Effect of prudent use of antimicrobials in the early phase of infection in pigs on the performance and meat quality of fattening pigs.

The presented study was a part of the ThermoEye project. The study examined the effect of prudent antibiotic treatment in response to illness on the fattening performance, slaughter traits, and meat quality of the pig fatteners. Pigs were divided into an experimental group - EXP and a control - CON. In both groups, the body temperature was monitored, and diseases were confirmed by a veterinarian. In the EXP group, metaphylaxis was used in each case of confirmed disease. The EXP fatteners had greater slaughter weight (by 4.7 kg) and meatiness (by 2.1 percentage points) compared to the CON pigs. The pH in pork was lower in EXP compared to CON pigs. The longissimus thoracis et lumborum of EXP pigs was characterised by a lower cooking loss (28.30 vs. 30.45%) and yellower colour compared to the CON group. Among the amino acids, only the content of histidine (by 5.2%; P < 0.01) and tyrosine (by 7.2%; P < 0.01) was significantly greater in the meat of the CON group compared to EXP, with no effect of sex noted. The content of SFA (by 14.6%; P < 0.05), UFA (by 15.6%; P < 0.05), MUFA (by 17.8%; P < 0.05), MCFA (by 14.1%; P < 0.05), and LCFA (by 15.6%; P < 0.05) was also greater in CON compared to EXP meat. In conclusion, automated monitoring of pig body temperature during the fattening period enables more precise, prudent treatment and effective animal health control while reducing costs due to disease losses and pharmacotherapy. It allows optimal production and affects meat quality.

Unveiling the attributes of rabbit milk.

Increasing the knowledge of rabbit milk can help in breeding practice to solve issues considering the health and growth of rabbit kits. The goal of the study was to perform a broad physicochemical analysis of rabbit milk and examine the effect of the reproductive status of the females on daily milk yield and milk attributes. The study was conducted on a commercial rabbit farm and included three consecutive lactations of Hycole does. It has been observed that the daily milk production increased from the 2nd till the 14th day of lactation when does produced almost 300 g of milk daily. The day of lactation caused a significant variation in the content of total solids, solids-not-fat, total protein, casein, lactose, C18: 2, C18: 3, Somatic Cell Count, and pH. The percentage of fat globules categorised according to their diameter changed with the ongoing lactation as well, and the diameter increased from 5 to 7 µm. The percentage of small milk fat globules decreased with lactation day, causing a possible decrease in the digestions rates of milk. Pregnancy had a negative impact on milk production, kits growth performance, and the content of total protein, solids-not-fat, and lactose in milk. Therefore, we can speculate about the negative impact of overlapping lactations and pregnancies on rabbit kits, as their growth is dependent on milk production and composition.

The impact of Box-Cox transformation on phenotypic and genomic characteristics of litter size variability in Landrace pigs.

