Summary
This narrative review examines pleiotropy—the phenomenon whereby a single genetic variant influences multiple traits—as a critical limitation to Mendelian randomization (MR), a method for inferring causality between traits from genetic data. The authors distinguish between vertical pleiotropy (where a variant influences one trait which then influences another) and horizontal pleiotropy (independent pathways), emphasising that horizontal pleiotropy violates MR assumptions. The paper outlines newly developed methods that, when used together, can enhance the reliability of phenome-wide causal inference from genome-wide association studies.
UK applicability
As a methodological review of genetic epidemiology techniques, this work is globally applicable and relevant to UK researchers conducting Mendelian randomization analyses in biomedical and public health research. The methods discussed could improve confidence in causal inference studies conducted within UK cohorts and biobanks.
Key measures
Pleiotropy detection and classification; validity of Mendelian randomization assumptions; reliability of causal effect estimates
Outcomes reported
The paper reviews methods for detecting and accounting for pleiotropy in Mendelian randomization analyses. It evaluates how recently developed approaches can be used together to improve the reliability of causal inference from genetic association data.
Topic tags
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