Chang Y-M, Gianola D, Heringstad B, Klemetsdal G
Department of Dairy Science, University of Wisconsin-Madison, Madison, WI, USA.
J Anim Breed Genet. 2006 Oct;123(5):290-300. doi: 10.1111/j.1439-0388.2006.00605.x.
Robust threshold models with multivariate Student's t or multivariate Slash link functions were employed to infer genetic parameters of clinical mastitis at different stages of lactation, with each cow defining a cluster of records. The robust fits were compared with that from a multivariate probit model via a pseudo-Bayes factor and an analysis of residuals. Clinical mastitis records on 36 178 first-lactation Norwegian Red cows from 5286 herds, daughters of 245 sires, were analysed. The opportunity for infection interval, going from 30 days pre-calving to 300 days postpartum, was divided into four periods: (i) -30 to 0 days pre-calving; (ii) 1-30 days; (iii) 31-120 days; and (iv) 121-300 days of lactation. Within each period, absence or presence of clinical mastitis was scored as 0 or 1 respectively. Markov chain Monte Carlo methods were used to draw samples from posterior distributions of interest. Pseudo-Bayes factors strongly favoured the multivariate Slash and Student's t models over the probit model. The posterior mean of the degrees of freedom parameter for the Slash model was 2.2, indicating heavy tails of the liability distribution. The posterior mean of the degrees of freedom for the Student's t model was 8.5, also pointing away from a normal liability for clinical mastitis. A residual was the observed phenotype (0 or 1) minus the posterior mean of the probability of mastitis. The Slash and Student's t models tended to have smaller residuals than the probit model in cows that contracted mastitis. Heritability of liability to clinical mastitis was 0.13-0.14 before calving, and ranged from 0.05 to 0.08 after calving in the robust models. Genetic correlations were between 0.50 and 0.73, suggesting that clinical mastitis resistance is not the same trait across periods, corroborating earlier findings with probit models.
采用具有多元学生t分布或多元斜线连接函数的稳健阈值模型来推断不同泌乳阶段临床乳腺炎的遗传参数，每头奶牛定义一组记录。通过伪贝叶斯因子和残差分析，将稳健拟合结果与多元概率单位模型的结果进行比较。对来自5286个牛群的36178头初产挪威红牛的临床乳腺炎记录进行了分析，这些牛是245头公牛的女儿。从产犊前30天到产后300天的感染间隔期分为四个阶段：(i)产犊前-30至0天；(ii)1至30天；(iii)31至120天；(iv)泌乳121至300天。在每个阶段，临床乳腺炎的有无分别记为0或1。采用马尔可夫链蒙特卡罗方法从感兴趣的后验分布中抽样。伪贝叶斯因子强烈支持多元斜线模型和多元学生t分布模型，而不是概率单位模型。斜线模型自由度参数的后验均值为2.2，表明责任分布的尾部较重。学生t分布模型自由度的后验均值为8.5，也表明临床乳腺炎的责任分布并非正态分布。残差是观察到的表型（0或1）减去乳腺炎概率的后验均值。在患乳腺炎的奶牛中，斜线模型和学生t分布模型的残差往往比概率单位模型的残差小。在稳健模型中，产犊前临床乳腺炎的责任遗传力为0.13 - 0.14，产犊后为0.05至0.08。遗传相关性在0.50至0.73之间，这表明不同阶段的临床乳腺炎抗性并非同一性状，这与概率单位模型的早期研究结果一致。
