bio-causal-genomics-mendelian-randomization

name	bio-causal-genomics-mendelian-randomization
description	Estimate causal effects between exposures and outcomes using genetic variants as instrumental variables with TwoSampleMR. Implements IVW, MR-Egger, weighted median, and MR-PRESSO methods for robust causal inference from GWAS summary statistics. Use when testing whether an exposure causally affects an outcome using genetic instruments.
tool_type	r
primary_tool	TwoSampleMR

Version Compatibility

Reference examples tested with: TwoSampleMR 0.5+, MendelianRandomization 0.9+

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.

Mendelian Randomization

"Test whether my exposure causally affects this outcome using GWAS data" → Use genetic variants as instrumental variables to estimate causal effects from GWAS summary statistics, applying IVW, MR-Egger, and weighted median methods for robust inference.

• R: TwoSampleMR::mr() for multi-method causal estimation
• R: MendelianRandomization::mr_ivw() for individual methods

Core Concepts

Mendelian randomization (MR) uses genetic variants as instrumental variables (IVs) to estimate causal effects of exposures on outcomes. Valid instruments must satisfy three assumptions:

1. Relevance - The variant is associated with the exposure (F-statistic > 10)
2. Independence - The variant is not associated with confounders
3. Exclusion restriction - The variant affects the outcome only through the exposure

TwoSampleMR Workflow

Goal: Estimate the causal effect of an exposure on an outcome using GWAS summary statistics and genetic instruments.

Approach: Extract instruments for the exposure, extract matching outcome data, harmonize allele directions, and run multiple MR methods (IVW, Egger, weighted median, weighted mode).

"Test if an exposure causally affects an outcome" -> Use genetic variants as instrumental variables to estimate causal effects from GWAS data.

• R: TwoSampleMR (extract_instruments + harmonise_data + mr)
• R: MendelianRandomization (mr_input + mr_ivw/mr_egger)

library(TwoSampleMR)

# --- Step 1: Extract instruments for the exposure ---
# From OpenGWAS (requires authentication -- see below)
exposure_dat <- extract_instruments(outcomes = 'ieu-a-2', p1 = 5e-08, clump = TRUE)

# From local GWAS summary statistics
exposure_dat <- read_exposure_data(
  filename = 'exposure_gwas.txt',
  sep = '\t',
  snp_col = 'SNP', beta_col = 'BETA', se_col = 'SE',
  effect_allele_col = 'A1', other_allele_col = 'A2',
  pval_col = 'P', eaf_col = 'EAF'
)

# Clump instruments to remove LD (r2 < 0.001, 10 Mb window)
# r2 < 0.001: Standard threshold to ensure instrument independence
# 10000 kb window: Wide enough to capture long-range LD
exposure_dat <- clump_data(exposure_dat, clump_r2 = 0.001, clump_kb = 10000)

# --- Step 2: Extract outcome data ---
outcome_dat <- extract_outcome_data(snps = exposure_dat$SNP, outcomes = 'ieu-a-7')

# From local summary statistics
outcome_dat <- read_outcome_data(
  filename = 'outcome_gwas.txt',
  sep = '\t',
  snp_col = 'SNP', beta_col = 'BETA', se_col = 'SE',
  effect_allele_col = 'A1', other_allele_col = 'A2',
  pval_col = 'P', eaf_col = 'EAF'
)

# --- Step 3: Harmonize ---
# Ensures effect alleles are aligned between exposure and outcome
dat <- harmonise_data(exposure_dat, outcome_dat, action = 2)

# action = 1: Assume all alleles on forward strand
# action = 2: Try to infer forward strand (default, recommended)
# action = 3: Correct strand for palindromic SNPs using allele frequencies

# --- Step 4: Perform MR ---
results <- mr(dat)

# Run all standard methods
results <- mr(dat, method_list = c(
  'mr_ivw',              # Inverse variance weighted (primary)
  'mr_egger_regression', # MR-Egger (detects pleiotropy)
  'mr_weighted_median',  # Weighted median (robust to 50% invalid)
  'mr_weighted_mode'     # Weighted mode (robust to outliers)
))

OpenGWAS Authentication

OpenGWAS (ieugwasr) requires authentication. The auth system has changed multiple times. Refer to the ieugwasr README for current instructions: https://github.com/MRCIEU/ieugwasr

For reproducibility, prefer downloading GWAS summary statistics directly and using read_exposure_data() / read_outcome_data() with local files.

