bio-copy-number-cnv-annotation

name	bio-copy-number-cnv-annotation
description	Annotate copy number variant segments with overlapping genes, dosage-sensitivity scores, cancer driver databases, population frequencies, and clinical-variant content. Covers bedtools/pybedtools interval intersection, AnnotSV comprehensive annotation and ranking, ClinGen haploinsufficiency/triplosensitivity scoring, gnomAD-SV/DGV frequency filtering, COSMIC Cancer Gene Census, and ClinVar overlap. Use when interpreting which genes a CNV affects, distinguishing the driver gene of a focal event from passengers, filtering against population CNVs, separating whole-gene from partial-gene overlap, or preparing CNVs for clinical classification.
tool_type	mixed
primary_tool	bedtools

Version Compatibility

Reference examples tested with: bedtools 2.31+, AnnotSV 3.4+, Python 3.10+ with pybedtools 0.9+, pandas 2.2+, pysam 0.22+; R 4.3+ with clusterProfiler 4.10+.

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

• CLI: bedtools --version, AnnotSV --version
• Python: pip show pybedtools pandas pysam
• R: packageVersion('clusterProfiler')

If code throws an error, introspect the installed package and adapt the example. AnnotSV output column names change between major versions — verify against the installed version.

CNV Annotation

"Annotate my CNV calls with the genes they affect" -> Overlap CNV segments with gene models, dosage-sensitivity maps, and clinical databases. The hard part is not the intersection — it is deciding which genes matter. A focal amplification overlapping 30 genes usually has one driver (the peak gene); a deletion's consequence depends on whether each gene is dosage-sensitive and whether the whole gene or only part is removed.

• CLI: bedtools intersect -a cnvs.bed -b genes.bed -wa -wb; AnnotSV for full annotation
• Python: pybedtools for interval logic; pysam for VCF database queries

Annotation Strategy — Pick the Database for the Question

Question	Resource	What it answers
Which genes does this CNV span?	RefSeq/GENCODE gene BED	Raw overlap (not yet consequence)
Is loss of this gene damaging?	ClinGen haploinsufficiency (HI) score	Dosage sensitivity to deletion
Is gain of this gene damaging?	ClinGen triplosensitivity (TS) score	Dosage sensitivity to duplication
Is this CNV common in the population?	gnomAD-SV, DGV, 1000G CNV	Benign-frequency filtering
Is this a known recurrent disorder locus?	ClinGen dosage regions, DECIPHER	Genomic-disorder context
Is this a cancer driver?	COSMIC Cancer Gene Census, OncoKB	Oncogene vs tumor-suppressor role
Is there pathogenic small-variant content?	ClinVar	Coincident SNV/indel pathogenicity
One-shot comprehensive annotation + ranking	AnnotSV	Aggregates most of the above

For constitutional CNV classification (assigning pathogenic/VUS/benign), the annotated output feeds the ACMG/ClinGen points framework — see germline-cnv-interpretation. For cohort-level recurrence and driver-peak identification, see recurrent-cnv.

The Core Distinction: Overlap Is Not Consequence

A CNV overlapping a gene does not necessarily change that gene's dosage in a way that matters. Three refinements separate annotation from interpretation:

1. Whole-gene vs partial overlap. A deletion spanning an entire gene removes one copy (clean haploinsufficiency test). A deletion removing only the last two exons creates a truncated allele — a different, often more damaging, consequence. Always record the fraction of each gene covered and whether coding exons or only introns/UTRs are hit.
2. Dosage sensitivity. Most genes tolerate single-copy loss. ClinGen HI/TS scores (3 = sufficient evidence for dosage sensitivity, 0 = no evidence, 30 = gene associated with an autosomal-recessive phenotype, 40 = dosage sensitivity unlikely) indicate which genes' loss/gain is actually consequential.
3. Driver vs passenger in focal events. A focal amplification carries many genes; the driver is the one under selection, typically at the recurrence peak across a cohort (GISTIC) and a known oncogene. Annotating all 30 genes as "amplified" overstates.

Gene Overlap with bedtools

Goal: Find genes overlapping each CNV segment, recording overlap extent.

Approach: Convert segments to BED, intersect with a gene model, keep both feature sets (-wo reports the overlap length) so partial vs whole-gene overlap is recoverable.

# Segments to BED (CNVkit .cns example; columns chrom/start/end/log2)
awk 'NR>1 {print $1"\t"$2"\t"$3"\t"$5}' sample.cns > sample.cnv.bed

# Intersect; -wo appends the number of overlapping bases
bedtools intersect -a sample.cnv.bed -b gencode.genes.bed -wo > cnv_gene_overlap.txt

Comprehensive Annotation with AnnotSV

Goal: Annotate CNVs against genes, dosage maps, population frequency, and clinical databases in one pass, with a built-in pathogenicity ranking.

