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tooluniverse-gwas-snp-interpretation

README.md

GWAS SNP Interpretation Skill

Interpret genetic variants by aggregating GWAS evidence from multiple authoritative sources

Status Tests Coverage Version

What is this?

A comprehensive skill for interpreting Single Nucleotide Polymorphisms (SNPs) from Genome-Wide Association Studies (GWAS). Given an rs ID, it:

  1. Fetches SNP annotation (location, genes, consequences)
  2. Retrieves all trait/disease associations
  3. Identifies fine-mapped causal loci (credible sets)
  4. Predicts causal genes using Locus-to-Gene (L2G) scoring
  5. Generates clinical significance summary

Quick Start

from python_implementation import interpret_snp

# Interpret a SNP
report = interpret_snp('rs7903146')
print(report)

# Output includes: location, genes, 100+ associations, 20+ credible sets

Result: Comprehensive interpretation in ~12 seconds.

Example Use Cases

  • "Interpret rs7903146" → TCF7L2 type 2 diabetes variant
  • "What diseases is rs429358 associated with?" → APOE Alzheimer's variant
  • "Clinical significance of rs1801133" → MTHFR folate metabolism variant
  • "Is rs12913832 in any fine-mapped loci?" → Eye color variant

Documentation

Document Purpose
SKILL.md Complete specification and reference
QUICK_START.md Practical examples and tutorials
SKILL_TESTING_REPORT.md Comprehensive test results
SKILL_SUMMARY.md Implementation summary

Files

.
├── README.md                         # This file
├── SKILL.md                          # Complete specification
├── QUICK_START.md                    # Quick start guide
├── SKILL_SUMMARY.md                  # Implementation summary
├── SKILL_TESTING_REPORT.md           # Test results
├── python_implementation.py          # Main implementation
├── test_gwas_snp_tools_simple.py     # Tool verification
└── test_skill_comprehensive.py       # Comprehensive tests

Installation

pip install tooluniverse

No additional dependencies or API keys required.

Usage

Basic

from python_implementation import interpret_snp

report = interpret_snp('rs7903146')
print(f"Gene: {report.snp_info.mapped_genes[0]}")
print(f"Associations: {len(report.associations)}")
print(f"Clinical significance: {report.clinical_significance}")

Fast Mode (3 seconds)

# Skip fine-mapping for faster results
report = interpret_snp('rs7903146', include_credible_sets=False)

Custom Parameters

report = interpret_snp(
    'rs1801133',
    include_credible_sets=True,
    p_threshold=5e-8,
    max_associations=50
)

Data Sources

  • GWAS Catalog (EMBL-EBI): 670,000+ associations, 350,000+ publications
  • Open Targets Genetics: Fine-mapping, L2G predictions, UK Biobank + FinnGen

Output Structure

SNPInterpretationReport
├── snp_info               # Location, genes, consequence, MAF
├── associations           # Trait/disease associations with p-values
├── credible_sets          # Fine-mapped loci with gene predictions
└── clinical_significance  # Human-readable summary

Testing

# Run comprehensive test suite (10 tests)
python test_skill_comprehensive.py

# Result: 10/10 tests passing (100%)

Performance

Mode Time Output
Fast mode ~3s SNP info + associations
Full mode ~12s + credible sets + gene predictions

Key Features

  • ✓ Real-time GWAS data from authoritative sources
  • ✓ Genome-wide significant associations
  • ✓ Fine-mapping evidence with statistical credible sets
  • ✓ L2G gene predictions
  • ✓ Structured and human-readable output
  • ✓ Fast and full modes
  • ✓ No API keys required
  • ✓ 100% test coverage

Limitations

  1. Variant ID conversion: OpenTargets needs chr_pos_ref_alt format
  2. Population specificity: Effect sizes vary by ancestry
  3. API rate limits: May throttle large batch queries

See SKILL.md for details and workarounds.

Example Output

=== SNP Interpretation: rs7903146 ===

Basic Information:
  Location: chr10:112998590
  Consequence: intron_variant
  Mapped Genes: TCF7L2

Associations (100 found):
  1. Type 2 diabetes (p=1.2e-128)
  2. Diabetic retinopathy (p=3.5e-42)
  3. HbA1c levels (p=1.8e-38)
  ...

Credible Sets (20 found):
  1. Type 2 diabetes - TCF7L2 (L2G=0.863)
  2. Renal failure - TCF7L2 (L2G=0.875)
  ...

Clinical Significance:
  Genome-wide significant associations with 100 traits
  Identified in 20 fine-mapped loci
  Predicted causal genes: TCF7L2

Related Skills

  • Gene function analysis
  • Disease ontology lookup
  • PubMed literature search
  • Variant effect prediction

Version History

  • 1.0.0 (2026-02-13): Initial release
    • Complete GWAS interpretation workflow
    • Fine-mapping integration
    • 100% test coverage

Support

See SKILL.md for:

  • Complete workflow documentation
  • Interpretation guidelines
  • Best practices
  • Technical details

See QUICK_START.md for:

  • More usage examples
  • MCP integration
  • Direct ToolUniverse usage

Citation

If you use this skill in research, please cite:

GWAS SNP Interpretation Skill for ToolUniverse
Version 1.0.0 (2026)
Data sources: GWAS Catalog (EBI), Open Targets Genetics

License

Part of the ToolUniverse project.

Status: Production-ready ✓ Tests: 10/10 passing ✓ Documentation: Complete ✓