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Cohort Stratification

Scenario

Your cohort includes samples from multiple disease groups and multiple sequencing technologies. You want to compare allele frequencies across groups to (a) identify technology-driven artifacts and (b) test whether a variant is enriched in a specific disease group.

Why Standard Databases Fall Short

General population databases do not allow stratification by disease group or sequencing technology. They provide a single aggregate frequency, hiding heterogeneity that may be scientifically or clinically significant.

Solution with AFQuery

AFQuery allows computing AF over any combination of metadata filters on the same database. You can compare:

  • WGS vs. WES (technology artifact detection)
  • Disease group A vs. disease group B (disease enrichment)
  • Within-technology disease comparisons (controlling for technology)

All comparisons happen on the same database.

Comparing WGS vs. WES and Disease Groups

Setup: Multi-technology, multi-disease cohort

sample_name vcf_path    sex tech_name   phenotype_codes
SAMP_001    vcfs/SAMP_001.vcf.gz    female  wgs epilepsy
SAMP_002    vcfs/SAMP_002.vcf.gz    male    wgs control
SAMP_003    vcfs/SAMP_003.vcf.gz    female  wes_v1  epilepsy
SAMP_004    vcfs/SAMP_004.vcf.gz    male    wes_v1  control
...

1. Technology comparison (artifact detection)

# WGS samples
afquery query --db ./db/ --locus chr1:925952 --tech wgs --format tsv

# WES samples
afquery query --db ./db/ --locus chr1:925952 --tech wes_v1 --format tsv

If AF differs substantially between technologies (>2×), this may indicate a capture artifact or systematic genotyping difference. Use afquery check to verify BED file coverage.

2. Disease group comparison

For the exclusion-based pseudo-control pattern, see Pseudo-controls.

# Epilepsy group
afquery query --db ./db/ --locus chr1:925952 --phenotype epilepsy --format json

# Control group (explicitly tagged)
afquery query --db ./db/ --locus chr1:925952 --phenotype control --format json

# Or use exclusion-based pseudo-controls
afquery query --db ./db/ --locus chr1:925952 --phenotype ^epilepsy --format json

3. Within-technology disease comparison

# Epilepsy WGS only (controls for technology)
afquery query --db ./db/ --locus chr1:925952 \
  --phenotype epilepsy --tech wgs --format tsv

# Control WGS only
afquery query --db ./db/ --locus chr1:925952 \
  --phenotype control --tech wgs --format tsv

4. Systematic stratification with dump

For genome-wide stratified analysis:

afquery dump \
  --db ./db/ \
  --by-phenotype epilepsy --by-phenotype control \
  --by-tech \
  --output stratified_af.csv

5. Python: compute enrichment ratio

from afquery import Database

db = Database("./db/")

disease = db.query("chr1", pos=925952, phenotype=["epilepsy"])
controls = db.query("chr1", pos=925952, phenotype=["control"])

if disease and controls and disease[0].AF and controls[0].AF:
    enrichment = disease[0].AF / controls[0].AF
    print(f"Disease AF: {disease[0].AF:.4f}  (AN={disease[0].AN})")
    print(f"Control AF: {controls[0].AF:.4f}  (AN={controls[0].AN})")
    print(f"Enrichment: {enrichment:.1f}×")

Biological Interpretation

Comparison Disease AF Control AF Enrichment Interpretation
Epilepsy vs. control (WGS) 0.012 0.003 4× Possible disease association
WGS vs. WES 0.011 0.010 1.1× No technology artifact
Epilepsy WGS vs. WES 0.012 0.013 0.9× Consistent across technologies

Low AN values (<100) indicate small group sizes — interpret those frequencies cautiously.

Next Steps