Genetic variability among and within domestic Old and New World camels at the α-lactalbumin gene (LALBA) reveals new alleles and polymorphisms responsible for differential expression.

α-Lactalbumin (α-LA), which is encoded by the LALBA gene, is a major whey protein that binds to Ca2+ and facilitates lactose synthesis as a regulatory subunit of the synthase enzyme complex. In addition, it has been shown to play central roles in immune modulation, cell-growth regulation, and antimicrobial activity. In this study, a multitechnical approach was used to fully characterize the LALBA gene and its variants in both coding and regulatory regions for domestic camelids (dromedary, Bactrian camel, alpaca, and llama). The gene analysis revealed a conserved structure among the camelids, but a slight difference in size (2,012 bp on average) due to intronic variations. Promoters were characterized for the transcription factor binding sites (11 found in total). Intraspecies sequence comparison showed 36 SNPs in total (2 in the dromedary, none in the Bactrian camel, 22 in the alpaca, and 12 in the llama), whereas interspecies comparison showed 86 additional polymorphic sites. Eight SNPs were identified as trans-specific polymorphisms, and 2 of them (g.112A>G and g.1229A>G) were particularly interesting in the New World camels. The first creates a new binding site for transcription factor SP1. An enhancing effect of the g.112G variant on the expression was demonstrated by 3 independent pGL3 gene reporter assays. The latter is responsible for the p.78Ile>Val AA replacement and represents novel allelic variants (named LALBA A and B). A link to protein variants has been established by isoelectric focusing (IEF), and bioinformatics analysis revealed that carriers of valine (g.1229G) have a higher glycosylation rate. Genotyping methods based on restriction fragment length polymorphism (PCR-RFLP) were set up for both SNPs. Overall, adenine was more frequent (0.54 and 0.76) at both loci. Four haplotypes were found, and the AA and GA were the most common with a frequency of 0.403 and 0.365, respectively. Conversely, a putative biological gain characterizes the haplotype GG. Therefore, opportunities for rapid directional selection can be realized if this haplotype is associated with favorable milk protein properties. This study adds knowledge at the gene and protein level for α-LA (LALBA) in camelids and importantly contributes to a relatively unexplored research area in these species.

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.

Tunisian camel casein gene characterization reveals similarities and differences with Sudanese and Nigerian populations.

Milk is a primary protein source that has always played a role in mammalian health. Despite the intensification of research projects on dromedary and the knowledge of the genetic diversity at the casein loci, the genetic structure of the Tunisian camel population still needs exploration. This study sought to determine the genetic diversity of 3 casein gene variants in 5 Tunisian camel ecotypes: c.150G>T at CSN1S1 (αS1-casein), g.2126A>G at CSN2 (ß-casein), and g.1029T>C at CSN3 (κ-casein). The obtained results were compared with data published on Sudanese and Nigerian camels to establish the level of differentiation within and between populations. A total of 159 blood samples were collected from 5 Tunisian camel ecotypes and the extracted DNA was genotyped by PCR-RFLP. A streamlined genotyping protocol was also developed for CSN3. Results indicated that allele T was quite rare (0.06) at CSN1S1 for all ecotypes. Minor allele frequency was found for G (0.462) in CSN2 except for Ardhaoui Medenine ecotype who deviated from the average CSN2 allele frequency of the total population. Allele C showed minor allele frequency of 0.384 in CSN3. Among the Tunisian population, GAT (0.343) was the most represented haplotype in all ecotypes except for Ardhaoui Medenine, where GGC (0.322) was the most frequent one. Significant differences in heterozygosity and local inbreeding were observed across the Tunisian, Sudanese, and Nigerian populations, although the global fixation index indicated that only 2.2% of the genetic variance is related to ecotype differences. Instead, phylogenetic analysis revealed a closer link between the Tunisian and Sudanese populations through a clade subdivision with 3 main branches among the ecotypes. This study represents the first attempt to understand casein gene variability in Tunisian camels; with further study, milk traits and genetic differentiation among populations can be associated with the history of camel domestication.

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.

Meta-analysis of spineless cactus feeding to meat lambs: performance and development of mathematical models to predict dry matter intake and average daily gain.

