Comparison and application of Mendelian randomization methods for correcting weak instrumental variable bias

YANG Boran; PENG Liuqing; GAO Xue; WANG Juping; WANG Tong

doi:10.16462/j.cnki.zhjbkz.2024.04.019

Volume 28 Issue 4

Apr. 2024

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YANG Boran, PENG Liuqing, GAO Xue, WANG Juping, WANG Tong. Comparison and application of Mendelian randomization methods for correcting weak instrumental variable bias[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(4): 490-496. doi: 10.16462/j.cnki.zhjbkz.2024.04.019

Citation:

YANG Boran, PENG Liuqing, GAO Xue, WANG Juping, WANG Tong. Comparison and application of Mendelian randomization methods for correcting weak instrumental variable bias[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(4): 490-496. doi: 10.16462/j.cnki.zhjbkz.2024.04.019

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Comparison and application of Mendelian randomization methods for correcting weak instrumental variable bias

doi: 10.16462/j.cnki.zhjbkz.2024.04.019

Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan 030001, China, Key Laboratory of Coal Environmental Pathogenicity and Prevention (Shanxi Medical University) Ministry of Education, Taiyuan 030001, China

Funds:

National Natural Science Foundation of China 81872715

National Natural Science Foundation of China 82103949

Basic Research Project of Shanxi Province, China 20210302124186

Basic Research Project of Shanxi Province, China 202103021223234

More Information

Corresponding author: WANG Tong, E-mail: tongwang@sxmu.edu.cn
Received Date: 2023-08-23
Rev Recd Date: 2024-01-06

Available Online: 2024-05-17

Publish Date: 2024-04-10

Abstract

Abstract

Objective To provide suggestions to choose the appropriate two-sample Mendelian randomization methods when no instrumental variables are available or weak instrumental variable bias exist. Methods In the case of no pleiotropy, balanced pleiotropy, and directional pleiotropy, respectively, the impact of weak instrumental variables on each method was investigated by changing the intensity of instrumental variables. The study simulated different number of instrumental variables to access the impact on MR-Mix under the condition that both directional pleiotropic effects and weak instrumental variables existed. MR-Mix served as the primary analytical method, while the other two methods were employed as sensitivity analyses to explore the causal associations between BMI, HDL, LDL, TG, TC, and serum uric acid. Results Under scenarios of no pleiotropy and balanced pleiotropy, MW-IVW performed the best, while MR-Mix performed the worst. In the case of directional pleiotropic, MR-Mix exhibited the best performed, whereas MW-IVW performed the worst. BMI(β=0.280, P=0.003) and TG(β=0.370, P < 0.001) were identified as risk factors for elevated serum uric acid. HDL(β=-0.250, P=0.002) was identified as a protective factor. Conclusions Under scenarios of no pleiotropy and balanced pleiotropy, MW-IVW demonstrates better statistical performance. However, in the presence of directional pleiotropy, MR-Mix exhibits superior robustness. BMI and TG are identified as risk factors for elevated serum uric acid.
- Mendelian randomizaiton,
- Pleiotropy,
- Weak instruments,
- Body mass index,
- Lipid traits,
- Serum urate

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References(13)

References

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