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Gwas Database

jimmc414/kosmos

  • 12 installs
  • 557 repo stars
  • Updated April 4, 2026
  • jimmc414/kosmos

Query the NHGRI-EBI GWAS Catalog for SNP-trait associations by rs ID, trait, or gene and retrieve p-values and summary statistics.

About

Queries the NHGRI-EBI GWAS Catalog of published genome-wide association studies. Developers use it to search variants by rs ID, disease/trait, or gene and retrieve p-values, effect sizes, and summary statistics for genetic epidemiology and polygenic risk scores.

  • SNP-trait associations by rs ID, trait, or gene
  • p-values, effect sizes, and full summary statistics

Gwas Database by the numbers

  • 12 all-time installs (skills.sh)
  • Ranked #3,529 of 4,348 Backend & APIs skills by installs in the Skillselion catalog
  • Data as of Aug 4, 2026 (Skillselion catalog sync)
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repo stars★ 557
Last updatedApril 4, 2026
Repositoryjimmc414/kosmos ↗

What it does

Query the NHGRI-EBI GWAS Catalog for SNP-trait associations by rs ID, trait, or gene and retrieve p-values and summary statistics.

Files

SKILL.mdMarkdownGitHub ↗

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}

   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()

2. Associations endpoint - /associations

   # 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()

3. Variants endpoint - /singleNucleotidePolymorphisms/{rsID}

   # 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()

4. Traits endpoint - /efoTraits/{efoID}

   # 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)

2. Query associations via API:

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

3. 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

4. Extract variant details:

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

5. 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:

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

2. Retrieve all trait associations:

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

3. Analyze pleiotropy:

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

4. 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:

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

2. Retrieve variants in gene region:

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

3. Analyze association patterns:

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

4. 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

2. Comprehensive variant extraction:

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

3. Quality assessment:

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

4. Data synthesis:

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

5. 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

2. Download summary statistics:

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

3. Query via API for specific variants:

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

4. 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

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