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.

Predicting feed efficiency of Angus steers using the gastrointestinal microbiome.

Microbial composition of the gastrointestinal tracts is an important factor affecting the variation in feed efficiency in ruminants. Several studies have investigated the composition of the ruminal and fecal microbiotas, as well as their impacts on feed efficiency and digestion. In addition, next-generation DNA sequencing techniques have allowed us to gain a better understanding of such microbiomes. In this study, the beef cattle microbiome data were analyzed using both a multivariate and a univariate approach and the results were compared. Moreover, a statistical procedure to classify calves in two groups with extreme Residual Feed Intake (RFI) values, using their microbiota profile, was developed. Both fecal and ruminal samples were collected from 63 Angus steers at two different time points for evaluation of their microbiomes: at the beginning and at the end of the feedlot. An additional fecal sample was collected at weaning. A total of 149 and 119 bacterial families (BFs) were retrieved from the ruminal and fecal samples, respectively. A Canonical Discriminant Analysis (CDA) was used to investigate whether BFs were able to distinguish between rumen and fecal samples. A sub-sample of 28 steers was divided in two groups based on their feed efficiency status: positive or negative for RFI. Fecal samples collected at weaning were used to assign the positive and negative RFI animals to their corresponding groups using both Stepwise Discriminant Analysis and CDA. Results revealed that CDA was able to distinguish between rumen and fecal samples. Peptostreptococcaceae was the family most associated with the fecal samples, whereas Prevotellaceae the most associated with the ruminal samples. The CDA using 19 BFs selected from the stepwise was able to correctly assign all animals to the proper RFI groups (negative or positive). Rhizobiaceae was the family most associated with negative RFI, whereas Comamonadacea was the family most linked with positive RFI. The results from this study showed that the multivariate approach can be used to improve microbiome data analysis, as well as to predict feed efficiency in beef cattle using information derived from the fecal microbiome.

Understanding the phenotypic and genetic background of the lactose content in Sarda dairy sheep.

Lactose, the principal carbohydrate found in milk, plays an important role in the physiological processes of milk production because it is related to milk volume, and it is responsible for the osmotic equilibrium between blood and milk in the mammary gland. In this study, factors affecting lactose content (LC) in sheep milk are investigated. For this purpose, 2,358 test-day records were sampled from 509 ewes (3-7 records per animal). The LC and other main milk traits were analyzed using a mixed linear model that included days in milk (DIM) class, parity, lambing month, and type of lambing as fixed effects and animal, permanent environment, and flock test day as random effects. The pedigree-based approach was used to estimate the heritability and repeatability of LC. Moreover, the genomic background of LC was investigated through a GWAS. The LC was affected by all tested factors (i.e., DIM class, parity, lambing month, and type of lambing). Low heritability (0.10 ± 0.05) and moderate repeatability (0.42 ± 0.02) were estimated for LC. High negative genetic correlations were estimated between LC and NaCl (-0.99 ± 0.01) and between LC and somatic cell count (-0.94 ± 0.05). Only 2 markers passed the chromosome-wide Bonferroni threshold. Results of the present study, although obtained on a relatively small sample, suggest the possibility to include LC in the breeding programs, particularly because of its strong relationship with NaCl and somatic cell count.

Strategies for choosing core animals in the algorithm for proven and young and their impact on the accuracy of single-step genomic predictions in cattle.