Approach: Export CNVs to VCF or BED and run AnnotSV; it returns a "full" line per gene plus a "split" summary, with an ACMG-aligned rank (1-5).

AnnotSV \
    -SVinputFile sample.cnv.vcf \
    -genomeBuild GRCh38 \
    -annotationMode both \
    -outputFile sample_annotated.tsv

# Output includes: overlapped genes, ClinGen HI/TS, gnomAD-SV/DGV frequency, OMIM,
# ClinVar, DECIPHER, and an ACMG-class rank per SV.

AnnotSV's rank is a useful triage signal, not a final classification — confirm against the ClinGen points framework for clinical reporting.

Dosage-Sensitivity and Driver Annotation

Goal: Tag each affected gene with its dosage sensitivity and, for tumors, its driver role, so passengers can be separated from drivers.

Approach: Join the gene-overlap table to the ClinGen dosage map (HI/TS scores) and to the COSMIC Cancer Gene Census; flag CNVs whose direction matches a known mechanism (oncogene amplified, tumor suppressor deleted).

import pandas as pd

def annotate_dosage_and_drivers(overlap_tsv, clingen_dosage, cgc_file):
    '''Tag overlapped genes with ClinGen HI/TS and COSMIC driver role.'''
    cols = ['cnv_chrom', 'cnv_start', 'cnv_end', 'log2',
            'gene_chrom', 'gene_start', 'gene_end', 'gene', 'overlap_bp']
    df = pd.read_csv(overlap_tsv, sep='\t', names=cols)
    df['gene_len'] = df['gene_end'] - df['gene_start']
    df['gene_frac_covered'] = (df['overlap_bp'] / df['gene_len']).clip(upper=1.0)
    df['whole_gene'] = df['gene_frac_covered'] >= 0.99

    dosage = pd.read_csv(clingen_dosage, sep='\t')  # gene, HI_score, TS_score
    df = df.merge(dosage, on='gene', how='left')

    cgc = pd.read_csv(cgc_file, sep='\t')
    role = dict(zip(cgc['Gene Symbol'], cgc['Role in Cancer']))
    df['driver_role'] = df['gene'].map(role)

    # Direction-consistent driver hits: oncogene amplified or TSG deleted.
    df['driver_hit'] = (
        ((df['log2'] > 0.3) & df['driver_role'].fillna('').str.contains('oncogene')) |
        ((df[] < -) & df[].fillna()..contains()))
     df

Population-Frequency Filtering

Goal: Remove common, presumed-benign CNVs before clinical interpretation.

Approach: Reciprocal-overlap match each CNV against a population SV catalog (gnomAD-SV, DGV); a CNV with high reciprocal overlap to a common population CNV of the same type is likely benign.

# 50% reciprocal overlap (-f 0.5 -r): same-type, similar-extent population match.
# Reciprocal overlap, not one-sided, prevents a tiny CNV inside a huge population CNV
# (or vice versa) from being wrongly matched.
bedtools intersect -a sample.cnv.bed -b gnomad_sv.bed -f 0.5 -r -wa -wb \
    > cnv_population_match.txt

Pathway Enrichment of Affected Genes

Goal: Test whether genes in amplified (or deleted) regions are enriched for pathways.

Approach: Extract genes by CNV direction, map to Entrez IDs, run GO/KEGG enrichment. Caveat: CNVs are large and gene-dense, so enrichment is biased toward whatever pathways cluster in CNV-prone genomic regions — interpret as hypothesis-generating.

library(clusterProfiler)
library(org.Hs.eg.db)

amp_genes <- unique(cnv_annot$gene[cnv_annot$log2 > 0.3])
entrez <- na.omit(mapIds(org.Hs.eg.db, keys = amp_genes,
                         keytype = 'SYMBOL', column = 'ENTREZID'))
go_bp <- enrichGO(gene = entrez, OrgDb = org.Hs.eg.db, ont = 'BP',
                  pAdjustMethod = 'BH', qvalueCutoff = 0.05)

Failure Modes

Genome-build mismatch between CNVs and annotation

Trigger: CNV coordinates on GRCh37 intersected with a GRCh38 gene model (or vice versa).

Mechanism: Coordinates silently shift; the intersection succeeds and returns wrong genes.

Symptom: Implausible gene assignments; a known driver locus annotated with the wrong gene; systematic offset.

