gnomad-database

gnomAD Database

Overview

gnomAD is the largest publicly available collection of human genetic variation. gnomAD v4 contains exome sequences from 730,947 individuals and genome sequences from 76,215 individuals across diverse ancestries.

Key resources:

• Browser: https://gnomad.broadinstitute.org/
• GraphQL API: https://gnomad.broadinstitute.org/api
• Downloads: https://gnomad.broadinstitute.org/downloads

When to Use This Skill

• Variant frequency lookup: Checking if a variant is rare, common, or absent
• Pathogenicity assessment: Filtering benign common variants (ACMG BA1/BS1/PM2)
• Loss-of-function intolerance: pLI and LOEUF scores for gene constraint
• Population-stratified frequencies: Comparing allele frequencies across ancestries
• Constraint analysis: Identifying genes depleted of missense or LoF variation

Supporting Files

• graphql_queries.md - Complete GraphQL query templates, population IDs, LoF annotation fields, in silico predictor IDs, Python helper with retry logic
• variant_interpretation.md - ACMG/AMP criteria thresholds, LoF assessment (LOFTEE), homozygous observations, in silico predictor score ranges, ancestry-specific considerations

GraphQL API

Endpoint: POST https://gnomad.broadinstitute.org/api

Datasets: gnomad_r4 (v4 exomes, GRCh38), gnomad_r4_genomes, gnomad_r3 (GRCh38), gnomad_r2_1 (GRCh37)

Query Variants by Gene

import requests

def query_gnomad_gene(gene_symbol, dataset="gnomad_r4", reference_genome="GRCh38"):
    """Fetch variants in a gene from gnomAD."""
    url = "https://gnomad.broadinstitute.org/api"
    query = """
    query GeneVariants($gene_symbol: String!, $dataset: DatasetId!, $reference_genome: ReferenceGenomeId!) {
      gene(gene_symbol: $gene_symbol, reference_genome: $reference_genome) {
        gene_id
        gene_symbol
        variants(dataset: $dataset) {
          variant_id
          pos
          ref
          alt
          consequence
          genome { af ac an ac_hom populations { id ac an af } }
          exome { af ac an ac_hom }
          lof
          lof_flags
          lof_filter
        }
      }
    }
    """
    variables = {"gene_symbol": gene_symbol, "dataset": dataset, "reference_genome": reference_genome}
    response = requests.post(url, json={"query": query, "variables": variables})
    return response.json()

# Filter to rare PTVs
result = query_gnomad_gene("BRCA1")
variants = result["data"]["gene"]["variants"]
rare_ptvs = [v for v in variants
    if v.get("lof") == "HC"
    and v.get("genome", {}).get("af", 1) < 0.001]

Query a Specific Variant

def query_gnomad_variant(variant_id, dataset="gnomad_r4"):
    """Fetch details for a variant (e.g., '17-43094692-G-A')."""
    url = "https://gnomad.broadinstitute.org/api"
    query = """
    query VariantDetails($variantId: String!, $dataset: DatasetId!) {
      variant(variantId: $variantId, dataset: $dataset) {
        variant_id
        chrom pos ref alt consequence lof rsids
        genome { af ac an ac_hom populations { id ac an af } }
        exome { af ac an ac_hom populations { id ac an af } }
        in_silico_predictors { id value flags }
        clinvar_variation_id
      }
    }
    """
    response = requests.post(url, json={"query": query, "variables": {"variantId": variant_id, "dataset": dataset}})
    return response.json()

Gene Constraint Scores

def query_gnomad_constraint(gene_symbol, reference_genome="GRCh38"):
    """Fetch constraint scores for a gene."""
    url = "https://gnomad.broadinstitute.org/api"
    query = """
    query GeneConstraint($gene_symbol: String!, $reference_genome: ReferenceGenomeId!) {
      gene(gene_symbol: $gene_symbol, reference_genome: $reference_genome) {
        gene_id gene_symbol
        gnomad_constraint {
          exp_lof exp_mis exp_syn obs_lof obs_mis obs_syn
          oe_lof oe_mis oe_syn oe_lof_lower oe_lof_upper
          lof_z mis_z syn_z pLI
        }
      }
    }
    """
    response = requests.post(url, json={"query": query, "variables": {"gene_symbol": gene_symbol, "reference_genome": reference_genome}})
    return response.json()

Constraint score interpretation:

Score	Range	Meaning
pLI	0-1	Probability of LoF intolerance; >0.9 = highly intolerant
LOEUF	0-inf	LoF observed/expected upper bound; <0.35 = constrained
oe_lof	0-inf	Observed/expected ratio for LoF variants
mis_z	-inf to inf	Missense constraint z-score; >3.09 = constrained
syn_z	-inf to inf	Synonymous z-score (control; should be near 0)

LOEUF is preferred over pLI (less sensitive to sample size).

Population Frequency Analysis

import pandas as pd

def get_population_frequencies(variant_id, dataset="gnomad_r4"):
    """Extract per-population allele frequencies."""
    url = "https://gnomad.broadinstitute.org/api"
    query = """
    query PopFreqs($variantId: String!, $dataset: DatasetId!) {
      variant(variantId: $variantId, dataset: $dataset) {
        variant_id
        genome { populations { id ac an af ac_hom } }
      }
    }
    """
    response = requests.post(url, json={"query": query, "variables": {"variantId": variant_id, "dataset": dataset}})
    populations = response.json()["data"]["variant"]["genome"]["populations"]
    df = pd.DataFrame(populations)
    return df[df["an"] > 0].sort_values("af", ascending=False)

Population IDs: afr (African), ami (Amish), amr (Admixed American), asj (Ashkenazi Jewish), eas (East Asian), fin (Finnish), mid (Middle Eastern), nfe (Non-Finnish European), sas (South Asian)

Key Workflows

Variant Pathogenicity Assessment

1. Check population frequency (AF < 1% recessive, < 0.1% dominant)
2. Check ancestry-specific frequencies (variant rare overall may be common in one population)
3. Assess LoF confidence: lof field HC = high-confidence, LC = low-confidence
4. Apply ACMG: BA1 (AF > 5%), BS1 (AF > prevalence), PM2 (absent/very rare)

Gene Prioritization in Rare Disease

1. Query constraint scores for candidate genes
2. Filter pLI > 0.9 or LOEUF < 0.35
3. Cross-reference with observed LoF variants
4. Integrate with ClinVar

Best Practices

• Use gnomAD v4 (gnomad_r4) by default; v2 only for GRCh37 compatibility
• Handle null responses: absence in gnomAD is informative but not conclusive
• Distinguish exome vs genome data: genome has more uniform coverage
• Rate limit GraphQL queries: add delays between requests
• Check ac_hom for recessive disease analysis

Attribution

Adapted from K-Dense-AI/claude-scientific-skills (CC0-1.0). Original skill by Kuan-lin Huang.