Genome-wide association study for the level of serum electrolytes in Italian Large White pigs.

Calcium, magnesium and phosphorus are essential electrolytes involved in a large number of biological processes. Imbalance of these minerals in blood may indicate clinically relevant conditions and are important in inferring acute or chronic pathologies in humans and animals. In this work, we carried out a genome-wide association study (GWAS) for the level of these three electrolytes in the serum of 843 performance-tested Italian Large White pigs. All pigs were genotyped with the Illumina PorcineSNP60 BeadChip, and GWAS was carried out using genome-wide efficient mixed-model association. For the level of Ca(2+) , eight single nucleotide polymorphisms (SNPs) were significant, considering a false discovery rate (FDR) < 0.05, and another eight were above the moderate association threshold (Pnominal value  < 5.00E-05). These SNPs are distributed in four porcine chromosomes (SSC): SSC8, SSC11, SSC12 and SSC13. In particular, a few putative different signals of association detected on SSC13 and one on SSC12 were in genes or close to genes involved in calcium metabolism (P2RY1, RAP2B, SLC9A9, C3orf58, TSC22D2, PLCH1 and CACNB1). Only one SNP (on SSC7) and six SNPs (on SSC2 and SSC7) showed moderate association with the level of magnesium and phosphorus respectively. The association signals for these two latter minerals might identify genes not known thus far for playing a role in their biological functions and regulations. In conclusion, our GWAS contributed to increased knowledge on the role that calcium, magnesium and phosphorus may play in the genetically determined physiological mechanisms affecting the natural variability of mineral levels in mammalian blood.

Twenty years of artificial directional selection have shaped the genome of the Italian Large White pig breed.

In this study, we investigated at the genome-wide level if 20 years of artificial directional selection based on boar genetic evaluation obtained with a classical BLUP animal model shaped the genome of the Italian Large White pig breed. The most influential boars of this breed (n = 192), born from 1992 (the beginning of the selection program of this breed) to 2012, with an estimated breeding value reliability of >0.85, were genotyped with the Illumina Porcine SNP60 BeadChip. After grouping the boars in eight classes according to their year of birth, filtered single nucleotide polymorphisms (SNPs) were used to evaluate the effects of time on genotype frequency changes using multinomial logistic regression models. Of these markers, 493 had a PBonferroni  < 0.10. However, there was an increasing number of SNPs with a decreasing level of allele frequency changes over time, representing a continuous profile across the genome. The largest proportion of the 493 SNPs was on porcine chromosome (SSC) 7, SSC2, SSC8 and SSC18 for a total of 204 haploblocks. Functional annotations of genomic regions, including the 493 shifted SNPs, reported a few Gene Ontology terms that might underly the biological processes that contributed to increase performances of the pigs over the 20 years of the selection program. The obtained results indicated that the genome of the Italian Large White pigs was shaped by a directional selection program derived by the application of methodologies assuming the infinitesimal model that captured a continuous trend of allele frequency changes in the boar population.

Deconstructing the pig sex metabolome: Targeted metabolomics in heavy pigs revealed sexual dimorphisms in plasma biomarkers and metabolic pathways.

Metabolomics has opened new possibilities to investigate metabolic differences among animals. In this study, we applied a targeted metabolomic approach to deconstruct the pig sex metabolome as defined by castrated males and entire gilts. Plasma from 545 performance-tested Italian Large White pigs (172 castrated males and 373 females) sampled at about 160 kg live weight were analyzed for 186 metabolites using the Biocrates AbsoluteIDQ p180 Kit. After filtering, 132 metabolites (20 AA, 11 biogenic amines, 1 hexose, 13 acylcarnitines, 11 sphingomyelins, 67 phosphatidylcholines, and 9 lysophosphatidylcholines) were retained for further analyses. The multivariate approach of the sparse partial least squares discriminant analysis was applied, together with a specifically designed statistical pipeline, that included a permutation test and a 10 cross-fold validation procedure that produced stability and effect size statistics for each metabolite. Using this approach, we identified 85 biomarkers (with metabolites from all analyzed chemical families) that contributed to the differences between the 2 groups of pigs ( < 0.05 at the stability statistic test). All acylcarnitines and almost all biogenic amines were higher in castrated males than in gilts. Metabolites involved in tryptophan catabolism had the largest differences (i.e., delta = 20% for serotonin) between castrated males (higher) and gilts (lower). The level of several AA (Ala, Arg, Gly, His, Lys, Ser, Thr, and Trp) was higher in gilts (delta was from approximately 1.0 to approximately 4.8%) whereas products of AA catabolism (taurine, 2-aminoadipic acid, and methionine sulfoxide) were higher in castrated males (delta was approximately 5.0-6.0%), suggesting a metabolic shift in castrated males toward energy storage and lipid production. Similar general patterns were observed for most sphingomyelins, phosphatidylcholines, and lysophosphatidylcholines. Metabolomic pathway analysis and pathway enrichment identified several differences between the 2 sexes. This metabolomic overview opened new clues on the biochemical mechanisms underlying sexual dimorphism that, on one hand, might explain differences in terms of economic traits between castrated male pigs and entire gilts and, on the other hand, could strengthen the pig as a model to define metabolic mechanisms related to fat deposition.

