MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK.
Department of Biostatistics, University of Liverpool, Liverpool, L69 3GL, UK.
Nat Commun. 2020 Feb 21;11(1):1010. doi: 10.1038/s41467-020-14452-4.
In Mendelian randomization (MR) analysis, variants that exert horizontal pleiotropy are typically treated as a nuisance. However, they could be valuable in identifying alternative pathways to the traits under investigation. Here, we develop MR-TRYX, a framework that exploits horizontal pleiotropy to discover putative risk factors for disease. We begin by detecting outliers in a single exposure-outcome MR analysis, hypothesising they are due to horizontal pleiotropy. We search across hundreds of complete GWAS summary datasets to systematically identify other (candidate) traits that associate with the outliers. We develop a multi-trait pleiotropy model of the heterogeneity in the exposure-outcome analysis due to pathways through candidate traits. Through detailed investigation of several causal relationships, many pleiotropic pathways are uncovered with already established causal effects, validating the approach, but also alternative putative causal pathways. Adjustment for pleiotropic pathways reduces the heterogeneity across the analyses.
在孟德尔随机化（MR）分析中，通常将表现出水平多效性的变体视为干扰因素。然而，它们在识别研究性状的替代途径方面可能具有重要价值。在这里，我们开发了 MR-TRYX，这是一种利用水平多效性来发现疾病潜在风险因素的框架。我们首先在单个暴露-结局 MR 分析中检测异常值，假设它们是由于水平多效性引起的。我们在数百个完整的 GWAS 汇总数据集之间进行搜索，以系统地识别与异常值相关的其他（候选）性状。我们针对候选性状通过途径对暴露-结局分析中的异质性开发了一种多性状多效性模型。通过对几个因果关系的详细研究，揭示了许多已经确立因果效应的多效性途径，验证了该方法，但也揭示了其他潜在的因果途径。对多效性途径的调整减少了分析之间的异质性。
