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Background: The interplay between the gut microbiota and feeding behavior has consequences for host metabolism and health. The present study aimed to explore gut microbiota overall influence on feeding behavior traits and to identify specific microbes associated with the traits in three commercial swine breeds at three growth stages. Feeding behavior measures were obtained from 651 pigs of three breeds (Duroc, Landrace, and Large White) from an average 73 to 163 days of age. Seven feeding behavior traits covered the information of feed intake, feeder occupation time, feeding rate, and the number of visits to the feeder. Rectal swabs were collected from each pig at 73 ± 3, 123 ± 4, and 158 ± 4 days of age. DNA was extracted and subjected to 16 S rRNA gene sequencing., Results: Differences in feeding behavior traits among breeds during each period were found. The proportion of phenotypic variances of feeding behavior explained by the gut microbial composition was small to moderate (ranged from 0.09 to 0.31). A total of 21, 10, and 35 amplicon sequence variants were found to be significantly (q-value < 0.05) associated with feeding behavior traits for Duroc, Landrace, and Large White across the three sampling time points. The identified amplicon sequence variants were annotated to five phyla, with Firmicutes being the most abundant. Those amplicon sequence variants were assigned to 28 genera, mainly including Christensenellaceae&#95;R-7&#95;group, Ruminococcaceae&#95;UCG-004, Dorea, Ruminococcaceae&#95;UCG-014, and Marvinbryantia., Conclusions: This study demonstrated the importance of the gut microbial composition in interacting with the host feeding behavior and identified multiple archaea and bacteria associated with feeding behavior measures in pigs from either Duroc, Landrace, or Large White breeds at three growth stages. Our study provides insight into the interaction between gut microbiota and feeding behavior and highlights the genetic background and age effects in swine microbial studies., (© 2021. The Author(s).)

Genomic selection increases accuracy and decreases generation interval, speeding up genetic changes in the populations. However, intensive changes caused by selection can reduce the genetic variation and can strengthen undesirable genetic correlations. The purpose of this study was to investigate changes in genetic parameters for fitness traits related with prolificacy (FT1) and litter survival (FT2 and FT3), and for growth (GT1 and GT2) traits in pigs over time. The data set contained 21,269 (FT1), 23,246 (FT2), 23,246 (FT3), 150,492 (GT1), and 150,493 (GT2) phenotypic records obtained from 2009 to 2018. The pedigree file included 369,776 animals born between 2001 and 2018, of which 39,103 were genotyped. Genetic parameters were estimated with bivariate models (FT1-GT1, FT1-GT2, FT2-GT1, FT2-GT2, FT3-GT1, and FT3-GT2) using 3-yr sliding subsets. With a Bayesian implementation using the GIBBS3F90 program computations were performed as genomic analysis (GEN) or pedigree-based analysis (PED), that is, with or without genotypes, respectively. For GEN (PED), the changes in heritability from the first to the last year interval, that is, from 2009-2011 to 2015-2018 were 8.6 to 5.6 (7.9 to 8.8) for FT1, 7.8 to 7.2 (7.7 to 10.8) for FT2, 11.4 to 7.6 (10.1 to 7.5) for FT3, 35.1 to 16.5 (32.5 to 23.7) for GT1, and 35.9 to 16.5 (32.6 to 24.1) for GT2. Differences were also observed for genetic correlations as they changed from -0.31 to -0.58 (-0.28 to -0.73) for FT1-GT1, -0.32 to -0.50 (-0.29 to -0.74) for FT1-GT2, -0.27 to -0.45 (-0.30 to -0.65) for FT2-GT1, -0.28 to -0.45 (-0.32 to -0.66) for FT2-GT2, 0.14 to 0.17 (0.11 to 0.04) for FT3-GT1, and 0.14 to 0.18 (0.11 to 0.05) for FT3-GT2. Strong selection in pigs reduced heritabilities and emphasized the antagonistic genetic relationships between fitness and growth traits. With genotypes considered, heritability estimates were smaller and genetic correlations were greater than estimates with only pedigree and phenotypes. When selection is based on genomic information, genetic parameters estimated without this information can be biased because preselection is not accounted for by the model., (© The Author(s) 2020. Published by Oxford University Press on behalf of the American Society of Animal Science.)

