While the human genome is often pictured as a static blueprint, it is dynamic. One significant type of genetic alteration is copy number variation (CNV), which involves changes in the number of copies of specific DNA segments. Analyzing these variations offers insights into human diversity, health, and disease.
What Are Copy Number Variations?
Copy number variations (CNVs) are structural variants where a large segment of DNA is either deleted or duplicated. Unlike a genetic typo, a CNV is like entire pages of an encyclopedia being missing or copied. These segments can range in size from short to millions of DNA bases long.
CNVs are distinct from single nucleotide polymorphisms (SNPs), which are changes to a single DNA base. Because CNVs involve larger stretches of genetic material, they can have a more substantial impact on an individual’s traits. A single CNV can encompass multiple genes, altering their “dosage” and affecting the amount of protein produced.
CNVs arise from errors when cells divide and replicate their DNA, causing segments to be accidentally deleted or duplicated. These recombination errors are a source of genetic diversity. While some CNVs are inherited, others can occur for the first time in an individual.
The gene for amylase, an enzyme that helps digest starch, illustrates how CNVs contribute to genetic differences. This gene is present in varying copy numbers among different human populations. Groups with historical diets high in starch tend to have more copies of the amylase gene, suggesting an evolutionary adaptation.
Techniques for Analyzing Copy Number Variations
A widely used technique for detecting CNVs is the chromosomal microarray (CMA). This method spreads a person’s DNA over a slide containing millions of probes that stick to specific locations in the genome. By measuring how much DNA binds to each probe, scientists identify regions with fewer or more copies than expected.
Another method is quantitative PCR (qPCR), a technique that measures the copy number of a specific, small DNA segment. It is often used to confirm a CNV that was first identified by a broader method like a microarray. This targeted approach precisely quantifies a DNA sequence to validate a deletion or duplication.
Next-generation sequencing (NGS) is also used for CNV analysis. Technologies like whole-genome sequencing (WGS) and whole-exome sequencing (WES) read a person’s genetic code. Computer programs then analyze this data for differences in read depth—the number of times a DNA segment is sequenced—to detect CNVs. This approach can identify smaller and more complex variations.
Each technique has different advantages. Microarrays provide a reliable, genome-wide view and are efficient for analyzing many samples. In contrast, NGS-based methods offer higher resolution for detecting smaller CNVs and mapping their precise boundaries. The choice of technique depends on the specific research question or clinical context.
Impact of Copy Number Variations on Human Traits and Diseases
Many CNVs are benign and contribute to the normal range of human traits without causing health problems. As a common feature of the genome, these variations influence physical characteristics and other non-disease traits, helping make each person genetically unique.
Some CNVs are associated with a wide range of human diseases. The impact of a CNV depends on its size, location, and whether it is a deletion or duplication. For example, certain CNVs that disrupt or change a gene’s copy number have been linked to developmental disorders like autism spectrum disorder and intellectual disability.
CNVs are also contributing factors in susceptibility to autoimmune diseases, certain types of cancer, and cardiovascular disease. In cancer, for instance, deletions of tumor suppressor genes or duplications of oncogenes can drive tumor growth. The specific genes contained within the CNV determine its potential health effect.
The effect of a CNV can be complex. A particular variation might have a strong, direct effect that leads to a specific genetic disorder. In other cases, a CNV might only slightly increase the risk for a condition by interacting with other genetic and environmental factors.
Understanding the Outcomes of CNV Analysis
Once a CNV is identified, the next step is interpreting its significance. The CNV is classified into categories: pathogenic (disease-causing), likely pathogenic, benign (harmless), likely benign, or a variant of uncertain significance (VUS). This classification is based on the CNV’s size and gene content, and on previous reports in scientific databases.
A CNV’s interpretation has direct implications for health care. A pathogenic finding can provide a diagnosis, predict a condition’s course, and guide treatment. For example, identifying a specific CNV can end a family’s diagnostic journey and provide information about the risk for other family members.
A significant challenge is interpreting a variant of uncertain significance (VUS), a CNV whose health impact is unknown. This result can be frustrating for families. Genetic counseling is often recommended to explain the uncertainty and discuss next steps, such as further testing or monitoring.
Beyond patient care, CNV analysis contributes to a broader understanding of human genetics. Each newly identified CNV adds to our collective knowledge base. This helps researchers discover new gene-disease connections, leading to the development of new diagnostic tools and therapies.