name: gwas-database description: "Query NHGRI-EBI GWAS Catalog for SNP-trait associations. Search variants by rs ID, disease/trait, gene, retrieve p-values and summary statistics, for genetic epidemiology and polygenic risk scores."

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:

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() ```

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()
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()
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

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)
Query associations via API: python url = f"https://www.ebi.ac.uk/gwas/rest/api/efoTraits/{efo_id}/associations"
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
Extract variant details:
rs IDs for each association
Effect alleles and directions
Effect sizes (odds ratios, beta coefficients)
Population allele frequencies
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

Query the variant: python url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{rs_id}"
Retrieve all trait associations: python url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{rs_id}/associations"
Analyze pleiotropy:
Identify all traits associated with this variant
Review effect directions across traits
Look for shared biological pathways
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

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}
Retrieve variants in gene region:
Get chromosomal coordinates for gene
Query variants in region
Include promoter and regulatory regions (extend boundaries)
Analyze association patterns:
Identify traits associated with variants in this gene
Look for consistent associations across studies
Review effect sizes and directions
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

Define research question:
Specific trait or disease of interest
Population considerations
Study design requirements
Comprehensive variant extraction:
Query all associations for trait
Set significance threshold
Note discovery and replication studies
Quality assessment:
Review study sample sizes
Check for population diversity
Assess heterogeneity across studies
Identify potential biases
Data synthesis:
Aggregate associations across studies
Perform meta-analysis if applicable
Create summary tables
Generate Manhattan or forest plots
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

Identify studies with summary statistics:
Browse summary statistics portal
Check FTP directory listings
Query API for available studies
Download summary statistics: bash # Via FTP wget ftp://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCSTXXXXXX/harmonised/GCSTXXXXXX-harmonised.tsv.gz
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}
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 DataFrame with association results
    """
    base_url = "https://www.ebi.ac.uk/gwas/rest/api"
    url = f"{base_url}/efoTraits/{trait_id}/associations"

    headers = {"Content-Type": "application/json"}
    results = []
    page = 0

    while True:
        params = {"page": page, "size": 100}
        response = requests.get(url, params=params, headers=headers)

        if response.status_code != 200:
            break

        data = response.json()
        associations = data.get('_embedded', {}).get('associations', [])

        if not associations:
            break

        for assoc in associations:
            pvalue = assoc.get('pvalue')
            if pvalue and float(pvalue) <= p_threshold:
                results.append({
                    'variant': assoc.get('rsId'),
                    'pvalue': pvalue,
                    'risk_allele': assoc.get('strongestAllele'),
                    'or_beta': assoc.get('orPerCopyNum') or assoc.get('betaNum'),
                    'trait': assoc.get('efoTrait'),
                    'pubmed_id': assoc.get('pubmedId')
                })

        page += 1
        sleep(0.1)  # Rate limiting

    return pd.DataFrame(results)

# Example usage
df = query_gwas_catalog('EFO_0001360')  # Type 2 diabetes
print(df.head())
print(f"\nTotal associations: {len(df)}")
print(f"Unique variants: {df['variant'].nunique()}")

Resources

references/api_reference.md

Comprehensive API documentation including: - Detailed endpoint specifications for both APIs - Complete list of query parameters and filters - Response format specifications and field descriptions - Advanced query examples and patterns - Error handling and troubleshooting - Integration with external databases

Consult this reference when: - Constructing complex API queries - Understanding response structures - Implementing pagination or batch operations - Troubleshooting API errors - Exploring advanced filtering options

Training Materials

The GWAS Catalog team provides workshop materials: - GitHub repository: https://github.com/EBISPOT/GWAS_Catalog-workshop - Jupyter notebooks with example queries - Google Colab integration for cloud execution

Important Notes

Data Updates

The GWAS Catalog is updated regularly with new publications
Re-run queries periodically for comprehensive coverage
Summary statistics are added as studies release data
EFO mappings may be updated over time

Citation Requirements

When using GWAS Catalog data, cite: - Sollis E, et al. (2023) The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource. Nucleic Acids Research. PMID: 37953337 - Include access date and version when available - Cite original studies when discussing specific findings

Limitations

Not all GWAS publications are included (curation criteria apply)
Full summary statistics available for subset of studies
Effect sizes may require harmonization across studies
Population diversity is growing but historically limited
Some associations represent conditional or joint effects

Data Access

Web interface: Free, no registration required
REST APIs: Free, no API key needed
FTP downloads: Open access
Rate limiting applies to API (be respectful)

Additional Resources

GWAS Catalog website: https://www.ebi.ac.uk/gwas/
Documentation: https://www.ebi.ac.uk/gwas/docs
API documentation: https://www.ebi.ac.uk/gwas/rest/docs/api
Summary Statistics API: https://www.ebi.ac.uk/gwas/summary-statistics/docs/
FTP site: http://ftp.ebi.ac.uk/pub/databases/gwas/
Training materials: https://github.com/EBISPOT/GWAS_Catalog-workshop
PGS Catalog (polygenic scores): https://www.pgscatalog.org/
Help and support: gwas-info@ebi.ac.uk

Gwas Database

📚 References