跨品种共享的SNP效应增加了数量较小的品种的基因组预测准确性
Varadanayakanahalli N Sahana1, Gopal Ramdasji Gowane1, Nilesh Nayee2
1Division of Animal Genetics and Breeding, ICAR-National Dairy Research Institute, Karnal, 132001, Haryana, India.
Scientific reports
|August 26, 2025
概括
多品种基因组评估提高了小牛品种的预测准确性. 使用Gir和Kankrej牛的综合数据显著提高了Kankrej的基因组估计育种值,为基因改进提供了可行的策略.
科学领域:
- 动物遗传学
- 定量遗传学
- 基因组选择
背景情况:
- 基因组选择加速了牲畜的遗传改进,但在发展中国家面临着挑战,特别是缺乏大量参考种群的小品种.
- 对于资源有限的本地牛种来说,建立大型单品种参考种群是困难的.
研究的目的:
- 研究多品种参考种群在数量较小的牛品种中提高基因组预测准确性的有效性.
- 用吉尔和萨希瓦尔品种的多品种参考数据评估坎克雷牛的基因组预测准确性.
主要方法:
- 使用了1,298只吉尔,1,291只萨希瓦尔和500只坎克雷牛的基因型和表型数据.
- 使用主要成分分析 (PCA) -K-means集群和链接不平衡 (LD) 衰变分析来评估遗传相似性.
- 对比了三个基因组评估策略:共享的基因组关系矩阵 (GRM),非共享的GRM和元创始人纠正的共享GRM.
主要成果:
- 基尔牛和坎克雷牛之间存在遗传相似性.
- 对于305天的第一次哺乳乳产量 (305-DMY) 的遗传概率因品种而异 (Gir:0.30,Sahiwal:0.27,Kankrej:0.17).
- 使用Gir-Kankrej数据的多种品种评估显著提高了Kankrej的预测准确度,比单种品种的预测准确度提高了23. 6% (共享GRM),24. 6% (非共享GRM) 和16. 9% (元创始人方法).
结论:
- 多品种基因组评估是一个可行的策略,
- 在多种品种评估框架中的"共享GRM"方法显示了Kankrej品种的最高预测准确性.
- 这项研究提供了在资源有限的情况下进行遗传改进的实用方法,克服了与小品种种群相关的挑战.
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