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使用开源软件优化全效的两阶段模型进行基因组选择.

Javier Fernández-González1, Julio Isidro Y Sánchez2

  • 1Centro de Biotecnologia y Genómica de Plantas (CBGP, UPM-INIA) - Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnologia Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223, Pozuelo de Alarcón, Madrid, Spain. javier.fgonzalez@upm.es.

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概括

完全有效的两阶段基因组选择模型提供了更好的预测准确性,特别是在增强设计中. 将估计误差共变性纳入随机效应 (Full_R模型) 增强了育种计划中的遗传收益.

关键词:
完全有效的完全有效的.基因组预测 基因组预测基因组选择 基因组选择这是一个开源的开源软件.植物育种 植物育种两个阶段的模型.变量-共变量 变量-共变量有权重的回归方法.

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科学领域:

  • 定量遗传学 是一种定量遗传学.
  • 植物和动物育种.

背景情况:

  • 基因组选择 (GS) 使用密集的遗传标记来预测基因组繁殖值 (GEBV).
  • 单阶段模型是计算密集型的,而双阶段模型提供了效率,但往往忽视了错误相关性.
  • 未加权 (UNW) 两阶段模型假设独立的错误,可能降低预测准确性.

研究的目的:

  • 评估完全有效的两阶段基因组选择模型的性能.
  • 将完全有效的模型与未加权的模型进行比较,特别是在增强的实验设计中.
  • 调查非添加效应和实验设计对预测准确性的影响.

主要方法:

  • 开发和模拟完全有效的两阶段模型,包括包含估计误差共变率的Full_R模型.
  • 使用模拟研究在各种场景和实验设计 (随机完整块和增强) 中比较模型性能.
  • 评估了非添加性遗传影响对预测准确性的影响.

主要成果:

  • 完全高效的两阶段模型在随机的完整块设计中表现类似于UNW模型,但在增强设计中表现要好得多.
  • 结合非添加效应和增强设计显著提高了预测准确性.
  • 包含估计误差共变率作为随机效应的Full_R模型展示了一致的性能和增加遗传收益的潜力.

结论:

  • 完全有效的两阶段模型,特别是Full_R模型,由于精度和效率的提高,建议用于基因组选择.
  • 实验设计和建模策略之间的协同作用对于最大限度地提高预测准确性至关重要.
  • 该研究提供了理论背景和开源R代码,以促进在育种计划中采用完全有效的模型,从而可能增加遗传收益.