The genetic background of variability remains of interest especially in traits of high economic importance, e.g. litter size in pigs. It has been indicated that the data transformation can affect the variability phenotype. This study aims to evaluate the phenotypic and genomic background of variability of litter size obtained from data before and after the Box-Cox transformation. In total, 67 500 records on the total number born (TNB) in Landrace pig population were used. Since the data presented skewness, the decision was made to perform Box-Cox transformation on TNB and obtain bcTNB. Next, the phenotypic variability was estimated as log-transformed variance of residuals (LnVar) for both TNB (LnVar_TNB) and bcTNB (LnVar_bcTNB). The variability traits were further used in the genome-wide association study (GWAS) performed on 10 688 sows genotyped with Axiom porcine 660 K or imputed to 660 K SNP-chip. The substantial difference in skewness was observed after data transformation, represented as a change from -0.46 to -0.02. Heritability for TNB was 0.118 vs 0.125 for bcTNB. The heritability for LnVar_TNB was 0.0025 vs 0.0037 for LnVar_bcTNB. The change in the genetic variance was confirmed when genetic coefficients on SD level were compared: 2% for LnVar_TNB vs 4% for LnVar_bcTNB. In bivariate analysis, the genetic correlation between the additive genetic effects of the mean TNB and its variability changed from 0.38 to 0.63. The observed positive genetic correlations indicated that selection focused on increasing the litter size will simultaneously cause an increase in litter size variability. Based on GWAS, 14 SNPs were detected for LnVar_TNB and eight for LnVar_bcTNB, with two of them indicating the most promising candidate genes. First candidate gene located on Sus scrofa chromosome (SSC) 3 is STAG3, which plays an essential role in gametogenesis. Second gene located on SSC 10 is ESRRG, which affects placenta development. The additional post-GWAS analysis indicated even more candidate genes for LnVar_TNB and LnVar_bcTNB. The most promising candidate gene was located on SSC 13 - MFN1, which is involved in embryonic development. The results of this study indicated a substantial change in variance components for variability when the Box-Cox transformation was applied to data presenting skewness. Moreover, the data transformation changed the phenotype substantially enough that only part of SNP overlapped between two variability traits. Our investigation shows that it is essential to perform Box-Cox transformation for skewed data in order to properly describe phenotypic and genomic properties of litter size variability in Landrace pigs.

Genome-wide association identifies methane production level relation to genetic control of digestive tract development in dairy cows.

The global temperatures are increasing. This increase is partly due to methane (CH4) production from ruminants, including dairy cattle. Recent studies on dairy cattle have revealed the existence of a heritable variation in CH4 production that enables mitigation strategies based on selective breeding. We have exploited the available heritable variation to study the genetic architecture of CH4 production and detected genomic regions affecting CH4 production. Although the detected regions explained only a small proportion of the heritable variance, we showed that potential QTL regions affecting CH4 production were located within QTLs related to feed efficiency, milk-related traits, body size and health status. Five candidate genes were found: CYP51A1 on BTA 4, PPP1R16B on BTA 13, and NTHL1, TSC2, and PKD1 on BTA 25. These candidate genes were involved in a number of metabolic processes that are possibly related to CH4 production. One of the most promising candidate genes (PKD1) was related to the development of the digestive tract. The results indicate that CH4 production is a highly polygenic trait.

Genome-wide association study reveals novel loci for litter size and its variability in a Large White pig population.

BACKGROUND: In many traits, not only individual trait levels are under genetic control, but also the variation around that level. In other words, genotypes do not only differ in mean, but also in (residual) variation around the genotypic mean. New statistical methods facilitate gaining knowledge on the genetic architecture of complex traits such as phenotypic variability. Here we study litter size (total number born) and its variation in a Large White pig population using a Double Hierarchical Generalized Linear model, and perform a genome-wide association study using a Bayesian method. RESULTS: In total, 10 significant single nucleotide polymorphisms (SNPs) were detected for total number born (TNB) and 9 SNPs for variability of TNB (varTNB). Those SNPs explained 0.83 % of genetic variance in TNB and 1.44 % in varTNB. The most significant SNP for TNB was detected on Sus scrofa chromosome (SSC) 11. A possible candidate gene for TNB is ENOX1, which is involved in cell growth and survival. On SSC7, two possible candidate genes for varTNB are located. The first gene is coding a swine heat shock protein 90 (HSPCB = Hsp90), which is a well-studied gene stabilizing morphological traits in Drosophila and Arabidopsis. The second gene is VEGFA, which is activated in angiogenesis and vasculogenesis in the fetus. Furthermore, the genetic correlation between additive genetic effects on TNB and on its variation was 0.49. This indicates that the current selection to increase TNB will also increase the varTNB. CONCLUSIONS: To the best of our knowledge, this is the first study reporting SNPs associated with variation of a trait in pigs. Detected genomic regions associated with varTNB can be used in genomic selection to decrease varTNB, which is highly desirable to avoid very small or very large litters in pigs. However, the percentage of variance explained by those regions was small. The SNPs detected in this study can be used as indication for regions in the Sus scrofa genome involved in maintaining low variability of litter size, but further studies are needed to identify the causative loci.

