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.

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

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:
        # v2 returns tissueSiteDetailId (not a display-name column), median, unit, geneSymbol
        df = df[["geneSymbol", "tissueSiteDetailId", "median", "unit"]].sort_values(
            "median", ascending=False
        )
    return df

# Example: get expression of APOE across tissues (v10 = GENCODE v39; APOE keeps .10 here)
df = get_gene_expression_by_tissue("ENSG00000130203.10")  # APOE GENCODE ID
print(df.head(10))
# Output: tissueSiteDetailId, median TPM, sorted by highest expression

3. eQTL Lookup

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("pValue", ascending=True)
    return df

# Example: Find eQTLs for PCSK9 (v10 GENCODE ID — version suffix must match the dataset)
df = query_eqtl("ENSG00000169174.11")
# v2 single-tissue eQTL fields: snpId, variantId, tissueSiteDetailId, nes, pValue, gencodeId
# (nes = normalized effect size of the alt allele; there is no separate slope/qval/maf here)
print(df[["snpId", "tissueSiteDetailId", "nes", "pValue", "gencodeId"]].head(20))

4. Single-Tissue eQTL by Variant

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)

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

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)

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

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", "nes", "pValue"]].sort_values("pValue")

# 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

• GENCODE version suffix must match the dataset (biggest gotcha): v10 maps to GENCODE v39, v8 maps to GENCODE v26, and the same gene gets a different .version in each. PCSK9 is ENSG00000169174.11 in v10 but .10 in v8 — querying the wrong suffix silently returns zero results. Resolve the correct ID per dataset with /reference/gene?geneId=SYMBOL&gencodeVersion=v39 (use v26 for v8). geneSymbol is also accepted by most endpoints and sidesteps the suffix.
• 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 threshold: GTEx calls eGenes/eQTLs at FDR < 0.05. The single-tissue eQTL/sQTL responses are already filtered to significant pairs and return pValue + nes (no per-row qval); the per-gene qValue lives on the /association/egene endpoint.
• Effect size: the QTL response field is nes (normalized effect size of the alternative allele), not slope; positive nes = higher expression with the alt allele.

Data Downloads (for large-scale analysis)

For genome-wide analyses, download full summary statistics rather than using the API:

# 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

Scripts

scripts/query_gtex.py — runnable helper for the GTEx Portal API v2 (no key):

python scripts/query_gtex.py expression ENSG00000130203.10
python scripts/query_gtex.py eqtl ENSG00000169174.11 --tissue Liver   # v10 GENCODE ID
python scripts/query_gtex.py tissues