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使用基因组选择来提高对猪多种群的基因组预测的准确性

Chang Yin¹, Peng Zhou¹, Yuwei Wang¹

¹Department of Animal Genetics and Breeding, College of Animal Science and Technology, National Experimental Teaching Demonstration Centre of Animal Science, Nanjing Agricultural University, Nanjing 210095, PR China.

Animal : an international journal of animal bioscience

|January 11, 2024

概括

结合参考组可以提高猪育种计划的基因组估计育种值 (GEBVs) 的准确性. 贝叶斯模型,特别是贝叶斯B模型,在群体被合并时显示出增强的性能,特别是具有高遗传性的群体.

科学领域:

动物遗传学动物遗传学
量化遗传学量化遗传学
生物信息学是一种生物信息学.

背景情况:

准确的基因组估计育种值 (GEBV) 对畜牧养殖至关重要.
小型和中型农场在积累足够的参考数据以确保GEBV的准确性方面面临着挑战.
结合不同的参考组是克服数据局限性的潜在策略.

研究的目的:

评估综合多种群体GEBV预测的统计模型.
为了提高GEBV对中小型猪群的准确性.
评估人口规模,遗传性和亲属关系对GEBV预测准确性的影响.

主要方法:

使用QMSim.模拟了三种不同大小的猪种群 (300,600,1500)
研究了四个遗传水平 (0.05到0.5) 和四个亲属情景.
根据不同的合并和预测策略,比较GBLUP,ssGBLUP,贝叶斯A和贝叶斯B模型的预测准确度.

主要成果:

人口合并提高了贝叶斯模型的准确性,贝叶斯B模型表现最高.
在独立种群中,BLUP模型是最准确的.
当种群相关且遗传性高时,贝叶斯B略高于BLUP模型的表现.

结论:

结合参考种群可以显著提高GEBV预测的准确性,特别是对于较小的种群.
统计模型的选择 (贝叶斯式与BLUP) 取决于人口结构和遗传性.
这些发现为优化资源有限的农场的育种计划提供了宝贵的见解.

关键词:

贝叶斯的方法贝叶斯的方法结合的猪种群,可以说是猪种群.基因组最好的线性无偏预测基因组估计的繁殖价值.单步基因组学最好的线性无偏预测

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