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Abstract Background Heat stress (HS) poses significant threats to the sustainability of livestock production. Genetically improving heat tolerance could enhance animal welfare and minimize production losses during HS events. Measuring phenotypic indicators of HS response and understanding their genetic background are crucial steps to optimize breeding schemes for improved climatic resilience. The identification of genomic regions and candidate genes influencing the traits of interest, including variants with pleiotropic effects, enables the refinement of genotyping panels used to perform genomic prediction of breeding values and contributes to unraveling the biological mechanisms influencing heat stress response. Therefore, the main objectives of this study were to identify genomic regions, candidate genes, and potential pleiotropic variants significantly associated with indicators of HS response in lactating sows using imputed whole-genome sequence (WGS) data. Phenotypic records for 18 traits and genomic information from 1,645 lactating sows were available for the study. The genotypes from the PorcineSNP50K panel containing 50,703 single nucleotide polymorphisms (SNPs) were imputed to WGS and after quality control, 1,622 animals and 7,065,922 SNPs were included in the analyses. Results A total of 1,388 unique SNPs located on sixteen chromosomes were found to be associated with 11 traits. Twenty gene ontology terms and 11 biological pathways were shown to be associated with variability in ear skin temperature, shoulder skin temperature, rump skin temperature, tail skin temperature, respiration rate, panting score, vaginal temperature automatically measured every 10 min, vaginal temperature measured at 0800 h, hair density score, body condition score, and ear area. Seven, five, six, two, seven, 15, and 14 genes with potential pleiotropic effects were identified for indicators of skin temperature, vaginal temperature, animal temperature, respiration rate, thermoregulatory traits, anatomical traits, and all traits, respectively. Conclusions Physiological and anatomical indicators of HS response in lactating sows are heritable but highly polygenic. The candidate genes found are associated with important gene ontology terms and biological pathways related to heat shock protein activities, immune response, and cellular oxidative stress. Many of the candidate genes with pleiotropic effects are involved in catalytic activities to reduce cell damage from oxidative stress and cellular mechanisms related to immune response.

Abstract Background Non-additive genetic effects are often ignored in livestock genetic evaluations. However, fitting them in the models could improve the accuracy of genomic breeding values. Furthermore, non-additive genetic effects contribute to heterosis, which could be optimized through mating designs. Traits related to fitness and adaptation, such as heat tolerance, tend to be more influenced by non-additive genetic effects. In this context, the primary objectives of this study were to estimate variance components and assess the predictive performance of genomic prediction of breeding values based on alternative models and two independent datasets, including performance records from a purebred pig population and heat tolerance indicators recorded in crossbred lactating sows. Results Including non-additive genetic effects when modelling performance traits in purebred pigs had no effect on the residual variance estimates for most of the traits, but lower additive genetic variances were observed, especially when additive-by-additive epistasis was included in the models. Furthermore, including non-additive genetic effects did not improve the prediction accuracy of genomic breeding values, but there was animal re-ranking across the models. For the heat tolerance indicators recorded in a crossbred population, most traits had small non-additive genetic variance with large standard error estimates. Nevertheless, panting score and hair density presented substantial additive-by-additive epistatic variance. Panting score had an epistatic variance estimate of 0.1379, which accounted for 82.22% of the total genetic variance. For hair density, the epistatic variance estimates ranged from 0.1745 to 0.1845, which represent 64.95–69.59% of the total genetic variance. Conclusions Including non-additive genetic effects in the models did not improve the accuracy of genomic breeding values for performance traits in purebred pigs, but there was substantial re-ranking of selection candidates depending on the model fitted. Except for panting score and hair density, low non-additive genetic variance estimates were observed for heat tolerance indicators in crossbred pigs.

