动物育种的进步:从孟德尔遗传学到机器学习
Manjit Panigrahi1, Divya Rajawat1, Sonali Sonejita Nayak1
1Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, Uttar Pradesh, India.
International journal of molecular sciences
|December 11, 2025
概括
动物育种从基本的观察发展到基因组和人工智能驱动的选择. 现代基因组预测模型通过分析复杂的遗传数据来提高牲畜的生产力和健康.
科学领域:
- 动物遗传学动物遗传学
- 基因组学就是基因组学.
- 机器学习在动物育种中的应用
背景情况:
- 畜牧养殖是从新石器时期的化和孟德尔的遗传学进化而来的.
- 定量遗传学推进了对身体重量和牛奶产量等复杂特征的研究.
- 基因组选择 (GS) 和基因组最佳线性无偏预测 (GBLUP) 提高了繁殖准确性.
研究的目的:
- 审查动物育种技术的历史进展.
- 突出基因组学和机器学习 (ML) 方法的整合.
- 展示畜牧业生产率,健康和福利方面的进步.
主要方法:
- 探索从表型到基因型选择的历史育种范式.
- 对定量遗传学,QTL映射和基因组预测方法的审查.
- 机器学习和人工智能应用在基因组预测中的分析.
主要成果:
- QTL映射使基于基因型的选择成为可能,提高了准确性.
- 在GBLUP下,基因组估计的繁殖值 (GEBV) 通过QTL关联得到增强.
- 深度学习模型在基因组预测中实现了高精度 (0.643相关性).
- 多种OMIC战略促进了畜牧业的疾病管理.
结论:
- 创新方法,特别是ML,正在改变动物育种.
- 这些进展显著影响了牛奶和肉类生产和疾病管理.
- 未来的前景集中在通过综合方法提高牲畜生产率,健康和福利.
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