一种标记物权重方法,以提高基因组预测中的家族内部准确性
Osval A Montesinos-López1, Leonardo Crespo-Herrera2, Alencar Xavier3,4
1Facultad de Telemática, Universidad de Colima, Colima, Colima, 28040, Mexico.
G3 (Bethesda, Md.)
|December 11, 2023
概括
新的Adversaria-Boruta (AB) 方法通过最大限度地减少培训测试数据差异,提高了植物育种中的基因组选择. 这提高了家庭内的预测准确性,这对于有效的选择过程至关重要.
科学领域:
- 植物育种 植物育种
- 遗传学 遗传学 是一个
- 生物信息学是一种生物信息学.
背景情况:
- 基因组选择 (GS) 正在改变植物育种,但在预测准确性方面面临挑战,特别是在家族内预测方面.
- 准确预测表型值对于提高育种计划的选择效率至关重要.
研究的目的:
- 引入和评估Adversaria-Boruta (AB) 方法,以提高基因组预测的准确性.
- 为了应对在家族内选择中预测准确度低的挑战.
主要方法:
- 阿德弗萨里亚-博鲁塔 (AB) 方法结合了对抗性验证 (AV) 和博鲁塔特征选择.
- 它通过向下加权不同的标志物来最大限度地减少培训-测试分布差异.
- 一个加权的基因组关系矩阵被整合到基因组最佳线性无偏预测器 (GBLUP) 模型中.
主要成果:
- 与标准GBLUP相比,AB方法在12个真实数据集中显示出更高的性能.
- 在皮尔森相关性 (8.6%),平均平方误差 (19.7%) 和正常化根平均平方误差 (9.8%) 中观察到改善.
结论:
- 在植物育种中,AB方法是提高预测准确度的宝贵工具,特别是在家族内选择方面.
- 鼓励其他研究人员进一步验证其实用性的经验证据.
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