Background: Non-additive genetic effects are often ignored in livestock genetic evaluations. However, fitting them in the models could improve the accuracy of genomic breeding values. Furthermore, non-additive genetic effects contribute to heterosis, which could be optimized through mating designs. Traits related to fitness and adaptation, such as heat tolerance, tend to be more influenced by non-additive genetic effects. In this context, the primary objectives of this study were to estimate variance components and assess the predictive performance of genomic prediction of breeding values based on alternative models and two independent datasets, including performance records from a purebred pig population and heat tolerance indicators recorded in crossbred lactating sows. Results: Including non-additive genetic effects when modelling performance traits in purebred pigs had no effect on the residual variance estimates for most of the traits, but lower additive genetic variances were observed, especially when additive-by-additive epistasis was included in the models. Furthermore, including non-additive genetic effects did not improve the prediction accuracy of genomic breeding values, but there was animal re-ranking across the models. For the heat tolerance indicators recorded in a crossbred population, most traits had small non-additive genetic variance with large standard error estimates. Nevertheless, panting score and hair density presented substantial additive-by-additive epistatic variance. Panting score had an epistatic variance estimate of 0.1379, which accounted for 82.22% of the total genetic variance. For hair density, the epistatic variance estimates ranged from 0.1745 to 0.1845, which represent 64.95–69.59% of the total genetic variance. Conclusions: Including non-additive genetic effects in the models did not improve the accuracy of genomic breeding values for performance traits in purebred pigs, but there was substantial re-ranking of selection candidates depending on the model fitted. Except for panting score and hair density, low non-additive genetic variance estimates were observed for heat tolerance indicators in crossbred pigs. [ABSTRACT FROM AUTHOR]

The study examined effects of feeding iso-nutrient (150 g CP, 17.3 MJ ME kg−1) raw (RCP), sprouted (SPC), or roasted (RSCP) cowpea diets to Windsnyer (W), Large White (LW) × Landrace (LR), and the 3-way crossbred (W × LW × LR) growing pigs. Diet dry matter (DM) digestibility was estimated using standard, 3-step (gastric, small intestines, colon) in vitro digestion. Dietary in vivo nutrient digestibility and nitrogen balance were evaluated using 3 weaned pigs of each genotype. Pigs were housed in individual metabolic cages. The diets were assigned to pigs in a 3 × 3 change-over factorial experiment within three balanced, 3 × 3 Latin squares. Feeding periods consisted of 7 days adaption + 5 days’ measurement of feed intake, and the total faecal and urine excretions. The SPC diet increased step 3 in vitro DM digestibility compared to RSCP (P < 0.05). Metabolic size-scaled feed consumption was higher on the RCP compared to the RSCP diet (P < 0.05). Cowpea processing reduced apparent DM and ash digestibility (P < 0.05). LW × LR pigs exhibited lower digestibility of ash and acid detergent fiber (ADF) compared to 3-way crossbred pigs (P < 0.05). Significant genotype-diet interactions were observed for nitrogen intake (P < 0.0001), digestible nitrogen (P = 0.043), urinary nitrogen output (P < 0.0001), faecal nitrogen output (P < 0.0001), total nitrogen excretion (P < 0.0001), and nitrogen retention (P < 0.001). The biological value of feed protein was higher for W pigs than 3-way crossbred pigs (P < 0.05). Genotype-diet interactions suggested unique digestive and, or metabolic adaptive traits in the utilization of the differently processed cowpeas, which need further investigation. [ABSTRACT FROM AUTHOR]

The article discusses the issues of productivity of sows depending on the technology of their maintenance and breeding methods. It was found that the technology of cultivation and breeding methods do not affect the multiplicity of sows, but have an effect on the individual weight of piglets at 21 and 35 days, as well as the weight of the nest at weaning. According to the complex indicator of reproductive qualities (KPVC), the best indicators are characterized by sows that are kept with a single-phase technology using non-replaceable litter.

