通过多品种基因组评估,对具有SNP影响的原始牛品种进行间接基因组预测
Marisol Londoño-Gil1,2, Jorge Hidalgo2, Andres Legarra2,3
1Faculdade de Ciências Agrárias e Veterinárias, Universidade Estadual Paulista Júlio de Mesquita Filho, Jaboticabal, São Paulo, Brazil.
概括
间接预测 (IP) 为缺乏表型的年轻基因型牛提供了一个解决方案,提高了基因组选择的准确性. 使用metafounders进行多品种分析可以提高IP的稳定性和准确性,特别是对于数据有限的品种.
科学领域:
- 动物遗传学和动物繁殖
- 量化遗传学 量化遗传学
- 基因组选择 基因组选择
背景情况:
- 间接预测 (IP) 对基因型的年轻动物缺乏表型或来自商业群体至关重要,防止从基因组评估中排除.
- 排除这些动物可能会导致偏差的基因组育种值 (GEBV),特别是在缺乏数据的巴西泽布因牛品种中.
- 使用更大的参考种群提高IP准确度对于改善内罗尔,布拉汉,古泽拉特和塔巴普瓦牛的基因组选择至关重要.
研究的目的:
- 为巴西的Nellore,Brahman,Guzerat和Tabapua年轻基因型牛计算间接预测 (IP).
- 评估单种和多种分析,带有或没有MF对IP准确性的影响.
- 用于IP计算,比较不同的参考种群 (有/没有MF的多品种,Nellore,品种内).
主要方法:
- 利用了来自巴西四个育种计划 (ANCP) 的血统,表型和基因型数据.
- 使用SNP效应计算IP,根据基因含量加权,在各种参考人口场景中计算IP.
- 在不同的建模方法中使用线性回归 (LR) 评估IP的偏差,分散和准确性.
主要成果:
- 整合元发射器 (MF) 减少了偏差和分散,同时略提高了IP准确性.
- 多品种分析显著提高了IP准确性,特别是有利于具有较少基因型动物的品种.
- 通过适当的建模,可以在未被包括在官方评估中的年轻基因型动物中实现强大的IP.
结论:
- 用metafounders进行多品种分析,为计算巴西斑牛间接预测提供了可靠的方法.
- 这种方法提高了基因组预测的准确性,并为将有价值的基因型动物纳入选择计划提供了实际解决方案.
- 有效的知识产权策略对于最大限度地提高畜牧业遗传收益至关重要,尤其是在数据有限的场景中.
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