This study aimed to conduct a meta-analysis using the random-effects model to merge published genetic parameter estimates for milk coagulation properties (MCP: comprising rennet coagulation time (RCT), curd-firming time (k20), curd firmness 30 min after rennet addition (a30), titrable acidity (TA) and milk acidity or pH) in dairy cows. Overall, 80 heritability estimates and 157 genetic correlations from 23 papers published between 1999 and 2020 were used. The heritability estimates for RCT, a30, k20, TA, and pH were 0.273, 0.303, 0.278, 0.189 and 0.276, respectively. The genetic correlation estimates between RCT-a30, RCT-pH, and RCT-TA were 0.842, 0.549 and −0.565, respectively. Genetic correlation estimates between RCT and production traits were generally low and ranged from −0.142 (between RCT and casein content) to 0.094 (between RCT and somatic cell score). Moderate and significant genetic correlations were observed between a30-pH (−0.396) and a30-TA (0.662). Also, the genetic correlation estimates between a30 and production traits were low to moderate and varied from −0.165 (between a30 and milk yield) to 0.481 (between a30 and casein content). Genetic correlation estimates between pH and production traits were low and varied from −0.190 (between pH and milk protein percentage) to 0.254 (between pH and somatic cell score). The results of this meta-analysis indicated the existence of additive genetic variation for MCP that could be used in genetic selection programs for dairy cows. Because of the moderate heritability of MCP and small genetic correlations with production traits, it could be possible to improve MCP with negligible correlated effects on production traits.

For this research communication our objective was to investigate to what extent milk coagulation properties and milk fatty acid (FA) composition were affected by different feeding systems, season and their interaction. Eighteen cows in total were subjected to one of three different feeding system treatments: full-time grazing or part-time grazing combined with indoor feeding of fresh grass with low or high concentrate supplementation. Milk was sampled in spring, summer and autumn. Milk coagulation time was 15.0, 19.0 and 17.7 min, coagulation dynamics 1.67, 3.41 and 1.79 min, and curd firmness 52.7, 32.4 and 47.0 mm in spring, summer and autumn, respectively. Thus, milk coagulation properties of the milk were lower during summer. There were strong seasonal effects on milk FA proportions, but there were not always changes with progressing season, or changes were different with respect to the impact of the feeding systems (system × season interaction). The milk fat was favourably rich in oleic acid, conjugated linoleic acid and α-linolenic acid and had a low n-6/n-3 fatty acid ratio in all systems. Factors like seasonal variations in grass composition and the energy balance of the cows were considered relevant for the milk FA composition. Overall, seasonal variations in milk quality were less pronounced with part-time grazing with fresh grass indoors as compared to full-time grazing without concentrate.

We investigated whether variation of the sheep Growth Hormone Receptor (GHR), Growth Hormone Releasing Hormone Receptor (GHRHR) and Insulin-Like Growth Factor 1 (IGF1) genes were associated with milk coagulation properties (MCP) in sheep. The GHR, GHRHR and IGF1 genes are part of the GH system, which is known to modulate metabolism, growth and reproduction as well as mammogenesis and galactopoiesis in dairy species. A total of 380 dairy Sarda sheep were genotyped for 36 SNPs mapping to these three genes. Traditional MCP were measured as rennet coagulation time (RCT), curd-firming time (k20) and curd firmness at 30 m (a30). Modeling of curd firming over time (CFt) was based on a 60 m lactodynamographic test, generating a total of 240 records of curd firmness (mm) for each milk sample. The model parameters obtained included: the rennet coagulation time as a result of modeling all data available (RCTeq, min); the asymptotic potential value of curd firmness (CFP, mm) at an infinite time; the CF instant rate constant (kCF, %/min); the syneresis instant rate constant (kSR, %/min); the maximum value of CF (CFmax, mm) and the time at achievement of CFmax (tmax, min). Statistical analysis revealed that variation of the GHR gene was significantly associated with RCT, kSR and CFP (P < 0.05). No other significant associations were detected. These findings may be useful for the dairy industry, as well as for selection programs.

The aims of this study were to estimate the genetic variation of traditional milk coagulation properties (MCPs), milk acidity, curd firmness (CF) modeled on time t (CFt; comprising: RCTeq, rennet coagulation time estimated from the equation; CFP, the asymptotic potential curd firmness; kCF, the curd firming instant rate constant; and kSR, the syneresis instant rate constant) and maximum CF traits (MCF; comprising CFmax, the maximum CF value; and tmax, the time of attainment). Furthermore, we investigated 96 single nucleotide polymorphisms (SNPs) from 54 candidate genes, testing their associations with the above-listed traits. Milk and blood samples were collected from 1271 cows (each sampled once) from 85 herds. Genotyping was performed using a custom Illumina VeraCode GoldenGate approach. A Bayesian linear animal model (including the effects of herd, days in milk, parity and additive polygenic effects) was used to estimate the genetic parameters of the studied traits. The same model with the addition of the SNP genotype effect was used for our association analysis. The heritability estimates of CFt and the MCF traits (RCTeq=0.258; kCF=0.230; CFmax=0.191; tmax=0.278) were similar to those obtained using traditional MCPs (0.187 to 0.267), except for the lower estimates for CFP (0.064) and kSR (0.077). A total of 13 of the 51 tested SNPs had relevant additive effects on at least one trait. We observed associations between MCPs and SNPs in the genes encoding ATP-binding cassette sub-family G member 2 (ABCG2), chemokine ligand 2 (CCL2), growth hormone 1 (GH1), prolactin (PRL) and toll-like receptor 2 (TLR2). Whereas, CFt and the MCF traits were associated with polymorphisms in the α-s1-casein (CSN1S1), β-casein (CSN2), GH1, oxidized low-density lipoprotein receptor 1 (OLR1), phospholipase C β1 (PLCB1), PRL and signal transducer and activator of transcription 5A (STAT5A) genes.

The aim of this study was to investigate the effects of numerous milk compositional factors on milk coagulation properties using Partial Least Squares (PLS). Milk from herds of Jersey and Holstein-Friesian cattle was collected across the year and blended (n=55), to maximise variation in composition and coagulation. The milk was analysed for casein, protein, fat, titratable acidity, lactose, Ca2+, urea content, micelles size, fat globule size, somatic cell count and pH. Milk coagulation properties were defined as coagulation time, curd firmness and curd firmness rate measured by a controlled strain rheometer. The models derived from PLS had higher predictive power than previous models demonstrating the value of measuring more milk components. In addition to the well-established relationships with casein and protein levels, CMS and fat globule size were found to have as strong impact on all of the three models. The study also found a positive impact of fat on milk coagulation properties and a strong relationship between lactose and curd firmness, and urea and curd firmness rate, all of which warrant further investigation due to current lack of knowledge of the underlying mechanism. These findings demonstrate the importance of using a wider range of milk compositional variables for the prediction of the milk coagulation properties, and hence as indicators of milk suitability for cheese making.

Ninety-nine individual milk samples from 37 cows in lactation week 10–35, selected for producing well or poorly/non-coagulating milk, were compared regarding protein composition, total calcium content, casein micelle size, pH, and coagulating properties after addition of 0·05% CaCl2. The results showed that a low κ-casein concentration in milk was a risk factor for non-coagulation. CaCl2 addition improved coagulating properties (coagulation time, curd firmness) of nearly all samples and eliminated differences between poorly/non-coagulating and well-coagulating milk, particularly regarding curd firmness. A second, independent data set with 18 non-coagulating or well-coagulating milk samples were analysed for protein composition, where indications of a similar association with κ-casein was observed.