Best (but oft-forgotten) practices: the design, analysis, and interpretation of Mendelian randomization studies

Summary

This methodological review examines Mendelian randomization as a tool for causal inference in observational epidemiology, exploiting the fixed nature of genotypes to reduce reverse causation and confounding bias. The authors provide guidance on design, analysis, and interpretation of MR studies, with particular attention to assumptions, limitations, and analytic strategies that strengthen causal inference. Though MR cannot definitively prove causality, it represents a cost-effective approach for prioritising intervention targets and informing public health policy.

UK applicability

This methodological guidance is broadly applicable to UK epidemiological research and evidence synthesis, particularly for studies utilising open-access genome-wide association study data and UK Biobank resources. The framework supports UK policy bodies and researchers in appraising causality claims and designing robust causal inference studies.

Key measures

Methodological frameworks for MR study design; assessment of assumptions and biases; analytic strategies for causal inference

Outcomes reported

The paper provides a methodological overview of Mendelian randomization (MR) study design, analysis, and interpretation, with emphasis on key assumptions and limitations. It reviews analytic strategies for strengthening causal inference and discusses the application of MR approaches in evidence synthesis for disease prevention and public health policy.

Topic tags