Nowadays, in some populations, the number of genotyped animals is too large to obtain the inverse of the genomic relationship matrix. The algorithm for proven and young animals (APY) can be used to overcome this problem. In the present work, different strategies for defining core animals in APY were tested using either simulated or real data. In particular, core definitions based on random choice or on the contribution to the genomic relationship matrix (GCONTR) calculated using Principal Component Analysis were tested. Core sizes able to explain 90, 95, 98, and 99% of the total variance of the genomic relationship matrix (G) were used. Analyzed phenotypes were three simulated traits for 3 000 individuals, and milkability records for 136 406 Italian Simmental cows. The number of genotypes was 4 100 for the simulated dataset, and 11 636 for the Simmental data, respectively. The GCONTR values in Simmental dataset were moderately correlated with the analyzed phenotype, and they showed a decreasing trend according to the year of birth of genotyped animals. The accuracy increased as the size of the core increased in both datasets. The inclusion in the core of animals with largest GCONTR values led to the lowest accuracies (0.50 and 0.71 for the simulated and Simmental datasets, respectively; average across traits and core sizes). On the contrary, the selection of animals with the lowest rank according to their contribution to the G provided slightly higher accuracies, especially in the simulated dataset (0.68 for the simulated dataset, and 0.76 for the Simmental data; average across traits and core sizes). In real data, particularly for larger sizes of core animals, the criteria of choice appear less important, confirming the results of earlier studies. Anyway, the inclusion in the core of animals with the lowest values of GCONTR led to increases in accuracy. These are preliminary results based on a small sample size that need to be confirmed on a larger number of genotypes.

Phenotypic and genetic characterization of the occurrence of noncoagulating milk in dairy sheep.

Milk coagulation ability is of central importance for the sheep dairy industry because almost all sheep milk is destined for cheese processing. The occurrence of milk with impaired coagulation properties is an obstacle to cheese processing and, in turn, to the profitability of the dairy companies. In this work, we investigated the causes of noncoagulation of sheep milk; specifically, we studied the effect of milk physicochemical properties on milk coagulation status [coagulating and noncoagulating (NC) milk samples, which do or do not coagulate within 30 min, respectively], and whether mid-infrared spectroscopy (MIR) could be used to assess variability in coagulation status. We also investigated the genetic background of milk coagulation ability. Individual milk samples were collected from 996 Sarda ewes farmed in 47 flocks located in Sardinia (Italy). Considered traits were daily milk yield, milk composition traits, and milk coagulation properties (rennet coagulation time, curd firming time, and curd firmness), and MIR spectra were acquired. About 9% of samples did not coagulate within 30 min. A logistic regression approach was used to test the effect of milk-related traits on milk coagulation status. A principal component (PC) analysis was carried out on the milk MIR spectra, and PC scores were then used as covariates in a logistic regression model to assess their relationship with milk coagulation status. Results of the present work demonstrated that the probability of having NC samples increases as milk contents of proteins and chlorides and somatic cell score increase. The analysis of PC extracted from milk spectra that influenced coagulation status highlighted key regions associated with lactose and protein concentrations, and others not associated with routinely collected milk composition traits. These results suggest that the occurrence of NC is mostly related to damage of the epithelium secretory mammary cells, which occurs with the advancement of a lactation or due to unhealthy mammary gland status. Genetic analysis of milk coagulation status and of the extracted PC confirmed the genetic background of the milk coagulability of sheep milk.

Genomic information allows for more accurate breeding values for milkability in dual-purpose Italian Simmental cattle.

Milkability is a trait related to the milking efficiency of an animal, and it is a component of the herd profitability. Due to its economic importance, milkability is currently included in the selection index of the Italian Simmental cattle breed with a weight of 7.5%. This lowly heritable trait is measured on a subjective scale from 1 to 3 (1 = slow, 3 = fast), and genetic evaluations are performed by pedigree-based BLUP. Genomic information is now available for some animals in the Italian Simmental population, and its inclusion in the genetic evaluation system could increase accuracy of breeding values and genetic progress for milkability. The aim of this study was to test the feasibility and advantages of having a genomic evaluation for this trait in the Italian Simmental population. Phenotypes were available for 131,308 cows. A total of 9,526 animals had genotypes for 42,152 loci; among the genotyped animals, 2,455 were cows with phenotypes, and the other were their relatives. The youngest cows with both phenotypes and genotypes (n = 900) were identified as selection candidates. Variance components and heritability were estimated using pedigree information, whereas genetic and genomic evaluations were carried out using BLUP and single-step genomic BLUP (ssGBLUP), respectively. In addition, a weighted ssGBLUP was assessed using genomic regions from a genome-wide association study. Evaluation models were validated using theoretical and realized accuracies. The estimated heritability for milkability was 0.12 ± 0.01. The mean theoretical accuracies for selection candidates were 0.43 ± 0.08 (BLUP) and 0.53 ± 0.06 (ssGBLUP). The mean realized accuracies based on linear regression statistics were 0.29 (BLUP) and 0.40 (ssGBLUP). No genomic regions were significantly associated with milkability, thus no improvements in accuracy were observed when using weighted ssGBLUP. Results indicated that genomic information could improve the accuracy of breeding values and increase genetic progress for milkability in Italian Simmental.

