Gnomad Variants

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

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• ExAC (GRCh37): Superseded by gnomAD.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
• Lek et al. 2016 (Nature, ~7,000 citations): ExAC — analysis of protein-coding variation in 60,706 humans. Established pLI for gene constraint. DOI
• Maurano et al. 2012 (Science, ~2,800 citations): Disease variants concentrate in DNase hypersensitive sites. DOI
• Finucane et al. 2015 (Nature Genetics, ~2,253 citations): Stratified LD score regression partitioning heritability into ENCODE annotations. DOI

Step 1: Determine the Query Type

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)

LOEUF is preferred over pLI for gnomAD v4+. LOEUF is continuous and better calibrated.

Applying Constraint to ENCODE Analysis

For genes identified as targets of ENCODE regulatory elements:

Step 4: Allele Frequency Filtering

Standard Frequency Thresholds

Population-Specific Frequencies

gnomAD provides frequencies for genetic ancestry groups:

• African/African American (afr)
• Admixed American/Latino (amr)
• Ashkenazi Jewish (asj)
• East Asian (eas)
• European (Finnish) (fin)
• European (non-Finnish) (nfe)
• Middle Eastern (mid)
• South Asian (sas)

Critical: A variant "rare" globally may be common in one population. Always check population-specific frequencies when interpreting regulatory variants in disease context.

Step 5: Integrated ENCODE + gnomAD Workflow

1. Identify regulatory variants from ENCODE:
   encode_search_files(output_type="IDR thresholded peaks", organ="pancreas", file_format="bed")
   → Intersect peaks with GWAS/eQTL variants using bedtools

2. Get allele frequencies from gnomAD:
   → GraphQL query for each variant
   → Filter by desired AF threshold

3. Check constraint of target genes:
   → For each variant-to-gene link (from ABC model, ENCODE enhancer-gene maps)
   → Query gnomAD gene constraint (LOEUF)

4. Prioritize:
   → Rare variant (AF < 0.01) + active enhancer + constrained gene (LOEUF < 0.35) = HIGH PRIORITY
   → Common variant (AF > 0.05) + active enhancer = potential GWAS mechanism
   → Absent from gnomAD + CRISPR-validated enhancer = potential de novo pathogenic

5. Track provenance:
   encode_log_derived_file(
       file_path="/path/to/prioritized_variants.tsv",
       source_accessions=["ENCSR...", "gnomAD_v4.1"],
       description="ENCODE regulatory variants filtered by gnomAD AF and constraint",
       tool_used="bedtools intersect + gnomAD GraphQL"
   )

Step 6: Bulk Data Access

For genome-wide analysis, downloading gnomAD VCFs is more efficient than API queries:

# gnomAD v4 genome VCFs (GRCh38)
# Available at: https://gnomad.broadinstitute.org/downloads
# Files hosted on Google Cloud Storage and AWS

# Download a single chromosome
gsutil cp gs://gcp-public-data--gnomad/release/4.1/vcf/genomes/gnomad.genomes.v4.1.sites.chr1.vcf.bgz .

# Or use Hail for cloud-native analysis

File sizes are large: Full genome VCF is ~1TB. Download per-chromosome files for targeted analysis.

Pitfalls and Caveats

1. gnomAD excludes pediatric disease cohorts: gnomAD explicitly removes individuals with severe pediatric disease. Pathogenic variants may be absent or at extremely low frequency.
2. Variant ID format matters: gnomAD uses CHR-POS-REF-ALT with 1-based positions and no "chr" prefix. Convert from VCF/BED coordinates carefully.
3. ExAC is superseded: Do not use ExAC for new analyses. All ExAC samples are included in gnomAD v2+. Use gnomAD v4.1 (GRCh38) for current work.
4. Assembly consistency: gnomAD v4 uses GRCh38, matching ENCODE. gnomAD v2.1.1 uses GRCh37 — do NOT mix with GRCh38 ENCODE data without liftOver.
5. Non-coding coverage gaps: gnomAD exome data only covers protein-coding regions. For non-coding regulatory variants, use gnomAD genome data specifically.
6. GraphQL rate limiting: No published threshold, but automated batch queries can trigger IP-level blocks. Throttle to ~1 req/sec for batch operations.
7. LOEUF vs pLI: Use LOEUF (continuous, better calibrated) for gnomAD v4. pLI is still reported for backward compatibility but is not recommended for new analyses.
8. Structural variants: gnomAD-SV provides structural variant data separately. Use for CNV/SV analysis around ENCODE regulatory elements.

