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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.

Previous studies have demonstrated evidence for a relationship between the vaginal microbiome and reproductive performance, suggesting the vaginal microbiota may serve as a tool to predict farrowing outcomes in commercial pigs. In this study, we compared the vaginal microbiome in sows with low and high farrowing performance and used it to classify animals with contrasting reproductive outcomes in commercial sows following immune challenge with porcine respiratory and reproductive syndrome (PRRS) vaccination. Eighteen microbes were differentially abundant (q-value Campylobacter, Bacteroides, Porphyromonas, Lachnospiraceae unclassified, Prevotella, and Phascolarctobacterium were also selected in the discriminant and linear regression analyses, and could be used as potential biomarkers for reproductive outcomes. The correct classification rate in the two groups was 100%. In conclusion, this study demonstrates that vaginal microbiota collected after PRRS vaccination could be potentially used to classify sows into having low or high farrowing performance in commercial herds.

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.

The goals of the study were to estimate the heritability and repeatability of boar sperm quality traits during the summer season, estimate phenotypic and genetic correlations between sperm quality traits, and assess the effect of these traits on total number born (TNB). Semen samples were collected and individually processed from Duroc boars (n = 363) from May through October 2017. Single sire litter information was available for 121 of the boars. Heritability, repeatability, genetic correlations, and phenotypic correlations were estimated for and among the following traits: total percentage of motile cells (MOT), total percentage of progressively motile cells (PROG), percentage of cells with a distal droplet (DIST), percentage of cells with a proximal droplet (PROX), percentage of cells with a bent tail (BENT), percentage of cells with a distal midpiece reflex (DMR), mean sperm head elongation (ELON), and total sperm cells per ejaculate (TOTSP). Heritability estimates ranged from 0.08 to 0.24 and repeatability estimates ranged from 0.21 to 0.62. The phenotypic and genetic correlations between sperm motility traits and morphological defects indicated a negative relationship. Sperm morphological defects had positive phenotypic and genetic correlations with each other. Total sperm per ejaculate showed positive genetic correlations with the motility traits. Positive phenotypic and genetic correlations existed between ELON and the motility traits. Total sperm motility (P 0.001) positively affected TNB while DIST (P 0.001), PROX (P 0.001), BENT (P = 0.05) and DMR (P = 0.002) negatively affected TNB. Results indicate genetic selection could enhance sperm quality during the summer season in boars.

Made available in DSpace on 2019-10-06T16:27:41Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-04-27 Background: In this study we integrated the CNV (copy number variation) and WssGWAS (weighted single-step approach for genome-wide association) analyses to increase the knowledge about number of piglets born alive, an economically important reproductive trait with significant impact on production efficiency of pigs. Results: A total of 3892 samples were genotyped with the Porcine SNP80 BeadChip. After quality control, a total of 57,962 high-quality SNPs from 3520 Duroc pigs were retained. The PennCNV algorithm identified 46,118 CNVs, which were aggregated by overlapping in 425 CNV regions (CNVRs) ranging from 2.5 Kb to 9718.4 Kb and covering 197 Mb (~ 7.01%) of the pig autosomal genome. The WssGWAS identified 16 genomic regions explaining more than 1% of the additive genetic variance for number of piglets born alive. The overlap between CNVR and WssGWAS analyses identified common regions on SSC2 (4.2-5.2 Mb), SSC3 (3.9-4.9 Mb), SSC12 (56.6-57.6 Mb), and SSC17 (17.3-18.3 Mb). Those regions are known for harboring important causative variants for pig reproductive traits based on their crucial functions in fertilization, development of gametes and embryos. Functional analysis by the Panther software identified 13 gene ontology biological processes significantly represented in this study such as reproduction, developmental process, cellular component organization or biogenesis, and immune system process, which plays relevant roles in swine reproductive traits. Conclusion: Our research helps to improve the understanding of the genetic architecture of number of piglets born alive, given that the combination of GWAS and CNV analyses allows for a more efficient identification of the genomic regions and biological processes associated with this trait in Duroc pigs. Pig breeding programs could potentially benefit from a more accurate discovery of important genomic regions. Department of Exact Science School of Agricultural and Veterinarian Sciences (FCAV) Sao Paulo State University (UNESP) Department of Animal and Dairy Science University of Georgia National Center for Cool and Cold Water Aquaculture (NCCCWA) Agricultural Research Service United States Department of Agriculture Department of Animal Science University of Connecticut Smithfield Premium Genetics Group Department of Exact Science School of Agricultural and Veterinarian Sciences (FCAV) Sao Paulo State University (UNESP)