Principal component and multivariate factor analysis of detailed sheep milk fatty acid profile.

Fatty acid (FA) profile is one of the most important aspects of the nutritional properties of milk. The FA content in milk is affected by several factors such as diet, physiology, environment, and genetics. Recently, principal component analysis (PCA) and multivariate factor analysis (MFA) have been used to summarize the complex correlation pattern of the milk FA profile by extracting a reduced number of new variables. In this work, the milk FA profile of a sample of 993 Sarda breed ewes was analyzed with PCA and MFA to compare the ability of these 2 multivariate statistical techniques in investigating the possible existence of latent substructures, and in studying the influence of physiological and environmental effects on the new extracted variables. Individual scores of PCA and MFA were analyzed with a mixed model that included the fixed effects of parity, days in milking, lambing month, number of lambs born, altitude of flock location, and the random effect of flock nested within altitude. Both techniques detected the same number of latent variables (9) explaining 80% of the total variance. In general, PCA structures were difficult to interpret, with only 4 principal components being associated with a clear meaning. Principal component 1 in particular was the easiest to interpret and agreed with the interpretation of the first factor, with both being associated with the FA of mammary origin. On the other hand, MFA was able to identify a clear structure for all the extracted latent variables, confirming the ability of this technique to group FA according to their function or metabolic origin. Key pathways of the milk FA metabolism were identified as mammary gland de novo synthesis, ruminal biohydrogenation, desaturation performed by stearoyl-coenzyme A desaturase enzyme, and rumen microbial activity, confirming previous findings in sheep and in other species. In general, the new extracted variables were mainly affected by physiological factors as days in milk, parity, and lambing month; the number of lambs born had no effect on the new variables, and altitude influenced only one principal component and factor. Both techniques were able to summarize a larger amount of the original variance into a reduced number of variables. Moreover, factor analysis confirmed its ability to identify latent common factors clearly related to FA metabolic pathways.

Sequencing of lipoprotein lipase gene in the Mediterranean river buffalo identified novel variants affecting gene expression.

Lipoprotein lipase (LPL) is a key enzyme for lipid metabolism, playing a fundamental role in the composition of fat in adipose tissue and milk. The LPL gene has been seldom investigated in dairy ruminants and barely studied in river buffalo (Bubalus bubalis). The aim of this work was to explore the genetic diversity of LPL and its promoter and to identify functional mutations, using a combined approach based on sequencing, dual-color electrophoretic mobility shift assay, and quantitative PCR. Thirteen consensus sequences for transcription factors were found in the promoter. Eleven SNP were detected, and the attention was focused on the SNP with potential functional effects: g.-446A>G, because the presence of G created a consensus motif for the transcription factor Sp1, and g.107A>G, which was the only exonic SNP. We developed PCR-RFLP methods for genotyping the 2 SNP and calculated the allele frequencies. A strong linkage disequilibrium (D' = 1; r2 = 0.903) was found between the 2 SNP. The dual-color electrophoretic mobility shift assay demonstrated that only genotype g.-446GG allowed the binding of the Sp1 transcription factor, resulting in overexpression of the gene (~2.5 fold), as confirmed by the quantitative PCR results. Haploinsufficiency is proposed as a regulation mechanism. This study adds further knowledge on the structure of the LPL gene and its expression in river buffalo, with potential effects on milk qualitative and quantitative production.

Genetic and genomic analyses of latent variables related to the milk fatty acid profile, milk composition, and udder health in dairy cattle.

