# Bio Causal Genomics Mendelian Randomization

Estimate causal effects between exposures and outcomes using genetic variants as...

- Repository: https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills (3090 stars)
- Category: Testing
- License: see repository
- Last updated: 2026-07-21
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-causal-genomics-mendelian-randomization -g -a claude-code -y`
Codex: `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-causal-genomics-mendelian-randomization -g -a codex -y`
Cursor: `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-causal-genomics-mendelian-randomization -g -a cursor -y`
Gemini CLI: `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-causal-genomics-mendelian-randomization -g -a gemini-cli -y`

## 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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

## 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)

```r
library(TwoSampleMR)

The rest: https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/blob/HEAD/skills/bio-causal-genomics-mendelian-randomization/SKILL.md