Genetic Analysis of Major Production and Reproduction Traits of Korean Duroc, Landrace and Yorkshire Pigs

Genomic Prediction of Growth Traits in Yorkshire Pigs of Different Reference Group Sizes Using Different Estimated Breeding Value Models

The need for sufficient reference population data poses a significant challenge in breeding programs aimed at improving pig farming on a small to medium scale. To overcome this hurdle, investigating the advantages of combing reference populations of varying sizes is crucial for enhancing the accuracy of the genomic estimated breeding value (GEBV). Genomic selection (GS) in populations with limited reference data can be optimized by combining populations of the same breed or related breeds. This study focused on understanding the effect of combing different reference group sizes on the accuracy of GS for determining the growth effectiveness and percentage of lean meat in Yorkshire pigs. Specifically, our study investigated two important traits: the age at 100 kg live weight (AGE100) and the backfat thickness at 100 kg live weight (BF100). This research assessed the efficiency of genomic prediction (GP) using different GEBV models across three Yorkshire populations with varying genetic backgrounds. The GeneSeek 50K GGP porcine high-density array was used for genotyping. A total of 2295 Yorkshire pigs were included, representing three Yorkshire pig populations with different genetic backgrounds-295 from Danish (small) lines from Huaibei City, Anhui Province, 500 from Canadian (medium) lines from Lixin County, Anhui Province, and 1500 from American (large) lines from Shanghai. To evaluate the impact of different population combination scenarios on the GS accuracy, three approaches were explored: (1) combining all three populations for prediction, (2) combining two populations to predict the third, and (3) predicting each population independently. Five GEBV models, including three Bayesian models (BayesA, BayesB, and BayesC), the genomic best linear unbiased prediction (GBLUP) model, and single-step GBLUP (ssGBLUP) were implemented through 20 repetitions of five-fold cross-validation (CV). The results indicate that predicting one target population using the other two populations yielded the highest accuracy, providing a novel approach for improving the genomic selection accuracy in Yorkshire pigs. In this study, it was found that using different populations of the same breed to predict small- and medium-sized herds might be effective in improving the GEBV. This investigation highlights the significance of incorporating population combinations in genetic models for predicting the breeding value, particularly for pig farmers confronted with resource limitations.

Application of deep learning with bivariate models for genomic prediction of sow lifetime productivity-related traits

OBJECTIVE

Pig breeders cannot obtain phenotypic information at the time of selection for sow lifetime productivity (SLP). They would benefit from obtaining genetic information of candidate sows. Genomic data interpreted using deep learning (DL) techniques could contribute to the genetic improvement of SLP to maximize farm profitability because DL models capture nonlinear genetic effects such as dominance and epistasis more efficiently than conventional genomic prediction methods based on linear models. This study aimed to investigate the usefulness of DL for the genomic prediction of two SLP-related traits; lifetime number of litters (LNL) and lifetime pig production (LPP).

METHODS

Two bivariate DL models, convolutional neural network (CNN) and local convolutional neural network (LCNN), were compared with conventional bivariate linear models (i.e., genomic best linear unbiased prediction, Bayesian ridge regression, Bayes A, and Bayes B). Phenotype and pedigree data were collected from 40,011 sows that had husbandry records. Among these, 3,652 pigs were genotyped using the PorcineSNP60K BeadChip.

RESULTS

The best predictive correlation for LNL was obtained with CNN (0.28), followed by LCNN (0.26) and conventional linear models (approximately 0.21). For LPP, the best predictive correlation was also obtained with CNN (0.29), followed by LCNN (0.27) and conventional linear models (approximately 0.25). A similar trend was observed with the mean squared error of prediction for the SLP traits.

CONCLUSION

This study provides an example of a CNN that can outperform against the linear model-based genomic prediction approaches when the nonlinear interaction components are important because LNL and LPP exhibited strong epistatic interaction components. Additionally, our results suggest that applying bivariate DL models could also contribute to the prediction accuracy by utilizing the genetic correlation between LNL and LPP.

