RASEL:选择核心SNP的整体模型及其用于牛品种的识别和分类
K K Kanaka1,2, Indrajit Ganguly1, Sanjeev Singh3
1ICAR-National Bureau of Animal Genetic Resources, Karnal, 132001, India.
Biochemical genetics
|August 22, 2025
概括
准确的牛品种和实用分类对于养殖管理至关重要. 使用祖先信息标记的整体机器学习方法在品种识别方面达到98%以上的准确性,在实用分类方面达到96%以上的准确性.
科学领域:
- 动物遗传学
- 生物信息学
- 动物科学中的机器学习
背景情况:
- 有效的牛养殖管理依赖于准确的品种和实用识别.
- 基因组标记提供了一个强大的工具来区分牛群.
研究的目的:
- 开发和验证用于准确的牛品种和实用分类的机器学习管道.
- 确定用于品种和实用性的信息性遗传标记.
主要方法:
- 使用了10个牛品种和657个祖先信息标记的参考面板.
- 使用集群机器学习模型 (逻辑回归,XGBoost,随机森林) 进行分类.
- 识别的品种信息标记 (BIM) 和实用信息标记 (UIM),包括选择签名.
主要成果:
- 综合模型使用207个BIM实现了品种识别的准确度超过98%.
- 通过使用17个UIM,实现了超过96%的准确性来对牛的实用性进行分类 (乳制品与 draft).
- 在印度马哈拉施特拉州成功应用UIM来对非描述性牛进行分类.
结论:
- 开发的RASEL管道为牛的品种和用途分类提供了准确和有效的方法.
- 这种基因组方法支持牛的改良.
- 这些标志物和管道在畜牧管理中具有实际应用.
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