Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK; UK Dementia Research Institute, Imperial College London, London, UK.
Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK.
Am J Hum Genet. 2023 Jul 6;110(7):1177-1199. doi: 10.1016/j.ajhg.2023.06.005.
The existing framework of Mendelian randomization (MR) infers the causal effect of one or multiple exposures on one single outcome. It is not designed to jointly model multiple outcomes, as would be necessary to detect causes of more than one outcome and would be relevant to model multimorbidity or other related disease outcomes. Here, we introduce multi-response Mendelian randomization (MR), an MR method specifically designed for multiple outcomes to identify exposures that cause more than one outcome or, conversely, exposures that exert their effect on distinct responses. MR uses a sparse Bayesian Gaussian copula regression framework to detect causal effects while estimating the residual correlation between summary-level outcomes, i.e., the correlation that cannot be explained by the exposures, and vice versa. We show both theoretically and in a comprehensive simulation study how unmeasured shared pleiotropy induces residual correlation between outcomes irrespective of sample overlap. We also reveal how non-genetic factors that affect more than one outcome contribute to their correlation. We demonstrate that by accounting for residual correlation, MR has higher power to detect shared exposures causing more than one outcome. It also provides more accurate causal effect estimates than existing methods that ignore the dependence between related responses. Finally, we illustrate how MR detects shared and distinct causal exposures for five cardiovascular diseases in two applications considering cardiometabolic and lipidomic exposures and uncovers residual correlation between summary-level outcomes reflecting known relationships between cardiovascular diseases.
现有的孟德尔随机化（MR）框架推断了一个或多个暴露因素对单一结果的因果效应。它不是为联合建模多个结果而设计的，因为这需要检测多个结果的原因，并且与建模多种疾病或其他相关疾病结果相关。在这里，我们介绍了多响应孟德尔随机化（MR），这是一种专门为多个结果设计的 MR 方法，用于识别导致多个结果的暴露因素，或者相反，识别对不同反应产生影响的暴露因素。MR 使用稀疏贝叶斯高斯 Copula 回归框架来检测因果效应，同时估计汇总水平结果之间的剩余相关性，即不能用暴露因素解释的相关性，反之亦然。我们从理论和综合模拟研究两方面展示了未测量的共同遗传多效性如何在没有样本重叠的情况下引起结果之间的剩余相关性。我们还揭示了影响多个结果的非遗传因素如何导致它们之间的相关性。我们证明，通过考虑剩余相关性，MR 具有更高的能力来检测导致多个结果的共享暴露因素。它还提供了比忽略相关反应之间依赖性的现有方法更准确的因果效应估计。最后，我们通过两个应用程序说明了 MR 如何检测五个心血管疾病的共享和独特的因果暴露因素，同时揭示了反映心血管疾病之间已知关系的汇总水平结果之间的剩余相关性。
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