CNV Interpretation

A copy number variant is, at root, a dosage problem. The reference human genome is diploid — two copies of every autosomal segment — and a CNV is any deviation from that count: a deletion drops a region to one copy (or zero, if homozygous), a duplication raises it to three. The clinical question is never simply is there a CNV but rather does changing the dose of the genes inside it matter. That reframing is the whole discipline of CNV interpretation.

Most genes tolerate having one copy knocked out or an extra copy added — the remaining allele, plus feedback regulation, keeps protein output close enough to normal. These genes are dosage-insensitive, and CNVs over them are usually benign. A minority of genes are dosage-sensitive: their phenotype depends on having exactly two functioning copies. Lose one and you get haploinsufficiency (50% of normal product is not enough); gain one and you get a triplosensitivity effect (150% is too much). Pathogenic CNVs are, almost by definition, the ones that sweep up a dosage-sensitive gene.

This is why size alone is a poor guide. A 2 Mb deletion across a gene desert can be benign, while a focused 300 kb deletion taking out a single haploinsufficient transcription factor is unambiguously pathogenic. CNVs span kilobases to whole chromosome arms, and the same locus can be benign as a duplication but pathogenic as a deletion (or vice versa), because haploinsufficiency and triplosensitivity are independent properties.

• Benign population CNVs are the genomic background noise of being human — common (often >1% frequency), frequently in gene-poor or highly polymorphic regions, and catalogued in the Database of Genomic Variants and gnomAD-SV.
• Pathogenic CNVs are rare, typically arose recently (often de novo), and overlap genes with established dosage sensitivity.

In neurodevelopmental practice this single variant class carries remarkable weight: CNVs explain roughly 15–20% of intellectual disability and autism diagnoses — more than any other single category of genetic variation, which is precisely why chromosomal microarray became the first-tier test for these indications.

Every CNV detection method ultimately answers the same question — how much DNA is here compared to a normal genome? — but they do it through different physics, and those differences explain both what each platform catches and what it silently misses.

Chromosomal microarray (CMA) is the workhorse. It became the recommended first-tier test for unexplained developmental delay, intellectual disability, autism, and multiple congenital anomalies because it roughly doubled the diagnostic yield of the karyotype it replaced (~15–20% vs ~3%) — the consensus that drove this shift in clinical practice Miller et al. 2010. There are two flavors. Array CGH competitively hybridizes patient DNA against a reference and reads the fluorescence ratio: more signal means a gain, less means a loss. SNP arrays instead genotype thousands of polymorphic sites and infer copy number from allele intensity and genotype pattern. That second channel is the key clinical advantage: SNP arrays see copy-neutral loss of heterozygosity — long stretches where copy number is a normal 2 but both copies came from one parent (segmental UPD) or one ancestor (identity by descent). aCGH is blind to this because the dose is normal; only the genotype pattern reveals it.

Understanding the mechanism also predicts the blind spots. CMA measures quantity, not arrangement, so it cannot see a balanced rearrangement — a reciprocal translocation or inversion has no net gain or loss of DNA, so the array reads normal even though a gene may be disrupted at the breakpoint. It also misses sequence-level variants, repeat expansions, and low-level mosaicism, because a small mutant fraction barely shifts the averaged signal.

Genome sequencing detects CNVs through three complementary signatures: read depth (fewer or more reads piling up over a deleted or duplicated region), discordant read pairs (mate pairs that map too far apart or in the wrong orientation), and split reads (single reads that straddle a breakpoint). Combining these gives finer resolution — down to hundreds of base pairs — and near base-pair breakpoint mapping, and in principle it can flag balanced events that CMA never could. The cost is bioinformatic: CNV calling from sequencing is genuinely hard, uneven coverage in GC-rich regions and segmental duplications degrades sensitivity exactly where many recurrent disease CNVs live, and callers disagree, so results need careful curation.

Before 2019, CNV interpretation was inconsistent and heavily size-driven — labs often called anything above an arbitrary length 'likely pathogenic.' The joint ACMG/ClinGen technical standards replaced that intuition with a quantitative, point-based scoring system, deletions and duplications scored on separate scoresheets Riggs et al. 2020. The logic mirrors the ACMG/AMP sequence-variant framework — accumulate evidence, sum it, land in one of five tiers (Pathogenic, Likely Pathogenic, VUS, Likely Benign, Benign) — but the evidence categories are tailored to dosage.

The core conceptual move is that points add up. Each line of evidence is worth a positive or negative value; a deletion reaching ≥0.99 points is Pathogenic, ≥0.90 is Likely Pathogenic, –0.89 to 0.89 is VUS, and increasingly negative totals are Likely Benign then Benign. No single observation usually decides the case — pathogenicity emerges from converging evidence, which is why a de novo CNV over a known haploinsufficient gene with a matching phenotype classifies confidently while any one of those facts alone would not.

