bio-causal-genomics-pleiotropy-detection

name	bio-causal-genomics-pleiotropy-detection
description	Detect and correct for horizontal pleiotropy in Mendelian randomization analyses using MR-PRESSO for outlier removal, MR-Egger regression for directional pleiotropy, and Steiger filtering for variant directionality. Use when validating MR results, detecting pleiotropic instruments, or running sensitivity analyses for causal inference.
tool_type	r
primary_tool	MR-PRESSO

Pleiotropy Detection

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

# 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

library(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 eggerb  
cat eggerse  
cat format.pvaleggerpval 





isq  Isqdatbeta.exposure datse.exposure
cat isq  
 isq   cat

Steiger Filtering

library(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_testdat
cat directioncorrect_causal_direction 
cat format.pvaldirectionsteiger_pval 

Additional Sensitivity Methods

library(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 ---


exposure1  extract_instruments  
exposure2  extract_instruments  

Comprehensive Sensitivity Framework

library(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_testdat

  
  resultsloo  mr_leaveoneoutdat

  
  presso_input  data.frame
    bx  datbeta.exposure by  datbeta.outcome
    bxse  datse.exposure byse  datse.outcome
  
  resultspresso  mr_presso
    BetaOutcome   BetaExposure  
    SdOutcome   SdExposure  
    OUTLIERtest   DISTORTIONtest  
    data  presso_input NbDistribution   SignifThreshold  
  

  results


summarize_sensitivity  sens 
  cat

  
  all_mr  rbindsensivw sensegger sensmedian sensmode
  cat
  printall_mr    

  
  cat
  cat senshetQ_pvalsenshetmethod   

  
  cat
  cat senspleioegger_intercept 
  cat senspleiopval 

  
  cat
      senspresso`MR-PRESSO results``Global Test`Pvalue 

  cat
  cat
  cat
  cat
  cat

STROBE-MR Reporting

When reporting MR analyses, follow STROBE-MR guidelines:

1. Report all MR methods tested (not just the most significant)
2. Report heterogeneity Q-statistic and p-value
3. Report Egger intercept with p-value
4. Report MR-PRESSO global test and number of outliers removed
5. Report F-statistics for instrument strength
6. Report Steiger directionality test
7. State whether results are consistent across sensitivity analyses
8. 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