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

Total score	Classification
>= 0.99	Pathogenic
0.90 to 0.98	Likely pathogenic
-0.89 to 0.89	Variant of uncertain significance (VUS)
-0.90 to -0.98	Likely benign
<= -0.99	Benign

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

Step	Source	Automatable
Gene content, functional elements	RefSeq/GENCODE	Yes (ClassifyCNV/AnnotSV)
Established HI/TS gene & region overlap	ClinGen dosage map	Yes
Protein-coding gene count	Gene model	Yes
Population frequency (benign evidence)	gnomAD-SV, DGV	Yes
Case-control / prior probands / phenotype fit	Literature, DECIPHER, internal DB	Partial — interpreter scores
De novo status, segregation	Trio/family data	No — interpreter scores

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.

Partial-gene overlap scored as whole-gene loss

Trigger: Scoring a deletion that removes only part of a haploinsufficient gene as a full-gene loss.

Mechanism: The rubric distinguishes whole-gene loss from partial overlap; a deletion of a few exons may create a truncating allele with different (sometimes greater) impact, scored under different criteria.

Symptom: Partial-gene CNVs mis-scored; truncating deletions under- or over-weighted.

Fix: Record whether the CNV removes the whole gene or part of it, and which exons; apply the rubric's partial-overlap criteria explicitly.

Reconciliation

Pattern	Likely cause	Action
ClassifyCNV VUS, AnnotSV rank 4	Different weighting of the same evidence	Re-derive points manually against the 2019 standard
Tool says benign, locus is a known disorder	Stale dosage database or build mismatch	Update databases; verify build
Two interpreters disagree on a VUS	Section 4-5 evidence weighted differently	Use the ClinGen calculator; document each criterion
De novo deletion still VUS	Section 5 points not added	Add confirmed-de-novo points

Operational rule: A clinical CNV classification is final only when (1) the CNV is confirmed constitutional, (2) databases and builds are current and consistent, (3) the automatable Sections 1-3 are scored by a tool, and (4) the interpreter has scored Sections 4-5 from case-specific evidence. Document each criterion and its points; the ClinGen web calculator is the reference tally.

Quantitative Thresholds

Threshold	Value	Source / Rationale
Pathogenic	total score >= 0.99	Riggs 2020 ACMG/ClinGen technical standards
Likely pathogenic	0.90 to 0.98	Riggs 2020
VUS	-0.89 to 0.89	Riggs 2020
Likely benign	-0.90 to -0.98	Riggs 2020
Benign	<= -0.99	Riggs 2020
Established dosage sensitivity	ClinGen HI/TS score = 3	ClinGen: sufficient evidence
Common-CNV benign frequency	high population frequency	Section 2/4 benign evidence

Common Errors

Error / symptom	Cause	Solution
Tumor CNVs classified "pathogenic germline"	Constitutional rubric applied to somatic	Use somatic oncology frameworks
Almost everything classified VUS	Sections 4-5 not scored	Add de novo/segregation/literature points
Known disorder locus scored benign	Stale dosage DB or build mismatch	Update ClinGen databases; check build
Wrong genes scored	Build mismatch	Align coordinates, --GenomeBuild, databases
Partial-gene deletion mis-scored	Whole-gene assumption	Apply partial-overlap criteria
ClassifyCNV vs AnnotSV disagree	Different evidence weighting	Re-derive against the 2019 standard manually

References

• Riggs ER et al 2020. Technical standards for the interpretation and reporting of constitutional copy-number variants: a joint consensus recommendation of ACMG and ClinGen. Genet Med 22:245
• Gurbich TA, Ilinsky VV 2020. ClassifyCNV: a tool for clinical annotation of copy-number variants. Sci Rep 10:20375
• Geoffroy V et al 2018. AnnotSV: an integrated tool for structural variations annotation. Bioinformatics 34:3572
• Rehm HL et al 2015 NEJM 372:2235 (ClinGen launch / framework). Dosage-sensitivity curation methodology is in Riggs ER et al 2012 Genet Med 14:680 (original ClinGen dosage-sensitivity workflow). Current ClinGen Dosage Sensitivity Map: clinicalgenome.org.

Related Skills

• copy-number/cnv-annotation - Gene, dosage, and database annotation feeding the rubric
• copy-number/gatk-cnv - GATK-gCNV germline CNV calling
• copy-number/cnvkit-analysis - Germline CNV calling from panels/exomes
• clinical-databases/clinvar-lookup - ClinVar CNV records and prior classifications
• clinical-databases/variant-prioritization - Somatic variant tiering (the non-germline path)
• clinical-databases/gnomad-frequencies - Population frequency for benign evidence