Celus C S, Ahmad Sheikh Firdous, Gangwar Munish, Kumar Subodh, Kumar Amit
Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh 243122, India.
Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh 243122, India; Livestock Production and Management Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh 243122, India.
Gene. 2025 Mar 5;939:149159. doi: 10.1016/j.gene.2024.149159. Epub 2024 Dec 11.
The basis of all improvement in (re)production performance of animals and plants lies in the genetic variation. The underlying genetic variation can be further explored through investigations using molecular markers including single nucleotide polymorphism (SNP) and microsatellite, and more recently structural variants like copy number variations (CNVs). Unlike SNPs, CNVs affect a larger proportion of the genome, making them more impactful vis-à-vis variation at the phenotype level. They significantly contribute to genetic variation and provide raw material for natural and artificial selection for improved performance. CNVs are characterized as unbalanced structural variations that arise from four major mechanisms viz., non-homologous end joining (NHEJ), non-allelic homologous recombination (NAHR), fork stalling and template switching (FoSTeS), and retrotransposition. Various detection methods have been developed to identify CNVs, including molecular techniques and massively parallel sequencing. Next-generation sequencing (NGS)/high-throughput sequencing offers higher resolution and sensitivity, but challenges remain in delineating CNVs in regions with repetitive sequences or high GC content. High-throughput sequencing technologies utilize different methods based on read-pair, split-read, read depth, and assembly approaches (or their combination) to detect CNVs. Read-pair based methods work by mapping discordant reads, while the read-depth approach works on detecting the correlation between read depth and copy number of genetic segments or a gene. Split-read methods involve mapping segments of reads to different locations on the genome, while assembly methods involve comparing contigs to a reference or de novo sequencing. Similar to other marker-trait association studies, CNV-association studies are not uncommon in humans and farm animals. Soon, extensive studies will be needed to deduce the unique evolutionary trajectories and underlying molecular mechanisms for targeted genetic improvements in different farm animal species. The present review delineates the importance of CNVs in genetic studies, their generation along with programs and principles to efficiently identify them, and finally throw light on the existing literature on studies in farm animal species vis-à-vis CNVs.
动植物（繁殖）性能所有改善的基础都在于遗传变异。潜在的遗传变异可以通过使用包括单核苷酸多态性（SNP）和微卫星在内的分子标记进行研究来进一步探索，最近还包括像拷贝数变异（CNV）这样的结构变异。与SNP不同，CNV影响基因组的比例更大，这使得它们在表型水平的变异方面更具影响力。它们对遗传变异有显著贡献，并为提高性能的自然和人工选择提供原材料。CNV被表征为不平衡的结构变异，其产生于四种主要机制，即非同源末端连接（NHEJ）、非等位基因同源重组（NAHR）、叉停滞和模板转换（FoSTeS）以及逆转座。已经开发了各种检测方法来识别CNV，包括分子技术和大规模平行测序。下一代测序（NGS）/高通量测序提供了更高的分辨率和灵敏度，但在划定具有重复序列或高GC含量区域的CNV方面仍然存在挑战。高通量测序技术基于读对、分裂读、读深度和组装方法（或它们的组合）使用不同方法来检测CNV。基于读对的方法通过映射不一致读来工作，而读深度方法则致力于检测读深度与遗传片段或基因拷贝数之间的相关性。分裂读方法涉及将读段映射到基因组上的不同位置，而组装方法涉及将重叠群与参考序列进行比较或进行从头测序。与其他标记-性状关联研究类似，CNV关联研究在人类和农场动物中并不罕见。很快，将需要进行广泛的研究，以推断不同农场动物物种中针对性遗传改良的独特进化轨迹和潜在分子机制。本综述阐述了CNV在遗传研究中的重要性、它们的产生以及有效识别它们所需的程序和原则，最后阐述了关于农场动物物种中与CNV相关的现有文献。
