将算法进行比较,以对单步基因组最佳线性无偏预测器的近似准确度进行比较
Pedro Ramos1,2,3, Andre Garcia2, Kelli Retallik2
1Department of Animal Science, University of Viçosa, Viçosa, Minas Gerais, Brazil.
Journal of animal science
|July 16, 2024
概括
算法2为估计的繁殖值提供了比算法1更精确的近似准确度. 这种方法是推用于遗传评估,特别是当包括基因型动物时.
科学领域:
- 动物育种和遗传学动物育种和遗传学
- 定量遗传学 是一种定量遗传学.
- 统计基因组学 统计基因组学
背景情况:
- 计算估计的繁殖值的准确性需要反转混合模型方程 (MME) 的左侧 (LHS).
- 对大型数据集而言,逆转LHS在计算上是不可行的,特别是对基因组信息.
- 接近算法是必要的,以估计大规模遗传评估的准确性.
研究的目的:
- 为了比较两个算法 (算法1和算法2),在BLUPF90软件中近似准确度.
- 为了验证这些近似准确度与来自MME反转的确切准确度.
- 评估包括基因型动物 (具有或没有表型) 在准确性估计上的影响.
主要方法:
- 对比了两个近似算法:算法1 (使用基因组关系矩阵对角) 和算法2 (结合精度与/或没有基因组数据).
- 根据使用单特征模型对数据子集的MME反转的精确度进行验证.
- 利用了美国安格斯协会 (American Angus Association) 的广泛数据集,包括数百万动物的血统和基因型信息,包括生长,尸体和大理石化特征.
主要成果:
- 与基因型动物的算法1 (0.87-0.90) 相比,算法2显示了更高的相关性 (0.98-0.99) 与精确度.
- 算法2对大多数特征的平均平方误差较低,回归斜率更接近1 (0.82-0.87),而算法1 (0.98-1.10),表现更好.
- 算法2的近似准确度更接近于将基因型动物纳入分析时的确切准确度.
结论:
- 算法2在基因评估中的近似准确性方面更加精确和可靠,特别是在大型基因组数据集中.
- 将基因型动物纳入其中显著有利于准确度估计,而算法2有效地捕获了这些改进.
- 建议使用算法2进行实际的遗传评估,因为它具有卓越的准确性和计算可行性.
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