Exploring the sources of variation of electrical conductivity and total and differential somatic cell count in Italian Mediterranean buffaloes.

In the buffalo dairy sector, a huge effort is still needed to improve mastitis prevention, detection, and management. Electrical conductivity (EC) and total somatic cell count (SCC) are well-known indirect indicators of mastitis. Differential somatic cell count (DSCC), which represents the proportion of neutrophils and lymphocytes on the total SCC, is instead a novel phenotype collected in the dairy cattle sector in the last lustrum. As little is known about this novel trait in dairy buffalo, in the present study we explored the nongenetic factors affecting DSCC, as well as EC and total somatic cell score (SCS), in the Italian Mediterranean buffalo. The data set used for the analysis included 14,571 test-day (TD) records of 1,501 animals from 6 herds, and climatic information of the sampling locations. The original data were filtered to exclude animals with less than 3 TD per lactation and, for the investigated traits, outliers beyond 4 standard deviations. In the statistical model we included the fixed effects of herd (6 classes), days in milk (DIM; 10 classes of 30 d, with the last being an open class until 360 d), parity (6 classes, from 1 to 6+), year-season of calving (11 classes, from summer 2019 to winter 2021/2022), year-season of sampling (9 classes, from spring 2020 to spring 2022), production level (4 classes based on quartiles of average milk yield by herd), and temperature-humidity index (THI; 4 classes based on quartiles, calculated using the average temperature and relative humidity of the 5 d before sampling). Average EC, SCS, and DSCC vary across herds. Considering DIM, greater EC values were observed at the beginning and the end of lactation; SCS was slightly lower, but DSCC was greater around the lactation peak. Increased EC, SCS, and DSCC levels with increasing parity were reported. Year-season calving and year-season sampling only slightly affected the variation of the investigated traits. Milk of high-producing buffaloes was characterized by lower EC and SCS mean values, nevertheless it had slightly greater DSCC percentages. Buffaloes grouped in the highest THI classes (classes 3 and 4) showed, on average, greater EC, SCS, and DSCC in comparison to the lower classes, especially to class 2. Results of the present study represent a preliminary as well as necessary step for the possible future inclusion of EC, SCS, or DSCC in breeding programs aimed to improve mastitis resistance in dairy buffaloes.

Exploiting machine learning methods with monthly routine milk recording data and climatic information to predict subclinical mastitis in Italian Mediterranean buffaloes.

Mastitis has detrimental effects on the world's dairy industry, reducing animal health, milk production and quality, as well as income for farmers. In addition, consumers' growing interest in food safety and rational usage of antibiotics highlights the need to develop novel strategies to improve mastitis detection, prevention, and management. In the present study we applied machine learning (ML) analyses to predict presence or absence of subclinical mastitis in Italian Mediterranean buffaloes, exploiting information collected the previous month during routine milk recording procedures, as well as climatic data. The data set included 3,891 records of 1,038 buffaloes from 6 herds located in Basilicata Region (South Italy). Prediction models were developed using 4 different ML algorithms (Generalized Linear Model, Support Vector Machines, Random Forest, and Neural Network) and 2 data set splitting approaches for the creation of the training and test sets (by record or by animal ID number, always with 80% of the data used for model training and the remaining 20% for model testing). Support Vector Machine was the best method to predict high or low somatic cell count at the subsequent test-day record in the validation set, and therefore it was used to estimate the contribution of each feature to the best model. Independently from the data set splitting approach, the most important features were somatic cell score, differential somatic cell count, electrical conductivity, and milk production. Among climatic data, the most informative were temperature and relative humidity. When the data were split by animal ID, an improvement in models' predictive performance on the test set was observed, suggesting this as the most appropriate data splitting approach in data sets with repeated measures to avoid data leakage. According to different metrics, Neural Network was the best method for making predictions on the test set. Our findings confirmed the promising role of ML methods to improve prevention and surveillance of subclinical mastitis, exploiting the large amount of data currently available to identify animals that would possibly have high somatic cell count the subsequent month.