Background: Commercial breeding programs seek to maximise the rate of genetic gain while minimizing the costs of attaining that gain. Genomic information offers great potential to increase rates of genetic gain but it is expensive to generate. Low-cost genotyping strategies combined with genotype imputation offer dramatically reduced costs. However, both the costs and accuracy of imputation of these strategies are highly sensitive to several factors. The objective of this paper was to explore the cost and imputation accuracy of several alternative genotyping strategies in pedigreed populations., Methods: Pedigree and genotype data from a commercial pig population were used. Several alternative genotyping strategies were explored. The strategies differed in the density of genotypes used for the ancestors and the individuals to be imputed. Parents, grandparents, and other relatives that were not descendants, were genotyped at high-density, low-density, or extremely low-density, and associated costs and imputation accuracies were evaluated., Results: Imputation accuracy and cost were influenced by the alternative genotyping strategies. Given the mating ratios and the numbers of offspring produced by males and females, an optimized low-cost genotyping strategy for a commercial pig population could involve genotyping male parents at high-density, female parents at low-density (e.g. 3000 SNP), and selection candidates at very low-density (384 SNP)., Conclusions: Among the selection candidates, 95.5% and 93.5% of the genotype variation contained in the high-density SNP panels were recovered using a genotyping strategy that costs respectively, $24.74 and $20.58 per candidate.

Genetic improvement of livestock productivity has resulted in greater production of metabolic heat and potentially greater susceptibility to heat stress. Various studies have demonstrated that there is genetic variability for heat tolerance and genetic selection for more heat tolerant individuals is possible. The rate of genetic progress tends to be greater when genomic information is incorporated into the analyses as more accurate breeding values can be obtained for young individuals. Therefore, this study aimed (1) to evaluate the predictive ability of genomic breeding values for heat tolerance based on routinely recorded traits, and (2) to investigate the genetic background of heat tolerance based on single‐step genome‐wide association studies for economically important traits related to body composition, growth and reproduction in Large White pigs. Pedigree information was available for 265,943 animals and genotypes for 8686 animals. The studied traits included ultrasound backfat thickness (BFT), ultrasound muscle depth (MDP), piglet weaning weight (WW), off‐test weight (OTW), interval between farrowing (IBF), total number of piglets born (TNB), number of piglets born alive (NBA), number of piglets born dead (NBD), number of piglets weaned (WN) and weaning‐to‐estrus interval (IWE). The number of phenotypic records ranged from 6059 (WN) to 172,984 (TNB). Single‐step genomic reaction norm predictions were used to calculate the genomic estimated breeding values for each individual. Predictions of breeding values for the validation population individuals were compared between datasets containing phenotypic records measured in the whole range of temperatures (WR) and datasets containing only phenotypic records measured when the weather station temperature was above 10°C (10C) or 15°C (15C), to evaluate the usefulness of these datasets that may better reflect the within‐barn temperature. The use of homogeneous or heterogeneous residual variance was found to be trait‐dependent, where homogeneous variance presented the best fit for MDP, BFT, OTW, TNB, NBA, WN and IBF, while the other traits (WW and IWE) had better fit with heterogeneous variance. The average prediction accuracy, dispersion and bias values considering all traits for WR were 0.36 ± 0.05, −0.07 ± 0.13 and 0.76 ± 0.10, respectively; for 10C were 0.39 ± 0.02, −0.05 ± 0.07 and 0.81 ± 0.05, respectively; and for 15C were 0.32 ± 0.05, −0.05 ± 0.11 and 0.84 ± 0.10, respectively. Based on the studied traits, using phenotypic records collected when the outside temperature (from public weather stations) was above 10°C provided better predictions for most of the traits. Forty‐three and 62 candidate genomic regions were associated with the intercept (overall performance level) and slope term (specific biological mechanisms related to environmental sensitivity), respectively. Our results contribute to improve genomic predictions using existing datasets and better understand the genetic background of heat tolerance in pigs. Furthermore, the genomic regions and candidate genes identified will contribute to future genomic studies and breeding applications. [ABSTRACT FROM AUTHOR]

