Short communication: Multi-trait estimation of genetic parameters for milk protein composition in the Danish Holstein

Abstract

Genetic parameters were estimated for the major milk proteins using bivariate and multi-trait models based on genomic relationships between animals. The analyses included, apart from total protein percentage, αS1-casein (CN), αS2-CN, β-CN, κ-CN, α-lactalbumin, and β-lactoglobulin, as well as the posttranslational sub-forms of glycosylated κ-CN and αS1-CN-8P (phosphorylated). Standard errors of the estimates were used to compare the models. In total, 650 Danish Holstein cows across 4 parities and days in milk ranging from 9 to 481d were selected from 21 herds. The multi-trait model generally resulted in lower standard errors of heritability estimates, suggesting that genetic parameters can be estimated with high accuracy using multi-trait analyses with genomic relationships for scarcely recorded traits. The heritability estimates from the multi-trait model ranged from low (0.05 for β-CN) to high (0.78 for κ-CN). Genetic correlations between the milk proteins and the total milk protein percentage were generally low, suggesting the possibility to alter protein composition through selective breeding with little effect on total milk protein percentage.

A Novel SNPs in Alpha-Lactalbumin Gene Effects on Lactation Traits in Chinese Holstein Dairy Cows

“…Polymorphism and milk quality studies have been widely used in the optimization of dairy population, and the link between milk composition and genetic variation in milk production has a profound impact on dairy production [2,3,24,25]. Recently, Poulsen et al found that α-LA is a major component, which has negative effect on milk coagulation in Danish Jersey [26].…”

“…Milk protein components have an important influence on milk production traits [1], and mainly affect by genetic factors [2,3]. There are six major milk protein components (α S1 -CN, α S2 -CN, β-CN, κ-CN, α-LA, and β-LG) in bovine milk [4,5].…”

Predictive ability of host genetics and rumen microbiome for subclinical ketosis

“…Genotypes were subject to quality control, including (1) minimum call rates of 90% for individuals and 95% for loci and (2) exclusion of SNP with a minor allele frequency lower than 5% before imputation to Illumina BovineHD markers using IMPUTE2, v. 2.3.1 (Howie et al, 2011). Imputation was based on a reference population of 3,383 animals, including sires and siblings of some of the sampled cows, as described in detail by Gebreyesus et al (2016). Imputation accuracies were assessed based on the built-in cross-validation system implemented in IMPUTE2, which showed that imputation error was, on average, less than 1%.…”

Combining multi-population datasets for joint genome-wide association and meta-analyses: The case of bovine milk fat composition traits

“…In the Danish data set, 278 cows were genotyped using the BovineSNP50 BeadChip. The remaining Danish Holstein cows were genotyped using the BovineHD BeadChip and used as reference to impute the 50K genotypes of the first part of the Danish cows to HD, as described by Gebreyesus et al (2016).…”