A retrospective analysis of allele frequency changes of major genes during 20 years of selection in the Italian Large White pig breed.

In this study, we investigated whether a selection programme based on boar genetic evaluation obtained with a classical BLUP animal model can change allele frequencies in a pig population. All Italian Large White boars born from 1992 to 2012 with estimated breeding value reliability >0.85 (n = 200) were selected among all boars of this breed. Boars were genotyped with markers in major genes (IGF2 intron3-g.3072G>A, MC4R p.D298N, VRTN PRE1 insertion, PRKAG3 p.I199V and FTO g.276T>G). Genotyping data were analysed grouping boars in eight classes according to their year of birth. To evaluate the influence of time on allele frequencies of the genotyped markers, multinomial logistic regression models were computed. Four of five polymorphic sites (IGF2, MC4R, VRTN and FTO) showed significant (p < 0.01) changes in allele frequencies over time due to a progressive and continuous increase of one allele (associated with higher lean meat content, higher average daily gain and favourable feed: gain ratio) and, consequently, decrease of the other one, following the directional selection of the selection programme of this pig breed. The retrospective analysis that was carried out in Italian Large White boars suggests that selection based on methodologies assuming the infinitesimal model is able to modify in a quite short period of time allele frequencies in major genes, increasing the frequency of alleles explaining a relevant (non-infinitesimal) fraction of the overall genetic variability for production traits.

Genomic selection in a pig population including information from slaughtered full sibs of boars within a sib-testing program.

Genomic selection is becoming a common practise in dairy cattle, but only few works have studied its introduction in pig selection programs. Results described for this species are highly dependent on the considered traits and the specific population structure. This paper aims to simulate the impact of genomic selection in a pig population with a training cohort of performance-tested and slaughtered full sibs. This population is selected for performance, carcass and meat quality traits by full-sib testing of boars. Data were simulated using a forward-in-time simulation process that modeled around 60K single nucleotide polymorphisms and several quantitative trait loci distributed across the 18 porcine autosomes. Data were edited to obtain, for each cycle, 200 sires mated with 800 dams to produce 800 litters of 4 piglets each, two males and two females (needed for the sib test), for a total of 3200 newborns. At each cycle, a subset of 200 litters were sib tested, and 60 boars and 160 sows were selected to replace the same number of culled male and female parents. Simulated selection of boars based on performance test data of their full sibs (one castrated brother and two sisters per boar in 200 litters) lasted for 15 cycles. Genotyping and phenotyping of the three tested sibs (training population) and genotyping of the candidate boars (prediction population) were assumed. Breeding values were calculated for traits with two heritability levels (h 2=0.40, carcass traits, and h 2=0.10, meat quality parameters) on simulated pedigrees, phenotypes and genotypes. Genomic breeding values, estimated by various models (GBLUP from raw phenotype or using breeding values and single-step models), were compared with the classical BLUP Animal Model predictions in terms of predictive ability. Results obtained for traits with moderate heritability (h 2=0.40), similar to the heritability of traits commonly measured within a sib-testing program, did not show any benefit from the introduction of genomic selection. None of the considered genomic models provided improvements in prediction ability of pigs with no recorded phenotype. However, a few advantages were found for traits with low heritability (h 2=0.10). These heritability levels are characteristic for meat quality traits recorded after slaughtering or for reproduction or health traits, typically recorded on field and not in performance stations. Other scenarios of data recording and genotyping should be evaluated before considering the implementation of genomic selection in a pig-selection scheme based on sib testing of boars.

