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基于机器学习的腹部脂肪特征的基因组育种价值估计模型的优化

Hengcong Chen¹, Dachang Dou¹, Min Lu¹

¹Key Laboratory of Chicken Genetics and Breeding, Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China.

Animals : an open access journal from MDPI

|October 16, 2025

概括

这项研究引入了一个新的机器学习框架,DAWSELF,用于预测的基因组繁殖值 (GEBV). 它提高了选择下腹脂肪的准确性,提高了肉质和繁殖效率.

科学领域:

动物遗传学动物遗传学
量化遗传学量化遗传学
机器学习在动物育种中的应用

背景情况:

的腹部脂肪显著影响肉质和料效率.
为了减少腹部脂肪的繁殖对于家禽生产的经济可行性至关重要.
基因组选择 (GS) 提供精确和早期选择复杂的特征,如腹部脂肪.

研究的目的:

开发一个先进的基因组预测框架,用于的腹部脂肪.
识别和利用腹部脂肪沉积的关键遗传标记.
提高基因组估计繁殖值 (GEBVs) 预测的准确性.

主要方法:

综合全基因组关联研究 (GWAS) 和链接不平衡 (LD) 用于SNP识别.
采用了两阶段的机器学习功能选择 (Lasso和RFE).
开发并验证了一个动态自适应加权堆叠合体学习框架 (DAWSELF),Ridge作为meta-learner.

主要成果:

确定了相关的单核酸多态 (SNPs) 用于腹部脂肪的预测.
线性和非线性模型显示高精度作为基础学习者.
在三个种群中,DAWSELF在预测准确性方面始终超过了个别模型和传统堆叠.

结论:

DAWSELF为GEBV预测复杂的特征提供了一个有效的框架,例如腹脂肪.
这项研究为家禽繁殖提供了可重复使用的SNP特征选择策略.
这种方法提高了育种精度,并提高了肉产品的质量.

关键词:

达威斯尔夫自己是什么?腹部脂肪 - 腹部脂肪估计育种价值的估计.基因组选择/预测机器学习是机器学习.

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