Genomic Prediction and Genome-Wide Association Study for Boar Taint Compounds

With a perspective future ban on surgical castration in Europe, selecting pigs with reduced ability to accumulate boar taint (BT) compounds (androstenone, indole, skatole) in their tissues seems a promising strategy. BT compound concentrations were quantified in the adipose tissue of 1075 boars genotyped at 29,844 SNPs. Traditional and SNP-based breeding values were estimated using pedigree-based BLUP (PBLUP) and genomic BLUP (GBLUP), respectively. Heritabilities for BT compounds were moderate (0.30-0.52). The accuracies of GBLUP and PBLUP were significantly different for androstenone (0.58 and 0.36, respectively), but comparable for indole and skatole (~0.43 and ~0.47, respectively). Several SNP windows, each explaining a small percentage of the variance of BT compound concentrations, were identified in a genome-wide association study (GWAS). A total of 18 candidate genes previously associated with BT (MX1), reproduction traits (TCF21, NME5, PTGFR, KCNQ1, UMODL1), and fat metabolism (CTSD, SYT8, TNNI2, CD81, EGR1, GIPC2, MIGA1, NEGR1, CCSER1, MTMR2, LPL, ERFE) were identified in the post-GWAS analysis. The large number of genes related to fat metabolism might be explained by the relationship between sexual steroid levels and fat deposition and be partially ascribed to the pig line investigated, which is selected for ham quality and not for lean growth.

Genetic Correlations between Boar Taint Compound Concentrations in Fat of Purebred Boars and Production and Ham Quality Traits in Crossbred Heavy Pigs

Selecting pigs with reduced ability to accumulate boar taint (BT) compounds in their tissues is an alternative to male surgical castration. As the majority of slaughter pigs are crossbred, before selecting against BT in purebreds, it is essential to consider possible impacts on commercial traits in crossbreds. This study estimated the genetic correlations between BT compound levels measured in 1115 purebred pigs and carcass and ham quality traits collected in 26,577 crossbred Italian heavy pigs. Genetic correlations were estimated in bivariate Bayesian analyses including one BT trait and one production or ham quality trait at a time. Heritability of androstenone, skatole, and indole was 0.41, 0.49, and 0.37, respectively. A moderate negative correlation between skatole and carcass yield (-0.40), and between all BT compounds and backfat (from -0.26 to -0.55) was observed. Conversely, positive correlations (from 0.11 to 0.54) were found between skatole and ham fat thickness traits. Correlations between BT compounds and iodine number ranged from -0.07 (for androstenone) to -0.64 (for skatole), whereas those with PUFA ranged from -0.13 (for indole) to -0.33 (for skatole). Hence, reducing BT could decrease ham fat thickness and increase unsaturated fatty acids, with potential negative impacts on product quality.

Concentrations of Boar Taint Compounds Are Weakly Associated with Sexual Behavior of Young Boars

Strategies to control boar taint (BT) in meat relies on the reduction of skatole, indole, and androstenone concentration. This might have unfavorable effects on the libido of breeding boars. The association between BT compound concentration in backfat and libido was investigated in 391 commercial breeding boars. Six sexual behavior traits (SBT; sexual arousal, salivation, mounting performance, interest in the dummy sow, penis unsheathing, and overall libido score) were scored during the training of the boars with the dummy sow. Variation in SBT was analyzed by proportional-odds cumulative logistic models. Overall, indole, skatole, and androstenone concentrations were weakly associated with libido. Farm of origin, age at training or body weight, and BT compound levels were poor predictors of boar performance (the area under the ROC curve ranged from 0.60 to 0.69). This indicates that BT compound concentrations were weakly associated with libido, even though the probability of observing good SBT scores increased with high levels of androstenone, intermediate or low levels of skatole, and intermediate to high levels of indole. Hence, practices aiming at reducing androstenone, and controlling the concentrations of skatole and indole to intermediate levels are not expected to impair the libido of young boars.

The Implications of Changing Age and Weight at Slaughter of Heavy Pigs on Carcass and Green Ham Quality Traits

Simple Summary

Conventional rearing systems for heavy pigs intended for Italian dry-cured ham production require pigs to be slaughtered at 160 ± 16 kg and a minimum age of 9 months. With the current animal genetic trends providing progressively leaner animals, the conventional rearing system fails to provide pigs with optimal characteristics for the dry-cured ham industry. In this research, new combinations of age and weight at slaughter were explored, using different feeding conditions, as possible alternative rearing strategies for heavy pigs. Such alternative rearing strategies aimed to manipulate the growth rate of pigs, first allowing them to reach 160 ± 16 kg slaughter weight at a younger age; second, allowing pigs to maximize their slaughter weight at 9 months of age; and third, inducing slow growth in the pigs to reach the 160 ± 16 kg body weight at an older age. The first two strategies were the most promising alternatives as they improved the rate of gain, feed efficiency, and ham adiposity of the pigs. While the first strategy was the most economically convenient, the second produced the hams with the highest quality.

