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
• 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
• 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
• 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
• 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
• 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

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

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"]["studies"]

print(f"Found {len(studies)} GWAS studies for '{trait}'")
for study in studies[:5]:
    print(f"  {study['accessionId']}: {study['publicationInfo']['title'][:80]}...")

Use EFO IDs for Standardized Trait Queries

The Experimental Factor Ontology (EFO) standardizes trait names:

# Query by EFO ID (more precise)
efo_id = "EFO_0001360"
url = f"https://www.ebi.ac.uk/gwas/rest/api/efoTraits/{efo_id}/associations"
params = {"size": 500}
response = requests.get(url, params=params)

Map Trait to ENCODE Tissues

Following the Maurano 2012 framework — disease-associated variants are enriched in tissue-specific regulatory elements:

# Check ENCODE data availability for disease-relevant tissue
encode_get_facets(organ="pancreas")
encode_get_facets(organ="liver")

Step 2: Retrieve GWAS Variants

Get Associations for a Variant

def get_gwas_associations(rs_id):
    """Get all GWAS associations for a variant."""
    url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{rs_id}/associations"
    response = requests.get(url)
    if response.status_code == 200:
        return response.json()["_embedded"]["associations"]
    return []

# Example: rs7903146 (strongest T2D variant, in TCF7L2)
associations = get_gwas_associations("rs7903146")
for assoc in associations:
    trait = assoc["efoTraits"][0]["trait"] if assoc["efoTraits"] else "Unknown"
    pval = assoc["pvalue"]
    print(f"  Trait: {trait}, p-value: {pval}")

Get All