/bio-causal-genomics-mediation-analysis

name: bio-causal-genomics-mediation-analysis
description: Decompose genetic effects into direct and indirect paths through mediating variables using the mediation R package. Tests whether gene expression, methylation, or other molecular phenotypes mediate the effect of genetic variants on disease. Use when testing whether a molecular phenotype mediates the genotype-to-phenotype relationship.
tool_type: r
primary_tool: mediation

Version Compatibility

Reference examples tested with: R stats (base), ggplot2 3.5+

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.

Mediation Analysis

**"Test whether gene expression mediates the effect of this variant on disease"** → Decompose the total genetic effect into direct and indirect (mediated) paths through a molecular phenotype, estimating ACME, ADE, and proportion mediated with bootstrap confidence intervals.

• R: `mediation::mediate()` for causal mediation analysis

Framework

Causal mediation decomposes the total effect of a treatment (genotype) on an outcome (phenotype) into:

• **ACME** (Average Causal Mediation Effect) - Indirect effect through the mediator
• **ADE** (Average Direct Effect) - Direct effect not through the mediator
• **Total effect** = ACME + ADE
• **Proportion mediated** = ACME / Total effect

Typical genomic applications:

• SNP -> gene expression (mediator) -> disease
• SNP -> DNA methylation (mediator) -> gene expression
• SNP -> protein levels (mediator) -> clinical outcome

Basic Mediation with the mediation Package

**Goal:** Decompose a genetic effect into direct and indirect (mediated) paths through a molecular phenotype.

**Approach:** Fit separate models for mediator and outcome, then run mediate() with bootstrap to estimate ACME (indirect), ADE (direct), and proportion mediated.

library(mediation)

# --- Step 1: Fit mediator model ---
# How does the treatment (genotype) affect the mediator (expression)?
mediator_model <- lm(expression ~ genotype + age + sex + pc1 + pc2, data = dat)

# --- Step 2: Fit outcome model ---
# How do treatment and mediator jointly affect the outcome?
# For binary outcome, use glm with family = binomial
outcome_model <- glm(
  disease ~ genotype + expression + age + sex + pc1 + pc2,
  data = dat, family = binomial
)

# --- Step 3: Run mediation analysis ---
# treat: name of treatment variable (genotype)
# mediator: name of mediator variable (expression)
# boot = TRUE: Use nonparametric bootstrap for CIs
# sims: Number of bootstrap simulations (1000 minimum for publication)
med_result <- mediate(
  mediator_model, outcome_model,
  treat = 'genotype', mediator = 'expression',
  boot = TRUE, sims = 1000
)

summary(med_result)
# Key outputs:
# ACME: Indirect effect (through expression)
# ADE: Direct effect (not through expression)
# Total Effect: ACME + ADE
# Prop. Mediated: ACME / Total

Interpreting Results

# Extract key quantities
acme <- med_result$d0           # Indirect (mediated) effect
acme_ci <- med_result$d0.ci     # 95% CI for ACME
ade <- med_result$z0            # Direct effect
total <- med_result$tau.coef    # Total effect
prop_med <- med_result$n0       # Proportion mediated

cat('ACME (indirect):', round(acme, 4), '\n')
cat('ACME 95% CI:', round(acme_ci[1], 4), 'to', round(acme_ci[2], 4), '\n')
cat('ADE (direct):', round(ade, 4), '\n')
cat('Total effect:', round(total, 4), '\n')
cat('Proportion mediated:', round(prop_med, 3), '\n')

# Significant ACME (CI excludes 0): Evidence for mediation
# Proportion mediated > 0.2: Meaningful mediation
# Proportion mediated > 0.8: Mediator explains most of the effect

eQTL Mediation

**Goal:** Test whether gene expression mediates the effect of an eQTL on a disease outcome across multiple genes.

**Approach:** Wrap the mediation workflow in a function, loop over candidate genes, and adjust p-values for multiple testing.

library(mediation)

run_eqtl_mediation <- function(dat, snp_col, expr_col, outcome_col, covariates) {
  covar_formula <- paste(covariates, collapse = ' + ')

  med_formula <- as.formula(paste(expr_col, '~', snp_col, '+', covar_formula))
  out_formula <- as.formula(paste(outcome_col, '~', snp_col, '+', expr_col, '+', covar_formula))

  med_model <- lm(med_formula, data = dat)

  if (length(unique(dat[[outcome_col]])) == 2) {
    out_model <- glm(out_formula, data = dat, family = binomial)
  } else {
    out_model <- lm(out_formula, data = dat)
  }

  result <- mediate(
    med_model, out_model,
    treat = snp_col, mediator = expr_col,
    boot = TRUE, sims = 1000
  )

  data.frame(
    snp = snp_col, gene = expr_col,
    acme = result$d0, acme_p = result$d0.p,
    ade = result$z0, ade_p = result$z0.p,
    total = result$tau.coef, total_p = result$tau.p,
    prop_mediated = result$n0
  )
}

# Example: test mediation for multiple genes
genes <- c('GENE_A', 'GENE_B', 'GENE_C')
covars <- c('age', 'sex', 'pc1', 'pc2', 'pc3')

mediation_results <- do.call(rbind, lapply(genes, function(g) {
  run_eqtl_mediation(dat, 'rs12345', g, 'disease_status', covars)
}))

# Adjust for multiple testing
mediation_results$acme_fdr <- p.adjust(mediation_results$acme_p, method = 'BH')

Multi-Omics Mediation

**Goal:** Test cascading mediation chains across multiple molecular layers (e.g., SNP -> methylation -> expression -> disease).

**Approach:** Fit sequential models for each link in the chain and run separate mediation analyses for each mediator-outcome pair.

# Test mediation chains: SNP -> methylation -> expression -> disease
library(mediation)

# Step 1: SNP -> methylation
mod_meth <- lm(methylation ~ genotype + age + sex, data = dat)

# Step 2: methylation -> expression (controlling for genotype)
mod_expr <- lm(express