一种用于基因组预测新环境中品种的新方法
Osval A Montesinos-López1, Sofia Ramos-Pulido2, Carlos Moisés Hernández-Suárez3
1Facultad de Telemática, Universidad de Colima, Colima, Colima, Mexico.
Frontiers in plant science
|August 11, 2023
概括
一种新的基因组选择 (GS) 方法通过修改响应变量以考虑环境差异来提高育种计划的预测准确性. 这种方法在未经测试的环境中提高了遗传进步和选择效率.
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.
- 生物信息学是一种生物信息学.
背景情况:
- 基因组选择 (GS) 对于加速作物和牲畜改进中的遗传收益至关重要.
- 在各种环境中预测线条性能仍然是育种计划中的一个挑战.
研究的目的:
- 开发和评估一种用于在新的未经测试环境中提高测试线路预测准确性的新方法.
- 解决基因组预测中培训和测试数据集之间的非静止分布问题.
主要方法:
- 提出了一个新的基因组预测模型 (M2),利用一个修改后的响应变量 (响应变量的差异).
- 将M2的预测性能与传统的基因组最佳线性无偏预测 (GBLUP) 模型 (M1_GE和M1_NO_GE) 进行比较.
- 在多个数据集上评估模型,以评估预测准确度的改进.
主要成果:
- 与传统的GBLUP模型相比,提出的方法 (M2) 显著提高了预测准确性.
- 预测准确度的增长至少为4.3% (皮尔森相关性),19.5% (捕获最高收益线) 和42.29% (NRMSE减少).
- 修改后的响应变量有效地减少了来自非静止分布的问题.
结论:
- 新型基因组选择方法为育种应用的预测准确性提供了实质性的改进.
- 这种方法提高了选择优质线路的效率,特别是在新环境中.
- 这种方法为优化育种计划中的遗传进步提供了有价值的工具.
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