Data & ReproStatistics & Data AnalysisFreedomIntelligence/OpenClaw-Medical-SkillsData & Reproduction

bio-causal-genomics-mediation-analysis

Maintainer FreedomIntelligence · Last updated April 1, 2026

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.

OpenClawNanoClawAnalysisReproductionbio-causal-genomics-mediation-analysis🧬 bioinformatics (gptomics bio-* suite)bioinformatics — epidemiological & causal genomicsdecompose

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-causal-genomics-mediation-analysis

Maintainer: FreedomIntelligence
License: MIT
Last updated: April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

R: mediation::mediate() for causal 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.
mediator_model <- lm(expression ~ genotype + age + sex + pc1 + pc2, data = dat).

Source Doc

Excerpt From SKILL.md

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)

## For binary outcome, use glm with family = binomial

outcome_model <- glm(
  disease ~ genotype + expression + age + sex + pc1 + pc2,
  data = dat, family = binomial
)

Use cases

Use when testing whether a molecular phenotype mediates the genotype-to-phenotype relationship.

Not for

Do not rely on this catalog entry alone for installation or maintenance details.

Upstream Related Skills

mendelian-randomization - Causal inference using genetic instruments
colocalization-analysis - Test if signals share a causal variant
population-genetics/association-testing - GWAS for treatment-outcome associations
multi-omics-integration/mofa-integration - Multi-omics data for mediation chains