• Interbreed differences in hormonal adipokine profiles in plasma and pituitary. • Adipokine variability across estrous cycle phases. • Correlation analysis of adipokines with metabolic and reproductive parameters. • Importance of breed-dependent correlations between Large White and Meishan pigs. • The potential impact of adipokines on metabolic and reproductive processes in swine. Adipokines play crucial roles in both reproductive and energy metabolic processes. This study aimed to compare the hormonal plasma profile of adiponectin, apelin, vaspin, chemerin, resistin, visfatin, and adipolin, and the expression of their receptors in the anterior pituitary (AP) between normal-weight Large White (LW) and fat Meishan (MS) pigs during different phases of the estrous cycle. We measured adipokine levels in the plasma and assessed their gene expression in the AP. We used Pearson's correlation analysis to examine potential links between adipokines levels, their receptors, and metabolic parameters (body weight; backfat thickness) and reproductive parameters (pituitary weight; age at puberty; levels of gonadotropins, steroid hormones; and gene expression of gonadotropin-releasing hormone receptor and gonadotropins in AP). The plasma levels of the evaluated adipokines fluctuated with phase and breed, except for visfatin and adipolin. Moreover, adipokine expression in AP varied significantly between breeds and estrous cycle phases, except for resistin receptor CAP1. Notably, we observed a positive correlation between plasma levels of adiponectin and its transcript in the AP only in MS pigs. Apelin gene expression correlated negatively with its receptor in MS, while we observed a breed-dependent correlation between chemerin gene expression and its receptor CMKLR1. We identified significant positive or negative correlations between adipokines or their receptor levels in plasma and AP as well as metabolic or reproductive parameters, depending on the breed. In conclusion, we have demonstrated breed-specific and estrous cycle-dependent regulation of adipokines in AP, underscoring their potential impact on metabolic and reproductive processes in swine. [ABSTRACT FROM AUTHOR]

Testes have several primary functions, such as male gametes production (spermatozoa) and secretion of several endocrine factors, including the production of steroid and protein hormones which facilitate elements in the healthy reproductive function of mammals. The potential of an animal functional endocrine reservoir could be an interesting finding in animal reproduction management. Therefore, the current study aimed to analyze the functional endocrine reserves of testes in 12- and 18-month-old boars of four different boar breeds, namely Large White, Landrace, Duroc, and Tempo, as the animal model in this research (n=10). To determine the functional endocrine reserves of the testes at 12 and 18 months of age, boars received injections of human chorionic gonadotropin (hCG) 3 times every 72 hours. Testosterone was measured in blood samples (10 mL) taken from the jugular vein before the administration of hCG as well as 2, 12, 24, 48, and 72 hours after administration of each of three hCG injections. The index of endocrine activity of the testes was determined by the formula: Ita = Т1 –Т0 / Т0, where Ita is the index of endocrine activity of the testes, T0 is the testosterone concentration before the administration of hCG, and T1 is the maximum testosterone concentration after the third administration of hCG. The effects of the hCG injection on boar testes at different age periods indicated that the testes of Large White and Duroc boars have higher potential endocrine reserves compared with the Landrace and Tempo breeds.

The aim of this study was to compare the quality of musculus longissimus dorsi in the breeds of Mangalitsa and Large White with regard to the slaughter weight. Large White (LW) breed and White Mangalitsa (Ma) breed were used in the experiment. The system of housing and feeding was the same in both of the monitored breeds. The pigs were fed with the same feeding mixture ad libitum. According to the slaughter weight, the pigs were divided into three groups: up to 100 kg, 101 – 110 kg and over 110 kg. The breed Ma had a significantly lower drip loss than the breed LW. Evaluating the color of the meat, the LW breed has showed significantly higher L* (lightness, white ±black) and lower a* (redness, red ± green) values than the Ma breed. Within the chemical meat composition, the Ma breed had a significantly higher water content in MLD compared to the LW breed. Generally, there were no major differences in the meat quality between the Mangalitsa and Large White breeds. Finally it can be concluded that the breed Mangalitsa showed more favorable values of the physico-chemical indicators. Comparing the quality of the meat with regard to the slaughter weight, there were no large differences between individual weight groups. A higher slaughter weight has positively influenced mainly the color of the meat, as pigs weighing more than 110 kg achieved a significantly lower value of L* and a higher value of a* in comparison to pigs of the lower weight. As a positive effect of a higher slaughter weight can be considered its effect on the protein content in the meat, as pigs weighing over 100 kg have a significantly higher protein content in the meat than pigs weighing below 100 kg.