The microbial composition resemblance among individuals in a group can be summarized in a square covariance matrix and fitted in linear models. We investigated eight approaches to create the matrix that quantified the resemblance between animals based on the gut microbiota composition. We aimed to compare the performance of different methods in estimating trait microbiability and predicting growth and body composition traits in three pig breeds. This study included 651 purebred boars from either breed: Duroc (n = 205), Landrace (n = 226), and Large White (n = 220). Growth and body composition traits, including body weight (BW), ultrasound backfat thickness (BF), ultrasound loin depth (LD), and ultrasound intramuscular fat (IMF) content, were measured on live animals at the market weight (156 ± 2.5 d of age). Rectal swabs were taken from each animal at 158 ± 4 d of age and subjected to 16S rRNA gene sequencing. Eight methods were used to create the microbial similarity matrices, including 4 kernel functions (Linear Kernel, LK ; Polynomial Kernel, PK ; Gaussian Kernel, GK ; Arc-cosine Kernel with one hidden layer, AK1), 2 dissimilarity methods (Bray-Curtis, BC ; Jaccard, JA), and 2 ordination methods (Metric Multidimensional Scaling, MDS ; Detrended Correspondence analysis, DCA). Based on the matrix used, microbiability estimates ranged from 0.07 to 0.21 and 0.12 to 0.53 for Duroc, 0.03 to 0.21 and 0.05 to 0.44 for Landrace, and 0.02 to 0.24 and 0.05 to 0.52 for Large White pigs averaged over traits in the model with sire, pen, and microbiome, and model with the only microbiome, respectively. The GK, JA, BC, and AK1 obtained greater microbiability estimates than the remaining methods across traits and breeds. Predictions were made within each breed group using four-fold cross-validation based on the relatedness of sires in each breed group. The prediction accuracy ranged from 0.03 to 0.18 for BW, 0.08 to 0.31 for BF, 0.21 to 0.48 for LD, and 0.04 to 0.16 for IMF when averaged across breeds. The BC, MDS, LK, and JA achieved better accuracy than other methods in most predictions. Overall, the PK and DCA exhibited the worst performance compared to other microbiability estimation and prediction methods. The current study shows how alternative approaches summarized the resemblance of gut microbiota composition among animals and contributed this information to variance component estimation and phenotypic prediction in swine. [ABSTRACT FROM AUTHOR]

The purpose of this study was to investigate the use of feeding behavior in conjunction with gut microbiome sampled at two growth stages in predicting growth and body composition traits of finishing pigs. Six hundred and fifty-one purebred boars of three breeds: Duroc (DR), Landrace (LR), and Large White (LW), were studied. Feeding activities were recorded individually from 99 to 163 days of age. The 16S rRNA gene sequences were obtained from each pig at 123 ± 4 and 158 ± 4 days of age. When pigs reached market weight, body weight (BW), ultrasound backfat thickness (BF), ultrasound loin depth (LD), and ultrasound intramuscular fat (IMF) content were measured on live animals. Three models including feeding behavior (Model_FB), gut microbiota (Model_M), or both (Model_FB_M) as predictors, were investigated. Prediction accuracies were evaluated through cross-validation across genetic backgrounds using the leave-one-breed-out strategy and across rearing environments using the leave-one-room-out approach. The proportions of phenotypic variance of growth and body composition traits explained by feeding behavior ranged from 0.02 to 0.30, and from 0.20 to 0.52 when using gut microbiota composition. Overall prediction accuracy (averaged over traits and time points) of phenotypes was 0.24 and 0.33 for Model_FB, 0.27 and 0.19 for Model_M, and 0.40 and 0.35 for Model_FB_M for the across-breed and across-room scenarios, respectively. This study shows how feeding behavior and gut microbiota composition provide non-redundant information in predicting growth in swine. [ABSTRACT FROM AUTHOR]

