Quantitative trait loci (QTL) mapping projects have been implemented mainly in the Holstein dairy cattle breed for several traits. The aim of this study is to map QTL for milk yield (MY) and milk protein percent (PP) in the Brown Swiss cattle populations of Austria, Germany, and Italy, considered in this study as a single population. A selective DNA pooling approach using milk samples was applied to map QTL in 10 paternal half-sib daughter families with offspring spanning from 1,000 to 3,600 individuals per family. Three families were sampled in Germany, 3 in Italy, 1 in Austria and 3 jointly in Austria and Italy. The pools comprised the 200 highest and 200 lowest performing daughters, ranked by dam-corrected estimated breeding value for each sire-trait combination. For each tail, 2 independent pools, each of 100 randomly chosen daughters, were constructed. Sire marker allele frequencies were obtained by densitometry and shadow correction analyses of 172 genome-wide allocated autosomal markers. Particular emphasis was placed on Bos taurus chromosomes 3, 6, 14, and 20. Marker association for MY and PP with a 10% false discovery rate resulted in nominal P-values of 0.071 and 0.073 for MY and PP, respectively. Sire marker association tested at a 20% false discovery rate (within significant markers) yielded nominal P-values of 0.031 and 0.036 for MY and PP, respectively. There were a total of 36 significant markers for MY, 33 for PP, and 24 for both traits; 75 markers were not significant for any of the traits. Of the 43 QTL regions found in the present study, 10 affected PP only, 8 affected MY only, and 25 affected MY and PP. Remarkably, all 8 QTL regions that affected only MY in the Brown Swiss, also affected MY in research reported in 3 Web-based QTL maps used for comparison with the findings of this study (http://www.vetsci.usyd.edu.au/reprogen/QTL_Map/; http://www.animalgenome.org/QTLdb/cattle.html; http://bovineqtl.tamu.edu/). Similarly, all 10 QTL regions in the Brown Swiss that affected PP only, affected only PP in the databases. Thus, many QTL appear to be common to Brown Swiss and other breeds in the databases (mainly Holstein), and an appreciable fraction of QTL appears to affect MY or PP primarily or exclusively, with little or no effect on the other trait. Although QTL information available today in the Brown Swiss population can be utilized only in a within family marker-assisted selection approach, knowledge of QTL segregating in the whole population should boost gene identification and ultimately the implementation and efficiency of an individual genomic program.

The aim of this study was to estimate genetic parameters for a set of new traits and to update values for production and morphological traits to be used in the selection index of Italian Brown Swiss dairy cattle. Longevity, milking speed and somatic cell scores (SCS) were considered for inclusion in the selection index, and (co)variances with all traits of the selection index were estimated. SCS was considered on a lactation basis while milk flow as the amount of milk (kg) released per time unit (minute) measured with a flowmeter. Cow functional longevity was the total herd life corrected for the production level. A total of 127,416 first lactation records of cows calving from 1985 to 2003 were considered. In order to maximize the number of records available for each combination of traits, 9 data sets were created. Estimates were obtained from multivariate linear sire models with equal design matrix in subsequent separated analysis. REML algorithms and canonical transformation were used to calculate (co)variance estimates among all traits: functional longevity, milking speed, SCS, 5 production traits (milk, fat and protein yields, fat and protein percent), and 19 type traits. Heritabilities estimated were 0.14 ± 0.02 for SCS, 0.33 ± 0.07 for milk flow, and 0.04 ± 0.01 for functional longevity. Genetic correlation values between SCS and milk yield, fat percent and protein percent resulted of 0.18 ± 0.09, -0.19 ± 0.08, and -0.22 ± 0.08 respectively. Functional longevity had a strong positive genetic correlation with udder depth (0.42 ± 0.10) while a negative correlation with rear legs set (-0.56 ± 0.10). Milk flow was positively correlated with most of the production measures: 0.30 ± 0.18 with milk yield, 0.24 ± 0.17 with fat yields 0.16 ± 0.20 with protein yield. Additionally milk flow resulted largely genetically correlated with some type traits (0.53 ± 0.14 rear udder width, 0.40 ± 0.16 hock quality, 0.32 ± 0.15 rump angle, -0.25 ± 0.19 with udder depth). The correlation between SCS and milk flow showed a value of 0.46 ± 0.26 indicating that faster cows are more susceptible to mastitis

