pku-yuangroup 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 Clin Genet 81:403 (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