Query gnomAD (Genome Aggregation Database) for population allele frequencies, variant constraint scores (pLI, LOEUF), and loss-of-function intolerance. Essential for variant pathogenicity interpretation, rare disease genetics, and identifying loss-of-function intolerant genes. Part of the AlterLab Academic Skills suite.
Scanned 5/27/2026
Install via CLI
Skills Directoryopenskills install AlterLab-IEU/AlterLab-Academic-Skills---
name: alterlab-gnomad
description: Query gnomAD (Genome Aggregation Database) for population allele frequencies, variant constraint scores (pLI, LOEUF), and loss-of-function intolerance. Essential for variant pathogenicity interpretation, rare disease genetics, and identifying loss-of-function intolerant genes. Part of the AlterLab Academic Skills suite.
license: CC0-1.0
metadata:
skill-author: AlterLab
version: "1.0.0"
---
# gnomAD Database
## Overview
The Genome Aggregation Database (gnomAD) is the largest publicly available collection of human genetic variation, aggregated from large-scale sequencing projects. gnomAD v4 contains exome sequences from 730,947 individuals and genome sequences from 76,215 individuals across diverse ancestries. It provides population allele frequencies, variant consequence annotations, and gene-level constraint metrics that are essential for interpreting the clinical significance of genetic variants.
**Key resources:**
- gnomAD browser: https://gnomad.broadinstitute.org/
- GraphQL API: https://gnomad.broadinstitute.org/api
- Data downloads: https://gnomad.broadinstitute.org/downloads
- Documentation: https://gnomad.broadinstitute.org/help
## When to Use This Skill
Use gnomAD when:
- **Variant frequency lookup**: Checking if a variant is rare, common, or absent in the general population
- **Pathogenicity assessment**: Rare variants (MAF < 1%) are candidates for disease causation; gnomAD helps filter benign common variants
- **Loss-of-function intolerance**: Using pLI and LOEUF scores to assess whether a gene tolerates protein-truncating variants
- **Population-stratified frequencies**: Comparing allele frequencies across ancestries (African/African American, Admixed American, Ashkenazi Jewish, East Asian, Finnish, Middle Eastern, Non-Finnish European, South Asian)
- **ClinVar/ACMG variant classification**: gnomAD frequency data feeds into BA1/BS1 evidence codes for variant classification
- **Constraint analysis**: Identifying genes depleted of missense or loss-of-function variation (z-scores, pLI, LOEUF)
## Core Capabilities
### 1. gnomAD GraphQL API
gnomAD uses a GraphQL API accessible at `https://gnomad.broadinstitute.org/api`. Most queries fetch variants by gene or specific genomic position.
**Datasets available:**
- `gnomad_r4` — gnomAD v4 exomes (recommended default, GRCh38)
- `gnomad_r4_genomes` — gnomAD v4 genomes (GRCh38)
- `gnomad_r3` — gnomAD v3 genomes (GRCh38)
- `gnomad_r2_1` — gnomAD v2 exomes (GRCh37)
**Reference genomes:**
- `GRCh38` — default for v3/v4
- `GRCh37` — for v2
### 2. Querying Variants by Gene
```python
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: $dataset) {
variant_id
pos
ref
alt
consequence
genome {
af
ac
an
ac_hom
populations {
id
ac
an
af
}
}
exome {
af
ac
an
ac_hom
}
lof
lof_flags
lof_filter
}
}
}
"""
variables = {
"gene_symbol": gene_symbol,
"dataset": dataset,
"reference_genome": reference_genome
}
response = requests.post(url, json={"query": query, "variables": variables})
return response.json()
# Example
result = query_gnomad_gene("BRCA1")
gene_data = result["data"]["gene"]
variants = gene_data["variants"]
# Filter to rare PTVs
rare_ptvs = [
v for v in variants
if v.get("lof") == "LC" or v.get("consequence") in ["stop_gained", "frameshift_variant"]
and v.get("genome", {}).get("af", 1) < 0.001
]
print(f"Found {len(rare_ptvs)} rare PTVs in {gene_data['gene_symbol']}")
```
### 3. Querying a Specific Variant
```python
import requests
def query_gnomad_variant(variant_id, dataset="gnomad_r4"):
"""Fetch details for a specific variant (e.g., '1-55516888-G-GA')."""
