Version Compatibility

Reference examples tested with: requests 2.31+, pandas 2.2+

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

• Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

gnomAD Frequency Queries

gnomAD REST API

Goal: Retrieve exome and genome allele frequencies from gnomAD for individual variants.

Approach: Send a GraphQL query to the gnomAD API with variant ID and dataset version, then parse exome/genome frequency fields.

"Check how common this variant is in the population" → Query gnomAD for allele frequency, allele count, and homozygote count.

• Python: GraphQL via requests.post() (requests)
• Python: myvariant.MyVariantInfo().getvariant() (myvariant)

Query Single Variant

python
import requests

def query_gnomad(chrom, pos, ref, alt, dataset='gnomad_r4'):
    '''Query gnomAD API for variant frequency

    dataset options: gnomad_r4, gnomad_r3, gnomad_r2_1
    '''
    url = 'https://gnomad.broadinstitute.org/api'

    query = '''
    query ($variantId: String!, $dataset: DatasetId!) {
        variant(variantId: $variantId, dataset: $dataset) {
            exome {
                ac
                an
                af
                homozygote_count
            }
            genome {
                ac
                an
                af
                homozygote_count
            }
        }
    }
    '''

    variant_id = f'{chrom}-{pos}-{ref}-{alt}'
    variables = {'variantId': variant_id, 'dataset': dataset}

    response = requests.post(url, json={'query': query, 'variables': variables})
    return response.json()

Parse gnomAD Response

python
def parse_gnomad_result(result):
    '''Extract allele frequencies from gnomAD response'''
    data = result.get('data', {}).get('variant', {})
    if not data:
        return None

    exome = data.get('exome', {}) or {}
    genome = data.get('genome', {}) or {}

    return {
        'exome_af': exome.get('af'),
        'exome_ac': exome.get('ac'),
        'exome_an': exome.get('an'),
        'exome_hom': exome.get('homozygote_count'),
        'genome_af': genome.get('af'),
        'genome_ac': genome.get('ac'),
        'genome_an': genome.get('an'),
        'genome_hom': genome.get('homozygote_count')
    }

Query via myvariant.info

Goal: Retrieve gnomAD frequencies through the myvariant.info aggregation layer for simpler API access.

Approach: Query myvariant.info by HGVS notation with gnomAD fields specified, extracting exome and genome allele frequencies.

python
import myvariant

mv = myvariant.MyVariantInfo()

def get_gnomad_via_myvariant(variant_hgvs):
    '''Get gnomAD frequencies via myvariant.info'''
    result = mv.getvariant(variant_hgvs, fields=['gnomad_exome', 'gnomad_genome'])

    exome = result.get('gnomad_exome', {})
    genome = result.get('gnomad_genome', {})

    return {
        'exome_af': exome.get('af', {}).get('af'),
        'genome_af': genome.get('af', {}).get('af')
    }

Population-Specific Frequencies

Goal: Retrieve ancestry-specific allele frequencies to assess variant rarity within relevant populations.

Approach: Query the gnomAD population-stratified AF fields (AFR, AMR, ASJ, EAS, FIN, NFE, SAS) via myvariant.info.

python
def get_population_frequencies(variant_hgvs):
    '''Get gnomAD frequencies by ancestry population'''
    mv = myvariant.MyVariantInfo()
    result = mv.getvariant(variant_hgvs, fields=['gnomad_exome.af'])

    af_data = result.get('gnomad_exome', {}).get('af', {})

    populations = {
        'af': af_data.get('af'),           # Global
        'af_afr': af_data.get('af_afr'),   # African
        'af_amr': af_data.get('af_amr'),   # Admixed American
        'af_asj': af_data.get('af_asj'),   # Ashkenazi Jewish
        'af_eas': af_data.get('af_eas'),   # East Asian
        'af_fin': af_data.get('af_fin'),   # Finnish
        'af_nfe': af_data.get('af_nfe'),   # Non-Finnish European
        'af_sas': af_data.get('af_sas'),   # South Asian
    }
    return populations

Filtering Thresholds

Common frequency cutoffs for variant filtering:

| Threshold | Use Case | |-----------|----------| | < 0.01 (1%) | Rare disease, ACMG PM2 | | < 0.001 (0.1%) | Stringent rare disease | | < 0.0001 (0.01%) | Ultra-rare | | Absent | Novel variant |

Filter Variants by Frequency

Goal: Apply population frequency thresholds to retain only rare variants for downstream analysis.

Approach: Compare the maximum allele frequency across exome and genome datasets against a configurable threshold (default 1% per ACMG PM2).

python
def is_rare(gnomad_af, threshold=0.01):
    '''Check if variant is rare based on gnomAD AF

    threshold: Default 0.01 (1%) per ACMG PM2 supporting criterion
    Use 0.001 for more stringent filtering
    '''
    if gnomad_af is None:
        return True  # Absent from gnomAD = rare
    return gnomad_af < threshold

def filter_rare_variants(variants, threshold=0.01):
    '''Filter list of variants to keep only rare ones'''
    rare = []
    for v in variants:
        exome_af = v.get('gnomad_exome_af')
        genome_af = v.get('gnomad_genome_af')
        max_af = max(filter(None, [exome_af, genome_af]), default=None)
        if is_rare(max_af, threshold):
            rare.append(v)
    return rare

Batch Query with Local gnomAD

Goal: Perform large-scale frequency lookups using a local gnomAD Hail Table for high throughput.

Approach: Load the gnomAD sites Hail Table from Google Cloud Storage and filter by allele frequency threshold.

For large-scale analysis, use local gnomAD VCF/Hail Table:

python
# Using Hail for gnomAD v4
import hail as hl

ht = hl.read_table('gs://gcp-public-data--gnomad/release/4.0/ht/exomes/gnomad.exomes.v4.0.sites.ht')

# Filter to rare variants
rare_ht = ht.filter(ht.freq[0].AF < 0.01)

Related Skills

• myvariant-queries - Aggregated queries including gnomAD
• variant-prioritization - Filter by frequency thresholds
• population-genetics/population-structure - Population stratification analysis