| Title: | Accounting for the impact of rare variants on causal inference with RARE: a novel multivariable Mendelian randomization method |
| Journal: | Briefings in Bioinformatics |
| Published: | 1 May 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40370099/ |
| DOI: | https://doi.org/10.1093/bib/bbaf214 |
| URL: | https://academic.oup.com/bib/article-pdf/26/3/bbaf214/63183280/bbaf214.pdf |
Publication 15706
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Abstract
Mendelian randomization (MR) method utilizes genetic variants as instrumental variables to infer the causal effect of an exposure on an outcome. However, the impact of rare variants on traits is often neglected, and traditional MR assumptions can be violated by correlated horizontal pleiotropy (CHP) and uncorrelated horizontal pleiotropy (UHP). To address these issues, we propose a multivariable MR approach, an extension of the standard MR framework: MVMR incorporating Rare variants Accounting for multiple Risk factors and shared horizontal plEiotropy (RARE). In the simulation studies, we demonstrate that RARE effectively detects the causal effects of exposures on outcome with accounting for the impact of rare variants on causal inference. Additionally, we apply RARE to study the effects of high density lipoprotein and low density lipoprotein on type 2 diabetes and coronary atherosclerosis, respectively, thereby illustrating its robustness and effectiveness in real data analysis.</p>
12 Keywords
- Causality
- Computer Simulation
- Coronary Artery Disease
- Diabetes Mellitus, Type 2
- Genetic Pleiotropy
- Genetic Variation
- Humans
- Lipoproteins, HDL
- Lipoproteins, LDL
- Mendelian Randomization Analysis
- Polymorphism, Single Nucleotide
- Risk Factors
10 Authors
- Yu Cheng
- Xinjia Ruan
- Xiaofan Lu
- Yuqing Yang
- Yuhang Wang
- Shangjin Yan
- Yuzhe Sun
- Fangrong Yan
- Liyun Jiang
- Tiantian Liu
Enabling scientific discoveries that improve human health