url = "https://gnomad.broadinstitute.org/api"
query = """
query VariantDetails($variantId: String!, $dataset: DatasetId!) {
variant(variantId: $variantId, dataset: $dataset) {
variant_id
chrom
pos
ref
alt
genome {
af
ac
an
ac_hom
populations {
id
ac
an
af
}
}
exome {
af
ac
an
ac_hom
populations {
id
ac
an
af
}
}
consequence
lof
rsids
in_silico_predictors {
id
value
flags
}
clinvar_variation_id
}
}
"""
response = requests.post(
url,
json={"query": query, "variables": {"variantId": variant_id, "dataset": dataset}}
)
return response.json()
# Example: query a specific variant
result = query_gnomad_variant("17-43094692-G-A") # BRCA1 missense
variant = result["data"]["variant"]
if variant:
genome_af = variant.get("genome", {}).get("af", "N/A")
exome_af = variant.get("exome", {}).get("af", "N/A")
print(f"Variant: {variant['variant_id']}")
print(f" Consequence: {variant['consequence']}")
print(f" Genome AF: {genome_af}")
print(f" Exome AF: {exome_af}")
print(f" LoF: {variant.get('lof')}")
```
### 4. Gene Constraint Scores
gnomAD constraint scores assess how tolerant a gene is to variation relative to expectation:
```python
import requests
def query_gnomad_constraint(gene_symbol, reference_genome="GRCh38"):
"""Fetch constraint scores for a gene."""
url = "https://gnomad.broadinstitute.org/api"
query = """
query GeneConstraint($gene_symbol: String!, $reference_genome: ReferenceGenomeId!) {
gene(gene_symbol: $gene_symbol, reference_genome: $reference_genome) {
gene_id
gene_symbol
gnomad_constraint {
exp_lof
exp_mis
exp_syn
obs_lof
obs_mis
obs_syn
oe_lof
oe_mis
oe_syn
oe_lof_lower
oe_lof_upper
lof_z
mis_z
syn_z
pLI
}
}
}
"""
response = requests.post(
url,
json={"query": query, "variables": {"gene_symbol": gene_symbol, "reference_genome": reference_genome}}
)
return response.json()
# Example
result = query_gnomad_constraint("KCNQ2")
gene = result["data"]["gene"]
constraint = gene["gnomad_constraint"]
print(f"Gene: {gene['gene_symbol']}")
print(f" pLI: {constraint['pLI']:.3f} (>0.9 = LoF intolerant)")
print(f" LOEUF: {constraint['oe_lof_upper']:.3f} (<0.35 = highly constrained)")
print(f" Obs/Exp LoF: {constraint['oe_lof']:.3f}")
print(f" Missense Z: {constraint['mis_z']:.3f}")
```
**Constraint score interpretation:**
| Score | Range | Meaning |
|-------|-------|---------|
| `pLI` | 0–1 | Probability of LoF intolerance; >0.9 = highly intolerant |
| `LOEUF` | 0–∞ | LoF observed/expected upper bound; <0.35 = constrained |
| `oe_lof` | 0–∞ | Observed/expected ratio for LoF variants |
| `mis_z` | −∞ to ∞ | Missense constraint z-score; >3.09 = constrained |
| `syn_z` | −∞ to ∞ | Synonymous z-score (control; should be near 0) |
### 5. Population Frequency Analysis
```python
import requests
import pandas as pd
def get_population_frequencies(variant_id, dataset="gnomad_r4"):
"""Extract per-population allele frequencies for a variant."""
