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Population-Specific Allele Frequency

Scenario

You manage a rare disease registry for a specific regional population. A variant in a candidate gene is reported at AF=0.001 in gnomAD (classified as very rare, supporting pathogenicity). But your cohort shows this variant at much higher frequency locally. Correct classification requires a locally calibrated AF.

Why Standard Databases Fall Short

gnomAD aggregates data from wide ancestry groups worldwide. A variant at AF=0.001 at European (non finish) group may be at AF=0.01 in a specific Iberian, French, or German population — a 10× difference that directly impacts ACMG criteria:

  • BA1 (benign standalone): AF > 0.05 in any population
  • BS1 (benign strong): AF > 0.01 in a matched population
  • PM2 (pathogenic moderate): AF < 0.001

Without a locally calibrated database, these thresholds are applied to population-averaged frequencies, potentially misclassifying variants enriched in your specific ancestry.

Solution with AFQuery

Build your AFQuery database from samples representative of your population. Queries then reflect the actual frequency distribution in your cohort — the most relevant reference for your patients.

Querying Population-Specific Subsets

1. Build a population-specific database

afquery create-db \
  --manifest cohort_manifest.tsv \
  --output-dir ./local_db/ \
  --genome-build GRCh38

2. Query a candidate variant

afquery query --db ./local_db/ --locus chr1:925952

chr1:925952 G>A  AC=142  AN=2742  AF=0.0518  n_eligible=1371  N_HET=138  N_HOM_ALT=2  N_HOM_REF=1231  N_FAIL=0

AF=0.052 locally, compared to gnomAD NFE AF=0.005 (10× higher). Under ACMG BS1, this variant is likely benign in this population.

3. Compare with a specific subgroup

If your cohort includes samples from multiple ancestry groups tagged in the manifest:

# Samples tagged with population label
afquery query --db ./local_db/ --locus chr1:925952 --phenotype iberian_population

4. Python: systematic comparison across many variants

from afquery import Database

db = Database("./local_db/")
variants = [(925952, "G", "A"), (1014541, "C", "T"), (3456789, "A", "G")]

results = db.query_batch("chr1", variants=variants)
for r in results:
    print(f"{r.variant.pos}: local_AF={r.AF:.4f}  AN={r.AN}")

Biological Interpretation

Variant gnomAD NFE AF Local AF Fold difference ACMG impact
chr1:925952 G>A 0.005 0.052 10× BS1 (benign strong)
chr1:1014541 C>T 0.0001 0.0001 1× PM2 supported
chr1:3456789 A>G 0.0008 0.009 11× BS1 in local pop

For the third variant, gnomAD would support PM2 (very rare), but local frequency shows it is common in this population (BS1 level). Using gnomAD alone would misclassify this variant.

Next Steps