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Individual methane emissions are a potential breeding goal for selection plans aimed at improving the sustainability of ruminant farming systems. However, a large-scale recording of individual methane emissions is hampered by the high costs of the equipment and the logistics of the experiments. Equations have been developed to predict methane emissions in dairy cattle based on variables such as dry matter intake (DMI), gross energy intake, body weight and milk fatty acids (FAs) profile. No equations are currently available for dairy sheep. The aims of this study were: (i) to estimate the enteric methane yield (eMY, expressed as g/kg of DMI) and enteric methane intensity (eMI, expressed as g/kg of FPCM) of dairy sheep using equations developed for dairy cattle that consider as predictors milk FA; (ii) to evaluate the effect of stage of lactation, parity and month of lambing on eMY and eMI; (iii) to estimate the heritability of eMY and eMI. For this purpose, 964 individual milk samples from Sarda dairy ewes were analysed for milk composition and FA profile. Nine different equations were used to predict eMY and eMI. Values of eMY ranged from 19.4 g/kg to 20.4 g/kg of DMI, whereas values of eMI ranged from 15.1 g/kg to 21.0 g/kg of DMI, respectively. Stage of lactation was an important factor in the variability of eMY (p <.01). Indeed, this trait increased towards the end of lactation. Both eMY and eMI were negatively correlated with milk yield, lactose concentration and urea and positively correlated with fat concentration and NaCl, respectively. Heritability of eMI and eMY estimated using a single trait model was 0.13 ± 0.05 and 0.05 ± 0.04, respectively. The values of heritability estimated for eMY using a bivariate models ranged from 0.10 to 0.16 and they were higher than the value estimated for eMI. Results of the present study, even if based on prediction equations developed for cows, increased the knowledge about the phenotypic and genetic background of methane emissions in Sarda dairy sheep. Methane emissions were predicted from fatty acids using equation developed for cows. Estimates were in agreement with the values reported in literature. Methane emissions were lowly heritable. [ABSTRACT FROM AUTHOR]

Residual feed intake (RFI) is the most used measure of feed efficiency. However, considering the importance of concentrates in the ration, a new index, the residual concentrate intake (RCI), was here defined. RCI aims to measure the individual efficiency in converting the concentrate into animal products. Brown Swiss young bulls (N = 736) were genotyped at 41,183 loci. Animals were housed in pens equipped with an automatic feeding system able to recognise the animal and record the concentrate intake. The diet consisted of concentrate and hay (ad libitum). The new RCI index was calculated as the residuals of the linear regression of concentrate intake on metabolic live weight and average daily gain. Animals were ranked according to their corrected RCI and divided into low (LRCI) and high phenotypes (HRCI). A low heritability (0.06 ± 0.03) was estimated using only genomics for this new index. Results from multivariate (M-GWAS) and Bayesian (B-GWAS) approaches were combined to identify SNP associated with RCI. The M-GWAS selected 698 SNPs potentially associated, whereas no significant markers were obtained in B-GWAS. Markers in the last approach were ranked according to their posterior inclusion probability and the first 698 were retained. Only SNPs in common between sorted B-GWAS and M-GWAS (N = 11) were considered associated with RCI. A total of 48 candidate genes were retrieved near these SNPs. Most of them were previously reported to be associated with feed efficiency and RFI. The combined use of multivariate and Bayesian techniques allow to identify SNPs associated with the investigated trait. RCI could be promising to select animals 48 candidate genes were found associated with RCI Multivariate technique allowed to identify significant SNPs [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Summary: 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. [ABSTRACT FROM AUTHOR]

Summary: 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. [ABSTRACT FROM AUTHOR]

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 temperaturehumidity 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. [ABSTRACT FROM AUTHOR]

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 ( k20) and curd firmness (a30) 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 k20. 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 a30 (-0.80 ± 0.12), a30 and k20 (-0.91 ± 0.09), and a30 and ILCY (-0.67 ± 0.08) were observed. Interesting genetic correlations between MCPs and milk composition ( rG>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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

