gtex-database — Анализ и доступ к данным GTEx

--- name: gtex-database description: Query GTEx (Genotype-Tissue Expression) portal for tissue-specific gene expression, eQTLs (expression quantitative trait loci), and sQTLs. Essential for linking GWAS variants to gene regulation, understanding tissue-specific expression, and interpreting non-coding variant effects. license: CC-BY-4.0 metadata: skill-author: Kuan-lin Huang --- # GTEx Database ## Overview The Genotype-Tissue Expression (GTEx) project provides a comprehensive resource for studying tissue-specific gene expression and genetic regulation across 54 non-diseased human tissues from nearly 1,000 individuals. GTEx v10 (the latest release) enables researchers to understand how genetic variants regulate gene expression (eQTLs) and splicing (sQTLs) in a tissue-specific manner, which is critical for interpreting GWAS loci and identifying regulatory mechanisms. **Key resources:** - GTEx Portal: https://gtexportal.org/ - GTEx API v2: https://gtexportal.org/api/v2/ - Data downloads: https://gtexportal.org/home/downloads/adult-gtex/ - Documentation: https://gtexportal.org/home/documentationPage ## When to Use This Skill Use GTEx when: - **GWAS locus interpretation**: Identifying which gene a non-coding GWAS variant regulates via eQTLs - **Tissue-specific expression**: Comparing gene expression levels across 54 human tissues - **eQTL colocalization**: Testing if a GWAS signal and an eQTL signal share the same causal variant - **Multi-tissue eQTL analysis**: Finding variants that regulate expression in multiple tissues - **Splicing QTLs (sQTLs)**: Identifying variants that affect splicing ratios - **Tissue specificity analysis**: Determining which tissues express a gene of interest - **Gene expression exploration**: Retrieving normalized expression levels (TPM) per tissue ## Core Capabilities ### 1. GTEx REST API v2 Base URL: `https://gtexportal.org/api/v2/` The API returns JSON and does not require authentication. All endpoints support pagination. ```python import requests BASE_URL = "https://gtexportal.org/api/v2" def gtex_get(endpoint, params=None): """Make a GET request to the GTEx API.""" url = f"{BASE_URL}/{endpoint}" response = requests.get(url, params=params, headers={"Accept": "application/json"}) response.raise_for_status() return response.json() ``` ### 2. Gene Expression by Tissue ```python import requests import pandas as pd def get_gene_expression_by_tissue(gene_id_or_symbol, dataset_id="gtex_v10"): """Get median gene expression across all tissues.""" url = "https://gtexportal.org/api/v2/expression/medianGeneExpression" params = { "gencodeId": gene_id_or_symbol, "datasetId": dataset_id, "itemsPerPage": 100 } response = requests.get(url, params=params) data = response.json() records = data.get("data", []) df = pd.DataFrame(records) if not df.empty: df = df[["tissueSiteDetailId", "tissueSiteDetail", "median", "unit"]].sort_values( "median", ascending=False ) return df # Example: get expression of APOE across tissues df = get_gene_expression_by_tissue("ENSG00000130203.10") # APOE GENCODE ID # Or use gene symbol (some endpoints accept both) print(df.head(10)) # Output: tissue name, median TPM, sorted by highest expression ``` ### 3. eQTL Lookup ```python import requests import pandas as pd def query_eqtl(gene_id, tissue_id=None, dataset_id="gtex_v10"): """Query significant eQTLs for a gene, optionally filtered by tissue.""" url = "https://gtexportal.org/api/v2/association/singleTissueEqtl" params = { "gencodeId": gene_id, "datasetId": dataset_id, "itemsPerPage": 250 } if tissue_id: params["tissueSiteDetailId"] = tissue_id all_results = [] page = 0 while True: params["page"] = page response = requests.get(url, params=params) data = response.json() results = data.get("data", []) if not results: break all_results.extend(results) if len(results) < params["itemsPerPage"]: break page += 1 df = pd.DataFrame(all_results) if not df.empty: df = df.sort_values("pval", ascending=True) return df # Example: Find eQTLs for PCSK9 df = query_eqtl("ENSG00000169174.14") print(df[["snpId", "tissueSiteDetailId", "slope", "pval", "gencodeId"]].head(20)) ``` ### 4. Single-Tissue eQTL by Variant ```python import requests def query_variant_eqtl(variant_id, tissue_id=None, dataset_id="gtex_v10"): """Get all eQTL associations for a specific variant.""" url = "https://gtexportal.org/api/v2/association/singleTissueEqtl" params = { "variantId": variant_id, # e.g., "chr1_55516888_G_GA_b38" "datasetId": dataset_id, "itemsPerPage": 250 } if tissue_id: params["tissueSiteDetailId"] = tissue_id response = requests.get(url, params=params) return response.json() # GTEx variant ID format: chr{chrom}_{pos}_{ref}_{alt}_b38 # Example: "chr17_43094692_G_A_b38" ``` ### 5. Multi-Tissue eQTL (eGenes) ```python import requests def get_egenes(tissue_id, dataset_id="gtex_v10"): """Get all eGenes (genes with at least one significant eQTL) in a tissue.""" url = "https://gtexportal.org/api/v2/association/egene" params = { "tissueSiteDetailId": tissue_id, "datasetId": dataset_id, "itemsPerPage": 500 } all_egenes = [] page = 0 while True: params["page"] = page response = requests.get(url, params=params) data = response.json() batch = data.get("data", []) if not batch: break all_egenes.extend(batch) if len(batch) < params["itemsPerPage"]: break page += 1 return all_egenes # Example: all eGenes in whole blood egenes = get_egenes("Whole_Blood") print(f"Found {len(egenes)} eGenes in Whole Blood") ``` ### 6. Tissue List ```python import requests def get_tissues(dataset_id="gtex_v10"): """Get all available tissues with metadata.""" url = "https://gtexportal.org/api/v2/dataset/tissueSiteDetail" params = {"datasetId": dataset_id, "itemsPerPage": 100} response = requests.get(url, params=params) return response.json()["data"] tissues = get_tissues() # Key fields: tissueSiteDetailId, tissueSiteDetail, colorHex, samplingSite # Common tissue IDs: # Whole_Blood, Brain_Cortex, Liver, Kidney_Cortex, Heart_Left_Ventricle, # Lung, Muscle_Skeletal, Adipose_Subcutaneous, Colon_Transverse, ... ``` ### 7. sQTL (Splicing QTLs) ```python import requests def query_sqtl(gene_id, tissue_id=None, dataset_id="gtex_v10"): """Query significant sQTLs for a gene.""" url = "https://gtexportal.org/api/v2/association/singleTissueSqtl" params = { "gencodeId": gene_id, "datasetId": dataset_id, "itemsPerPage": 250 } if tissue_id: params["tissueSiteDetailId"] = tissue_id response = requests.get(url, params=params) return response.json() ``` ## Query Workflows ### Workflow 1: Interpreting a GWAS Variant via eQTLs 1. **Identify the GWAS variant** (rs ID or chromosome position) 2. **Convert to GTEx variant ID format** (`chr{chrom}_{pos}_{ref}_{alt}_b38`) 3. **Query all eQTL associations** for that variant across tissues 4. **Check effect direction**: is the GWAS risk allele the same as the eQTL effect allele? 5. **Prioritize tissues**: select tissues biologically relevant to the disease 6. **Consider colocalization** using `coloc` (R package) with full summary statistics ```python import requests, pandas as pd def interpret_gwas_variant(variant_id, dataset_id="gtex_v10"): """Find all genes regulated by a GWAS variant.""" url = "https://gtexportal.org/api/v2/association/singleTissueEqtl" params = {"variantId": variant_id, "datasetId": dataset_id, "itemsPerPage": 500} response = requests.get(url, params=params) data = response.json() df = pd.DataFrame(data.get("data", [])) if df.empty: return df return df[["geneSymbol", "tissueSiteDetailId", "slope", "pval", "maf"]].sort_values("pval") # Example results = interpret_gwas_variant("chr1_154453788_A_T_b38") print(results.groupby("geneSymbol")["tissueSiteDetailId"].count().sort_values(ascending=False)) ``` ### Workflow 2: Gene Expression Atlas 1. Get median expression for a gene across all tissues 2. Identify the primary expression site(s) 3. Compare with disease-relevant tissues 4. Download raw data for statistical comparisons ### Workflow 3: Tissue-Specific eQTL Analysis 1. Select tissues relevant to your disease 2. Query all eGenes in that tissue 3. Cross-reference with GWAS-significant loci 4. Identify co-localized signals ## Key API Endpoints | Endpoint | Description | |----------|-------------| | `/expression/medianGeneExpression` | Median TPM by tissue for a gene | | `/expression/geneExpression` | Full distribution of expression per tissue | | `/association/singleTissueEqtl` | Significant eQTL associations | | `/association/singleTissueSqtl` | Significant sQTL associations | | `/association/egene` | eGenes in a tissue | | `/dataset/tissueSiteDetail` | Available tissues with metadata | | `/reference/gene` | Gene metadata (GENCODE IDs, coordinates) | | `/variant/variantPage` | Variant lookup by rsID or position | ## Datasets Available | ID | Description | |----|-------------| | `gtex_v10` | GTEx v10 (current; ~960 donors, 54 tissues) | | `gtex_v8` | GTEx v8 (838 donors, 49 tissues) — older but widely cited | ## Best Practices - **Use GENCODE IDs** (e.g., `ENSG00000130203.10`) for gene queries; the `.version` suffix matters for some endpoints - **GTEx variant IDs** use the format `chr{chrom}_{pos}_{ref}_{alt}_b38` (GRCh38) — different from rs IDs - **Handle pagination**: Large queries (e.g., all eGenes) require iterating through pages - **Tissue nomenclature**: Use `tissueSiteDetailId` (e.g., `Whole_Blood`) not display names for API calls - **FDR correction**: GTEx uses FDR < 0.05 (q-value) as the significance threshold for eQTLs - **Effect alleles**: The `slope` field is the effect of the alternative allele; positive = higher expression with alt allele ## Data Downloads (for large-scale analysis) For genome-wide analyses, download full summary statistics rather than using the API: ```bash # All significant eQTLs (v10) wget https://storage.googleapis.com/adult-gtex/bulk-qtl/v10/single-tissue-cis-qtl/GTEx_Analysis_v10_eQTL.tar # Normalized expression matrices wget https://storage.googleapis.com/adult-gtex/bulk-gex/v10/rna-seq/GTEx_Analysis_v10_RNASeQCv2.4.2_gene_reads.gct.gz ``` ## Additional Resources - **GTEx Portal**: https://gtexportal.org/ - **API documentation**: https://gtexportal.org/api/v2/ - **Data downloads**: https://gtexportal.org/home/downloads/adult-gtex/ - **GitHub**: https://github.com/broadinstitute/gtex-pipeline - **Citation**: GTEx Consortium (2020) Science. PMID: 32913098