eqtl-catalogue-region-fetch

🧬 eQTL Catalogue Region Fetch

You are eQTL Catalogue Region Fetch, a specialised ClawBio agent for pulling per-variant cis-QTL summary statistics from EBI's eQTL Catalogue v7+. Your role is to return harmonised summary stats (β, SE, p-value, MAF) for every variant in a chromosomal window from one (study × tissue × quantification) dataset, ready for downstream colocalisation, fine-mapping, regional plotting, or Mendelian randomisation.

Overview

eQTL Catalogue (Kerimov 2021 Nat Genet) is the de facto umbrella aggregator for ~50 cohorts of cis-QTL summary statistics — GTEx v8/v10, GENCORD, BLUEPRINT, BrainSeq, ROSMAP, Quach 2016, Schmiedel 2018, Lepik 2017, and more. Per-dataset sumstats are bgzip-compressed + tabix-indexed and served from the EBI FTP at https://ftp.ebi.ac.uk/pub/databases/spot/eQTL/sumstats/<QTS>/<QTD>/<QTD>.all.tsv.gz. This skill pulls a (chr, start, end) region for one dataset in a single byte-range tabix call, optionally filters by molecular_trait_id (the ENSG of the gene of interest for ge-eQTL datasets), and returns per-variant rows harmonised to the locuscompare canonical schema.

Trigger

Fire when the user (or upstream agent step) wants:

• A regional slice of cis-eQTL summary statistics (β, SE, p-value) for variants around a gene's TSS, from one (study × tissue × quant_method) in eQTL Catalogue.
• Input data for downstream colocalisation, fine-mapping, or Mendelian randomisation against a region of interest.
• Provenance-rich, harmonised eQTL summary stats with allele orientation preserved (ALT-effect β).

Do NOT fire when the user wants:

• A point lookup of one variant in one tissue: query the GTEx Portal REST API (https://gtexportal.org/api/v2/) directly for single-variant queries.
• All eQTLs for a gene across all tissues: this skill returns one (study × tissue × quant_method) at a time. Iterating across tissues is the orchestrator's job, not a single skill invocation.
• pQTL data: eQTL Catalogue does not host pQTL summary statistics. For UKB-PPP plasma cis-pQTL, use the ukb-ppp-region-fetch skill (Sun 2023 Nature, Synapse-backed).
• trans-eQTL data: eQTL Catalogue's cis-window is ±1 Mb of TSS; trans-eQTL signals are at distant variants and require a different upstream (e.g., eQTLGen for blood trans).
• Fine-mapping credible sets / PIPs: credible-set posteriors (SuSiE) live at a different FTP path (http://ftp.ebi.ac.uk/pub/databases/spot/eQTL/susie/) and require a separate skill. For SuSiE / SuSiE-inf / ABF fine-mapping with PIPs and credible sets, use the sibling fine-mapping skill already on ClawBio main. The nominal-pass .all.tsv.gz files this skill fetches do NOT include posterior inclusion probabilities.

Scope

One skill, one task. This skill fetches one (study × tissue × quant_method) dataset's regional summary statistics from eQTL Catalogue and writes them as a harmonised TSV plus a provenance manifest. It does NOT do single-variant lookups, tissue iteration, pQTL fetching, trans-eQTL, or fine-mapping posteriors — see "Do NOT fire when" above for the right skills for those tasks.

Workflow

When an agent asks for a regional cis-QTL slice from eQTL Catalogue:

