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MubasherMohammed
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GWAS sumstats analysis — seed

Identify significant variants and compute genomic inflation factor from GWAS summary statistics.

Updated May 10, 2026
Search Tags:#bioinformatics#genomics#GWAS#variant analysis#genomic inflation factor
One-click install
npx skills add https://github.com/MubasherMohammed/opencode-BioInfo --skill gwas-sumstats-analysis-seed
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: GWAS sumstats analysis — seed
Source: https://github.com/MubasherMohammed/opencode-BioInfo/tree/main/python/skills/clawpathy_autoresearch/examples/demo_task/skill
Command: npx skills add https://github.com/MubasherMohammed/opencode-BioInfo --skill gwas-sumstats-analysis-seed

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill simplifies the process of analyzing Genome-Wide Association Studies (GWAS) summary statistics files, allowing users to identify genome-wide significant lead variants and compute the genomic inflation factor lambda, all while providing a QQ plot visualization.

Core Features & Use Cases

  • Lead Variant Identification: Quickly identify genome-wide significant lead variants from the sumstats file.
  • Genomic Inflation Factor: Compute the genomic inflation factor lambda to assess potential inflation in p-values.
  • QQ Plot Generation: Produce a QQ plot for visualizing the distribution of observed and expected p-values.
  • Summary JSON Output: Provide a final summary JSON file with lead count, lambda, and plot path for easy access to the results.

Quick Start

Execute the 'gwas-sumstats-analysis' skill with the input sumstats file.

Frequently Asked Questions about GWAS sumstats analysis — seed

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

FAQPage Schema
How do I analyze GWAS summary statistics to identify significant variants?▼

To analyze GWAS summary statistics, this skill processes the input sumstats file to identify genome-wide significant lead variants and outputs a summary JSON file for easy access to the results.

What is the genomic inflation factor lambda and how is it calculated from sumstats?▼

The genomic inflation factor lambda is a metric computed from GWAS summary statistics to assess potential inflation in p-values. This skill calculates lambda directly from the input sumstats file.

How do I generate a QQ plot for GWAS p-value distributions?▼

To generate a QQ plot for GWAS p-value distributions, this skill visualizes the observed and expected p-values from your sumstats file and provides the plot path in the output summary.

Can I use this GWAS analysis skill with summary statistics files of any scale?▼

Yes, you can use this skill with any genome-wide association study summary statistics file. It processes the input to identify lead variants, compute lambda, and generate a QQ plot regardless of scale.

What outputs do I get from GWAS summary statistics analysis?▼

The outputs from GWAS summary statistics analysis include identified genome-wide significant lead variants, a computed genomic inflation factor lambda, a QQ plot visualization, and a summary JSON file.

Why do I need to check the genomic inflation factor in GWAS data?▼

You need to check the genomic inflation factor in GWAS data to assess potential inflation in p-values. This skill computes the lambda value to help you evaluate the presence of systematic bias.

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