Genetic parameters for reproductive traits in the Italian Mediterranean buffalo using milk yield as a correlated trait.

Until now, the genetic evaluation of the Italian Mediterranean Buffalo has been mainly focused on production traits. However, female fertility affects the efficiency of the dairy industry as it is essential to maintain the profitability of dairy farms. Indeed, the estimation of its genetic component is crucial for its improvement. In this study, 3 measures of buffalo's fertility were analyzed: the age at first calving (AFC), the interval between first and second calving (CIV1), and the interval between second and successive calvings (CIV2_12). Milk yield at 270 d (MY270) was used as a correlated trait. First, genetic parameters were estimated using 7,915 buffalo cows with first calving from 1991 to 2018, then breeding values were calculated from 236,087 buffalo cows. Genetic parameters were estimated by Bayesian inference fitting a multiple-trait animal model using the GIBBS1F90 program, and BLUPF90 was used for estimation of breeding value. The heritability and repeatability estimates of fertility traits were low. The genetic correlations among fertility traits ranged from 0.10 (AFC-CIV1) to 0.92 (CIV1-CIV2_12). Genetic correlation between MY270 and fertility traits was unfavorable, ranging from 0.23 to 0.48. The results from this study can be used as a basis for the future genetic improvement of fertility traits in the Italian Mediterranean Buffaloes.

Meta-analysis of the energy and protein requirements of hair sheep raised in the tropical region of Brazil.

The objective of this study was to estimate, through mathematical models, energy and protein requirements for maintenance and gain of hair sheep raised in the tropical region of Brazil. To determine the equation parameters, a meta-analysis of seven independent experiments of nutrient requirements was performed, comprising a total of 243 experimental units (animals), which were conducted under tropical conditions, using hair sheep in growing and finishing phases and endowed of the following quantitative data for each animal: body weight (BW), empty body weight (EBW), average daily gain (ADG), empty body gain (EBG), heat production (HP), metabolizable energy intake (MEI), retained energy (RE), metabolizable protein intake (MPI) and body protein content. The regression equations generated were as follows: for Net Energy for maintenance, (NEm ): LogHP(MJEBW-0.75day-1)=-0.6090(±0.07470)+0.5149(±0.07216)×MEI(MJEBW-0.75day-1); for Net Energy for gain, (NEg ): LogRE(MJEBW-0.75day-1)=0.03084(±0.05334)+0.8455(±0.04355)×LogEBG(kg/day); for Metabolizable Protein for maintenance,(MPm ): MPI(g/day)  = 24.8470 (±7.3646) + 560.28 (±99.6582) × EBG(kg/day) ; for Net Protein for gain, (NPg ): NPg(kg/day)=0.1941×EBW(kg)-0.1058. The NEm requirement was 0.246 MJ EBW-0.75  day-1 . The metabolizable energy for maintenance requirement was 0.391 MJ EBW-0.75  day-1 . Considering an ADG of 100 g, the NEg requirement ranged from 0.496 to 1.701 MJ/day for animals with BW ranging from 10 to 40 kg respectively. The efficiencies of use of the metabolizable energy for maintenance and gain were 0.63 and 0.36 respectively. The MPm requirement was 3.097 g EBW-0.75  day-1 . Considering an ADG of 100 g, the NPg requirement ranged from 12.4 to 10.5 g/day for animals with BW ranging from 10 to 40 kg respectively. The total metabolizable energy and protein requirements were lower than those reported by the NRC and AFRC systems. Thus, our results support the hypothesis that nutrient requirements of hair sheep raised in tropical regions differ from wool sheep raised in temperate regions. Therefore, the use of the equations designed in this study is recommended.

Derivation and genome-wide association study of a principal component-based measure of heat tolerance in dairy cattle.

