用metafounders建模缺失的血统,并在一个小奶牛种群中验证单步基因组预测,外国遗传学有很大的影响
R D López-Correa1, A Legarra2, I Aguilar3
1Universidad de la República, Facultad de Agronomía, 12900 Montevideo, Uruguay; Universidad de la República, Facultad de Veterinaria, 13000 Montevideo, Uruguay.
Journal of dairy science
|February 4, 2024
概括
在基因组评估中,将未知的父组 (UPG) 或元组 (MF) 纳入基因组评估对于遗传改进至关重要. 该研究发现,虽然MFbounded模型表现良好,但在基因组估计繁殖值 (GEBV) 预测中,偏差和过度分散仍然存在.
科学领域:
- 动物育种与遗传学
- 基因组评估模型
- 统计遗传学 统计遗传学
背景情况:
- 小国家在动物改良方面严重依赖外国遗传学.
- 准确的基因组评估模型是必不可少的,特别是当处理未知父组 (UPG) 在进口的sires.
- 使用UPG可以在基因组估计育种值 (GEBV) 中引入偏差.
研究的目的:
- 在乌拉圭霍尔斯坦遗传评估中评估不同的模型,包括UPG或Metafounders (MF).
- 用BLUP和单步基因组BLUP (ssGBLUP) 来分析GEBV预测的偏差,分散和准确性.
- 为了比较不同玛矩阵 (Γ) 估计方法对MF的性能.
主要方法:
- 实施BLUP和单步基因组BLUP (ssGBLUP) 模型.
- 使用边界线性回归 (MFbounded) 和两值方法 (MFrobust) 估计玛矩阵 (Γ).
- 对验证群体的GEBV偏差,分散和预测准确性的分析.
主要成果:
- 两种MFbounded和MFrobust估计器都显示出良好的GEBV预测性能,MFbounded是优越的.
- 在所有模型中观察到对验证公牛的偏见,在验证母牛的BLUP中观察到UPG.
- 在所有模型中检测到过度分散,特别是在验证公牛中,这表明在建模遗传变异方面存在潜在问题.
- 单步基因组BLUP (ssGBLUP) 通常提供比传统BLUP更准确的预测.
结论:
- 基因组 (MF) 和未知父组 (UPG) 有效地模拟遗传进步,除了早期世代.
- 在基因组评估中,MFbounded是估计马矩阵 (Γ) 的推方法.
- 尽管有所改善,但在基因组评估中,偏见和过度分散仍然是挑战,需要进一步研究它们的起源和缓解策略.
- 在这种情况下,ssGBLUP在基因组评估方面表现出比BLUP更好的预测能力.
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