bio-clinical-databases-gnomad-frequencies

name	bio-clinical-databases-gnomad-frequencies
description	Queries gnomAD v4 (807k samples), v3, v2.1.1, and constraint metrics with grpmax FAF95, bottleneck-group exclusion, LOEUF interpretation, SV/CNV/mtDNA catalogs, and Whiffin max-credible-AF framework. Use when filtering rare variants, applying ACMG BS1/BA1, ranking genes by LoF intolerance, or selecting between v2 (GRCh37 + chrX/Y constraint) and v4 (GRCh38 + 807k samples).
origin	openai4s
category	bioskills/clinical-databases
metadata	{"tool_type":"python","primary_tool":"requests","third_party":{"name":"GPTomics/bioSkills","repository":"https://github.com/GPTomics/bioSkills","commit":"d91ed3d563019e649dc854c56ccd62551359488a","license":"MIT"}}

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.

Release	Build	Samples	Use when	Fails when
v2.1.1	GRCh37	125,748 exomes + 15,708 genomes	Constraint metrics needed (LOEUF v2 most-validated); chrX/Y constraint required; GRCh37 native non-negotiable	GRCh38 native cohort; modern rare-variant FAF95 (use v4)
v3.1.2	GRCh38	76,156 genomes (NO exomes)	Non-coding region rare variants on GRCh38; mtDNA frequencies	Exome variants needed (no exomes); 76k cohort smaller than v4
v4.0/v4.1	GRCh38	730,947 exomes + 76,215 genomes = 807,162 total	Default for everything; rare-variant filtering, FAF95, gene queries	chrX/Y constraint (not released); cancer-cohort analysis (no TCGA in v4)

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.

Code	Threshold	Notes
BA1	AF > 5% in any non-bottleneck group	ClinGen SVI default; VCEPs may override (Hearing Loss VCEP uses 0.5%)
BS1	AF > gene-specific max-credible-AF	Computed per gene via Whiffin formula
PM2_Supporting	Absent or ultra-rare in gnomAD	Downgraded from PM2_Moderate in SVI 2020

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.

Metric	What	Interpretation
LOEUF	Upper bound of 90% CI of LoF observed/expected ratio	Lower = more LoF-intolerant; first decile (LOEUF < 0.35 v2; < 0.6 v4) = strongly intolerant
pLI	Probability LoF intolerant	Still used; gnomAD team recommends LOEUF for ranking
Missense Z	Z-score of observed-vs-expected missense	Z > 3.09 = 'constrained' set (~p < 0.001, one-tailed)
Missense O/E	Observed/expected missense ratio	Continuous form of missense Z

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

Release	Subset	Removes	Use when
v2.1.1	non_cancer	TCGA	Cancer-related variant analysis (avoids circularity)
v2.1.1	non_neuro	Psychiatric/neuro cohorts	Neuropsychiatric variant analysis
v2.1.1	controls	Cases with known disease (~60k samples)	Disease-association calibration
v3.1.2	non_v2	v2 overlapping samples	Independent of v2
v3.1.2	controls_and_biobanks	Disease cases retained, biobanks emphasized	Population-level reference
v4	non_ukb	UK Biobank exomes	When EUR-skew of UKB problematic
v4	non_neuro	Deprecated	--
v4	non_cancer	Unnecessary (no TCGA in v4)	--

SV Catalog and CNV

Resource	Release	Samples	Coverage
gnomAD-SV v2	Collins 2020 Nature 581:444	14,891 unrelated WGS	433k SVs, GRCh37
gnomAD-SV v4	Nov 2023	63,046 unrelated WGS	1,199,117 high-confidence SVs, GRCh38
gnomAD-CNV v4	Nov 2023	464,297 individuals (exome-derived gCNV)	Rare (AF < 1%) autosomal coding CNVs

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

Scenario	Recommended path	Why
Single variant AF lookup	GraphQL API or myvariant.info	Lowest latency; returns full per-ancestry breakdown
ACMG BS1/BA1 application	grpmax_faf95 from v4	The ClinGen-recommended field
Gene-level LoF constraint (autosomes)	LOEUF from v4 March 2024 release	Larger sample, more stable
Gene-level LoF constraint (chrX/Y)	LOEUF from v2.1.1	v4 X/Y constraint NOT released
Bulk rare-variant filter (cohort-scale)	Hail Table on GCS	No rate limits; full schema
SV frequency	gnomAD-SV v4 (WGS) or gnomAD-CNV v4 (exome)	Choose by data type
mtDNA frequency	v3.1 mtDNA release (Laricchia 2022)	Only gnomAD release with mtDNA
Cancer-variant analysis	v2.1.1 non_cancer subset OR v4 (no TCGA)	Avoid TCGA circularity in v2
Comparison across builds	Use canonical SPDI or CA ID, normalize first	Liftover != native

