Bio Clinical Databases Gnomad Frequencies

Version Compatibility

Reference examples tested with: requests 2.31+, hail 0.2.130+, pandas 2.2+, myvariant 1.0+. Current gnomAD release is v4.1 (May 2024); v4.1 fixed the v4.0 AN under-counting issue that inflated rare-variant AF estimates by 5-10%.

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• Hail: hl.version(); pin to >=0.2.130 for v4 schema

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. The gnomAD browser GraphQL API at https://gnomad.broadinstitute.org/api is the supported public endpoint; Hail Tables on Google Cloud Storage at gs://gcp-public-data--gnomad/ are the supported bulk access.

gnomAD Frequency Queries and Constraint

'How rare is this variant in the general population?' -> Pull allele frequency, grpmax FAF95 (the ACMG-grade frequency), LOEUF gene-level constraint, structural variant catalog, mtDNA frequencies, and the appropriate dataset version per use case.

• Python (single variant): GraphQL via requests.post('https://gnomad.broadinstitute.org/api', json={'query': ..., 'variables': ...})
• Python (aggregator): myvariant.MyVariantInfo().getvariant(hgvs, fields=['gnomad_exome', 'gnomad_genome'])
• Python (bulk): hl.read_table('gs://gcp-public-data--gnomad/release/4.1/ht/exomes/gnomad.exomes.v4.1.sites.ht')

v2.1.1 / v3.1.2 / v4.x: When to Use Which

This is the most consequential decision in any gnomAD query. The releases are not interchangeable; choice determines what can and cannot be said about a variant.

Critical caveats:

• v4 genomes are the SAME 76,215 v3 samples reprocessed against GRCh38 with updated pipelines; not independent.
• ~81% of v2 genomes are also in v3; joint v2+v3 meta-analysis must dedupe at sample ID.
• v4 includes 416,555 UK Biobank exomes under a specific collaboration agreement; check use terms.
• v4 does NOT include TCGA, so the non_cancer subset is unnecessary; the v4 subset is non_ukb (excludes UKB exomes for ancestry rebalancing).
• Liftover v2 (GRCh37 -> GRCh38) is NOT equivalent to v4 native; variant representation differs at ~0.5-1% of sites due to assembly fixes.

v4 Ancestry Groups: popmax -> grpmax Terminology

v4 ancestry groups: AFR, AMR, ASJ, EAS, FIN, MID, NFE, SAS, AMI, REMAINING. The MID (Middle Eastern) group was new in v4; previously absorbed into "OTH". The REMAINING group (31,256 v4 samples) is individuals who did not cluster with any reference; they contribute to overall AF but not to grpmax.

Terminology shift: gnomAD documentation and ACMG-facing narrative uses grpmax (genetic ancestry group max) -- replacing the older popmax ("population max") term -- to disambiguate genetic ancestry from self-reported race/ethnicity. The public GraphQL schema still exposes legacy field names containing popmax (e.g. faf95.popmax, faf95.popmax_population); these are the grpmax values under the modern terminology. Always check the schema version when writing queries; new browsers may rename these fields.

grpmax_faf95 is the operational ACMG field. It computes the maximum 95% lower-CI allele frequency, excluding bottleneck groups (AMI, ASJ, FIN, REMAINING) because pathogenic founder variants in those groups would otherwise falsely trigger BS1/BA1. MID is included in grpmax but is the smallest non-bottleneck group with highest per-allele variance.

Filtering Allele Frequency (FAF95): The ACMG-Grade AF

Whiffin 2017 Genet Med 19:1151 introduced FAF95 = Poisson lower bound of 95% CI for AF. By construction, AF > FAF95; FAF95 is the conservative frequency for ACMG application.

Max-credible-AF formula: (prevalence x heterogeneity x allelic-contribution) / (penetrance x 2). Plug in disease parameters to get the gene-specific BA1 / BS1 threshold; compare against grpmax_faf95.

Use grpmax_faf95, not raw AF, for BS1/BA1 application; this is the ClinGen-recommended approach.

Constraint Metrics: pLI, LOEUF, missense Z

Karczewski 2020 Nature 581:434 defined LOEUF as the upper bound of the 90% CI of observed/expected pLoF count per gene. LOEUF is recommended over pLI because it is continuous and accounts for gene size more rigorously.