High-throughput cow genotyping has opened new perspectives for genome-wide association studies (GWAS). Directly recorded phenotypes and several records per animal could be used. In this study, a GWAS on lactation curve traits of 337 Italian Simmental cows genotyped with the Illumina (San Diego, CA) low-density BeadChip (7K) was carried out. Scores of the first 2 principal components extracted from test-day records (7 for each lactation) for milk yield, fat and protein percentages, and somatic cell score were used as phenotypes. The first component described the average level of the lactation curve, whereas the second summarized its shape. Data were analyzed with a mixed linear model that included fixed effects of herd, calving month, calving year, parity, SNP genotype, and random effects of animal and permanent environment. All statistically significant markers (Bonferroni corrected) were associated with the average level component (2 for milk yield, 9 for fat percentage, 6 for protein percentages, and 1 for somatic cell score). No markers were found to be associated with the lactation curve shape. Gene discovery was performed using windows of variable size, according to the linkage disequilibrium level of the specific genomic region. Several suggestive candidate genes were identified, some of which already reported to be associated with dairy traits, such as DGAT1. Others were involved in lipid metabolism, in protein synthesis, in the immune response, in cellular processes, and in early development. The large number of genes flagged in the present study suggests interesting perspectives for the use of low-density genotyped females for GWAS, also for novel phenotypes that are not currently considered as breeding goals. [ABSTRACT FROM AUTHOR]

The aim of the present study was to investigate the variability at CSN1S1 locus of the Italian Mediterranean river buffalo and to study possible allele effects on milk yield and its composition. Effects of parity, calving season and month of production were also evaluated. Three single-nucleotide polymorphisms were detected. The first mutation, located at position 89 of the 17th exon (c.628C>T), is responsible for the amino acid change p.Ser178 (B allele)/Leu178 (A allele). The other two polymorphisms, detected at the positions 144 (c.882G>A) and 239 (c.977A>G) of 19th exon, respectively, are silent (3ʹ UTR, untranslated region). Associations between the CSN1S1 genotypes and milk production traits were investigated using 4122 test day records of 503 lactations from 175 buffalo cows. Milk yield, fat and protein percentages were analysed using a mixed linear model. A significant association between the c.628C>T SNP and the protein percentage was found. In particular, the CC genotype showed an average value ~0.04% higher than the CT and TT genotypes. The allele substitution effect of cytosine into thymine was -0.014, with a quite low (0.3%) protein percentage contribution to total phenotypic variance. A large dominance effect was detected. Characterisation of the CSN1S1 transcripts and a method based on MboI amplification created restriction site PCR for a rapid genotyping of c.628C>T are provided. The αs1-casein is the most polymorphic milk protein and its genetic variants influence the quali-quantitative characteristics and the technological properties of the milk. This study investigates the genetic variability at the CSN1S1 in the Mediterranean buffalo. A significant association between the c.628C>T SNP and the protein percentage was found. This result offers useful indications for future applications of marker-assisted selection programmes. [ABSTRACT FROM AUTHOR]

In this study, the effects of breed composition and predictor dimensionality on the accuracy of direct genomic values (DGV) in a multiple breed (MB) cattle population were investigated. A total of 3559 bulls of three breeds were genotyped at 54 001 single nucleotide polymorphisms: 2093 Holstein (H), 749 Brown Swiss (B) and 717 Simmental (S). DGV were calculated using a principal component (PC) approach for either single (SB) or MB scenarios. Moreover, DGV were computed using all SNP genotypes simultaneously with SNPBLUP model as comparison. A total of seven data sets were used: three with a SB each, three with different pairs of breeds (HB, HS and BS), and one with all the three breeds together (HBS), respectively. Editing was performed separately for each scenario. Reference populations differed in breed composition, whereas the validation bulls were the same for all scenarios. The number of SNPs retained after data editing ranged from 36 521 to 41 360. PCs were extracted from actual genotypes. The total number of retained PCs ranged from 4029 to 7284 in Brown Swiss and HBS respectively, reducing the number of predictors by about 85% (from 82% to 89%). In all, three traits were considered: milk, fat and protein yield. Correlations between deregressed proofs and DGV were used to assess prediction accuracy in validation animals. In the SB scenarios, average DGV accuracy did not substantially change when either SNPBLUP or PC were used. Improvement of DGV accuracy were observed for some traits in Brown Swiss, only when MB reference populations and PC approach were used instead of SB-SNPBLUP (+10% HBS, +16%HB for milk yield and +3% HBS and +7% HB for protein yield, respectively). With the exclusion of the abovementioned cases, similar accuracies were observed using MB reference population, under the PC or SNPBLUP models. Random variation owing to sampling effect or size and composition of the reference population may explain the difficulty in finding a defined pattern in the results. [ABSTRACT FROM PUBLISHER]

