Invited review: Genetics and modeling of milk coagulation properties

Abstract

Milk coagulation properties (MCP) are conventionally measured using computerized renneting meters, mechanical or optical devices that record curd firmness over time (CF(t)). The traditional MCP are rennet coagulation time (RCT, min), curd firmness (a(30), mm), and curd-firming time (k(20), min). The milk of different ruminant species varies in terms of CF(t) pattern. Milk from Holstein-Friesian and some Scandinavian cattle breeds yields higher proportions of noncoagulating samples, samples with longer RCT and lower a(30), and samples for which k(20) is not estimable, than does milk from Brown Swiss, Simmental, and other local Alpine breeds. The amount, proportion, and genetic variants (especially κ-casein) of milk protein fractions strongly influence MCP and explain variable proportions of the observed differences among breeds and among individuals of the same breed. In addition, other major genes have been shown to affect MCP. Individual repeatability of MCP is high, whereas any herd effect is low; thus, the improvement of MCP should be based principally on selection. Exploitable additive genetic variation in MCP exists and has been assessed using different breeds in various countries. Several models have been formulated that either handle noncoagulating samples or not. The heritability of MCP is similar to that of other milk quality traits and is higher than the heritability of milk yield. Rennet coagulation time and a(30) are highly correlated, both phenotypically and genetically. This means that the use of a(30) data does not add valuable information to that obtainable from RCT; both traits are genetically correlated mainly with milk acidity. Moreover, a(30) is correlated with casein content. The major limitations of traditional MCP can be overcome by prolonging the observation period and by using a novel CF(t) modeling, which uses all available information provided by computerized renneting meters and allows the estimation of RCT, the potential asymptotic curd firmness, the curd-firming rate, and the syneresis rate. Direct measurements of RCT obtained from both mechanical and optical devices show similar heritabilities and exhibit high phenotypic and genetic correlations. Moreover, mid-infrared reflectance spectroscopy can predict MCP. The heritabilities of predicted MCP are higher than those of measured MCP, and the 2 sets of values are strongly correlated. Therefore, mid-infrared reflectance spectroscopy is a reliable and cheap method whereby MCP can be improved at the population level; this is because such spectra are already routinely acquired from the milk of cows enrolled in milk recording schemes.

Genetic variation and effects of candidate-gene polymorphisms on coagulation properties, curd firmness modeling and acidity in milk from Brown Swiss cows

“…This finding is similar to that from a previous report on the Brown Swiss (Chessa et al, 2013). At present, it is not possible to establish whether the less favorable effect of the CSN2 rs43703011 A variant on RCT is due to the presence of the A 1 or B alleles, and the positive effect of the CSN2 B allele reported by Bittante et al (2012) remains controversial. Moreover, the CSN2*A 2 variant is usually found in a haplotype with the CSN3*B variant (frequency, 0.56 to 0.67; Chessa et al, 2013), suggesting that the positive effect of the CSN2 rs43703011 C allele could be confused with that of the CSN3*B variant.…”

“…The instrument recorded the width (in mm) of the oscillatory graph every 15 s during testing and directly provided the traditional MCP traits: RCT (min), defined as the time from enzyme addition to milk gelation; k 20 (min), defined as the time from gelation to that at which the width of the graph reached 20 mm; and the widths of the graph at 30 min (a 30 , mm), from rennet addition (measuring the extent of CF). Figure 1 Modeling prolonged observations of curd firmness (CF) at t time (CF t ), model parameters: RCT = rennet coagulation time; k 20 = curd-firming time of samples reaching 20 mm of firmness within 45 min from enzyme addition; a 30 = curd firmness at 30 min after enzyme addition; CF P = potential asymptotical CF in absence of syneresis; k CF = curd firming instant rate constant; k SR = syneresis instant rate constant and representation of maximum CF traits MCF: CF max = maximum CF t value; t max = time at CF max ; modified from Bittante et al (2012).…”

“…The fraction of milk used for cheesemaking is on the rise worldwide (International Dairy Federation, 2013), increasing the importance of the relevant milk parameters, which include cheese yield, milk coagulation properties (MCPs), and the fat and protein contents . MCPs, which are important measures of the technological qualities of milk (Annibaldi et al, 1977;Bittante et al, 2012), include rennet reactivity, curd-firming capacity, syneresis ability and whey drainage, all of which are crucial features for cheesemaking. The suitability of milk for cheesemaking is traditionally evaluated by measuring the rennet coagulation time (RCT), the time required for curd firming (k 20 ) and the firmness (a 30 or a 45 , depending on the testing time used), elasticity, permeability, contractility and syneresis of the curd.…”

Behavior of the Physiochemical Parameters of Raw Milk Stored in Temporary Horizontal Storage Tanks

“…The physiochemical characteristics of milk and their inter-relationships are a valuable tool to evaluate the productive performance of dairy cattle, to provide information about the physiological state of the lactation, and to diagnose metabolic disorders and their possible impacts on the industrial processing and the final quality of milk products (Rowbotham and Ruegg, 2016). In the tanks of the trucks arriving at the dairy industry, samples are collected for laboratory tests, which must be taken after agitation for five to ten minutes by means of an agitator of the total volume of the tank (Di Domenico, 2009;Ponce, 2009;Bittante et al, 2012;Tonini, 2014) The fat globules are suspended in water and have a lower density than it. This causes the formation of a fat layer on top of the rest of the milk, which must be constantly stirred to prevent the formation of a too thick layer of fat at the top.…”

Mapping and fine-mapping of genetic factors affecting bovine milk composition

“…Targeted research on NC milk can help geneticists develop breeding programs to modify milk composition and technological properties of milk and thus reduce the prevalence of NC milk. Bittante et al (2012) suggested that effects of herd have little influence on milk coagulation properties (MCP) including NC milk, although several factors can influence the composition of bovine milk (e.g., breed, a cow's diet, age of a cow, and the stage of lactation; Chilliard et al, 2001). In addition, MCP seem be influenced by many factors, such as SCC (e.g., Ikonen et al, 2004;Cassandro et al, 2008), titratable acidity (e.g., Penasa et al, 2010), casein composition (Okigbo et al, 1985b), pH (Nájera et al, 2003), stage of lactation (Okigbo et al, 1985a;Ostersen et al,1997), and breed (e.g., Auldist et al, 2004;De Marchi et al, 2007, Bittante et al, 2012, among many other factors.…”