Critical version mismatch:

• v2.1.1 constraint metrics published 2020; v4 constraint published March 2024 (4 months after v4 data release).
• v4 constraint is autosomes only; chrX and chrY constraint metrics in v4 are NOT released. For X/Y constraint, fall back to v2.1.1.
• LOEUF first decile shifted v2 to v4: v2 < 0.35; v4 < 0.6 (larger sample shifted the distribution). Gene rank in deciles is stable across versions but absolute thresholds are NOT interchangeable.

Subsets: non_cancer, non_neuro, controls

SV Catalog and CNV

gnomAD-CNV v4 is the resource that democratized exome-derived CNV background frequencies; previously only ExAC-CNV provided this at scale.

mtDNA (Laricchia 2022 Genome Res 32:569)

10,850 unique mtDNA variants across 56,434 individuals (v3.1). Frequencies reported per nuclear-ancestry AND per mitochondrial-haplogroup. Heteroplasmy >=10% threshold; ~1/250 individuals carry pathogenic mtDNA variant at heteroplasmy >=10%. mtDNA inheritance is non-Mendelian; standard ACMG criteria do not apply directly; use MITOMAP and HmtVar in parallel.

VEP Version Pinning

Each gnomAD release pins to a VEP version:

• v4 uses VEP 105 with GENCODE 39 / Ensembl 105 transcripts
• v2.1.1 uses VEP 85

A variant's consequence prediction can flip between v2 and v4 due to MANE Select adoption and transcript-set updates. Always pin VEP version when reproducing gnomAD annotations.

Decision Tree by Query Scenario

Single Variant Query (GraphQL)

Goal: Retrieve exome + genome AF, grpmax, FAF95, and per-ancestry breakdown for one variant.

Approach: Hit gnomAD's GraphQL API with explicit dataset version; parse the nested response.

import requests

GNOMAD_API = 'https://gnomad.broadinstitute.org/api'

def query_variant(chrom, pos, ref, alt, dataset='gnomad_r4'):
    '''Query gnomAD GraphQL for variant frequency + grpmax FAF95.

    dataset options: gnomad_r4 (v4.1, default), gnomad_r3, gnomad_r2_1
    '''
    query = '''
    query VariantById($variantId: String!, $dataset: DatasetId!) {
      variant(variantId: $variantId, dataset: $dataset) {
        variant_id
        rsids
        exome {
          ac
          an
          af
          homozygote_count
          filters
          populations { id ac an }
          faf95 { popmax popmax_population }
        }
        genome {
          ac
          an
          af
          homozygote_count
          filters
          populations { id ac an }
          faf95 { popmax popmax_population }
        }
      }
    }
    '''
    variant_id = f'{chrom}-{pos}-{ref}-{alt}'
    r = requests.post(GNOMAD_API,
                      json={'query': query, 'variables': {'variantId': variant_id, 'dataset': dataset}},
                      timeout=30)
    r.raise_for_status()
    return r.json().get('data', {}).get('variant')


def grpmax_faf95(payload):
    '''Extract the grpmax FAF95; the ACMG-grade frequency. Excludes bottleneck groups.'''
    exome = payload.get('exome') if payload else None
    if exome and exome.get('faf95'):
        return {
            'faf95': exome['faf95'].get('popmax'),
            'grpmax_ancestry': exome['faf95'].get('popmax_population'),
            'source': 'exome'
        }
    genome = payload.get('genome') if payload else None
    if genome and genome.get('faf95'):
        return {
            'faf95': genome['faf95'].get('popmax'),
            'grpmax_ancestry': genome['faf95'].get('popmax_population'),
            'source': 'genome'
        }
    return {'faf95': 0.0, 'grpmax_ancestry': None, 'source': 'absent'}

ACMG BS1/BA1 Application

Goal: Apply Whiffin max-credible-AF framework to a candidate variant.

Approach: Compute the gene-specific BS1 threshold from disease parameters, compare to grpmax_faf95.

def max_credible_af(prevalence, max_allelic_contribution=1.0, max_genetic_contribution=1.0,
                    penetrance=1.0):
    '''Whiffin 2017 max-credible-AF formula.