Immunoglobulin G antibody response, measured as sample-to-positive (S/P) ratio, to Porcine Reproductive and Respiratory Syndrome virus (PRRSV) has been proposed as an indicator trait for improved reproductive performance in PRRSV-infected purebred sows and PRRSV-vaccinated crossbred gilts. In this study, we investigated the genetic correlations (rg) of S/P ratio following a PRRSV outbreak and PRRSV-vaccination with performance in non-exposed and PRRSV-exposed sows. PRRSV outbreak phase was defined based on previously described methodologies after the detection of typical clinical signs of PRRSV infection. 541 Landrace sows had S/P ratio measured at ~54 days after the beginning of the PRRSV outbreak (S/Poutbreak), and 906 Landrace x Large White naïve F1 gilts had S/P ratio measured at ~50 days after vaccination with a commercial modified live PRRSV vaccine (S/PVx). 711 and 428 Landrace sows had reproductive performance recorded before and during the PRRSV outbreak, respectively. 811 vaccinated F1 animals had farrowing performance for up to 3 parities. All animals were genotyped for ~28K SNPs. The estimate of rg of S/Poutbreakwith S/PVx was high (rg±SE = 0.72±0.18). Estimates of rg of S/Poutbreak with reproductive performance in F1 sows were low to moderate, ranging from 0.05±0.23 (number stillborn) to 0.30±0.20 (total number born). Estimates of rg of S/PVxwith reproductive performance in non-infected purebred sows were moderate and favorable with number born alive (0.50±0.23), but low (0 to -0.11±0.23) with litter mortality traits. Estimates of rg of S/PVx were moderate and negative (-0.47±0.18) with the number of mummies in PRRSV-infected purebred sows and low with other traits (-0.29±0.18 for total number born to 0.05±0.18 for number stillborn). These results indicate that selection for antibody response following a PRRSV outbreak collected in purebred sows and to PRRSV vaccination collected in commercial crossbred gilts may increase litter size of non-infected and PRRSV-exposed purebred and commercial crossbred sows.

Antibody response, measured as sample-to-positive (S/P) ratio, to porcine reproductive and respiratory syndrome virus (PRRSV) following a PRRSV-outbreak (S/POutbreak) in a purebred nucleus and following a PRRSV-vaccination (S/PVx) in commercial crossbred herds have been proposed as genetic indicator traits for improved reproductive performance in PRRSV-infected purebred and PRRSV-vaccinated crossbred sows, respectively. In this study, we investigated the genetic relationships of S/POutbreak and S/PVx with performance at the commercial (vaccinated crossbred sows) and nucleus level (non-infected and PRRSV-infected purebred sows), respectively, and tested the effect of previously identified SNP for these indicator traits. Antibody response was measured on 541 Landrace sows ~54 d after the start of a PRRSV outbreak, and on 906 F1 (Landrace × Large White) gilts ~50 d after vaccination with a commercial PRRSV vaccine. Reproductive performance was recorded for 711 and 428 Landrace sows before and during the PRRSV outbreak, respectively, and for 811 vaccinated F1 animals. The estimate of the genetic correlation (rg) of S/POutbreak with S/PVx was 0.72 ± 0.18. The estimates of rg of S/POutbreak with reproductive performance in vaccinated crossbred sows were low to moderate, ranging from 0.05 ± 0.23 to 0.30 ± 0.20. The estimate of rg of S/PVx with reproductive performance in non-infected purebred sows was moderate and favorable with number born alive (0.50 ± 0.23) but low (0 ± 0.23 to −0.11 ± 0.23) with piglet mortality traits. The estimates of rg of S/PVx were moderate and negative (−0.38 ± 0.21) with number of mummies in PRRSV-infected purebred sows and low with other traits (−0.30 ± 0.18 to 0.05 ± 0.18). Several significant associations (P0 > 0.90) of previously reported SNP for S/P ratio (ASGA0032063 and H3GA0020505) were identified for S/P ratio and performance in non-infected purebred and PRRSV-exposed purebred and crossbred sows. Genomic regions harboring the major histocompatibility complex class II region significantly contributed to the genetic correlation of antibody response to PRRSV with most of the traits analyzed. These results indicate that selection for antibody response in purebred sows following a PRRSV outbreak in the nucleus and for antibody response to PRRSV vaccination measured in commercial crossbred sows are expected to increase litter size in purebred and commercial sows.

