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:
    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

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

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

• Identify the GWAS variant (rs ID or chromosome position)

• Convert to GTEx variant ID format (chr{chrom}{pos}{ref}_{alt}_b38 )

• Query all eQTL associations for that variant across tissues

• Check effect direction: is the GWAS risk allele the same as the eQTL effect allele?

• Prioritize tissues: select tissues biologically relevant to the disease

• 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", "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

• Get median expression for a gene across all tissues

• Identify the primary expression site(s)

• Compare with disease-relevant tissues

• Download raw data for statistical comparisons

Workflow 3: Tissue-Specific eQTL Analysis

• Select tissues relevant to your disease

• Query all eGenes in that tissue

• Cross-reference with GWAS-significant loci

• 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:

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