Integrating GTEx Tissue Expression with ENCODE Regulatory Data

Use GTEx gene expression across 54 human tissues to validate ENCODE regulatory element activity, establish enhancer-gene links, and provide tissue-specific expression context for functional genomics findings.

Scientific Rationale

The question: "Is the gene near my ENCODE regulatory element actually expressed in the tissue where the element is active?"

ENCODE catalogs where regulatory elements exist (enhancers, promoters, insulators) but does not directly measure gene expression across a broad tissue panel. GTEx (Genotype-Tissue Expression) fills this gap by providing RNA-seq-based gene expression measurements across 54 human tissues from ~1,000 post-mortem donors. Integrating the two answers a fundamental question: does the regulatory landscape match the transcriptional output?

An active enhancer (H3K27ac+, ATAC-seq+) near a gene in pancreas tissue is much more meaningful if GTEx confirms the gene is highly expressed in pancreas. Conversely, an ENCODE enhancer near a gene with zero expression in the relevant tissue suggests the enhancer regulates a different gene, or acts in a cell-type subpopulation not captured by bulk GTEx.

What GTEx Provides

• Median gene expression (TPM) across 54 human tissues from ~1,000 donors
• Transcript-level expression for isoform analysis
• eQTLs — variants associated with gene expression in specific tissues (cis and trans)
• sQTLs — variants associated with alternative splicing
• Single-nucleus RNA-seq for selected tissues (GTEx v8+)
• Allele-specific expression data

Why Integrate with ENCODE

Key Literature

• GTEx Consortium 2020 "The GTEx Consortium atlas of genetic regulatory effects across human tissues" (Science, ~4,000 citations). The flagship publication describing the v8 release with 17,382 samples across 54 tissues from 948 donors. Identified cis-eQTLs for 95% of genes, tissue-sharing patterns, and cell-type interaction eQTLs. DOI: 10.1126/science.aaz1776
• Aguet et al. 2017 "Genetic effects on gene expression across human tissues" (Nature, ~3,000 citations). The v6p analysis establishing the multi-tissue eQTL framework, demonstrating widespread tissue-specific genetic regulation. DOI: 10.1038/nature24277
• ENCODE Project Consortium 2020 (Nature, ~1,656 citations). Registry of 926,535 human cCREs. Provides the regulatory element catalog to cross-reference with GTEx expression. DOI: 10.1038/s41586-020-2493-4
• Nasser et al. 2021 (Nature, ~468 citations). ABC model linking enhancers to genes using ENCODE data. GTEx expression validates ABC-predicted enhancer-gene pairs. DOI: 10.1038/s41586-021-03446-x

When to Use This Skill

GTEx REST API Reference

Base URL: https://gtexportal.org/api/v2

No authentication required. Responses are JSON.

Key Endpoints

Dataset IDs

• gtex_v8 — Current release (54 tissues, 948 donors, 17,382 samples)

Step 1: Identify the Gene of Interest from ENCODE Data

The starting point is typically an ENCODE finding — an active regulatory element near a gene:

# Find enhancers in pancreas
encode_search_experiments(assay_title="Histone ChIP-seq", target="H3K27ac", organ="pancreas")

# Get peak files
encode_list_files(
    experiment_accession="ENCSR...",
    file_format="bed",
    output_type="IDR thresholded peaks",
    assembly="GRCh38"
)

From the peak file, identify the nearest gene(s) to the enhancer. You will need the Ensembl gene ID (ENSG...) for GTEx queries.

Step 2: Query GTEx for Gene Expression

Get median expression across all tissues

import requests

gene_id = "ENSG00000254647"  # INS (insulin)
url = f"https://gtexportal.org/api/v2/expression/geneExpression"
params = {
    "geneId": gene_id,
    "datasetId": "gtex_v8"
}

response = requests.get(url, params=params)
data = response.json()

# Each entry has: tissueSiteDetailId, median, geneSymbol, etc.
for entry in sorted(data["data"], key=lambda x: x["median"], reverse=True)[:10]:
    print(f"{entry['tissueSiteDetailId']}: {entry['median']:.1f} TPM")

Get transcript-level expression

url = "https://gtexportal.org/api/v2/expression/medianTranscriptExpression"
params = {
    "geneId": gene_id,
    "datasetId": "gtex_v8"
}
response = requests.get(url, params=params)

Step 3: Map GTEx Tissues to ENCODE Biosamples

GTEx and ENCODE use different tissue nomenclature. Key mappings:

To get the full list of GTEx tissue IDs:

url = "https://gtexportal.org/api/v2/dataset/tissueSiteDetail"
response = requests.get(url)
tissues = response.json()["data"]
for t in tissues:
    print(f"{t['tissueSiteDetailId']}: {t['tissueSiteDetail']}")

Step 4: Interpret TPM Values

Expression Thresholds