Selecting causal risk factors from high-throughput experiments using multivariable Mendelian randomization

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Colijn, Johanna Maria 
Klaver, Caroline 
Burgess, Stephen 

Modern high-throughput experiments provide a rich resource to investigate causal determinants of disease risk. Mendelian randomization (MR) is the use of genetic variants as instrumental variables to infer the causal effect of a specific risk factor on an outcome. Multivariable MR is an extension of the standard MR framework to consider multiple potential risk factors in a single model. However, current implementations of multivariable MR use standard linear regression and hence perform poorly with many risk factors. Here, we propose a novel approach to two-sample multivariable MR based on Bayesian model averaging (MR-BMA) that scales to high-throughput experiments. In a realistic simulation study, we show that MR-BMA can detect true causal risk factors even when the candidate risk factors are highly correlated. We illustrate MR-BMA by analysing publicly-available summarized data on metabolites to prioritise likely causal biomarkers for age-related macular degeneration.

4202 Epidemiology, 4905 Statistics, 42 Health Sciences, 49 Mathematical Sciences, Prevention
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Nature Communications
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Springer Nature
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Wellcome Trust (204623/Z/16/Z)
Medical Research Council (MC_UU_00002/7)
Wellcome Trust