/gnomad-variants

name: gnomad-variants
description: Query gnomAD (Genome Aggregation Database) for population allele frequencies, gene constraint scores, and variant annotations to interpret ENCODE regulatory variants. Use when the user needs allele frequencies for variants in ENCODE regulatory elements, wants to assess gene constraint (pLI, LOEUF) for ENCODE target genes, needs population-specific frequencies for GWAS variants overlapping cCREs, wants to filter variants by rarity before functional annotation, or is interpreting ENCODE CRISPR/MPRA results in the context of population genetics. Also use when the user mentions gnomAD, allele frequency, pLI, LOEUF, constraint, rare variants, population frequency, ExAC, or variant filtering.

Query gnomAD for Population Variant Data

When to Use

• User wants to check population allele frequencies for variants in ENCODE regulatory regions
• User asks about "gnomAD", "allele frequency", "population genetics", "gene constraint", or "variant frequency"
• User needs to filter regulatory variants by rarity (common vs rare) using population data
• User wants to assess gene constraint (pLI, LOEUF) for targets identified from ENCODE ChIP-seq
• Example queries: "check gnomAD frequency for variants in my peaks", "is this regulatory variant rare?", "what's the constraint score for CTCF?"

Annotate ENCODE-identified regulatory variants with population allele frequencies and gene constraint scores from the Genome Aggregation Database.

Scientific Rationale

**The question**: "How common is this variant in the population, and how constrained is the gene it regulates?"

ENCODE identifies regulatory elements and the variants within them, but does not provide population frequency data. gnomAD (v4.1: 807,162 individuals, 730,947 exomes + 76,215 genomes) fills this gap — enabling researchers to distinguish common regulatory variants (likely benign or with modest effect) from rare variants (potentially pathogenic or high-impact).

Why gnomAD + ENCODE Together

| ENCODE Provides | gnomAD Provides | Combined Insight | |----------------|----------------|------------------| | Variant overlaps cCRE (dELS) | AF = 0.0001 (rare) | Rare variant disrupting an enhancer → high priority | | Variant in TF binding site | AF = 0.15 (common) | Common regulatory variant → likely modest effect or GWAS candidate | | Target gene identified | LOEUF = 0.12 (highly constrained) | Constrained gene + rare enhancer variant → strong candidate | | Variant in CRISPR-validated enhancer | Not in gnomAD (absent) | Ultra-rare/de novo → possible pathogenic regulatory variant |

Key Resources

• **gnomAD v4.1** (GRCh38): 807,162 individuals, 62.9M SNVs + 6.2M indels
• **gnomAD v3.1.2** (GRCh38): Genome-only dataset, 76,156 genomes
• **gnomAD v2.1.1** (GRCh37/hg19): Legacy dataset, still widely used
• **ExAC** (GRCh37): Superseded by gnomAD. All ExAC samples included in gnomAD v2+.
• **gnomAD browser**: https://gnomad.broadinstitute.org

Literature Support

• **Karczewski et al. 2020** (Nature, ~5,000 citations): gnomAD — mutational constraint spectrum from 141,456 individuals. Defined LOEUF as primary constraint metric. [DOI](https://doi.org/10.1038/s41586-020-2308-7)
• **Lek et al. 2016** (Nature, ~7,000 citations): ExAC — analysis of protein-coding variation in 60,706 humans. Established pLI for gene constraint. [DOI](https://doi.org/10.1038/nature19057)
• **Maurano et al. 2012** (Science, ~2,800 citations): Disease variants concentrate in DNase hypersensitive sites. [DOI](https://doi.org/10.1126/science.1222794)
• **Finucane et al. 2015** (Nature Genetics, ~2,253 citations): Stratified LD score regression partitioning heritability into ENCODE annotations. [DOI](https://doi.org/10.1038/ng.3404)

Step 1: Determine the Query Type

| User Has | Query Strategy | |----------|---------------| | Specific variant (rs ID or chr-pos-ref-alt) | Single variant lookup | | List of GWAS/eQTL variants | Batch variant query | | Gene of interest (ENCODE target) | Gene constraint lookup | | Genomic region with ENCODE peaks | Region variant query |

Step 2: Query gnomAD via GraphQL API

**Endpoint**: `https://gnomad.broadinstitute.org/api` **Method**: POST with GraphQL query in JSON body **Authentication**: None required **Rate limit**: IP-level throttling; throttle to ~1 request/second for batch queries

Single Variant Lookup

curl -X POST https://gnomad.broadinstitute.org/api \
  -H "Content-Type: application/json" \
  -d '{
    "query": "query { variant(variantId: \"1-55517991-C-CAT\", dataset: gnomad_r4) { exome { ac an af } genome { ac an af } joint { ac an af } } }"
  }'

**Variant ID format**: `CHR-POS-REF-ALT` (1-based position, no "chr" prefix)

Gene Constraint Lookup

curl -X POST https://gnomad.broadinstitute.org/api \
  -H "Content-Type: application/json" \
  -d '{
    "query": "query { gene(gene_symbol: \"BRCA2\", reference_genome: GRCh38) { symbol gene_id gnomad_constraint { pLI oe_lof oe_lof_lower oe_lof_upper oe_mis oe_mis_lower oe_mis_upper } } }"
  }'

Region Variant Query

curl -X POST https://gnomad.broadinstitute.org/api \
  -H "Content-Type: application/json" \
  -d '{
    "query": "query { region(chrom: \"1\", start: 55505222, stop: 55530526, reference_genome: GRCh38) { variants(dataset: gnomad_r4) { variant_id pos ref alt exome { ac af } genome { ac af } } } }"
  }'

Step 3: Interpret Constraint Scores

Gene Constraint (Karczewski et al. 2020)

| Metric | Definition | Interpretation | |--------|-----------|----------------| | **LOEUF** | Loss-of-function observed/expected upper bound 90% CI | <0.35 = highly constrained (v2); <0.6 = constrained (v4) | | **pLI** | Probability of being loss-of-function intolerant | >0.9 = LoF-intolerant (legacy metric from ExAC) | | **oe_lof** | Observed/expected loss-of-function ratio | <0.2 = highly constrained | | **oe_mis** | Observed/expected missense ratio | <0.6 = missense constrain