gwas-database

GWAS Catalog Database

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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)

  1. 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.

  1. REST API Access

The GWAS Catalog provides two REST APIs for programmatic access:

Base URLs:

API Documentation:

Core Endpoints:

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()

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()

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()

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()

  1. 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()

  1. 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:

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/

  1. 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:

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:

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

Retrieve all trait associations:

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:

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:

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:

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) &#x3C;= 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:

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

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