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Updated: Feb 17, 2026

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用随机回归模型对黄色羽毛肉产卵的单步基因组选择的准确性

Shaoyan Jia¹, Tianfei Liu¹, Jie Ma¹

¹State Key Laboratory of Swine and Poultry Breeding Industry, Guangdong Key Laboratory of Animal Breeding and Nutrition, Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China.

Poultry science

|February 15, 2026

概括

随机回归模型 (RRM) 与单步基因组选择相结合,提高了家禽蛋生产的准确性. 这种方法比传统方法更有效地提高了估计的育种价值.

科学领域:

动物遗传学和动物繁殖
量化遗传学量化遗传学
禽畜科学禽畜科学禽畜科学

背景情况:

蛋生产是家禽的关键经济特征,受到动态环境和生理因素的影响.
传统模型 (STM,MTM) 在捕捉卵生产的连续时间模式方面存在局限性.
随机回归模型 (RRM) 提供了一种灵活的方法来分析纵向数据和时间依赖的遗传效应.

研究的目的:

为了比较单步基因组选择 (ssGBLUP) 与RRM相结合的准确性和公正性,与卵子生产的多特征模型 (MTM) 和单特征模型 (STM) 相比.
评估RRM在剖析时间依赖的遗传效应中的有效性,以改善黄色羽毛肉的繁殖价值预测.

主要方法:

利用了来自18221只黄羽毛肉的168,596个蛋生产记录的数据集.
应用RRM (Legendre多项式),MTM (三个产卵阶段) 和STM (累计产量) 进行分析.
集成ssGBLUP与RRM,MTM和STM,以估计育种价值.

主要成果:

使用ssGBLUP的RRM在估计育种值 (EBV) 的准确性和公正性方面明显优于MTM和STM.
与STM相比,RRM提高了EBV准确度,高达7% (血统) 和6.8% (基因组),与MTM相比,高达3% (血统) 和2.9% (基因组).
结合RRM与ssGBLUP,EBV准确度提高了高达84%,与基于血统的模型相比,不偏见率提高了26%.

结论:

整合RRM与ssGBLUP是一种高度有效的策略,用于增强家禽的基因组选择计划.
这种方法为纵向生产特征 (如蛋数量) 提供了更准确,更公正的预测.
RRM-ssGBLUP为加速家禽养殖的遗传改进提供了一个有希望的途径.

关键词:

遗传参数是一个遗传参数.基因组选择基因组选择纵向的特征纵向的特征随机回归模型是一个随机回归模型.黄色羽毛的肉肉是什么意思

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