    Args:
        prevalence: disease prevalence (e.g., 1/10000 = 1e-4)
        max_allelic_contribution: max contribution of single allele to disease in any case
        max_genetic_contribution: max contribution of this gene to disease in any case
        penetrance: probability that variant carriers develop disease

    Returns: max-credible per-allele frequency under dominant inheritance (use /2 for AR)
    '''
    return (prevalence * max_genetic_contribution * max_allelic_contribution) / (penetrance * 2)


def apply_bs1_ba1(grpmax_faf95_val, max_credible, ba1_threshold=0.05):
    '''Apply ClinGen SVI BS1/BA1 criteria.

    BA1 default 5% per ClinGen SVI; VCEP-specific overrides exist (Hearing Loss = 0.5%).
    BS1 = max-credible-AF specific to gene+disease.
    '''
    if grpmax_faf95_val is None:
        return 'PM2_Supporting'  # Absent or ultra-rare
    if grpmax_faf95_val > ba1_threshold:
        return 'BA1'
    if grpmax_faf95_val > max_credible:
        return 'BS1'
    return None  # No criterion triggered; variant is consistent with rare-disease causation

Gene-Level Constraint (LOEUF)

Goal: Retrieve gene constraint metrics with awareness of version mismatch for chrX/Y.

Approach: Use v4 LOEUF for autosomes; fall back to v2.1.1 for chrX/Y. Report LOEUF decile, not raw value, to avoid cross-version comparison errors.

def query_gene_constraint(gene_symbol, dataset='gnomad_r4'):
    '''Pull gene constraint metrics. Note: v4 has no chrX/Y constraint; use v2 fallback.'''
    query = '''
    query GeneById($symbol: String!) {
      gene(gene_symbol: $symbol, reference_genome: GRCh38) {
        gene_id
        symbol
        chrom
        gnomad_constraint {
          oe_lof
          oe_lof_lower
          oe_lof_upper
          oe_mis
          oe_mis_upper
          pli
          mis_z
        }
      }
    }
    '''
    r = requests.post(GNOMAD_API,
                      json={'query': query, 'variables': {'symbol': gene_symbol}},
                      timeout=30)
    r.raise_for_status()
    gene = r.json().get('data', {}).get('gene')
    if gene is None:
        return None
    if gene.get('chrom') in ('X', 'Y'):
        gene['constraint_note'] = ('v4 constraint NOT released for chrX/Y; query v2.1.1 '
                                   'via gnomad_r2_1 dataset on the v2 endpoint')
    return gene

Bulk Query via Hail (cohort-scale)

Goal: Filter millions of variants by AF, grpmax, or LOEUF without API rate limits.

Approach: Read gnomAD v4 Hail Table from Google Cloud Storage; use hl.read_table() + filter operations.

import hail as hl

def init_hail_for_gnomad():
    '''Initialize Hail for gnomAD v4 GCS access. Requires Hail 0.2.130+.'''
    hl.init(default_reference='GRCh38')


def filter_rare_variants_hail(input_vcf, max_grpmax_faf95=0.0001, output_path='filtered.mt'):
    '''Filter input MT to variants below grpmax FAF95 threshold using gnomAD v4 exomes.'''
    ht_v4 = hl.read_table('gs://gcp-public-data--gnomad/release/4.1/ht/exomes/'
                          'gnomad.exomes.v4.1.sites.ht')
    mt = hl.import_vcf(input_vcf, reference_genome='GRCh38')
    mt = mt.annotate_rows(gnomad=ht_v4[mt.locus, mt.alleles])
    mt = mt.filter_rows(
        (hl.is_missing(mt.gnomad.grpmax_faf95)) |
        (mt.gnomad.grpmax_faf95.faf95 < max_grpmax_faf95)
    )
    mt.write(output_path, overwrite=True)
    return mt

Per-Operation Failure Modes

1. Using popmax/AF where grpmax_faf95 belongs

• Trigger: Apply BS1 with raw AF instead of FAF95.
• Mechanism: Raw AF inflates for low-N populations; FAF95 is the lower-bound CI; conservative.
• Symptom: Pathogenic variants falsely categorized BS1 in small-N ancestry groups (especially MID with v4's smallest sample size).
• Fix: Use grpmax_faf95.popmax field; not populations[i].af.

2. Failing to exclude bottleneck groups

• Trigger: Compute grpmax including AMI, ASJ, FIN, REMAINING.
• Mechanism: Founder variants in bottleneck groups can reach AF > 5% but are not population-general; would falsely trigger BA1.
• Symptom: Founder-population pathogenic variants reported benign.
• Fix: Use gnomAD's pre-computed grpmax_faf95 which excludes bottleneck groups by design.

