Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN 55414, USA.
Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN 55414, USA.
Am J Hum Genet. 2024 Sep 5;111(9):1834-1847. doi: 10.1016/j.ajhg.2024.07.007. Epub 2024 Aug 5.
Mendelian randomization (MR) utilizes genome-wide association study (GWAS) summary data to infer causal relationships between exposures and outcomes, offering a valuable tool for identifying disease risk factors. Multivariable MR (MVMR) estimates the direct effects of multiple exposures on an outcome. This study tackles the issue of highly correlated exposures commonly observed in metabolomic data, a situation where existing MVMR methods often face reduced statistical power due to multicollinearity. We propose a robust extension of the MVMR framework that leverages constrained maximum likelihood (cML) and employs a Bayesian approach for identifying independent clusters of exposure signals. Applying our method to the UK Biobank metabolomic data for the largest Alzheimer disease (AD) cohort through a two-sample MR approach, we identified two independent signal clusters for AD: glutamine and lipids, with posterior inclusion probabilities (PIPs) of 95.0% and 81.5%, respectively. Our findings corroborate the hypothesized roles of glutamate and lipids in AD, providing quantitative support for their potential involvement.
孟德尔随机化（MR）利用全基因组关联研究（GWAS）汇总数据来推断暴露与结局之间的因果关系，为识别疾病风险因素提供了一种有价值的工具。多变量 MR（MVMR）估计了多种暴露对结局的直接影响。本研究解决了代谢组学数据中常见的高度相关暴露的问题，在这种情况下，由于共线性，现有的 MVMR 方法通常面临统计效力降低的问题。我们提出了一种稳健的 MVMR 框架扩展，利用约束最大似然（cML）并采用贝叶斯方法来识别暴露信号的独立聚类。通过两样本 MR 方法，我们将该方法应用于英国生物库代谢组学数据中最大的阿尔茨海默病（AD）队列，确定了 AD 的两个独立信号聚类：谷氨酰胺和脂质，后验纳入概率（PIP）分别为 95.0%和 81.5%。我们的发现证实了谷氨酸和脂质在 AD 中的假设作用，为它们潜在的参与提供了定量支持。