Genetic analysis of within-litter variation in piglets' birth weight using genomic or pedigree relationship matrices.

The objective of this study was to estimate the genetic variance for within-litter variation of birth weight (BW0) using genomic (GRM) or pedigree relationship matrices (PRM) and to compare the accuracy of estimated breeding values (EBV) for within-litter variation of BW0 using GRM and PRM. The BW0 and residual variance of BW0 were modeled by the double hierarchical generalized linear model using GRM or PRM. Data came from 2 dam lines: Landrace and Large White. After editing, the data set in Landrace consisted of 748 sows with 1,938 litters and 29,430 piglets and in Large White of 989 sows with 3,320 litters and 51,818 piglets. To construct GRM, 46,466 (Landrace) and 44,826 (Large White) single nucleotide polymorphisms were used, whereas to construct PRM, 5 generations of pedigree were used. The accuracy of EBV with GRM was estimated with 8-fold cross-validation and compared to PRM. Estimated variance components were highly similar for GRM and PRM. The maternal genetic variance in residual variance of BW0 in Landrace was 0.05 with GRM and 0.06 with PRM. In Large White these were 0.04 with GRM and 0.05 with PRM. The genetic coefficient of variation (GCV SDe) was about 0.10 in both dam lines. This indicates a change of 10% in residual SD of BW0 when achieving a genetic response of 1 genetic standard deviation. The genetic correlation between birth weight and its residual variance was about 0.6 in both dam lines. The accuracies of selection for within-litter variation of birth weight were 0.35 with GRM and 0.23 with PRM in Landrace and 0.29 with GRM and 0.34 with PRM in Large White. In this case, using GRM did not significantly increase accuracies of selection. Results, however, show good opportunities to select for reduced within-litter variation of BW0. Genomic selection can increase accuracy of selection when reference populations contain at least 2,000 sows.

Comparison of methods to study uniformity of traits: Application to birth weight in pigs.

Increasing uniformity of traits is an important objective in livestock production. This study focused on the BWcomparison of a double hierarchical GLM (DHGLM) with the conventional analysis of uniformity, using within-litter variation in birth weight (BW0) in pigs as a case. In pigs, within-litter variation of BW0 is a trait in which uniformity is important in breeding practice. Traditionally, uniformity has been studied by analysis of SD or variances. In DHGLM, differences between animals are studied by analyzing the residual variance of the trait and estimating its variance components. Here we used data on BW0, recorded in 2 sow lines (Large White and Landrace), to compare the estimation of genetic parameters and breeding values for uniformity from DHGLM and traditional analysis of the variance. Comparison of DHGLM with the conventional analysis using the logarithm-transformed variance of BW0 was possible because both methods were on the same scale and the models contained the same random effects. In addition, the genetic CV at the residual SD level (GCV) was proposed as a measure expressing the potential response to selection. Three-fold cross-validation was performed to study predictive ability of both methods. The estimated GCV was highly similar using both methods. Results indicate that the SD of BW0 can be decreased by up to approximately 10% after 1 generation of selection, indicating good prospects for response to selection. The correlation between EBV (0.88 in both sow lines) obtained from both methods indicated high similarity between conventional analysis and DHGLM. Comparison of accuracies of EBV showed that the methods were comparable, with moderate accuracies achieved with approximately 100 piglets per maternal grandsire. Cross-validation also indicated very similar predictive ability in estimating EBV for BW0 variation for both methods. Therefore, it was concluded that conventional analysis and DHGLM produced highly comparable results. Still, the DHGLM potentially has a broader application than conventional analysis to study uniformity of traits, because it also can be used for traits with single observations per animal.

Effect of gestating sow body condition, feed refusals, and group housing on growth and feed intake in grower-finishing pigs.