Spineless cactus is a useful feed for various animal species in arid and semiarid regions due to its adaptability to dry and harsh soil, high efficiency of water use and carbohydrates storage. This meta-analysis was carried out to assess the effect of spineless cactus on animal performance, and develop and evaluate equations to predict dry matter intake (DMI) and average daily gain (ADG) in meat lambs. Equations for predicting DMI and ADG as a function of animal and diet characteristics were developed using data from eight experiments. The dataset was comprised of 40 treatment means from 289 meat lambs, in which cactus was included from 0 to 75% of the diet dry matter (DM). Accuracy and precision were evaluated by cross-validation using the mean square error of prediction (MSEP), which was decomposed into mean bias, systematic bias and random error; concordance correlation coefficient, which was decomposed into accuracy (Cb) and precision (ρ); and coefficient of determination (R2). In addition, the data set was used to evaluate the predicting accuracy and precision of the main lamb feeding systems (Agricultural and Food Research Council, Small Ruminant Nutritional System, National Research Council and Institut National de la Recherche Agronomique) and also two Brazilian studies. The DMI, CP intake (CPI), metabolizable energy (ME) intake and ADG increased when cactus was included up to 499 g/kg DM (P<0.001). In contrast, animals fed high levels of cactus (>500 g/kg DM) had a decreased DMI, CPI and NDF intake, but increased feed efficiency (P<0.001) and similar ADG compared with those without cactus addition. The DMI was positively correlated with initial BW, final BW, concentrate and ADG, while it was negatively correlated with cactus inclusion and ME of the diet. On other hand, ADG was positively correlated with DMI, initial and mean BW and concentrate, and it was negatively correlated with cactus inclusion. The two developed equations had high accuracy (Cb of 0.95 for DMI and 0.94 for ADG) and the random error of MSEP was 99% for both equations. The precision of both equations was moderate, with R2 values of 0.53 and 0.50 and ρ values of 0.73 and 0.71 for DMI and ADG, respectively. In conclusion, the developed equation to predict DMI had moderate precision and high accuracy, nonetheless, it was more efficient than those reported in the literature. The proposed equations can be a useful alternative to estimate intake and performance of lambs fed cactus.

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.

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.

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.

Genomewide analysis of bull sperm quality and fertility traits.

Because the priority of AI industry is to identify subfertile bulls, a predictive model that allowed for the prediction of 91% bulls of low fertility was implemented based on seminological (motility) parameters and DNA status assessed both as DNA fragmentation index (DFI) and by TUNEL assay using sperm of 105 Holstein-Friesian bulls (four batches per bull) selected based on in vivo estimated relative conception rates (ERCR). Thereafter, sperm quality and male fertility traits of bulls were explored by GWAS using a high-density (777K) Illumina chip. After data editing, 85 bulls and 591,988 SNPs were retained for GWAS. Of 12 SNPs with false discovery rate <0.2, four SNPs located on BTA28 and BTA18 were significantly associated (LD-adjusted Bonferroni <0.05) with the non-compensatory sperm parameters DFI and TUNEL. Other SNPs of interest for potential association with TUNEL were found on BTA3, in the same chromosome where associations with non-compensatory in vivo bull fertility were already reported. Further suggestive SNPs for sperm membrane integrity were located on BTA28, the chromosome where QTL studies previously reported associations with sperm quality traits. Suggestive SNPs for ERCR were found on BTA18 in the vicinity of a site already associated with in vivo bull fertility. Additional SNPs associated with ERCR and sperm kinetic parameters were also identified. In contrast to other, but very few GWAS on fertility traits in bovine spermatozoa, which reported significant SNPs located on BTX, we have not identified SNPs of interest in this sexual chromosome.

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.

Grape seed and linseed, alone and in combination, enhance unsaturated fatty acids in the milk of Sarda dairy sheep.

