Vazquez A I, Weigel K A, Gianola D, Bates D M, Perez-Cabal M A, Rosa G J M, Chang Y M
Department of Dairy Science, University of Wisconsin, Madison 53706, USA.
J Dairy Sci. 2009 Oct;92(10):5239-47. doi: 10.3168/jds.2009-2085.
Typically, clinical mastitis is coded as the presence or absence of disease in a given lactation, and records are analyzed with either linear models or binary threshold models. Because the presence of mastitis may include cows with multiple episodes, there is a loss of information when counts are treated as binary responses. Poisson models are appropriated for random variables measured as the number of events, and although these models are used extensively in studying the epidemiology of mastitis, they have rarely been used for studying the genetic aspects of mastitis. Ordinal threshold models are pertinent for ordered categorical responses; although one can hypothesize that the number of clinical mastitis episodes per animal reflects a continuous underlying increase in mastitis susceptibility, these models have rarely been used in genetic analysis of mastitis. The objective of this study was to compare probit, Poisson, and ordinal threshold models for the genetic evaluation of US Holstein sires for clinical mastitis. Mastitis was measured as a binary trait or as the number of mastitis cases. Data from 44,908 first-parity cows recorded in on-farm herd management software were gathered, edited, and processed for the present study. The cows were daughters of 1,861 sires, distributed over 94 herds. Predictive ability was assessed via a 5-fold cross-validation using 2 loss functions: mean squared error of prediction (MSEP) as the end point and a cost difference function. The heritability estimates were 0.061 for mastitis measured as a binary trait in the probit model and 0.085 and 0.132 for the number of mastitis cases in the ordinal threshold and Poisson models, respectively; because of scale differences, only the probit and ordinal threshold models are directly comparable. Among healthy animals, MSEP was smallest for the probit model, and the cost function was smallest for the ordinal threshold model. Among diseased animals, MSEP and the cost function were smallest for the Poisson model, followed by the ordinal threshold model. In general, the models for count variables more accurately identified diseased animals and more accurately predicted mastitis costs. Healthy animals were more accurately identified by the probit model.
通常，临床型乳腺炎被编码为特定泌乳期疾病的存在与否，记录通过线性模型或二元阈值模型进行分析。由于乳腺炎的存在可能包括多次发病的奶牛，将计数视为二元反应时会损失信息。泊松模型适用于作为事件数量测量的随机变量，尽管这些模型在乳腺炎流行病学研究中被广泛使用，但它们很少用于研究乳腺炎的遗传方面。有序阈值模型适用于有序分类反应；尽管可以假设每头动物的临床乳腺炎发作次数反映了乳腺炎易感性的持续潜在增加，但这些模型很少用于乳腺炎的遗传分析。本研究的目的是比较概率单位模型、泊松模型和有序阈值模型对美国荷斯坦公牛临床型乳腺炎的遗传评估。乳腺炎被测量为二元性状或乳腺炎病例数。收集、编辑和处理了来自农场畜群管理软件记录的44908头头胎奶牛的数据用于本研究。这些奶牛是1861头公牛的女儿，分布在94个畜群中。通过使用2种损失函数的5折交叉验证评估预测能力：以预测均方误差（MSEP）为终点和成本差异函数。在概率单位模型中，作为二元性状测量的乳腺炎遗传力估计值为0.061，在有序阈值模型和泊松模型中，乳腺炎病例数的遗传力估计值分别为0.085和0.132；由于尺度差异，只有概率单位模型和有序阈值模型可以直接比较。在健康动物中，概率单位模型的MSEP最小，有序阈值模型的成本函数最小。在患病动物中，泊松模型的MSEP和成本函数最小，其次是有序阈值模型。一般来说，计数变量模型更准确地识别患病动物，并更准确地预测乳腺炎成本。概率单位模型更准确地识别健康动物。
