Short communication: Genetic parameters for milk protein composition predicted using mid-infrared spectroscopy in the French Montbéliarde, Normande, and Holstein dairy cattle breeds

Abstract

Genetic parameters for the major milk proteins were estimated in the 3 main French dairy cattle breeds (i.e. Montbéliarde, Normande, and Holstein) as part of the PhénoFinlait program. The 6 major milk protein contents as well as the total protein content (PC) were estimated from mid-infrared spectrometry on 133,592 test-day milk samples from 20,434 cows in first lactation. Lactation means, expressed as a percentage of milk (protein contents) or of protein (protein fractions), were analyzed with an animal mixed model including fixed environmental effects (herd, year × month of calving, and spectrometer) and a random genetic effect. Genetic parameter estimates were very consistent across breeds. Heritability estimates (h) were generally higher for protein fractions than for protein contents. They were moderate to high for α-casein, α-casein, β-casein, κ-casein, and α-lactalbumin (0.25 < h < 0.72). In each breed, β-lactoglobulin was the most heritable trait (0.61 < h < 0.86). Genetic correlations (r) varied depending on how the percentage was expressed. The PC was strongly positively correlated with protein contents but almost genetically independent from protein fractions. Protein fractions were generally in opposition, except between κ-casein and α-lactalbumin (0.39 < r < 0.46) and κ-casein and α-casein (0.36 < r < 0.49). Between protein contents, r estimates were positive, with highest values found between caseins (0.83 < r < 0.98). In the 3 breeds, β-lactoglobulin was negatively correlated with caseins (-0.75 < r < -0.08), in particular with κ-casein (-0.75 < r < -0.55). These results, obtained from a large panel of cows of the 3 main French dairy cattle breeds, show that routinely collected mid-infrared spectra could be used to modify milk protein composition by selection.

Genetic analysis of Fourier transform infrared milk spectra in Danish Holstein and Danish Jersey

“…These associations could explain weaker r g to wavenumbers from the protein-, fat-, and lactose-region (Table 2). Consistent weaker genetic correlations have also been observed for LGB content in milk, when studying genetic correlations between milk contents of LGB, α-lactose, α S1 -CN, α S2 -CN, β-CN, and κ-CN (Sanchez et al, 2017). Within the fat-regions, wavenumbers in fat-i were associated with fat A (Kaylegian et al, 2009), transfat (Hewavitharana and van Brakel, 1997), and DGAT1 (Wang et al, 2016).…”

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].…”

Short communication: Confirmation of candidate causative variants on milk composition and cheesemaking properties in Montbéliarde cows

“…Cheesemaking properties (CMP) are strongly related to bovine milk composition (Wedholm et al, 2006). In the PhénoFinlait project, we predicted protein and fatty acid milk composition using mid-infrared (MIR) spectrometry in the French Holstein, Montbéliarde, and Normande dairy cattle breeds (Ferrand-Calmels et al, 2014;Sanchez et al, 2017a). Genome-wide association study (GWAS) on whole-genome sequences imputed using data of run 4 of the 1,000 bull genome project (Daetwyler et al, 2014) led to the identification of candidate causative mutations in 24 candidate genes (Boichard et al, 2014;Sanchez et al, 2017b).…”

“…Moreover, 15 milk composition traits (Table 1) were predicted from MIR spectra using equations of prediction previously developed in PhénoFinlait (Ferrand et al, 2012;Ferrand-Calmels et al, 2014;Sanchez et al, 2017a) and Optimir (Gengler et al, 2016) projects. These included 6 proteins (α-LA and β-LG whey proteins and α S1 -CN, α S2 -CN, β-CN and κ-CN), 4 fatty acids (total SFA, UFA, MUFA, and PUFA), and 5 min- BeadChips.…”