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GWAS Fine-Mapping & Causal Variant Prioritization

Identify and prioritize causal variants at GWAS loci using statistical fine-mapping and locus-to-gene predictions.

Overview

This ToolUniverse skill enables systematic identification of causal variants from GWAS data using:

  • Statistical fine-mapping (SuSiE, FINEMAP, etc.) to compute posterior probabilities
  • Locus-to-gene (L2G) predictions to link variants to their likely causal genes
  • Functional annotations from GWAS Catalog and Open Targets Genetics
  • Integration of multiple data sources for comprehensive variant prioritization

Quick Start

from python_implementation import prioritize_causal_variants

# Prioritize variants in TCF7L2 for diabetes
result = prioritize_causal_variants("TCF7L2", "type 2 diabetes")
print(result.get_summary())

# Output:
# Query Gene: TCF7L2
# Credible Sets Found: 8
# Top Causal Genes:
#   - TCF7L2 (L2G score: 0.863)

Documentation

Key Features

1. Variant Prioritization

Rank variants by posterior probability of being causal:

result = prioritize_causal_variants("APOE", "alzheimer")
for cs in result.credible_sets:
    print(f"{cs.trait}: {cs.lead_variant.rs_ids[0]}")
    print(f"  Method: {cs.finemapping_method}")
    print(f"  Top gene: {cs.l2g_genes[0]}")

2. Study Exploration

Get all fine-mapped loci from a GWAS:

from python_implementation import get_credible_sets_for_study

credible_sets = get_credible_sets_for_study("GCST90029024")
print(f"Found {len(credible_sets)} independent loci")

3. Disease Study Search

Find relevant GWAS studies:

from python_implementation import search_gwas_studies_for_disease

studies = search_gwas_studies_for_disease("type 2 diabetes", "MONDO_0005148")
for study in studies:
    print(f"{study['id']}: {study.get('nSamples', 'N/A')} samples")

4. Validation Planning

Get experimental validation suggestions:

suggestions = result.get_validation_suggestions()
# Suggests: CRISPR assays, eQTL analysis, colocalization, replication

Use Cases

  1. Locus Prioritization: "Which variant at this locus is causal?"
  2. Gene Discovery: "Which genes does this variant affect?"
  3. Study Exploration: "What are all the T2D risk loci?"
  4. Validation Planning: "How should we experimentally validate this?"
  5. Meta-Analysis: "Compare fine-mapping across multiple studies"

Testing Status

✓ 100% Test Pass Rate (10/10 comprehensive tests)

Tested with real-world examples:

  • APOE and Alzheimer's disease
  • TCF7L2 and type 2 diabetes
  • FTO and obesity

See SKILL_TESTING_REPORT.md for details.

Tools Used

Open Targets Genetics (GraphQL)

  • Get variant info and credible sets
  • Study-level credible set queries
  • Disease-based study search
  • L2G predictions

GWAS Catalog (REST)

  • SNP search by gene/rsID
  • Association queries
  • Study metadata

No API keys required - all data is public.

Installation

pip install tooluniverse

Files

  • python_implementation.py: Main Python SDK implementation
  • SKILL.md: Complete documentation
  • QUICK_START.md: Quick reference
  • test_skill_comprehensive.py: Test suite (10 tests)
  • SKILL_TESTING_REPORT.md: Testing report
  • README.md: This file

Requirements

  • Python 3.8+
  • ToolUniverse >= 1.0.0
  • No API keys needed

Examples

Example 1: Complete Fine-Mapping Workflow

from python_implementation import (
    search_gwas_studies_for_disease,
    get_credible_sets_for_study,
    prioritize_causal_variants
)

# Step 1: Find T2D studies
studies = search_gwas_studies_for_disease("type 2 diabetes", "MONDO_0005148")
largest = max(studies, key=lambda s: s.get('nSamples', 0) or 0)

# Step 2: Get all loci
credible_sets = get_credible_sets_for_study(largest['id'])

# Step 3: Prioritize TCF7L2 variants
result = prioritize_causal_variants("TCF7L2", "type 2 diabetes")

# Step 4: Generate report
print(result.get_summary())
for suggestion in result.get_validation_suggestions():
    print(suggestion)

Example 2: Variant-Centric Analysis

# Start with a known variant
result = prioritize_causal_variants("rs429358")  # APOE4

# Check all traits
print(f"Associated with {len(set(result.associated_traits))} traits")

# Find credible sets
for cs in result.credible_sets:
    print(f"{cs.trait}: {cs.l2g_genes[0] if cs.l2g_genes else 'No gene'}")

Key Concepts

Credible Sets

A minimal set of variants containing the causal variant with 95-99% probability. Each variant has a posterior probability of causality.

Posterior Probability

The probability that a specific variant is causal, given GWAS data and LD structure. Higher values indicate stronger candidates.

L2G Score

Locus-to-gene score (0-1) integrating distance, eQTLs, chromatin interactions, and functional annotations. Higher scores = stronger gene-variant links.

Fine-Mapping Methods

  • SuSiE: Handles multiple causal variants per locus
  • FINEMAP: Fast Bayesian stochastic search
  • PAINTOR: Integrates functional annotations

Biological Validation

Results validated against known biology:

  • rs429358 (APOE4) → High L2G for APOE, Alzheimer's association ✓
  • rs7903146 (TCF7L2) → Strong diabetes association, TCF7L2 top gene ✓
  • rs9939609 (FTO) → BMI/obesity traits, intergenic variant ✓

Contributing

This skill follows the ToolUniverse skill creation workflow:

  1. Domain analysis
  2. Tool testing
  3. Implementation
  4. Documentation
  5. Comprehensive testing

License

MIT License - see ToolUniverse main repository

Citation

If you use this skill, please cite:

  • Open Targets Genetics: Ghoussaini et al. (2021) Nature Genetics
  • GWAS Catalog: Sollis et al. (2023) Nucleic Acids Research
  • SuSiE: Wang et al. (2020) JRSS-B
  • L2G method: Mountjoy et al. (2021) Nature Genetics

Support

  • Documentation: See SKILL.md
  • Issues: Create GitHub issue in ToolUniverse repository
  • Questions: Check QUICK_START.md

Changelog

v1.0.0 (2026-02-13)

  • Initial release
  • 10 comprehensive tests (100% pass rate)
  • Support for gene and variant queries
  • Study-level analysis
  • Validation suggestions
  • Complete documentation