多变量基因组混合预测与内核和家长信息
Osval A Montesinos-López1, José Crossa2,3, Carolina Saint Pierre2
1Facultad de Telemática, Universidad de Colima, Colima 28040, Colima, Mexico.
International journal of molecular sciences
|September 28, 2023
概括
基因组选择 (GS) 通过结合父母数据来改善杂交预测. 直接使用父母的表型信息 (Pmean) 略高于使用繁殖值 (BV),提高混合繁殖效率.
科学领域:
- 植物育种 植物育种
- 定量遗传学 是一种定量遗传学.
- 基因组学就是基因组学.
背景情况:
- 基因组选择 (GS) 对于预测杂交性能和优化育种策略至关重要.
- 利用基因组信息可以提高混合繁殖计划的决策和成功率.
- 通过整合家长信息来提高基因组预测准确度,可以改进目前的方法.
研究的目的:
- 探索将家长表型信息作为多特征框架内的共变量纳入的影响,以改善混合性能的基因组预测.
- 为了比较两个方法:直接使用父母的表型信息 (Pmean) 与使用估计的繁殖值 (BV).
主要方法:
- 为了整合家长信息,采用了多特征框架.
- 测试了两种方法:Pmean (直接的父母表型) 和BV (父母的育种价值).
- 预测性能使用正常化根平均平方误差 (NRMSE) 进行评估,并通过各种方法和内核类型 (线性/非线性) 进行比较.
主要成果:
- 无论是Pmean还是BV方法都改善了预测性能,减少了NRMSE至少4.24%.
- 直接使用家长表型信息的Pmean方法在BV方法上显示出轻微但一致的优势.
- 使用线性与非线性内核时没有观察到显著的性能增长.
结论:
- 整合父母的表型信息显著提高了对杂交动物的基因组选择的预测性能.
- 与仅使用繁殖值相比,Pmean方法提供了一种稍微更有效的策略来改善混合预测.
- 这些发现提供了经验证据,支持父母表型数据在优化杂交育种计划中的价值.
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