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Pseudo-Controls: Using Unrelated Patients as Background Frequency

The Clinical Challenge

Hospital genomics laboratories rarely sequence healthy individuals. Sequencing is ordered for patients with suspected genetic disease — meaning any local cohort is, by definition, enriched for disease. This creates a fundamental problem: you have no healthy controls to use as background frequency.

Public databases (gnomAD) fill this gap partially, but they may not match your cohort's ancestry, capture technology, or phenotypic composition (see Why Cohort-Specific AF Matters).

The Pseudo-Control Strategy

The key insight is that not all diseases share the same genetic architecture. A patient diagnosed with cardiomyopathy is not enriched — compared to the general population — for variants in retinal degeneration genes. Their variant frequencies in unrelated gene sets are, for practical purposes, equivalent to those of a healthy individual.

AFQuery enables you to dynamically select pseudo-controls by excluding samples with phenotypes related to the disease under study at query time — without modifying or rebuilding the database.

graph TB
    DB["AFQuery Database<br/>2,000 samples"]

    subgraph Full["Query 1: Full Cohort"]
        FA["AF = 0.0045<br/>AC=18, AN=4000<br/>includes I42 patients"]
    end

    subgraph Pseudo["Query 2: Pseudo-controls (exclude I42)"]
        FB["AF = 0.0013<br/>AC=4, AN=3200<br/>excludes I42 patients"]
    end

    Ratio["3.5× enrichment<br/>in cardiomyopathy"]

    DB -->|all samples| Full
    DB -->|exclude I42| Pseudo
    Full --> Ratio
    Pseudo --> Ratio

    style DB fill:#e3f2fd
    style FA fill:#fff3e0
    style FB fill:#e8f5e9
    style Ratio fill:#ffcccc

When Is This Valid?

Pseudo-controls are appropriate when:

  • The excluded phenotype is genetically unrelated to the study disease
  • The remaining cohort is large enough (AN ≥ 100 recommended)
  • You want to test for variant enrichment in a specific disease group

Examples of valid pseudo-control pairs:

Study disease Exclude from pseudo-controls
Retinopathy Cardiomyopathy patients (I42)
Epilepsy Metabolic disease patients (E70–E90)
Hearing loss Oncology patients
Cardiomyopathy Ophthalmology patients

Example: Cardiomyopathy Study

1. Manifest setup

Your manifest tags each sample with relevant ICD-10 codes:

sample_name vcf_path    sex tech_name   phenotype_codes
SAMP_001    vcfs/SAMP_001.vcf.gz    female  wgs I42,I10
SAMP_002    vcfs/SAMP_002.vcf.gz    male    wgs I42
SAMP_003    vcfs/SAMP_003.vcf.gz    female  wgs I10
SAMP_004    vcfs/SAMP_004.vcf.gz    male    wgs H35
...

No special setup is required — pseudo-controls are defined entirely at query time.

2. Query full cohort

afquery query --db ./db/ --locus chr1:925952 --format tsv

chrom   pos ref alt AC  AN  AF  n_eligible  N_HET   N_HOM_ALT   N_HOM_REF   N_FAIL
chr1    925952  G   A   18  4000    0.00450 2000    16  1   1983    0

3. Query pseudo-controls (exclude cardiomyopathy)

afquery query --db ./db/ --locus chr1:925952 --phenotype ^I42 --format tsv

chrom   pos ref alt AC  AN  AF  n_eligible  N_HET   N_HOM_ALT   N_HOM_REF   N_FAIL
chr1    925952  G   A   4   3200    0.00125 1600    4   0   1596    0

The variant is 3.5× enriched in cardiomyopathy patients versus the pseudo-control background.

4. Python API

from afquery import Database

db = Database("./db/")

# Full cohort
full = db.query("chr1", pos=925952)

# Pseudo-controls (exclude cardiomyopathy)
controls = db.query("chr1", pos=925952, phenotype=["^I42"])

enrichment = full[0].AF / controls[0].AF
print(f"Full cohort AF:      {full[0].AF:.5f}")     # 0.00450
print(f"Pseudo-control AF:   {controls[0].AF:.5f}") # 0.00125
print(f"Enrichment:          {enrichment:.1f}×")    # 3.5×

Biological Interpretation

The pseudo-control AF provides a cleaner background frequency than a gnomAD lookup because:

  • It reflects the same sequencing technology and same capture kit as your cases
  • It controls for population-level AF in your specific ancestry
  • It is not subject to the disease enrichment that may be present even in gnomAD (patients with undiagnosed genetic conditions may be included in population databases)

Key advantage: The pseudo-control query runs in under 100 ms. No database rebuild is needed — you can explore different disease stratifications interactively within a single clinical session.

Next Steps