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用metafounders对杂交的霍尔斯坦和泽西牛进行单步基因组预测

I Ampofo¹, G Vargas², D Gonzalez-Peña²

¹Zoetis Genetics, Kalamazoo, MI 49007; Department of Animal Science, University of Connecticut, Storrs, CT 06269.

Journal of dairy science

|September 20, 2025

概括

将metafounders (MF) 纳入单步基因组最佳线性无偏预测 (ssGBLUP) 模型可以改善杂交牛的遗传预测,特别是低遗传性特征. 然而,需要小心量身定制,以避免过度适应高遗传性特征.

科学领域:

动物育种与遗传学
量化遗传学量化遗传学
基因组评估基因组评估

背景情况:

准确的遗传评估对于畜牧业的改善至关重要.
血统差距可以限制多品种种群的基因组预测的准确性.
超级创始人 (MF) 提供了一个潜在的解决方案,以解决不完整的血统信息.

研究的目的:

调查将元发现者 (MF) 纳入基因评估的单步GBLUP (ssGBLUP) 模型的影响.
评估不同MF场景对各种特征和品种 (荷尔斯坦,泽西及其杂交品种) 的预测指标的影响.
评估模型复杂性和预测准确性之间的权衡.

主要方法:

利用了霍尔斯坦,泽西和杂交牛的大量数据集,并使用了广泛的表型和基因型记录.
应用了使用ssGBLUP的5特征可重复性模型,其中包含三个不同的MF场景 (4,24,和32MF) 和一个常规模型 (NO_MF).
进行了前期验证,以评估模型的可预测性,通货膨胀率和稳定性.

主要成果:

包括MF影响预测指标变化基于特征,品种和MF配置.
某些MF场景 (例如,4MF) 提高了可预测性,并减少了对特定特征的杂交品种的偏差.
在杂交品种中,MF场景提高了低遗传性特征 (SCS,DPR) 的预测能力,但在某些情况下降低了高遗传性特征 (MY,PY,FY) 的稳定性,表明潜在的过拟合.

结论:

超级创始人为管理多品种遗传评估中的血统差距提出了一个有价值的策略.
多发性菌的有效性取决于仔细考虑特征遗传性和种群结构.
优化MF应用对于防止过度配合和确保牛群中可靠的遗传预测至关重要.

关键词:

基本人口基础人口.基因组评估的基因组评估超级创始人超级创始人单步基因组BLUP BLUP是一个步骤.

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