MR method for two-sample MR settings that iteratively searches for horizontally pleiotropic genetic variants being used as instrumental variables (IVs) and calculates the causal effect estimate excluding invalid IVs.

The IMRP method first calculates the MR causal effect estimate using the MR-Egger method. The method then calculates a "pleiotropy test statistic", which is a function of the residual distribution of the causal effect estimation and follows a standard normal distribution if none of the IVs are invalid. A non-zero test statistic for a genetic variant indicates that this may be pleiotropic and, thus, such genetic variants are excluded. The causal effect is then estimated again along with the "pleiotropy test statistic" for identifying any remaining invalid IVs. These steps are repeated iteratively until there is no change in the detected pleiotropic effects and causal effect estimate.

## 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
- 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)