Madsen P, Shariati M M, Odegård J
Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, University of Aarhus, PO Box 50, DK-8830 Tjele, Denmark.
J Dairy Sci. 2008 Nov;91(11):4355-64. doi: 10.3168/jds.2008-1128.
Mixture models are appealing for identifying hidden structures affecting somatic cell score (SCS) data, such as unrecorded cases of subclinical mastitis. Thus, liability-normal mixture (LNM) models were used for genetic analysis of SCS data, with the aim of predicting breeding values for such cases of mastitis. Here, putative mastitis statuses and breeding values for liability to putative mastitis were inferred solely from SCS observations. In total, there were 395,906 test-day records for SCS from 50,607 Danish Holstein cows. Four different statistical models were fitted: A) a classical (nonmixture) random regression model for test-day SCS; B1) an LNM test-day model assuming homogeneous (co)variance components for SCS from healthy (IMI-) and infected (IMI+) udders; B2) an LNM model identical to B1, but assuming heterogeneous residual variances for SCS from IMI- and IMI+ udders; and C) an LNM model assuming fully heterogeneous (co)variance components of SCS from IMI- and IMI+ udders. For the LNM models, parameters were estimated with Gibbs sampling. For model C, variance components for SCS were lower, and the corresponding heritabilities and repeatabilities were substantially greater for SCS from IMI- udders relative to SCS from IMI+ udders. Further, the genetic correlation between SCS of IMI- and SCS of IMI+ was 0.61, and heritability for liability to putative mastitis was 0.07. Models B2 and C allocated approximately 30% of SCS records to IMI+, but for model B1 this fraction was only 10%. The correlation between estimated breeding values for liability to putative mastitis based on the model (SCS for model A) and estimated breeding values for liability to clinical mastitis from the national evaluation was greatest for model B1, followed by models A, C, and B2. This may be explained by model B1 categorizing only the most extreme SCS observations as mastitic, and such cases of subclinical infections may be the most closely related to clinical (treated) mastitis.
混合模型对于识别影响体细胞评分（SCS）数据的隐藏结构很有吸引力，例如未记录的亚临床乳腺炎病例。因此，使用责任-正态混合（LNM）模型对SCS数据进行遗传分析，目的是预测此类乳腺炎病例的育种值。在此，假定的乳腺炎状态和对假定乳腺炎的易感性育种值仅从SCS观测值推断得出。总共收集了来自50607头丹麦荷斯坦奶牛的395906条SCS测定日记录。拟合了四种不同的统计模型：A）用于测定日SCS的经典（非混合）随机回归模型；B1）一种LNM测定日模型，假定来自健康（IMI-）和感染（IMI+）乳房的SCS具有同质（协）方差分量；B2）一个与B1相同的LNM模型，但假定来自IMI-和IMI+乳房的SCS具有异质残差方差；C）一个LNM模型，假定来自IMI-和IMI+乳房的SCS具有完全异质的（协）方差分量。对于LNM模型，使用吉布斯抽样估计参数。对于模型C，相对于来自IMI+乳房的SCS，来自IMI-乳房的SCS的方差分量较低，相应的遗传力和重复性显著更高。此外，IMI-的SCS与IMI+的SCS之间的遗传相关性为0.61，对假定乳腺炎的易感性遗传力为0.07。模型B2和C将大约30%的SCS记录归类为IMI+，但对于模型B1，这一比例仅为10%。基于该模型（模型A的SCS）的假定乳腺炎易感性估计育种值与国家评估的临床乳腺炎易感性估计育种值之间的相关性，模型B1最大，其次是模型A、C和B2。这可能是因为模型B1仅将最极端的SCS观测值归类为乳腺炎，而此类亚临床感染病例可能与临床（治疗过的）乳腺炎关系最为密切。