Heat stress represents a key factor that negatively affects the productive and reproductive performance of farm animals. In the present work, a new measure of tolerance to heat stress for dairy cattle was developed using principal component analysis. Data were from 590,174 test-day records for milk yield, fat and protein percentages, and somatic cell score of 39,261 Italian Holstein cows. Test-day records adjusted for main systematic factors were grouped into 11 temperature-humidity index (THI) classes. Daughter trait deviations (DTD) were calculated for 1,540 bulls as means of the adjusted test-day records for each THI class. Principal component analysis was performed on the DTD for each bull. The first 2 principal components (PC) explained 42 to 51% of the total variance of the system across the 4 traits. The first PC, a measure of the level at which the curve is located, was interpreted as a measure of the level at which the DTD curve was located. The second PC, which shows the slope of increasing or decreases DTD curves, synthesized the behavior of the DTD pattern. Heritability of the 2 component scores was moderate to high for level across all traits (range = 0.23-0.82) and low to moderate for slope (range = 0.16-0.28). For each trait, phenotypic and genetic correlations between level and slope were equal to zero. A genome-wide association analysis was carried out on a subsample of 423 bulls genotyped with the Illumina 50K bovine bead chip (Illumina, San Diego, CA). Two single nucleotide polymorphisms were significantly associated with slope for milk yield, 4 with level for fat percentage, and 2 with level and slope of protein percentage, respectively. The gene discovery was carried out considering windows of 0.5 Mb surrounding the significant markers and highlighted some interesting candidate genes. Some of them have been already associated with the mechanism of heat tolerance as the heat shock transcription factor (HSF1) and the malonyl-CoA-acyl carrier protein transacylase (MCAT). The 2 PC were able to describe the overall level and the slope of response of milk production traits across increasing levels of THI index. Moreover, they exhibited genetic variability and were genetically uncorrelated. These features suggest their use as measures of thermotolerance in dairy cattle breeding schemes.

Genome-wide association study for cheese yield and curd nutrient recovery in dairy cows.

Cheese production and consumption are increasing in many countries worldwide. As a result, interest has increased in strategies for genetic selection of individuals for technological traits of milk related to cheese yield (CY) in dairy cattle breeding. However, little is known about the genetic background of a cow's ability to produce cheese. Recently, a relatively large panel (1,264 cows) of different measures of individual cow CY and milk nutrient and energy recoveries in the cheese (REC) became available. Genetic analyses showed considerable variation for CY and for aptitude to retain high proportions of fat, protein, and water in the coagulum. For the dairy industry, these characteristics are of major economic importance. Nevertheless, use of this knowledge in dairy breeding is hampered by high costs, intense labor requirement, and lack of appropriate technology. However, in the era of genomics, new possibilities are available for animal breeding and genetic improvement. For example, identification of genomic regions involved in cow CY might provide potential for marker-assisted selection. The objective of this study was to perform genome-wide association studies on different CY and REC measures. Milk and DNA samples from 1,152 Italian Brown Swiss cows were used. Three CY traits expressing the weight (wt) of fresh curd (%CYCURD), curd solids (%CYSOLIDS), and curd moisture (%CYWATER) as a percentage of weight of milk processed, and 4 REC (RECFAT, RECPROTEIN, RECSOLIDS, and RECENERGY, calculated as the % ratio between the nutrient in curd and the corresponding nutrient in processed milk) were analyzed. Animals were genotyped with the Illumina BovineSNP50 Bead Chip v.2. Single marker regressions were fitted using the GenABEL R package (genome-wide association using mixed model and regression-genomic control). In total, 103 significant associations (88 single nucleotide polymorphisms) were identified in 10 chromosomes (2, 6, 9, 11, 12, 14, 18, 19, 27, 28). For RECFAT and RECPROTEIN, high significance peaks were identified in Bos taurus autosome (BTA) 6 and BTA11, respectively. Marker ARS-BFGL-NGS-104610 (∼104.3 Mbp) was highly associated with RECPROTEIN and Hapmap52348-rs29024684 (∼87.4 Mbp), closely located to the casein genes on BTA6, with RECFAT. Genomic regions identified may enhance marker-assisted selection in bovine cheese breeding beyond the use of protein (casein) and fat contents, whereas new knowledge will help to unravel the genomic background of a cow's ability for cheese production.

