An efficient and robust approach to Mendelian randomization with measured pleiotropic effects in a high-dimensional setting

TL;DR

This paper introduces a regularization-based method for Mendelian randomization that efficiently accounts for pleiotropic effects using summary data, improving causal inference in high-dimensional genetic studies.

Contribution

It proposes a novel regularization approach to identify relevant pleiotropic covariates, enabling robust and efficient causal effect estimation even with many covariates and summary data.

Findings

Method performs well in realistic simulations

Outperforms standard methods in high-dimensional settings

Applied successfully to study urate and heart disease

Abstract

Valid estimation of a causal effect using instrumental variables requires that all of the instruments are independent of the outcome conditional on the risk factor of interest and any confounders. In Mendelian randomization studies with large numbers of genetic variants used as instruments, it is unlikely that this condition will be met. Any given genetic variant could be associated with a large number of traits, all of which represent potential pathways to the outcome which bypass the risk factor of interest. Such pleiotropy can be accounted for using standard multivariable Mendelian randomization with all possible pleiotropic traits included as covariates. However, the estimator obtained in this way will be inefficient if some of the covariates do not truly sit on pleiotropic pathways to the outcome. We present a method which uses regularization to identify which out of a set of potential covariates need to be accounted for in a Mendelian randomization analysis in order to produce an efficient and robust estimator of a causal effect. The method can be used in the case where individual-level data are not available and the analysis must rely on summary-level data only. It can also be used in the case where there are more covariates under consideration than instruments, which is not possible using standard multivariable Mendelian randomization. We show the results of simulation studies which demonstrate the performance of the proposed regularization method in realistic settings. We also illustrate the method in an applied example which looks at the causal effect of urate plasma concentration on coronary heart disease.