The aim of this study was to perform genetic, genome-wide association (GWAS), and gene-set enrichment analyses with latent variables related to milk fatty acid profile (i.e., fatty acids factor scores; FAF), milk composition, and udder health in a cohort of 1,158 Italian Brown Swiss cows. The phenotypes under study were 12 FAF previously identified through factor analysis and classified as follows: de novo FA (F1), branched-chain FA-milk yield (F2), biohydrogenation (F3), long-chain fatty acids (F4), desaturation (F5), short-chain fatty acids (F6), milk protein and fat contents (F7), odd fatty acids (F8), conjugated linoleic acids (F9), linoleic acid (F10), udder health (F11) and vaccelenic acid (F12). (Co)variance components were estimated for factor scores using a Bayesian linear animal model via Gibbs sampling. The animals were genotyped with the Illumina BovineSNP50 BeadChip v.2 (Illumina Inc., San Diego, CA). A single marker regression model was fitted for GWAS analysis. The gene-set enrichment analysis was run on the GWAS results using the Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes pathway databases to identify the ontologies and pathways associated with the FAF. Marginal posterior means of the heritabilities of the aforementioned FAF ranged from 0.048 for F12 to 0.310 for F5. Factors F1 and F6 had the highest number of relevant genetic correlations with the other traits. The genomic analysis detected a total of 39 significant SNP located on 17 Bos taurus autosomes. All latent variables produced signals except for F2 and F10. The traits with the highest number of significant associations were F11 (17) and F12 (7). Gene-set enrichment analyses identified significant pathways (false discovery rate 5%) for F3 and F7. In particular, systemic lupus erythematosus was enriched for F3, whereas the MAPK (mitogen-activated protein kinase) signaling pathway was overrepresented for F7. The results support the existence of important and exploitable genetic and genomic variation in these latent explanatory phenotypes. Information acquired might be exploited in selection programs and when designing further studies on the role of the putative candidate genes identified in the regulation of milk composition and udder health.

Genomic selection of milk fatty acid composition in Sarda dairy sheep: Effect of different phenotypes and relationship matrices on heritability and breeding value accuracy.

Fatty acid (FA) composition is one of the most important aspects of milk nutritional quality. However, the inclusion of this trait as a breeding goal for dairy species is hampered by the logistics and high costs of phenotype recording. Fourier-transform infrared spectroscopy (FTIR) is a valid and cheap alternative to laboratory gas chromatography (GC) for predicting milk FA composition. Moreover, as for other novel phenotypes, the efficiency of selection for these traits can be enhanced by using genomic data. The objective of this research was to compare traditional versus genomic selection approaches for estimating genetic parameters and breeding values of milk fatty acid composition in dairy sheep using either GC-measured or FTIR-predicted FA as phenotypes. Milk FA profiles were available for a total of 923 Sarda breed ewes. The youngest 100 had their own phenotype masked to mimic selection candidates. Pedigree relationship information and genotypes were available for 923 and 769 ewes, respectively. Three statistical approaches were used: the classical-pedigree-based BLUP, the genomic BLUP that considers the genomic relationship matrix G, and the single-step genomic BLUP (ssGBLUP) where pedigree and genomic relationship matrices are blended into a single H matrix. Heritability estimates using pedigree were lower than ssGBLUP, and very similar between GC and FTIR regarding the statistical approach used. For some FA, mostly associated with animal diet (i.e., C18:2n-6, C18:3n-3), random effect of combination of flock and test date explained a relevant quota of total variance, reducing the heritability estimates accordingly. Genomic approaches (genomic BLUP and ssGBLUP) outperformed the traditional pedigree method both for GC and FTIR FA. Prediction accuracies in the older cohort were larger than the young cohort. Genomic prediction accuracies (obtained using either G or H relationship matrix) in the young cohort of animals, where their own phenotypes were masked, were similar for GC and FTIR. Multiple-trait analysis slightly affected genomic breeding value accuracies. These results suggest that FTIR-predicted milk FA composition could represent a valid option for inclusion in breeding programs.

Genetic parameters of milk fatty acid profile in sheep: comparison between gas chromatographic measurements and Fourier-transform IR spectroscopy predictions.