Relationships play a very important role in studies on quantitative genetics. In traditional breeding, pedigree records are used to establish relationships between animals; while this kind of relationship actually represents one kind of relatedness, it cannot distinguish individual specificity, capture the variation between individuals or determine the actual genetic superiority of an animal. However, with the popularization of high-throughput genotypes, assessments of relationships among animals based on genomic information could be a better option. In this study, we compared the relationships between animals based on pedigree and genomic information from two pig breeding herds with different genetic backgrounds and a simulated dataset. Two different methods were implemented to calculate genomic relationship coefficients and genomic kinship coefficients, respectively. Our results show that, for the same kind of relative, the average genomic relationship coefficients (G matrix) were very close to the pedigree relationship coefficients (A matrix), and on average, the corresponding values were halved in genomic kinship coefficients (K matrix). However, the genomic relationship yielded a larger variation than the pedigree relationship, and the latter was similar to that expected for one relative with no or little variation. Two genomic relationship coefficients were highly correlated, for farm1, farm2 and simulated data, and the correlations for the parent-offspring, full-sib and half-sib were 0.95, 0.90 and 0.85; 0.93, 0.96 and 0.89; and 0.52, 0.85 and 0.77, respectively. When the inbreeding coefficient was measured, the genomic information also yielded a higher inbreeding coefficient and a larger variation than that yielded by the pedigree information. For the two genetically divergent Large White populations, the pedigree relationship coefficients between the individuals were 0, and 62 310 and 175 271 animal pairs in the G matrix and K matrix were greater than 0. Our results demonstrated that genomic information outperformed the pedigree information; it can more accurately reflect the relationships and capture the variation that is not detected by pedigree. This information is very helpful in the estimation of genomic breeding values or gene mapping. In addition, genomic information is useful for pedigree correction. Further, our findings also indicate that genomic information can establish the genetic connection between different groups with different genetic background. In addition, it can be used to provide a more accurate measurement of the inbreeding of an animal, which is very important for the assessment of a population structure and breeding plan. However, the approaches for measuring genomic relationships need further investigation.

Summary: The number of teats is a reproductive‐related trait of great economic relevance as it affects the mothering ability of the sows and thus the number of properly weaned piglets. Moreover, genetic improvement of this trait is fundamental to parallelly help the selection for increased litter size. We present the results of single‐marker and haplotypes‐based genome‐wide association studies for the number of teats in two large cohorts of heavy pig breeds (Italian Large White and Italian Landrace) including 3990 animals genotyped with the 70K GGP Porcine BeadChip and other 1927 animals genotyped with the Illumina PorcineSNP60 BeadChip. In the Italian Large White population, genome scans identified three genome regions (SSC7, SSC10, and SSC12) that confirmed the involvement of the VRTN gene (as we previously reported) and highlighted additional loci known to affect teat counts, including the FRMD4A and HOXB1 gene regions. A different picture emerged in the Italian Landrace population, with a total of 12 genome regions in eight chromosomes (SSC3, SSC6, SSC8, SSC11, SSC13, SSC14, SSC15, and SSC16) mainly detected via the haplotype‐based genome scan. The most relevant QTL was close to the ARL4C gene on SSC15. Markers in the VRTN gene region were not significant in the Italian Landrace breed. The use of both single‐marker and haplotype‐based genome‐wide association analyses can be helpful to exploit and dissect the genome of the pigs of different populations. Overall, the obtained results supported the polygenic nature of the investigated trait and better elucidated its genetic architecture in Italian heavy pigs. [ABSTRACT FROM AUTHOR]

We present a genome assembly from an individual female Pieris brassicae (the large white; Arthropoda; Insecta; Lepidoptera; Pieridae). The genome sequence is 292 megabases in span. The majority of the assembly is scaffolded into 16 chromosomal pseudomolecules, with the W and Z sex chromosome assembled. Gene annotation of this assembly on Ensembl has identified 12,229 protein coding genes.

