Abstract：
Genetic improvement of udder health in dairy cows is of high relevance as mastitis is one of the most prevalent diseases. Since it is known that the heritability of mastitis is low and direct data on mastitis cases are often not available in large numbers, auxiliary traits, such as somatic cell count (SCC) are used for the genetic evaluation of udder health. In previous studies, models to predict clinical mastitis based on mid-infrared (MIR) spectral data and a somatic cell count-derived score (SCS) were developed. Those models can provide a probability of mastitis for each cow at every test-day, which is potentially useful as an additional auxiliary trait for the genetic evaluation of udder health. Furthermore, MIR spectral data were used to estimate contents of lactoferrin, a glycoprotein positively associated with immune response. The present study aimed to estimate heritabilities (h2) and genetic correlations (ra) for clinical mastitis diagnosis (CM), SCS, MIR-predicted mastitis probability (MIRprob), MIR + SCS-predicted mastitis probability (MIRSCSprob) and lactoferrin estimates (LF). Data for this study were collected within the routine milk recording and health monitoring system of Austria from 2014 to 2021 and included records of approximately 54,000 Fleckvieh cows. Analyses were performed in two datasets, including test-day records from 5 to 150 or 5 to 305 days in milk. Prediction models were applied to obtain MIR- and SCS-based phenotypes (MIRprob, MIRSCSprob, LF). To estimate heritabilities and genetic correlations bivariate linear animal models were applied for all traits. A lactation model was used for CM, defined as a binary trait, and a test-day model for all other continuous traits. In addition to the random animal genetic effect, the fixed effects year-season of calving and parity-age at calving and the random permanent environmental effect were considered in all models. For CM the random herd-year effect, for continuous traits the random herd-test day effect and the covariate days in milk (linear and quadratic) were additionally fitted. The obtained genetic parameters were similar in both datasets. The heritability found for CM was expectedly low (h2 = 0.02). For SCS and MIRSCSprob, heritability estimates ranged from 0.23 to 0.25, and for MIRprob and LF from 0.15 to 0.17. CM was highly correlated with SCS and MIRSCSprob (ra = 0.85 to 0.88). Genetic correlations of CM were moderate with MIRprob (ra = 0.26 and 0.37) during 150 and 305 days in milk, respectively and low with LF (h2 = 0.10 and 0.11). However, basic selection index calculations indicate that the added value of the new MIR-predicted phenotypes is limited for genetic evaluation of udder health.
摘要：
奶牛乳房健康的遗传改善具有高度相关性,因为乳腺炎是最普遍的疾病之一。由于已知乳腺炎的遗传率很低，而且乳腺炎病例的直接数据通常无法大量获得，辅助性状，例如体细胞计数（SCC）用于乳房健康的遗传评估。在以往的研究中,建立了基于中红外（MIR）光谱数据和体细胞计数衍生评分（SCS）预测临床乳腺炎的模型。这些模型可以提供每头牛在每个测试日的乳腺炎概率，它可能作为乳房健康遗传评估的额外辅助性状。此外，MIR光谱数据用于估计乳铁蛋白的含量，一种与免疫反应正相关的糖蛋白。本研究旨在估计临床乳腺炎诊断（CM）的遗传力（H2）和遗传相关性（RA），SCS,MIR预测的乳腺炎概率（MIRprob），MIR+SCS预测的乳腺炎概率(MIRSCSprob)和乳铁蛋白估计值(LF)。这项研究的数据是在2014年至2021年奥地利的常规牛奶记录和健康监测系统中收集的，其中包括约54,000头Fleckvieh奶牛的记录。在两个数据集中进行了分析，包括牛奶中5到150天或5到305天的测试日记录。应用预测模型来获得基于MIR和SCS的表型(MIRprob，MIRSCSprob,LF).为了估计遗传力和遗传相关性,对所有性状应用双变量线性动物模型。泌乳模型用于CM，定义为二元特征，和所有其他连续性状的测试日模型。除了随机的动物遗传效应，在所有模型中都考虑了产卵年季节和产卵年龄的固定效应以及随机永久性环境效应。对于CM，随机羊群年效应，对于连续性状，还拟合了随机羊群试验日效应和牛奶中的协变量日（线性和二次）。在两个数据集中获得的遗传参数相似。对于CM发现的遗传力预期较低(h2=0.02)。对于SCS和MIRSCSprob，遗传力估计值为0.23至0.25，MIRprob和LF的遗传力估计值为0.15至0.17。CM与SCS和MIRSCSprob高度相关（ra=0.85至0.88）。在150天和305天的牛奶中，CM与MIRprob的遗传相关性中等（ra=0.26和0.37），分别为低LF（h2=0.10和0.11）。然而,基本选择指数计算表明，新的MIR预测表型的附加值对于乳房健康的遗传评估是有限的。