Short communication: Effect of heat stress on nonreturn rate of Italian Holstein cows.

The data set consisted of 1,016,856 inseminations of 191,012 first, second, and third parity Holstein cows from 484 farms. Data were collected from year 2001 through 2007 and included meteorological data from 35 weather stations. Nonreturn rate at 56 d after first insemination (NR56) was considered. A logit model was used to estimate the effect of temperature-humidity index (THI) on reproduction across parities. Then, least squares means were used to detect the THI breakpoints using a 2-phase linear regression procedure. Finally, a multiple-trait threshold model was used to estimate variance components for NR56 in first and second parity cows. A dummy regression variable (t) was used to estimate NR56 decline due to heat stress. The NR56, both for first and second parity cows, was significantly (unfavorable) affected by THI from 4 d before 5 d after the insemination date. Additive genetic variances for NR56 increased from first to second parity both for general and heat stress effect. Genetic correlations between general and heat stress effects were -0.31 for first parity and -0.45 for second parity cows.

Genome-wide association of coagulation properties, curd firmness modeling, protein percentage, and acidity in milk from Brown Swiss cows.

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.

Software solutions for the livestock genomics SNP array revolution.

Since the beginning of the genomic era, the number of available single nucleotide polymorphism (SNP) arrays has grown considerably. In the bovine species alone, 11 SNP chips not completely covered by intellectual property are currently available, and the number is growing. Genomic/genotype data are not standardized, and this hampers its exchange and integration. In addition, software used for the analyses of these data usually requires not standard (i.e. case specific) input files which, considering the large amount of data to be handled, require at least some programming skills in their production. In this work, we describe a software toolkit for SNP array data management, imputation, genome-wide association studies, population genetics and genomic selection. However, this toolkit does not solve the critical need for standardization of the genotypic data and software input files. It only highlights the chaotic situation each researcher has to face on a daily basis and gives some helpful advice on the currently available tools in order to navigate the SNP array data complexity.

Imputation accuracy is robust to cattle reference genome updates.

Genotype imputation is routinely applied in a large number of cattle breeds. Imputation has become a need due to the large number of SNP arrays with variable density (currently, from 2900 to 777,962 SNPs). Although many authors have studied the effect of different statistical methods on imputation accuracy, the impact of a (likely) change in the reference genome assembly on imputation from lower to higher density has not been determined so far. In this work, 1021 Italian Simmental SNP genotypes were remapped on the three most recent reference genome assemblies. Four imputation methods were used to assess the impact of an update in the reference genome. As expected, the four methods behaved differently, with large differences in terms of accuracy. Updating SNP coordinates on the three tested cattle reference genome assemblies determined only a slight variation on imputation results within method.

Genomic characteristics of Staphylococcus aureus strains associated with high within-herd prevalence of intramammary infections in dairy cows.

Staphylococcus aureus is one of the most important causes of mastitis in dairy cattle. Based on previous research, Staph. aureus genotypes with different pathogenic and contagious properties can cause intramammary infection (IMI) and coexist in the same herd. Our study aimed to compare Staph. aureus strains from herds that differed in IMI prevalence using different molecular approaches such as ribosomal spacer (RS)-PCR, multilocus sequence typing (MLST), spa typing, ribotyping, pulsed-field gel electrophoresis (PFGE), and multiplex PCR. For this purpose, 31 dairy herds with Staph. aureus IMI were selected, and 16 of these were chosen for a comparison study: the 8 high-prevalence (HP) herds had Staph. aureus IMI prevalence >28% and the 8 low-prevalence (LP) herds had an IMI prevalence <4%. A total of 650 isolates of Staph. aureus from mammary quarters of all positive cows were genotyped with RS-PCR, a technique based on amplification of a portion of the intergenic spacer 16S-23S rRNA, and a subset of 54 strains was also analyzed by multiplex PCR, ribotyping, PFGE, MLST, and spa typing. The RS-PCR analysis revealed 12 different profiles. Staphylococcus aureus strains isolated from 5 out of 8 HP herds showed a profile identical to the genotype B (GTB), described in previous studies as being strongly associated with high within-herd prevalence of Staph. aureus mastitis and the presence of the genes coding for enterotoxins sea, sed, and sej, a long x-region of spa gene, and 3 lukE fragments. Moreover, all strains isolated in the HP herds possessed genes coding for staphylococcal enterotoxins. In LP herds, a limited number of strains of 6 genotypes, different from those isolated in HP herds, were identified and GTB was not found. Within these genotypes, 4 strains were positive for the mecA gene. Preliminary results and comparison with other genotyping methods confirmed that genotyping by RS-PCR is an accurate, rapid, and inexpensive tool for future field studies on Staph. aureus mastitis strains and generates clinically relevant results.