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Objective As one of the most important metabolic organs, the liver plays vital roles in modulating the lipid metabolism. This study was to compare miRNA expression profiles of the Large White liver between two different developmental periods and to identify candidate miRNAs for lipid metabolism. Methods Eight liver samples were collected from White Large of 70-day fetus (P70) and of 70-day piglets (D70) (with 4 biological repeats at each development period) to construct sRNA libraries. Then the eight prepared sRNA libraries were sequenced using Illumina next-generation sequencing technology on HiSeq 2500 platform. Results As a result, we obtained 346 known and 187 novel miRNAs. Compared with the D70, 55 down- and 61 up-regulated miRNAs were shown to be significantly differentially expressed (DE). Gene ontology and Kyoto encyclopedia of genes and genomes enrichment analysis indicated that these DE miRNAs were mainly involved in growth, development and diverse metabolic processes. They were predicted to regulate lipid metabolism through adipocytokine signaling pathway, mitogen-activated protein kinase, AMP-activated protein kinase, cyclic adenosine monophosphate, phosphatidylinositol 3 kinase/protein kinase B, and Notch signaling pathway. The four most abundantly expressed miRNAs were miR-122, miR-26a and miR-30a-5p (miR-122 only in P70), which play important roles in lipid metabolism. Integration analysis (details of mRNAs sequencing data were shown in another unpublished paper) revealed that many target genes of the DE miRNAs (miR-181b, miR-145-5p, miR-199a-5p, and miR-98) might be critical regulators in lipid metabolic process, including acyl-CoA synthetase long chain family member 4, ATP-binding casette A4, and stearyl-CoA desaturase. Thus, these miRNAs were the promising candidates for lipid metabolism. Conclusion Our study provides the main differences in the Large White at miRNA level between two different developmental stages. It supplies a valuable database for the further function and mechanism elucidation of miRNAs in porcine liver development and lipid metabolism.

Genetic parameters for sow stayability were estimated from farrowing records of 10,295 Landrace sows and 8192 Large White sows. The record for sow stayability from parity k to parity k + 1 (k = 1, ..., 6) was 0 when a sow had a farrowing record at parity k but not at parity k + 1, and 1 when a sow had both records. Heritability was estimated by using single-trait linear and threshold animal models. Genetic correlations among parities were estimated by using two-trait linear-linear and single-trait random regression linear animal models. Genetic correlations with litter traits at birth were estimated by using a two-trait linear-linear animal model. Heritability estimates by linear model analysis were low (0.065-0.119 in Landrace & 0.061-0.157 in Large White); those by threshold model analysis were higher (0.136-0.200 & 0.110-0.283). Genetic correlations among parities differed between breeds and models. Genetic correlation between sow stayability and number born alive was positive in many cases, implying that selection for number born alive does not reduce sow stayability. The results seem to be affected by decisions on culling made by farmers. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate breed differences and heterosis in the number of teats from 5,788 performance-tested pigs (1,663 boars and 4,125 gilts) belonging to Landrace (LDR), Large White (LRW), F1 LDR x LRW crosses, and R1 LRW x LDR crosses produced from 2016 to 2018 in a local nucleus/multiplier breeding farm. The average number of the left, right, and total teats was 6.72, 6.70, and 13.42, respectively. Teat number was positively correlated (P<0.01) with body length at the end of test (r = 0.06 to 0.09). Teat number was not affected (P>0.05) by gender and backfat thickness. Large White pigs had significantly (P<0.05) more total teats than Landrace pigs by 0.29 teat. The R1 LRW x LDR crossbred pigs had more (P<0.05) total teats than F1 LDR x LRW cross by 0.48 teat. Heterosis estimates in F1 crosses were 1.04%, 0.45%, and 2.25% equivalent to additional 0.07 teat, 0.03 teat, and 0.30 teat for the number of the left, right and total teats, respectively. While teat number can be improved within a breed by selection, teat number may also be improved by heterosis resulting from the production of F1 crosses. [ABSTRACT FROM AUTHOR]