Abstract

Italian dry-cured ham production requires pigs to be slaughtered at 160 ± 16 kg at 9 months of age (control, C). The study explored three alternatives, based on different feeding conditions: (1) allowing pigs to express their growth potential by letting them reach 160 ± 16 kg slaughter weight (SW) at younger slaughter age (SA) (younger Age, YA); (2) allowing pigs to express their growth potential by maximizing their SW at 9 months SA (greater weight, GW); (3) increasing the SA required to reach 160 ± 16 kg SW (older age, OA). Pigs (336 C21 Goland, 95 kg initial body weight) were slaughtered on average at 257, 230, 257, and 273 d SA and 172.7, 172.3, 192.9, and 169.3 SW kg for the four treatments, respectively. C pigs had an average daily gain (ADG) of 715 g/d and feed efficiency (FE) of 0.265 (gain to feed). Compared to C, YA pigs had higher ADG (+32%), FE (+7.5%), and better ham adiposity; GW pigs had higher carcass weight (+12%), ADG (+25%), trimmed ham weight (+10.9%), and better ham adiposity. OA treatment affected ADG (−16.4%), FE (−16.6%), and trimmed ham weight (−3.6%). YA and GW could be promising alternatives to C as they improved FE and ham quality traits.

Estimates of non-genetic effects and genetic parameters for semen traits in Piemontese bulls

Male reproductive performances are often ignored in cattle breeding programmes, although semen traits might be used to improve bull breeding soundness. Effects of genetic and environmental factors on semen production and quality traits were estimated in 693 Piemontese bulls with the aim of providing the first estimates of genetic parameters for semen traits for this breed. Volume and concentrations of individual ejaculates (up to three per each test-day), and volume, concentration, total number of spermatozoa and post-thawing progressive motility of within test-day pooled semen were available for 19 060 ejaculates. Bulls reached the maximum amount of daily semen production after their third year of age, with concentration rapidly increasing until 23 months of age, and then slowly decreasing. Semen volume was at its highest when collection days were at least 15 days apart, whereas the maximum concentration was reached when the interval was 6 days. Heritability estimates were generally moderate (0.14-0.26), and low for progressive motility (0.08). Estimates of genetic correlation among the volumes of the individual ejaculates were high and positive (≥0.79), as were the genetic correlations among their concentrations (≥0.46). Genetic correlations among volume and concentration traits varied from -0.47 (with a 95% high posterior density interval ranging from -0.65 to -0.23) to -0.32 (with a 95% high posterior density interval ranging from -0.55 to -0.09). Progressive motility was unrelated with the other traits, but moderately positively correlated with volumes of the second and third ejaculates. The magnitude of heritabilities showed that selection for semen traits is possible. However, the unfavourable relationship between volume and concentration must be taken into account if a future selection programme is to be established.

On-site visible-near IR prediction of iodine number and fatty acid composition of subcutaneous fat of raw hams as phenotypes for a heavy pig breeding program

The quality of subcutaneous fat of raw hams is a trait of interest in selective breeding programs for pig lines used in dry-cured ham production, and rapid, non-invasive methods for its assessment are available. However, the efficacy of such methods to provide indicator traits for breeding programs needs to be proven. The study investigated the accuracy of on-site visible-near IR spectroscopy predictions of iodine number and fatty acid (FA) composition of raw ham subcutaneous fat, and it evaluated their effectiveness as indicator traits of ham fat quality in a pig breeding program. Prediction equations were developed using visible-near IR spectra acquired at the slaughterhouse from five sites in subcutaneous fat of raw hams of 1025 crossbred pigs. Pigs were raised, under standardized rearing and feeding conditions, in the sib-testing program of the Goland C21 boar line and slaughtered at nine months of age and average body weight of 166 ± 15 kg. Accuracy was generally relatively poor, but R² in external validation was >0.7 for iodine number and concentration of C18:2n-6, polyunsaturated FAs and omega-6 FAs. To assess the effectiveness of the on-site predictions as indicator traits in a breeding program, (co)variance components of the measured traits (OBS) and of their predictions using in-lab (in-lab-PR) or on-site (on-site-PR) spectrometers were estimated. Available records for OBS were 6814 and 2048, for iodine number and FA composition, respectively. Predictions using in-lab were available for pigs slaughtered between 2006 and 2014, for a total of 10 153 records. Predictions using on-site were obtained from spectra collected since 2011, for a total of 10 296 records. The estimated heritabilities for the investigated traits ranged from 0.34 to 0.50 and were greater for on-site-PR than for OBS. Genetic correlations between OBS and in-lab-PR were very close to 1.00 for all the investigated traits, whereas those between OBS and on-site-PRED ranged from 0.86 to 0.94. On-site visible-IR predictions are accurate enough to support the use of this technique for large-scale phenotyping of raw ham fat quality, even when dealing with animals of a single genetic line raised in standardized conditions, and may be implemented as indicator traits in breeding programs.