The effects of heat stress in Italian Holstein dairy cattle.

The data set for this study comprised 1,488,474 test-day records for milk, fat, and protein yields and fat and protein percentages from 191,012 first-, second-, and third-parity Holstein cows from 484 farms. Data were collected from 2001 through 2007 and merged with meteorological data from 35 weather stations. A linear model (M1) was used to estimate the effects of the temperature-humidity index (THI) on production traits. Least squares means from M1 were used to detect the THI thresholds for milk production in all parities by using a 2-phase linear regression procedure (M2). A multiple-trait repeatability test-model (M3) was used to estimate variance components for all traits and a dummy regression variable (t) was defined to estimate the production decline caused by heat stress. Additionally, the estimated variance components and M3 were used to estimate traditional and heat-tolerance breeding values (estimated breeding values, EBV) for milk yield and protein percentages at parity 1. An analysis of data (M2) indicated that the daily THI at which milk production started to decline for the 3 parities and traits ranged from 65 to 76. These THI values can be achieved with different temperature/humidity combinations with a range of temperatures from 21 to 36°C and relative humidity values from 5 to 95%. The highest negative effect of THI was observed 4 d before test day over the 3 parities for all traits. The negative effect of THI on production traits indicates that first-parity cows are less sensitive to heat stress than multiparous cows. Over the parities, the general additive genetic variance decreased for protein content and increased for milk yield and fat and protein yield. Additive genetic variance for heat tolerance showed an increase from the first to third parity for milk, protein, and fat yield, and for protein percentage. Genetic correlations between general and heat stress effects were all unfavorable (from -0.24 to -0.56). Three EBV per trait were calculated for each cow and bull (traditional EBV, traditional EBV estimated with the inclusion of THI covariate effect, and heat tolerance EBV) and the rankings of EBV for 283 bulls born after 1985 with at least 50 daughters were compared. When THI was included in the model, the ranking for 17 and 32 bulls changed for milk yield and protein percentage, respectively. The heat tolerance genetic component is not negligible, suggesting that heat tolerance selection should be included in the selection objectives.

Selection for milk coagulation properties predicted by Fourier transform infrared spectroscopy in the Italian Holstein-Friesian breed.

