gnomad-database
gnomAD v4 population variant frequencies via GraphQL API. Allele counts and frequencies stratified by ancestry (AFR, AMR, EAS, NFE, SAS, FIN, ASJ, MID), gene-level constraint (pLI, LOEUF, missense z), and coverage. Identify rare or constrained variants. For clinical pathogenicity use clinvar-database; for GWAS use gwas-database.
npx skills add BioTender-max/awesome-bio-agent-skills --skill gnomad-database --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Gnomad-database provides access to gnomAD v4 data, including population allele frequencies stratified by ancestry, gene-level constraint metrics (pLI, LOEUF, missense z), and coverage information. It supports identifying rare variants and constrained genes, with guidance to use clinvar-database for clinical pathogenicity and gwas-database for GWAS associations.
How it works
- It exposes a GraphQL API at the gnomad endpoint to query gene-level variants, variant details, population frequencies, coverage, and gene constraint scores.
- Core queries include:
- Gene Variant Query: fetch variants in a gene with genome-wide frequencies and annotations.
- Variant Lookup: fetch detailed information for a single variant by variantId and dataset.
- Population Frequencies: retrieve per-ancestry population frequencies for a variant, computing allele frequency as ac/an when needed.
- Coverage Query: obtain per-base read depth coverage for a specified chromosomal region (exome/genome).
- Gene Constraint: retrieve pLI, LOEUF, and missense z-score for given genes.
- Region Variant Search: fetch all variants within a chromosomal region and summarize by consequence, including high-confidence LoF filtering.
- Examples show how to construct GraphQL queries, call the API, and process results (e.g., filtering rare variants by AF, computing AF from ac/an).
When to use it
- When checking if a candidate variant is rare across populations (AF < 0.1% in all populations).
- When retrieving frequencies stratified by ancestry for a given variant.
- When identifying rare loss-of-function variants in a gene for burden testing or prioritization.
- When obtaining gene constraint metrics to assess tolerance to LoF variants.
- When evaluating read depth coverage for a region to distinguish low frequency due to coverage.
- When filtering a VCF by population frequency before clinical interpretation.
- For clinical pathogenicity use clinvar-database; for GWAS use gwas-database.
What it can touch
- Tools: claude-code
- Network access to the GraphQL API at https://gnomad.broadinstitute.org/api
- It executes Python code that calls the API, parses JSON responses, and prints or processes values.
Caveats
- The skill notes that gnomAD provides frequency evidence but does not classify pathogenicity.
- Some fields and structures in the API responses may vary between queries (e.g., canonical transcript selection in variant details).
- Rate limits are not officially published; a polite-access sleep (e.g., time.sleep(0.5)) is suggested between requests.
- The GraphQL schema evolves; users should adapt to changes in fields like transcript_consequences vs. top-level consequence.
# gnomAD Database ## Overview The Genome Aggregation Database (gnomAD) is a resource of aggregated exome and genome sequencing data from 730,000+ individuals. It provides population variant frequencies stratified by 9 ancestry groups, gene-level constraint scores (pLI, LOEUF), and read coverage information. Access is free via a GraphQL API at `https://gnomad.broadinstitute.org/api` — no authentication required, no official SDK. ## When to Use - Checking whether a candidate variant is rare enough to be clinically relevant (AF < 0.1% in all populations) - Retrieving allele frequencies stratified by ancestry group (AFR, AMR, EAS, NFE, SAS, FIN, ASJ, MID) for a variant - Identifying all rare loss-of-function variants in a gene for burden testing or candidate prioritization - Getting gene constraint metrics (pLI, LOEUF) to assess tolerance to loss-of-function variants - Checking read depth coverage for a region to evaluate if low variant frequency reflects low sequencing coverage - Filtering a VCF by population frequency — query gnomAD AF to discard common variants before clinical interpretation - For clinical pathogenicity classifications use `clinvar-database`; gnomAD provides frequen
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Query 1: Gene Variant Query
- Query 2: Variant Lookup
- Query 3: Population Frequencies
- Query 4: Coverage Query
- Query 5: Gene Constraint
- Query 6: Variant Search by Region
- Key Concepts
- gnomAD Data Model
- Ancestry Groups
pip install requests pandas matplotlib
What does the gnomad-database skill do?
gnomAD v4 population variant frequencies via GraphQL API. Allele counts and frequencies stratified by ancestry (AFR, AMR, EAS, NFE, SAS, FIN, ASJ, MID), gene-level constraint (pLI, LOEUF, missense z), and coverage. Identify rare or constrained variants. For clinical pathogenicity use clinvar-database; for GWAS use gwas-database.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill gnomad-database --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.