用随机回归模型模拟阿尔卑斯山 × 甲虫杂交的奶山山羊的哺乳曲线,并配备Legendre多项式和B-spline函数
Amritanshu Upadhyay1, Rani Alex1, Mahesh Shivanand Dige2
1Animal Genetics and Breeding Division, ICAR-National Dairy Research Institute, Karnal, Haryana, India.
概括
B-spline模型在基因评估阿尔卑斯山羊 × 甲山羊方面优越,为哺乳曲线提供了洞察力,并有助于繁殖策略. 适度的遗传性表明基于测试日牛奶产量的选择潜力.
科学领域:
- 动物遗传学和动物繁殖
- 乳制品科学 乳制品科学
- 量化遗传学 量化遗传学
背景情况:
- 准确的基因评估乳性能对于乳牛山羊的有效育种计划至关重要.
- 哺乳期曲线为整个哺乳期的牛奶生产动态提供了宝贵的信息.
- 随机回归模型 (RRM) 是用于分析测试日牛奶产量等纵向数据的先进统计工具.
研究的目的:
- 通过使用随机回归模型 (RRM) 来基因评估阿尔卑斯山 × 甲杂交山羊的哺乳曲线.
- 估计第一个哺乳期测试日 (TDMY) 牛奶产量的遗传参数,以告知育种策略.
- 为了比较直角的莱根德尔多项式 (LEG) 和B-splines (BS),以建模哺乳曲线变化.
主要方法:
- 利用了25998份来自阿尔卑斯山山羊和牛羊杂交的第一次哺乳测试日 (TDMY) 牛奶产量记录.
- 应用单特征随机回归模型 (RRM),使用直角的莱根德多项式 (LEG) 和B-splines (BS).
- 根据其捕捉遗传和环境变异组件的能力,确定了最佳模型.
主要成果:
- 在这种人群中,B-spline模型在遗传评估哺乳曲线方面表现出优于LEG模型的优势.
- 平均第一次哺乳期TDMY为1.22 ± 0.03公斤,峰值产量 (1.35 ± 0.02公斤) 约在第7个测试日.
- 观察到适度的遗传性估计值 (0.09 ± 0.04 到 0.33 ± 0.06),表明遗传选择的可能性.
结论:
- 建议使用六个节点的正方形B-spline函数作为阿尔卑斯山山羊 × Beetal山羊遗传分析的最佳RRM.
- 适度的遗传性和显著的永久性环境影响表明,选择策略应该考虑多个测试日.
- 这些发现支持使用基于B-spline的RRM来改进旨在增强杂交乳山羊乳生产的育种计划.
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