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

中国畜禽种业 ›› 2026, Vol. 22 ›› Issue (5): 70-78.doi: 10.19543/j.cnki.1673-4556.20260330.003

家畜基因组选种选配技术的研究进展

刘凤娟¹^,²(), 吴铁成², 刘俊阳², 王涛², 意乐其², 高玉林², 闫新刚¹, 刘斌²()

^1.鄂尔多斯市立新实业有限公司，内蒙古鄂尔多斯 017000
^2.内蒙古自治区农牧业科学院，内蒙古呼和浩特 010031

收稿日期:2025-10-14 出版日期:2026-05-26 发布日期:2026-06-17
通讯作者: 刘斌 E-mail:1452460782@qq.com;liubin0613@126.com
作者简介:刘凤娟（2001—），女，硕士研究生，研究方向：动物遗传育种与繁殖，E-mail：1452460782@qq.com。
刘凤娟，女，2001年生，硕士研究生，畜牧专业。
刘斌，研究员，动物遗传育种与繁殖专业博士。内蒙古自治区农牧业科学院畜牧所绒毛羊研究室主任，硕士研究生导师。内蒙古自治区“英才兴蒙”工程五类人才、新世纪“321人才工程”二层次人选。任内蒙古自治区农畜产品羊绒加工团队岗位专家，全国畜牧业标准化技术委员会羊业及特色畜产业标准化工作组委员，中国畜牧兽医学会养羊学分会理事，中国农业国际合作促进会动物福利养殖合作委员会农场动物福利评估专家。长期扎根绒毛羊科研生产一线，致力于绒山羊遗传改良与标准化养殖技术研发，主持国家自然科学基金、自治区科技计划等项目15项。建立了绒山羊毛囊发育多组学研究平台，系统阐明了光周期调控羊绒生长的核心机制，主导研发“绒山羊优质种羊培育、两年三产繁育、光控增绒、优质羊绒生产”等四大技术体系，突破羊绒细度控制与繁殖效率瓶颈，推动自治区绒山羊福利养殖技术升级，累计推广示范245.84万只羊，新增经济效益1.65亿元。获内蒙古自治区技术发明二等奖（2025，第1）、科技进步奖二等奖（2019，第4）等5项省部级奖励，获国际发明专利1项、国家发明专利5项，制定地方标准16项，发表论文30余篇，主编著作2部。其成果通过专利转化和技术服务实现收入135万元，推动内蒙古绒山羊产业突破国际福利贸易壁垒，助力乡村振兴与农牧民增收。
基金资助:
鄂尔多斯市重点研发计划项目(YF20250271);2023年度内蒙古引进人才科研启动支持项目

Research progress on breeding and selection techniques for domestic animal genomes

Fengjuan Liu¹^,²(), Tiecheng Wu², Junyang Liu², Tao Wang², Leqi Yi², Yulin Gao², Xingang Yan¹, Bin Liu²()

^1.Erdos LiXin Industrial Co. , Ltd. Ordos, 017000, Inner Mongolia
^2.Inner Mongolia Academy of Agricultural Sciences, Hohhot, 010031, Inner Mongolia

Received:2025-10-14 Online:2026-05-26 Published:2026-06-17
Contact: Bin Liu E-mail:1452460782@qq.com;liubin0613@126.com

摘要/Abstract

摘要：

随着基因组学技术的快速发展，家畜育种已进入基因组时代。基因组选种选配技术通过整合全基因组标记信息与智能算法，既显著提升了育种选择准确性、缩短世代间隔，又有效平衡了遗传进展与近交控制，从而保障了群体遗传健康，推动实现高效、精准、可持续的现代家畜育种。该文系统综述了基因组选种选配的基本原理与常用模型，包括以GBLUP、ssBLUP为代表的直接法、以贝叶斯法为代表的间接法以及机器学习方法（如支持向量机、随机森林、人工神经网络等），并分析了各模型的适用范围与优缺点。在应用进展方面，基因组选择技术已广泛应用于牛、猪、山羊和绵羊等主要家畜育种中，显著提升了乳用性状、繁殖性能、饲料效率等重要经济性状的遗传进展；基因组选配技术在奶牛、猪等物种中的研究表明，其在协同提升遗传进展、控制近交系数、维持遗传多样性等方面发挥了重要作用。同时，该文还分析了当前该技术面临的数据质量与标准化、计算资源需求、多性状整合、结构变异利用不足以及推广应用中的人才与成本等问题，并对未来研究方向进行了展望。

关键词: 基因组选择, 基因组选配, 遗传进展, 基因组育种值, 近交控制

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

中图分类号:

S831

刘凤娟, 吴铁成, 刘俊阳, 王涛, 意乐其, 高玉林, 闫新刚, 刘斌. 家畜基因组选种选配技术的研究进展[J]. 中国畜禽种业, 2026, 22(5): 70-78.

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.

图/表 2

图1

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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]

家畜基因组选种选配技术的研究进展

Research progress on breeding and selection techniques for domestic animal genomes

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