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.

中文翻译：

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一种新的多变量孟德尔随机化框架，用于理清高度相关的暴露与代谢组学的应用


孟德尔随机化 （MR） 利用全基因组关联研究 （GWAS） 摘要数据来推断暴露和结果之间的因果关系，为识别疾病风险因素提供了有价值的工具。多变量 MR （MVMR） 估计多次暴露对结果的直接影响。本研究解决了代谢组学数据中常见的高度相关暴露问题，在这种情况下，由于多重共线性，现有的 MVMR 方法经常面临统计功效降低的问题。我们提出了 MVMR 框架的稳健扩展，该框架利用约束最大似然 （cML） 并采用贝叶斯方法来识别独立的暴露信号集群。通过双样本 MR 方法将我们的方法应用于最大的阿尔茨海默病 （AD） 队列的英国生物样本库代谢组学数据，我们确定了 AD 的两个独立信号簇：谷氨酰胺和脂质，后验包含概率 （PIP） 分别为 95.0% 和 81.5%。我们的研究结果证实了谷氨酸和脂质在 AD 中的假设作用，为它们的潜在参与提供了定量支持。