bio-causal-genomics-mendelian-randomization

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)
))