Method developed for two-sample MR settings that accounts for correlated pleiotropy when estimating causal effect of an exposure on an outcome using Welch-weighted Egger regression.
Akin to Steiger filtering, the estimation method relies on the assumption that, if the exposure causes the outcome and the genetic variant being used as an instrumental variable (IV) influences the exposure, then, if all MR assumptions are met, the variance explained in the outcome by the IV must be no larger than the variance explained in the exposure by the IV multiplied by the effect of the exposure on the outcome. It uses this information to construct new weights for MR-Egger regression using the Welch test statistic for a two-sample difference in mean with unequal variances and the standard inverse-variance weight, down-weighting likely pleiotropic single nucleotide polymorphisms (SNPs).
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 with regularization
- MR-Clust
- MR-Egger regression and extensions
- MR-Link
- MR-Path
- Multivariable MR (MVMR) and extensions