Pleiotropy robust methods for multivariable Mendelian randomization

TL;DR

This paper introduces three pleiotropy-robust methods for multivariable Mendelian randomization, improving causal inference accuracy when multiple risk factors are involved, especially under varying levels of pleiotropy.

Contribution

The paper develops and compares three novel pleiotropy-robust methods for multivariable Mendelian randomization, addressing a gap in existing approaches for multiple risk factors.

Findings

MVMR-Robust outperforms existing methods with low pleiotropy.

MVMR-Lasso achieves lowest mean squared error at high pleiotropy.

MVMR-Median provides reliable estimates up to moderate pleiotropy.

Abstract

Mendelian randomization is a powerful tool for inferring the presence, or otherwise, of causal effects from observational data. However, the nature of genetic variants is such that pleiotropy remains a barrier to valid causal effect estimation. There are many options in the literature for pleiotropy robust methods when studying the effects of a single risk factor on an outcome. However, there are few pleiotropy robust methods in the multivariable setting, that is, when there are multiple risk factors of interest. In this paper we introduce three methods which build on common approaches in the univariable setting: MVMR-Robust; MVMR-Median; and MVMR-Lasso. We discuss the properties of each of these methods and examine their performance in comparison to existing approaches in a simulation study. MVMR-Robust is shown to outperform existing outlier robust approaches when there are low levels of pleiotropy. MVMR-Lasso provides the best estimation in terms of mean squared error for moderate to high levels of pleiotropy, and can provide valid inference in a three sample setting. MVMR-Median performs well in terms of estimation across all scenarios considered, and provides valid inference up to a moderate level of pleiotropy. We demonstrate the methods in an applied example looking at the effects of intelligence, education and household income on the risk of Alzheimer's disease.