Classifying the fertility of dairy cows using milk mid-infrared spectroscopy

The objective of this study was to investigate the potential of milk mid-infrared (MIR) spectroscopy, MIR-derived traits including milk composition, milk fatty acids, and blood metabolic profiles (fatty acids, β-hydroxybutyrate, and urea), and other on-farm data for discriminating cows of good versus poor likelihood of conception to first insemination (i.e., pregnant vs. open). A total of 6,488 spectral and milk production records of 2,987 cows from 19 commercial dairy herds across 3 Australian states were used. Seven models, comprising different explanatory variables, were examined. Model 1 included milk production; concentrations of fat, protein, and lactose; somatic cell count; age at calving; days in milk at herd test; and days from calving to insemination. Model 2 included, in addition to the variables in model 1, milk fatty acids and blood metabolic profiles. The MIR spectrum collected before first insemination was added to model 2 to form model 3. Fat, protein, and lactose percentages, milk fatty acids, and blood metabolic profiles were removed from model 3 to create model 4. Model 5 and model 6 comprised model 4 and either fertility genomic estimated breeding value or principal components obtained from a genomic relationship matrix derived using animal genotypes, respectively. In model 7, all previously described sources of information, but not MIR-derived traits, were used. The models were developed using partial least squares discriminant analysis. The performance of each model was evaluated in 2 ways: 10-fold random cross-validation and herd-by-herd external validation. The accuracy measures were sensitivity (i.e., the proportion of pregnant cows that were correctly classified), specificity (i.e., the proportion of open cows that were correctly classified), and area under the curve (AUC) for the receiver operating curve. The results showed that in all models, prediction accuracy obtained through 10-fold random cross-validation was higher than that of herd-by-herd external validation, with the difference in AUC ranging between 0.01 and 0.09. In the herd-by-herd external validation, using basic on-farm information (model 1) was not sufficient to classify good- and poor-fertility cows; the sensitivity, specificity, and AUC were around 0.66. Compared with model 1, adding milk fatty acids and blood metabolic profiles (model 2) increased the sensitivity, specificity, and AUC by 0.01, 0.02, and 0.02 unit, respectively (i.e., 0.65, 0.63, and 0.678). Incorporating MIR spectra into model 2 resulted in sensitivity, specificity, and AUC values of 0.73, 0.63, and 0.72, respectively (model 3). The comparable prediction accuracies observed for models 3 and 4 mean that useful information from MIR-derived traits is already included in the spectra. Adding the fertility genomic estimated breeding value and animal genotypes (model 7) produced the highest prediction accuracy, with sensitivity, specificity, and AUC values of 0.75, 0.66, and 0.75, respectively. However, removing either the fertility estimated breeding value or animal genotype from model 7 resulted in a reduction of the prediction accuracy of only 0.01 and 0.02, respectively. In conclusion, this study indicates that MIR and other on-farm data could be used to classify cows of good and poor likelihood of conception with promising accuracy.

Prediction of blood β-hydroxybutyrate content and occurrence of hyperketonemia in early-lactation, pasture-grazed dairy cows using milk infrared spectra

The objective of this study was to evaluate the ability of milk infrared spectra to predict blood β-hydroxybutyrate (BHB) concentration for use as a management tool for cow metabolic health on pasture-grazed dairy farms and for large-scale phenotyping for genetic evaluation purposes. The study involved 542 cows (Holstein-Friesian and Holstein-Friesian × Jersey crossbreds), from 2 farms located in the Waikato and Taranaki regions of New Zealand that operated under a seasonal-calving, pasture-based dairy system. Milk infrared spectra were collected once a week during the first 5 wk of lactation. A blood "prick" sample was taken from the ventral labial vein of each cow 3 times a week for the first 5 wk of lactation. The content of BHB in blood was measured immediately using a handheld device. After outlier elimination, 1,910 spectra records and corresponding BHB measures were used for prediction model development. Partial least square regression and partial least squares discriminant analysis were used to develop prediction models for quantitative determination of blood BHB content and for identifying cows with hyperketonemia (HYK). Both quantitative and discriminant predictions were developed using the phenotypes and infrared spectra from two-thirds of the cows (randomly assigned to the calibration set) and tested using the remaining one-third (validation set). A moderate accuracy was obtained for prediction of blood BHB. The coefficient of determination (R²) of the prediction model in calibration was 0.56, with a root mean squared error of prediction of 0.28 mmol/L and a ratio of performance to deviation, calculated as the ratio of the standard deviation of the partial least squares model calibration set to the standard error of prediction, of 1.50. In the validation set, the R² was 0.50, with root mean squared error of prediction values of 0.32 mmol/L, which resulted in a ratio of performance to deviation of 1.39. When the reference test for HYK was defined as blood concentration of BHB ≥1.2 mmol/L, discriminant models indicated that milk infrared spectra correctly classified 76% of the HYK-positive cows and 82% of the HYK-negative cows. The quantitative models were not able to provide accurate estimates, but they could differentiate between high and low BHB concentrations. Furthermore, the discriminant models allowed the classification of cows with reasonable accuracy. This study indicates that the prediction of blood BHB content or occurrence of HYK from milk spectra is possible with moderate accuracy in pasture-grazed cows and could be used during routine milk testing. Applicability of infrared spectroscopy is not likely suited for obtaining accurate BHB measurements at an individual cow level, but discriminant models might be used in the future as herd-level management tools for classification of cows that are at risk of HYK, whereas quantitative models might provide large-scale phenotypes to be used as an indicator trait for breeding cows with improved metabolic health.

