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Selecting likely causal risk factors from high-throughput experiments using multivariable Mendelian randomization.
- Source :
- Nature Communications; 1/7/2020, Vol. 11 Issue 1, p1-11, 11p
- Publication Year :
- 2020
-
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. Multivariable Mendelian randomization (MR) extends the standard MR framework to consider multiple risk factors in a single model. Here, Zuber et al. propose MR-BMA, a Bayesian variable selection approach to identify the likely causal determinants of a disease from many candidate risk factors as for example high-throughput data sets. [ABSTRACT FROM AUTHOR]
- Subjects :
- RETINAL degeneration
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Details
- Language :
- English
- ISSN :
- 20411723
- Volume :
- 11
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Nature Communications
- Publication Type :
- Academic Journal
- Accession number :
- 141099479
- Full Text :
- https://doi.org/10.1038/s41467-019-13870-3