Emerging insights into copy number variations: genomic mechanisms, advanced detection approaches, and clinical implications

Copy number variations (CNVs) are significant structural genomic alterations involving duplications or deletions of DNA segments ranging from kilobases to megabases. They originate through mechanisms such as non-allelic homologous recombination (NAHR), replication errors like fork stalling and template switching (FOSTES), non-homologous end joining (NHEJ), and mobile element insertion (MEI). These rearrangements can alter gene dosage and disrupt regulatory elements, leading to variations in gene expression and protein function. Consequently, CNVs contribute to phenotypic diversity, evolutionary adaptation, and the etiology of complex diseases, including cancers, neurological disorders, and metabolic syndromes. With the advent of next-generation sequencing (NGS), particularly whole-genome sequencing (WGS), CNV detection has become more accurate and comprehensive. WGS offers unbiased, genome-wide coverage, allowing the identification of CNVs in both coding and non-coding regions. Several analytical approaches have been developed to exploit NGS data for CNV detection. The read-depth (RD) method estimates CNVs based on the number of sequencing reads aligned to genomic regions and is highly effective for identifying large CNVs. Split-read (SR) and read-pair (RP) methods improve resolution at CNV breakpoints but may be limited by read length and repetitive regions. Assembly-based (AS) approaches reconstruct genomic segments de novo, while combined approaches (CA) integrate multiple methods to improve sensitivity and reduce false positives. Interpreting the biological and clinical significance of CNVs requires integrating detection data with annotation tools such as the Variant Effect Predictor (VEP) and databases like DECIPHER. These resources help determine whether a CNV affects gene function, overlaps regulatory elements, or is associated with known phenotypes. In silico techniques such as gene set enrichment analysis, pathway-based annotation, and network-guided analyses further aid in prioritizing functionally relevant CNVs. Accurate CNV interpretation depends on robust detection strategies and multidisciplinary analysis. As genomic technologies advance, the integration of multiple analytical methods with biological context will continue to enhance our understanding of CNVs and their role in health, disease, and evolution. KEYWORDS: ,,