Interpreting Results

Goal: Evaluate MR evidence through method comparison, heterogeneity testing, and sensitivity analyses.

Approach: Compare estimates across methods for consistency, test for heterogeneity (Cochran's Q), pleiotropy (Egger intercept), and single-SNP influence (leave-one-out).

# Method comparison table
results

# Key columns: method, nsnp, b (causal estimate), se, pval
# IVW is the primary method; others are sensitivity analyses
# Consistent direction/magnitude across methods strengthens evidence

# --- Heterogeneity (Cochran's Q) ---
het <- mr_heterogeneity(dat)
# Significant Q-statistic suggests pleiotropy or invalid instruments
# Q p-value < 0.05: Evidence of heterogeneity

# --- Pleiotropy (Egger intercept) ---
pleiotropy <- mr_pleiotropy_test(dat)
# Significant intercept (p < 0.05): Evidence of directional pleiotropy
# Non-significant intercept: No evidence (but low power with few SNPs)

# --- Leave-one-out ---
loo <- mr_leaveoneout(dat)
# Check if any single SNP drives the result
# Causal estimate should remain stable when each SNP is removed

# --- Single SNP analysis ---
single <- mr_singlesnp(dat)

Instrument Strength

Goal: Assess whether genetic instruments are strong enough for valid MR inference.

Approach: Compute per-instrument F-statistics from exposure effect sizes and standard errors, removing weak instruments (F < 10).

# F-statistic for each instrument
# F = (beta_exposure / se_exposure)^2
# F > 10: Sufficient instrument strength (conventional threshold)
# F < 10: Weak instrument bias (toward confounded observational estimate)
dat$f_statistic <- (dat$beta.exposure / dat$se.exposure)^2

# Mean F-statistic across all instruments
mean_f <- mean(dat$f_statistic)
cat('Mean F-statistic:', mean_f, '\n')
cat('Instruments with F < 10:', sum(dat$f_statistic < 10), '\n')

# Remove weak instruments
dat <- dat[dat$f_statistic >= 10, ]

Bidirectional MR

Goal: Test whether causal direction runs from exposure to outcome or vice versa.

Approach: Run Steiger directionality test and perform MR in both directions to distinguish true causation from reverse causation.

# Steiger test: Verify variant explains more variance in exposure than outcome
steiger <- directionality_test(dat)
# correct_causal_direction = TRUE: Instruments are valid
# correct_causal_direction = FALSE: Reverse causation likely

# Run MR in reverse direction
exposure_rev <- extract_instruments(outcomes = 'ieu-a-7')
outcome_rev <- extract_outcome_data(snps = exposure_rev$SNP, outcomes = 'ieu-a-2')
dat_rev <- harmonise_data(exposure_rev, outcome_rev)
results_rev <- mr(dat_rev)

Visualization

Goal: Create diagnostic plots for MR results including scatter, forest, leave-one-out, and funnel plots.

Approach: Use TwoSampleMR built-in plotting functions to visualize method slopes, per-SNP estimates, and potential asymmetry.

library(TwoSampleMR)

# Scatter plot: SNP-exposure vs SNP-outcome effects with method slopes
mr_scatter_plot(results, dat)

# Forest plot: Individual SNP and combined estimates
mr_forest_plot(single)

# Leave-one-out plot
mr_leaveoneout_plot(loo)

# Funnel plot: Precision vs causal estimate (asymmetry = pleiotropy)
mr_funnel_plot(single)

Power Calculation

Goal: Estimate MR power based on instrument strength, sample size, and expected effect size.

Approach: Compute total variance explained by instruments from allele frequencies and effect sizes.

# MR power depends on: sample size, variance explained by instruments, effect size
# Use mRnd web tool: https://shiny.cnsgenomics.com/mRnd/
# Or approximate:
variance_explained <- sum(2 * dat$eaf.exposure * (1 - dat$eaf.exposure) * dat$beta.exposure^2)
cat('Variance explained by instruments:', variance_explained, '\n')
# Higher R^2 = more power; typically need R^2 > 0.01 for reasonable power

Related Skills

• pleiotropy-detection - Sensitivity analyses for MR assumptions
• colocalization-analysis - Confirm shared causal variants
• fine-mapping - Prioritize causal variants at instrument loci
• population-genetics/association-testing - GWAS for exposure and outcome data