畜牧兽医学报 ›› 2021, Vol. 52 ›› Issue (10): 2803-2813.doi: 10.11843/j.issn.0366-6964.2021.010.011

• 遗传育种 • 上一篇    下一篇

基因芯片技术在晋南牛种公牛选育中的应用

王宏浩1, 任小康1, 张毅2, 高会江3, 车雷杰4, 王曦1*   

  1. 1. 山西农业大学动物科学学院, 太原 030032;
    2. 中国农业大学动物科技学院, 北京 100193;
    3. 国家肉牛遗传评估中心, 北京 100193;
    4. 运城市国家级晋南牛遗传资源基因保护中心, 运城 044000
  • 收稿日期:2020-11-18 出版日期:2021-10-23 发布日期:2021-10-27
  • 通讯作者: 王曦,主要从事牛的传统改良与分子选育研究,E-mail:wxphilip@aliyun.com
  • 作者简介:王宏浩(1990-),男,山西汾阳人,硕士,主要从事牛羊遗传育种与分子选育工作,E-mail:whonghao2025@163.com
  • 基金资助:
    山西农业大学生物育种工程(YZGC131);山西省农业科学院育种工程项目(17yzgc109);山西省面上自然基金项目(201901D111452);山西省重点研发计划项目(201703d22021-1;201903D211012)

Application of Gene Chip Technology in Jinnan Bull Breeding

WANG Honghao1, REN Xiaokang1, ZHANG Yi2, GAO Huijiang3, CHE Leijie4, WANG Xi1*   

  1. 1. College of Animal Science, Shanxi Agricultural University, Taiyuan 030032, China;
    2. College of Animal Science and Technology, China Agricultural University, Beijing 100193, China;
    3. National Center of Beef Cattle Genetic Evaluation, Beijing 100193, China;
    4. Yuncheng National Jinnan Cattle Genetic Resources Gene Protection Center, Yuncheng 044000, China
  • Received:2020-11-18 Online:2021-10-23 Published:2021-10-27

摘要: 为了更好的保护开发利用晋南牛,确保晋南牛的遗传多样性,本研究应用基因芯片技术,对晋南牛进行群体遗传特性的检测及后备种公牛的遗传评估,为晋南牛的分子辅助选育与保种提供理论与技术支持。采集18月龄健康、体重相近((350±20)kg)的荷斯坦牛、和顺肉牛、西门塔尔牛、延边牛及利木赞牛血样各10份,及晋南牛后备公牛血样25份,根据不同牛品种分为6组,其中前5组每组10个重复,晋南牛后备公牛25个重复。应用Illumina SNP 50K高密度牛SNP芯片进行基因型检测,分析比较晋南牛的群体遗传特征,运用亲缘矩阵计算晋南牛后备公牛的亲缘系数,同时用BLUP进行遗传评估。结果表明,晋南牛在遗传结构上与荷斯坦牛、和顺肉牛、西门塔尔牛及利木赞牛关系较远,与延边牛较近,为中国地方品种群体;对晋南牛后备公牛进行遗传评估,得出了牛的基因组胴体重方差育种值排名,JN23的胴体重倍数性状标准差最大,从基因组水平可选作肉用种公牛;应用亲缘分析对晋南牛后备公牛家系进行分类,避免群体间的近交。本研究对晋南牛后备公牛进行了遗传评估、近交家系分析、传统表型选择及遗传疾病检测,最终选留的种公牛为JN07、JN23、JN05、JN08、JN02、JN13、JN19、JN14,通过多种选育方法结合提高了公牛的选择准确性,为晋南牛的群体选育提高奠定了基础。

关键词: 基因芯片, 晋南牛, 遗传结构, 遗传评估, 分子辅助选育

Abstract: In order to better protect, develop and utilize Jinnan cattle and ensure the genetic diversity of Jinnan cattle, in this study, gene chip technology was used to detect Jinnan cattle population genetic characteristics and perform genetic evaluation of reserve bulls, and provide theoretical and technical support for molecular-assisted breeding and breeding of Jinnan cattle. Ten blood samples of Holstein, Heshun, Simmental, Yanbian and Limousin cattle with healthy and similar weight of ((350±20) kg) at the age of 18 months were collected, and 25 blood samples of Jinnan reserve bulls were collected, these were divided into 6 groups according to different breeds of cattle, the first 5 groups had 10 repetitions in each group, and Jinnan cattle reserve bulls had 25 repe-titions. The Illumina SNP 50K high-density cattle SNP chip was used to detect their genotypes, the genetic characteristics of Jinnan cattle was analyzed and compared, and the kinship matrix was used to calculate the kinship coefficient of Jinnan cattle reserve bulls, the genetic evaluation was performed by BLUP. The results showed that Jinnan cattle had a farther relationship with Holstein, Heshun, Simmental and Limousin cattle in genetic structure, and was closer to Yanbian cattle, and it was a Chinese local breed group. The Jinnan cattle reserve bulls were genetically evaluated, and the breeding value ranking of the genomic carcass weight variance of the cattle was obtained; the JN23 had the largest carcass weight multiple trait standard deviation, and it could be selected at the genome level as a beef breeding bull. The kinship was analyzed, the Jinnan cattle reserve bull family was classified to avoid inbreeding between groups.The genetic evaluation, inbred family analysis, traditional phenotypic selection and genetic disease detection were carried out on the reserve bulls of Jinnan cattle. Finally, the selected and remained bulls were JN07, JN23, JN05, JN08, JN02, JN13, JN19 and JN14 in Jinnan cattle, the combination of a variety of breeding methods has enhanced the accuracy of bull selection, which laid a foundation for improving the group selection and breeding of Jinnan cattle.

Key words: gene chip, Jinnan cattle, genetic structure, genetic evaluation, molecular-assisted selection

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