选择环境共变量的特征选择方法是否提高了基因组预测的准确性?
Osval A Montesinos-López1, Leonardo Crespo-Herrera2, Carolina Saint Pierre2
1Facultad de Telemática, Universidad de Colima, Colima, Mexico.
Frontiers in genetics
|August 9, 2023
概括
通过整合环境数据,可以改进基因组选择 (GS). 特征选择方法提高了植物和动物育种中复杂特征的预测准确度,特别是当环境因素相关时.
科学领域:
- 农业科学 农业科学
- 遗传学 是一个遗传学.
- 生物信息学是一种生物信息学.
背景情况:
- 基因组选择 (GS) 正在彻底改变育种,但面临着复杂特征和多环境数据的挑战.
- 将环境共变量集成到GS模型中对于提高预测准确性至关重要.
研究的目的:
- 研究环境信息与基因型数据在基因组选择中的整合.
- 评估特征选择方法 (皮尔森相关性和博鲁塔) 对这种整合的有效性.
主要方法:
- 应用了两种特征选择技术来确定相关的环境共变量.
- 使用了一个离开一个环境的交叉验证策略.
- 使用规范化根平均平方误差 (NRMSE) 和皮尔森相关性评估预测准确性.
主要成果:
- 简单地包含环境共变量对预测准确性产生了可变的影响.
- 通过特征选择进行最佳整合,在6个数据集中的4个数据集中显著提高了预测准确性 (14.25%218.71%的NRMSE改进).
- 没有观察到皮尔森相关性的显著增长;当共变量与特征无关时,特征选择是无效的.
结论:
- 特征选择是优化环境共变量在基因组选择中的整合的一个有价值的策略.
- 这种方法在经验上增强了在多环境条件下复杂特征的预测能力.
- 其有效性取决于环境共变量与目标特征的相关性.
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