Selecting likely causal risk factors from high-throughput experiments using multivariable Mendelian randomization
oleh: Verena Zuber, Johanna Maria Colijn, Caroline Klaver, Stephen Burgess
| Format: | Article |
|---|---|
| Diterbitkan: | Nature Portfolio 2020-01-01 |
Deskripsi
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.