Casellas J, Caja G, Ferret A, Piedrafita J
Grup de Recerca en Remugants, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain.
J Anim Sci. 2007 Mar;85(3):618-24. doi: 10.2527/jas.2006-365. Epub 2006 Oct 13.
The analysis focused on model fitting of 2 ewe reproductive traits, litter size, and days to lambing (interval between the introduction of the ram into the flock and the subsequent parturition of the ewes). The experimental data set of the Universitat Autònoma of Barcelona flock was used, including 1,598 records of litter size and 1,699 records of days to lambing from 376 Ripollesa ewes between 1986 and 2005. Univariate and bivariate models were considered as beginning points with linear or threshold approximation for litter size. Model fitting was evaluated in terms of goodness-of-fit and predictive ability, using the mean square error and the correlation between phenotypic and predicted records (rho(y,ŷ)) as reference parameters. The bivariate model was preferable for both variables, minimizing mean square error and maximizing rho(y,ŷ). A threshold approximation for litter size was preferable over a linear approximation. Models were also compared with a simulation study, comparing the correlation coefficient between simulated and predicted breeding values (rho(a,â)). The bivariate threshold model was favored, with a rho(y,ŷ) of 0.677 and 0.834 for litter size and days to lambing, respectively. Correlation coefficients between simulated and predicted breeding values in the bivariate linear model were reduced slightly to 0.651 and 0.831, respectively, and they were lowest with linear univariate models (0.642 and 0.802). Although the bivariate models for ewe litter size and days to lambing were more accurate than the univariate models, the threshold approaches showed a greater advantage under the bivariate model. For the purpose of genetic evaluation of litter size in sheep, use of the threshold-linear model seems justified. In the Ripollesa breed, the evaluation of litter size can benefit from recording birth weight.
该分析聚焦于2个母羊繁殖性状的模型拟合,即产仔数和产羔天数(从公羊引入羊群到随后母羊分娩的间隔时间)。使用了巴塞罗那自治大学羊群的实验数据集,其中包括1986年至2005年间376只里波列萨母羊的1598条产仔数记录和1699条产羔天数记录。单变量和双变量模型被视为起点,对产仔数采用线性或阈值近似法。根据拟合优度和预测能力对模型拟合进行评估,使用均方误差以及表型记录与预测记录之间的相关性(rho(y,ŷ))作为参考参数。双变量模型对两个变量均更为适用,可使均方误差最小化并使rho(y,ŷ)最大化。对于产仔数,阈值近似法优于线性近似法。还通过模拟研究对模型进行了比较,比较模拟育种值与预测育种值之间的相关系数(rho(a,â))。双变量阈值模型更受青睐,产仔数和产羔天数的rho(y,ŷ)分别为0.677和0.834。双变量线性模型中模拟育种值与预测育种值之间的相关系数分别略有降低,降至0.651和0.831,而在单变量线性模型中则最低(0.642和0.802)。尽管母羊产仔数和产羔天数的双变量模型比单变量模型更准确,但在双变量模型下,阈值方法显示出更大优势。为了对绵羊产仔数进行遗传评估,使用阈值 - 线性模型似乎是合理的。在里波列萨品种中,产仔数的评估可受益于记录出生体重。