Selecting causal risk factors from high-throughput experiments using multivariable Mendelian randomization
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Zuber, V., Colijn, J. M., Klaver, C., & Burgess, S. (2018). Selecting causal risk factors from high-throughput experiments using multivariable Mendelian randomization. https://doi.org/10.1101/396333
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 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.
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External DOI: https://doi.org/10.1101/396333
This record's URL: https://www.repository.cam.ac.uk/handle/1810/299452
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