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.

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.

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.

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.

Different physical forms of one diet fed to growing pigs induce morphological changes in mandubular glands and local leptin (Ob) production and receptor (ObR) expression.

The physical form of the diet plays an important role for morphological adaptations of organs in the gastrointestinal tract. It was hypothesized that different physical forms of one diet could exert extra-enteric effects, under local and systemic neuroendocrine regulation. Gross morphology, fresh mass and cytoarchitecture of mandibular glands (MG) were studied in growing pigs fed with one diet processed under four different physical forms. Four dietary treatments were offered for 4 weeks to 32 growing pigs (initial BW: 8.30 ± 0.83 kg) allotted into 4 experimental groups: FP, finely ground pellet (dMean, 0.46 mm); CM, coarsely ground meal (dMean, 0.88 mm); CP, coarsely ground pellet (dMean, 0.84); CE, coarsely ground extruded (dMean, 0.66). Conventional and immuonohistochemical techniques were used to immunolocalize, in particular, leptin (Ob) and its receptor (ObR). A significant effect was observed on the relative mass of the MG, depending on the diet (p < 0.03) and on the BW (p < 0.04), with no interactions (diet*BW). The immunohistochemical reactions for Ob and ObR showed a marked positivity in the MG from the group fed with the CM diet, displaying Ob-positive acinar cells and ObR-positive cells in the striated ducts, together with endocrine-like cells. The intensity of chromogenic reactions positively testing to ObR was used to evaluate the cytoarchitecture of the MG and its possible correlations. Pearson's correlation coefficient resulted to positively link (p < 0.0001) the ObR expression with the absolute mass of MG in the 61.1% of pigs. The physical form of the diet is related to extra-enteral effects, inducing changes in gross and microscopic morphology of the MG in the growing pig. The local production of Ob and the expression of the respective ObR in the striated duct cells shed a new light on the mitogenic activity of Ob in extra-enteral organs, like the MG, in relation to the physical form of 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.

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.

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.

The mathematical modeling of the lactation curve for dairy traits of the donkey (Equus asinus).

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.

Associations of acetyl-coenzyme A carboxylase α, stearoyl-coenzyme A desaturase, and lipoprotein lipase genes with dairy traits in Alpine goats.

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 α (ACACA), 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 0.50L 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.

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.

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.

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.

Exploring the relationship between bacterial genera and lipid metabolism in bovine rumen.

The rumen is characterised by a complex microbial ecosystem, which is particularly active in lipid metabolism. Several studies demonstrated a role of diet and breed on bacterial community profile, with the effect on metabolic pathways. Despite the knowledge achieved on metabolism and the bacterial profile, little is known about the relationship between individual bacteria and metabolic pathways. Therefore, a multivariate approach was used to search for possible relationships between bacteria and products of several pathways. The correlation between rumen bacterial community composition and rumen lipid metabolism was assessed in 40 beef steers (20 Maremmana and 20 Aubrac) reared with the same system and fed the same diet. A canonical discriminant analysis combined with a canonical correlation analysis (CCA) was performed to explore this correlation. The variables showing a Pearson correlation higher than 0.6 as absolute value and significant were retained for CCA considering the relationship of bacterial composition with several metabolic pathways. The results indicated that some bacterial genera could have significant impacts on the presence of several fatty acids. However, the relationship between genera and fatty acid changes according to the breed, demonstrating that the metabolic pathways change according to the host genetic background, related to breed evolution, although there is also an intra-breed genetic background which should not be ignored. In Maremmana, Succiniclasticum and Rikenellaceae_RC9_gut_group showed a high positive correlation with dimethylacetals (DMAs) DMAC13:0, DMAC14:0, DMAC14:0iso, DMAC15:0, DMAC15:0iso, and DMAC18:0. Prevotellaceae_UCG-003 correlates with C18:3c9c12c15 and C18:1t11, while Fibrobacter and Succiniclasticum correlate with C18:2c9t11 and Lachnospiraceae_NK3A20_group correlates with C18:1c12. Prevotellaceae_UCG-003, Ruminococcaceae_UCG-010, and Oribacterium showed a positive correlation with C13:0iso, and C17:0. Conversely, in Aubrac, Treponema_2 and Rikenellaceae_RC9_gut_group correlated with DMAC14:0iso, DMAC16:0iso, DMAC17:0iso, while Ruminococcaceae_UCG-010, Christensenellaceae_R-7_group and Ruminococcaceae_NK4A214_group correlated with DMAC18:1t11, DMAC14:0, DMAC18:1c12. Acetitomaculum correlated with C18:2c9c12, C18:1c12, C18:1c13, C18:1t12 and Lachnospiraceae_NK3A20_group with C18:1t6-8 and C18:1t9. Saccharofermentas, Ruminococcaceae_UCG-010 and Rikenellaceae_RC9_gut_group correlated with C18:2c9t11 while, Prevotellaceae_UCG-001 and Ruminococcus_1 correlated with C14:0iso, C15:0, C15:0iso, C17:0. Saccharofermentans, Rikenellaceae_RC9_gut_group, Ruminococcaceae_NK4A214_group, and Ruminococcaceae_UCG-010 correlated with C13:1c12 and C16:0iso. These results lead to hypothesise a possible association between several metabolic pathways and one or a few bacterial genera. If these associations are confirmed by further investigations that verify the causality of a bacterial genus with a particular metabolic process, it will be possible to deepen the knowledge on the activity of the rumen population in lipid metabolism. This approach appears to be a promising tool for uncovering the correlation between bacterial genera and products of rumen lipid metabolism.

The impact of the rank of marker variance-covariance matrix in principal component evaluation for genomic selection applications.

In genomic selection (GS) programmes, direct genomic values (DGV) are evaluated using information provided by high-density SNP chip. Being DGV accuracy strictly dependent on SNP density, it is likely that an increase in the number of markers per chip will result in severe computational consequences. Aim of present work was to test the effectiveness of principal component analysis (PCA) carried out by chromosome in reducing the marker dimensionality for GS purposes. A simulated data set of 5700 individuals with an equal number of SNP distributed over six chromosomes was used. PCs were extracted both genome-wide (ALL) and separately by chromosome (CHR) and used to predict DGVs. In the ALL scenario, the SNP variance-covariance matrix (S) was singular, positive semi-definite and contained null information which introduces 'spuriousness' in the derived results. On the contrary, the S matrix for each chromosome (CHR scenario) had a full rank. Obtained DGV accuracies were always better for CHR than ALL. Moreover, in the latter scenario, DGV accuracies became soon unsettled as the number of animals decreases, whereas in CHR, they remain stable till 900-1000 individuals. In real applications where a 54k SNP chip is used, the largest number of markers per chromosome is approximately 2500. Thus, a number of around 3000 genotyped animals could lead to reliable results when the original SNP variables are replaced by a reduced number of PCs.

Using eigenvalues as variance priors in the prediction of genomic breeding values by principal component analysis.

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.

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.

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.

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.