Comparison of Bayesian regression models and partial least squares regression for the development of infrared prediction equations

The objective of this study was to compare the prediction accuracy of 92 infrared prediction equations obtained by different statistical approaches. The predicted traits included fatty acid composition (n = 1,040); detailed protein composition (n = 1,137); lactoferrin (n = 558); pH and coagulation properties (n = 1,296); curd yield and composition obtained by a micro-cheese making procedure (n = 1,177); and Ca, P, Mg, and K contents (n = 689). The statistical methods used to develop the prediction equations were partial least squares regression (PLSR), Bayesian ridge regression, Bayes A, Bayes B, Bayes C, and Bayesian least absolute shrinkage and selection operator. Model performances were assessed, for each trait and model, in training and validation sets over 10 replicates. In validation sets, Bayesian regression models performed significantly better than PLSR for the prediction of 33 out of 92 traits, especially fatty acids, whereas they yielded a significantly lower prediction accuracy than PLSR in the prediction of 8 traits: the percentage of C18:1n-7 trans-9 in fat; the content of unglycosylated κ-casein and its percentage in protein; the content of α-lactalbumin; the percentage of α_S2-casein in protein; and the contents of Ca, P, and Mg. Even though Bayesian methods produced a significant enhancement of model accuracy in many traits compared with PLSR, most variations in the coefficient of determination in validation sets were smaller than 1 percentage point. Over traits, the highest predictive ability was obtained by Bayes C even though most of the significant differences in accuracy between Bayesian regression models were negligible.

Prediction of milk fatty acid content with mid-infrared spectroscopy in Canadian dairy cattle using differently distributed model development sets

The fatty acid profile of milk is a prevailing issue due to the potential negative or positive effects of different fatty acids to human health and nutrition. Mid-infrared spectroscopy can be used to obtain predictions of otherwise costly fatty acid phenotypes in a widespread and rapid manner. The objective of this study was to evaluate the prediction of fatty acid content for the Canadian dairy cattle population from mid-infrared spectral data and to compare the results produced by altering the partial least squares (PLS) model development set used. The PLS model development sets used to develop the predictions were reference fatty acids expressed as (1) grams per 100 g of fatty acid, (2) grams per 100 g of milk, (3) the natural logarithmic transform of grams per 100 g of milk, and (4) subsets of samples randomly selected by removing excess records around the mean to present a more uniform distribution, repeated 10 times. Gas chromatography measured fatty acid concentration and spectral data for 2,023 milk samples of 373 cows from 4 breeds and 44 herds were used in the model development. The coefficient of determination of cross-validation (R_cv²) increased when fatty acids were expressed on a per 100 g of milk basis compared with on a per 100 g of fat basis for all examined fatty acids. The logarithmic transformation used to create a more Gaussian distribution in the development set had little effect on the prediction accuracy. The individual fatty acids C12:0, C14:0, C16:0, C18:0, C18:1n-9 cis, and saturated, monounsaturated, unsaturated, short-chain, medium-chain, and long-chain fatty acid groups had (R_cv²) greater than 0.70. When model development was performed with subsets of the original samples, slight increases in (R_cv²) values were observed for the majority of fatty acids. The difference in (R_cv²) between the top- and bottom-performing prediction equation across the different subsets for a single predicted fatty acid was on average 0.055 depending on which samples were randomly selected to be used in the PLS model development set. Predictions for fatty acids with high accuracies can be used to monitor fatty acid contents for cows in milk recording programs and possibly for genetic evaluation.