The present study aimed to explore gut microbiota’s overall influence on feeding behavior and identify specific microbes associated with the traits in three commercial swine breeds at different growth stages. Feeding behavior measures were obtained from 651 pigs of three breeds (Duroc, Landrace, and Large White) from 67 to 175 days of age. Seven feeding behavior traits cover 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 16S rRNA gene sequencing. Sequences were analyzed using the QIIME2. The amplicon sequence variant table was constructed using the DADA2. Mixed linear models were used to perform statistical analyses. Differences in feeding behavior traits among breeds and across time periods were found. The proportion of phenotypic variances explained by the microbiome ranged from 0.10 to 0.26, 0.14 to 0.25, and 0.12 to 0.28 in Duroc pigs, from 0.09 to 0.20, 0.10 to 0.23, and 0.12 to 0.23 in Landrace pigs, from 0.10 to 0.17, 0.11 to 0.19, and 0.10 to 0.31 in Large White pigs at three time points, respectively. 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 three time points. This study demonstrated the importance of the gut microbiome composition in influencing the host feeding behavior and identified multiple microbes associated with the feeding behavior of 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 breed effects in swine microbial studies.

Antibody response, measured as sample-to-positive ratio (S/P), to porcine reproductive and respiratory syndrome (PRRS) after PRRS outbreaks has been proposed as an indicator trait to improve reproductive performance in PRRS-infected sows. However, waiting for a PRRS outbreak to occur and having different relationships in healthy pigs may limit the use of this trait. Thus, we proposed to investigate if this relationship also occurs between S/P to PRRS vaccination and reproductive performance in sows without exposure to PRRS. Nine hundred six F1 replacement gilts (139□17 days old) from two commercial farms were vaccinated with a commercial modified live PRRS virus vaccine. Blood samples were collected at 52 days after vaccination to measure S/P to PRRS and SNP genotyping. Reproductive performance included: number born alive (NBA), number of piglets weaned, number born mummified (MUM), number stillborn (NSB), and pre-weaning mortality (PWM) at parities (P) 1 to 3. Average performance was calculated for each trait per sow per year (PSY). Farrowing rate and age at first service were also analyzed. BayesC0 was used to estimate genetic correlations between S/P and reproductive performance. Bivariate genome-wide association studies of antibody response and reproductive traits were performed using BayesB. High genetic correlations between S/P and farrowing performance were identified for NBA_P1 (0.61±0.16), PWM_P2 (-0.64±0.15), PWM_P2 (-0.63±0.20), NSB_P3 (-0.84±0.05), MUM_P3 (-0.83±0.11), and NSB_PSY (-0.90±0.05). A QTL was identified on chromosome 7 (MHC region) for these reproductive traits and for S/P, explaining from 1.2% (PWM_P2) to 22.4% (S/P) of the genetic variance. SNP H3GA0020505 explained most of the variance in this region for these traits. Heterozygote animals for this SNP had overall better performance: greater S/P (P = 0.001), greater (P = 0.06) NBA_P1, and lesser (P = 0.06) MUM_P3 than other genotypes. These results suggest that antibody response to PRRS vaccination may be used as a genetic indicator to improve reproductive performance in commercial pigs.

