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回归和深度学习模型用于新墨西哥智利胡中复杂特征的全基因组选择

Dennis N Lozada^1,2, Karansher Singh Sandhu³, Madhav Bhatta³

¹Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM, 88003, USA. dlozada@nmsu.edu.

BMC genomic data

|December 19, 2023

概括

全基因组的选择模型显示出智利胡繁殖的前景. 不同的模型对各种特征表现最好,组合方法增强了对产量的选择反应.

科学领域:

植物育种和遗传学
农业科学农业科学
基因组学就是基因组学.

背景情况:

全基因组选择 (GWS) 对于估计作物改进中的基因组育种值至关重要.
这项研究评估了回归和深度学习模型,用于 (Capsicum annuum) 的繁殖.

研究的目的:

评估不同GWS模型对的产量和农学特征的准确性.
确定最优的预测模型,以增强品种发展和种群改善.

主要方法:

实现了回归和深度学习 (例如,多层感知器) 模型.
在多环境试验中,利用了14922个SNP标记在204种基因型中.
执行十倍交叉验证以评估预测准确性.

主要成果:

预测准确性因特征和模型而异;像植物高度这样的高度遗传性特征显示出更高的准确性.
贝叶斯脊回归在第一日期和每种植物的总产量方面表现出色.
多层感知器 (MLP) 在开花时间和植物高度方面优越.
基因组BLUP显示了植物宽度的最高准确性.
减少SNP标记物提高了某些特征的准确性,特别是在较小的培训人群中.
综合表型和GWS方法改善了产量特征的选择反应.

结论:

回归和深度学习模型在培养计划中提供了显著的遗传改进潜力.
较大的训练数据集有利于通过深度学习模型提高预测准确性.

关键词:

豆种类 (Capsicum spp.) 是一种植物.基因组估计的育种价值.基因组预测基因组预测链接不平衡链接不平衡机器学习机器学习植物形态学植物形态学单个核酸的多态性.收益率收益率是指收益率.

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