Milk coagulation is based on a series of physicochemical changes at the casein micelle level, resulting in formation of a gel. Milk coagulation properties (MCP) are relevant for cheese quality and yield, important factors for the dairy industry. They are also evaluated in herd bulk milk to reward or penalize producers of Protected Designation of Origin cheeses. The economic importance of improving MCP justifies the need to account for this trait in the selection process. A pilot study was carried out to determine the feasibility of including MCP in the selection schemes of the Italian Holstein. The MCP were predicted in 1,055 individual milk samples collected in 16 herds (66 ± 24 cows per herd) located in Brescia province (northeastern Italy) by means of Fourier transform infrared (FTIR) spectroscopy. The coefficient of determination of prediction models indicated moderate predictions for milk rennet coagulation time (RCT=0.65) and curd firmness (a30=0.68), and poor predictions for curd-firming time (k20=0.49), whereas the range error ratio (8.9, 6.9, and 9.5 for RCT, k20, and a30, respectively) indicated good practical utility of the predictive models for all parameters. Milk proteins were genotyped and casein haplotypes (αS1-, ß-, αS2-, and κ-casein) were reconstructed. Data from 51 half-sib families (19.9 ± 16.4 daughters per sire) were analyzed by an animal model to estimate (1) the genetic parameters of predicted RCT, k20, and a30; (2) the breeding values for these predicted clotting variables; and (3) the effect of milk protein genotypes and casein haplotypes on predicted MCP (pMCP). This is the first study to estimate both genetic parameters and breeding values of pMCP, together with the effects of milk protein genotypes and casein haplotypes, that also considered k20, probably the most important parameter for the dairy industry (because it indicates the time for the beginning of curd-cutting). Heritability of predicted RCT (0.26) and k20 (0.31) were close to the average heritability described in literature, whereas the heritability of a30 was higher (0.52 vs. 0.27). The effects of milk proteins were statistically significant and similar to those obtained on measured MCP. In particular, haplotypes including uncommon variants showed positive (B-I-A-B) or negative (B-A(1)-A-E) effects. Based on these findings, FTIR spectroscopy-pMCP is proposed as a potential selection criterion for the Italian Holstein.

Evaluating genotype by environment interaction for growth traits in Limousine cattle.

Environmental conditions affect the growth and health of animals, making it crucial to quantify heat stress and the genetic basis of heat tolerance in animal breeding. The main objective of this study was to evaluate heat stress on growth and investigate the genetic background of tolerance to harsh environmental conditions in the Italian Limousine beef cattle. Three growth traits were analysed: average daily gain (ADG), weaning weight (WW), and yearling weight (YW). Data were collected from animals raised between 1991 and 2022 and combined with 14 environmental covariates. Records for ADG, WW, and YW encompassed 108 205, 100 058, and 24 939 individuals, respectively, with 4 617, 4 670, and 2 048 genotyped individuals. Climatic variables were compared for inclusion in a linear mixed model using the Deviance Information Criterion. Multiple-trait models and genomic information incorporated environmental conditions with the largest impact on the studied traits Genotype by environment interaction (G × E) was detected in all the studied traits, showing substantial heterogeneity of the variance components across the different environments (Env). Heritability for WW remains constant among Env; instead, for ADG and YW decreased under uncomfortable environmental conditions. YW showed the lowest genetic correlation (0.28) between divergent conditions (Env 2 and Env 5,) for ADG and WW correlations dropped below 0.50 among Env. The values of genetic correlations indicate that growth traits are moderately to strongly affected by G × E. Eigenvalue decomposition of the additive genetic (co)variance matrix for ADG, WW, and YW indicated that three components accounted for over 0.80 of the proportion of the variance explained, suggesting different animal performances across Env. Spearman rank correlations showed potential re-ranking of genotyped sires, because ADG, WW, and YW showed correlations between Env below 0.80. The accuracy of single-step genomic EBV was higher compared to EBV for al traits. Overall, the result confirms the existence of G × E for growth traits in the Italian Limousine population. Including G × E in the model allows for more environment-aware predictions, helping breeders understand how different genetic bases respond to varying conditions. Genomic predictions incorporating G × E could accelerate genetic gains and improve response to selection for heat tolerance.

A comparison of genetic and genomic breeding values in Saanen and Alpine goats.

Nowadays, several countries are developing or adopting genomic selection in the dairy goat sector. The most used method to estimate breeding values is Single-Step Genomic Best Linear Unbiased Prediction (ssGBLUP) which offers several advantages in terms of computational process and accuracy of the estimated breeding values (EBVs). Saanen and Alpine are the predominant dairy goat breeds in Italy, and both have similar breeding programs where EBVs for productive traits are currently calculated using BLUP. This work describes the implementation of genomic selection for these two breeds in Italy, aligning with the selection practices already carried out in the international landscape. The available dataset included 3 611 genotyped animals, 11 470 lactation records, five traits (milk, protein and fat yields, and fat and protein percentages), and three-generation pedigrees. EBVs were estimated using BLUP, GBLUP, and ssGBLUP both with single and multiple trait approaches. The methods were compared in terms of correlation between EBVs and genetic trends. Results were also validated with the linear regression method excluding part of the phenotypic data. In both breeds, EBVs and GEBVs were strongly correlated and the trend of each trait was similar comparing the three methods. The average increase in accuracy across traits and methods amounted to +13 and +10% from BLUP to ssGBLUP for Alpine and Saanen breeds, respectively. Results indicated higher prediction accuracy and correlation for GBLUP and ssGBLUP compared to BLUP, implying that the use of genotypes increases the accuracy of EBVs, particularly in the absence of phenotypic data. Therefore, ssGBLUP is likely to be the most effective method to enhance genetic gain in Italian Saanen and Alpine goats.

