通过历史数据优化,最大限度地提高日繁殖的效率
Javier Fernández-González1, Bertrand Haquin2, Eliette Combes2
1Centro de Biotecnologia y Genómica de Plantas (CBGP, UPM-INIA)-Instituto Nacional de Investigación y Tecnologia Agraria y Alimentaria (INIA), Universidad Politécnica de Madrid (UPM), Campus de Montegancedo-UPM, Pozuelo de Alarcón, Madrid, 28223, Spain. javier.fgonzalez@upm.es.
Plant methods
|March 17, 2024
概括
基因组选择模型可以使用历史数据子集进行优化. 像Tails_GEGVs这样的新算法通过最大限度地提高训练集中的遗传多样性来提高复杂特征的预测能力.
科学领域:
- 植物育种 植物育种
- 基因组学就是基因组学.
- 统计遗传学 统计遗传学
背景情况:
- 基因组选择 (GS) 在植物育种中被广泛使用,原因是基因型化成本较低,计算能力更强.
- 广泛的历史基因型和表型数据需要在GS模型中最佳利用策略.
- 跨年预测准确性对于开发强大的育种计划至关重要.
研究的目的:
- 调查最佳的数据子集选择,用于校准跨年预测的GS模型.
- 开发和评估优化培训集大小和遗传组成的方法.
- 为了提高GS模型的预测能力,使用太阳花育种中的历史数据.
主要方法:
- 采用多目标优化方法来选择理想的培训年.
- 开发了Min_GRM方法,以优化训练集大小,减少维度.
- 使用Tails_GEGVs算法来优化遗传组成并利用异质性.
主要成果:
- 通过Min_GRM方法,可减少20%的维度,预测能力的损失最小.
- 使用所有数据,Tails_GEGVs的表现优于使用所有数据,仅使用60%的谷物产量预测.
- 在训练组中最大限度地提高遗传多样性,确保了跨基因型值的一致预测能力.
结论:
- 通过战略子集选择对历史数据的最佳利用可以提高GS模型的性能.
- 开发的优化方法,Min_GRM和Tails_GEGVs,提供了提高预测能力的有效方法.
- 这项研究为最大限度地提高GS在植物育种计划中的有效性提供了宝贵的见解.
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