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+---
+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:**
+
+1. **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()
+   ```
+
+2. **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()
+   ```
+
+3. **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()
+   ```
+
+4. **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**
+```python
+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**
+```python
+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**
+```python
+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**
+```python
+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**
+```python
+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:**
+```python
+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:**
+   ```python
+   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:**
+   ```python
+   url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{rs_id}"
+   ```
+
+2. **Retrieve all trait associations:**
+   ```python
+   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:
+   ```python
+   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:**
+   ```bash
+   # 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:**
+   ```python
+   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:
+
+```python
+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