Bio Copy Number Gatk Cnv

Version Compatibility

Reference examples tested with: GATK 4.5+ (gatk4), Python 3.10+ (gcnv conda env), R 4.3+.

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

• CLI: gatk --version then gatk <ToolName> --help to confirm arguments
• gCNV requires a working gatkcondaenv (theano/tensorflow stack) — gatk will report if the Python environment is missing

GATK 4.5+ gCNV inference defaults are tuned for whole-exome data; whole-genome runs generally need parameter changes. If a tool reports an unrecognized argument, check the help for that exact GATK version rather than retrying.

GATK CNV Workflows

"Call CNVs the GATK way" -> GATK has two separate CNV workflows that share almost no tools. Picking the wrong one is the most common mistake.

• Somatic CNV: CollectReadCounts -> DenoiseReadCounts -> ModelSegments -> CallCopyRatioSegments. Tumor copy-ratio segments, optionally allele-aware.
• Germline gCNV: DetermineGermlineContigPloidy -> GermlineCNVCaller -> PostprocessGermlineCNVCalls. Per-sample germline CN genotypes (VCF).

Critical: What GATK Somatic CNV Does NOT Provide

ModelSegments + CallCopyRatioSegments produce copy-ratio segments and a minor-allele fraction per segment, and the "call" is a simple t-test emitting + / - / 0. This is not integer allele-specific copy number, not tumor purity, and not ploidy. Practitioners routinely assume parity with ASCAT/FACETS and there is none. For integer allele-specific CN, purity, ploidy, LOH state, or whole-genome-doubling status, use allele-specific-copy-number (ASCAT, Sequenza, FACETS, or PureCN — PureCN can even reuse the GATK ModelSegments segmentation as input).

Somatic vs Germline — Choosing the Workflow

Decision Tree by Scenario

Somatic CNV Pipeline

# 1. Preprocess and annotate intervals (WES: bin-length 0 = use exome targets as-is)
gatk PreprocessIntervals -R ref.fa -L targets.interval_list \
    --bin-length 0 --interval-merging-rule OVERLAPPING_ONLY -O preprocessed.interval_list
gatk AnnotateIntervals -R ref.fa -L preprocessed.interval_list \
    --interval-merging-rule OVERLAPPING_ONLY -O annotated.tsv     # GC content for FilterIntervals

# 2. Collect read counts (each BAM)
gatk CollectReadCounts -R ref.fa -I sample.bam -L preprocessed.interval_list \
    --interval-merging-rule OVERLAPPING_ONLY -O sample.counts.hdf5

# 3. Build the panel of normals (tangent-normalization basis)
# --minimum-interval-median-percentile 5.0 is the GATK CNV tutorial value (tool default 10.0)
gatk CreateReadCountPanelOfNormals \
    -I normal1.counts.hdf5 -I normal2.counts.hdf5 -I normalN.counts.hdf5 \
    --annotated-intervals annotated.tsv \
    --minimum-interval-median-percentile 5.0 -O cnv.pon.hdf5

# 4. Denoise tumor against the PoN (tangent normalization)
gatk DenoiseReadCounts -I tumor.counts.hdf5 --count-panel-of-normals cnv.pon.hdf5 \
    --standardized-copy-ratios tumor.standardizedCR.tsv \
    --denoised-copy-ratios tumor.denoisedCR.tsv

# 5. Allelic counts at common biallelic SNPs (tumor and matched normal)
gatk CollectAllelicCounts -R ref.fa -I tumor.bam -L common_snps.interval_list \
    -O tumor.allelicCounts.tsv
gatk CollectAllelicCounts -R ref.fa -I normal.bam -L common_snps.interval_list \
    -O normal.allelicCounts.tsv

# 6. Joint segmentation of copy ratio and allele fraction
gatk ModelSegments --denoised-copy-ratios tumor.denoisedCR.tsv \
    --allelic-counts tumor.allelicCounts.tsv \
    --normal-allelic-counts normal.allelicCounts.tsv \
    --output-prefix tumor -O segments/

# 7. Call each segment +/-/0 (simple t-test against the copy-ratio baseline)
gatk CallCopyRatioSegments -I segments/tumor.cr.seg -O segments/tumor.called.seg

AnnotateIntervals (step 1) and supplying --annotated-intervals to the PoN are frequently skipped — they enable explicit GC-bias correction and are recommended.

