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

Summary

This methodological review examines Mendelian randomization (MR) as a tool for appraising causality in observational epidemiology, exploiting the principle that genotypes are resistant to reverse causation bias and confounding. The authors provide comprehensive guidance on design, analysis, and interpretation of MR studies, emphasising key assumptions and limitations that, if unaddressed, can lead to erroneous conclusions. The review discusses two-sample MR approaches and their increasing utility in evidence synthesis and public health policy, positioning MR as a cost-effective strategy for causal inference despite its methodological constraints.

UK applicability

This methodological framework is directly applicable to UK epidemiological research and public health policy development, as UK researchers have access to large population biobanks and genome-wide association study consortia. The guidance would strengthen the evidence base for intervention targeting in UK disease prevention programmes.

Key measures

Methodological framework for Mendelian randomization; assessment of study design quality; evaluation of causal inference approaches

Outcomes reported

The study reviewed the design, analysis, and interpretation of Mendelian randomization studies, with particular emphasis on assumptions, limitations, and analytic strategies for strengthening causal inference in observational epidemiology.

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