Genetic evaluation for production and body size traits using different animal models in purebred-Duroc pigs

Duroc pigs are popular crossbred terminal sires, and accurate assessment of genetic parameters in the population can help to rationalize breeding programmes. The principle aim of this study were to evaluate the genetic parameters of production (birth weight, BW; age at 115 kg, AGE; feed conversion ratio, FCR) and body size (body length, BL; body height, BH; front cannon circumference, FCC) traits of Duroc pigs. The second objective was to analyze the fit of different genetic assessment models. The variance components and correlations of BW (28,348 records), AGE (28,335 records), FCR (11,135 records), BL (31,544 records), BH (21,862 records), and FCC (14,684 records) traits were calculated by using DMU and AIREMLF90 from BLUPF90 package. In the common environment model, the heritability of BW, AGE, FCR, BL, BH, and FCC traits were 0.17 ± 0.014, 0.30 ± 0.019, 0.28 ± 0.024, 0.16 ± 0.013, 0.14 ± 0.017, and 0.081 ± 0.016, with common litter effect values of 0.25, 0.20, 0.18, 0.23, 0.19, and 0.16, respectively. According to the results of the Akaike information criterion (AIC) calculations, models with smaller AIC values have a better fit. We found that the common environment model with litter effects as random effects for estimating genetic parameters had a better fit. In this Model, the estimated genetic correlations between AGE with BW, FCR, BL, BH, and FCC traits were -0.28 (0.040), 0.76 (0.038), -0.71 (0.036), -0.44 (0.060), and -0.60 (0.073), respectively, with phenotypic correlations of -0.17, 0.52, -0.22, -0.13 and -0.24, respectively. In our analysis of genetic trends for six traits in the Duroc population from 2012 to 2021, we observed significant genetic trends for AGE, BL, and BH. Particularly noteworthy is the rapid decline in the genetic trend for AGE, indicating an enhancement in the pig's growth rate through selective breeding. Therefore, we believe that some challenging-to-select traits can benefit from the genetic correlations between traits. By selecting easily measurable traits, they can gain from synergistic selection effects, leading to genetic progress. Conducting population genetic parameter analysis can assist us in devising breeding strategies.

Estimation of genetic parameters of pig reproductive traits

Introduction

In this study, we aimed to estimate the genetic parameters of the reproductive traits in three popular commercial pig breeds: Duroc, Landrace, and Yorkshire. Additionally, we evaluated the factors that influence these traits.

Method

We collected data from a large number of litters, including 1,887 Duroc, 21,787 Landrace, and 74,796 Yorkshire litters. Using the ASReml-R software to analyze 11 traits, which included: total number of pigs born (TNB); number of piglets born alive (NBA); number of piglets born healthy (NBH); number of piglets born weak (NBW); number of new stillborn piglets (NS); number of old stillborn piglets (OS); number of piglets born with malformation (NBM); number of mummified piglets (NM); total litter birthweight (LBW); litter average weight (LAW); duration of gestational period (GP). We investigated the effects of 4 fixed factors on the genetic parameters of these traits.

Results

Among the 11 reproductive-related traits, the gestational period belonged to the medium heritability traits (0.251-0.430), while remaining traits showed low heritability, ranging from 0.005 to 0.159. TNB, NBA, NBH, LBW had positive genetic correlation (0.737 ~ 0.981) and phenotype correlation (0.711 ~ 0.951). There was a negative genetic correlation between NBW and LAW (-0.452 ~ -0.978) and phenotypic correlation (-0.380 ~ -0.873). LBW was considered one of the most reasonable reproductive traits that could be used for breeding improvement. Repeatability of the three varieties was within the range of 0.000-0.097. In addition, the fixed effect selected in this study had a significant effect on Landrace and Yorkshire (p < 0.05).

Discussion

We found a positive correlation between LBW and TNB, NBA, and NBH, suggesting the potential for multi-trait association breeding. Factors such as farm, farrowing year, breeding season, and parity should be taken into consideration in practical production, as they may impact the reproductive performance of breeding pigs.

Performance of crossbred pigs with indigenous and Hampshire inheritance under a smallholder production system in the Eastern Himalayan hill region

Pig productivity is very low in the Eastern Himalayan hill region due to the poor performance of local pigs. To improve pig productivity, it was decided to develop a crossbred pig of Niang Megha indigenous and Hampshire as an exotic germplasm. The performance of crossbred pigs with different levels of Hampshire and indigenous inheritance—H-50 × NM-50 (HN-50), H-75 × NM-25 (HN-75), and H-87.5 × NM-12.5 (HN-87.5)—was compared for their performance to find a suitable level of genetic inheritance. Among the crossbreds, HN-75 performed better in terms of production, reproduction performance, and adaptability. Inter se mating and selection were carried out on six generations of HN-75 pigs, and genetic gain and trait stability were evaluated and released as a crossbred. These crossbred pigs attained body weights of 77.5–90.7 kg by 10 months of age, with FCR of 4.3:1. Age at puberty was 276.66 ± 2.25 days, and average birth weight was 0.92 ± 0.06 kg. Litter size at birth and weaning were 9.12 ± 0.55 and 8.52 ± 0.81. These pigs have good mothering abilities with a weaning percentage of 89.32 ± 2.52%, good carcass quality, and consumer preference. The lifetime productivity for an average of six farrowings/sow showed a total litter size at birth of 51.83 ± 1.61 and total litter size at weaning of 47.17 ± 2.69. In a smallholder production system, the crossbred pigs showed a better growth rate and a higher litter size at birth and at weaning than average local pigs. Hence, the popularization of this crossbreed would enhance the production, productivity, livelihood, and income of the regionʼs farmers.

