis is an important tool to elucidate the causal relevance of environmental and biological risk factors for disease. However, causal inference is undermined if genetic variants used to instrument a risk factor also influence alternative disease-pathways (horizontal pleiotropy). Here we report how the ‘no horizontal pleiotropy assumption’ is strengthened when proteins are the risk factors of interest. Proteins are typically the proximal effectors of biological processes encoded in the genome. Moreover, proteins are the targets of most medicines, so MR studies of drug targets are becoming a fundamental tool in drug development. To enable such studies, we introduce a mathematical framework that contrasts MR analysis of proteins with that of risk factors located more distally in the causal chain from gene to disease. We illustrate key model decisions and introduce an analytical framework for maximising power and evaluating the robustness of analyses.

孟德尔随机化 (MR) 分析是阐明环境和生物风险因素与疾病之间因果关系的重要工具。然而，如果用于检测危险因素的遗传变异也影响其他疾病途径（水平多效性），那么因果推断就会受到损害。在这里，我们报告了当蛋白质是感兴趣的风险因素时，如何加强“无水平多效性假设”。蛋白质通常是基因组中编码的生物过程的近端效应器。此外，蛋白质是大多数药物的靶点，因此药物靶点的磁共振研究正在成为药物开发的基本工具。为了进行此类研究，我们引入了一个数学框架，将蛋白质的 MR 分析与位于从基因到疾病的因果链中更远端的风险因素的分析进行对比。我们说明了关键模型决策，并引入了一个分析框架，用于最大化功效和评估分析的稳健性。