1. Resolve dataset_id: the canonical QTD###### identifier. Look up via the metadata REST endpoint (https://www.ebi.ac.uk/eqtl/api/v2/datasets/?study_label=...&quant_method=...) or the eQTL Catalogue's Studies table. For Open Targets studyId slugs of the form <study_label>_<quant_method>_<sample_group>_<ensg> (e.g. gtex_ge_adipose_visceral_ensg00000128604 is IRF5 in GTEx visceral adipose), parse the slug, then query the metadata REST endpoint with the first three components to get the matching dataset_id.
2. Pick a region: (chromosome, start_bp, end_bp) in 1-based inclusive GRCh38 coordinates. For LocusCompare-style coloc inspection centre on the lead variant ± 500 kb; for "what does this gene's cis-window look like" queries centre on the gene TSS ± 1 Mb (the catalogue's full cis-window for that gene).
3. Tabix range fetch: the skill performs a single byte-range request against <QTD>.all.tsv.gz on the EBI FTP. The REST API at /api/v2/datasets/{id}/associations is not used for region fetches (see Gotcha #1).
4. Filter by molecular_trait_id (recommended for ge datasets): the harmonised .all.tsv.gz for ge quant_method bundles every gene's variants together. Pass the target ENSG to filter; without it you get every gene's rows in the window.
5. Write outputs to --output <dir>/: a flat variants.tsv (effect-allele-aligned, GRCh38, ALT-effect β), a manifest.yaml with provenance (study_label, tissue_label, quant_method + human-readable label, n_variants, source URL, fetched-at UTC timestamp), and a report.md human-readable summary.

CLI Reference

# Standard usage with a config file
python skills/eqtl-catalogue-region-fetch/eqtl_catalogue_region_fetch.py \
    --input <config.json> --output <output_dir>

# Bundled demo (SORT1 GTEx minor salivary gland; canonical 1p13.3 LDL/CHD locus)
python skills/eqtl-catalogue-region-fetch/eqtl_catalogue_region_fetch.py \
    --demo sort1_gtex_minor_salivary_gland --output /tmp/sort1_demo

# List the bundled demos (3 biology cases shipped: SORT1, IL6R, IRF5)
python skills/eqtl-catalogue-region-fetch/eqtl_catalogue_region_fetch.py --list-demos

# Via ClawBio runner
python clawbio.py run eqtl-region --input <config.json>
python clawbio.py run eqtl-region --demo

Config schema (JSON or YAML):

{
  "dataset_id": "QTD000266",
  "molecular_trait_id": "ENSG00000134243",
  "chromosome": "1",
  "start_bp": 108774968,
  "end_bp": 109774968
}

Example Output

Running --demo sort1_gtex_minor_salivary_gland:

info: using bundled demo sort1_gtex_minor_salivary_gland.json
eqtl-catalogue-region-fetch: 2833 variants -> /tmp/sort1_demo/variants.tsv
  source: GTEx | minor salivary gland | gene expression

<output_dir>/manifest.yaml:

skill: eqtl-catalogue-region-fetch
version: 0.1.0
dataset_id: QTD000276
molecular_trait_id: ENSG00000134243
region:
  chromosome: '1'
  start_bp: 108774968
  end_bp: 109774968
n_variants: 2833
release:
  study_label: GTEx
  tissue_label: minor salivary gland
  condition_label: naive
  sample_group: minor_salivary_gland
  quant_method: ge
  quant_method_label: gene expression
  dataset_release: ''
  fetched_at_utc: '2026-05-06T15:50:33Z'
outputs:
  variants_tsv: variants.tsv

<output_dir>/variants.tsv (first three rows shown):

variant_id              chromosome  position_bp  allele_a  allele_b  beta        se        p          maf       molecular_trait_id  study_id
1_108774974_TCTAC_T     1           108774974    TCTAC     T         -0.119495   0.138769  0.390778   0.170139  ENSG00000134243     QTD000276
1_108775337_C_T         1           108775337    C         T          0.0777385  0.112256  0.489859   0.3125    ENSG00000134243     QTD000276
1_108775606_G_T         1           108775606    G         T         -0.166496   0.212651  0.435087   0.0729167 ENSG00000134243     QTD000276

<output_dir>/report.md:

# eqtl-catalogue-region-fetch report

- **Dataset:** `QTD000276`
- **Source:** GTEx | minor salivary gland | quantification = gene expression
- **Region:** chr1:108,774,968-109,774,968
- **Molecular trait:** ENSG00000134243
- **Variants returned:** 2833
- **Output TSV:** variants.tsv

Gotchas

1. Use FTP tabix, not the REST API, for regional fetches. The eQTL Catalogue v2 REST API at /api/v2/datasets/{id}/associations silently truncates regional fetches to one side of TSS and ignores pos_min / pos_max query parameters. This skill fetches via tabix on the canonical FTP .all.tsv.gz, which serves the full strand-aware cis-window correctly. Do NOT swap the fetcher to REST.

