Disentangling the effects of traits with shared clustered genetic predictors using multivariable Mendelian randomization

TL;DR

This paper introduces multivariable Mendelian randomization methods using principal component analysis to disentangle causal effects of correlated genetic variants within gene clusters, improving estimate precision and stability.

Contribution

The authors develop PCA-based multivariable Mendelian randomization techniques to handle correlated genetic variants, addressing issues of instability and imprecision in causal inference.

Findings

PCA-based methods yield more precise estimates.

Methods are less sensitive to numerical instability.

Application identifies monocyte chemoattractant protein-1 as a likely causal factor for stroke.

Abstract

When genetic variants in a gene cluster are associated with a disease outcome, the causal pathway from the variants to the outcome can be difficult to disentangle. For example, the chemokine receptor gene cluster contains genetic variants associated with various cytokines. Associations between variants in this cluster and stroke risk may be driven by any of these cytokines. Multivariable Mendelian randomization is an extension of standard univariable Mendelian randomization to estimate the direct effects of related exposures with shared genetic predictors. However, when genetic variants are clustered, a Goldilocks dilemma arises: including too many highly-correlated variants in the analysis can lead to ill-conditioning, but pruning variants too aggressively can lead to imprecise estimates or even lack of identification. We propose multivariable methods that use principal component analysis to reduce many correlated genetic variants into a smaller number of orthogonal components that are used as instrumental variables. We show in simulations that these methods result in more precise estimates that are less sensitive to numerical instability due to both strong correlations and small changes in the input data. We apply the methods to demonstrate the most likely causal risk factor for stroke at the chemokine gene cluster is monocyte chemoattractant protein-1.