Inferring the Direction of a Causal Link and Estimating Its Effect via a Bayesian Mendelian Randomization Approach
This work addresses the challenge of inferring causal links and estimating their effects from observational data for epidemiologists, particularly when dealing with pleiotropy and reverse causation in genetic studies. This is an incremental improvement to an existing method.
This paper proposes BayesMR, a Bayesian Mendelian Randomization approach that accounts for pleiotropic effects and reverse causation to infer the direction and estimate the magnitude of a causal link between an exposure and an outcome. The method provides a posterior distribution for the causal effect and uses Bayesian model averaging to quantify the likelihood of the inferred causal direction.
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 called BayesMR 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.