评估调整的ssGBLUP模型用于南非霍尔斯坦牛的基因组预测和矩阵兼容性
Kgaogelo Stimela Mafolo1,2, Michael D MacNeil1,2,3, Frederick W C Neser2
1Animal Production, Agricultural Research Council, Private Bag X2, Irene 0062, South Africa.
Animals : an open access journal from MDPI
|February 13, 2026
概括
优化的一步基因组最佳线性无偏预测 (ssGBLUP) 模型在荷尔斯坦牛中提高了6-7%的基因组预测准确度. 这些调整后的模型减少了偏差,并提高了育种价值估计的可靠性.
科学领域:
- 动物遗传学动物遗传学
- 量化遗传学 量化遗传学
- 乳制品科学 乳制品科学
背景情况:
- 单步基因组最佳线性无偏预测 (ssGBLUP) 模型可以在具有有限基因类型数据的人群中产生偏见或不准确的基因组预测.
- 基因组和血统关系矩阵之间的不兼容性是这些预测问题的主要原因.
研究的目的:
- 评估五种替代ssGBLUP模型对南非霍尔斯坦牛的牛奶,脂肪和蛋白质产量特征的基因组预测准确性的影响.
- 确定最佳模型调整,以提高基因组估计育种值 (GEBV) 的可靠性.
主要方法:
- 利用了696,413个牛奶生产记录和541,325个南非霍尔斯坦牛的血统数据集.
- 我们比较了五种ssGBLUP模型:标准ssGBLUP,ssGBLUP考虑内生 (ssGBLUP_Fx),ssGBLUP与未知父组 (ssGBLUP_upg),以及两个调整的ssGBLUP模型 (ssGBLUP_adjusted) 具有优化的混合参数.
- 使用Illumina 50K芯片数据估计的305天哺乳期牛奶,蛋白质和脂肪产量的GEBV.
主要成果:
- 调整后的ssGBLUP_模型显示了实现的最高预测准确度,比标准的ssGBLUP有6-7%的改进.
- 对于牛奶,蛋白质和脂肪产量的准确性在0.23-0.30 (ssGBLUP/ssGBLUP_Fx),0.26-0.34 (ssGBLUP_upg) 和0.29-0.37 (ssGBLUP_adjusted) 之间.
- 通过回归系数表示的偏差在调整后的模型中降低了, ssGBLUP_adjusted 的值在0.44-0.53之间,而 ssGBLUP_adjusted 的值在0.30-0.36.
结论:
- 优化血统和基因组关系的混合对于可靠的Holstein乳牛GEBV估计至关重要.
- ssGBLUP_调整模型提供了更高的准确性和更少的偏差,从而导致更有效的选择决策.
- 这些发现强调了ssGBLUP的方法改进的重要性,以最大限度地提高乳制品种群的遗传收益.
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