In the current study, principal component (PC) analysis was used to reduce the number of predic-tors in the estimation of direct genomic breeding values (DGV) for meat traits in a sample of 479 Italian Sim-mental bulls. Single nucleotide polymorphism mark-er genotypes were determined with the 54K Illumina beadchip. After edits, 457 bulls and 40,179 SNP were retained. Principal component extraction was performed separately for each chromosome and 2466 new variables able to explain 70% of total variance were obtained. Bulls were divided into reference and validation popu-lation. Three scenarios of the ratio reference:validation were tested: 70:30, 80:20, 90:10. Effect of PC scores on polygenic EBV was estimated in the reference popula-tion using different models and methods. Traits analyzed were 7 beef traits: daily BW gain, size score, muscularity score, feet and legs score, beef index (economic index), calving ease direct effect, and cow muscularity. Accu-racy was calculated as correlation between DGV and polygenic EBV in the validation bulls. Muscularity, feet and legs, and the beef index showed the greatest accura-cies; calving ease, the least. In general, accuracies were slightly greater when reference animals were selected at random and the best scenario was 90:10 and no substan-tial differences in accuracy were found among different methods. Principal component analysis is entirely based on the factorization of the SNP (co)variance matrix and produced a reduced set of variables (6% of the original variables) which may be used for different phenotypic traits. In spite of this huge reduction in the number of in-dependent variables, DGV accuracies resulted similar to those obtained by using the whole set of SNP markers. Accuracies of direct genomic values found in the pres-ent work were always greater than those of traditional parental average (PA). Thus, results of the present study may suggest a possible advantage of use of genomic in-dexes in the preselection of performance test candidates for beef traits. Moreover, the relevant reduction of vari-able space might allow genomic selection implementa-tion also in small populations. [ABSTRACT FROM AUTHOR]

The aim of this study was to elucidate the structure of relationships between milk yield, composition, and coagulation properties of Brown Swiss cattle. Multi-variate factor analysis was used to derive new synthetic variables that can be used for selection purposes. For this reason, genetic parameters of these new variables were estimated. Individual records on milk yield, fat and protein percentages, casein content, lactose percentage, somatic cell count, titratable acidity, and pH were taken on 1,200 Italian Brown Swiss cows located in 38 herds. Factor analysis was able to extract 4 latent variables with an associated communality equal to 70% of the total original variance. The 4 latent factors were interpreted as indicators of milk composition, coagulation, acidity, and mammary gland health, respectively. Factor scores calculated for each animal exhibited coherent patterns along the lactation and across different parities. Estimation of genetic parameters of factor scores carried out with a multiple-trait Bayesian hierarchical model showed moderate to low heritabilities (raging from 0.10 to 0.23) and genetic correlations (from -0.15 to 0.46). Results of the present study support the hypothesis of a simpler structure that controls, at least in part, the covariance of milk composition and coagulation properties. Moreover, extracted variables may be useful for both breeding and management purposes, being able to represent, with a single value for each animal, complex traits such as milk coagulation properties or health status of the mammary gland. [ABSTRACT FROM AUTHOR]

Genome-wide selection aims to predict genetic merit of individuals by estimating the effect of chromosome segments on phenotypes using dense single nucleotide polymorphism (SNP) marker maps. In the present paper, principal component analysis was used to reduce the number of predictors in the estimation of genomic breeding values for a simulated population. Principal component extraction was carried out either using all markers available or separately for each chromosome. Priors of predictor variance were based on their contribution to the total SNP correlation structure. The principal component approach yielded the same accuracy of predicted genomic breeding values obtained with the regression using SNP genotypes directly, with a reduction in the number of predictors of about 96% and computation time of 99%. Although these accuracies are lower than those currently achieved with Bayesian methods, at least for simulated data, the improved calculation speed together with the possibility of extracting principal components directly on individual chromosomes may represent an interesting option for predicting genomic breeding values in real data with a large number of SNP. The use of phenotypes as dependent variable instead of conventional breeding values resulted in more reliable estimates, thus supporting the current strategies adopted in research programs of genomic selection in livestock. [ABSTRACT FROM AUTHOR]