The main focus of this study was to identify sow gestation features that affect growth rate (GR) and feed intake (FI) of their offspring during grower-finishing stage. Because the sow provides a specific environment to her offspring during gestation, certain features (e.g., BW of the sow), feed refusals or gestation group, may affect her ability to deliver and feed a healthy litter. Data on 17,743 grower-finishing pigs, coming from 604 sires and 681 crossbred sows, were obtained from the Institute for Pigs Genetics. Sow gestation features were collected during multiple gestations and divided into 3 clusters describing i) sow body condition (i.e., BW, backfat, and gestation length), ii) sow feed refusals (FR), the difference between offered and eaten feed during 3 periods of gestation: 1 to 28, 25 to 50, 45 to 80 d, and iii) sow group features (i.e., number of sows, and average parity). Sow gestation features were added to the base model 1 at a time to study their effect on GR and FI. Significant gestation features (P < 0.1) were fitted simultaneously in animal model to investigate whether they could explain common litter and permanent sow effects. Gestation length had effect on GR [1.4 (g/d)/d; P = 0.04] and FI [6.8 (g/d)/d; P = 0.007]. Body weights of the sow at insemination [0.07 (g/d)/kg; P = 0.08], at farrowing [0.14 (g/d)/kg; P < 0.0001], and after lactation [0.1 (g/d)/kg; P = 0.003] had effect on GR. Sow parturition-lactation loss in backfat thickness and weight were not significant for GR and FI. Days with FR during 25 to 50 and 45 to 80 d of gestation and average FR during 45 to 80 d of gestation had negative effect on GR and when substantially increased had also a positive effect on FI. Sow FR from 1 to 28 d of gestation were not significant. Number of sows in gestation group had effect on FI [-9 (g/d)/group member; P = 0.04] and day sow entered group had an effect on GR [-0.9 (g/d)/day; P = 0.04]. Sow gestation features explained 1 to 3% of the total variance in grower-finishing pigs. Gestation features did explain phenotypic variance due to permanent sow and part of phenotypic variance due to common litter effects for FI but not for GR.

Effect of sow history features on growth and feed intake in grow-finish pigs.

The sow provides a specific environment to her offspring during gestation and lactation. Certain features in the early life of the sow (sow history features) may affect her ability to deliver and feed a healthy litter. In genetic analyses of grow-finish traits, these effects are estimated as common litter or permanent sow effects. The objective of this research was to identify sow history features that affect the growth rate (GR) and feed intake (FI) of her offspring during the grow-finish stage. Data from 17,743 grow-finish pigs, coming from 604 sires and 681 crossbred sows, were recorded between May 2001 and February 2010 at the experimental farm of the Institute for Pig Genetics (Beilen, the Netherlands). The grow-finish stage was divided into 2 phases (phase 1: 26 to 75 kg; phase 2: 75 to 115 kg). The sow history features were birth litter size, birth year and season, birth farm, weaning age, age of transfer to the experimental farm, and age at first insemination. The sow features were added to the basic model one at a time to study their effect on the grow-finish traits of the pigs. Subsequently, significant sow features (P < 0.1) were fitted simultaneously in an animal model. With every extra piglet in the birth litter of the sow, the GR of her offspring decreased by 1 g/d and the FI decreased by 4 g/d. Every extra day to the first insemination increased the GR of grow-finish pigs by 0.1 g/d. The heritability estimates for GR and FI (only in phase 2 of the grow-finish stage) decreased after adding the sow features to the model. No differences were found in estimates of the common litter effects between the basic model and the model with all significant sow features. The estimates of the permanent sow effect changed for FI from 0.03 (basic model) to 0.00 (model with sow features), and for FI in phase 1, the permanent sow effect decreased from 0.03 (basic model) to 0.01 (model with sow features). In conclusion, selected sow features do affect the grow-finish traits of the pigs, but their estimates are small and explain only a small proportion of the differences in the GR and FI of grow-finish pigs. The sow features partially explained the permanent sow effect of FI-related traits and did not explain the common litter effect. Although the sow early life features can affect piglet traits, they do not predict which sows produce better performing offspring in the grow-finish stage.