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:

SNP Annotation

Retrieves basic variant information including genomic coordinates, alleles, functional consequence, and mapped genes

Association Discovery

Finds all GWAS trait/disease associations with statistical significance

Fine-Mapping Evidence

Identifies credible sets the variant belongs to (fine-mapped causal loci)

Gene Mapping

Uses Locus-to-Gene (L2G) predictions to identify likely causal genes

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

gwas_get_snp_by_id

Get SNP annotation

gwas_get_associations_for_snp

Get all trait associations

Open Targets Tools

OpenTargets_get_variant_info

Get variant details with population frequencies

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

High

Multiple genome-wide significant associations + in credible sets + high L2G scores

Moderate

Genome-wide significant associations but limited fine-mapping

Low

Suggestive associations or limited replication

Limitations

Variant ID Conversion

OpenTargets requires chr_pos_ref_alt format, which may need allele lookup

Population Specificity

Associations may vary by ancestry

Effect Sizes

Beta values are study-dependent (different phenotype scales)

Causality

Associations don't prove causation; fine-mapping improves confidence

Currency

Data reflects published GWAS; latest studies may not be included

Best Practices

Use Full Interpretation

Enable

include_credible_sets=True

for clinical decisions

Check Multiple Variants

Look at other variants in the same locus

Validate Populations

Consider ancestry-specific effect sizes

Review Publications

Check original studies for context

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)