# Eqtl Catalogue Region Fetch > | - **Type:** Skill - **Install:** `agentstack add skill-clawbio-clawbio-eqtl-catalogue-region-fetch` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [ClawBio](https://agentstack.voostack.com/s/clawbio) - **Installs:** 0 - **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [ClawBio](https://github.com/ClawBio) - **Source:** https://github.com/ClawBio/ClawBio/tree/main/skills/eqtl-catalogue-region-fetch - **Website:** https://clawbio.github.io/ClawBio/ ## Install ```sh agentstack add skill-clawbio-clawbio-eqtl-catalogue-region-fetch ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # 🧬 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///.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](https://www.ebi.ac.uk/eqtl/Studies/). For Open Targets `studyId` slugs of the form `___` (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 `.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 /`: 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 ```bash # Standard usage with a config file python skills/eqtl-catalogue-region-fetch/eqtl_catalogue_region_fetch.py \ --input --output # 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 python clawbio.py run eqtl-region --demo ``` Config schema (JSON or YAML): ```json { "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 ``` `/manifest.yaml`: ```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 ``` `/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 ``` `/report.md`: ```markdown # 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 . ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [ClawBio](https://github.com/ClawBio) - **Source:** [ClawBio/ClawBio](https://github.com/ClawBio/ClawBio) - **License:** MIT - **Homepage:** https://clawbio.github.io/ClawBio/ Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-clawbio-clawbio-eqtl-catalogue-region-fetch - Seller: https://agentstack.voostack.com/s/clawbio - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. 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