Zhang Jinxin, Nie Changsheng, Li Xinghua, Zhao Xiurong, Jia Yaxiong, Han Jianlin, Chen Yu, Wang Liang, Lv Xueze, Yang Weifang, Li Kaiyang, Zhang Jianwei, Ning Zhonghua, Bao Haigang, Zhao Chunjiang, Li Junying, Qu Lujiang
Department of Animal Genetics and Breeding, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China.
Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China.
Front Genet. 2022 Oct 20;13:971588. doi: 10.3389/fgene.2022.971588. eCollection 2022.
Structural variants (SVs) are one of the main sources of genetic variants and have a greater impact on phenotype evolution, disease susceptibility, and environmental adaptations than single nucleotide polymorphisms (SNPs). However, SVs remain challenging to accurately type, with several detection methods showing different limitations. Here, we explored SVs from 10 different chickens using PacBio technology and detected 49,501 high-confidence SVs. The results showed that the PacBio long-read detected more SVs than Illumina short-read technology genomes owing to some SV sites on chromosomes, which are related to chicken growth and development. During chicken domestication, some SVs beneficial to the breed or without any effect on the genomic function of the breed were retained, whereas deleterious SVs were generally eliminated. This study could facilitate the analysis of the genetic characteristics of different chickens and provide a better understanding of their phenotypic characteristics at the SV level, based on the long-read sequencing method. This study enriches our knowledge of SVs in chickens and improves our understanding of chicken genomic diversity.
结构变异（SVs）是遗传变异的主要来源之一，与单核苷酸多态性（SNPs）相比，对表型进化、疾病易感性和环境适应性的影响更大。然而，准确分型SVs仍然具有挑战性，几种检测方法都存在不同的局限性。在这里，我们使用PacBio技术探索了10种不同鸡的SVs，并检测到49,501个高可信度的SVs。结果表明，由于染色体上的一些SV位点与鸡的生长发育有关，PacBio长读长检测到的SVs比Illumina短读长技术基因组更多。在鸡的驯化过程中，一些对品种有益或对品种基因组功能没有任何影响的SVs被保留下来，而有害的SVs通常被消除。本研究基于长读长测序方法，有助于分析不同鸡的遗传特征，并在SV水平上更好地理解它们的表型特征。这项研究丰富了我们对鸡SVs的认识，提高了我们对鸡基因组多样性的理解。
