Mendelian Randomization versus Path Models: Making Causal Inferences in Genetic Epidemiology

Area of Science:

• Epidemiology
• Genetics
• Biostatistics

Background:

• Mendelian randomization (MR) is a widely used method for causal inference in observational studies.
• The application of MR relies on several key assumptions that can be difficult to satisfy in practice.
• Integrating multi-omics data requires advanced analytical frameworks for robust causal discovery.

Purpose of the Study:

• To elucidate the core principles and assumptions of Mendelian randomization studies.
• To compare and contrast Mendelian randomization with path modeling, particularly when analyzing diverse 'omics' data.
• To highlight the utility of path models for causal inference in complex biological systems.

Main Methods:

• A comprehensive review of the foundational concepts and assumptions of Mendelian randomization, referencing Katan (1986).
• Introduction of path modeling as a complementary and often superior analytical tool for 'omics' data integration.
• Illustrative examples using real-world data on lipid levels and coronary artery disease to demonstrate path model application.

Main Results:

• Mendelian randomization is underpinned by numerous assumptions that are frequently unmet in real-world applications.
• Path models are well-suited for causal inference and should be distinguished from Mendelian randomization.
• Path modeling provides a more appropriate analytical strategy for many applications, often in conjunction with basic Mendelian randomization.

Conclusions:

• Mendelian randomization and path models represent distinct conceptual frameworks for causal inference.
• Path modeling, unlike simple Mendelian randomization, is highly effective for investigating causality across multiple 'omics' data levels.