gwas-database

# 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