Short communication: Mid-infrared spectroscopy prediction of fine milk composition and technological properties in Italian Simmental

The objective of this study was to evaluate the ability of mid-infrared predictions of fine milk composition and technological traits to serve as a tool for large-scale phenotyping of the Italian Simmental population. Calibration equations accurately predicted the fatty acid profile of the milk, but we obtained moderate or poor accuracy for detailed protein composition, coagulation properties, curd yield and composition, lactoferrin, and concentration of major minerals. To evaluate the role of infrared predictions as indicator traits of fine milk composition in indirect selective breeding programs, the genetic parameters of the traits predicted using mid-infrared spectra need to be estimated.

Estimates of genetic parameters for content of boar taint compounds in adipose tissue of intact males at 160 and 220 days of age

The aims of this study were to investigate variation in content of androstenone (AND), skatole (SKA), and indole (IND), quantified in adipose tissue of intact male pigs at 160 d of age (105 kg BW) and 220 d of age (155 kg BW), to estimate genetic parameters and to investigate the genetic relationships for AND, SKA, IND, and growth traits. A sample of adipose tissue was collected in vivo, using a biopsy device, from the neck of 500 intact males at the 2 ages and at slaughter from the ham of 100 of the investigated animals. Backfat depth was measured at 220 d of age, whereas BW was recorded at each sampling. Quantification of AND, SKA, and IND was performed by HPLC with fluorescence detection. Estimates of genetic parameters were obtained through Bayesian analyses after logarithmic transformations of original measures. Contents of boar taint compounds (BTC) measured at 220 d were higher than those at 160 d of age. Correlations between contents of BTC in backfat and ham fat ranged from 0.7 (IND) to 0.88 (SKA). Medium-high h were estimated for BTC at both ages, but estimates at 220 d (0.58, 0.60, and 0.69 for AND, SKA, and IND, respectively) were greater than those at 160 d. The genetic correlation between contents at 160 and 220 d of each BTC was positive, but the probability that such estimates were greater than 0.8 was very low, indicating that contents at 160 and 220 d were traits controlled by different genetic backgrounds. Different rankings were observed when breeding values for the content at 160 and 220 d of age were used to rank animals. As a consequence, performance testing programs for BTC should be based preferably on phenotypes measured at 220 d of age. Weak genetic correlations were observed between content of BT compounds and growth traits (BW, backfat depth, and daily gain from 160 to 220 d of age), indicating that selective breeding to reduce the risk of tainted pork is expected to exert trivial effects on growth performance and fat deposition. Results indicate that prevalence of BTC is high in mature and heavy pigs relative to young and light pigs. High heritability; positive genetic correlations between AND, SKA, and IND; and trivial effects on growth traits suggest that reduction of BTC through selective breeding is feasible and exploitable as an alternative to surgical castration also for pigs slaughtered at heavy BW.

Direct and social genetic effects on body weight at 270 days and carcass and ham quality traits in heavy pigs

The aims of this study were to estimate covariance components for BW at 270 d (BW270) and carcass and ham quality traits in heavy pigs using models accounting for social effects and to compare the ability of such models to fit the data relative to models ignoring social interactions. Phenotypic records were from 9,871 pigs sired by 293 purebred boars mated to 456 crossbred sows. Piglets were born and reared at the same farm and randomly assigned at 60 d of age to groups (6.1 pigs per group on average) housed in finishing pens, each having an area of 6 m(2). The average additive genetic relationship among group mates was 0.11. Pigs were slaughtered at 277 ± 3 d of age and 169.7 ± 13.9 kg BW in groups of nearly 70 animals each. Four univariate animal models were compared: a basic model (M1) including only direct additive genetic effects, a model (M2) with nonheritable social group (pen) effects in addition to effects in M1, a model (M3) accounting for litter effects in addition to M2, and a model (M4) accounting for social genetic effects in addition to effects in M3. Restricted maximum likelihood estimates of covariance components were obtained for BW270; carcass backfat depth; carcass lean meat content (CLM); iodine number (IOD); and linoleic acid content (LIA) of raw ham subcutaneous fat; subcutaneous fat depth in the proximity of semimembranosus muscle (SFD1) and quadriceps femoris muscle (SFD2); and linear scores for ham round shape (RS), subcutaneous fat (SF), and marbling. Likelihood ratio tests indicated that, for all traits, M2 fit the data better than M1 and that M3 was superior to M2 except for SFD1 and SFD2. Model M4 was significantly better than M3 for BW270 (P < 0.001) and CLM, IOD, RS, and SF (P < 0.05). The contribution of social genetic effects to the total heritable variance was large for CLM and BW270, ranging from 33.2 to 35%, whereas the one for ham quality traits ranged from 6.8 (RS) to 11.2% (SF). Direct and social genetic effects on BW270 were uncorrelated, whereas there was a negative genetic covariance between direct and social effects on CLM, IOD, RS, and SF, which reduced the total heritable variance. This variance, measured relative to phenotypic variance, ranged from 21 (CLM) to 54% (BW270). Results indicate that social genetic effects affect variation in traits relevant for heavy pigs used in dry-cured hams manufacturing. Such effects should be exploited and taken into account in design of breeding programs for heavy pigs.

