使用随机回归模型对肉牛料效率特征的基因组分析
Pedro Vital Brasil Ramos1,2, Gilberto Romeiro de Oliveira Menezes3, Delvan Alves da Silva1
1Department of Animal Science, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil.
使用随机回归模型与B-splines进行基因组评估,可以提高肉牛的料效率. 这可以缩短测试时间,提高对干物质摄入量和体重增加等特征的选择策略.
科学领域:
- 动物遗传学和动物繁殖
- 量化遗传学 量化遗传学
- 牛肉牛生产 牛肉牛生产
背景情况:
- 料效率对于肉牛的利能力和可持续性至关重要,影响了投入需求和甲排放.
- 传统的料效率计算使用平均每日摄入量和体重增加.
- 通过随机回归模型 (RRMs) 进行纵向分析,可以随着时间的推移计算遗传和环境影响.
研究的目的:
- 建议使用RRMs对干物摄入量 (DMI),体重增加 (BWG),剩余料摄入量 (RFI) 和剩余体重增加 (RWG) 进行基因组评估.
- 为了比较RRMs的适用性,使用Legendre多项式 (LP) 和B-spline函数.
- 评估遗传参数,并为料效率特征提供新的选择策略.
主要方法:
- 基因组繁殖值 (GEBVs) 估计是使用ssGBLUP下Nellore牛的RRMs.
- 采用了直角LP和B-spline函数,基于偏差信息标准 (DIC) 的模型比较.
- 使用斯皮尔曼相关性和普通人的百分比来比较每周和整体GEBV的排名.
主要成果:
- 具有异质残余方差的线性B-spline函数显示出最好的适合性.
- 在84天的测试中,对DMI,BWG,RFI和RWG的遗传概率估计在0.03到0.30之间.
- 对DMI和RFI观察到高遗传相关性,而BWG和RWG在测试的早期显示出负相关性.
- 在第八周,GEBV对DMI和RFI的排名与整体排名非常相匹配 (斯皮尔曼相关性0.95-1.00).
- 11周的BWG和RWG排名与整体排名有很高的相关性 (斯皮尔曼相关性为0.94-0.98).
结论:
- 使用线性B-splines的随机回归模型是基因组评估料效率的可行方法.
- 对于DMI,RFI,BWG和RWG存在足够的添加基因变异,支持适度的选择反应.
- 性能测试可以缩短到DMI/RFI选择的56天和BWG/RWG选择的77天.
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