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基准机器学习和参数方法用于基因组预测Nellore牛的料效率相关的特征

Lucio F M Mota¹, Leonardo M Arikawa², Samuel W B Santos²

¹School of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal, SP, 14884-900, Brazil. flaviommota.zoo@gmail.com.

Scientific reports

|March 17, 2024

概括

使用机器学习 (MLNN,SVR) 和多特征基因组预测 (MTGBLUP) 的基因组选择 (GS) 与传统方法相比,提高了Nellore牛的料效率预测准确度.

科学领域:

动物遗传学和动物繁殖
基因组选择基因组选择
机器学习在农业中的应用

背景情况:

基因组选择 (GS) 对于提高动物生产力和环境可持续性至关重要.
料效率 (FE) 是一个复杂的特征,影响畜牧业的利能力.
精确预测FE对于有效的育种计划至关重要.

研究的目的:

为了比较机器学习 (MLNN,SVR) 和基因组预测模型对Nellore牛的料效率特征的预测准确度.
评估多层神经网络 (MLNN),支持向量回归 (SVR),多特征基因组最佳线性无偏预测 (MTGBLUP),单特征基因组最佳线性无偏预测 (STGBLUP) 和各种贝叶斯回归方法的性能.

主要方法:

利用了来自1156头Nellore牛的基因组数据,基因型为约300K标记物.
基于出生年份的雇员前期验证用于预测准确性评估.
经过训练的MLNN和SVR模型使用五倍交叉验证进行超参数选择.

主要成果:

机器学习方法 (MLNN,SVR) 和MTGBLUP显著优于STGBLUP和贝叶斯回归方法.
与基线方法相比,预测准确度增加了8.9% (MLNN),14.6% (SVR) 和13.7% (MTGBLUP).
对于复杂的FE特征,SVR和MTGBLUP显示出具有竞争力的预测准确度 (0.62-0.69).

结论:

支持向量回归 (SVR) 和MTGBLUP对于预测牛的料效率特征非常准确.
这些先进的方法为复杂的表型的基因组选择提供了竞争优势.
这些发现支持整合机器学习和多特征方法来改善畜牧养殖.

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