[Mendelian randomization approach, used for causal inferences]

Area of Science:

• Genetics
• Epidemiology
• Biostatistics

Background:

• The Mendelian randomization (MR) approach utilizes Mendelian's law of random genetic inheritance.
• MR enables causal inference from observational data by employing genetic variants as instrumental variables.
• Its application has surged due to advancements in statistical methods and large-scale omics datasets.

Purpose of the Study:

• To provide a comprehensive overview of Mendelian randomization strategies.
• To discuss the assumptions, implications, reliability, and limitations of the MR approach.
• To highlight the utility of MR in causal inference for disease risk.

Main Methods:

• Utilizing genetic variants as instrumental variables (IV).
• Assessing the association between genotype, phenotype, and disease risk.
• Applying advanced statistical methods to observational data.

Main Results:

• Mendelian randomization offers a robust framework for causal inference.
• The method's reliability is contingent on the validity of its underlying assumptions.
• Understanding limitations is crucial for accurate interpretation of MR findings.

Conclusions:

• Mendelian randomization is a powerful tool for establishing causality in epidemiological research.
• The approach requires careful consideration of assumptions and potential biases.
• Continued methodological development enhances the power and applicability of MR.