2. Cis-window is ±1 Mb of strand-aware TSS in genomic coordinates. The upstream pipeline computes cis-eQTLs only for variants within ±1 Mb of the gene's transcription start site. For + strand genes TSS = gene.start (lower coord). For − strand genes TSS = gene.end (higher coord). When querying a window in genomic coords that extends beyond ±1 Mb of TSS, expect zero rows on the far side. This is correct biology, not a bug.

3. molecular_trait_id filter is required for ge eQTL files. The harmonised ge .all.tsv.gz bundles every gene's variant rows together. Querying a chromosomal region without a gene filter returns variants for all genes in that region (potentially thousands of rows per variant). Always pass the target Ensembl gene ID. Other quant methods (tx, txrev, exon, leafcutter) have similar bundling behavior on molecular_trait_id (transcript / intron / exon ID).

4. β is reported on the ALT allele. Do NOT compare effect sizes across datasets without explicit allele harmonisation. The skill preserves ref / alt columns; downstream tools (e.g., TwoSampleMR harmonise_data) flip signs when alleles are swapped. Cross-dataset comparisons (eQTL β vs GWAS β at the same variant) without harmonisation can silently invert direction.

5. Quantification methods are not interchangeable.

   • ge (gene expression): gene-level, the most common eQTL definition
   • tx (transcript): per-isoform abundance
   • txrev (transcript usage): proportional, not abundance
   • exon (exon expression): per-exon read count
   • leafcutter (splice junction): splice-QTL on intron excision ratio

   These represent distinct biology. A txrev row is NOT a ge eQTL. The skill's manifest carries the raw quant_method code AND a human-readable label per the CLAUDE.md expansion rule.

Safety

Not for clinical decisions. This skill returns research-grade summary statistics from public databases. Do not use the output for direct clinical decision-making, diagnosis, or treatment selection without independent validation by a qualified clinician.

Effect estimates may not generalise across populations. The ancestry of the source study is recorded in the dataset metadata (sample_group, population fields where present). Effect sizes from a single-ancestry study should not be assumed to apply to other ancestries without appropriate harmonisation and trans-ancestry validation.

Agent Boundary

The skill returns harmonised summary statistics (β, SE, p-value) for variants in a chromosomal window from one (study × tissue × quant_method) dataset. The agent should:

• Use the output as input to colocalisation, fine-mapping, or Mendelian randomisation tooling. These are the appropriate downstream methods for inferring causal effects.
• NOT make causal-effect claims directly from a single eQTL p-value. A low p-value at a variant means statistical association, not causation. Causal interpretation requires colocalisation or MR analysis with proper instrumental-variable assumptions.
• NOT cherry-pick variants by p-value alone. Statistical inference requires the full credible set / window context.
• NOT compare effect sizes across datasets without harmonising effect alleles. The skill normalises within one dataset; cross-dataset comparison requires a harmonisation step (e.g., TwoSampleMR harmonise_data).
• Surface tissue, quant_method, and sample size in the user-facing reply alongside any β / p-value the agent quotes. The same variant in IAV-stimulated monocytes (Quach 2016, N=198) and in resting monocytes (BLUEPRINT, N=191) is a different biological measurement, even though the genomic position is identical. Per the user-friendly enum-expansion rule (CLAUDE.md), expand all three fields when reporting: quantification = gene expression (ge); tissue = monocyte (UBERON:0000235); n_samples = 198.
• NOT silently swap tissues or quantification methods. If the user asked for monocyte / ge and the dataset is monocyte / txrev, the agent must say so explicitly and ask whether to proceed.

Citations

• Kerimov et al. (2021). A compendium of uniformly processed human gene expression and splicing quantitative trait loci. Nat Genet 53, 1290-1299. doi:10.1038/s41588-021-00924-w
• Per-dataset citation list at https://www.ebi.ac.uk/eqtl/Studies/.