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Credible Mendelian Randomization Studies in the Presence of Selection Bias Using Control Exposures
Zhao Yang1, C Mary Schooling1,2, Man Ki Kwok1
1School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
Control exposures can validate Mendelian randomization (MR) estimates by detecting selection bias. This method helps ensure the reliability of causal inference in genetic studies.
Area of Science:
- Epidemiology
- Genetic Epidemiology
- Biostatistics
Background:
- Selection bias is a significant limitation in Mendelian randomization (MR) studies.
- Existing methods for assessing selection bias in MR are limited.
- Understanding and mitigating selection bias is crucial for accurate causal inference.
Purpose of the Study:
- To propose and validate a novel method using control exposures to detect selection bias in MR studies.
- To assess the potential effect of higher transferrin on stroke using MR and control exposures.
- To provide a framework for selecting appropriate control exposures in MR analyses.
Main Methods:
- Simulation studies to illustrate the data-generating process of selection bias in MR.
- Conceptualization and application of control exposures (transferrin saturation, iron status) to validate MR estimates.
- Investigation of MR-Pleiotropy RESidual Sum and Outliers (MR-PRESSO) and other statistical methods.
Main Results:
- The application showed inconsistent effects of genetically predicted transferrin and transferrin saturation on stroke, suggesting potential selection bias.
- Genetically predicted iron status showed expected associations with stroke and longevity, indicating no systematic selection bias in that context.
- The proposed control exposure method successfully identified potential biases in the primary MR analysis.
Conclusions:
- Routine use of control exposures is a valuable tool for validating MR estimates and detecting selection bias.
- Appropriate control exposures include antagonists, decoys, or exposures with similar biological activity and shared bias sources.
- An additional validated control exposure with a known outcome association can further explore systematic selection bias.
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