Fix: Confirm both inputs are the same build. If not, liftOver the CNVs (note that liftOver can split or drop segments across assembly gaps) and verify a known landmark.

Annotating all overlapped genes as the "affected" genes

Trigger: Reporting every gene a focal amplification spans as amplified/driver.

Mechanism: Focal events are megabases wide and gene-dense; only the selected gene is the driver.

Symptom: A 2 Mb amplicon "amplifies" 40 genes; the report cannot distinguish ERBB2 from its passengers.

Fix: For focal events, prioritize the gene at the cohort recurrence peak (GISTIC, see recurrent-cnv) and known drivers (CGC/OncoKB). Report passengers separately or not at all.

ClinVar CLNSIG parsing errors

Trigger: Naive string matching on the ClinVar CLNSIG INFO field.

Mechanism: CLNSIG is multi-valued, mixes terms ("Conflicting_classifications", "Pathogenic/Likely_pathogenic", "Benign/Likely_benign"), and is per-small-variant — not per-CNV. A substring match for "pathogenic" silently captures "Likely_pathogenic" (intended) but a careless match also fires on records that are conflicting or benign once underscores and slashes are involved.

Symptom: Benign or conflicting variants reported as pathogenic; CNV flagged on incidental nearby SNVs.

Fix: Parse CLNSIG against the controlled vocabulary; exclude "Conflicting" and benign terms explicitly. Remember ClinVar SNV/indel pathogenicity does not transfer to a CNV — use it as context, and use ClinVar's own CNV records or ClinGen dosage regions for the CNV itself.

Equating overlap with consequence

Trigger: Treating any gene-overlapping CNV as functionally significant.

Mechanism: Most single-copy losses are tolerated; partial overlaps may hit only introns/UTRs.

Symptom: Long lists of "affected" dosage-insensitive genes; benign CNVs over-called as significant.

Fix: Require dosage evidence (ClinGen HI/TS) and record coding-exon overlap and whole-gene-vs-partial status before calling a gene affected.

Quantitative Thresholds

Threshold	Value	Source / Rationale
Population-CNV reciprocal overlap	>= 50% (-f 0.5 -r)	Standard reciprocal-overlap match for benign filtering (convention traces to gnomAD-SV / DGV workflows; Collins SR et al 2020 Nature 581:444 uses comparable reciprocal-overlap thresholds for benign-population matching)
Common-CNV benign frequency	> 1% population frequency	ACMG/ClinGen: high frequency supports benign
ClinGen HI/TS dosage-sensitive	score = 3	ClinGen: sufficient evidence for dosage sensitivity
Whole-gene overlap	>= 99% gene length covered	Distinguishes clean haploinsufficiency from partial/truncating
AnnotSV pathogenic ranks	rank 4-5	AnnotSV ACMG-aligned ranking (1 benign - 5 pathogenic)

Common Errors

Error / symptom	Cause	Solution
Wrong genes assigned to a CNV	hg19/hg38 build mismatch	Match builds; liftOver and verify a landmark
40 genes called "amplified" for one amplicon	All overlapped genes reported as drivers	Prioritize recurrence-peak + known-driver genes
Benign variants flagged pathogenic	Substring match on ClinVar CLNSIG	Parse the controlled vocabulary explicitly
Tiny CNV matched to a huge population CNV	One-sided overlap used for frequency filter	Use reciprocal overlap (-f 0.5 -r)
AnnotSV columns not found	Column names differ across AnnotSV versions	Check head of the output for the installed version
Enrichment dominated by gene-dense loci	CNVs span gene clusters	Treat CNV-gene enrichment as hypothesis-generating

References

• Geoffroy V et al 2018. AnnotSV: an integrated tool for structural variations annotation. Bioinformatics 34:3572
• Riggs ER et al 2020. Technical standards for the interpretation and reporting of constitutional copy-number variants: ACMG and ClinGen. Genet Med 22:245
• Collins RL et al 2020. A structural variation reference for medical and population genetics (gnomAD-SV). Nature 581:444
• Sondka Z et al 2018. The COSMIC Cancer Gene Census. Nat Rev Cancer 18:696

Related Skills

• copy-number/germline-cnv-interpretation - ACMG/ClinGen points-based CNV classification
• copy-number/recurrent-cnv - GISTIC2 recurrence peaks and driver-gene identification
• copy-number/cnvkit-analysis - Generates the CNV segments to annotate
• copy-number/cnv-visualization - Visualizing annotated CNVs
• pathway-analysis/go-enrichment - GO/KEGG enrichment methodology and caveats
• genome-intervals/bed-file-basics - BED interval operations
• clinical-databases/clinvar-lookup - Querying ClinVar for variant pathogenicity