/gwas-catalog

name: gwas-catalog
description: "Guide for integrating NHGRI-EBI GWAS Catalog associations with ENCODE regulatory data. Use when users need to find GWAS variants in ENCODE peaks, connect regulatory elements to disease associations, or prioritize functional variants using ENCODE annotations. Trigger on: GWAS, genome-wide association, SNP association, trait association, GWAS Catalog, disease association, risk variant, lead SNP, LD proxy."

Integrating NHGRI-EBI GWAS Catalog with ENCODE Regulatory Data

When to Use

• User wants to intersect ENCODE regulatory elements with GWAS-associated variants
• User asks about "GWAS", "genome-wide association", "disease variants", or "trait-associated SNPs"
• User needs to find which GWAS hits overlap enhancers, promoters, or TF binding sites
• User wants to prioritize GWAS loci by functional annotation from ENCODE data
• Example queries: "find GWAS variants in my H3K27ac peaks", "which diabetes GWAS hits overlap pancreas enhancers?", "annotate GWAS loci with ENCODE regulatory marks"

Connect genome-wide association study findings with ENCODE functional annotations to identify which regulatory elements harbor disease-associated variants and prioritize causal mechanisms for non-coding GWAS hits.

Scientific Rationale

**The question**: "Which of the disease-associated variants from GWAS fall within active regulatory elements, and what can ENCODE tell us about their functional impact?"

The GWAS Catalog (maintained by NHGRI-EBI) contains over 500,000 variant-trait associations from 6,000+ publications. The central challenge of post-GWAS analysis is that >90% of these associations point to non-coding regions of the genome. ENCODE provides the essential functional annotation layer: if a GWAS variant falls within an active enhancer in disease-relevant tissue, that enhancer becomes a candidate causal mechanism.

This was first demonstrated systematically by Maurano et al. (2012, Science), who showed that disease-associated variants are enriched in DNase I hypersensitive sites (DHSs), and that the cell-type specificity of the DHS predicts the relevant disease tissue. This foundational insight drives the entire GWAS-ENCODE integration framework.

Scale of the Problem

• GWAS Catalog: 500,000+ associations, 100,000+ unique variants, 5,000+ traits
• ENCODE cCREs: 926,535 regulatory elements covering 7.9% of the genome
• Overlap expectation: ~8% of random variants would overlap a cCRE by chance
• Observed enrichment: GWAS variants show 2-5x enrichment in regulatory elements (higher for tissue-matched elements)

Key Literature

• **Sollis et al. 2023** "The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource" (Nucleic Acids Research). The current GWAS Catalog publication describing the REST API, summary statistics hosting, and expanded annotation pipeline. [DOI: 10.1093/nar/gkac1010](https://doi.org/10.1093/nar/gkac1010)
• **Buniello et al. 2019** "The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019" (Nucleic Acids Research, ~3,500 citations). The widely-cited GWAS Catalog reference describing curation standards and the move to EFO ontology for traits. [DOI: 10.1093/nar/gky1120](https://doi.org/10.1093/nar/gky1120)
• **Maurano et al. 2012** "Systematic localization of common disease-associated variation in regulatory DNA" (Science, ~3,000 citations). The foundational demonstration that GWAS variants concentrate in DNase I hypersensitive sites, with cell-type-specific enrichment predicting disease-relevant tissues. Enabled de novo identification of pathogenic cell types from variant sets. [DOI: 10.1126/science.1222794](https://doi.org/10.1126/science.1222794)
• **ENCODE Project Consortium 2020** (Nature, ~1,656 citations). Registry of 926,535 human cCREs that provides the regulatory annotation layer for GWAS variant interpretation. [DOI: 10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
• **Finucane et al. 2015** (Nature Genetics, ~2,253 citations). Stratified LD Score Regression (S-LDSC) for partitioning heritability into ENCODE-defined functional categories. [DOI: 10.1038/ng.3404](https://doi.org/10.1038/ng.3404)
• **Nasser et al. 2021** (Nature, ~468 citations). ABC model linked 5,036 GWAS signals to 2,249 genes using ENCODE data. [DOI: 10.1038/s41586-021-03446-x](https://doi.org/10.1038/s41586-021-03446-x)

GWAS Catalog REST API Reference

**Base URL**: `https://www.ebi.ac.uk/gwas/rest/api`

No authentication required. Responses are JSON (HAL format).

Key Endpoints

| Endpoint | Purpose | Parameters | |---------|---------|-----------| | `/singleNucleotidePolymorphisms/{rsId}` | Get variant details | rsId (e.g., rs7903146) | | `/singleNucleotidePolymorphisms/{rsId}/associations` | Get associations for a variant | rsId | | `/associations?pubmedId={pmid}` | Get associations from a study | PubMed ID | | `/studies?diseaseTrait={trait}` | Find studies by trait name | Trait string | | `/efoTraits/{efoId}` | Get trait details by EFO ID | EFO ID | | `/efoTraits/{efoId}/associations` | Associations for a trait | EFO ID | | `/studies/{studyId}` | Study details | Study accession (GCST...) |

Pagination

All list endpoints support pagination:

• `?page=0&size=20` (default page size is 20, max is 500)

Bulk Downloads

For genome-wide analysis, use the GWAS Catalog downloads (faster than API):

• All associations: `https://www.ebi.ac.uk/gwas/api/search/downloads/full`
• Alternative: `https://www.ebi.ac.uk/gwas/docs/file-downloads`
• Format: TSV with columns for variant, trait, p-value, OR/beta, study, etc.

Step 1: Define the Disease/Trait and Relevant Tissues

Query GWAS Catalog for a Trait

import requests

# Search by trait name
trait = "type 2 diabetes"
url = "https://www.ebi.ac.uk/gwas/rest/api/studies"
params = {"diseaseTrait": trait}
response = requests.get(url, params=params)
studies = response.json()["_embedded"]["studie