url = "https://gnomad.broadinstitute.org/api"
query = """
query PopFreqs($variantId: String!, $dataset: DatasetId!) {
variant(variantId: $variantId, dataset: $dataset) {
variant_id
genome {
populations {
id
ac
an
af
ac_hom
}
}
}
}
"""
response = requests.post(
url,
json={"query": query, "variables": {"variantId": variant_id, "dataset": dataset}}
)
data = response.json()
populations = data["data"]["variant"]["genome"]["populations"]
df = pd.DataFrame(populations)
df = df[df["an"] > 0].copy()
df["af"] = df["ac"] / df["an"]
df = df.sort_values("af", ascending=False)
return df
# Population IDs in gnomAD v4:
# afr = African/African American
# ami = Amish
# amr = Admixed American
# asj = Ashkenazi Jewish
# eas = East Asian
# fin = Finnish
# mid = Middle Eastern
# nfe = Non-Finnish European
# sas = South Asian
# remaining = Other
```
### 6. Structural Variants (gnomAD-SV)
gnomAD also contains a structural variant dataset:
```python
import requests
def query_gnomad_sv(gene_symbol):
"""Query structural variants overlapping a gene."""
url = "https://gnomad.broadinstitute.org/api"
query = """
query SVsByGene($gene_symbol: String!) {
gene(gene_symbol: $gene_symbol, reference_genome: GRCh38) {
structural_variants {
variant_id
type
chrom
pos
end
af
ac
an
}
}
}
"""
response = requests.post(url, json={"query": query, "variables": {"gene_symbol": gene_symbol}})
return response.json()
```
## Query Workflows
### Workflow 1: Variant Pathogenicity Assessment
1. **Check population frequency** — Is the variant rare enough to be pathogenic?
- Use gnomAD AF < 1% for recessive, < 0.1% for dominant conditions
- Check ancestry-specific frequencies (a variant rare overall may be common in one population)
2. **Assess functional impact** — LoF variants have highest prior probability
- Check `lof` field: `HC` = high-confidence LoF, `LC` = low-confidence
- Check `lof_flags` for issues like "NAGNAG_SITE", "PHYLOCSF_WEAK"
3. **Apply ACMG criteria:**
- BA1: AF > 5% → Benign Stand-Alone
- BS1: AF > disease prevalence threshold → Benign Supporting
- PM2: Absent or very rare in gnomAD → Pathogenic Moderate
### Workflow 2: Gene Prioritization in Rare Disease
1. Query constraint scores for candidate genes
2. Filter for pLI > 0.9 (haploinsufficient) or LOEUF < 0.35
3. Cross-reference with observed LoF variants in the gene
4. Integrate with ClinVar and disease databases
### Workflow 3: Population Genetics Research
1. Identify variant of interest from GWAS or clinical data
2. Query per-population frequencies
3. Compare frequency differences across ancestries
4. Test for enrichment in specific founder populations
## Best Practices
- **Use gnomAD v4 (gnomad_r4)** for the most current data; use v2 (gnomad_r2_1) only for GRCh37 compatibility
- **Handle null responses**: Variants not observed in gnomAD are not necessarily pathogenic — absence is informative
- **Distinguish exome vs. genome data**: Genome data has more uniform coverage; exome data is larger but may have coverage gaps
- **Rate limit GraphQL queries**: Add delays between requests; batch queries when possible
- **Homozygous counts** (`ac_hom`) are relevant for recessive disease analysis
- **LOEUF is preferred over pLI** for gene constraint (less sensitive to sample size)
## Data Access
- **Browser**: https://gnomad.broadinstitute.org/ — interactive variant and gene browsing
- **GraphQL API**: https://gnomad.broadinstitute.org/api — programmatic access
- **Downloads**: https://gnomad.broadinstitute.org/downloads — VCF, Hail tables, constraint tables
- **Google Cloud**: gs://gcp-public-data--gnomad/
## Additional Resources
- **gnomAD website**: https://gnomad.broadinstitute.org/
- **gnomAD blog**: https://gnomad.broadinstitute.org/news
- **Downloads**: https://gnomad.broadinstitute.org/downloads
- **API explorer**: https://gnomad.broadinstitute.org/api (interactive GraphiQL)
- **Constraint documentation**: https://gnomad.broadinstitute.org/help/constraint
- **Citation**: Karczewski KJ et al. (2020) Nature. PMID: 32461654; Chen S et al. (2024) Nature. PMID: 38conservation
- **GitHub**: https://github.com/broadinstitute/gnomad-browser
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