Walkthrough: Population Frequency Context for ENCODE Regulatory Variants

Goal: Use gnomAD population allele frequencies to contextualize variants found in ENCODE regulatory elements, distinguishing rare disease-causing variants from common regulatory polymorphisms. Context: gnomAD provides allele frequencies across 76,000+ genomes. Variants in ENCODE regulatory regions with low population frequency are candidates for disease-causing regulatory mutations.

Step 1: Find ENCODE regulatory peaks

encode_search_experiments(assay_title="ATAC-seq", organ="kidney", organism="Homo sapiens")

Expected output:

{
  "total": 8,
  "results": [
    {"accession": "ENCSR900KID", "assay_title": "ATAC-seq", "biosample_summary": "kidney", "status": "released"}
  ]
}

Step 2: Download peak files

encode_list_files(accession="ENCSR900KID", file_format="bed", output_type="IDR thresholded peaks", assembly="GRCh38")

Expected output:

{
  "files": [
    {"accession": "ENCFF950KID", "output_type": "IDR thresholded peaks", "file_format": "bed narrowPeak", "file_size_mb": 0.7}
  ]
}

Step 3: Query gnomAD for variants in regulatory peaks

Using gnomAD GraphQL API (via skill guidance):

{
  region(dataset: gnomad_r4, chrom: "16", start: 68771195, stop: 68771395) {
    variants {
      variant_id
      pos
      ref
      alt
      genome {
        ac
        an
        af
        populations { id ac an af }
      }
    }
  }
}

Expected response:

{
  "data": {
    "region": {
      "variants": [
        {"variant_id": "16-68771250-C-T", "genome": {"af": 0.00012, "ac": 18, "an": 152312}},
        {"variant_id": "16-68771300-G-A", "genome": {"af": 0.35, "ac": 53321, "an": 152312}}
      ]
    }
  }
}

Interpretation:

• 16-68771250-C-T (AF=0.012%) — rare variant in kidney ATAC-seq peak → candidate disease variant
• 16-68771300-G-A (AF=35%) — common polymorphism → likely benign regulatory variant

Step 4: Filter for rare regulatory variants

Apply gnomAD frequency filters:

• Ultra-rare (AF < 0.01%): highest priority for Mendelian disease
• Rare (AF 0.01–1%): consider for complex disease risk
• Common (AF > 1%): likely benign, but may contribute to common disease via GWAS

Step 5: Check gene constraint scores

{
  gene(gene_symbol: "PKD1", reference_genome: GRCh38) {
    gnomad_constraint {
      pLI
      oe_lof
      oe_lof_upper
    }
  }
}

Interpretation: pLI near 1.0 = gene is loss-of-function intolerant. Rare regulatory variants near constrained genes are higher priority.

Integration with downstream skills

• Rare variants in ENCODE peaks feed into → variant-annotation for comprehensive annotation
• Population frequencies contextualize → clinvar-annotation pathogenicity calls
• Gene constraint scores inform → disease-research gene prioritization
• Frequency-filtered variants integrate with → gwas-catalog for allele frequency comparison

Code Examples

1. Find ENCODE data for gnomAD-relevant tissues

encode_get_facets(facet_field="organ", assay_title="ATAC-seq", organism="Homo sapiens")

Expected output:

{
  "facets": {
    "organ": {"brain": 32, "heart": 18, "liver": 14, "kidney": 8, "lung": 10}
  }
}

2. Get experiment details before variant annotation

encode_get_experiment(accession="ENCSR900KID")

Expected output:

{
  "accession": "ENCSR900KID",
  "assay_title": "ATAC-seq",
  "biosample_summary": "kidney",
  "replicates": 2,
  "status": "released",
  "audit": {"WARNING": 0, "ERROR": 0}
}

3. Track experiments used for variant frequency analysis

encode_track_experiment(accession="ENCSR900KID", notes="Kidney ATAC-seq for gnomAD regulatory variant filtering")

Expected output:

{
  "status": "tracked",
  "accession": "ENCSR900KID",
  "notes": "Kidney ATAC-seq for gnomAD regulatory variant filtering"
}

Integration

Related Skills

Presenting Results

• Present population frequency data as: variant | rsID | allele_freq | population | homozygotes. Flag variants absent from gnomAD (potentially novel). Suggest: "Would you like to check ClinVar significance for these variants?"

For the request: "$ARGUMENTS"