mendelian-randomisation

Conduct Mendelian Randomization analysis on GWAS summary statistics for causal inference.

Updated May 10, 2026

Search Tags:#causal inference #genetic epidemiology #GWAS #genetic association #mendelian randomization

Ready to run

npx skills add https://github.com/MubasherMohammed/opencode-BioInfo --skill mendelian-randomisation

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: mendelian-randomisation
Source: https://github.com/MubasherMohammed/opencode-BioInfo/tree/main/python/skills/mendelian-randomisation
Command: npx skills add https://github.com/MubasherMohammed/opencode-BioInfo --skill mendelian-randomisation

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, scipy, matplotlib, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill simplifies and automates Mendelian Randomization analysis for causal inference, saving users the need to manually handle complex calculations and statistical diagnostics.

Core Features & Use Cases

Estimation Methods: Supports multiple Mendelian Randomization estimators (IVW, MR-Egger, weighted median/mode, full sensitivity analysis).
Causal Inference: Determines potential causal relationships between genetic markers and diseases/traits.
Use Case: A user wants to explore if a particular gene variant is associated with an increased risk of a certain disease. They would input their genetic and disease outcome data, and this skill would output the causal effect estimate along with sensitivity analysis.

Quick Start

Run Mendelian Randomization analysis for genetic association by inputting the genetic instrument file 'instruments.json' and specify the output directory 'output'.

Frequently Asked Questions about mendelian-randomisation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I perform Mendelian randomization on GWAS summary statistics for causal inference?▼

You can perform Mendelian randomization by inputting your GWAS summary statistics as a JSON file, which the tool processes to generate causal effect estimates and sensitivity analyses in markdown and JSON reports.

What estimation methods are available for genetic association analysis?▼

Genetic association analysis supports multiple estimation methods including IVW, MR-Egger, weighted median, and weighted mode, alongside full sensitivity analyses to validate causal inference results.

Do I need specific Python libraries to run Mendelian randomization analysis?▼

Yes, you need numpy, scipy, and matplotlib installed to run Mendelian randomization analysis, as these libraries handle the statistical computation and data visualization required for causal inference.

Can I use GWAS summary statistics in JSON format for causal effect estimation?▼

Yes, you can use GWAS summary statistics formatted as JSON by providing a genetic instrument file, which the tool parses to estimate causal effects and output diagnostic reports.

What is the best way to validate drug target causal relationships using genetic epidemiology?▼

Validating drug target causal relationships is best done by applying multiple Mendelian randomization estimators and sensitivity analyses to GWAS summary statistics, ensuring robust causal inference for genetic epidemiology.