bio-causal-genomics-pleiotropy-detection

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_name to 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.

# 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')