Alterlab Gtex

---
name: alterlab-gtex
description: Query the GTEx (Genotype-Tissue Expression) portal v2 REST API for tissue-specific gene expression (median TPM across 54 human tissues), expression QTLs (eQTLs), and splicing QTLs (sQTLs). Use when checking which tissues express a gene, finding which gene a non-coding/GWAS variant regulates via eQTLs, or interpreting variant regulatory effects across tissues. NOT for curated trait-variant associations (use alterlab-gwas), population allele frequencies or variant constraint (use alterlab-gnomad), or gene/transcript structure and ID mapping (use alterlab-ensembl). Part of the AlterLab Academic Skills suite.
license: CC-BY-4.0
allowed-tools: Read WebFetch Bash(curl:*) Bash(python:*)
compatibility: Keyless GTEx Portal REST API v2 (no authentication required); datasets gtex_v10 (GENCODE v39) and gtex_v8 (GENCODE v26)
metadata:
    skill-author: AlterLab
    version: "1.0.1"
    last_updated: "2026-09-23"
---

# 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

### Does NOT Trigger

| Scenario | Use Instead |
|----------|-------------|
| Curated trait-variant associations, reported traits, study provenance | `alterlab-gwas` |
| Population allele frequencies, LoF constraint (pLI/LOEUF) | `alterlab-gnomad` |
| Gene/transcript models, VEP consequences, ID mapping | `alterlab-ensembl` |
| Genetics-backed target-disease evidence scores (incl. colocalisation/L2G) | `alterlab-opentargets` |
| Single-cell expression atlases (cell-type resolution) | `alterlab-cellxgene` |

## 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(gencode_id, dataset_id="gtex_v10"):
    """Get median gene expression across all tissues (versioned GENCODE ID required)."""
    url = "https://gtexportal.org/api/v2/expression/medianGeneExpression"
    params = {
        "gencodeId": gencode_id,
        "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

```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("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

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

# Example: rs2228145 (IL6R p.Asp358Ala). Resolve an rsID to the GTEx ID with
# GET /dataset/variant?snpId=rs2228145&datasetId=gtex_v10 -> "chr1_154454494_A_C_b38"
results = interpret_gwas_variant("chr1_154454494_A_C_b38")
if not results.empty:
    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; resolves a symbol to the dataset's versioned GENCODE ID |
| `/dataset/variant` | Variant lookup by `snpId` (rsID) or `variantId`; returns the b38 GTEx ID |

## Datasets Available

| ID | Description |
|----|-------------|
| `gtex_v10` | GTEx v10 (current; 946 donors, 54 tissues, eQTLs mapped in 50; GENCODE v39) |
| `gtex_v8` | GTEx v8 (948 donors, 838 genotyped; eQTLs in 49 of 54 tissues; GENCODE v26) — 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). The expression and QTL endpoints only accept versioned `gencodeId` values — a bare symbol such as `APOE` also returns zero rows.
- **Always pass `datasetId`**: some endpoints (`/association/singleTissueSqtl`, `/dataset/tissueSiteDetail`, `/dataset/variant`) still default to `gtex_v8` when it is omitted, so you silently get v8 data — and a v10 GENCODE ID returns nothing.
- **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:

```bash
# All significant eQTLs (v10)
wget https://storage.googleapis.com/adult-gtex/bulk-qtl/v10/single-tissue-cis-qtl/GTEx_Analysis_v10_eQTL.tar

# Gene read counts (use ..._gene_tpm.gct.gz for TPM, ..._gene_median_tpm.gct.gz for per-tissue medians)
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):

```bash
python scripts/query_gtex.py gene APOE                     # symbol -> versioned GENCODE ID for gtex_v10
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
```