CNVs (Copy Number Variations) are a type of structural variants of submicroscopic size, such as deletions, duplications, insertions, inversions and translocations, which involve a change in copy number with respect to the reference genome (Figure 1). Although they are relatively common in the structure of our genomes and contribute greatly to interindividual variability, their involvement in a large number of diseases such as autism, Parkinson's, Alzheimer's, mitochondrial diseases or cancer, among others, has also been proven.
Next-generation Sequencing (NGS) technologies have become a key tool for the identification of CNVs, almost completely replacing others such as CGH arrays and MLPAs. However, the analysis of the data from exome sequencing exome sequencing has limitations that must be taken into account, as they may affect the accuracy of the results:
- Only imbalances of genomic information can be defined, while rearrangements that do not involve a net loss or gain, such as inversions or balanced translocations, will go undetected.
- The whole genome sequence is not available, which affects the detection of alterations that include uncaptured regions.
- The library preparation and capture process may result in regions with different coverage.
- Repetitive sequences make it difficult to uniquely map reads from sequencing.
- In tumour samples, copy number quantification may be compromised by low tumour percentage and intratumoural heterogeneity.
Although there are no reference standards for the analysis of CNVs, a number of recommendations should be followed whenever possible to achieve optimal results:
- Starting from good quality DNA.
- When several samples are analysed together, carry out DNA extraction, library generation, sequencing and bioinformatics analysis as uniformly as possible.
- Ensure proper coverage to increase the sensitivity and specificity of variant detection and facilitate quantification.
- Sequencing on platforms that offer longer read lengths. This will make it more likely to include the entire sequence of a CNV in a single read, improving its discovery and mapping.
- Have a set of controls that have the same origin and have been processed in the same way.
- Ensure tumour purity of more than 60% in neoplastic samples.
The characterisation of CNVs remains complicated, so it is necessary to be aware of their limitations. Furthermore, proper validation of those variants found, especially in the clinical setting, is key. The development of publicly accessible databases, both in controls and patients, will contribute in the future to elucidate the true significance of all CNVs analysed.
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- Zhao M. et al. (2013) Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives. BMC Bioinformatics.