Short communication: imputing genotypes using PedImpute fast algorithm combining pedigree and population information.

Routine genomic evaluations frequently include a preliminary imputation step, requiring high accuracy and reduced computing time. A new algorithm, PedImpute (http://dekoppel.eu/pedimpute/), was developed and compared with findhap (http://aipl.arsusda.gov/software/findhap/) and BEAGLE (http://faculty.washington.edu/browning/beagle/beagle.html), using 19,904 Holstein genotypes from a 4-country international collaboration (United States, Canada, UK, and Italy). Different scenarios were evaluated on a sample subset that included only single nucleotide polymorphism from the Bovine low-density (LD) Illumina BeadChip (Illumina Inc., San Diego, CA). Comparative criteria were computing time, percentage of missing alleles, percentage of wrongly imputed alleles, and the allelic squared correlation. Imputation accuracy on ungenotyped animals was also analyzed. The algorithm PedImpute was slightly more accurate and faster than findhap and BEAGLE when sire, dam, and maternal grandsire were genotyped at high density. On the other hand, BEAGLE performed better than both PedImpute and findhap for animals with at least one close relative not genotyped or genotyped at low density. However, computing time and resources using BEAGLE were incompatible with routine genomic evaluations in Italy. Error rate and allelic squared correlation attained by PedImpute ranged from 0.2 to 1.1% and from 96.6 to 99.3%, respectively. When complete genomic information on sire, dam, and maternal grandsire are available, as expected to be the case in the close future in (at least) dairy cattle, and considering accuracies obtained and computation time required, PedImpute represents a valuable choice in routine evaluations among the algorithms tested.

Genetic parameters for body condition score, locomotion, angularity, and production traits in Italian Holstein cattle.

The objectives of this research were to estimate genetic parameters for body condition score (BCS) and locomotion (LOC), and to assess their relationships with angularity (ANG), milk yield, fat and protein content, and fat to protein content ratio (F:P) in the Italian Holstein Friesian breed. The Italian Holstein Friesian Cattle Breeders Association collects type trait data once on all registered first lactation cows. Body condition score and LOC were introduced in the conformation scoring system in 2007 and 2009, respectively. Variance (and covariance) components among traits were estimated with a Bayesian approach via a Gibbs sampling algorithm and an animal model. Heritability estimates were 0.114 and 0.049 for BCS and LOC, respectively. The genetic correlation between BCS and LOC was weak (-0.084) and not different from zero; therefore, the traits seem to be genetically independent, but further investigation on possible departures from linearity of this relationship is needed. Angularity was strongly negatively correlated with BCS (-0.612), and strongly positively correlated with LOC (0.650). The genetic relationship of milk yield with BCS was moderately negative (-0.386), and was moderately positive (0.238) with LOC. These results indicate that high-producing cows tend to be thinner and tend to have better locomotion than low-producing cows. The genetic correlation of BCS with fat content (0.094) and F:P (-0.014) was very weak and not different from zero, and with protein content (0.173) was weak but different from zero. Locomotion was weakly correlated with fat content (0.071), protein content (0.028), and F:P (0.074), and correlations were not different from zero. Phenotypic correlations were generally weaker than their genetic counterparts, ranging from -0.241 (BCS with ANG) to 0.245 (LOC with ANG). Before including BCS and LOC in the selection index of the Italian Holstein breed, the correlations with other traits currently used to improve type and functionality of animals need to be investigated.

