Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression

TLDR

Two‑sample Mendelian randomization studies are traditionally visualized with scatter plots of SNP‑outcome versus SNP‑exposure associations, providing an immediate view of causal‑effect estimates per variant. This paper proposes replacing the conventional scatter plot with a Galbraith radial plot to better accommodate varying precision of SNP‑outcome estimates and to facilitate detection of outliers and influential points. The radial plot overlays the inverse‑variance weighted causal estimate, supports MR‑Egger regression, offers additional diagnostic plots, and is implemented in accompanying software. Using a two‑sample MR study of systolic blood pressure and coronary heart disease, the radial plot identified a single outlier driving discrepancies between IVW and MR‑Egger estimates and simplified data recoding and outlier detection.

Abstract

data furnishing a two-sample Mendelian randomization (MR) study are often visualized with the aid of a scatter plot, in which single-nucleotide polymorphism (SNP)-outcome associations are plotted against the SNP-exposure associations to provide an immediate picture of the causal-effect estimate for each individual variant. It is also convenient to overlay the standard inverse-variance weighted (IVW) estimate of causal effect as a fitted slope, to see whether an individual SNP provides evidence that supports, or conflicts with, the overall consensus. Unfortunately, the traditional scatter plot is not the most appropriate means to achieve this aim whenever SNP-outcome associations are estimated with varying degrees of precision and this is reflected in the analysis.We propose instead to use a small modification of the scatter plot-the Galbraith Radial plot-for the presentation of data and results from an MR study, which enjoys many advantages over the original method. On a practical level, it removes the need to recode the genetic data and enables a more straightforward detection of outliers and influential data points. Its use extends beyond the purely aesthetic, however, to suggest a more general modelling framework to operate within when conducting an MR study, including a new form of MR-Egger regression.We illustrate the methods using data from a two-sample MR study to probe the causal effect of systolic blood pressure on coronary heart disease risk, allowing for the possible effects of pleiotropy. The Radial plot is shown to aid the detection of a single outlying variant that is responsible for large differences between IVW and MR-Egger regression estimates. Several additional plots are also proposed for informative data visualization.The Radial plot should be considered in place of the scatter plot for visualizing, analysing and interpreting data from a two-sample summary data MR study. Software is provided to help facilitate its use.