Gut microbiota and growth performance of offspring are influenced by wet nurse in pigs using cross-fostering trial

BACKGROUND

Nursing mom can regulate the gut microbiome succession in offspring. However, it remains unclear whether these effects are long-term and what effect it has on the growth performance of piglets. This study aimed to develop a cross-fostering model of piglets and investigate the effect of maternal environment on gut microbiota, even the growth performance of the offspring, and if this effect could be maintained in the long term.

RESULTS

Four groups of piglets were generated as follows (n = 12): Duroc piglets nursed by their birth mom (Dd) or Yorkshire sows (Yd), Yorkshire piglets nursed by Duroc sows (Dy) or their birth mom (Yy). The study found that cross-fostering improved the growth performance of piglets for a long time. The gut microbiota of piglets was mainly determined by the breeds of nursing moms before weaning, and it was more and more influenced by their breeds after weaning, but the influence of birth mom breeds still existed. The linear discriminant analysis (LDA) effect size (LEfSe) analysis and Spearman correlation analysis showed that Sutterella, Butyricimonas and Alistipes, which were affected by nursing mom before weaning, had a strong positive correlation with the growth performance of piglets before weaning. Candidatus_Soleaferrea and Treponema, which were affected by both nursing mom and piglet breed after weaning, were significantly negatively correlated with the growth performance of piglets long after weaning.

CONCLUSION

Our results revealed that both the breeds of piglets and their birth moms influence the gut microbiota of piglets for a long time, even after weaning. Additionally, this effect might be related to the growth performance of piglets. © 2022 Society of Chemical Industry.

Direct-Maternal Genetic Parameters for Litter Size and Body Weight of Piglets of a New Black Breed for the Taiwan Black Hog Market

The objective of this study was to estimate the genetic parameters of litter size and piglet weight from farrowing to weaning in KHAPS Black sows. The genetic parameters investigated were the direct (h²_d), maternal (h²_m), realized (h²_r), and total (h²_T) heritability, as well as correlations (r_d, r_m, and r_dm) within and between traits. The analyses were performed using single- and three-trait animal models with and without maternal genetic effects. In the three-trait model with maternal genetic effect, all estimates of h²_d and h²_m were significantly different from zero except the h²_d of mean birth weight. Positive values of r_d and r_m between traits were observed as expected in the range of 0.322-1.000. Negative values of r_dm were found within and between traits and were less associated with mean piglet weight traits than litter size traits. Estimates of h²_T were consistently larger than those of h²_r in both the single- and three-trait model analyses. In addition, the three-trait model can take into account the association between the traits, so the estimates are more accurate with smaller SEs. In conclusion, maternal genetic effects were not negligible in this study, and thus, a multiple-trait animal model with maternal genetic effects and full pedigree is recommended to assist future pig breeding decisions in this new breed.

Genetic Parameter Estimation and Genome-Wide Association Analysis of Social Genetic Effects on Average Daily Gain in Purebreds and Crossbreds

Simple Summary

Average daily gain (ADG) is influenced by both an individual’s direct genetic effect (DGE) and by a social genetic effect (SGE) derived from pen mates. Therefore, identifying the DGE and SGE on ADG is essential for a better understanding of pig breeding systems. We conducted this study to elucidate the genetic characteristics and relationships of DGE and SGE on ADG using purebred and crossbred pigs. We found that the DGE and SGE both contributed to ADG in both populations. In addition, the SGE of purebred pigs was highly correlated with the DGE of crossbred pigs. Furthermore, we identified several genomic regions that may be associated with the DGE and SGE on ADG. Our findings will contribute to future genomic evaluation studies of socially affected traits.

Abstract

Average daily gain (ADG) is an important growth trait in the pig industry. The direct genetic effect (DGE) has been studied mainly to assess the association between genetic information and economic traits. The social genetic effect (SGE) has been shown to affect ADG simultaneously with the DGE because of group housing systems. We conducted this study to elucidate the genetic characteristics and relationships of the DGE and SGE of purebred Korean Duroc and crossbred pigs by single-step genomic best linear unbiased prediction and a genome-wide association study. We used the genotype, phenotype, and pedigree data of 1779, 6022, and 7904 animals, respectively. Total heritabilities on ADG were 0.19 ± 0.04 and 0.39 ± 0.08 for purebred and crossbred pigs, respectively. The genetic correlation was the greatest (0.77 ± 0.12) between the SGE of purebred and DGE of crossbred pigs. We found candidate genes located in the quantitative trait loci (QTLs) for the SGE that were associated with behavior and neurodegenerative diseases, and candidate genes in the QTLs for DGE that were related to body mass, size of muscle fiber, and muscle hypertrophy. These results suggest that the genomic selection of purebred animals could be applied for crossbred performance.

