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Tooluniverse Gwas Finemapping

Checked
mims-harvard·vlatest·other·Apache-2.0

This skill precisely refines GWAS results by leveraging ToolUniverse's GWA Finemapping technology, pinpointing causal variants for enhanced genomic insights.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add mims-harvard/tooluniverse --skill tooluniverse-gwas-finemapping -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

mims-harvard

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Install target

Live repository

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Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

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    Scanned mims-harvard/tooluniverse at f5c37eebeb.

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Commit
f5c37eebeb
Manifest
skills/tooluniverse-gwas-finemapping/SKILL.md
Scanned
7 files, 2,800 lines
Copies in repo
3 — all of them scanned
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About this skill

What it does

This skill enables AI agents to perform fine-mapping analysis on Genome-Wide Association Study (GWAS) summary statistics. It helps identify specific causal genetic variants within broad genomic regions associated with a trait or disease by leveraging statistical methods and reference panels.

When to use it

  • You have GWAS summary data and need to narrow down associations from hundreds of thousands of SNPs to a credible set of causal variants.
  • You are conducting genetic research where pinpointing the exact biological mechanism behind a phenotypic association is critical.
  • You require integration of functional annotations or reference panels (like 1000 Genomes) to prioritize variants in specific populations.
  • Your workflow involves downstream tasks like polygenic risk score refinement or Mendelian randomization that depend on high-resolution variant localization.

Key capabilities

  • Processes GWAS summary statistics files directly within the agent environment.
  • Implements fine-mapping algorithms to calculate posterior inclusion probabilities (PIP) for variants.
  • Integrates with external reference panels to account for linkage disequilibrium patterns.
  • Outputs prioritized lists of candidate causal variants with statistical confidence metrics.

Example prompts

  • "Run a fine-mapping analysis on my uploaded GWAS summary statistics using the default reference panel and return the top 10 credible set variants."
  • "Perform conditional fine-mapping to distinguish independent signals within the identified locus for height, adjusting for population stratification."
  • "Execute a Bayesian fine-mapping workflow incorporating functional annotation weights to prioritize non-coding regulatory variants in the FTO region."

Tips & gotchas

Ensure your input GWAS summary statistics include standard columns (e.g., SNP ID, effect allele, non-effect allele, effect size, p-value) to avoid parsing errors. Fine-mapping accuracy heavily depends on the quality and ancestry match of the reference panel used; mismatched populations can lead to inflated false positives or missed causal signals.