Porcine Reproductive and Respiratory Syndrome (PRRS) is historically the most economically important swine disease worldwide that severely affects the reproductive performance of sows. However, little is still known about the genetic basis of reproductive performance in purebred herds during a PRRS outbreak through the comparison of maternal and terminal breeds. Thus, the objective of this work was to explore the host genetics of response to PRRS in purebred sows from two breeds. Reproductive data included 2546 Duroc and 2522 Landrace litters from 894 and 813 purebred sows, respectively, which had high-density genotype data available (29,799 single nucleotide polymorphisms; SNPs). The data were split into pre-PRRS, PRRS, and post-PRRS phases based on standardized farrow-year-week estimates. Heritability estimates for reproductive traits were low to moderate (≤0.20) for Duroc and Landrace across PRRS phases. On the other hand, genetic correlations of reproductive traits between PRRS phases were overall moderate to high for both breeds. Several associations between MARC0034894, a candidate SNP for response to PRRS, with reproductive performance were identified (P -value < 0.05). Genomic analyses detected few QTL for reproductive performance across all phases, most explaining a small percentage of the additive genetic variance (≤8.2%, averaging 2.1%), indicating that these traits are highly polygenic. None of the identified QTL within a breed and trait overlapped between PRRS phases. Overall, our results indicate that Duroc sows are phenotypically more resilient to PRRS than Landrace sows, with a similar return to PRRS-free performance between breeds for most reproductive traits. Genomic prediction results indicate that genomic selection for improved reproductive performance under a PRRS outbreak is possible, especially in Landrace sows, by training markers using data from PRRS-challenged sows. On the other hand, the high genetic correlations with reproductive traits between PRRS phases suggest that selection for improved reproductive performance in a clean environment could improve performance during PRRS, but with limited efficiency due to their low heritability estimates. Thus, we hypothesize that an indicator trait that could be indirectly selected to increase the response to selection for these traits would be desirable and would also improve the reproductive performance of sows during a PRRS outbreak. [ABSTRACT FROM AUTHOR]

Antibody response to porcine reproductive and respiratory syndrome (PRRS) virus (PRRSV) infection, measured as sample-to-positive (S/P) ratio, has been proposed as an indicator trait for improved reproductive performance during a PRRS outbreak in Landrace sows. However, this result has not yet been validated in Landrace sows or evaluated in terminal sire lines. The main objectives of this work were to validate the use of S/P ratio as an indicator trait to select pigs during a PRRS outbreak and to explore the genetic basis of antibody response to PRRSV. Farrowing data included 2,546 and 2,522 litters from 894 Duroc and 813 Landrace sows, respectively, split into pre-PRRS, PRRS, and post-PRRS phases. Blood samples were taken from 1,231 purebred sows (541 Landrace and 690 Duroc) following a PRRS outbreak for subsequent PRRSV ELISA analysis for S/P ratio measurement. All animals had high-density genotype data available (29,799 single nucleotide polymorphisms; SNPs). Genetic parameters and genome-wide association studies (GWAS) for S/P ratio were performed for each breed separately. Heritability estimates (± standard error) of S/P ratio during the PRRS outbreak were moderate, with 0.35 ± 0.08 for Duroc and 0.34 ± 0.09 for Landrace. During the PRRS outbreak, favorable genetic correlations of S/P ratio with the number of piglets born alive (0.61 ± 0.34), number of piglets born dead (−0.33 ± 0.32), and number of stillborn piglets (−0.27 ± 0.31) were observed for Landrace sows. For Duroc, the GWAS identified a major quantitative trait locus (QTL) on chromosome (Chr) 7 (24-15 megabases; Mb) explaining 15% of the total genetic variance accounted for by markers (TGVM), and another one on Chr 8 (25 Mb) explaining 2.4% of TGVM. For Landrace, QTL on Chr 7 (24–25 Mb) and Chr 7 (108–109 Mb), explaining 31% and 2.2% of TGVM, respectively, were identified. Some of the SNPs identified in these regions for S/P ratio were associated with reproductive performance but not during the PRRS outbreak. Genomic prediction accuracies for S/P ratio were moderate to high for the within-breed analysis. For the between-breed analysis, these were overall low. These results further support the use of S/P ratio as an indicator trait for improved reproductive performance during a PRRS outbreak in Landrace sows. [ABSTRACT FROM AUTHOR]