This study evaluated backfat thickness (BFT) of 12,272 performance-tested pigs (3,351 boars and 8,921 gilts) belonging to Landrace (LDR), Large White (LRW), and their F1 crosses. Backfat thickness at the shoulder (BFT1), midback (BFT2), loin (BFT3), and their average were significantly affected (P<0.01) by breed, year, month, and weight at the end of test. Backfat thickness was highest for BFT1 (15.67 mm for boars; 15.80 mm for gilts), followed by BFT3 (14.21 mm for boars; 15.21 mm for gilts), and lowest for BFT2 (11.95 mm for boars; 12.30 mm for gilts). Average BFT was positively (P<0.01) correlated with average daily gain, body length, age, and weight at the end of test. Average BFT was significantly (P<0.05) lower in boars than in gilts. Average BFT was significantly (P<0.05) higher in Landrace than in Large White. Differences in average BFT between F1 LDR x LRW and R1 LRW x LDR crosses were not significant (P>0.05). Heterosis for average BFT in boars (-3.37% or -0.45 mm) was higher than in gilts (-1.48% or -0.21 mm). While backfat thickness can be reduced by selection within a breed, backfat thickness may also be decreased by heterosis resulting from the production of F1 crosses. [ABSTRACT FROM AUTHOR]

The aim of this study was to determine the effect of parity on reproductive performance by sows of different breeds. The sows were raised on three farms, and had seven or more litters of not fewer than seven piglets. A total of 1511 litters from various parities of Lithuanian White (N =721), Large White (N = 162) and Landrace (N = 628) sows were contained in the data that were used in this study. Multiparous sows had larger litters than primiparous sows. Piglet survival rate declined from parity 7. An increase in litter size was observed until parity 5 in Large White and until parity 6 in improved Lithuanian White (P <0.05). In comparison with parity 1, Landrace sows up to parity 5 showed not only increased prolificacy, but also increased number and litter weight of piglets at 21 days old, and the number and litter weight of weaned piglets (P <0.01). The current findings indicate that sows of these breeds can be used effectively in breeding herds until parity 5 and parity 6. [ABSTRACT FROM AUTHOR]

Relationships play a very important role in studies on quantitative genetics. In traditional breeding, pedigree records are used to establish relationships between animals; while this kind of relationship actually represents one kind of relatedness, it cannot distinguish individual specificity, capture the variation between individuals or determine the actual genetic superiority of an animal. However, with the popularization of high-throughput genotypes, assessments of relationships among animals based on genomic information could be a better option. In this study, we compared the relationships between animals based on pedigree and genomic information from two pig breeding herds with different genetic backgrounds and a simulated dataset. Two different methods were implemented to calculate genomic relationship coefficients and genomic kinship coefficients, respectively. Our results show that, for the same kind of relative, the average genomic relationship coefficients (G matrix) were very close to the pedigree relationship coefficients (A matrix), and on average, the corresponding values were halved in genomic kinship coefficients (K matrix). However, the genomic relationship yielded a larger variation than the pedigree relationship, and the latter was similar to that expected for one relative with no or little variation. Two genomic relationship coefficients were highly correlated, for farm1, farm2 and simulated data, and the correlations for the parent-offspring, full-sib and half-sib were 0.95, 0.90 and 0.85; 0.93, 0.96 and 0.89; and 0.52, 0.85 and 0.77, respectively. When the inbreeding coefficient was measured, the genomic information also yielded a higher inbreeding coefficient and a larger variation than that yielded by the pedigree information. For the two genetically divergent Large White populations, the pedigree relationship coefficients between the individuals were 0, and 62 310 and 175 271 animal pairs in the G matrix and K matrix were greater than 0. Our results demonstrated that genomic information outperformed the pedigree information; it can more accurately reflect the relationships and capture the variation that is not detected by pedigree. This information is very helpful in the estimation of genomic breeding values or gene mapping. In addition, genomic information is useful for pedigree correction. Further, our findings also indicate that genomic information can establish the genetic connection between different groups with different genetic background. In addition, it can be used to provide a more accurate measurement of the inbreeding of an animal, which is very important for the assessment of a population structure and breeding plan. However, the approaches for measuring genomic relationships need further investigation. [ABSTRACT FROM AUTHOR]

