Gwas Database

GWAS Catalog Database

Overview

The GWAS Catalog is a comprehensive repository of published genome-wide association studies maintained by the National Human Genome Research Institute (NHGRI) and the European Bioinformatics Institute (EBI). The catalog contains curated SNP-trait associations from thousands of GWAS publications, including genetic variants, associated traits and diseases, p-values, effect sizes, and full summary statistics for many studies.

When to Use This Skill

This skill should be used when queries involve:

• Genetic variant associations: Finding SNPs associated with diseases or traits
• SNP lookups: Retrieving information about specific genetic variants (rs IDs)
• Trait/disease searches: Discovering genetic associations for phenotypes
• Gene associations: Finding variants in or near specific genes
• GWAS summary statistics: Accessing complete genome-wide association data
• Study metadata: Retrieving publication and cohort information
• Population genetics: Exploring ancestry-specific associations
• Polygenic risk scores: Identifying variants for risk prediction models
• Functional genomics: Understanding variant effects and genomic context
• Systematic reviews: Comprehensive literature synthesis of genetic associations

Core Capabilities

1. Understanding GWAS Catalog Data Structure

The GWAS Catalog is organized around four core entities:

• Studies: GWAS publications with metadata (PMID, author, cohort details)
• Associations: SNP-trait associations with statistical evidence (p ≤ 5×10⁻⁸)
• Variants: Genetic markers (SNPs) with genomic coordinates and alleles
• Traits: Phenotypes and diseases (mapped to EFO ontology terms)

Key Identifiers:

• Study accessions: GCST IDs (e.g., GCST001234)
• Variant IDs: rs numbers (e.g., rs7903146) or variant_id format
• Trait IDs: EFO terms (e.g., EFO_0001360 for type 2 diabetes)
• Gene symbols: HGNC approved names (e.g., TCF7L2)

2. Web Interface Searches

The web interface at https://www.ebi.ac.uk/gwas/ supports multiple search modes:

By Variant (rs ID):

rs7903146

Returns all trait associations for this SNP.

By Disease/Trait:

type 2 diabetes
Parkinson disease
body mass index

Returns all associated genetic variants.

By Gene:

APOE
TCF7L2

Returns variants in or near the gene region.

By Chromosomal Region:

10:114000000-115000000

Returns variants in the specified genomic interval.

By Publication:

PMID:20581827
Author: McCarthy MI
GCST001234

Returns study details and all reported associations.

3. REST API Access

The GWAS Catalog provides two REST APIs for programmatic access:

Base URLs:

• GWAS Catalog API: https://www.ebi.ac.uk/gwas/rest/api
• Summary Statistics API: https://www.ebi.ac.uk/gwas/summary-statistics/api

API Documentation:

• Main API docs: https://www.ebi.ac.uk/gwas/rest/docs/api
• Summary stats docs: https://www.ebi.ac.uk/gwas/summary-statistics/docs/

Core Endpoints:

1. Studies endpoint - /studies/{accessionID}

```python import requests

# Get a specific study url = "https://www.ebi.ac.uk/gwas/rest/api/studies/GCST001795" response = requests.get(url, headers={"Content-Type": "application/json"}) study = response.json() ```

1. Associations endpoint - /associations

``python # Find associations for a variant variant = "rs7903146" url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{variant}/associations" params = {"projection": "associationBySnp"} response = requests.get(url, params=params, headers={"Content-Type": "application/json"}) associations = response.json() ``

1. Variants endpoint - /singleNucleotidePolymorphisms/{rsID}

``python # Get variant details url = "https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs7903146" response = requests.get(url, headers={"Content-Type": "application/json"}) variant_info = response.json() ``

1. Traits endpoint - /efoTraits/{efoID}

``python # Get trait information url = "https://www.ebi.ac.uk/gwas/rest/api/efoTraits/EFO_0001360" response = requests.get(url, headers={"Content-Type": "application/json"}) trait_info = response.json() ``

4. Query Examples and Patterns

Example 1: Find all associations for a disease

import requests

trait = "EFO_0001360"  # Type 2 diabetes
base_url = "https://www.ebi.ac.uk/gwas/rest/api"

# Query associations for this trait
url = f"{base_url}/efoTraits/{trait}/associations"
response = requests.get(url, headers={"Content-Type": "application/json"})
associations = response.json()

# Process results
for assoc in associations.get('_embedded', {}).get('associations', []):
    variant = assoc.get('rsId')
    pvalue = assoc.get('pvalue')
    risk_allele = assoc.get('strongestAllele')
    print(f"{variant}: p={pvalue}, risk allele={risk_allele}")

Example 2: Get variant information and all trait associations

import requests

variant = "rs7903146"
base_url = "https://www.ebi.ac.uk/gwas/rest/api"

# Get variant details
url = f"{base_url}/singleNucleotidePolymorphisms/{variant}"
response = requests.get(url, headers={"Content-Type": "application/json"})
variant_data = response.json()

# Get all associations for this variant
url = f"{base_url}/singleNucleotidePolymorphisms/{variant}/associations"
params = {"projection": "associationBySnp"}
response = requests.get(url, params=params, headers={"Content-Type": "application/json"})
associations = response.json()

# Extract trait names and p-values
for assoc in associations.get('_embedded', {}).get('associations', []):
    trait = assoc.get('efoTrait')
    pvalue = assoc.get('pvalue')
    print(f"Trait: {trait}, p-value: {pvalue}")

Example 3: Access summary statistics

import requests

# Query summary statistics API
base_url = "https://www.ebi.ac.uk/gwas/summary-statistics/api"

# Find associations by trait with p-value threshold
trait = "EFO_0001360"  # Type 2 diabetes
p_upper = "0.000000001"  # p < 1e-9
url = f"{base_url}/traits/{trait}/associations"
params = {
    "p_upper": p_upper,
    "size": 100  # Number of results
}
response = requests.get(url, params=params)
results = response.json()

# Process genome-wide significant hits
for hit in results.get('_embedded', {}).get('associations', []):
    variant_id = hit.get('variant_id')
    chromosome = hit.get('chromosome')
    position = hit.get('base_pair_location')
    pvalue = hit.get('p_value')
    print(f"{chromosome}:{position} ({variant_id}): p={pvalue}")

Example 4: Query by chromosomal region

import requests

# Find variants in a specific genomic region
chromosome = "10"
start_pos = 114000000
end_pos = 115000000

base_url = "https://www.ebi.ac.uk/gwas/rest/api"
url = f"{base_url}/singleNucleotidePolymorphisms/search/findByChromBpLocationRange"
params = {
    "chrom": chromosome,
    "bpStart": start_pos,
    "bpEnd": end_pos
}
response = requests.get(url, params=params, headers={"Content-Type": "application/json"})
variants_in_region = response.json()

5. Working with Summary Statistics

The GWAS Catalog hosts full summary statistics for many studies, providing access to all tested variants (not just genome-wide significant hits).

Access Methods:

1. FTP download: http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/
2. REST API: Query-based access to summary statistics
3. Web interface: Browse and download via the website

Summary Statistics API Features:

• Filter by chromosome, position, p-value
• Query specific variants across studies
• Retrieve effect sizes and allele frequencies
• Access harmonized and standardized data

Example: Download summary statistics for a study

import requests
import gzip

# Get available summary statistics
base_url = "https://www.ebi.ac.uk/gwas/summary-statistics/api"
url = f"{base_url}/studies/GCST001234"
response = requests.get(url)
study_info = response.json()

# Download link is provided in the response
# Alternatively, use FTP:
# ftp://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCSTXXXXXX/

6. Data Integration and Cross-referencing

The GWAS Catalog provides links to external resources:

Genomic Databases:

• Ensembl: Gene annotations and variant consequences
• dbSNP: Variant identifiers and population frequencies
• gnomAD: Population allele frequencies

Functional Resources:

• Open Targets: Target-disease associations
• PGS Catalog: Polygenic risk scores
• UCSC Genome Browser: Genomic context

Phenotype Resources:

• EFO (Experimental Factor Ontology): Standardized trait terms
• OMIM: Disease gene relationships
• Disease Ontology: Disease hierarchies

Following Links in API Responses:

import requests

# API responses include _links for related resources
response = requests.get("https://www.ebi.ac.uk/gwas/rest/api/studies/GCST001234")
study = response.json()

# Follow link to associations
associations_url = study['_links']['associations']['href']
associations_response = requests.get(associations_url)

Query Workflows

Workflow 1: Exploring Genetic Associations for a Disease

1. Identify the trait using EFO terms or free text:

• Search web interface for disease name
• Note the EFO ID (e.g., EFO_0001360 for type 2 diabetes)

1. Query associations via API:

``python url = f"https://www.ebi.ac.uk/gwas/rest/api/efoTraits/{efo_id}/associations" ``

1. Filter by significance and population:

