Agent skill

Gwas Snp Interpretation

by lamm-mit in lamm-mit/scienceclaw

ToolUniverse workflow — Gwas Snp Interpretation. An agent skill from lamm-mit/scienceclaw.

Apache-2.0Auto-check passed

Install Gwas Snp Interpretation

skills CLI
$ npx skills add lamm-mit/scienceclaw --skill gwas-snp-interpretation -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install lamm-mit/scienceclaw gwas-snp-interpretation --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/gwas-snp-interpretation .claude/skills/gwas-snp-interpretation && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
gwas-snp-interpretation
GitHub stars
244
Token cost
~1.9k tokens
SKILL.md length
639 words
Files
3 (incl. scripts)
Skills in repo
86
Repo updated
First seen
Licence
Apache-2.0

At a glance

ToolUniverse workflow — Gwas Snp Interpretation. An agent skill from lamm-mit/scienceclaw.

  • Works in 4 steps: SNP Basic Info → Trait Associations → Credible Sets (Fine-Mapping) → …
  • SKILL.md covers Overview, What It Does, Workflow and Data Sources, plus 10 more sections
  • Runs Python scripts from its folder

What it does

Gwas Snp Interpretation is an agent skill from lamm-mit/scienceclaw. ToolUniverse workflow — Gwas Snp Interpretation

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including scripts (for example `scripts/run.py`).

The licence is Apache-2.0.

Example prompts

  • “/gwas-snp-interpretation”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. SNP Basic Info
  2. Trait Associations
  3. Credible Sets (Fine-Mapping)
  4. Clinical Significance

What it can do on your machine

Read from SKILL.md and the folder at commit ab9aba1. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • ebi.ac.uk
    • genetics.opentargets.org

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Gwas Snp Interpretation loads about 1.9k tokens when it runs. Until then it costs about 18 tokens; SKILL.md has 639 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~18
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from lamm-mit/scienceclaw at commit ab9aba1, republished under its Apache-2.0 licence (© lamm-mit). 639 words, ~1,877 tokens.

Download SKILL.mdSave it as .claude/skills/gwas-snp-interpretation/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
gwas-snp-interpretation
description
ToolUniverse workflow — Gwas Snp Interpretation
source
https://github.com/mims-harvard/ToolUniverse/tree/main/skills/tooluniverse-gwas-snp-interpretation

name: tooluniverse-gwas-snp-interpretation description: Interpret genetic variants (SNPs) from GWAS studies by aggregating evidence from multiple databases (GWAS Catalog, Open Targets Genetics, ClinVar). Retrieves variant annotations, GWAS trait associations, fine-mapping evidence, locus-to-gene predictions, and clinical significance. Use when asked to interpret a SNP by rsID, find disease associations for a variant, assess clinical significance, or answer questions like "What diseases is rs429358 associated with?" or "Interpret rs7903146".

GWAS SNP Interpretation Skill

Overview

Interpret genetic variants (SNPs) from GWAS studies by aggregating evidence from multiple sources to provide comprehensive clinical and biological context.

Use Cases:

  • "Interpret rs7903146" (TCF7L2 diabetes variant)
  • "What diseases is rs429358 associated with?" (APOE Alzheimer's variant)
  • "Clinical significance of rs1801133" (MTHFR variant)
  • "Is rs12913832 in any fine-mapped loci?" (Eye color variant)

What It Does

The skill provides a comprehensive interpretation of SNPs by:

  1. SNP Annotation: Retrieves basic variant information including genomic coordinates, alleles, functional consequence, and mapped genes
  2. Association Discovery: Finds all GWAS trait/disease associations with statistical significance
  3. Fine-Mapping Evidence: Identifies credible sets the variant belongs to (fine-mapped causal loci)
  4. Gene Mapping: Uses Locus-to-Gene (L2G) predictions to identify likely causal genes
  5. Clinical Summary: Aggregates evidence into actionable clinical significance

Workflow

User Input: rs7903146
    ↓
[1] SNP Lookup
    → Get location, consequence, MAF
    → gwas_get_snp_by_id
    ↓
[2] Association Search
    → Find all trait/disease associations
    → gwas_get_associations_for_snp
    ↓
[3] Fine-Mapping (Optional)
    → Get credible set membership
    → OpenTargets_get_variant_credible_sets
    ↓
[4] Gene Predictions
    → Extract L2G scores for causal genes
    → (embedded in credible sets)
    ↓
[5] Clinical Summary
    → Aggregate evidence
    → Identify key traits and genes
    ↓
Output: Comprehensive Interpretation Report

