在线混合模型框架的背景下,用于基因组预测的图形模型
Osval A Montesinos-López1, Gloria Isabel Huerta Prado2, José Cricelio Montesinos-López3
1Facultad de Telemática, Universidad de Colima, Colima, Mexico.
The plant genome
|October 7, 2024
概括
基因组选择中的图形模型在与基因型效应相结合时略有提高了预测准确性,仅使用基因型数据的模型表现优于基因型数据. 这项研究验证了14个植物育种数据集的发现.
科学领域:
- 农业科学 农业科学
- 遗传学 是一个遗传学.
- 生物信息学是一种生物信息学.
背景情况:
- 基因组选择 (GS) 对于推进植物和动物育种至关重要.
- 高预测准确度对于GS的成功应用至关重要.
- 线性混合模型 (LMM) 常用于基因组预测.
研究的目的:
- 为了提高基因组选择中的预测准确性.
- 在线性混合模型框架内研究图形模型的实用性.
- 评估结合谱系连接和基因型效应的影响.
主要方法:
- 在线混合模型框架中集成的图形模型的探索.
- 使用单独的基因型效应,单独的图形结构和组合效应对预测准确性的比较.
- 通过14个不同的植物育种数据集进行验证.
主要成果:
- 当仅使用图形结构 (线连接) 时,基因组预测的准确性下降.
- 整合基因型效应和图形结构,比单独使用基因型效应略有改善.
- 这些结果在多个数据集中一致.
结论:
- 在这种情况下,单独的图形模型并不能提高基因组预测的准确性.
- 将基因型效应与图形结构相结合,在预测准确度上提供了微小的改进.
- 这些发现为优化植物育种中的基因组选择策略提供了洞察力.
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