| Title: | Bayesian network-based Mendelian randomization for variant prioritization and phenotypic causal inference |
| Journal: | Human Genetics |
| Published: | 21 Feb 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38381161/ |
| DOI: | https://doi.org/10.1007/s00439-024-02640-x |
| URL: | https://www.researchsquare.com/article/rs-3609205/latest.pdf |
Publication 8702
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
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.</p>
8 Authors
- Jianle Sun
- Jie Zhou
- Yuqiao Gong
- Chongchen Pang
- Yanran Ma
- Jian Zhao
- Zhangsheng Yu
- Yue Zhang
1 Application
| Application ID | Title |
|---|---|
| 100014 | Whole-genome sequencing studies for disease-related and imaging phenotypes using UKBB data. |
Enabling scientific discoveries that improve human health