Associations between stearoyl-CoA desaturase (SCD) gene polymorphisms and milk production traits (milk, fat, and protein yields, fat and protein contents, somatic cell score) were investigated on a sample of 701 lactations of 313 Italian Holsteins. Test-day records (5,097) were analyzed with a mixed linear model that included the fixed effects of herd, date of test, parity, genotype at the SCD locus, and lactation interval nested within SCD genotype, and the random effect of cow. An effect of the SCD genotype on milk and protein yields was detected, with VV cows producing more milk (about 2 kg/d) and protein (about 0.07 kg/ d) compared with AA cows. The contribution of the SCD locus to the phenotypic variance of tile 2 traits was about 0.015. These results suggest a possible use of the SCD locus in gene-assisted selection programs for the improvement of milk production traits in dairy cattle, although large-scale studies in different breeds are required. [ABSTRACT FROM AUTHOR]

Multivariate factor analysis and principal component analysis were used to decompose the correlation matrix of test-day milk yields of 48,374 lactations of 21,721 Italian Simmental cows. Two common latent factors related to level of production in early lactation and lactation persistency, and 2 principal components associated with the whole lactation yield and persistency were obtained. Factor and principal component scores were treated as new quantitative phenotypes related to prominent features of lactation curve shape. Genetic parameters were estimated by univariate and bivariate animal models. Estimates of heritability were moderately low for both latent factors (0.13 for persistency and yield early in lactation). Heritabilities of the principal component related to total lactation yield and 305-d yield were similar (0.19 and 0.20, respectively). Finally, heritability was quite low for the principal component related to lactation persistency (0.07). Repeatabilities between lactations were about 0.27 for both latent factors, around 0.4 for the first principal component and 305-d yield, and 0.11 for the second principal component. Moderate genetic correlation among common factors (0.26) and their high genetic correlation with total lactation yield (>0.60) suggest that selection can be used to change the shape of lactation curve as well as improve yield. Scores of the second principal component can be used to genetically improve persistency while maintaining constant total lactation yield. [ABSTRACT FROM AUTHOR]

Milk test-day records of 5728 lactations of Italian Simmental cows were analyzed with multivariate factor analysis in order to extract 2 common factors, whose scores were used as quantitative measures of 2 main features of lactation curve shape--i.e., the increasing rate of yield in the first part of lactation and the rate of decline of milk yield after the lactation peak. The 2 indices, objectively derived from the correlation matrix of original test-day records, showed a high discriminant power in separating lactation curves with different shapes. The weak correlation between the 2 factors (0.11), together with the high correlation of factors and the total 305-d yield (about 0.70), suggests that an increase in lactation yield could be achieved by acting only on one of the 2 factors related to lactation-curve shape, with the other kept constant at a medium or low value. The suitability of the 2 factors as descriptors of lactation patterns has been confirmed by the relationships found between factor scores and the main environmental effects known to affect the shape of the lactation curve, such as parity and season of calving. [ABSTRACT FROM AUTHOR]

Multivariate factor Analysis (MFA) and Principal Component Analysis (PCA) are dimension reduction techniques able to extract from the correlation matrix of milk test day records new latent variables related to lactation curve shape. In this paper, MFA and PCA were applied to a data set of 16,364 lactations of Italian Simmental cows, each with seven TD records for milk yield and Somatic Cell Count (SCC). The aim was to study relationships between lactation curve shape and two measures of SCC: lactational geometric mean of SCC (LGSCC) and lactational means of Somatic Cell Score (LSCS). MFA extracted two latent variables, related with level of production in early lactation (PEL) and lactation persistency (PERS) respectively, whereas PCA yields a leading component related to the average level of production (AVY) and a second component positively related with tests of early lactation and negatively with tests of the second part of lactation (SLOPE). Both for MFA and PCA, latent variables were able to explain more than 80% of the original variance. Correlations between indexes of lactation curve shape obtained with MFA and measures of somatic cells are favourable, i.e near zero or negative as in the case of PERS. Around zero are also correlations of the principal component related with level of production whereas positive and slightly higher (0.18) is the correlation between LSCS and SLOPE, indicating that curves with higher peaks and more pronounced slope in the second part of lactation tend to have higher values of SCC. Results of the present study highlight the ability of both MFA and PCA to extract latent variables related to lactation curve shape that, with the exception of the variable SHAPE, show very low or negative correlations with measures of SCC along the lactation. [ABSTRACT FROM AUTHOR]

