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基于贝叶斯网络的孟德尔随机化用于变异优先级排序和表型因果推断。

Bayesian network-based Mendelian randomization for variant prioritization and phenotypic causal inference.

机构信息

Department of Bioinformatics and Biostatistics, Shanghai Jiao Tong University, Shanghai, China.

School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen, China.

出版信息

Hum Genet. 2024 Oct;143(9-10):1081-1094. doi: 10.1007/s00439-024-02640-x. Epub 2024 Feb 21.

Abstract

Mendelian randomization is a powerful method for inferring causal relationships. However, obtaining suitable genetic instrumental variables is often challenging due to gene interaction, linkage, and pleiotropy. We propose Bayesian network-based Mendelian randomization (BNMR), a Bayesian causal learning and inference framework using individual-level data. BNMR employs the random graph forest, an ensemble Bayesian network structural learning process, to prioritize candidate genetic variants and select appropriate instrumental variables, and then obtains a pleiotropy-robust estimate by incorporating a shrinkage prior in the Bayesian framework. Simulations demonstrate BNMR can efficiently reduce the false-positive discoveries in variant selection, and outperforms existing MR methods in terms of accuracy and statistical power in effect estimation. With application to the UK Biobank, BNMR exhibits its capacity in handling modern genomic data, and reveals the causal relationships from hematological traits to blood pressures and psychiatric disorders. Its effectiveness in handling complex genetic structures and modern genomic data highlights the potential to facilitate real-world evidence studies, making it a promising tool for advancing our understanding of causal mechanisms.

摘要

孟德尔随机化是一种推断因果关系的强大方法。然而,由于基因相互作用、连锁和多效性,获得合适的遗传工具变量通常具有挑战性。我们提出了基于贝叶斯网络的孟德尔随机化(BNMR),这是一种使用个体水平数据的贝叶斯因果学习和推理框架。BNMR 采用随机图森林,这是一种集成的贝叶斯网络结构学习过程,用于优先考虑候选遗传变异,并选择适当的工具变量,然后通过在贝叶斯框架中纳入收缩先验来获得抗多效性的估计。模拟表明,BNMR 可以有效地减少变异选择中的假阳性发现,并在效应估计的准确性和统计功效方面优于现有的 MR 方法。通过对英国生物银行(UK Biobank)的应用,BNMR 展示了其处理现代基因组数据的能力,并揭示了从血液特征到血压和精神障碍的因果关系。它在处理复杂遗传结构和现代基因组数据方面的有效性突出了促进真实世界证据研究的潜力,使其成为推进我们对因果机制理解的有前途的工具。

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