This study aimed to investigate interpopulation variation due to sex, breed and age, and the intrapopulation variation in the form of genetic variance for recombination in swine. Genome‐wide recombination rate and recombination occurrences (RO) were traits studied in Landrace (LR) and Large White (LW) male and female populations. Differences were found for sex, breed, sex‐breed interaction, and age effects for genome‐wide recombination rate and RO at one or more chromosomes. Dams were found to have a higher genome‐wide recombination rate and RO at all chromosomes than sires. LW animals had higher genome‐wide recombination rate and RO at seven chromosomes but lower at two chromosomes than LR individuals. The sex‐breed interaction effect did not show any pattern not already observable by sex. Recombination increased with increasing parity in females, while in males no effect of age was observed. We estimated heritabilities and repeatabilities for both investigated traits and obtained the genetic correlation between male and female genome‐wide recombination rate within each of the two breeds studied. Estimates of heritability and repeatability were low (h2 = 0.01–0.26; r = 0.18–0.42) for both traits in all populations. Genetic correlations were high and positive, with estimates of 0.98 and 0.94 for the LR and LW breeds, respectively. We performed a GWAS for genome‐wide recombination rate independently in the four sex/breed populations. The results of the GWAS were inconsistent across the four populations with different significant genomic regions identified. The results of this study provide evidence of variability for recombination in purebred swine populations. [ABSTRACT FROM AUTHOR]

Simple Summary: A major problem in the swine meat industry is significant weight loss due to pigs overheating. Our approach to solving this problem is breeding swine based on their ability to survive in high temperatures. We determined in this study that genetically there is a difference between the most successful animal in hot conditions and the most successful animal in cooler conditions. This can be applied to selection of breeding animals for swine companies. Additionally, we found a few genes that may be related to this heat resistance, but more studies need to be completed to validate this finding. The purpose of this study was to perform a genome-wide association study to determine the genomic regions associated with heat stress tolerance in swine. Phenotypic information on carcass weight was available for 227,043 individuals from commercial farms in North Carolina and Missouri, U.S. Individuals were from a commercial cross of a Duroc sire and a dam resulting from a Landrace and Large White cross. Genotypic information was available for 8232 animals with 33,581 SNPs. The pedigree file contained a total of 553,448 animals. A threshold of 78 on the Temperature Humidity Index (THI) was used to signify heat stress. A two-trait analysis was used with the phenotypes heat stress (Trait One) and non-heat stress (Trait Two). Variance components were calculated via AIREML and breeding values were calculated using single step GBLUP (ssGBLUP). The heritability for Traits One and Two were calculated at 0.25 and 0.20, respectively, and the genetic correlation was calculated as 0.63. Validation was calculated for 163 genotyped sires with progeny in the last generation. The benchmark was the GEBV with complete data, and the accuracy was determined as the correlation between the GEBV of the reduced and complete data for the validation sires. Weighted ssGBLUP did not increase the accuracies. Both methods showed a maximum accuracy of 0.32 for Trait One and 0.54 for Trait Two. Manhattan Plots for Trait One, Trait Two, and the difference between the two were created from the results of the two-trait analysis. Windows explaining more than 0.8% of the genetic variance were isolated. Chromosomes 1 and 14 showed peaks in the difference between the two traits. The genetic correlation suggests a different mechanism for Hot Carcass Weight under heat stress. The GWAS results show that both traits are highly polygenic, with only a few genomic regions explaining more than 1% of variance. [ABSTRACT FROM AUTHOR]