Institute of Animal Breeding and Genetics, Justus-Liebig-University of Gießen, 35390 Gießen, Germany.
Institute of Animal Breeding and Genetics, Justus-Liebig-University of Gießen, 35390 Gießen, Germany.
J Dairy Sci. 2018 Mar;101(3):2171-2186. doi: 10.3168/jds.2017-13575. Epub 2017 Dec 14.
The most common approach in dairy cattle to prove genotype by environment interactions is a multiple-trait model application, and considering the same traits in different environments as different traits. We enhanced such concepts by defining continuous phenotypic, genetic, and genomic herd descriptors, and applying random regression sire models. Traits of interest were test-day traits for milk yield, fat percentage, protein percentage, and somatic cell score, considering 267,393 records from 32,707 first-lactation Holstein cows. Cows were born in the years 2010 to 2013, and kept in 52 large-scale herds from 2 federal states of north-east Germany. The average number of genotyped cows per herd (45,613 single nucleotide polymorphism markers per cow) was 133.5 (range: 45 to 415 genotyped cows). Genomic herd descriptors were (1) the level of linkage disequilibrium (r) within specific chromosome segments, and (2) the average allele frequency for single nucleotide polymorphisms in close distance to a functional mutation. Genetic herd descriptors were the (1) intra-herd inbreeding coefficient, and (2) the percentage of daughters from foreign sires. Phenotypic herd descriptors were (1) herd size, and (2) the herd mean for nonreturn rate. Most correlations among herd descriptors were close to 0, indicating independence of genomic, genetic, and phenotypic characteristics. Heritabilities for milk yield increased with increasing intra-herd linkage disequilibrium, inbreeding, and herd size. Genetic correlations in same traits between adjacent levels of herd descriptors were close to 1, but declined for descriptor levels in greater distance. Genetic correlation declines were more obvious for somatic cell score, compared with test-day traits with larger heritabilities (fat percentage and protein percentage). Also, for milk yield, alterations of herd descriptor levels had an obvious effect on heritabilities and genetic correlations. By trend, multiple trait model results (based on created discrete herd classes) confirmed the random regression estimates. Identified alterations of breeding values in dependency of herd descriptors suggest utilization of specific sires for specific herd structures, offering new possibilities to improve sire selection strategies. Regarding genomic selection designs and genetic gain transfer into commercial herds, cow herds for the utilization in cow training sets should reflect the genomic, genetic, and phenotypic pattern of the broad population.
在奶牛中，证明基因型与环境互作的最常见方法是应用多性状模型，并将不同环境下的相同性状视为不同的性状。我们通过定义连续的表型、遗传和基因组群描述符，并应用随机回归种公牛模型来增强这些概念。所关注的性状是产奶量、脂肪百分比、蛋白质百分比和体细胞评分的测试日性状，共考虑了来自 32707 头首产荷斯坦奶牛的 267393 条记录。奶牛出生于 2010 年至 2013 年，饲养于德国东北部 2 个联邦州的 52 个大型牛群中。每个牛群的平均基因分型奶牛数量（每头牛 45613 个单核苷酸多态性标记）为 133.5（范围：45 至 415 头基因分型奶牛）。基因组群描述符为：（1）特定染色体片段内的连锁不平衡（r）水平，以及（2）功能突变附近单核苷酸多态性的平均等位基因频率。遗传群描述符为：（1）群内近交系数，以及（2）来自外国种公牛的女儿比例。表型群描述符为：（1）牛群规模，以及（2）非返情率的牛群平均值。群描述符之间的大多数相关性接近 0，表明基因组、遗传和表型特征相互独立。产奶量的遗传力随群内连锁不平衡、近交和牛群规模的增加而增加。相邻群描述符水平的同性状遗传相关接近 1，但距离较大的描述符水平的遗传相关下降。与具有较大遗传力的测试日性状（脂肪百分比和蛋白质百分比）相比，体细胞评分的遗传相关下降更为明显。此外，对于产奶量，牛群描述符水平的改变对遗传力和遗传相关有明显影响。趋势上，多性状模型结果（基于创建的离散牛群类别）证实了随机回归估计。根据牛群描述符确定的育种值变化表明，特定公牛可用于特定牛群结构，为改进种公牛选择策略提供了新的可能性。关于基因组选择设计和遗传增益在商业牛群中的转移，用于牛训练集的奶牛牛群应反映广泛群体的基因组、遗传和表型模式。
