Version Compatibility
Reference examples tested with: MR-PRESSO 1.0+, TwoSampleMR 0.5+
Before using code patterns, verify installed versions match. If versions differ:
- R:
packageVersion("<pkg>")then?function_nameto verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Pleiotropy Detection
"Check my MR results for pleiotropic bias" → Detect and correct for horizontal pleiotropy using outlier removal (MR-PRESSO), directional pleiotropy testing (MR-Egger intercept), and variant directionality filtering (Steiger) to validate causal inference results.
- R:
MRPRESSO::mr_presso()for global and distortion tests - R:
TwoSampleMR::mr_egger_regression()for Egger intercept test
Overview
Horizontal pleiotropy violates the exclusion restriction assumption of MR: instruments affect the outcome through pathways other than the exposure. Detecting and correcting for pleiotropy is essential for valid causal inference.
Types of pleiotropy:
- Vertical (mediated): Instrument -> exposure -> outcome (valid, not a problem)
- Horizontal (direct): Instrument -> outcome bypassing exposure (violates MR assumptions)
- Balanced: Pleiotropic effects cancel out (IVW still valid, Egger intercept ~0)
- Directional: Pleiotropic effects are systematic (biases IVW, Egger detects this)
MR-PRESSO
Goal: Detect and remove pleiotropic outlier instruments from an MR analysis.
Approach: Run MR-PRESSO to test for global pleiotropy, identify individual outlier SNPs, test whether their removal changes the causal estimate (distortion test), and obtain a corrected estimate.
r# install.packages('remotes') # remotes::install_github('rondolab/MR-PRESSO') library(MRPRESSO) # Input: harmonized data from TwoSampleMR # Columns needed: beta.exposure, beta.outcome, se.exposure, se.outcome presso_input <- data.frame( bx = dat$beta.exposure, by = dat$beta.outcome, bxse = dat$se.exposure, byse = dat$se.outcome ) # --- Run MR-PRESSO --- # NbDistribution: Number of simulations for null distribution (minimum 1000) # SignifThreshold: P-value threshold for outlier detection (0.05 standard) presso_result <- mr_presso( BetaOutcome = 'by', BetaExposure = 'bx', SdOutcome = 'byse', SdExposure = 'bxse', OUTLIERtest = TRUE, DISTORTIONtest = TRUE, data = presso_input, NbDistribution = 5000, SignifThreshold = 0.05 ) # --- Global test --- # Tests whether there is any pleiotropy among instruments # Significant p-value: Evidence of horizontal pleiotropy global_p <- presso_result$`MR-PRESSO results`$`Global Test`$Pvalue cat('Global test p-value:', global_p, '\n') # --- Outlier test --- # Identifies individual pleiotropic SNPs outliers <- presso_result$`MR-PRESSO results`$`Outlier Test` cat('\nOutlier test results:\n') print(outliers) # Outlier SNPs (p < 0.05) outlier_indices <- which(outliers$Pvalue < 0.05) cat('Outlier SNPs:', length(outlier_indices), '\n') # --- Distortion test --- # Tests whether removing outliers significantly changes the causal estimate # Significant: Outliers were meaningfully biasing the estimate distortion_p <- presso_result$`MR-PRESSO results`$`Distortion Test`$Pvalue cat('Distortion test p-value:', distortion_p, '\n') # --- Corrected estimate --- # MR estimate after removing outlier SNPs main_results <- presso_result$`Main MR results` cat('\nRaw IVW estimate:', main_results$`Causal Estimate`[1], '\n') cat('Corrected IVW estimate:', main_results$`Causal Estimate`[2], '\n')
MR-Egger Diagnostics
Goal: Test for directional pleiotropy and obtain a pleiotropy-adjusted causal estimate.
Approach: Fit MR-Egger regression where the intercept estimates average pleiotropic bias, and check I-squared for instrument strength under the NOME assumption.
rlibrary(TwoSampleMR) # MR-Egger regression allows for a non-zero intercept # The intercept estimates the average pleiotropic effect egger <- mr_egger_regression(dat$beta.exposure, dat$beta.outcome, dat$se.exposure, dat$se.outcome) # --- Egger intercept --- # Significant intercept (p < 0.05): Directional pleiotropy present # Non-significant: No evidence (but low power with < 10 SNPs) cat('Egger intercept:', round(egger$b_i, 5), '\n') cat('Intercept SE:', round(egger$se_i, 5), '\n') cat('Intercept p-value:', format.pval(egger$pval_i), '\n') # --- Egger slope --- # Valid causal estimate EVEN with directional pleiotropy (InSIDE assumption) cat('\nEgger causal estimate:', round(egger$b, 4), '\n') cat('Egger SE:', round(egger$se, 4), '\n') cat('Egger p-value:', format.pval(egger$pval), '\n') # --- I-squared for Egger --- # I^2 measures instrument strength for MR-Egger specifically # I^2 > 0.9: Egger estimate reliable # I^2 < 0.6: Egger has low power, interpret with caution (NOME violation) isq <- Isq(dat$beta.exposure, dat$se.exposure) cat('\nI-squared:', round(isq, 3), '\n') if (isq < 0.9) cat('Warning: I-squared < 0.9; Egger estimate may be unreliable (NOME violation)\n')
Steiger Filtering
Goal: Verify that instruments act in the correct causal direction (exposure -> outcome, not reverse).