Effect of polymorphisms in candidate genes on carcass and meat quality traits in double muscled Piemontese cattle

The aim of this study was to investigate the association between 10 candidate genes and carcass weight and conformation, carcass daily gain, and meat quality (pH, color, cooking loss, drip loss and shear force) in 990 double-muscled Piemontese young bulls. Animals were genotyped at each of the following genes: growth hormone, growth hormone receptor, pro-opiomelanocortin, pro-opiomelanocortin class 1 homeobox 1, melanocortin-4 receptor, corticotrophin-releasing hormone, diacylglycerol O-acyltransferase-1, thyroglobulin, carboxypeptidase E and gamma-3 regulatory subunit of the AMP-activated protein kinase. All the investigated SNPs had additive effects which were relevant for at least one of the traits. Relevant associations between the investigated SNPs and carcass weight, carcass daily gain and carcass conformation were detected, whereas associations of SNPs with meat quality were moderate. Results confirmed some of previously reported associations, but diverged for others. Validation in other cattle breeds is required to use these SNPs in gene-assisted selection programs for enhancement of carcass traits and meat quality.

Genetic associations between daily BW gain and live fleshiness of station-tested young bulls and carcass and meat quality traits of commercial intact males in Piemontese cattle

The aim of this study was to investigate genetic relationships between beef traits of station-tested young bulls and carcass and meat quality traits (MQ) of commercial intact males in Piemontese cattle. Phenotypes for daily gain (DG) and live fleshiness traits (width at withers: WW; shoulder muscularity: SM; loin width: LW; loin thickness: LT; thigh muscularity: TM; thigh profile: TP) and thinness of the shin bone (BT) were available for 3,109 and 2,183 performance-tested young bulls, respectively. Carcass daily gain (CDG), carcass conformation (SEUS), pH at 24 h (pH24h) and 8 d after slaughter (pH8d), lightness (L*), redness (a*), yellowness (b*), hue angle (HA), saturation index (SI), drip loss (DL), cooking loss (CL), and shear force (SF) were assessed for 1,208 commercial intact males. (Co) variance components were estimated in a set of twelve 9-traits analyses using REML and linear animal models including all performance-test traits and 1 carcass or MQ trait at a time. Heritabilities ± SE of beef traits ranged from 0.26 ± 0.03 (LW) to 0.47 ± 0.01 (DG), whereas those of carcass traits and MQ from 0.06 ± 0.03 (CL) to 0.63 ± 0.04 (HA). The genetic correlation (rg) between DG and CDG was 0.75 ± 0.10, indicating that DG, as measured at the test station, is a good indicator of the carcass gain achieved by commercial animals under farms conditions. Daily BW gain of station-tested bulls correlated positively with color traits (from 0.11 ± 0.12 to 0.54 ± 0.09), ph8d (rg ± SE = 0.31 ± 0.11), DL (rg ± SE = 0.29 ± 0.17), and CL (rg ± SE = 0.27 ± 0.18). Live fleshiness of station-tested bulls exhibited genetic correlations with MQ of commercial animals that were positive for L* and b* (from 0.13 ± 0.08 to 0.65 ± 0.14) and negative for pH (from -0.27 ± 0.15 to -0.57 ± 0.11), CL (from -0.16 ± 0.23 to -0.43 ± 0.22), and SF (TM: rg ± SE = -0.31 ± 0.15; TP: rg ± SE = -0.41 ± 0.17). The thinness of the shin bone correlated unfavorably with CDG (rg ± SE = -0.74 ± 0.07) and favorably with SEUS (rg ± SE = 0.65 ± 0.17), CL (rg ± SE = -0.39 ± 0.13), and SF (rg ± SE = -0.32 ± 0.17). The estimated genetic correlations indicate that selection to increase DG, as measured at the test station, exerts moderate adverse effects on MQ. Because selection emphasis is greater for live fleshiness than for DG, the correlated response in MQ and carcass traits is expected to be influenced to a greater extent by selection for muscularity, even though these traits are less heritable than DG.

