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Updated: Sep 12, 2025

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改善热带适应性肉牛的归算准确性:使用全基因组测序数据对Brahman和Nellore的应用

G A Fernandes Júnior¹, R Costilla², R Carvalheiro³

¹School of Agricultural and Veterinarian Sciences, UNESP, Jaboticabal, SP 14884-900, Brazil; Acarau Valley State University, UVA, Sobral, CE 62040-370, Brazil.

Animal : an international journal of animal bioscience

|August 7, 2025

概括

在多品种模型中将布拉曼和内罗尔牛的序列数据结合起来,与单品种方法相比,显著提高了全基因组赋值的准确性. 这一策略增强了热带适应性肉牛基因组预测的遗传多样性和准确性.

科学领域:

动物遗传学和动物基因组学
生物信息学和计算生物学

背景情况:

结合多样化的品种信息可以增强参考种群规模和遗传多样性,这对于提高归算和基因组预测准确度至关重要.
像Brahman和Nellore这样的热带适应的肉牛品种在经济上具有重要意义,需要准确的基因组评估.

研究的目的:

评估将Brahman和Nellore序列数据结合起来对整个基因组序列水平的归算准确度的影响.
为了比较这些热带适应牛的多品种归算策略与单品种评估.

主要方法:

他们利用了来自279只动物 (128只布拉汉,151只内罗尔) 的全基因组测序数据.
使用FImpute3软件设计了10个归算场景,从高密度SNP面板归算到全基因组序列.
在动物和SNP两种水平上,观察到的和归算的基因型之间的二次相关性来评估归算准确性.

主要成果:

多品种模型改善了每只动物的平均归算精度,从布拉汉的0.89到0.91,以及内罗尔的0.94到0.96.
在混合品种时,平均SNP智能归算准确度从布拉汉的0.78增加到0.82,在内罗尔的0.86增加到0.92.
在以前难以归因的多个染色体的基因组区域中观察到显著的归因精度增长.

结论:

使用多品种模型将布拉曼和内罗尔牛的序列信息结合起来,与单品种评估相比,可以提高归算准确性.
当参考小组将两种品种的遗传变异分开时,这种方法特别有效,从而提高了基因组洞察力.

关键词:

牛群是 Bos indicus 的一个类型.基因组数据分析.难以归纳的地区.链接不平衡链接不平衡多品种评估多品种评估

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