Approach: Apply the Steiger test to each instrument, remove those explaining more outcome variance than exposure variance, and re-run MR on filtered instruments.
rlibrary(TwoSampleMR) # Steiger test: Verify each instrument explains more variance in # the exposure than the outcome. Instruments failing this test # may act through a reverse causal pathway. steiger <- steiger_filtering(dat) # Keep only correctly oriented instruments dat_steiger <- steiger[steiger$steiger_dir == TRUE, ] cat('Instruments passing Steiger filter:', nrow(dat_steiger), 'of', nrow(steiger), '\n') # Re-run MR with filtered instruments results_steiger <- mr(dat_steiger) print(results_steiger[, c('method', 'nsnp', 'b', 'se', 'pval')]) # Directionality test (aggregate) direction <- directionality_test(dat) cat('\nCorrect causal direction:', direction$correct_causal_direction, '\n') cat('Steiger p-value:', format.pval(direction$steiger_pval), '\n')
Additional Sensitivity Methods
rlibrary(TwoSampleMR) # --- Contamination mixture --- # Assumes some instruments are valid, others are not # Does not require majority valid assumption mr_conmix <- mr(dat, method_list = 'mr_raps') # --- MR-RAPS --- # NOTE: MRAPS CRAN package was archived March 2025. # Use the MendelianRandomization package instead, or install from GitHub: # remotes::install_github('qingyuanzhao/mr.raps') library(MendelianRandomization) mr_input <- mr_input( bx = dat$beta.exposure, bxse = dat$se.exposure, by = dat$beta.outcome, byse = dat$se.outcome ) raps_result <- mr_raps(mr_input) cat('MR-RAPS estimate:', raps_result$Estimate, '\n') cat('MR-RAPS p-value:', raps_result$Pvalue, '\n') # --- Multivariable MR --- # Controls for pleiotropy by including multiple exposures simultaneously # e.g., adjust for BMI when estimating effect of lipids on CHD exposure1 <- extract_instruments('ieu-a-300') # LDL exposure2 <- extract_instruments('ieu-a-302') # HDL # Combine and perform multivariable MR # (See TwoSampleMR vignette for full multivariable workflow)
Comprehensive Sensitivity Framework
Goal: Run a complete battery of MR sensitivity analyses to validate causal findings.
Approach: Apply IVW, MR-Egger, weighted median, weighted mode, heterogeneity, Egger intercept, leave-one-out, and MR-PRESSO in a single function and summarize results.
rlibrary(TwoSampleMR) library(MRPRESSO) run_sensitivity <- function(dat) { results <- list() # 1. IVW (primary) results$ivw <- mr(dat, method_list = 'mr_ivw') # 2. MR-Egger results$egger <- mr(dat, method_list = 'mr_egger_regression') # 3. Weighted median (robust to 50% invalid instruments) results$median <- mr(dat, method_list = 'mr_weighted_median') # 4. Weighted mode results$mode <- mr(dat, method_list = 'mr_weighted_mode') # 5. Heterogeneity results$het <- mr_heterogeneity(dat) # 6. Egger intercept results$pleio <- mr_pleiotropy_test(dat) # 7. Leave-one-out results$loo <- mr_leaveoneout(dat) # 8. MR-PRESSO presso_input <- data.frame( bx = dat$beta.exposure, by = dat$beta.outcome, bxse = dat$se.exposure, byse = dat$se.outcome ) results$presso <- mr_presso( BetaOutcome = 'by', BetaExposure = 'bx', SdOutcome = 'byse', SdExposure = 'bxse', OUTLIERtest = TRUE, DISTORTIONtest = TRUE, data = presso_input, NbDistribution = 5000, SignifThreshold = 0.05 ) results } summarize_sensitivity <- function(sens) { cat('=== MR Sensitivity Analysis Summary ===\n\n') # Method comparison all_mr <- rbind(sens$ivw, sens$egger, sens$median, sens$mode) cat('Method comparison:\n') print(all_mr[, c('method', 'b', 'se', 'pval')]) # Heterogeneity cat('\nHeterogeneity (Cochran Q):\n') cat(' Q p-value (IVW):', sens$het$Q_pval[sens$het$method == 'Inverse variance weighted'], '\n') # Egger intercept cat('\nEgger intercept:\n') cat(' Intercept:', sens$pleio$egger_intercept, '\n') cat(' P-value:', sens$pleio$pval, '\n') # MR-PRESSO global test cat('\nMR-PRESSO global test p-value:', sens$presso$`MR-PRESSO results`$`Global Test`$Pvalue, '\n') cat('\n--- Interpretation ---\n') cat('Consistent estimates across methods: Evidence strengthened\n') cat('Significant Egger intercept: Directional pleiotropy present\n') cat('Significant MR-PRESSO global: Horizontal pleiotropy detected\n') cat('Significant heterogeneity: Instruments may be invalid\n') }
STROBE-MR Reporting
When reporting MR analyses, follow STROBE-MR guidelines:
- Report all MR methods tested (not just the most significant)
- Report heterogeneity Q-statistic and p-value
- Report Egger intercept with p-value
- Report MR-PRESSO global test and number of outliers removed
- Report F-statistics for instrument strength
- Report Steiger directionality test
- State whether results are consistent across sensitivity analyses
- Acknowledge limitations of the MR assumptions
Related Skills
- mendelian-randomization - Primary MR analysis that pleiotropy tests validate
- fine-mapping - Identify causal variants at instrument loci
- population-genetics/association-testing - GWAS data for MR instruments
