家畜基因组选种选配技术的研究进展

doi:10.19543/j.cnki.1673-4556.20260330.003

Abstract

Abstract:

With the rapid advancement of genomics technologies, livestock breeding has entered the genomic era. Genomic selection and mating techniques, by integrating genome-wide marker information with intelligent algorithms, not only significantly enhance the accuracy of breeding selection and shorten generation intervals, but also effectively balance genetic gain with inbreeding control, thereby safeguarding population genetic health and promoting efficient, precise, and sustainable modern livestock breeding. This article systematically reviews the fundamental principles and common models of genomic selection and mating, including direct methods represented by GBLUP and ssGBLUP, indirect methods such as Bayesian approaches, and machine learning methods (e.g., support vector machines, random forests, artificial neural networks), while analyzing the applicable scope, advantages, and limitations of each model. Regarding application progress, this article indicates that genomic selection technology has been widely implemented in the breeding of major livestock species such as cattle, pigs, goats, and sheep, significantly enhancing genetic gain for economically important traits including milk production, reproductive performance, and feed efficiency. Studies on genomic mating in dairy cattle and pigs demonstrate its important role in synergistically improving genetic gain, controlling inbreeding coefficients, and maintaining genetic diversity. Furthermore, this article analyzes current challenges facing this technology, including data quality and standardization, computational resource requirements, multi-trait integration, underutilization of structural variations, as well as personnel and cost issues in practical implementation, while providing prospects for future research directions.

Key words: Genomic selection, Genomic selection mating, Genetic progress, Genomic breeding value, Inbreeding control

CLC Number:

S831

Fengjuan Liu, Tiecheng Wu, Junyang Liu, Tao Wang, Leqi Yi, Yulin Gao, Xingang Yan, Bin Liu. Research progress on breeding and selection techniques for domestic animal genomes[J].Chinese Livestock and Poultry Breeding, 2026, 22(5): 70-78.

Figures/Tables 2

Fig. 1

Table 1

The research and application of genomic selection in livestock breeding"

物种 Species	研究焦点 Research focus	主要方法/模型 Main methods/Models	主要贡献/发现 Main contributions/Findings	参考文献References
牛 Cattle	荷斯坦牛乳成分性状遗传评估	考虑母体遗传效应的基因组评估	深化了对乳酪产量和乳蛋白含量等遗传参数的理解，为精准评估提供了方案。	[30]
	繁殖性状预测	机器学习结合基因组亲缘关系矩阵与主成分分析	证明了结合基因组信息与机器学习策略可有效提升预测准确性。	[31]
	奶牛酮病预测	改进的优化算法	提升了疾病预测效能，为生产实践提供了新工具。	[32]
	肉牛育种（多样化群体）	基因组选择应用评估	证实GS技术尤其适用于品种多样化及小规模群体，显示出良好的应用潜力。	[33]
猪 Swine	种猪选育效率	GEBV与常规选择对比	基因组选择方向一致，且GEBV准确性更高，有效提升了选育效率。	[34]
	饲料效率性状跨群体预测	合并杜洛克公猪、商品猪，构建参考群	成功提高了平均日增重、料重比等性状的预测准确性，优化了育种策略	[35]
	多性状基因组预测	多性状模型整合辅助性状信息	提高目标性状（如产仔数）的基因组预测精度。	[36]
	早期选种与遗传进展	高密度SNP芯片+GBLUP模型	能提升基因组预测能力，支持早期选种，加快遗传进展。	[37-38]
绒山羊 Cashmere goats	内蒙古绒山羊育种	SSGBLUP	确定为最适合该群体的方法，世代间隔可从4.5年缩短至2年，显著加速遗传进展。	[39]
绵羊 Sheep	中国美利奴羊生长与毛用性状	ABLUP, GBLUP, ssGBLUP；五倍交叉验证等	结合个体加性和母体遗传效应的模型，并使用ssGBLUP法，可获得准确性最高的基因组估计育种值，为超细型细毛羊选育提供支撑。	[40]

Table 1

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