Application of Genetic, Genomic and Biological Pathways in Improvement of Swine Feed Efficiency

Despite the significant improvement of feed efficiency (FE) in pigs over the past decades, feed costs remain a major challenge for producers profitability. Improving FE is a top priority for the global swine industry. A deeper understanding of the biology underlying FE is crucial for making progress in genetic improvement of FE traits. This review comprehensively discusses the topics related to the FE in pigs including: measurements, genetics, genomics, biological pathways and the advanced technologies and methods involved in FE improvement. We first provide an update of heritability for different FE indicators and then characterize the correlations of FE traits with other economically important traits. Moreover, we present the quantitative trait loci (QTL) and possible candidate genes associated with FE in pigs and outline the most important biological pathways related to the FE traits in pigs. Finally, we present possible ways to improve FE in swine including the implementation of genomic selection, new technologies for measuring the FE traits, and the potential use of genome editing and omics technologies.

The results of fattening hybrid pigs of Danish selection

The results of a study of the breed combinations that influence pigs of Danish selection on fattening, meat, and economic indicators are presented. The experimental animals were divided into two groups according to their genotype. The method of groups-analogs was applied. The pigs were similar in terms of conditional heredity, sex, age, and body weight within each group. Five animals from each group were slaughtered at the meat processing plant to study the meat qualities. These studies arose due to the constant search for the most successful and cost-effective options for breed combinations that guarantee the effect of heterosis in hybrid offspring, which determined the scientific novelty and relevance. The dependence on breed combinations of fattening and meat qualities of young pigs obtained by simple two-breed and complex three-breed industrial crossbreeding of breeds of Danish selection has been established. At the final stage of fattening, a significant intergroup difference was established in terms of the average daily increase in body weight in favor of pigs of the breed combination F2 ¼Y ¼L ½D at 5.4% at P > 0.999 compared with analogs of the breed combination F1 ½Y ½L. Crossbreeds of the F2 generation turned out to be more early maturing and reached a body weight of 100 kg in 168.1 days, compared to 173.2 days in crossbreeds of the F1 generation with a difference of 5.1 days (P > 0.95) and a slaughter weight of 110 kg with a difference of 5.5 days (P > 0.95). They also spent less feed by 0.18 feed units per 1 kg of body weight gain, but the intergroup difference was insignificant. At slaughter, the weight of the steamed carcass in the crossbreeds of generation F2 ¼Y ¼L ½D was higher by 6.7 kg (P<0.95), and the slaughter yield was higher by 5.9 % (P > 0.999). It is concluded that the combination of Yorkshire breeds with Landraces and Durocs achieves a high economic effect on pork production since the level of profitability of pork production in three-breed hybrids is higher by 6.6%, respectively than in two-breed ones.

Genetic and phenotypic correlations between backfat thickness and weight at 28 weeks of age, and reproductive performance in primiparous Landrace sows raised under tropical conditions

Backfat thickness could reflect the energy reserve of female pigs that is required for their reproductivity, especially gilts that might be selected as replacements. In this study, genetic and phenotypic correlations between backfat thickness (BF) and body weight (BW) at 28 weeks of age, and reproduction traits were estimated. They were considered for the possibility of using BF at the pre-selective stage as an early indicator of sow's reproduction potential. Pedigree information, BF and BW at 28 weeks of age, age at first farrowing (AFF), transformed proportion of piglet loss at birth (tPL), and transformed weaning to first service interval (tWSI) of 806 primiparous Landrace sows were used to estimate the variance components by a restricted maximum likelihood procedure with an average information algorithm for multivariate analysis. The genetic correlation between BF and BW was 0.70 ± 0.13. Both BF and BW had a negative genetic correlation with AFF but not with tWSI. Genetic correlation estimates between tPL and other traits were unclear due to high standard error. The genetic correlation between AFF and tWSI was 0.78 ± 0.36. There were 19.35% of sires, 26.34% of dams, and 25.81% of sows that had preferable estimated breeding values for BF, BW, AFF, and WSI. These values indicated the feasibility of using selection index to improve BF and BW at the pre-selective stage and reduce AFF and tWSI of replacement gilt simultaneously. The estimation of genetic correlation between PL and other traits warrants further study in larger populations.