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gnomad-database

The gnomAD Database skill queries the Genome Aggregation Database via GraphQL to retrieve population variant frequencies, ancestry-stratified allele counts, gene constraint metrics (pLI, LOEUF), and sequencing coverage for over 730,000 individuals. Use it to determine whether candidate variants are rare enough for clinical relevance, filter variants by population frequency before interpretation, assess gene tolerance to loss-of-function variants, or evaluate coverage at specific genomic regions; do not use it for clinical pathogenicity classification or GWAS-level association studies, which require clinvar-database and gwas-database respectively.

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git clone --depth 1 https://github.com/jaechang-hits/SciAgent-Skills /tmp/gnomad-database && cp -r /tmp/gnomad-database/skills/genomics-bioinformatics/databases/gnomad-database ~/.claude/skills/gnomad-database
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SKILL.md

# 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 frequency evidence but does not classify pathogenicity
- For GWAS associations at the study level use `gwas-database`; gnomAD is for population frequency lookups

## Prerequisites

- **Python packages**: `requests`, `pandas`, `matplotlib`
- **Data requirements**: gene symbols (e.g., `BRCA1`), variant IDs (`1-69511-A-G` format, or rsIDs)
- **Environment**: internet connection; no API key required
- **Rate limits**: no official published limits; use `time.sleep(0.5)` between requests for polite access; avoid bursts over 10 requests/second

```bash
pip install requests pandas matplotlib
```

## Quick Start

```python
import requests
import time

GNOMAD_API = "https://gnomad.broadinstitute.org/api"

def gnomad_query(query: str, variables: dict = None) -> dict:
    """Execute a gnomAD GraphQL query and return the data payload."""
    payload = {"query": query, "variables": variables or {}}
    r = requests.post(GNOMAD_API, json=payload, timeout=30)
    r.raise_for_status()
    result = r.json()
    if "errors" in result:
        raise ValueError(f"GraphQL errors: {result['errors']}")
    return result["data"]

# Quick check: get pLI / LOEUF for BRCA1
# GnomadConstraint fields are FLAT (no nested `lof { oe_ci { upper } }` type).
# `pli` is the current field; `pLI` is preserved as a deprecated alias.
query = """
query GeneConstraint($gene_symbol: String!, $reference_genome: ReferenceGenomeId!) {
  gene(gene_symbol: $gene_symbol, reference_genome: $reference_genome) {
    gnomad_constraint { pli oe_lof_upper }
  }
}
"""
data = gnomad_query(query, {"gene_symbol": "BRCA1", "reference_genome": "GRCh38"})
constraint = data["gene"]["gnomad_constraint"]
print(f"BRCA1 pLI:   {constraint['pli']:.3e}")        # ~5.5e-38 (very high LoF-intolerant)
print(f"BRCA1 LOEUF: {constraint['oe_lof_upper']:.3f}") # 0.928
```

## Core API

### Query 1: Gene Variant Query

Fetch all variants in a gene with population allele frequencies. Returns a list of variants with their genome-level frequencies.

```python
import requests, time

GNOMAD_API = "https://gnomad.broadinstitute.org/api"

def gnomad_query(query, variables=None):
    r = requests.post(GNOMAD_API, json={"query": query, "variables": variables or {}}, timeout=30)
    r.raise_for_status()
    result = r.json()
    if "errors" in result:
        raise ValueError(f"GraphQL errors: {result['errors']}")
    return result["data"]

GENE_VARIANTS_QUERY = """
query GeneVariants($gene_symbol: String!, $reference_genome: ReferenceGenomeId!, $dataset: DatasetId!) {
  gene(gene_symbol: $gene_symbol, reference_genome: $reference_genome) {
    gene_id
    symbol
    variants(dataset: $dataset) {
      variant_id
      rsids
      chrom
      pos
      ref
      alt
      consequence
      lof
      genome {
        an
        ac
        af
        faf95 { popmax popmax_population }
      }
    }
  }
}
"""

data = gnomad_query(GENE_VARIANTS_QUERY, {
    "gene_symbol": "PCSK9",
    "reference_genome": "GRCh38",
    "dataset": "gnomad_r4"
})
variants = data["gene"]["variants"]
print(f"Gene: {data['gene']['symbol']} ({data['gene']['gene_id']})")
print(f"Total variants: {len(variants)}")
# Filter to rare variants (AF < 0.001)
rare = [v for v in variants if v["genome"] and v["genome"]["af"] is not None and v["genome"]["af"] < 0.001]
print(f"Rare variants (AF < 0.1%): {len(rare)}")
for v in rare[:3]:
    print(f"  {v['variant_id']} | {v['consequence']} | AF={v['genome']['af']:.2e}")
```

### Query 2: Variant Lookup

Fetch detailed information for a single variant by its gnomAD variant ID (CHROM-POS-REF-ALT format) or search by rsID.

```python
VARIANT_QUERY = """
query VariantDetails($variantId: String!, $dataset: DatasetId!) {
  variant(variantId: $variantId, dataset: $dataset) {
    variant_id
    rsids
    chrom
    pos
    ref
    alt
    transcript_consequences {
      gene_symbol
      transcript_id
      is_canonical
      major_consequence
      lof
      lof_filter
      lof_flags
    }
    genome {
      an
      ac
      af
      faf95 { popmax popmax_population }
      populations { id ac an homozygote_count }
    }
  }
}
"""

# Query.variant() arg is `variantId` (camelCase). The top-level deprecated
# `consequence`/`lof`/`lof_filter`/`lof_flags` fields on VariantDetails were
# removed — read them from `transcript_consequences` (plural list; pick the
# canonical transcript with is_canonical=True).
data = gnomad_query(VARIANT_QUERY, {
    "variantId": "1-55039974-G-T",    # PCSK9 p.Tyr142Ter (LoF)
    "dataset": "gnomad_r4"
})
v = data["variant"]
canon = next((t for t in (v.get("transcript_consequences") or []) if t.get("is_canonical")),
             (v.get("transcript_consequences") or [{}])[0])
print(f"Variant     : {v['variant_id']}")
print(f"rsIDs       : {v['rsids']}")
print(f"Gene
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