MR accounting for Linkage Disequilibrium and Pleiotropy (MR-LDP)

Probabilistic MR method for two-sample MR settings that accounts for linkage disequilibrium amongst genetic variants being used as instrumental variables (IVs).

Within MR analyses, especially those focusing on complex traits with likely many associated common single nucleotide polymorphisms (SNPs) with small effects, LD amongst genetic variants being used as IVs can bias estimates and lead to false positives. Including correlated SNPs as IVs can not only overrepresent the genetic contribution on a particular trait (e.g., the exposure) in an MR context and can induce bias through horizontal pleiotropy. MR-LDP uses the marginal effect sizes and accompanying standard errors from genome-wide association study (GWAS) summary statistics and relies on a reference panel to construct an LD matrix and provide information about the correlation amongst genetic variants, to build a probabilistic model for estimating the causal effect of an exposure on an outcome. The properties of this probabilistic model accounts for correlation amongst genetic variants and the variance explained in the outcome by the IVs (i.e., horizontal pleiotropy.

References

• Cheng Q, Yang Y, Shi X, et al. MR-LDP: a two-sample Mendelian randomization for GWAS summary statistics accounting for linkage disequilibrium and horizontal pleiotropy. NAR Genomics and Bioinformatics 2020; 2; lqaa028,

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 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)