Fatty acid (FA) composition is a key component of sheep milk nutritional quality. However, breeding for FA composition in dairy sheep is hampered by the logistic and phenotyping costs. This study was aimed at estimating genetic parameters for sheep milk FA and to test the feasibility of their routine measurement by using Fourier-transform IR (FTIR) spectroscopy. Milk FA composition of 989 Sarda ewes farmed in 48 flocks was measured by gas chromatography (FAGC). Moreover, FTIR spectrum was collected for each sample, and it was used to predict FA composition (FAFTIR) by partial least squares regression: 700 ewes were used for estimating model parameters, whereas the remaining 289 animals were used to validate the model. One hundred replicates were performed by randomly assigning animals to estimation and validation data set, respectively. Variance components for both measured and predicted traits were estimated with an animal model that included the fixed effects of parity, days in milking interval, lambing month, province, altitude of flock location, the random effects of flock-test-date and animal genetic additive. Genetic correlations among FAGC, and between corresponding FAGC and FAFTIR were estimated by bivariate analysis. Coefficients of determination between FAGC and FAFTIR ranged from moderate (about 0.50 for odd- and branched-chain FA) to high (about 0.90 for de novo FA) values. Low-to-moderate heritabilities were observed for individual FA (ranging from 0.01 to 0.47). The largest value was observed for GC measured C16:0. Low-to-moderate heritabilities were estimated for FA groups. In most of cases, heritabilites were slightly larger for FAGC than FAFTIR. Estimates of genetic correlations among FAGC showed a large variability in magnitude and sign. The genetic correlation between FAFTIR and FAGC was higher than 60% for all investigated traits. Results of the present study confirm the existence of genetic variability of the FA composition in sheep and suggest the feasibility of using FAFTIR as proxies for these traits in large scale breeding programs.

The single nucleotide polymorphism g.133A>C in the stearoyl CoA desaturase gene (SCD) promoter affects gene expression and quali-quantitative properties of river buffalo milk.

The stearoyl-CoA desaturase (SCD) gene has been investigated in depth in ruminants because of its effect on milk fat composition. In river buffalo, the single nucleotide polymorphism (SNP) g.133A>C in the gene promoter has been associated with milk quality and yield. However, the biological reason for such effects remains unexplored. In this study, we combined mRNA profile analysis, an electromobility shift assay, and quantitative PCR to elucidate the role of this SNP on gene transcription and its effects on milk fat traits. A preliminary genotyping of g.133A>C was carried out on a group of 303 river buffaloes to choose individuals for the downstream applications. Analysis of allele frequencies showed an increase in the minor allele C (0.25) compared with previous findings (0.16). Six animals (2 for each genotype) were chosen for cloning and 216 positive cDNA recombinant clones for SCD (72 per genotype) were analyzed by PCR. All clones showed the same length on agarose gel; therefore, random clones were chosen for sequencing. No qualitative differences were found and all gene transcripts assembled correctly. An electrophoretic mobility shift assay was performed to evaluate the binding of the transcription factor Sp1 to DNA sequences including g.133A>C. Genotype CC showed a higher binding (mean ± standard error of the mean) than genotype AA in 2 different conditions [Enzo buffer (EB), Enzo Life Science Inc., Farmingdale, NY: 201.77 ± 4.06 vs. 141.65 ± 3.77 band intensity values and Poletto buffer (PB): 95.90 ± 1.15 vs. 67.30 ± 2.14 band intensity values]. The subsequent quantitative PCR confirmed the upregulation of the CC genotype compared with the AA and AC genotypes. The association study with milk fat traits revealed a favorable effect of allele C. The heterozygous genotype had the highest values for monounsaturated fatty acids, oleic acid (C18:1 cis-9), polyunsaturated fatty acids, and odd- and branched-chain fatty acids, and the lowest values for saturated fatty acids and atherogenic and thrombogenic indices; the heterozygous genotype differed significantly from the AA genotype. The AC genotype has previously been associated with higher milk yield. Therefore, the g.133A>C SNP is a marker with dual effects and is an interesting candidate for assisted selection programs in river buffalo. These data clarified the biological role of the SNP g.133A>C in the SCD promoter and how it affects gene function, providing important knowledge on the genetic background of lipid metabolism, including the future possibility of selecting alleles with quantitatively or qualitatively favorable effects.

