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Mediation Analysis - Usage Guide

Overview

Decompose genetic effects into direct and indirect paths through mediating variables. Tests whether molecular phenotypes such as gene expression or methylation mediate the relationship between genotype and disease.

Prerequisites

install.packages('mediation')

# For high-dimensional mediation
install.packages('HIMA')

# Visualization
install.packages('ggplot2')

Quick Start

Tell your AI agent what you want to do:

  • "Test whether gene expression mediates the effect of this SNP on disease"
  • "Run mediation analysis with methylation as the mediator between genotype and phenotype"
  • "Decompose the total effect of rs12345 into direct and indirect paths through GENE_X"

Example Prompts

eQTL Mediation

"I have genotype, expression, and disease data -- test if expression of BRCA1 mediates the SNP effect on breast cancer"

"Run mediation analysis for all eQTL genes at this locus"

Multi-Omics

"Test the mediation chain: SNP -> methylation -> expression -> disease"

"Use HIMA to find which CpG sites mediate the genotype-disease association"

Sensitivity

"How robust are my mediation results to unmeasured confounding?"

"Run sensitivity analysis on the mediation result and plot the sensitivity curve"

What the Agent Will Do

  1. Fit mediator model (treatment -> mediator)
  2. Fit outcome model (treatment + mediator -> outcome)
  3. Run mediation analysis with bootstrap confidence intervals
  4. Report ACME, ADE, total effect, and proportion mediated
  5. Run sensitivity analysis for unmeasured confounding
  6. Adjust p-values for multiple testing if testing many mediators

Tips

  • Sequential ignorability - The key assumption is untestable; always run sensitivity analysis
  • Bootstrap CIs - Use at least 1000 simulations; 5000 for publication
  • Proportion mediated - Values above 0.2 suggest meaningful mediation; above 0.8 suggests the mediator explains most of the effect
  • Binary outcomes - Use glm with family = binomial for the outcome model
  • Multiple mediators - Use HIMA for high-dimensional mediation with penalized regression
  • Covariates - Include population structure PCs, age, and sex in both models

Related Skills

  • causal-genomics/mendelian-randomization - Causal inference with genetic instruments
  • causal-genomics/colocalization-analysis - Confirm shared causal variants
  • population-genetics/association-testing - GWAS data for mediation inputs
  • multi-omics-integration/mofa-integration - Multi-omics data integration