Quantitative trait loci mapping for conjugated linoleic acid, vaccenic acid and ∆(9) -desaturase in Italian Brown Swiss dairy cattle using selective DNA pooling.

A selective DNA pooling approach was applied to identify QTL for conjugated linoleic acid (CLA), vaccenic acid (VA) and Δ(9) -desaturase (D9D) milk content in Italian Brown Swiss dairy cattle. Milk samples from 60 animals with higher values (after correction for environmental factors) and 60 animals with lower values for each of these traits from each of five half-sib families were pooled separately. The pools were genotyped using the Illumina BovineSNP50 BeadChip. Sire allele frequencies were compared between high and low tails at the sire and marker level for SNPs for which the sires were heterozygous. An r procedure was implemented to perform data analysis in a selective DNA pooling design. A correction for multiple tests was applied using the proportion of false positives among all test results. BTA 19 showed the largest number of markers in association with CLA. Associations between SNPs and the VA and Δ(9) -desaturase traits were found on several chromosomes. A bioinformatics survey identified genes with an important role in pathways for milk fat and fatty acids metabolism within 1 Mb of SNP markers associated with fatty acids contents.

Bimodality and the genetics of milk flow traits in the Italian Holstein-Friesian breed.

The overall goal of this study was to investigate milk flow traits in Italian Holstein-Friesian cows and, in particular, the bimodality of milk flow, defined as delayed milk ejection at the start of milking. Using a milkometer, 2,886 records were collected from 133 herds in northern Italy from 2001 to 2007. All records included 5 time-period measurements for milk flow, somatic cell score (SCS), milk yield, 8 udder type traits, and the presence or absence of bimodality in milk flow. Genetic parameters were estimated using linear animal models for continuous traits such as milk flow, udder type, SCS, and milk production, whereas bimodality was analyzed as a categorical trait. With the exception of decreasing time (which had a very small heritability value of 0.06), heritability values for milk flow traits were moderate, ranging from 0.10 (ascending time) to 0.41 (maximum milk flow). In addition, moderate to high genetic correlations were estimated between total milking time and other time measures (from 0.78 to 0.87), and among time flow traits (from 0.62 to 0.91). The decreasing time was the trait most genetically correlated with udder type traits, with correlation values of 0.92 with rear udder height, 0.85 with rear udder width, and 0.73 with teat placement. Large udders with strong attachments were also associated with greater milk production. Heritability estimated for bimodality was 0.43, and its genetic correlation with milk flow traits and SCS indicated a sizable genetic component underlying this trait. Bimodality was negatively associated with milk production; shorter milking times and greater peak milk levels were genetically correlated with more frequent bimodal flows, indicating that faster milk release would result in an increase in bimodal patterns. The negative genetic correlation of bimodality with SCS (-0.30) and the genetic correlation between milk flow traits and SCS suggest that the relationship between milkability and SCS is probably nonlinear and that intermediate flow rates are optimal with respect to mastitis susceptibility. Quicker milk flow over a shorter period would increase the frequency of bimodal curves in milking, whereas the correlation between bimodality and both ascending and descending time was less clear.

Genetic evaluations for measures of the milk-flow curve in the Italian Brown Swiss population.

The objective of this study was to estimate heritabilities and genetic correlations between milk-release parameters, somatic cell score, milk yield, and udder functional traits in the Italian Brown Swiss population. Data were available from 37,511 cows over a span of 12 yr (1997-2008) from 1,592 herds. Milking flows were recorded for each individual once during lactation. Three different analyses were performed to estimate variance components for all the traits of interest. The first analysis included single control data milk yield, somatic cell score, maximum milk flow, average milk flow, time of plateau, decreasing time, and total milking time, whereas the second analysis included milk-release parameters as well as total udder score, udder depth, and 305-d milk yield and somatic cell score as dependent variables. The third analysis included total milking time, 305-d milk yield and somatic cell score, total udder score, udder depth, and ratios of maximum milk flow over total milking time (R1), time of plateau (R2), and decreasing time (R3) to estimate the relationship between the shape of the milk-release curves and important milking traits. Results from the first and second analysis found similar heritabilities for milkability traits ranging from 0.05 to 0.41 with genetic correlations between production traits and flow traits ranging from low to moderate values. Positive genetic correlations were found among production, somatic cell score, and milkability traits. The third analysis showed that R1 had the greatest heritability of the ratio traits (0.37) with large genetic correlations with R2 and R3, a low correlation with 305-d somatic cell score, and no correlation with 305-d milk yield. Estimated responses to selection over 5 generations were also calculated using different indexes, which included either flow or ratio traits. The results of this study show that it is possible to use information collected through portable flowmeters to improve milkability traits. Using a set of variables or traits to describe the overall release of milk can be an advantageous selection strategy to decrease management costs while maintaining milk production.