Genome-wide variability and selection signatures in Italian island cattle breeds.

In the present study, a sample of 88 animals belonging to four local (Modicana, Sarda, Sardo-Bruna and Sardo-Modicana) and one cosmopolitan (Italian Brown Swiss) cattle breeds were genotyped with a medium density SNP beadchip and compared to investigate their genetic diversity and the existence of selection signatures. A total of 43 012 SNPs distributed across all 29 autosomal chromosomes were retained after data quality control. Basic population statistics, Wright fixation index and runs of homozygosity (ROH) analyses confirmed that the Italian Brown Swiss genome was shaped mainly by selection, as underlined by the low values of heterozygosity and minor allele frequency. As expected, local cattle exhibited a large within-breed genetic heterogeneity. The FST comparison revealing the largest number of significant SNPs was Sardo-Bruna vs. Sardo-Modicana, whereas the smallest was observed for Italian Brown Swiss vs. Sardo-Modicana. Modicana exhibited the largest number of detected ROHs, whereas the smallest was observed for Sardo-Modicana. Signatures of selection were detected in genomic regions that harbor genes involved in milk production traits for Italian Brown Swiss and fitness traits for local breeds. According to the results of multi-dimensional scaling and the admixture analysis the Sardo-Bruna is more similar to the Sarda than to the Italian Brown Swiss breed. Moreover, the Sardo-Modicana is genetically closer to the Modicana than to the Sarda breed. Results of the present work confirm the usefulness of single nucleotide polymorphisms in deciphering the genetic architecture of livestock breeds.

A canonical discriminant analysis to study the association between milk fatty acids of ruminal origin and milk fat depression in dairy cows.

Although milk fat depression (MFD) has been observed and described since the beginning of the last century, all the molecular and biochemical mechanisms involved are still not completely understood. Some fatty acids (FA) originating during rumen biohydrogenation have been proposed as causative elements of MFD. However, contradictory results were obtained when studying the effect of single FA on MFD. An alternative could be the simultaneous evaluation of the effect of many FA using a multivariate approach. The aim of this study was to evaluate the relationship between individual milk FA of ruminal origin and MFD using canonical discriminant analysis, a multivariate technique able to distinguish 2 or more groups on the basis of a pool of variables. In a commercial dairy herd, a diet containing 26% starch on a DM basis induced an unintentional MFD syndrome in 14 cows out of 40. Milk yielded by these 14 animals showed a fat content lower than 50% of the ordinary value, whereas milk production and protein content were normal. The remaining 26 cows secreted typical milk fat content and therefore were considered the control group, even though they ate the same diet. The stepwise discriminant analysis selected 14 milk FA of ruminal origin most able to distinguish the 2 groups. This restricted pool of FA was used, as variables, in a run of the canonical discriminant analysis that was able to significantly discriminate between the 2 groups. Out of the 14 FA, 5 conjugated linoleic acid isomers (C18:2 trans-10,trans-12, C18:2 trans-8,trans-10, C18:2 trans-11,cis-13, C18:2 cis-9,cis-11, C18:2 cis-10,cis-12) and C15:0 iso were more related to the control group, whereas C18:2 trans-10,cis-12, C16:1 trans-6-7, C16:1 trans-9, C18:1 trans-6-8, C18:1 trans-9, C18:1 trans-10, C18:1 cis-11, and C18:3n-3 were positively associated with the MFD group, allowing a complete discrimination. On the basis of these results, we can conclude that (1) the shift of ruminal biohydrogenation from C18:1 trans-11 to C18:1 trans-10 seemed to be strongly associated with MFD; (2) at the same time, other C18:1 trans isomers showed a similar association; (3) on the contrary, conjugated linoleic acid isomers other than C18:2 trans-10,cis-12 seemed to be associated with a normal fat secretion. Results confirmed that MFD is the consequence of a combined effect of the outflow of many ruminal FA, which collectively affect mammary fat synthesis. Because the animals of the 2 groups were fed the same diet, these results suggested that factors other than diet are involved in the MFD syndrome. Feeding behavior (i.e., ability to select dietary ingredients in a total mixed ration), rumen environment and the composition of ruminal bacteria are additional factors able to modify the products of rumen biohydrogenation. Results of the present work confirmed that the multivariate approach can be a useful tool to evaluate a metabolic pathway that involves several parameters, providing interesting suggestions about the role of some FA involved in MFD. However, results about the MFD syndrome obtained in the present research require a deep molecular investigation to be confirmed.