Summary: The Piétrain pig originates from the Belgian village Piétrain some time between 1920 and 1950. Owing to its superior conformation, the Piétrain has spread worldwide since the 1960s. As initial population sizes were limited and close inbreeding was commonplace, the breed's genetic diversity has been questioned. Therefore, this study examines Piétrain breed substructure, diversity and selection signatures using SNP data in comparison with Duroc, Landrace and Large White populations. Principal component analysis indicated three subpopulations, and FST analysis showed that US Piétrains differ most from European Piétrains. Average inbreeding based on runs of homozygosity (ROH) segments larger than 4 Mb ranged between 16.7 and 20.9%. The highest chromosomal inbreeding levels were found on SSC8 (42.7%). ROH islands were found on SSC8, SSC15 and SSC18 in all Piétrain populations, but numerous population‐specific ROH islands were also detected. Moreover, a large ROH island on SSC8 (34–126 Mb) appears nearly fixed in all Piétrain populations, with a unique genotype. Chromosomal ROH patterns were similar between Piétrain populations. This study shows that Piétrain populations are genetically diverging, with at least three genetically distinct populations worldwide. Increasing genetic diversity in local Piétrain populations by introgression from other Piétrain populations seems to be only limited. Moreover, a unique 90 Mb region on SSC8 appeared largely fixed in the Piétrain breed, indicating that fixation was already present before the 1960s. We believe that strong selection and inbreeding during breed formation fixed these genomic regions in Piétrains. Finally, we hypothesize that independent coat color selection may have led to large ROH pattern similarities on SSC8 between unrelated pig breeds. [ABSTRACT FROM AUTHOR]

The performance of the two‐trait animal model that regards the first parity and later parities as two different traits in estimating genetic parameters for number of born alive (NBA) was examined using real and simulated data. Genetic parameters for NBA were estimated in purebred Landrace and Large White pigs using a single‐trait repeatability model (Model 1) that regards all parities as the same trait and a two‐trait animal model (Model 2) that regards the first and the later parities as different traits. For Model 2, the permanent environmental effect was fitted to only the records of the later parities. Heritability for NBA estimated using Model 1 was 0.12 for Landrace and 0.11 for Large White. Estimated heritability for NBA of the first parity and the later parities was 0.21 and 0.16, respectively, for Landrace; 0.18 and 0.16, respectively, for Large White obtained using Model 2, and higher than those in both breeds obtained using Model 1. Further results based on data simulated using the Monte Carlo method suggest that estimated additive genetic variance could be more biased using Model 2 than Model 1. [ABSTRACT FROM AUTHOR]

The aim of this study was to compare the quality of musculus longissimus dorsi in the breeds of Mangalitsa and Large White with regard to the slaughter weight. Large White (LW) breed and White Mangalitsa (Ma) breed were used in the experiment. The system of housing and feeding was the same in both of the monitored breeds. The pigs were fed with the same feeding mixture ad libitum. According to the slaughter weight, the pigs were divided into three groups: up to 100 kg, 101 - 110 kg and over 110 kg. The breed Ma had a significantly lower drip loss than the breed LW. Evaluating the color of the meat, the LW breed has showed significantly higher L* (lightness, white ±black) and lower a* (redness, red ± green) values than the Ma breed. Within the chemical meat composition, the Ma breed had a significantly higher water content in MLD compared to the LW breed. Generally, there were no major differences in the meat quality between the Mangalitsa and Large White breeds. Finally it can be concluded that the breed Mangalitsa showed more favorable values of the physico-chemical indicators. Comparing the quality of the meat with regard to the slaughter weight, there were no large differences between individual weight groups. A higher slaughter weight has positively influenced mainly the color of the meat, as pigs weighing more than 110 kg achieved a significantly lower value of L* and a higher value of a* in comparison to pigs of the lower weight. As a positive effect of a higher slaughter weight can be considered its effect on the protein content in the meat, as pigs weighing over 100 kg have a significantly higher protein content in the meat than pigs weighing below 100 kg. [ABSTRACT FROM AUTHOR]

