MR that analyses marginal summary statistics (i.e., information about associations between single nucleotide polymorphisms (SNPs) and traits from genome-wide association studies (GWASs)) under a hierarchical joint multi-SNP model to identify genetic variants for fine mapping.
Originally proposed to harmonize frameworks that exist between MR and transcriptome-wide association studies (TWASs), the latter of which identifies SNPs associated with gene expression and uses these to estimate the association between predicted gene expression and traits. Both methods of which suffer from pleiotropy due to the multi-functional nature of many genes. The method is useful in situations where multiple traits exist on the causal pathway between the instrumental variables (IVs) and the outcome and/or the SNPs being used as IVs are correlated.
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)
- 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)