Remarkable genetic diversity detected at river buffalo prolactin receptor (PRLR) gene and association studies with milk fatty acid composition.

Prolactin is an anterior pituitary peptide hormone involved in many different endocrine activities and is essential for reproductive performance. This action is mediated by its receptor, the prolactin receptor, encoded by the PRLR gene. In this study, we sequenced and characterized the Mediterranean river buffalo PRLR gene (from exon 3 to 10), and we found remarkable genetic diversity. In particular, we found 24 intronic polymorphisms and 13 exonic SNPs, seven of which were non-synonymous. Furthermore, the polymorphisms identified in the 3'-UTR were investigated to establish their possible influence on microRNA binding sites. Considering all the amino acid changes and the observed allelic combinations, it is possible to deduce at least six different translations of the buffalo prolactin receptor and, consequently, the presence at the PRLR gene of at least six alleles. Furthermore, we identified a deletion of a CACTACC heptamer between nucleotides 1102 and 1103 of exon 10 (3'-UTR), and we developed an allele-specific PCR to identify the carriers of this genetic marker. Finally, the SNP g.11188A>G, detected in exon 10 and responsible for the amino acid replacement p.His328Arg, was genotyped in 308 Italian Mediterranean river buffaloes, and an association study with milk fat traits was carried out. The statistical analysis showed a tendency that approached significance for the AA genotype with higher contents of odd branched-chain fatty acids. Thus, our results suggest that the PRLR gene is a good candidate for gene association studies with qualitative traits related to buffalo milk production.

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 on beef production traits in Marchigiana cattle breed.

A genomewide association study was carried out on a sample of Marchigiana breed cattle to detect markers significantly associated with carcass and meat traits. Four hundred and nine young bulls from 117 commercial herds were genotyped by Illumina 50K BeadChip assay. Eight growth and carcass traits (average daily gain, carcass weight, dressing percentage, body weight, skin weight, shank circumference, head weight and carcass conformation) and two meat quality traits (pH at slaughter and pH 24 h after slaughter) were measured. Data were analysed with a linear mixed model that included fixed effects of herd, slaughter date, fixed covariables of age at slaughter and SNP genotype, and random effects of herd and animal. A permutation test was performed to correct SNP genotype significance level for multiple testing. A total of 96 SNPs were significantly associated at genomewide level with one or more of the considered traits. Gene search was performed on genomic regions identified on the basis of significant SNP position and level of linkage disequilibrium. Interesting loci affecting lipid metabolism (SOAT1), bone (BMP4) and muscle (MYOF) biology were highlighted. These results may be useful to better understand the genetic architecture of growth and body composition in cattle.

Estimates of heritability and genetic correlations for milk coagulation properties and individual laboratory cheese yield in Sarda ewes.

Objective of this study was to estimate genetic parameters of milk coagulation properties (MCPs) and individual laboratory cheese yield (ILCY) in a sample of 1018 Sarda breed ewes farmed in 47 flocks. Rennet coagulation time (RCT), curd-firming time (k 20) and curd firmness (a 30) were measured using Formagraph instrument, whereas ILCY were determined by a micromanufacturing protocol. About 10% of the milk samples did not coagulate within 30 min and 13% had zero value for k 20. The average ILCY was 36%. (Co)variance components of considered traits were estimated by fitting both single- and multiple-trait animal models. Flock-test date explained from 13% to 28% of the phenotypic variance for MCPs and 26% for ILCY, respectively. The largest value of heritability was estimated for RCT (0.23±0.10), whereas it was about 0.15 for the other traits. Negative genetic correlations between RCT and a 30 (-0.80±0.12), a 30 and k 20 (-0.91±0.09), and a 30 and ILCY (-0.67±0.08) were observed. Interesting genetic correlations between MCPs and milk composition (r G>0.40) were estimated for pH, NaCl and casein. Results of the present study suggest to use only one out of three MCPs to measure milk renneting ability, due to high genetic correlations among them. Moreover, negative correlations between ILCY and MCPs suggest that great care should be taken when using these methods to estimate cheese yield from small milk samples.