The scoresheet walks through five evidence sections:

• Section 1 — genomic content. Does the CNV even contain protein-coding genes or known functional elements? An empty region scores nothing; this is where the framework deliberately abandoned size cutoffs in favor of what's inside.
• Section 2 — overlap with established dosage-sensitive regions. Complete overlap of a ClinGen-curated haploinsufficient/triplosensitive gene or an established genomic-disorder region carries the most weight (up to ~0.90 on its own); partial overlap that does or doesn't disrupt the critical gene scores differently.
• Section 3 — number of protein-coding genes. A crude proxy: more genes, slightly more points, capping the contribution so gene count never dominates.
• Section 4 — literature/database evidence for this specific region and phenotype, including case-level data and segregation.
• *Section 5 — inheritance and de novo status* in the patient at hand.

The engine underneath Section 2 is ClinGen's curated dosage map. Each gene and region gets a haploinsufficiency (HI) score and a triplosensitivity (TS) score on a 0–3 scale, where 3 means 'sufficient evidence that losing (or gaining) a single copy causes disease.' Crucially, the standards insist on uncoupling the variant's intrinsic classification from its meaning for the individual: a CNV can be objectively Pathogenic yet, at a reduced-penetrance or imprinted locus, still not explain a given patient's presentation — two separate judgments that should not be blurred on the report.

Some deletions and duplications appear over and over, in unrelated patients, with nearly identical breakpoints and the same size every time. This recurrence is not coincidence — it is written into the architecture of the genome itself. These loci are flanked by low-copy repeats (segmental duplications): long blocks of DNA, often >95% identical, sitting on either side of the region.

The mechanism is non-allelic homologous recombination (NAHR). During meiosis, chromosomes pair up and the recombination machinery looks for matching sequence to align. The flanking repeats are so similar that the machinery can mistake the repeat on one side for the repeat on the other — aligning paralogous (non-allelic) copies instead of true alleles. A crossover in that misaligned configuration excises the intervening segment on one product (deletion) and adds it to the reciprocal product (duplication). Because the repeats define the alignment, the breakpoints — and therefore the CNV size — are stereotyped. This is what makes these conditions genomic disorders: the disease is a property of where the repeats sit, not of any single mutation.

This explains several clinical observations at once. It explains why deletion and duplication of the same region often coexist as paired syndromes (they are reciprocal products of one event). It explains the consistent ~1.5–3 Mb sizes. And it explains why these are among the most common identifiable causes of syndromic neurodevelopmental disease — the genomic architecture keeps regenerating the same CNVs in the population, generation after generation. Contrast this with non-recurrent CNVs, which have scattered, patient-specific breakpoints and arise through different routes (non-homologous end joining, or replication-based mechanisms such as FoSTeS/MMBIR), where the deleted region varies from case to case.

The classic neurogenetic genomic disorders — 22q11.2 deletion (DiGeorge/velocardiofacial), Williams-Beuren (7q11.23), 1p36 deletion, and the imprinted 15q11–q13 region underlying Angelman and Prader-Willi — all share this NAHR signature, which is why a microarray can recognize them at a glance from their reproducible coordinates.

A CNV report is a clinical instrument, and its job is to let a clinician act without re-deriving the lab's reasoning. Three things make that possible: unambiguous coordinates, an honest classification, and a clear statement of what the finding means for this patient.

Coordinates must be reproducible. Because CNV size and breakpoints are the whole story, the report has to anchor them to a specified genome build — the same deletion has different numerical coordinates on GRCh37 versus GRCh38, and mixing builds is a recurring source of error. Good reports give ISCN nomenclature, the linear coordinates, the build, and a summary of the gene content so the reader can judge dosage at a glance.

Inheritance is often the deciding evidence, and this is where the report's value is highest. Testing both parents (a 'trio') converts an uninterpretable VUS into something actionable surprisingly often: a de novo CNV is roughly an order of magnitude more likely to be pathogenic than an inherited one, because if a variant were benign you would usually find it sitting harmlessly in a parent. The asymmetry must be read with care, though — inheritance from an unaffected parent argues against pathogenicity only when the locus shows full penetrance and no imprinting. At reduced-penetrance loci, an unaffected carrier parent is fully compatible with a pathogenic call; at imprinted loci like 15q11–q13, the parent of origin, not mere inheritance, determines the phenotype.

The VUS must be communicated as genuinely uncertain — neither reassurance nor diagnosis. A VUS cannot drive medical decisions, and the report should say so plainly while offering the concrete next steps that can resolve it: parental testing, deeper phenotyping, and periodic reclassification as databases mature. Honest uncertainty prevents both false reassurance and overtreatment.

Finally, genome-wide assays produce incidental and secondary findings — a CNV unrelated to the referral question, sometimes with implications for an adult-onset condition or for relatives. Because microarray scans the whole genome at once, this possibility is foreseeable, so it belongs in pre-test counseling rather than arriving as a surprise on the report.