| Title: | Identifying Modifiable Risk Factors for Colorectal Cancer with MUSE: A Novel Two-Sample Mendelian Randomization Method |
| Journal: | - |
| Published: | 1 Sep 2024 |
| DOI: | https://doi.org/10.2139/ssrn.4955304 |
Publication 13528
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Abstract
Background: Identifying modifiable causal risk factors for cancer is challenging because both exposure and outcome are strongly impacted by genetics and environment, leading to complex instrumental variable-to-trait structures. Methods: To accurately capture these intricate structures, we propose a novel two-sample MR method, namely, Multiple forms of IV-to-exposure effects Unraveling shared confounding using GWAS Summary-level Effects (MUSE). Findings: Through rigorous analyses of simulated and real-world data, MUSE has been shown to be more suitable for comprehensive real-world scenarios than other existing methods, in terms of controlling the type I error rate and boosting statistical power, even when accounting for linkage disequilibrium among instrumental variables. We further apply MUSE to GWAS summary-level statistics and combine evidence from the UK Biobank individual-level data to identify putative causal risk factors for colorectal cancer. Interpretation: In conclusion, this paper aimed to highlight the applicability of MUSE and to provide practical insights into the potential causal mechanisms of cancer. Funding: This work was supported by the National Natural Science Foundation of China [No. 82273735, No. 82304252, No. 82473737], the National Science Foundation for Young Scientists of China [No.82404380], and the Fundamental Research Funds for the Central Universities of China Pharmaceutical University [2632023FY04].Declaration of Interest: The authors declare no competing interests.</p>
10 Authors
- Yu Cheng
- Ruyue He
- Xinjia Ruan
- Xinyi Chen
- Xiaofan Lu
- Yuqing Yang
- Yuhang Wang
- Yuqing Ye
- Tiantian Liu
- Fangrong Yan
Enabling scientific discoveries that improve human health