Radial Mendelian randomization

Radial Mendelian randomization: Novel approaches to estimating and visualising causal effects using GWAS summary data

Background: Multivariable Mendelian randomization (MVMR) is a statistical approach using genetic variants as instrumental variables to estimate direct causal effects of multiple exposures on an outcome simultaneously. In univariable MR findings are typically illustrated using scatter or radial plots created using summary data from GWAS, however, analogous plots for MVMR analyses have so far been unavailable due to the multidimensional nature of the analysis.

Methods: We propose a radial formulation of MVMR, and an adapted Galbraith radial plot, which allow for the direct effect of each exposure within an MVMR analysis to be visualised. Radial MVMR plots facilitate the detection of outlier variants, indicating a violation of one or more assumptions of MVMR. The RMVMR R package is also provided as accompanying software for implementation of the methods described.

Results: We demonstrate the effectiveness of the radial MVMR approach through simulation and applied analyses considering the effect of lipid fractions upon coronary heart disease (CHD). We find evidence of a protective effect of high-density lipoprotein (HDL) and a positive effect of low-density lipoprotein (LDL) on CHD, however, the protective effect of HDL appeared to be smaller in magnitude when removing potentially pleiotropic genetic variants.

In combination with simulated examples, we highlight how important features of MVMR analyses can be explored using a range of tools incorporated within the RMVMR R package.

Conclusions: Radial MVMR effectively visualises causal effect estimates, and provides valuable diagnostic information with respect to the underlying assumptions of MVMR. 

Speaker

Wes Spiller, University of Bristol

How to attend

If not a member of the Dept. Mathematical Science at the University of Essex, you can register your interest in attending the seminar and request the Zoom’s meeting password by emailing Dr Osama Mahmoud (o.mahmoud@essex.ac.uk)