This study evaluated the effect of dietary inclusion of grape seed and linseed, alone or in combination, on sheep milk fatty acids (FA) profile using 24 Sarda dairy ewes allocated to 4 isoproductive groups. Groups were randomly assigned to 4 dietary treatments consisting of a control diet (CON), a diet including 300 g/d per animal of grape seed (GS), a diet including 220 g/d per animal of extruded linseed (LIN), and a diet including a mix of 300 g/d per animal of grape seed and 220 g/d per animal of extruded linseed (MIX). The study lasted 10 wk, with a 2-wk adaptation period and an 8-wk experimental period. Milk FA composition was analyzed in milk samples collected in the last 4 wk of the trial. The milk concentration of saturated fatty acids (SFA) decreased and that of unsaturated, monounsaturated, and polyunsaturated fatty acids (UFA, MUFA, and PUFA, respectively) increased in GS, LIN, and MIX groups compared with CON. The MIX group showed the lowest values of SFA and the highest of UFA, MUFA, and PUFA. Milk from ewes fed linseed (LIN and MIX) showed an enrichment of vaccenic acid (VA), oleic acid (OA), α-linolenic acid (LNA), and cis-9,trans-11 conjugated linoleic acid (CLA) compared with milk from the CON group. The GS group showed a greater content of milk oleic acid (OA) and linoleic acid (LA) and tended to show a greater content of VA and cis-9,trans-11 CLA than the CON group. The inclusion of grape seed and linseed, alone and in combination, decreased the milk concentration of de novo synthesized FA C10:0, C12:0, and C14:0, with the MIX group showing the lowest values. In conclusion, grape seed and linseed could be useful to increase the concentration of FA with potential health benefits, especially when these ingredients are included in combination in the diet.

Genome-wide association analysis in Italian Simmental cows for lactation curve traits using a low-density (7K) SNP panel.

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.

Dissection of genomic correlation matrices of US Holsteins using multivariate factor analysis.

The aim of this study was to compare correlation matrices between direct genomic predictions for 31 traits at the genomic and chromosomal levels in US Holstein bulls. Multivariate factor analysis carried out at the genome level identified seven factors associated with conformation, longevity, yield, feet and legs, fat and protein content traits. Some differences were found at the chromosome level; variations in covariance structure on BTA 6, 14, 18 and 20 were interpreted as evidence of segregating QTL for different groups of traits. For example, milk yield and composition tended to join in a single factor on BTA 14, which is known to harbour the DGAT1 locus that affects these traits. Another example was on BTA 18, where a factor strongly correlated with sire calving ease and conformation traits was identified. It is known that in US Holstein, there is a segregating QTL on BTA18 influencing these traits. Moreover, a possible candidate gene for daughter pregnancy rate was suggested for BTA28. The methodology proposed in this study could be used to identify individual chromosomes, which have covariance structures that differ from the overall (whole genome) covariance structure. Such differences can be difficult to detect when a large number of traits are evaluated, and covariances may be affected by QTL that do not have large allele substitution effects.

Multiple-breed genomic evaluation by principal component analysis in small size populations.

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.

Use of the canonical discriminant analysis to select SNP markers for bovine breed assignment and traceability purposes.

Several market research studies have shown that consumers are primarily concerned with the provenance of the food they eat. Among the available identification methods, only DNA-based techniques appear able to completely prevent frauds. In this study, a new method to discriminate among different bovine breeds and assign new individuals to groups was developed. Bulls of three cattle breeds farmed in Italy - Holstein, Brown, and Simmental - were genotyped using the 50K SNP Illumina BeadChip. Multivariate canonical discriminant analysis was used to discriminate among breeds, and discriminant analysis (DA) was used to assign new observations. This method was able to completely identify the three groups at chromosome level. Moreover, a genome-wide analysis developed using 340 linearly independent SNPs yielded a significant separation among groups. Using the reduced set of markers, the DA was able to assign 30 independent individuals to the proper breed. Finally, a set of 48 high discriminant SNPs was selected and used to develop a new run of the analysis. Again, the procedure was able to significantly identify the three breeds and to correctly assign new observations. These results suggest that an assay with the selected 48 SNP could be used to routinely track monobreed products.

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.

Use of principal component approach to predict direct genomic breeding values for beef traits in Italian Simmental cattle.

In the current study, principal component (PC) analysis was used to reduce the number of predictors in the estimation of direct genomic breeding values (DGV) for meat traits in a sample of 479 Italian Simmental bulls. Single nucleotide polymorphism marker 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 population. 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 population 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. Accuracy was calculated as correlation between DGV and polygenic EBV in the validation bulls. Muscularity, feet and legs, and the beef index showed the greatest accuracies; 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 substantial 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 independent variables, DGV accuracies resulted similar to those obtained by using the whole set of SNP markers. Accuracies of direct genomic values found in the present 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 indexes in the preselection of performance test candidates for beef traits. Moreover, the relevant reduction of variable space might allow genomic selection implementation also in small populations.

Prediction of genomic breeding values for dairy traits in Italian Brown and Simmental bulls using a principal component approach.

The large number of markers available compared 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_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.