规范化累积增益作为基因组选择模型的替代评估措施
Felix Heinrich1, Thomas M Lange2, Faisal Ramzan3
1Breeding Informatics Group, Department of Animal Sciences, Georg-August University, Göttingen, Margarethe von Wrangell-Weg 7, 37075, Göttingen, Germany. felix.heinrich@uni-goettingen.de.
Genetics, selection, evolution : GSE
|December 16, 2025
概括
评估基因组选择模型需要超越标准回归的指标. 新的正常化累积收益 (NCG) 衡量了直接选择的效率,通过专注于顶级个体来改善育种决策.
科学领域:
- 基因组学就是基因组学.
- 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:
- 基因组选择 (GS) 使用统计和机器学习模型从基因组数据中预测表型.
- 将GS模型性能进行比较至关重要,因为没有一种方法在所有数据集中脱而出.
- 像皮尔森相关性和MSE这样的传统指标可以评估整体适应性,但不能评估用于繁殖的选择有效性.
研究的目的:
- 为基因组预测引入一种替代的评估指标,即规范累积收益 (NCG).
- 评估GS模型在为繁殖计划选择表现最佳的个体方面的有效性.
主要方法:
- 开发并应用了标准化累积收益 (NCG) 度量.
- 在四个动物和植物数据集中比较了九种常见的基因组预测方法.
- 在所有可能的选择值中分析模型性能.
主要成果:
- NCG直接量化了被选中的个体的表型收益,为选择效率提供了直观的衡量标准.
- 性能比较显示,在不同的选择强度下,方法有效性存在差异.
- 所有门的综合分析提供了比单个门评估更深入的见解.
结论:
- 与传统的回归指标相比,NCG为育种选择提供了更相关的评估.
- 选择选择强度的选择显著影响方法性能,指导最佳模型选择.
- 对于NCG计算的R包可用于基因组选择研究中的更广泛应用.
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