Data Sources

GWAS Catalog (EMBL-EBI)
  • SNP annotations: Functional consequences, mapped genes, population frequencies
  • Associations: P-values, effect sizes, study metadata
  • Coverage: 350,000+ publications, 670,000+ associations
Open Targets Genetics
  • Fine-mapping: Statistical credible sets from SuSiE, FINEMAP methods
  • L2G predictions: Machine learning-based gene prioritization
  • Colocalization: QTL evidence for causal genes
  • Coverage: UK Biobank, FinnGen, and other large cohorts

Input Parameters

Required
  • rs_id (str): dbSNP rs identifier
    • Format: "rs" + number (e.g., "rs7903146")
    • Must be valid rsID in GWAS Catalog
Optional
  • include_credible_sets (bool, default=True): Query fine-mapping data
    • True: Complete interpretation (slower, ~10-30s)
    • False: Fast associations only (~2-5s)
  • p_threshold (float, default=5e-8): Genome-wide significance threshold
  • max_associations (int, default=100): Maximum associations to retrieve

Output Format

Returns SNPInterpretationReport containing:

1. SNP Basic Info
python
{
    'rs_id': 'rs7903146',
    'chromosome': '10',
    'position': 112998590,
    'ref_allele': 'C',
    'alt_allele': 'T',
    'consequence': 'intron_variant',
    'mapped_genes': ['TCF7L2'],
    'maf': 0.293
}
2. Trait Associations
python
[
    {
        'trait': 'Type 2 diabetes',
        'p_value': 1.2e-128,
        'beta': '0.28 unit increase',
        'study_id': 'GCST010555',
        'pubmed_id': '33536258',
        'effect_allele': 'T'
    },
    ...
]
3. Credible Sets (Fine-Mapping)
python
[
    {
        'study_id': 'GCST90476118',
        'trait': 'Renal failure',
        'finemapping_method': 'SuSiE-inf',
        'p_value': 3.5e-42,
        'predicted_genes': [
            {'gene': 'TCF7L2', 'score': 0.863}
        ],
        'region': '10:112950000-113050000'
    },
    ...
]
4. Clinical Significance
Genome-wide significant associations with 100 traits/diseases:
  - Type 2 diabetes
  - Diabetic retinopathy
  - HbA1c levels
  ...

Identified in 20 fine-mapped loci.
Predicted causal genes: TCF7L2

Example Usage

See QUICK_START.md for platform-specific examples.

Tools Used

GWAS Catalog Tools
  1. gwas_get_snp_by_id: Get SNP annotation
  2. gwas_get_associations_for_snp: Get all trait associations
Open Targets Tools
  1. OpenTargets_get_variant_info: Get variant details with population frequencies
  2. OpenTargets_get_variant_credible_sets: Get fine-mapping credible sets with L2G

Interpretation Guide

P-value Significance Levels
  • p < 5e-8: Genome-wide significant (strong evidence)
  • p < 5e-6: Suggestive (moderate evidence)
  • p < 0.05: Nominal (weak evidence)
Show full SKILL.md (256 more words)Show less
L2G Score Interpretation
  • > 0.5: High confidence causal gene
  • 0.1-0.5: Moderate confidence
  • < 0.1: Low confidence
Clinical Actionability
  1. High: Multiple genome-wide significant associations + in credible sets + high L2G scores
  2. Moderate: Genome-wide significant associations but limited fine-mapping
  3. Low: Suggestive associations or limited replication

Limitations

  1. Variant ID Conversion: OpenTargets requires chr_pos_ref_alt format, which may need allele lookup
  2. Population Specificity: Associations may vary by ancestry
  3. Effect Sizes: Beta values are study-dependent (different phenotype scales)
  4. Causality: Associations don't prove causation; fine-mapping improves confidence
  5. Currency: Data reflects published GWAS; latest studies may not be included

Best Practices

  1. Use Full Interpretation: Enable include_credible_sets=True for clinical decisions
  2. Check Multiple Variants: Look at other variants in the same locus
  3. Validate Populations: Consider ancestry-specific effect sizes
  4. Review Publications: Check original studies for context
  5. Integrate Evidence: Combine with functional data, eQTLs, pQTLs

Technical Notes

Performance
  • Fast mode (no credible sets): 2-5 seconds
  • Full mode (with credible sets): 10-30 seconds
  • Bottleneck: OpenTargets GraphQL API rate limits
Error Handling
  • Invalid rs_id: Returns error message
  • No associations: Returns empty list with note
  • API failures: Graceful degradation (returns partial results)
  • Gene Function Analysis: Interpret predicted causal genes
  • Disease Ontology Lookup: Understand trait classifications
  • PubMed Literature Search: Find original GWAS publications
  • Variant Effect Prediction: Functional consequence analysis

