MRC Integrative Epidemiology Unit (IEU) Seminar series

Title: Bayesian Variable Selection with A Pleiotropic Loss Function in Mendelian Randomization 

Abstract: Mendelian randomization is the use of genetic variants as instruments to assess the existence of a causal relationship between a risk factor and an outcome. A Mendelian randomization analysis requires a set of genetic variants that are strongly associated with the risk factor and only associated with the outcome through their effect on the risk factor. We describe a novel variable selection algorithm for Mendelian randomization that can identify sets of genetic variants which are suitable in both these respects. Our algorithm is applicable in the context of two-sample summary-data Mendelian randomization and employs a Bayesian framework to model genetic associations with the risk factor and a loss function to penalize genetic variants that exhibit pleiotropic effects. Variable selection is done using reversible-jump MCMC. We compare our algorithm against established pleiotropy-robust Mendelian randomization methods in a range of simulations. In a real data application, we study the effect of systolic and diastolic blood pressure on the risk of suffering from coronary heart disease.

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