Effect of CSN1S1-CSN3 (α(S1)-κ-casein) composite genotype on milk production traits and milk coagulation properties in Mediterranean water buffalo

The aim of this study was to estimate effects of CSN1S1-CSN3 (α(S1)-κ-casein) composite genotypes on milk production traits and milk coagulation properties (MCP) in Mediterranean water buffalo. Genotypes at CSN1S1 and CSN3 and coagulation properties [rennet clotting time (RCT), curd firming time (K₂₀), and curd firmness (A₃₀)] were assessed by reversed-phase HPLC and computerized renneting meter analysis, respectively, using single test-day milk samples of 536 animals. Alternative protein variants of α(S1)-CN and κ-CN were detected by HPLC, and identification of the corresponding genetic variants was carried out by DNA analysis. Two genetic variants were detected at CSN1S1 (A and B variants) and 2 at CSN3 (X1 and X2 variants). Statistical inference was based on a linear model including the CSN1S1-CSN3 composite genotype effect (7 genotypes), the effects of herd-test-day (8 levels), and a combined days in milk (DIM)-parity class. Composite genotype AB-X2X2 was associated with decreased test-day milk yield [-0.21 standard deviation (SD) units of the trait] relative to genotype BB-X2X2. Genotypes did not affect milk protein content, but genotype AB-X1X1 was associated with increased fat content compared with genotype BB-X2X2 (+0.28 SD units of the trait) and AB-X1X1 (+0.43 SD units of the trait). For RCT, the largest difference (+1.91 min; i.e., 0.61 SD units of the trait) was observed between genotype AA-X1X2 and AB-X1X1. Direction of genotype effects on K(20) was consistent with that for RCT. The maximum variation in K₂₀ due to genotype effects (between AA-X1X2 and AB-X1X1 genotypes) was almost 0.9 SD units of the trait. Magnitude of genotype effects was smaller for A₃₀ than for RCT and K₂₀, with a maximum difference of 0.5 SD units of the trait between genotype AA-X1X2 and AA-X1X1. The B allele at CSN1S1 was associated with increased RCT and K₂₀ and with weaker curds compared with allele A. Allele X2 at CSN3 exerted opposite effects on MCP relative to CSN1S1 B. Because of linkage disequilibrium, allele B at CSN1S1 and allele X2 at CSN3 tend to be associated and this likely makes their effects cancel each other. This study indicates a role for casein genes in variation of MCP of buffalo milk. Further studies are necessary to estimate the effects of casein genetic variants on variation of cheese yield.

Short communication: CSN1S1-CSN3 (α(S1)-κ-casein) composite genotypes affect detailed milk protein composition of Mediterranean water buffalo

The aim of the study was to investigate the effect of composite CSN1S1-CSN3 [α(S1)-κ-casein (CN)] genotype on milk protein composition in Mediterranean water buffalo. Content of α(S1)-CN, α(S2)-CN, β-CN, γ-CN, κ-CN, glycosylated and unglycosylated κ-CN, α-lactalbumin, and β-lactoglobulin was measured by reversed-phase HPLC using 621 individual milk samples. Genotypes at CSN1S1 and CSN3 were also obtained by reversed-phase HPLC. Two alleles were detected at CSN1S1 (corresponding to the A and B variants, O62823: p.Leu193Ser,) and at CSN3 (corresponding to the X1 and X2 variants, CAP12622.1: p.Ile156Thr). Increased proportions of α(S1)-CN in total casein (TCN) were associated with genotypes carrying CSN1S1 A. Genotypes associated with a marked decrease of the proportion of α(S1)-CN in TCN (composite genotypes AB-X1X1 and BB-X1X2) were associated with marked increases in the proportion of α(S2)-CN. In addition, composite genotypes carrying the X1 allele at CSN3 were associated with a greater proportion of α(S2)-CN in TCN relative to those carrying CSN3 X2. Composite genotypes greatly affected also the variability of ratios of κ-CN to TCN, with genotypes carrying the X1 allele at CSN3 being associated with decreased ratios. The decreased content of glycosylated κ-CN associated with CSN3 X1 was responsible for the overall lower content of total κ-CN in milk of X1-carrying animals. Increasing the frequency of specific genotypes might be an effective way to alter milk protein composition, namely the proportion of α(S1)-CN, α(S2)-CN, and κ-CN in TCN, and the degree of glycosylation of κ-CN.

Association of polymorphisms in calpain 1, (mu/I) large subunit, calpastatin, and cathepsin D genes with meat quality traits in double-muscled Piemontese cattle

Five single-nucleotide polymorphisms (SNPs) located in the calpain 1, (mu/I) large subunit (CAPN1), calpastatin (CAST), and cathepsin D (CTSD) genes were analyzed in a large sample of Piemontese cattle. The aim of this study was to evaluate allele and genotype frequencies of these SNPs and to investigate associations of CAPN1, CAST, and CTSD gene variants with meat quality traits. Minor allele frequencies ranged from 30 to 48%. The presence of the A allele at CAPN530 increased yellowness and drip loss. The CAST282 G allele was associated with an increased drip loss compared to the C allele, and the CAST2959 A allele decreased redness compared to the G allele.

