Summary
This paper proposes the Galbraith Radial plot as a superior alternative to traditional scatter plots for visualizing two-sample Mendelian randomization data, particularly when SNP-outcome associations are estimated with varying precision. The Radial plot framework enables more straightforward outlier detection and a generalized modelling approach, including a novel MR-Egger regression form. Illustrated with a blood pressure–coronary heart disease example, the method is shown to improve identification of influential variants responsible for heterogeneity between causal effect estimates.
UK applicability
This is a methodological contribution of potential relevance to UK epidemiological and genetic research institutions conducting causal inference studies. The improved visualization and statistical framework may enhance rigour in UK-based Mendelian randomization investigations across health topics, though it does not directly address farming systems or nutritional phenotypes.
Key measures
Radial plot visualization, Radial regression coefficients, outlier detection, pleiotropy assessment, causal effect estimates via inverse-variance weighted and MR-Egger regression
Outcomes reported
The study presents methodological improvements to visualize and analyse two-sample Mendelian randomization data, demonstrating the approach using a case study of systolic blood pressure and coronary heart disease risk. The Radial plot method enables improved detection of outlying variants and pleiotropy in genetic association studies.
Topic tags
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