Principal Component Analysis (PCA) has been used by several authors to address issues of genetic analysis of multivariate complex phenotypes, i.e. large number of parameters to be estimated, computational constraints and sampling error. PCA is able to describe the maximum amount of original variation with the minimum number of orthogonal new variables. However, it is scarcely flexible and results are not always easy to interpret. In order to cope with these shortcomings, the multivariate Factor Analysis (FA) could be proposed. FA, via the rotation technique, is able to extract latent variables with defined relationships with original variables. In the present work the ability of PCA and FA to factorize genetic correlation matrices (published data) of milk yield, logSCC for dairy cattle and weights at different ages for beef cattle is compared. The PCA yields a first component related to all original variables, able to explain most of the original variance (82%, 50%, 82% for milk yield, logSCC and body weight, respectively). Other principal components highlight the contribution of specific original variables to the total variance in decreasing order. On the other hand, FA extracts common latent factors related to specific groups of variables and that have a clear biological meaning: in the case of milk yield, for example, the fist latent common factor is able to explain 55% of the original variance, it is related to the milk tests of the second part of lactation and can be considered as an index of lactation persistency; the second factor (42% of explained variance) is related with tests of the first part of lactation and can be considered as an index of level of production in early lactation. Similar results are obtained for body weights and, even if less defined, for logSCC. Results of the present work suggest that a combined used of PCA and FA could be usefully for a deep understanding of latent structure of correlations in multivariate complex phenotypes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In recent years, an increase in the number of donkeys farmed in Italy as a consequence of the growing demand for donkey milk for direct consumption has been observed. Some research has been carried out on jenny milk composition and on its nutritional properties, whereas milk production features are scarcely described for this species. In this work, the lactation curve shape of donkeys for milk yield and composition was investigated. A total of 453 test-day records for milk yield, fat and protein percentage, and somatic cell count of 62 lactations measured on 46 multiparous jennies of the Ragusano breed were considered. Effects of herd, age, and foaling season were assessed by using a mixed model analysis. Average and individual lactation curves were fitted using the Wood incomplete gamma function, the Cappio-Borlino modified gamma, and a third-order Legendre orthogonal polynomial model. Donkeys foaling between 6- and 10-yr-old had the highest test-day milk yield (about 1.85 kg/d). Donkeys foaling in winter and autumn had a higher daily milk yield compared with those foaling in summer and spring. Less defined results were obtained for composition traits. The general pattern of the donkey lactation curve is similar to the standard shape reported for the main dairy ruminant species, with a peak yield occurring at about 5 wk from parturition. Younger jennies tended to have lower production peaks and higher lactation persistency. Similarly to what is reported for dairy cattle, a large variability in individual patterns has been observed. No differences in goodness of fit have been observed between the models in the case of average lactation curves, whereas orthogonal polynomials were more efficient in fitting individual patterns. [ABSTRACT FROM AUTHOR]

Milk yield and composition are of great economic importance for the dairy goat industry. The identification of genes associated with phenotypic differences for these traits could allow for the implementation of gene-assisted selection programs in goats. Associations between polymorphisms at 3 candidate genes and milk production traits in Alpine goats farmed in Italy were investigated in the present research. Considered genes were acetyl-coenzyme A carboxylase a (ACAC A), the major regulatory enzyme of fatty acid biosynthesis; stearoyl-coenzyme A desaturase (SCD), involved in the biosynthesis of monounsaturated fatty acids in the mammary gland; and lipoprotein lipase (LPL), which plays a central role in plasma triglyceride metabolism. An approach somewhat similar to the granddaughter design for detecting quantitative trait loci in dairy cattle was followed. Effects of genotypes of a sample of 59 Alpine bucks on phenotypes of their 946 daughters raised in 75 flocks were investigated. Data comprised 13,331 daily records for milk yields (L/d), fat and protein yields (kg/d), and fat and protein contents (%) of 2,200 lactations. Population genetics parameters were calculated and associations between milk production traits and 10 single nucleotide polymorphisms (SNP) at the 3 genes were tested. Two markers at the ACACA, 1 for the SCD and 1 at the LPL locus, deviated significantly from the Hardy-Weinberg equilibrium, with an observed heterozygosity lower than expected. Flock, age of the goat, kidding season, and stage of lactation affected all traits considered, except fat percentage. Three SNP were found to be significantly associated with milk production traits. The SNP located on the ACACA gene showed an effect on milk yield, with daughters of TT bucks having an average test-day milk yield of about 0.3 to 0.25 L/d lower than the other 2 genotypes. The marker on the LPL locus was highly associated with milk yield, with the largest values for CC daughters (about 0.50 L more than GG). The TGT deletion located on the untranslated region of the SCD gene showed significant effects on average milk and protein yields. The homozygote-deleted genotype had values about 0.5 L/d and 16 g/d lower for milk and protein daily yield, respectively, compared with the TGT/TGT genotype. Differences between genotypes were quite constant across most of the lactation. Associations found in the present study, which should be tested in a larger sample, especially for those markers that show rare genotypes, may offer useful indications for the genetic improvement of dairy traits in goats. [ABSTRACT FROM AUTHOR]

