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Inferring Relationships Between Somatic Cell Score and Milk Yield Using Simultaneous and Recursive Models

机译:使用同时递归模型推断体细胞评分与产奶量之间的关系

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A Bayesian analysis via Markov chain Monte Carlo methods extending the simultaneous and recursive model of Gianola and Sorensen (2004) was proposed to account for possible population heterogeneity. The method was used to infer relationships between milk yield and somatic cell scores of Norwegian Red cows. Data consisted of test-day records of milk yield and somatic cell score of first-lactation cows during the first 120 d of lactation. Results suggested large negative direct effects from somatic cell score to milk yield and small reciprocal effects from milk yield to somatic cell score. The direct effects were larger in the first 60 d of lactation than in the subsequent period. Bayesian model selection strongly favored the simultaneous and recursive models for milk yield and somatic cell score over the corresponding mixed model without considering simultaneity or recursiveness. Estimated effects between milk yield and somatic cell score seemed to be yield-dependent, larger in higher producing cows than in lower producing cows. Heritability estimates from the simultaneous and recursive models were similar to those from the mixed model, but some genetic correlations differed considerably among models.
机译:提出了一种通过马尔可夫链蒙特卡罗方法进行的贝叶斯分析,扩展了Gianola和Sorensen(2004)的同时递归模型,以解决可能的人口异质性。该方法用于推断挪威红牛的产奶量与体细胞评分之间的关​​系。数据由泌乳前120 d内第一胎母牛的产奶量和体细胞评分的测试日记录组成。结果表明,从体细胞评分到牛奶产量的负面影响很大,而从牛奶产量到体细胞评分的负面影响却很小。在哺乳期的前60天,其直接影响大于随后的时期。在不考虑同时性或递归性的情况下,贝叶斯模型选择相对于相应的混合模型,强烈赞成牛奶产量和体细胞得分的同时模型和递归模型。产奶量和体细胞评分之间的估计影响似乎与产量有关,在高产奶牛中比在低产奶牛中更大。同时模型和递归模型的遗传力估计与混合模型的遗传力估计相似,但是一些遗传相关性在模型之间存在很大差异。

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