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[].get(),
            : 
        }
    genome = payload.get()  payload  
     genome  genome.get():
         {
            : genome[].get(),
            : genome[].get(),
            : 
        }
     {: , : , : }

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

Pattern	Likely cause	Action
ClinVar P vs gnomAD grpmax_faf95 > 1%	Founder-population pathogenic; or ClinVar is stale low-star	Apply Whiffin max-credible-AF for the gene; check ClinVar star/freshness
v2 LOEUF < 0.35 vs v4 LOEUF = 0.5	Distribution shifted with v4 sample size, not biology	Use deciles; v4 first decile = < 0.6
v2 AF != v4 AF for same variant	Sample overlap (v3 in v4) + new exomes; expected	Trust v4 default; non-overlapping subsets via non_v2 or non_ukb
Variant present in v3 genomes, absent v4 exomes	Variant outside exome capture region (intronic, intergenic)	Use v3.1.2 or v4 genomes for non-coding
gnomAD-SV v2 vs v4 different breakpoints	v2 GRCh37, v4 GRCh38; assembly fixes shift coords	Use v4 native; document build
Browser shows lower AF than Hail Table	Browser pre-filters with filters=PASS; Hail Table includes all	Apply filters filter in Hail explicitly

Quantitative Thresholds and Conventions

Threshold	Convention	Source
BA1 default	grpmax_faf95 > 5% in non-bottleneck group	Richards 2015 + ClinGen SVI
BS1	grpmax_faf95 > gene-specific max-credible-AF	Whiffin 2017
PM2_Supporting	Absent or ultra-rare in gnomAD	SVI 2020 downgrade
LOEUF first decile v2	< 0.35	Karczewski 2020
LOEUF first decile v4	< 0.6	gnomAD constraint release March 2024
Missense Z constrained	Z > 3.09 (~p < 0.001)	Samocha 2014
mtDNA heteroplasmy carrier threshold	>=10% heteroplasmy	Laricchia 2022
v4 sample size	730,947 exomes + 76,215 genomes = 807,162	gnomAD v4.0 release Nov 2023
Bottleneck groups (excluded from grpmax)	AMI, ASJ, FIN, REMAINING	gnomAD v4 documentation
API rate limit	None published; ~10 req/s practical	gnomAD browser GraphQL

Common Errors

Symptom	Cause	Solution
Cannot read property 'af' of undefined	Variant not in dataset; variant returned null	Check if payload is None; absence is biologically informative
FAF95 = 0 for a known common variant	grpmax_faf95 only computed when AN sufficient	Check AC and AN directly; FAF95 is 0 when N too low to estimate
Variant filter status AC0 or RF	Failed gnomAD QC	Variants with non-PASS should usually be excluded from analysis
Different AFs between gnomAD browser and Hail Table	Browser auto-applies PASS filter; Hail does not	Filter filters.size() == 0 (i.e., PASS) in Hail
LOEUF appears worse in v4 vs v2	Distribution shifted with larger sample	Compare deciles, not absolute values
SV not found in v4-SV	v2-SV is GRCh37, v4-SV is GRCh38; or variant not called in WGS	Try v2-SV with liftover; or check gnomAD-CNV for exome-derived
mtDNA variant missing	Only v3.1 has mtDNA; not in v4	Query v3.1 directly

Anticipated Reviewer Pushback

Pushback	Standard response
"Why FAF95 instead of AF?"	Raw AF is point estimate; FAF95 is Poisson lower-bound 95% CI; ClinGen SVI recommendation for BS1/BA1.
"Why exclude FIN and ASJ from grpmax?"	Founder-population pathogenic variants reach high AF locally; including them would trigger false BA1.
"This LOEUF differs from the 2020 paper"	We use v4 March 2024 constraint (807k samples); 2020 paper used v2 (141k samples). Decile rank is stable; absolute shifted.
"Why not v4 for chrX constraint?"	v4 March 2024 constraint release is autosomes only; chrX/Y not yet released as of 2025. Fall back to v2.
"Why v3 if v4 exists?"	v4 genomes = v3 genomes reprocessed; for genome-only analysis they are equivalent.
"Variant exists in liftover v2 but not v4"	~0.5-1% of sites differ post-assembly fixes; use v4 native, not liftover, as ground truth.
"Browser AF higher than this value"	Browser includes flagged variants by default; we filter on PASS.

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