Giustino Gaspa

Analysis of runs of homozygosity and their relationship with inbreeding in five cattle breeds farmed in Italy

Increased inbreeding is an inevitable consequence of selection in livestock populations. The analysis of high-density single nucleotide polymorphisms (SNPs) facilitates the identification of long and uninterrupted runs of homozygosity (ROH) that can be used to identify chromosomal regions that are identical by descent. In this work, the distribution of ROH of different lengths in five Italian cattle breeds is described. A total of 4095 bulls from five cattle breeds (2093 Italian Holstein, 749 Italian Brown, 364 Piedmontese, 410 Marchigiana and 479 Italian Simmental) were genotyped at 54K SNP loci. ROH were identified and used to estimate molecular inbreeding coefficients (FROH ), which were compared with inbreeding coefficients estimated from pedigree information (FPED ) and using the genomic relationship matrix (FGRM ). The average number of ROH per animal ranged from 54 ± 7.2 in Piedmontese to 94.6 ± 11.6 in Italian Brown. The highest number of short ROH (related to ancient consanguinity) was found in Piedmontese, followed by Simmental. The Italian Brown and Holstein had a higher proportion of longer ROH distributed across the whole genome, revealing recent inbreeding. The FPED were moderately correlated with FROH > 1 Mb (0.662, 0.700 and 0.669 in Italian Brown, Italian Holstein and Italian Simmental respectively) but poorly correlated with FGRM (0.134, 0.128 and 0.448 for Italian Brown, Italian Holstein and Italian Simmental respectively). The inclusion of ROH > 8 Mb in the inbreeding calculation improved the correlation of FROH with FPED and FGRM . ROH are a direct measure of autozygosity at the DNA level and can overcome approximations and errors resulting from incomplete pedigree data. In populations with high linkage disequilibrium (LD) and recent inbreeding (e.g. Italian Holstein and Italian Brown), a medium-density marker panel, such as the one used here, may provide a good estimate of inbreeding. However, in populations with low LD and ancient inbreeding, marker density would have to be increased to identify short ROH that are identical by descent more precisely.

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.

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.

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.

Pre-selection of most significant SNPS for the estimation of genomic breeding values

The availability of a large amount of SNP markers throughout the genome of different livestock species offers the opportunity to estimate genomic breeding values (GEBVs). However, the estimation of many effects in a data set of limited size represent a severe statistical problem. A pre-selection of SNPS based on single regression may provide a reasonable compromise between accuracy of results, number of independent variables to be considered and computing requirements.A total of 595 and 618 SNPS were pre-selected using a simple linear regression for each SNP, based on phenotypes or polygenic EBVs, respectively, with an average distance of 9–10 cM between them. Chromosome four had the largest frequency of selected SNPS. Average correlations between GEBVs and TBVs were about 0.82 and 0.73 for the TRAINING generations when phenotypes or polygenic EBVs were considered as dependent variable, whereas they tend to decrease to 0.66 and 0.54 for the PREDICTION generations. The pre-selection of SNPs using the phenotypes as dependent variable together with a BLUP estimation of marker genotype effects using a variance contribution of each marker equal to σ2a/nsnps resulted in a remarkable accuracy of GEBV estimation (0.77) in the PREDICTION generations.

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.

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.

Detection of selection signatures in Piemontese and Marchigiana cattle, two breeds with similar production aptitudes but different selection histories

BackgroundDomestication and selection are processes that alter the pattern of within- and between-population genetic variability. They can be investigated at the genomic level by tracing the so-called selection signatures. Recently, sequence polymorphisms at the genome-wide level have been investigated in a wide range of animals. A common approach to detect selection signatures is to compare breeds that have been selected for different breeding goals (i.e. dairy and beef cattle). However, genetic variations in different breeds with similar production aptitudes and similar phenotypes can be related to differences in their selection history.MethodsIn this study, we investigated selection signatures between two Italian beef cattle breeds, Piemontese and Marchigiana, using genotyping data that was obtained with the Illumina BovineSNP50 BeadChip. The comparison was based on the fixation index (Fst), combined with a locally weighted scatterplot smoothing (LOWESS) regression and a control chart approach. In addition, analyses of Fst were carried out to confirm candidate genes. In particular, data were processed using the varLD method, which compares the regional variation of linkage disequilibrium between populations.ResultsGenome scans confirmed the presence of selective sweeps in the genomic regions that harbour candidate genes that are known to affect productive traits in cattle such as DGAT1, ABCG2, CAPN3, MSTN and FTO. In addition, several new putative candidate genes (for example ALAS1, ABCB8, ACADS and SOD1) were detected.ConclusionsThis study provided evidence on the different selection histories of two cattle breeds and the usefulness of genomic scans to detect selective sweeps even in cattle breeds that are bred for similar production aptitudes.

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.

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.