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

Verena Zuber, Johanna Maria Colijn, Caroline Klaver and Stephen Burgess ()
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Verena Zuber: University of Cambridge
Johanna Maria Colijn: Erasmus University Medical Center
Caroline Klaver: Erasmus University Medical Center
Stephen Burgess: University of Cambridge

Nature Communications, 2020, vol. 11, issue 1, 1-11

Abstract: Abstract 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 two-sample multivariable MR approach 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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-019-13870-3

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DOI: 10.1038/s41467-019-13870-3

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