Alterlab Gnomad

---
name: alterlab-gnomad
description: Query gnomAD (Genome Aggregation Database) for population allele frequencies and gene constraint scores (pLI, LOEUF) reflecting loss-of-function intolerance. Use when checking how common a variant is across populations, filtering rare-disease candidate variants, assessing variant pathogenicity, or identifying loss-of-function intolerant genes. Part of the AlterLab Academic Skills suite.
license: CC0-1.0
allowed-tools: Read WebFetch Bash(curl:*) Bash(python:*)
compatibility: Keyless gnomAD GraphQL API (no authentication required)
metadata:
    skill-author: AlterLab
    version: "1.1.0"
    last_updated: "2026-09-23"
---

# 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. The current release is **v4.1.1** (30 March 2026), which updated gene constraint (Bayesian LOEUF, chrX/chrY coverage) and LOFTEE flags; the API dataset ID is still `gnomad_r4`. 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)

### Does NOT Trigger

| Scenario | Use Instead |
|----------|-------------|
| Clinical significance / ACMG classification submitted for a variant | `alterlab-clinvar` |
| Somatic mutation frequency in tumors | `alterlab-cbioportal` |
| Variant consequence prediction (VEP) or gene/transcript models | `alterlab-ensembl` |
| GWAS associations for a trait or variant | `alterlab-gwas` |
| Tissue expression or eQTLs for a gene | `alterlab-gtex` |

## 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** (values of the `DatasetId` enum):
- `gnomad_r4` — gnomAD v4 (currently v4.1.1), GRCh38 (recommended default; returns both `exome` and `genome` blocks per variant — there is no separate `gnomad_r4_genomes` id)
- `gnomad_r4_non_ukb` — v4 excluding UK Biobank samples
- `gnomad_r3` — gnomAD v3 genomes, GRCh38 (subsets: `gnomad_r3_non_cancer`, `_non_neuro`, `_non_topmed`, `_non_v2`, `_controls_and_biobanks`)
- `gnomad_r2_1` — gnomAD v2, GRCh37 (use only for GRCh37 compatibility; subsets `_controls`, `_non_neuro`, `_non_cancer`, `_non_topmed`)
- `exac` — legacy ExAC

(Verified by introspecting the `DatasetId` enum on 2026-09-23; re-check with
`{ __type(name: "DatasetId") { enumValues { name } } }`.)

Structural and copy-number variants use separate enums: `structural_variants(dataset: gnomad_sv_r4)` (v2 SVs: `gnomad_sv_r2_1`) and `copy_number_variants(dataset: gnomad_cnv_r4)`.

**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
        symbol
        variants(dataset: $dataset) {
          variant_id
          pos
          ref
          alt
          consequence
          genome {
            af
            ac
            an
            ac_hom
            populations {
              id
              ac
              an
              ac_hom
            }
          }
          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 protein-truncating variants.
# Note the parentheses: without them `and` binds tighter than `or`, so the AF
# filter would silently apply only to the consequence branch.
# Per-population frequency is af = ac / an (the populations entries expose
# ac/an, not af). Genome `af` here is absent for variants seen only in exomes,
# so fall back to exome af.
def variant_af(v):
    g = (v.get("genome") or {}).get("af")
    return g if g is not None else (v.get("exome") or {}).get("af", 1)

rare_ptvs = [
    v for v in variants
    if (v.get("lof") == "HC" or v.get("consequence") in ["stop_gained", "frameshift_variant"])
    and variant_af(v) < 0.001
]
print(f"Found {len(rare_ptvs)} rare PTVs in {gene_data['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"

    # On a single variant, consequence/lof are NOT top-level fields — they
    # live under transcript_consequences[] (per transcript). Populations
    # expose ac/an only; compute per-population af = ac / an.
    query = """
    query VariantDetails($variantId: String!, $dataset: DatasetId!) {
      variant(variantId: $variantId, dataset: $dataset) {
        variant_id
        chrom
        pos
        ref
        alt
        rsids
        genome {
          af
          ac
          an
          ac_hom
          populations { id ac an ac_hom }
        }
        exome {
          af
          ac
          an
          ac_hom
          populations { id ac an ac_hom }
        }
        transcript_consequences {
          gene_symbol
          major_consequence
          lof
          lof_flags
          is_canonical
        }
        in_silico_predictors {
          id
          value
          flags
        }
      }
    }
    """

    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-C")  # BRCA1 missense, rs80357199
variant = result["data"]["variant"]

if variant:
    genome_af = (variant.get("genome") or {}).get("af", "N/A")
    exome_af = (variant.get("exome") or {}).get("af", "N/A")
    # Pick the consequence from the canonical transcript when present.
    tcs = variant.get("transcript_consequences") or []
    canonical = next((t for t in tcs if t.get("is_canonical")), tcs[0] if tcs else {})
    print(f"Variant: {variant['variant_id']}")
    print(f"  Consequence: {canonical.get('major_consequence')}")
    print(f"  Genome AF: {genome_af}")
    print(f"  Exome AF: {exome_af}")
    print(f"  LoF: {canonical.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
        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['symbol']}")
print(f"  pLI:   {constraint['pLI']:.3f}  (>0.9 = LoF intolerant)")
print(f"  LOEUF: {constraint['oe_lof_upper']:.3f}  (<0.45 = constrained, v4.1.1 guidance)")
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; <0.45 = constrained in v4.1.1 (the older <0.35 cutoff was calibrated on v2 and is too strict for v4) |
| `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
            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"

    # structural_variants requires a dataset argument (gnomad_sv_r4).
    query = """
    query SVsByGene($gene_symbol: String!, $dataset: StructuralVariantDatasetId!) {
      gene(gene_symbol: $gene_symbol, reference_genome: GRCh38) {
        structural_variants(dataset: $dataset) {
          variant_id
          type
          chrom
          pos
          end
          af
          ac
          an
        }
      }
    }
    """

    response = requests.post(
        url,
        json={"query": query, "variables": {"gene_symbol": gene_symbol, "dataset": "gnomad_sv_r4"}}
    )
    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 LOEUF < 0.45 (gnomAD v4.1.1 recommendation) — or pLI > 0.9 if you must match older literature
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); v4.1.1 computes it in a Bayesian (Gamma-posterior) framework and extends constraint to chrX/chrY, so values differ from v4.1 — record which version you used

## 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: 38057664
- **GitHub**: https://github.com/broadinstitute/gnomad-browser

## Scripts

`scripts/query_gnomad.py` — runnable helper for the gnomAD GraphQL API (no key). It carries inline PEP 723 deps, so run the file directly with `uv run` (not `uv run python ...`) to auto-install `requests`:

```bash
uv run scripts/query_gnomad.py variant 17-43094692-G-C --dataset gnomad_r4
uv run scripts/query_gnomad.py constraint BRCA1 --genome GRCh38
```