3. Querying v4 constraint for chrX/Y

• Trigger: Pull LOEUF for DMD or USP9Y from v4 release.
• Mechanism: v4 March 2024 constraint release excluded sex chromosomes.
• Symptom: Missing or stale constraint metrics for X/Y genes.
• Fix: Query v2.1.1 LOEUF for chrX/Y; use v4 for autosomes; report LOEUF decile rather than raw value.

4. Comparing LOEUF absolute values across v2/v4

• Trigger: "v4 LOEUF for GENE-X is 0.45; v2 was 0.30; has it become more tolerant?"
• Mechanism: Larger v4 sample shifts the LOEUF distribution upward; first-decile threshold shifted v2 < 0.35 -> v4 < 0.6.
• Symptom: Genes appear to lose constraint between versions when they have not.
• Fix: Compare deciles, not absolute values; or stay within one version.

5. v2 -> v4 liftover assumed equivalent

• Trigger: Project v2 GRCh37 variants onto GRCh38 with CrossMap, treat as v4 native.
• Mechanism: ~0.5-1% of sites have different representations after liftover due to assembly fixes (e.g., gaps closed, contigs joined).
• Symptom: Inconsistent AFs at low rate; failed cross-version reproducibility.
• Fix: Query v4 native by GRCh38 coordinates directly; do not use liftover output as v4-equivalent.

6. UKB sample contamination of grpmax

• Trigger: Compute grpmax across v4 default subset; observe inflated NFE/SAS.
• Mechanism: 416,555 UK Biobank exomes dominate the v4 NFE+SAS subsets.
• Symptom: Variants common in UKB but rare globally falsely look common.
• Fix: Use non_ukb subset for grpmax when ancestry composition matters.

7. v3 vs v4 confusion; "I want WGS"

• Trigger: User says "I want WGS AFs" and pipeline pulls v4 genomes.
• Mechanism: v4 genomes are the SAME 76,215 v3 samples reprocessed against GRCh38; not independent.
• Symptom: WGS AFs appear identical to v3.1.2; not a bug, but worth flagging.
• Fix: Document that v4 genomes = v3 genomes reprocessed; for true independent WGS, no such resource yet exists at scale.

8. Constraint applied to multi-isoform gene without transcript awareness

• Trigger: Apply LOEUF "for the gene" when LoF is isoform-specific.
• Mechanism: gnomAD constraint is computed on the canonical transcript; tissue-specific or alternative isoforms may have different LoF tolerance.
• Symptom: Mis-prioritization of variants on minor transcripts.
• Fix: Cross-check with MANE Select; for isoform-specific LoF, use isoform-level constraint where available (rare).

Reconciliation: When Sources Disagree

Quantitative Thresholds and Conventions

Common Errors

Anticipated Reviewer Pushback

References

• Chen S et al. 2024. A genomic mutational constraint map using variation in 76,156 human genomes. Nature 625:92.
• Karczewski KJ et al. 2020. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581:434.
• Samocha KE et al. 2014. A framework for the interpretation of de novo mutation in human disease. Nat Genet 46:944.
• Collins RL et al. 2020. A structural variation reference for medical and population genetics. Nature 581:444.
• Laricchia KM et al. 2022. Mitochondrial DNA variation across 56,434 individuals in gnomAD. Genome Res 32:569.
• Whiffin N et al. 2017. Using high-resolution variant frequencies to empower clinical genome interpretation. Genet Med 19:1151.
• ClinGen guidance on gnomAD v4 (March 2024): https://clinicalgenome.org/site/assets/files/9445/clingen_guidance_to_vceps_regarding_the_use_of_gnomad_v4_march_2024.pdf
• gnomAD v4 release notes: https://gnomad.broadinstitute.org/news/2023-11-gnomad-v4-0/
• gnomAD v4.1 updates: https://gnomad.broadinstitute.org/news/2024-05-gnomad-v4-1-updates/

Related Skills

• clinical-databases/clinvar-lookup - Pathogenicity classification (gnomAD AF used for BS1/BA1)
• clinical-databases/acmg-classification - Whiffin FAF95 framework applied to ACMG criteria
• clinical-databases/variant-prioritization - Rare-disease pipeline using grpmax_faf95
• clinical-databases/myvariant-queries - Aggregated queries including gnomAD overlay
• population-genetics/population-structure - Population stratification background