Germline gCNV Pipeline

# 1. Determine contig ploidy across the cohort (karyotype + global depth)
gatk DetermineGermlineContigPloidy -L preprocessed.interval_list \
    --interval-merging-rule OVERLAPPING_ONLY \
    -I sample1.counts.hdf5 -I sampleN.counts.hdf5 \
    --contig-ploidy-priors ploidy_priors.tsv --output-prefix cohort -O ploidy-calls/

# 2. FilterIntervals — remove low-mappability / extreme-GC / low-count intervals
gatk FilterIntervals -L preprocessed.interval_list --annotated-intervals annotated.tsv \
    -I sample1.counts.hdf5 -I sampleN.counts.hdf5 \
    --interval-merging-rule OVERLAPPING_ONLY -O filtered.interval_list

# 3. GermlineCNVCaller, cohort mode (builds the model AND calls the cohort)
gatk GermlineCNVCaller --run-mode COHORT -L filtered.interval_list \
    --interval-merging-rule OVERLAPPING_ONLY \
    --contig-ploidy-calls ploidy-calls/cohort-calls \
    -I sample1.counts.hdf5 -I sampleN.counts.hdf5 \
    --output-prefix cohort -O gcnv-calls/

# 4. Post-process per sample into a genotyped VCF
gatk PostprocessGermlineCNVCalls \
    --calls-shard-path gcnv-calls/cohort-calls \
    --model-shard-path gcnv-calls/cohort-model \
    --contig-ploidy-calls ploidy-calls/cohort-calls \
    --sample-index 0 \
    --output-genotyped-intervals sample0.intervals.vcf.gz \
    --output-genotyped-segments sample0.segments.vcf.gz \
    --output-denoised-copy-ratios sample0.denoisedCR.tsv

Case mode (--run-mode CASE) scores a new sample against the cohort *-model shards; it must use the identical filtered.interval_list and the same scatter count as the cohort run, or it fails or produces incomparable calls.

Failure Modes

Tangent normalization removes a real CNV

Trigger: PoN is small (< ~20 normals) or contains samples that share a recurrent CNV (e.g. a common germline CNV, or a PoN accidentally built from tumors).

Mechanism: DenoiseReadCounts projects the tumor coverage profile onto the subspace spanned by the PoN's principal components. Any copy-number pattern present in that subspace is treated as "systematic noise" and subtracted. A CNV shared by PoN members is therefore normalized out of the tumor.

Symptom: A known event (recurrent amplification/deletion, or a common germline CNV) is absent from denoisedCR.tsv; denoised profile is suspiciously flat at that locus.

Fix: Build the PoN from >= 20-40 unrelated, tumor-free, process-matched normals. Never put tumors in the PoN. Cross-check against the standardizedCR.tsv (pre-tangent) profile — if the event is there but gone after denoising, the PoN ate it.

Mistaking ModelSegments output for allele-specific integer CN

Trigger: Treating tumor.modelFinal.seg minor-allele fraction as integer minor copy number, or expecting a purity/ploidy field.

Mechanism: GATK somatic CNV models copy ratio and allele fraction but never fits the purity/ploidy grid that converts log-ratio to integer absolute CN.

Symptom: No purity/ploidy in any output; "copy number" is continuous log2; LOH is a low minor-allele fraction, not an explicit CN-LOH state.

Fix: Accept GATK somatic CNV as a relative caller. For integer ASCN, feed the data to PureCN (segmentationGATK4 reuses GATK segments), FACETS, ASCAT, or Sequenza — see allele-specific-copy-number.

FilterIntervals silently drops intervals containing real variants

Trigger: Aggressive mappability or segmental-duplication cutoffs in FilterIntervals.

Mechanism: A minimum mappability > 0 or a maximum segmental-duplication content < 1 excludes intervals overlapping segdups and low-mappability regions — exactly where many disease-relevant CNVs (e.g. recurrent genomic-disorder loci flanked by segdups) live.

Symptom: Known recurrent CNVs at segdup-mediated loci are never called; the gene of interest has no intervals in filtered.interval_list.

Fix: Inspect filtered.interval_list for genes of interest before calling. Relax mappability/segdup cutoffs for targeted analyses; for genomic-disorder loci, depth-based callers are inherently limited near segdups — confirm with an orthogonal assay.

Raw gCNV output has ~22% precision

Trigger: Using unfiltered GermlineCNVCaller / PostprocessGermlineCNVCalls output for association or de novo analysis.

Mechanism: gCNV is tuned for high recall (~95% of rare coding CNVs >= 2 exons) at the cost of precision; raw calls are dominated by false positives.

Symptom: Implausibly many rare CNVs per sample; de novo CNV rate far above the expected ~0.01-0.02/genome.

Fix: Apply the QS (quality score) filter. QS > 100 is a common starting threshold; QS > 1000 reaches ~96% precision. Also apply sample-level filters (call rate, number of CNVs per sample) per Babadi 2023.