Multivariate factor analysis of detailed milk fatty acid profile: Effects of dairy system, feeding, herd, parity, and stage of lactation.

We investigated the potential of using multivariate factor analysis to extract metabolic information from data on the quantity and quality of milk produced under different management systems. We collected data from individual milk samples taken from 1,158 Brown Swiss cows farmed in 85 traditional or modern herds in Trento Province (Italy). Factor analysis was carried out on 47 individual fatty acids, milk yield, and 5 compositional milk traits (fat, protein, casein, and lactose contents, somatic cell score). According to a previous study on multivariate factor analysis, a variable was considered to be associated with a specific factor if the absolute value of its correlation with the factor was ≥0.60. The extracted factors were representative of the following 12 groups of fatty acids or functions: de novo fatty acids, branched fatty acid-milk yield, biohydrogenation, long-chain fatty acids, desaturation, short-chain fatty acids, milk protein and fat contents, odd fatty acids, conjugated linoleic acids, linoleic acid, udder health, and vaccelenic acid. Only 5 fatty acids showed small correlations with these groups. Factor analysis suggested the existence of differences in the metabolic pathways for de novo short- and medium-chain fatty acids and Δ9-desaturase products. An ANOVA of factor scores highlighted significant effects of the dairy farming system (traditional or modern), season, herd/date, parity, and days in milk. Factor behavior across levels of fixed factors was consistent with current knowledge. For example, compared with cows farmed in modern herds, those in traditional herds had higher scores for branched fatty acids, which were inversely associated with milk yield; primiparous cows had lower scores than older cows for de novo fatty acids, probably due to a larger contribution of lipids mobilized from body depots on milk fat yield. The statistical approach allowed us to reduce a large number of variables to a few latent factors with biological meaning and able to represent groups of fatty acids with a common origin and function. Multivariate factor analysis would therefore be a valuable tool for studying the influence of different production environments and individual animal factors on milk fatty acid composition, and for developing nutritional strategies able to manipulate the milk fatty acid profile according to consumer demand.

Derivation of multivariate indices of milk composition, coagulation properties, and individual cheese yield in dairy sheep.

Milk composition and its technological properties are traits of interest for the dairy sheep industry because almost all milk produced is processed into cheese. However, several variables define milk technological properties and a complex correlation pattern exists among them. In the present work, we measured milk composition, coagulation properties, and individual cheese yields in a sample of 991 Sarda breed ewes in 47 flocks. The work aimed to study the correlation pattern among measured variables and to obtain new synthetic indicators of milk composition and cheese-making properties. Multivariate factor analysis was carried out on individual measures of milk coagulation parameters; cheese yield; fat, protein, and lactose percentages; somatic cell score; casein percentage; NaCl content; pH; and freezing point. Four factors that were able to explain about 76% of the original variance were extracted. They were clearly interpretable: the first was associated with composition and cheese yield, the second with udder health status, the third with coagulation, and the fourth with curd characteristics. Factor scores were then analyzed by using a mixed linear model that included the fixed effect of parity, lambing month, and lactation stage, and the random effect of flock-test date. The patterns of factor scores along lactation stages were coherent with their technical meaning. A relevant effect of flock-test date was detected, especially on the 2 factors related to milk coagulation properties. Results of the present study suggest the existence of a simpler latent structure that regulates relationships between variables defining milk composition and coagulation properties in sheep. Heritability estimates for the 4 extracted factors were from low to moderate, suggesting potential use of these new variables as breeding goals.