The objective of this study was to estimate heritabilities and genetic correlations between milk-release parameters, somatic cell score, milk yield, and udder functional traits in the Italian Brown Swiss population. Data were available from 37,511 cows over a span of 12 yr (1997-2008) from 1,592 herds. Milking flows were recorded for each individual once during lactation. Three different analyses were performed to estimate variance components for all the traits of interest. The first analysis included single control data milk yield, somatic cell score, maximum milk flow, average milk flow, time of plateau, decreasing time, and total milking time, whereas the second analysis included milk-release parameters as well as total udder score, udder depth, and 305-d milk yield and somatic cell score as dependent variables. The third analysis included total milking time, 305-d milk yield and somatic cell score, total udder score, udder depth, and ratios of maximum milk flow over total milking time (R1), time of plateau (R2), and decreasing time (R3) to estimate the relationship between the shape of the milk-release curves and important milking traits. Results from the first and second analysis found similar heritabilities for milkability traits ranging from 0.05 to 0.41 with genetic correlations between production traits and flow traits ranging from low to moderate values. Positive genetic correlations were found among production, somatic cell score, and milkability traits. The third analysis showed that R1 had the greatest heritability of the ratio traits (0.37) with large genetic correlations with R2 and R3, a low correlation with 305-d somatic cell score, and no correlation with 305-d milk yield. Estimated responses to selection over 5 generations were also calculated using different indexes, which included either flow or ratio traits. The results of this study show that it is possible to use information collected through portable flowmeters to improve milkability traits. Using a set of variables or traits to describe the overall release of milk can be an advantageous selection strategy to decrease management costs while maintaining milk production.

Cheese production is increasing in many countries, and a desire toward genetic selection for milk coagulation properties in dairy cattle breeding exists. However, measurements of individual cheesemaking properties are hampered by high costs and labor, whereas traditional single-point milk coagulation properties (MCP) are sometimes criticized. Nevertheless, new modeling of the entire curd firmness and syneresis process (CFt equation) offers new insight into the cheesemaking process. Moreover, identification of genomic regions regulating milk cheesemaking properties might enhance direct selection of individuals in breeding programs based on cheese ability rather than related milk components. Therefore, the objective of this study was to perform genome-wide association studies to identify genomic regions linked to traditional MCP and new CFt parameters, milk acidity (pH), and milk protein percentage. Milk and DNA samples from 1,043 Italian Brown Swiss cows were used. Milk pH and 3 MCP traits were grouped together to represent the MCP set. Four CFt equation parameters, 2 derived traits, and protein percentage were considered as the second group of traits (CFt set). Animals were genotyped with the Illumina SNP50 BeadChip v.2 (Illumina Inc., San Diego, CA). Multitrait animal models were used to estimate variance components. For genome-wide association studies, the genome-wide association using mixed model and regression-genomic control approach was used. In total, 106 significant marker traits associations and 66 single nucleotide polymorphisms were identified on 12 chromosomes (1, 6, 9, 11, 13, 15, 16, 19, 20, 23, 26, and 28). Sharp peaks were detected at 84 to 88 Mbp on Bos taurus autosome (BTA) 6, with a peak at 87.4 Mbp in the region harboring the casein genes. Evidence of quantitative trait loci at 82.6 and 88.4 Mbp on the same chromosome was found. All chromosomes but BTA6, BTA11, and BTA28 were associated with only one trait. Only BTA6 was in common between MCP and CFt sets. The new CFt traits reinforced the support of MCP signals and provided with additional information on genomic regions that might be involved in regulation of the coagulation process of bovine milk.

The aims of this study were (1) to analyze rennet coagulation time (RCT), curd-firming rate, and curd firmness obtained by extending the standard 30-min testing time to 45 min; (2) to estimate heritabilities of the aforementioned traits determined by mechanical (Formagraph; Foss Electric, Hillerød, Denmark) and near-infrared optical (Optigraph; Ysebaert, Frépillon, France) instruments, and to assess the statistical relevance of their genetic background by using the Bayes factor procedure, the deviance information criterion, and the mean squared error; (3) to estimate phenotypic and genetic relationships between instruments within trait and between traits within instrument; and (4) to obtain correlations for sire rankings based on the used instruments. Individual milk samples were collected from 913 Brown Swiss cows reared in 63 herds located in Trento Province (Italy). Milk coagulation properties (MCP) were measured using 2 different instruments: Formagraph and Optigraph. Both instruments were housed in the same laboratory and operated by the same technician. Each sample was analyzed simultaneously on each instrument. All experimental conditions (milk temperature and the concentration and type of rennet) were identical. For the analysis, univariate and bivariate animal models were implemented using Bayesian methods. Univariate analyses were conducted to test the hypothesis that the traits showed additive genetic determination. Deviance information criterion, Bayes factor, and mean squared error were used as model choice criteria. The main results were that (1) RCT could be measured on all samples by extending the observation time to 45 min, and its genetic parameters (h(2)=0.23) and breeding values could be estimated while avoiding the bias of noncoagulating samples; (2) curd-firming rate could be measured on almost all milk samples, and its genetic parameters could be estimated for the first time on a field data set (h(2)=0.21); (3) for the first time, genetic parameters of curd firmness 45 min after rennet addition (h(2)=0.12) were estimated, and they were compared with curd firmness 30 min after rennet addition (h(2)=0.17); and (4) MCP estimated using the Optigraph appeared to be genetically different from those determined by Formagraph, with the partial exception of RCT (genetic correlation=0.97). Breeding strategies for the improvement of MCP must be planned with caution. Currently, the high throughput, ease of use, and reduced costs of analysis make predictions obtained from mid-infrared spectroscopy (MIRS) on untreated milk samples a promising alternative to produce relevant data at the population level. The use of mechanical lactodynamographs to establish reference data for MIRS calibrations have been already studied, whereas the use of near-infrared optical lactodynamographs as a reference method for MIRS calibrations needs to be investigated.