References

  1. GWAS Catalog: https://www.ebi.ac.uk/gwas/
  2. Open Targets Genetics: https://genetics.opentargets.org/
  3. GWAS Significance Thresholds: Fadista et al. 2016
  4. L2G Method: Mountjoy et al. 2021 (Nature Genetics)

Version

  • Version: 1.0.0
  • Last Updated: 2026-02-13
  • ToolUniverse Version: >= 1.0.0
  • Tools Required: gwas_get_snp_by_id, gwas_get_associations_for_snp, OpenTargets_get_variant_credible_sets

© lamm-mit, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (scripts) in skills/gwas-snp-interpretation of lamm-mit/scienceclaw.

  • SKILL.md
  • scripts/__pycache__/run.cpython-313.pyc
  • scripts/run.py

Open the folder on GitHubat commit ab9aba1

Compare with similar skills

Gwas Snp Interpretation next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Gwas Snp Interpretation compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Gwas Snp Interpretation this skilllamm-mit/scienceclaw244—~1.9kAutomated safety check: PassApache-2.0
Tooluniverse Gwas Snp Interpretationwu-yc/LabClaw1.1k2 repos~1.8kAutomated safety check: PassNone
Gwas Databasedavila7/claude-code-templates32k10 repos~5kAutomated safety check: PassMIT
Tooluniverse Gwas Finemappingwu-yc/LabClaw1.1k2 repos~3kAutomated safety check: PassNone
Tooluniverse Variant Interpretationwu-yc/LabClaw1.1k2 repos~9.5kAutomated safety check: PassNone
Tooluniverse Gwas Trait To Genewu-yc/LabClaw1.1k2 repos~2.2kAutomated safety check: PassNone

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Questions about Gwas Snp Interpretation

What does Gwas Snp Interpretation do?

ToolUniverse workflow — Gwas Snp Interpretation. An agent skill from lamm-mit/scienceclaw. Gwas Snp Interpretation is an agent skill from lamm-mit/scienceclaw.

How do I install Gwas Snp Interpretation in Claude Code?

Run `npx skills add lamm-mit/scienceclaw --skill gwas-snp-interpretation -a claude-code`. Or copy the skill folder (skills/gwas-snp-interpretation in lamm-mit/scienceclaw) into .claude/skills/gwas-snp-interpretation in your project. Claude Code loads it when a task matches its description.

How do I install Gwas Snp Interpretation in Codex?

Run `npx skills add lamm-mit/scienceclaw --skill gwas-snp-interpretation -a codex`. Or copy the skill folder (skills/gwas-snp-interpretation in lamm-mit/scienceclaw) into .agents/skills/gwas-snp-interpretation in your project. Codex loads it when a task matches its description.

Can I use Gwas Snp Interpretation in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add lamm-mit/scienceclaw --skill gwas-snp-interpretation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/gwas-snp-interpretation, .gemini/skills/gwas-snp-interpretation, .github/skills/gwas-snp-interpretation and .opencode/skills/gwas-snp-interpretation in your project.

What does Gwas Snp Interpretation need to run?

Going by SKILL.md and its folder, Gwas Snp Interpretation needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Gwas Snp Interpretation access the network?

SKILL.md names 2 domains. As links in the text: ebi.ac.uk and genetics.opentargets.org. This is read from the text; nothing was executed.

Is Gwas Snp Interpretation safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Gwas Snp Interpretation use?

Gwas Snp Interpretation is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Gwas Snp Interpretation use?

About 1.9k tokens (SKILL.md is roughly 7.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Gwas Snp Interpretation?

Skills that share tags, products or a category with Gwas Snp Interpretation: Tooluniverse Gwas Snp Interpretation (wu-yc/LabClaw, 1.1k stars), Gwas Database (davila7/claude-code-templates, 32k stars), Tooluniverse Gwas Finemapping (wu-yc/LabClaw, 1.1k stars) and Tooluniverse Variant Interpretation (wu-yc/LabClaw, 1.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Gwas Snp Interpretation?

lamm-mit (a GitHub user) maintains it in lamm-mit/scienceclaw, which has 244 GitHub stars. The repository holds 86 skills in this directory. The repository was last updated on August 21, 2026.

Source: lamm-mit/scienceclaw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.