• Check p-values (genome-wide significant: p ≤ 5×10⁻⁸)
• Review ancestry information in study metadata
• Filter by sample size or discovery/replication status

1. Extract variant details:

• rs IDs for each association
• Effect alleles and directions
• Effect sizes (odds ratios, beta coefficients)
• Population allele frequencies

1. Cross-reference with other databases:

• Look up variant consequences in Ensembl
• Check population frequencies in gnomAD
• Explore gene function and pathways

Workflow 2: Investigating a Specific Genetic Variant

1. Query the variant:

``python url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{rs_id}" ``

1. Retrieve all trait associations:

``python url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{rs_id}/associations" ``

1. Analyze pleiotropy:

• Identify all traits associated with this variant
• Review effect directions across traits
• Look for shared biological pathways

1. Check genomic context:

• Determine nearby genes
• Identify if variant is in coding/regulatory regions
• Review linkage disequilibrium with other variants

Workflow 3: Gene-Centric Association Analysis

1. Search by gene symbol in web interface or:

``python url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/search/findByGene" params = {"geneName": gene_symbol} ``

1. Retrieve variants in gene region:

• Get chromosomal coordinates for gene
• Query variants in region
• Include promoter and regulatory regions (extend boundaries)

1. Analyze association patterns:

• Identify traits associated with variants in this gene
• Look for consistent associations across studies
• Review effect sizes and directions

1. Functional interpretation:

• Determine variant consequences (missense, regulatory, etc.)
• Check expression QTL (eQTL) data
• Review pathway and network context

Workflow 4: Systematic Review of Genetic Evidence

1. Define research question:

• Specific trait or disease of interest
• Population considerations
• Study design requirements

1. Comprehensive variant extraction:

• Query all associations for trait
• Set significance threshold
• Note discovery and replication studies

1. Quality assessment:

• Review study sample sizes
• Check for population diversity
• Assess heterogeneity across studies
• Identify potential biases

1. Data synthesis:

• Aggregate associations across studies
• Perform meta-analysis if applicable
• Create summary tables
• Generate Manhattan or forest plots

1. Export and documentation:

• Download full association data
• Export summary statistics if needed
• Document search strategy and date
• Create reproducible analysis scripts

Workflow 5: Accessing and Analyzing Summary Statistics

1. Identify studies with summary statistics:

• Browse summary statistics portal
• Check FTP directory listings
• Query API for available studies

1. Download summary statistics:

``bash # Via FTP wget ftp://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCSTXXXXXX/harmonised/GCSTXXXXXX-harmonised.tsv.gz ``

1. Query via API for specific variants:

``python url = f"https://www.ebi.ac.uk/gwas/summary-statistics/api/chromosomes/{chrom}/associations" params = {"start": start_pos, "end": end_pos} ``

1. Process and analyze:

• Filter by p-value thresholds
• Extract effect sizes and confidence intervals
• Perform downstream analyses (fine-mapping, colocalization, etc.)

Response Formats and Data Fields

Key Fields in Association Records:

• rsId: Variant identifier (rs number)
• strongestAllele: Risk allele for the association
• pvalue: Association p-value
• pvalueText: P-value as text (may include inequality)
• orPerCopyNum: Odds ratio or beta coefficient
• betaNum: Effect size (for quantitative traits)
• betaUnit: Unit of measurement for beta
• range: Confidence interval
• efoTrait: Associated trait name
• mappedLabel: EFO-mapped trait term

Study Metadata Fields:

• accessionId: GCST study identifier
• pubmedId: PubMed ID
• author: First author
• publicationDate: Publication date
• ancestryInitial: Discovery population ancestry
• ancestryReplication: Replication population ancestry
• sampleSize: Total sample size

Pagination: Results are paginated (default 20 items per page). Navigate using:

• size parameter: Number of results per page
• page parameter: Page number (0-indexed)
• _links in response: URLs for next/previous pages

Best Practices

Query Strategy

• Start with web interface to identify relevant EFO terms and study accessions
• Use API for bulk data extraction and automated analyses
• Implement pagination handling for large result sets
• Cache API responses to minimize redundant requests

Data Interpretation

• Always check p-value thresholds (genome-wide: 5×10⁻⁸)
• Review ancestry information for population applicability
• Consider sample size when assessing evidence strength
• Check for replication across independent studies
• Be aware of winner's curse in effect size estimates

Rate Limiting and Ethics

• Respect API usage guidelines (no excessive requests)
• Use summary statistics downloads for genome-wide analyses
• Implement appropriate delays between API calls
• Cache results locally when performing iterative analyses
• Cite the GWAS Catalog in publications

Data Quality Considerations

• GWAS Catalog curates published associations (may contain inconsistencies)
• Effect sizes reported as published (may need harmonization)
• Some studies report conditional or joint associations
• Check for study overlap when combining results
• Be aware of ascertainment and selection biases

Python Integration Example

Complete workflow for querying and analyzing GWAS data:

import requests
import pandas as pd
from time import sleep

def query_gwas_catalog(trait_id, p_threshold=5e-8):
    """
    Query GWAS Catalog for trait associations

    Args:
        trait_id: EFO trait identifier (e.g., 'EFO_0001360')
        p_threshold: P-value threshold for filtering

    Returns:
        pandas

…

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [silverstein](https://github.com/silverstein)
- **Source:** [silverstein/claude-scientific-skills-desktop](https://github.com/silverstein/claude-scientific-skills-desktop)
- **License:** MIT
- **Homepage:** https://businessvacation.com

Install and usage instructions live in the source repository linked above.