Selecting likely causal risk factors from high-throughput experiments using multivariable Mendelian randomization
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...
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| Main Authors: | , , , |
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| Format: | article |
| Language: | EN |
| Published: |
Nature Portfolio
2020
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| Subjects: | |
| Online Access: | https://doaj.org/article/25ac00d0332e4bc4a929fc5680c24f8b |
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| Summary: | 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. |
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