Understanding the assumptions underlying Mendelian randomization.

Area of Science:

• Genetics
• Epidemiology
• Biostatistics

Background:

• Large genetic datasets are increasingly available, driving the adoption of Mendelian Randomization (MR).
• MR is a valuable secondary analysis method for estimating causal relationships when experimental studies are not feasible.
• The validity of MR relies heavily on several key assumptions.

Purpose of the Study:

• To elucidate the core assumptions underpinning Mendelian Randomization (MR).
• To explain the role of these assumptions in ensuring valid causal effect estimation.
• To discuss how diverse data types can support the validation of MR assumptions.

Main Methods:

• Leveraging genetic variants as instrumental variables to infer causal effects.
• Focus on the theoretical framework and assumptions of MR.
• Discussion of data requirements for assumption validation.

Main Results:

• MR can provide informative causal estimates but is susceptible to bias if assumptions are violated.
• A thorough understanding of MR assumptions is crucial for researchers.
• The perspective highlights the importance of evaluating assumption validity within specific analytical contexts.

Conclusions:

• Understanding and validating MR assumptions is paramount for reliable causal inference.
• This perspective aims to equip researchers with the knowledge to critically assess MR applications.
• Further research should explore how different data modalities can strengthen MR's evidential basis.