Inferring the direction of a causal link and estimating its effect via a Bayesian Mendelian randomization approach. (April 2020)

• Record Type: Journal Article
• Authors: Bucur, Ioan Gabriel; Claassen, Tom; Heskes, Tom
• Other Names: Fox Jean-Paul guest-editor.
• Is Part Of: Statistical methods in medical research. Volume 29:Number 4(2020)
• Page Start: 1081
• Page End: 1111
• Publication Date: 2020-04
• Subjects: Causal inference -- Mendelian randomization -- Bayesian model averaging -- instrumental variables -- sparsity prior -- genetic epidemiology -- robust estimation; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280219851817
• Languages: English
• ISSNs: 0962-2802

Abstract

The use of genetic variants as instrumental variables – an approach known as Mendelian randomization – is a popular epidemiological method for estimating the causal effect of an exposure (phenotype, biomarker, risk factor) on a disease or health-related outcome from observational data. Instrumental variables must satisfy strong, often untestable assumptions, which means that finding good genetic instruments among a large list of potential candidates is challenging. This difficulty is compounded by the fact that many genetic variants influence more than one phenotype through different causal pathways, a phenomenon called horizontal pleiotropy. This leads to errors not only in estimating the magnitude of the causal effect but also in inferring the direction of the putative causal link. In this paper, we propose a Bayesian approach calledBayesMR that is a generalization of the Mendelian randomization technique in which we allow for pleiotropic effects and, crucially, for the possibility of reverse causation. The output of the method is a posterior distribution over the target causal effect, which provides an immediate and easily interpretable measure of the uncertainty in the estimation. More importantly, we use Bayesian model averaging to determine how much more likely the inferred direction is relative to the reverse direction.