König S, Wu X L, Gianola D, Heringstad B, Simianer H
Institute of Animal Breeding and Genetics, University of Göttingen, 37075 Göttingen, Germany.
J Dairy Sci. 2008 Jan;91(1):395-406. doi: 10.3168/jds.2007-0170.
Relationships between claw disorders and test-day milk yield recorded in 2005 on 5,360 Holstein cows, kept on 11 large-scale dairy farms in eastern Germany, were analyzed in a Bayesian framework with standard linear and threshold models and recursive linear and threshold models. Four different claw disorders, digital dermatitis (DD), sole ulcer (SU), wall disorder (WD), and interdigital hyperplasia (IH), were scored as binary traits within 200 d after calving and analyzed separately. Incidences of disorders were 13.7% for DD, 16.5% for SU, 9.8% for WD, and 6.7% for IH. Heritabilities of disorders were greater when applying threshold or recursive threshold models than with linear or linear recursive models. Posterior means of genetic correlations between test-day milk production and claw disorders ranged from 0.17 to 0.44, suggesting that breeding strategies focusing on increased milk yield will increase incidences of disorders as a correlated response. A progressive path of lagged relationships was postulated for recursive models describing first the influence of test-day milk yield (MY1) on claw disorders and second, the effect of the disorder on milk production level at the following test day (MY2). In recursive models, structural coefficients describe recursive relationships at the phenotypic level. The structural coefficient lambda21 was the gradient of disease (trait 2) with respect to MY1 (trait 1) for a model with a recursive effect of trait 1 on trait 2. The increase of disease incidence of the 4 different disorders per 1-kg increase of MY1 ranged from lambda21 = 0.006 to lambda21 = 0.024 on the visible scale when applying recursive linear models, and from lambda21 = 0.003 to lambda21 = 0.016 on the underlying liability scale for recursive threshold models. The rate of change in MY2 (trait 3) with respect to the previous claw disorder is given by lambda32 for a model with a recursive effect from trait 2 to trait 3. Structural coefficients lambda32 ranged from -0.12 to -0.68 predicting that a 1-unit increase in the incidence of any disorder reduces milk yield at the following test day by up to 0.67 kg. Rank correlations between sire posterior means for the same claw disorders among different models were >0.84, but some changes in rank of sires in distinct top-10 lists were observed. Structural equation models are of increasing importance in genetic evaluations, and this study showed the possible application of recursive systems, even for categorical data.
对2005年德国东部11个大型奶牛场的5360头荷斯坦奶牛记录的蹄病与产奶量之间的关系，采用标准线性和阈值模型以及递归线性和阈值模型在贝叶斯框架下进行了分析。四种不同的蹄病，即趾间皮炎（DD）、蹄底溃疡（SU）、蹄壁病（WD）和指间增生（IH），在产犊后200天内被记为二元性状并分别进行分析。疾病发生率分别为：DD为13.7%，SU为16.5%，WD为9.8%，IH为6.7%。应用阈值或递归阈值模型时疾病的遗传力比线性或线性递归模型时更高。产奶量与蹄病之间遗传相关性的后验均值范围为0.17至0.44，这表明专注于提高产奶量的育种策略将导致疾病发生率作为相关反应而增加。对于递归模型，假设了一个滞后关系的渐进路径，首先描述产奶量（MY1）对蹄病的影响，其次描述疾病对下一个检测日产奶水平（MY2）的影响。在递归模型中，结构系数描述了表型水平上的递归关系。结构系数lambda21表示具有性状1对性状2递归效应的模型中疾病（性状2）相对于MY1（性状1）的梯度。应用递归线性模型时，在可见尺度上，MY1每增加1千克，4种不同疾病的发病率增加范围为lambda21 = 0.006至lambda21 = 0.024；对于递归阈值模型，在潜在易感性尺度上为lambda21 = 0.003至lambda21 = 0.016。对于具有从性状2到性状3递归效应的模型，MY2（性状3）相对于先前蹄病的变化率由lambda32给出。结构系数lambda32范围为 -0.12至 -0.68，预测任何疾病发病率增加1个单位会使下一个检测日产奶量降低多达0.67千克。不同模型中相同蹄病的父本后验均值之间的秩相关>0.84，但在不同的前10名列表中观察到了一些父本排名的变化。结构方程模型在遗传评估中的重要性日益增加，本研究展示了递归系统的可能应用，即使对于分类数据也是如此。
