Multivariable Mendelian Randomization: The Use of Pleiotropic Genetic Variants to Estimate Causal Effects

TLDR

Mendelian randomization typically uses genetic variants that affect only a single risk factor, but pleiotropic variants that influence multiple related factors complicate causal inference, prompting the development of a multivariable approach analogous to factorial trials. The authors propose extending Mendelian randomization to use multiple genetic variants associated with several measured risk factors in order to simultaneously estimate each factor’s causal effect on an outcome. They develop and compare multivariable MR estimation methods with real and simulated data, outlining the assumptions required for valid inference. Under these assumptions, the study shows that triglyceride‑related pathways causally increase coronary heart disease risk independently of low‑density lipoprotein and high‑density lipoprotein cholesterol effects.

Abstract

A conventional Mendelian randomization analysis assesses the causal effect of a risk factor on an outcome by using genetic variants that are solely associated with the risk factor of interest as instrumental variables. However, in some cases, such as the case of triglyceride level as a risk factor for cardiovascular disease, it may be difficult to find a relevant genetic variant that is not also associated with related risk factors, such as other lipid fractions. Such a variant is known as pleiotropic. In this paper, we propose an extension of Mendelian randomization that uses multiple genetic variants associated with several measured risk factors to simultaneously estimate the causal effect of each of the risk factors on the outcome. This “multivariable Mendelian randomization” approach is similar to the simultaneous assessment of several treatments in a factorial randomized trial. In this paper, methods for estimating the causal effects are presented and compared using real and simulated data, and the assumptions necessary for a valid multivariable Mendelian randomization analysis are discussed. Subject to these assumptions, we demonstrate that triglyceride-related pathways have a causal effect on the risk of coronary heart disease independent of the effects of low-density lipoprotein cholesterol and high-density lipoprotein cholesterol.