Chang-heng Zhao, Dan Wang, Cheng Yang, Yan Chen, Jun Teng, Xin-yi Zhang, Zhi Cao, Xian-ming Wei, Chao Ning, Qi-en Yang, Wen-fa Lv, Qin Zhang
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Using WGS-based SNPs and InDels, we revealed the phylogenetic relationships between 11 Chinese indigenous and two foreign sheep breeds and quantified their purities through estimated genomic breed compositions. We found that the optimal strategy for identifying these breeds was the combination of DFI_union for breed-informative marker detection, which integrated the methods of Delta, Pairwise Wright's FST, and Informativeness for Assignment (namely DFI) by merging the breed-informative markers derived from the three methods, and KSR for breed assignment, which integrated the methods of K-Nearest Neighbor, Support Vector Machine, and Random Forest (namely KSR) by intersecting their results. Using SI markers improved the identification accuracy compared to using SNPs or InDels alone. We achieved accuracies over 97.5% when using at least the 1000 most breed-informative (MBI) SI markers and even 100% when using 5000 SI markers. 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引用次数: 0
摘要
准确的品种鉴定对于本土农场动物遗传资源的保护和可持续利用至关重要。在本研究中,我们利用全基因组测序的 SNP 和 InDel 数据评估了 13 个绵羊品种的系统发育关系和基因组品种组成。这些品种包括 11 个中国本土品种和 2 个国外商业品种。我们比较了不同标记类型(即SNPs、InDels以及SNPs和InDels的组合(命名为SIs))、不同品种信息标记检测方法以及不同机器学习分类方法的不同品种鉴定策略。利用基于 WGS 的 SNPs 和 InDels,我们揭示了 11 个中国本土绵羊品种和 2 个外国绵羊品种之间的系统发育关系,并通过估计的基因组品种组成量化了它们的纯度。我们发现,鉴定这些品种的最佳策略是将 DFI_union 与 KSR 结合起来,前者用于品种信息标记检测,通过合并三种方法得出的品种信息标记,整合了 Delta、配对赖特 FST 和分配信息度方法(即 DFI);后者用于品种分配,通过交叉它们的结果,整合了 K-近邻、支持向量机和随机森林方法(即 KSR)。与单独使用 SNP 或 InDels 相比,使用 SI 标记提高了鉴定准确率。当使用至少 1000 个最具品种信息(MBI)的 SI 标记时,我们的准确率超过了 97.5%,而当使用 5000 个 SI 标记时,准确率甚至达到了 100%。我们的研究结果不仅为这些中国地方绵羊品种的保护提供了重要依据,也为本土农畜品种的品种识别提供了一般方法。
Population structure and breed identification of Chinese indigenous sheep breeds using whole genome SNPs and InDels
Accurate breed identification is essential for the conservation and sustainable use of indigenous farm animal genetic resources. In this study, we evaluated the phylogenetic relationships and genomic breed compositions of 13 sheep breeds using SNP and InDel data from whole genome sequencing. The breeds included 11 Chinese indigenous and 2 foreign commercial breeds. We compared different strategies for breed identification with respect to different marker types, i.e. SNPs, InDels, and a combination of SNPs and InDels (named SIs), different breed-informative marker detection methods, and different machine learning classification methods. Using WGS-based SNPs and InDels, we revealed the phylogenetic relationships between 11 Chinese indigenous and two foreign sheep breeds and quantified their purities through estimated genomic breed compositions. We found that the optimal strategy for identifying these breeds was the combination of DFI_union for breed-informative marker detection, which integrated the methods of Delta, Pairwise Wright's FST, and Informativeness for Assignment (namely DFI) by merging the breed-informative markers derived from the three methods, and KSR for breed assignment, which integrated the methods of K-Nearest Neighbor, Support Vector Machine, and Random Forest (namely KSR) by intersecting their results. Using SI markers improved the identification accuracy compared to using SNPs or InDels alone. We achieved accuracies over 97.5% when using at least the 1000 most breed-informative (MBI) SI markers and even 100% when using 5000 SI markers. Our results provide not only an important foundation for conservation of these Chinese local sheep breeds, but also general approaches for breed identification of indigenous farm animal breeds.
期刊介绍:
Genetics Selection Evolution invites basic, applied and methodological content that will aid the current understanding and the utilization of genetic variability in domestic animal species. Although the focus is on domestic animal species, research on other species is invited if it contributes to the understanding of the use of genetic variability in domestic animals. Genetics Selection Evolution publishes results from all levels of study, from the gene to the quantitative trait, from the individual to the population, the breed or the species. Contributions concerning both the biological approach, from molecular genetics to quantitative genetics, as well as the mathematical approach, from population genetics to statistics, are welcome. Specific areas of interest include but are not limited to: gene and QTL identification, mapping and characterization, analysis of new phenotypes, high-throughput SNP data analysis, functional genomics, cytogenetics, genetic diversity of populations and breeds, genetic evaluation, applied and experimental selection, genomic selection, selection efficiency, and statistical methodology for the genetic analysis of phenotypes with quantitative and mixed inheritance.