with phenotypes represents one of the main issues in genomic selection. In this work, principal component analysis was used to reduce the number of predictors for calculating genomic breeding values (GEBV). Bulls of 2 cattle breeds farmed in Italy (634 Brown and 469 Simmental) were genotyped with the 54K Illumina beadchip (Illumina Inc., San Diego, CA). After data editing, 37,254 and 40,179 single nucleotide polymorphisms (SNP) were retained for Brown and Simmental, respectively. Principal component analysis carried out on the SNP genotype matrix extracted 2,257 and 3,596 new variables in the 2 breeds, respectively. Bulls were sorted by birth year to create reference and prediction populations. The effect of principal components on deregressed proofs in reference animals was estimated with a BLUP model. Results were compared with those obtained by using SNP genotypes as predictors with either the BLUP or Bayes&lowbar;A method. Traits considered were milk, fat, and protein yields, fat and protein percentages, and somatic cell score. The GEBV were obtained for prediction population by blending direct genomic prediction and pedigree indexes. No substantial differences were observed in squared correlations between GEBV and EBV in prediction animals between the 3 methods in the 2 breeds. The principal component analysis method allowed for a reduction of about 90% in the number of independent variables when predicting direct genomic values, with a substantial decrease in calculation time and without loss of accuracy. [ABSTRACT FROM AUTHOR]

Associations of allelic variants of the ß-lactoglobulin (LGB), stearoyl-coenzyme A desaturase 1 (SCD), and sterol regulatory element binding protein (SREBP-1) genes with milk production, composition (fat, protein, and casein content), acidity (pH and titratable acidity), and coagulation properties (rennet coagulation time and curd firmness) were investigated in Brown Swiss cows. In total, 294 animals (progeny of 15 sires) reared in 16 herds were milk-sampled once. The additive effects of LGB (rs109625649:C > T), SCD (ss469414220:C > T), and SREBP-1 (AB355704: g.101&lowbar;185ins) poly- morphisms on the aforementioned traits were analyzed through Bayesian linear models. The LGB genotype affected rennet coagulation time, with TT (or BB) alleles showing longer rennet coagulation time compared with CC (or AA) cows. The SCD gene allelic variants were found to be associated with protein and casein contents and curd firmness: CC animals had the lowest values for the aforementioned traits. An association was found between SREBP-1 alleles and fat content, with the highest values for cows carrying the 84-bp insertion (or L) allele. Results suggest a possible use of these loci in gene-assisted selection programs for the improvement of milk quality traits and coagulation properties in Brown Swiss cattle. [ABSTRACT FROM AUTHOR]

Milk fatty acid composition is a parameter of great interest for evaluation of nutritional quality of milk. Stearoyl-CoA desaturase (SCD) is a key enzyme in mammary lipid metabolism because it is able to add a double bond in the cis &Delta9-position in a large spectrum of medium- and long-chain fatty acids. A polymorphism with 2 alleles (A and V) in the fifth exon of the SCD gene has been reported. The effect of SCD genotype on individual milk fatty acid composition and on cis-9 unsaturated/saturated fatty acid ratios of 297 Holstein Italian Friesian cows was investigated in this paper. The SCD genotypes were determined by using a single strand conformation polymorphism method. Relative frequencies of SCD genotypes were 27, 60, and 13% for AA, AV, and VV, respectively. Milk of AA cows had a greater content of cis-9 C 18:1 and total monounsaturated fatty acids and a higher C14:1/C14 ratio than did milk of VV cows. The relative contribution of SCD genotype to variation of monounsaturated fatty acids, cis-9 C18:1, and cis-9 C14:1 was 5, 4, and 7.7%, respectively. No significant differences were detected between SCD genotypes in the milk content of cis-9, trans-11 C 18:2. Results of the present work provide some indication of an association between SCD locus and the fatty acid profile in the examined sample of Italian Holsteins, thus suggesting a possible role of this gene in the genetic variation of milk nutritional properties. [ABSTRACT FROM AUTHOR]