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

Accepted version
Peer-reviewed

Type

Article

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Authors

Colijn, Johanna Maria 
Klaver, Caroline 
Burgess, Stephen 

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.

Description

Keywords

4202 Epidemiology, 4905 Statistics, 42 Health Sciences, 49 Mathematical Sciences, Prevention, Women's Health, 2.1 Biological and endogenous factors, 2.5 Research design and methodologies (aetiology)

Journal Title

Nature Communications

Conference Name

Journal ISSN

2041-1723

Volume Title

Publisher

Springer Nature

Rights

All rights reserved
Sponsorship
Wellcome Trust (204623/Z/16/Z)
Medical Research Council (MC_UU_00002/7)
Wellcome Trust