[Review on correction methods related to the pleiotropic effect in Mendelian randomization]

Area of Science:

• Epidemiology
• Genetics
• Biostatistics

Background:

• Mendelian randomization (MR) is a powerful causal inference method.
• Genetic variants serve as instrumental variables (IVs) for exposures.
• Pleiotropy, where IVs affect outcomes through pathways other than the exposure, is a major challenge in MR.

Purpose of the Study:

• To review and present methods for addressing pleiotropic bias in Mendelian randomization.
• To guide researchers in selecting appropriate instrumental variables and analytical techniques.

Main Methods:

• Discusses criteria for valid instrumental variables in MR.
• Explains methods for testing and correcting for pleiotropic effects.
• Covers the construction of pleiotropic effect correction models.
• Details sensitivity analyses for robust causal estimations.

Main Results:

• Pleiotropy is common due to complex biological pathways.
• Correction methods are essential for reliable MR findings.
• Careful consideration of data types and sample size is crucial for method selection.

Conclusions:

• Addressing pleiotropy is vital for valid Mendelian randomization studies.
• The presented methods enhance the robustness of causal inference.
• Researchers should carefully select MR methods based on study specifics for consistent causal estimations.