机器学习用于复合肉牛种群中生长特征的基因组预测
1USDA Agricultural Research Service, Fort Keogh Livestock and Range Research Laboratory, Miles City, MT 59301, USA.
Animals : an open access journal from MDPI
|October 26, 2024
概括
基因组最佳线性无偏预测 (GBLUP) 与机器学习模型相比,在预测畜牧养殖价值方面表现出更高的准确性,用于预测出生和年幼体重. 随机森林在断奶体重方面表现出色.
科学领域:
- 动物育种与遗传学
- 基因组选择 基因组选择
- 量化遗传学 量化遗传学
背景情况:
- 基因组选择在植物和牲畜育种中被广泛使用,但预测模型往往需要改进.
- 机器学习 (ML) 通过建模复杂的遗传关系来提高预测准确性的潜力.
研究的目的:
- 评估和比较四个ML模型的预测性能与基因组预测的传统方法.
- 评估模型准确度,以预测牲畜的出生体重 (BW),断奶体重 (WW) 和年幼体重 (YW).
主要方法:
- 评估随机森林,支持向量机,卷积神经网络和多层感知子.
- 与基因组最佳线性无偏预测 (GBLUP),贝叶斯A和贝叶斯B的ML模型进行比较.
- 评估预测准确度和模型合适度,使用平均平方误差和回归系数等指标.
主要成果:
- GBLUP实现了BW和YW的最高预测准确度.
- 随机森林证明了对第二次世界大战的卓越预测准确性.
- GBLUP显示出更好的模型匹配,由较低的平均平方误差和改进的回归系数表明.
结论:
- 与测试的ML模型相比,GBLUP模型为研究的特征提供了更高的预测准确性和模型匹配.
- 虽然ML模型显示出有希望的结果,但像GBLUP这样的传统方法在牲畜中的基因组预测中仍然非常有效.
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