Genetic analysis of carcass traits in beef cattle using random regression models
Genetic analysis of carcass traits in beef cattle using random regression models
复制标题
DOI：
10.2527/jas.2015-0246
复制
发表时间：
2016-04-01
影响因子：
3.3
通讯作者：
Berry, D. P.
中科院分区：
文献类型：
--
作者：
Englishby, T. M.;Banos, G.;Berry, D. P.
牲畜的成熟速度不同，部分取决于它们的遗传价值；因此，某些公牛的后代的最佳屠宰年龄可能有所不同。本研究的目的是检查多种肉牛和乳制品品种（包括杂交品种）牛的胴体重量、胴体构象和胴体脂肪的父系水平遗传谱。分析中使用了 126,214 头小母牛和 124,641 头年龄在 360 至 1,200 日龄之间的小公牛以及 86,089 头年龄在 360 至 720 日龄之间的年轻公牛的屠宰记录；动物来自 15,127 头公牛。使用父系随机回归模型生成屠宰时每个性状跨年龄的方差分量，其中包括用于固定和随机效应的二次多项式；假设各年龄段存在异质残差方差。不同性别的胴体重量遗传力估计范围为 0.08 (+/- 0.02) 至 0.34 (+/- 0.02)，体形遗传力估计范围为 0.24 (+/- 0.02) 至 0.42 (+/- 0.01)，脂肪评分范围为 0.16 (+/- 0.03) 至 0.40 (+/- 0.02)。随着年龄比较间隔的延长，各年龄特征内的遗传相关性减弱，但均>0.64，这表明不同年龄各特征的遗传背景相似。加性遗传协方差矩阵的特征值和特征函数揭示了动物胴体性状生长曲线的遗传变异性，尽管大多数遗传变异性与生长曲线的高度相关。在同一龄期，胴体重量与体形之间存在正向遗传相关性（0.60～0.78；SE范围为0.01～0.04），而脂肪与体形（-0.46～0.08；SE范围为0.02～0.09）和胴体重量（-0.48～-0.16；SE范围为0.02～0.02）之间存在负遗传相关。 0.14）在同一年龄。本研究中估计的遗传参数表明牛生长轨迹的遗传变异性，可以通过育种计划加以利用并用于决策支持工具。
Livestock mature at d ifferent rates depending, in part, on their genetic merit; therefore, the optimal age at slaughter for progeny of certain sires may differ. The objective of the present study was to examine sire-level genetic profiles for carcass weight, carcass conformation, and carcass fat in cattle of multiple beef and dairy breeds, including crossbreeds. Slaughter records from 126,214 heifers and 124,641 steers aged between 360 and 1,200 d and from 86,089 young bulls aged between 360 and 720 d were used in the analysis; animals were from 15,127 sires. Variance components for each trait across age at slaughter were generated using sire random regression models that included quadratic polynomials for fixed and random effects; heterogeneous residual variances were assumed across ages. Heritability estimates across genders ranged from 0.08 (+/- 0.02) to 0.34 (+/- 0.02) for carcass weight, from 0.24 (+/- 0.02) to 0.42 (+/- 0.01) for conformation, and from 0.16 (+/- 0.03) to 0.40 (+/- 0.02) for fat score. Genetic correlations within each trait across ages weakened as the interval between ages compared lengthened but were all >0.64, suggesting a similar genetic background for each trait across different ages. Eigenvalues and eigenfunctions of the additive genetic covariance matrix revealed genetic variability among animals in their growth profiles for carcass traits, although most of the genetic variability was associated with the height of the growth profile. At the same age, a positive genetic correlation (0.60 to 0.78; SE ranged from 0.01 to 0.04) existed between carcass weight and conformation, whereas negative genetic correlations existed between fatness and both conformation (-0.46 to 0.08; SE ranged from 0.02 to 0.09) and carcass weight (-0.48 to -0.16; SE ranged from 0.02 to 0.14) at the same age. The estimated genetic parameters in the present study indicate genetic variability in the growth trajectory in cattle, which can be exploited through breeding programs and used in decision support tools.