Bio Copy Number Germline Cnv Interpretation

Version Compatibility

Reference examples tested with: ClassifyCNV 1.1+, AnnotSV 3.4+, Python 3.10+ with pandas 2.2+; bedtools 2.31+.

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

• CLI: python ClassifyCNV.py --help, AnnotSV --version
• Update the bundled ClinGen/dosage databases — ClassifyCNV ships an update_clingen.sh; dosage curation changes, and a stale database silently mis-scores.

This skill is for constitutional/germline CNVs only. Somatic tumor CNVs use a different framework (AMP/ASCO/CAP and OncoKB tiers) — do not apply ACMG/ClinGen constitutional scoring to a tumor.

Germline CNV Interpretation

"Is this constitutional CNV pathogenic" -> Apply the 2019 ACMG/ClinGen technical standards: a semiquantitative, points-based rubric that sums evidence into one of five clinical categories. There are two separate rubrics — one for copy-number loss, one for copy-number gain — because the evidence for deletion and duplication pathogenicity is different. The total score maps to a five-tier classification.

• CLI: ClassifyCNV (automates the observed-evidence sections), AnnotSV (ACMG-aligned rank)
• Manual: case-specific evidence (de novo status, segregation, prior literature) is scored by the interpreter, not the tool

The Points Framework

Evidence is grouped into sections (the loss and gain rubrics each have five). For copy-number loss: Section 1 — does the CNV contain protein-coding or functionally important elements; Section 2 — overlap with established haploinsufficient genes/regions (strong positive) or established benign regions (strong negative); Section 3 — number of protein-coding genes; Section 4 — detailed case/literature evidence (case-control, prior probands, phenotype specificity); Section 5 — inheritance (de novo with confirmed parentage is strong positive; inherited from an unaffected parent is negative). The gain rubric is structured the same way but keyed to triplosensitivity and the distinct evidence base for duplications.

The decisive postdoc-level point: a tool can only score the evidence it is given. ClassifyCNV and AnnotSV automate Sections 1-3 (gene content, dosage-region overlap, population frequency) well; Sections 4-5 (de novo status, segregation, literature) require the interpreter to supply points. An unsupervised tool run therefore systematically lands CNVs in VUS — the absence of family/literature evidence is not neutral, it is unscored.

Classification Workflow

Semi-Automated Scoring with ClassifyCNV

Goal: Score the automatable ACMG/ClinGen sections for a set of constitutional CNVs.

Approach: Provide CNVs as a BED with an explicit DEL/DUP type; ClassifyCNV applies the 2019 rubric against the bundled ClinGen databases and emits a per-CNV scoresheet.

# Input BED: chrom, start, end, type  (type = DEL or DUP)
python ClassifyCNV.py \
    --infile constitutional_cnvs.bed \
    --GenomeBuild hg38 \
    --precise \
    --outdir classifycnv_out

# Output Scoresheet.txt: per-CNV total score, classification, and per-criterion points.

import pandas as pd

def review_classifycnv(scoresheet):
    '''Flag CNVs whose ACMG class likely changes once case-specific evidence is added.'''
    df = pd.read_csv(scoresheet, sep='\t')
    # VUS CNVs near a tier boundary are the ones where de novo / segregation evidence
    # (Sections 4-5, not scored automatically) would tip the classification.
    df['near_boundary'] = df['Total score'].between(0.60, 0.89) | \
                          df['Total score'].between(-0.89, -0.60)
    df['needs_manual_evidence'] = (df['Classification'] == 'VUS') & df['near_boundary']
    return df

Comprehensive Annotation Cross-Check with AnnotSV

AnnotSV -SVinputFile constitutional_cnvs.vcf -genomeBuild GRCh38 \
    -annotationMode both -outputFile annotsv_out.tsv
# AnnotSV emits an ACMG-aligned rank (1 benign - 5 pathogenic) per SV; use it to
# cross-check ClassifyCNV, not as a standalone clinical classification.

Failure Modes

Applying constitutional scoring to a somatic CNV

Trigger: Running ACMG/ClinGen germline classification on tumor copy number.

Mechanism: The 2019 standards are explicitly constitutional; somatic CNV clinical significance uses the AMP/ASCO/CAP tier system and oncology evidence (therapy, prognosis).

Symptom: Tumor amplifications classified as "pathogenic germline variants"; clinically meaningless report.

Fix: Confirm the CNV is constitutional (present in germline DNA). For tumors, use somatic oncology frameworks — see clinical-databases/variant-prioritization.

Treating a tool's VUS as a final answer

Trigger: Reporting ClassifyCNV/AnnotSV output verbatim without adding case evidence.

Mechanism: Tools score gene content, dosage overlap, and frequency, but not de novo status, segregation, or literature; absent that input the score sits in the VUS band.

Symptom: Nearly every novel CNV classified VUS; clinically relevant de novo deletions under-called.

Fix: Treat tool output as the Section 1-3 baseline. Add Section 4-5 points from trio data, segregation, DECIPHER, and literature before issuing a classification. A VUS near a tier boundary specifically signals missing case evidence.

Stale ClinGen dosage database

Trigger: Using ClassifyCNV/AnnotSV bundled databases without updating.

Mechanism: ClinGen dosage curation is ongoing; HI/TS scores and dosage-sensitive regions change. A stale database scores Section 2 wrong.

Symptom: A gene with a newly curated HI score 3 is scored as having no dosage evidence; classification too low.

Fix: Run the database update script before a classification batch; record the ClinGen release date in the report.

Genome-build mismatch

Trigger: CNV coordinates and the --GenomeBuild argument (or annotation databases) on different builds.

Mechanism: Coordinates silently shift; the wrong genes and dosage regions are scored.

Symptom: Implausible gene content; a known disorder locus scored as gene-poor.

Fix: Confirm CNV coordinates, --GenomeBuild, and all databases are the same build; verify a landmark CNV.