Use of different statistical models to predict direct genomic values for productive and functional traits in Italian Holsteins.

One of the main issues in genomic selection was the huge unbalance between number of markers and phenotypes available. In this work, principal component analysis is used to reduce the number of predictors for calculating direct genomic breeding values (DGV) for production and functional traits. 2093 Italian Holstein bulls were genotyped with the 54 K Illumina beadchip, and 39,555 SNP markers were retained after data editing. Principal Components (PC) were extracted from SNP matrix, and 15,207 PC explaining 99% of the original variance were retained and used as predictors. Bulls born before 2001 were included in the reference population, younger animals in the test population. A BLUP model was used to estimate the effect of principal component on deregressed proof (DRPF) for 35 traits and results were compared to those obtained by using SNP genotypes as predictors either with BLUP or with Bayes_A models. Correlations between DGV and DRPF did not substantially differ among the three methods except for milk fat content. The lowest prediction bias was obtained for the method based on the use of principal component. Regression coefficients of DRPF on DGV were lower than one for the approach based on the use of PC and higher than one for the other two methods. The use of PC as predictors resulted in a large reduction of number of predictors (approximately 38%) and of computational time that was approximately 2% of the time needed to estimate SNP effects with the other two methods. Accuracies of genomic predictions were in most of cases only slightly higher than those of the traditional pedigree index, probably due to the limited size of the considered population.

CpG DNA methylation changes during epididymal sperm maturation in bulls.

BACKGROUND: During epididymal transit spermatozoa acquire specific morphological features which enhance their ability to swim in a progressive manner and interact with the oocytes. At the same time, sperm cells undergo specific molecular rearrangements essential for the fertilizing sperm to drive a correct embryo development. To assess epigenetic sperm changes during epididymal maturation, the caput, corpus and cauda epididymis sperm tracts were isolated from eight bulls and characterized for different sperm quality parameters and for CpG DNA methylation using Reduced Representation Bisulfite Sequencing (RRBS) able to identify differentially methylated regions (DMRs) in higher CpG density regions. RESULTS: Caput sperm showed significant variation in motility and sperm kinetics variables, whereas spermatozoa collected from the corpus presented morphology variation and significant alterations in variables related to acrosome integrity. A total of 57,583 methylated regions were identified across the eight bulls, showing a significantly diverse distribution for sperm collected in the three epididymal regions. Differential methylation was observed between caput vs corpus (n = 11,434), corpus vs cauda (n = 12,372) and caput vs cauda (n = 2790). During epididymal transit a high proportion of the epigenome was remodeled, showing several regions in which methylation decreases from caput to corpus and increases from corpus to cauda. CONCLUSIONS: Specific CpG DNA methylation changes in sperm isolated from the caput, corpus, and cauda epididymis tracts are likely to refine the sperm epigenome during sperm maturation, potentially impacting sperm fertilization ability and spatial organization of the genome during early embryo development.

Heritability and genetic correlation of body weight and Kleiber ratio in Limousin and Charolais beef cattle breeds.

Reducing the environmental impact of livestock production is now indispensable and genetic selection can be of great support for this purpose. Measures that can identify high body growth at low maintenance costs in production animals are particularly useful since resources have been increasingly limited. Therefore, the goal of this study was to estimate genetic parameters for BW and Kleiber ratio (KR) in 210-day-old and 365-day-old Charolais and Limousin breeds. A database comprising animals born from 1999 to 2018 was used in a multitrait model applying Bayesian inference. The heritability for BW is high in Charolais (0.39 and 0.42 for BW210 and BW365, respectively) and moderate in Limousin (0.22), indicating possible genetic gains for BW in both breeds. The genetic variability of KR should also allow satisfactory genetic gains. In addition, the genetic correlation between BW and KR ranged from low to moderate. Thus, selection over KR should have no effects on BW, showing that high body growth can be obtained without changes in efficiency.