Copyright of Nihon Chikusan Gakkaiho is the property of Japanese Society of Animal Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A research aimed at evaluating the reproductive performance of pigs as influenced by breed, season, and parity was carried out. Reproductive performance records of 1383 sows made up of 204 primiparous and 1179 multiparous large white (687) and Duroc × large white crosses (696) obtained over a 3-year period (2013 to 2016) were used. Data on reproductive performance taken were farrowing rate, litter size at birth and at weaning, piglet weight at birth and at weaning farrowing interval and pre-weaning mortality. To determine the effect of season, parity and breed on reproductive performance, the 4-way analysis of variance (ANOVA) was used. A mixed model was fitted using the generalized linear model (GLM) procedure of the GenStat (Discovery Edition) to investigate the fixed effects of breed (two classes), season of birth (three classes) and parity (five classes) on the reproductive traits. Where differences in means were observed, the means were separated using the least significant difference at 5% level of significance. Results obtained indicated litter size at birth of 12.5 ± 0.3 and 14.2 ± 0.5 for the large white and the Duroc × large white respectively and farrowing rates of 90.0 ± 2.3 and 94.5 ± 2.5% for the large white and the Duroc × large white respectively. Litter size at birth and at weaning, farrowing rate and piglet weight at birth were all influenced by parity and season with increasing parity significantly reducing litter size at birth and at weaning. Reproductive traits of the sows were superior during the rainy season, an indication of the need to consider season of birth in making decision. [ABSTRACT FROM AUTHOR]

The Large White pig is the most commonly raised commercial pig breed in the world. The aim of this work was to investigate D-loop mtDNA in Large White pigs (n = 402) of various selections bred in the Russian Federation from 2000 to 2019. The general sample consisted of three groups: Old (n = 78) (Russian selection, 2000–2010); Imp (n = 123) (imported to Russia in 2008–2014); New (n = 201) (2015–2019). The synthesized score (Fz) was calculated by analyzing the main PCA (principal component analysis components). An affiliation to Asian or European haplogroups was determined according to the NCBI (National Center for Biotechnology Information). In the study, we defined 46 polymorphic sites and 42 haplotypes. Significant distinctions between groups Old, Imp and New in frequencies of haplotypes and haplogroups were established. The distribution of Asian and European haplotypes in the groups was Old: 50%/50%, Imp: 43%/57%, New: 75%/25%, respectively. The variety of haplotypes and haplogroups in the pigs of the group New is related to the farms in which they breed. Haplotype frequencies significantly differ between the clusters Old_Center, Old_Siberia and Old_South. This study will provide information on the genetic diversity of Large White breed pigs. The results will be useful for the conservation and sustainable use of these resources.

Restricted maximum likelihood (REML) was used to analyze 6500 on-farm performance test records obtained from a local swine breeding farm from 2014 and 2016, consisting of 2651 Landrace (759 boars; 1912 gilts) and 3829 Large White (1145 boars; 2684 gilts). The high heritability estimates based on paternal half sib correlation for average daily gain, ADG (0.65-0.85 in boars, 0.26-0.37 in gilts), feed conversion ratio, FCR (0.26-0.28), weight at end of test, WtEOT (0.60-0.87 in boars, 0.35-0.43 in gilts), backfat thickness - from shoulder, BFT1 (0.52-0.58 in boars, 0.30-0.51 in gilts), midback, BFT2 (0.45-0.76 in boars, 0.14-0.38 in gilts), ham, BFT3 (0.32-0.61 in boars, 0.36 for gilts), and their average, AveBFT (0.62-0.77 for boars, 0.36-0.42 for gilts) suggest that local selection for performance test traits in Landrace and Large White pigs may lead to substantial response to selection while the significant genetic correlations among them is expected to bring correlated responses in the other traits. Significant phenotypic correlations among performance test traits (except FCR) were positively correlated with each other, ranging from 0.39 to 0.96. FCR was negatively correlated to other boar performance test traits, ranging from -0.32 to -0.83. Significant genetic correlations varied considerably among performance test traits. [ABSTRACT FROM AUTHOR]