This study aimed to estimate the genetic parameters for somatic cell count (SCC) and the genetic association between SCC and milk production traits using two different methods of SCC normalization. The dataset contained information on 8870 lactation records of 6172 Guzerá dairy cows selected for dual-purpose from 95 herds. The lactation means of SCC were normalized in two ways: (a) SCC1 = log10 (SCC) and (b) SCC2 = log2 (SCC/100) + 3. Multivariate analyses were performed considering milk production traits over the course of 305 days of lactation. Estimates of the variance components and genetic parameters were carried out by the Bayesian inference method, applying Gibbs sampling. Single chains of 2,000,000 iterations were used, with sampling discards of the first 5000 chains and a sampling period of every 50 iterations. The deviation of information criteria (DIC) was used to evaluate the best transformation for standardization of the SCC data, comparing analysis 1 (milk production traits over 305 days and SCC1) with analysis 2 (milk production traits over 305 days and SCC2). According to the data structure of this study, SCC1 normalization was the most efficient method and produced better estimates than normalization by the SCC2 method. The heritability estimates for SCC were low regardless of the transformation method used, indicating a small possibility of expressive genetic gains from the direct selection of these traits. However, the repeatability indicated the potential for increasing heritability estimates if the effects of the permanent environment were reduced. The genetic correlations between the milk yield and SCC traits do not indicate the possibility of a correlated genetic gain from the direct selection of one trait. However, concomitant selection for milk production traits and SCC will likely not affect the individual response either.

中文翻译：

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使用不同程序归一化的数据对古泽拉牛的体细胞计数（SCC）和产奶性状的遗传参数。

这项研究旨在使用两种不同的SCC归一化方法来估算体细胞计数（SCC）的遗传参数以及SCC与乳汁生产性状之间的遗传关联。该数据集包含有关从95个牛群中选作双重用途的6172具Guzerá奶牛的8870个泌乳记录的信息。通过两种方式对SCC的泌乳方式进行标准化：（a）SCC1 = log10（SCC）和（b）SCC2 = log2（SCC / 100）+3。考虑到305天的产奶特性，进行了多变量分析。哺乳期。方差分量和遗传参数的估计是通过贝叶斯推断方法，使用吉布斯采样法进行的。使用2,000,000次迭代的单链，前5000个链的采样丢弃，每50次迭代的采样周期。信息标准偏差（DIC）用于评估SCC数据标准化的最佳转换，将分析1（超过305天的牛奶生产性状和SCC1）与分析2（超过305天的牛奶生产性状和SCC2）进行比较。根据本研究的数据结构，SCC1归一化是最有效的方法，并且比SCC2归一化产生的估计值更好。无论采用哪种转化方法，SCC的遗传力估计值都较低，这表明直接选择这些性状获得表达遗传增益的可能性很小。但是，如果减少了永久环境的影响，则可重复性表明有可能增加遗传力估计值。产奶量与SCC性状之间的遗传相关性并不表明直接选择一种性状可获得相关遗传增益的可能性。但是，牛奶生产性状和SCC的伴随选择也可能不会影响个体反应。