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Tooluniverse Gwas Drug Discovery

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mims-harvard·vlatest·other·Apache-2.0

Tooluniverse Gwas Drug Discovery leverages GWAS data to predict potential drug targets and accelerate genetic insights for therapeutic development.

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-drug-discovery -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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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.

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    Scanned mims-harvard/tooluniverse at f5c37eebeb.

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Commit
f5c37eebeb
Manifest
skills/tooluniverse-gwas-drug-discovery/SKILL.md
Scanned
23 files, 7,300 lines
Copies in repo
3 — all of them scanned
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About this skill

tooluniverse-gwas-drug-discovery

What it does

This skill enables AI agents to perform Genome-Wide Association Studies (GWAS) specifically tailored for drug discovery workflows. It automates the analysis of genetic variants to identify associations with diseases or traits, accelerating the identification of potential therapeutic targets.

When to use it

  • Target Identification: Quickly scan genomic data to pinpoint specific genetic markers linked to complex diseases like diabetes or Alzheimer's.
  • Biomarker Discovery: Analyze variant frequencies to discover new biomarkers that could serve as indicators for drug efficacy or patient stratification.
  • Replication Studies: Validate previously identified genetic associations against new datasets to ensure robustness before clinical trials.
  • Polygenic Risk Scoring: Generate initial polygenic risk scores to understand the cumulative effect of multiple genetic variants on disease susceptibility.

Key capabilities

  • Executes comprehensive Genome-Wide Association Study (GWAS) pipelines automatically.
  • Processes large-scale genomic datasets to find statistically significant variant-disease associations.
  • Integrates directly into AI agent workflows for automated drug target prioritization.
  • Provides structured output suitable for downstream pharmacological analysis.

Example prompts

  • "Run a GWAS analysis on this dataset of 10,000 samples to identify genetic variants associated with type 2 diabetes."
  • "Identify top candidate genes from the recent GWAS results that could serve as targets for cardiovascular drug development."
  • "Perform a meta-analysis of GWAS data to find novel biomarkers for early-stage cancer detection."

Tips & gotchas

Ensure your input datasets are properly formatted and pre-processed (e.g., imputed, QC'd) before running the analysis to avoid false positives. This tool focuses on statistical association; results must always be validated biologically and clinically before advancing to drug development stages.