Tooluniverse Gwas Snp Interpretation

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

{
    '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

[
    {
        '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)

[
    {
        '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

3. OpenTargets_get_variant_info: Get variant details with population frequencies
4. 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)

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)

Related Skills

• 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