Bayesian approach for two-sample MR settings that models and accounts for instrumental variables (IVs) that exert horizontally pleiotropic effects on the outcome independent of the exposure.
This Bayesian method represents horizontally pleiotropic effects by unknown parameters in the MR estimation model and imposes a shrinkage prior distribution that assumes an unspecified subset of the effects to be zero. The resulting posterior distribution can then be sampled by Markov chain Monte Carlo methods to obtain the causal effect estimate and related confidence intervals. The method is reasonably robust to the presence of directional pleiotropy and moderate correlation between the IVs. The method can also be extended to estimate the direct and indirect effects of scenarios with two exposures on an outcome using MR methodology.
References
Other terms in 'Pleiotropy-robust two-sample MR methods':
- Bayes MR
- Bayesian implementation of the MR-Egger Estimator (BMRE)
- Bayesian network analysis
- Causal Analysis Using Summary Effect estimates (MR-CAUSE)
- Contamination mixture models
- Generalized Summary MR (GSMR)
- Genetic Instrumental Variable (GIV)
- Hierarchical joint Analysis of Marginal summary statistics (hJAM)
- Inverse variance weighted (IVW) random effects model
- Iterative Mendelian Randomization and Pleiotropy (IMRP)
- Leave-one-out analysis
- Median-based estimate
- Mode-based estimate
- MR accounting for Correlated and Idiosyncratic Pleiotropy (MRCIP)
- MR accounting for Linkage Disequilibrium and Pleiotropy (MR-LDP)
- MR Lasso
- MR Mixture (MRMix)
- MR using Robust regression (MR Robust)
- MR with penalized weights
- MR with regularization
- MR-Clust
- MR-Egger regression and extensions
- MR-Link
- MR-Path
- Multivariable MR (MVMR) and extensions
- Welch-weighted Egger regression (WWER)