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

pip install requests pandas matplotlib

Quick Start

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.

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.

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        : {canon.get('gene_symbol')}")
print(f"Consequence : {canon.get('major_consequence')}  |  LoF: {canon.get('lof')}")
g = v["genome"]
print(f"Genome AF   : {g['af']:.2e}  (AC={g['ac']}, AN={g['an']})")
print(f"FAF95 popmax: {g['faf95']['popmax']:.2e} in {g['faf95']['popmax_population']}")

Query 3: Population Frequencies

Retrieve allele frequency broken down by ancestry group for a specific variant.

import pandas as pd

POPULATION_FREQ_QUERY = """
query PopFreqs($variantId: String!, $dataset: DatasetId!) {
  variant(variantId: $variantId, dataset: $dataset) {
    variant_id
    genome {
      populations {
        id
        ac
        an
        homozygote_count
      }
    }
  }
}
"""

ANCESTRY_LABELS = {
    "afr": "African/African American",
    "amr": "Admixed American",
    "eas": "East Asian",
    "fin": "Finnish",
    "nfe": "Non-Finnish European",
    "sas": "South Asian",
    "asj": "Ashkenazi Jewish",
    "mid": "Middle Eastern",
    "oth": "Other",
}

data = gnomad_query(POPULATION_FREQ_QUERY, {
    "variantId": "1-55039974-G-T",
    "dataset": "gnomad_r4"
})
pops = data["variant"]["genome"]["populations"]

# VariantPopulation no longer exposes `af` directly — compute from ac/an.
main_pops = [p for p in pops if p["id"] in ANCESTRY_LABELS and p["an"] > 0]
df = pd.DataFrame(main_pops)
df["af"] = df["ac"] / df["an"]
df["label"] = df["id"].map(ANCESTRY_LABELS)
df = df.sort_values("af", ascending=False)
print(df[["label", "ac", "an", "af", "homozygote_count"]].to_string(index=False))

Query 4: Coverage Query

Retrieve per-base read depth coverage for a gene region to assess data completeness.

COVERAGE_QUERY = """
query Coverage($chrom: String!, $start: Int!, $stop: Int!,
               $reference_genome: ReferenceGenomeId!, $dataset: DatasetId!) {
  region(chrom: $chrom, start: $start, stop: $stop, reference_genome: $reference_genome) {
    coverage(dataset: $dataset) {
      exome  { mean median over_1 over_10 over_20 over