Genetic correlation patterns between somatic cell score and protein yield in the Italian Holstein-Friesian population.

Genetic parameters for somatic cell score (SCS) in the Italian Holstein-Friesian population were estimated addressing the pattern of genetic correlation with protein yield in different parities (first, second, and third) and on different days in milk within each parity. Three approaches for parameter estimation were applied using random samples of herds from the national database of the Italian Holstein Association. Genetic correlations for lactation measures (305-d protein yield and lactation SCS) were positive in the first parity (0.31) and close to zero in the second (0.01) and third (0.09) parities. These results indicated that larger values of SCS were genetically associated with increased production. The second and third sets of estimates were based on random regression test-day models, modeling the shape of lactation curve with the Wilmink function and fourth-order Legendre polynomials, respectively. Genetic correlations from both random regression models showed a specific pattern associated with days in milk within and across parities. Estimates varied from positive to negative in the first and second parity, and from null to negative in the third parity. Patterns were similar for both random regression models. The average overall correlation between SCS and protein yield was zero or slightly positive in the first lactation and ranged from zero to negative in later lactations. Correlation estimates differed by parity and stage of lactation. They also demonstrated the dubiousness of applying a single genetic correlation measure between SCS and protein in setting selection strategies. Differences in magnitude and the sign of genetic correlations between SCS and yields across and within parities should be accounted for in selection schemes.

Short communication: quantitative trait loci affecting the somatic cell score on chromosomes 4 and 26 in Italian Holstein cattle.

This work aimed to confirm previously reported quantitative trait loci (QTL) affecting the somatic cell score (SCS) in dairy cattle on Bos taurus autosomes (BTA) 4 and 26. A granddaughter design with selective genotyping was implemented that included half-sib families from 12 male lines of Italian Holstein cattle. The animals were genotyped for 5 microsatellite markers each on regions of BTA 4 (average marker spacing 9.42 cM) and BTA 26 (average marker spacing 5.26 cM), previously reported by other authors as carrying QTL for somatic cell count. Quantitative trait loci analyses were performed using interval mapping by regressing sire breeding values for SCS onto genotype probabilities at 1-cM intervals along the 2 chromosome regions. Breeding values for SCS were estimated for the whole population using a test-day repeatability animal model. Results were not significant on a chromosome basis, but a possible QTL was found at BM4505 on BTA 26, confirming this region for further studies of QTL affecting SCS in the Italian Holstein population.

Impact of area and sire by herd interaction on heritability estimates for somatic cell count in Italian Holstein Friesian cows.

The aim of the paper was to estimate variance components for somatic cell scores for Italian Holsteins using data from three different areas of the country. A total of 2,202,804 first-parity test-day records, collected from 1990 to 1997 in three areas of Italy (Mantova, Milano, and Parmigiano cheese area), were available for study. The areas differ in herd size, feeding systems and especially in milk use. A minimum standard of quality is also required by some specific methods of cheese production, as for example from the Parmigiano Reggiano cheese chain. These reasons, taken together, affect the attention given to the quality of milk production in herds, and, therefore, to the sanitation levels. A pedigree file was extracted from the national database of Holstein Friesian breed. For computational reasons, eight samples of the data were extracted per area. Variance components were estimated by sample using two different test-day repeatability models. The first model included fixed effects of herd-test date, days in milk (30-d intervals) and calving month, and random effects of permanent environment, additive genetic and residual error. Estimated heritabilities in the first model ranged from 0.06 to 0.09 and repeatabilities from 0.36 to 0.45. Only small differences were detected among areas. In the second model, a random sire x herd interaction effect was added. Including the sire x herd effect resulted in heritability estimates ranging between 0.05 and 0.08 and repeatabilities from 0.35 to 0.45. The analysis revealed that only a small fraction of the total variance (0.35 to 1.5%) could be explained by sire x herd interaction effect. Based on this research, it appears that parameter estimates for somatic cell count do not differ by region, and inclusion of a sire x herd interaction effect is unnecessary.