Copy Number Variations: Deciphering Their Role in Complex Diseases

Copy number variation (CNV) refers to the difference in the dosage of genomic segments, ranging in size from one kilobase (kb) to several megabases (Mb), when compared to a reference human genome. CNVs can result from structural variations within the genome, including deletions, duplications, insertions, or unbalanced translocations and inversions, which can lead to either a loss or gain of genomic segments. They can influence gene expression, chromatin organization, and gene regulation. CNVs have been implicated in the etiology of various complex diseases, including neuropsychiatric disorders such as autism and schizophrenia.

Some of the impacts of CNVs on complex diseases include:
- Dosage imbalance: CNVs can lead to the loss or gain of genetic material, which can alter the expression of genes and potentially cause diseases.
- Gene disruption: CNVs can disrupt genes, leading to the loss of function or the gain of new functions, which can contribute to the development of complex diseases.
- Complex trait associations: CNVs have been associated with various complex traits, including genetic regions linked to Alzheimer's disease and schizophrenia. However, the relationship between CNVs and diseases is not straightforward, and further research is needed to understand the underlying mechanisms.
- Evolutionary implications: CNVs have been found to play an important role in human evolution, with copy number changes accounting for more differences between the human and chimpanzee genomes than other forms of mutation.
- Clinical impact: CNV analysis has become a powerful tool for identifying genomic alterations in clinical diagnostic laboratories, leading to the discovery of new syndromes and refining genotype-phenotype relationships in known disorders.

Despite the growing evidence for the involvement of CNVs in complex diseases, the understanding of the functional mechanisms by which CNVs cause diseases is still limited. As studies relating CNV to diseases expand, our understanding of human diversity, the causes and development of complex diseases, and disease resistance will grow accordingly.

CNVs are categorized into two main types: copy number polymorphisms (CNPs), which are common and often small (less than 10 kilobases), and rare variants, which are much longer (hundreds of thousands to over 1 million base pairs). CNPs are associated with complex genetic diseases like psoriasis, Crohn's disease, and glomerulonephritis, while the larger, rarer CNVs are disproportionately found in patients with mental retardation, developmental delay, schizophrenia, and autism.

There are several methods for determining CNVs:
- Microarray-Based CNV Analysis: This involves genome-wide genotyping arrays used for detecting genetic variants, including CNVs. This approach is reliable and efficient for large-scale analysis, allowing for the profiling of chromosomal aberrations like amplifications, deletions, rearrangements, and copy-neutral loss of heterozygosity.
- NGS-Based Copy Number Analysis: Next-Generation Sequencing (NGS) is effective for detecting smaller CNVs that microarrays might miss. NGS provides a base-by-base view of the genome, detecting small or novel CNVs and mapping their exact locations. This high resolution complements the high throughput of arrays for a comprehensive genomic view.
- Gold Standard Methods: The gold standards for CNV detection in genetic diagnostics are multiplex ligation-dependent probe amplification (MLPA) and array comparative genomic hybridization (aCGH). These methods are time-consuming and costly, often leading to only a subset of genes being tested. The use of NGS data as a first CNV screening step can reduce the need for MLPA/aCGH tests, thus freeing up resources.

Detecting CNVs from targeted NGS data remains challenging. Existing tools generally perform well for large CNVs but struggle with single and multi-exon alterations, which are often involved in several genetic diseases. Identifying a tool capable of detecting CNVs from NGS panel data at a single-exon resolution with sufficient sensitivity for use as a screening step in a diagnostic setting is a key challenge.

In a study evaluating CNV detection tools for NGS panel data in genetic diagnostics, five tools were tested against four datasets for a total of 495 samples with 231 single and multi-exon validated CNVs. This evaluation aimed to identify the most suitable tools for genetic diagnostics. The tools DECoN and panelcn.MOPS showed the highest performance for CNV screening before orthogonal confirmation.

These methods highlight the ongoing efforts and challenges in accurately detecting and cataloging CNVs in human populations, as well as determining their association with biological function and disease. The research and development of effective diagnostic tools and techniques for CNV analysis continue to be crucial in the understanding and treatment of complex genetic diseases.