Effect of κ-casein B relative content in bulk milk κ-casein on Montasio, Asiago, and Caciotta cheese yield using milk of similar protein composition

The aim of this study was to investigate the effect exerted by the relative content of κ-casein (κ-CN) B in bulk milk κ-CN on coagulation properties and cheese yield of 3 Italian cheese varieties (Montasio, Asiago, and Caciotta). Twenty-four cheese-making experiments were carried out in 2 industrial and 1 small-scale dairy plant. Detailed protein composition of bulk milk of 380 herds providing milk to these dairies was analyzed by reversed-phase HPLC. To obtain 2 experimental milks differing in the relative content of κ-CN B in κ-CN, herds were selected on the basis of bulk milk protein composition and relative content of κ-CN genetic variants. Milk was collected and processed separately for the 2 groups of selected herds. A difference of 20% in the relative content of κ-CN B in κ-CN was obtained for the 2 experimental milks for Montasio and a difference of 15% for Asiago and Caciotta. The 2 experimental milks were of similar protein and CN content, casein number, pH, CN composition, and β-CN genetic composition. For each cheese-making trial, amounts of milk, ranging from 2,000 to 6,000kg, were manufactured. Each vat contained milk collected at least from 4 dairy herds. Cheese yield after brining and at the end of the aging was recorded. Milk with a greater proportion of κ-CN B in κ-CN (HIGHB) exhibited similar coagulation properties and greater cheese yield compared with milk with a lower proportion of κ-CN B in κ-CN (LOWB). The increased cheese yield observed for HIGHB when manufacturing Montasio cheese was ascribed to a greater fat content compared with LOWB. The probability of HIGHB giving a cheese yield 5% greater than that of LOWB ranged from 51 to 67% for Montasio cheese, but was less than 21% for Asiago and Caciotta cheeses. Variation in relative content of κ-CN B in κ-CN content did not relevantly affect industrial cheese yield when milks of similar CN composition were processed. An indirect effect due to the increased κ-CN content of κ-CN B milk is thought to explain the favorable effects of κ-CN B on cheese yield reported in the literature.

Survival analysis of preweaning piglet survival in a dry-cured ham-producing crossbred line

The aim of this study was to investigate piglet preweaning survival and its relationship with a total merit index (TMI) used for selection of Large White terminal boars for dry-cured ham production. Data on 13,924 crossbred piglets (1,347 litters), originated by 189 Large White boars and 328 Large White-derived crossbred sows, were analyzed under a frailty proportional hazards model, assuming different baseline hazard functions and including sire and nursed litter as random effects. Estimated hazard ratios (HR) indicated that sex, cross-fostering, year-month of birth, parity of the nurse sow, size of the nursed litter, and class of TMI were significant effects for piglet preweaning survival. Female piglets had less risk of dying than males (HR = 0.81), as well as cross-fostered piglets (HR = 0.60). Survival increased when piglets were nursed by sows of third (HR = 0.85), fourth (HR = 0.76), and fifth (HR = 0.79) parity in comparison with first and second parity sows. Piglets of small (HR = 3.90) or very large litters (HR >1.60) had less chance of surviving in comparison with litters of intermediate size. Class of TMI exhibited an unfavorable relationship with survival (HR = 1.20 for the TMI top class). The modal estimates of sire variance under different baseline hazard functions were 0.06, whereas the variance for the nursed litter was close to 0.7. The estimate of the nursed litter effect variance was greater than that of the sire, which shows the importance of the common environment generated by the nurse sow. Relationships between sire rankings obtained from different survival models were high. The heritability estimate in equivalent scale was low and reached a value of 0.03. Nevertheless, the exploitable genetic variation for this trait justifies the inclusion of piglet preweaning survival in the current breeding program for selection of Large White terminal boars for dry-cured ham production.

Validation of a new reversed-phase high-performance liquid chromatography method for separation and quantification of bovine milk protein genetic variants

A new RP-HPLC method for the separation and quantification of the most common genetic variants of bovine milk proteins is described. A reversed-phase analytical column C8 (Zorbax 300SB-C8 RP, 3.5 microm, 300A, 150 x 4.6 I.D.) was used. All the most common casein (CN) and whey protein genetic variants, including beta-CN(I) were detected and separated simultaneously in less then 40 min, with the exception of alpha(S1)-CN(B) and CN(C) variants. Purified protein genetic variants were employed in calibration and showed different absorbances at 214 nm. The procedure was developed using 40 raw individual milk samples of cows belonging to four different breeds and certified skim milk powder BCR-063R. Method validation consisted in testing linearity, repeatability, reproducibility and accuracy. A linear relationship (R(2)>0.99) between the concentrations of proteins and peak areas was observed over the concentration range, with low detection limits. Repeatability and reproducibility were satisfactory for both retention times and peak areas. The RSD of peak areas ranged from 0.92 to 4.32% within analytical day and from 0.85 to 9.52% across analytical days. The recoveries, calculated using mixtures of samples previously quantified, ranged from 98.1 to 103.7%.