MR method developed for two-sample MR settings that uses regularization to identify variables that need to be accounted for in the MR model to produce a causal effect estimate robust to violations of MR assumptions.
The method can be used where the number of potentially pleiotropic variables (i.e., the pathways by which the genetic variants being used as instrumental variables (IVs) influence the outcome independent from the exposure) is less than the total number of IVs minus one. Once the pleiotropic variables are identified and excluded, multivariable MR analysis methods can be applied with the remaining variables included in the model to estimate the causal effect. The method can be used with both individual-level (i.e., one-sample MR and summary-level data (i.e., two-sample MR) and does not require any of the genetic variants to be valid IVs.
References
Other terms in 'Pleiotropy-robust two-sample MR methods':
- Bayes MR
- Bayesian implementation of the MR-Egger Estimator (BMRE)
- Bayesian multi-instrument Mendelian randomization (MIMR)
- 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-Clust
- MR-Egger regression and extensions
- MR-Link
- MR-Path
- Multivariable MR (MVMR) and extensions
- Welch-weighted Egger regression (WWER)