Background Feed efficiency is a crucial parameter in swine production, given both its economic and environmental impact. The gut microbiota plays an essential role in nutrient digestibility and is, therefore, likely to affect feed efficiency. This study aimed to characterize feed efficiency, fatness traits, and gut microbiome composition in three major breeds of domesticated swine and investigate a possible link between feed efficiency and gut microbiota composition. Results Average daily feed intake (ADFI), average daily gain (ADG), feed conversion ratio (FCR), residual feed intake (RFI), backfat, loin depth, and intramuscular fat of 615 pigs belonging to the Duroc (DR), Landrace (LR), and Large White (LW) breeds were measured. Gut microbiota composition was characterized by 16S rRNA gene sequencing. Orthogonal contrasts between paternal line (DR) and maternal lines (LR+LW) and between the two maternal lines (LR versus LW) were performed. Average daily feed intake and ADG were statistically different with DR having lower ADFI and ADG compared to LR and LW. Landrace and LW had a similar ADG and RFI, with higher ADFI and FCR for LW. Alpha diversity was higher in the fecal microbial communities of LR pigs than in those of DR and LW pigs for all time points considered. Duroc communities had significantly higher proportional representation of the Catenibacterium and Clostridium genera compared to LR and LW, while LR pigs had significantly higher proportions of Bacteroides than LW for all time points considered. Amplicon sequence variants from multiple genera (including Anaerovibrio, Bacteroides, Blautia, Clostridium, Dorea, Eubacterium, Faecalibacterium, Lactobacillus, Oscillibacter, and Ruminococcus) were found to be significantly associated with feed efficiency, regardless of the time point considered. Conclusions In this study, we characterized differences in the composition of the fecal microbiota of three commercially relevant breeds of swine, both over time and between breeds. Correlations between different microbiome compositions and feed efficiency were established. This suggests that the microbial community may contribute to shaping host productive parameters. Moreover, our study provides important insights into how the intestinal microbial community might influence host energy harvesting capacity. A deeper understanding of this process may allow us to modulate the gut microbiome in order to raise more efficient animals. Video Abstract

Background Feed efficiency is a crucial parameter in swine production given both its economic and environmental impact. The gut microbiota plays an essential role in nutrient digestibility and is therefore likely to affect feed efficiency. This study aimed to characterize feed efficiency, production traits, and gut microbiome composition of three major breeds of domesticated swine, and to investigate a possible link between feed efficiency and gut microbiota composition. Results Average daily feed intake ( ADFI ), average daily gain ( ADG ), feed conversion ratio ( FCR ), residual feed intake ( RFI ), backfat, loin depth, and intramuscular fat of 615 pigs belonging to the Duroc ( DR ), Landrace ( LR ), and Large White ( LW ) breeds were measured. Gut microbiota composition was characterized by 16S rRNA gene sequencing. Orthogonal contrasts between paternal line (DR) and maternal lines (LR+LW) and between the two maternal lines (LR versus LW) were performed. Average daily feed intake and ADG were statistically different with DR having lower ADFI and ADG compared to LR and LW. Landrace and LW had a similar ADG and RFI, with higher ADFI and FCR for LW. Alpha diversity was higher in the fecal microbial communities of LR pigs than in those of DR and LW pigs for all time points considered. Duroc communities had significantly higher proportional representation of the Catenibacterium and Clostridium genera compared to LR and LW, while LR pigs had significantly higher proportions of Bacteroides than LW for all time points considered. We identified several amplicon sequencing variants members of certain genera including Anaerovibrio , Bacteroides , Blautia , Clostridium , Dorea , Eubacterium , Faecalibacterium , Lactobacillus, Oscillibacter , and Ruminococcus significantly associated with feed efficiency of pigs for all time points considered. Conclusions In this study we characterized differences in the composition of the fecal microbiota of three commercially relevant breeds of swine, both over time and between breeds. Correlations between different microbiome compositions and feed efficiency were established. This suggests that the microbial community may contribute in shaping host productive parameters. Moreover, our study provides important insights to understand how the intestinal microbial community might influence the host energy harvesting capacity. A deeper understanding of this process may allow us to modulate the gut microbiome for more efficient animals.

Vaginal and rectal prolapse incidence has been increasing in the U.S. commercial breeding sow population in recent years. This has resulted in increased sow breeding mortality rates in the U.S. due to both increased mortality and euthanasia. The objective of the present study was to estimate the potential role that genetics plays in the prolapse incidence of the U.S. commercial sow population. Data from 11,481 purebred Large White sows from two commercial farms were used in this study. Genetic parameters for prolapse incidence were estimated based on linear and threshold animal models utilizing AIREMLF90 and THRGIBBS1F90, respectively. Across both models, the fixed effects included contemporary group as year-month of removal and removal parity and a random effect was sow. A pedigree-based relationship matrix was included in the model. The highest prolapse incidence occurred in the 1st when compared to other parities. Heritability estimates for combined vaginal and rectal prolapse incidence were 0.03 ± 0.01 and 0.003 ± 0.003 for the linear and threshold models, respectively. The results from this study indicate that there is very little if any genetic impact on the prolapse incidence. Pork producers should focus on the environmental effects contributing to reducing prolapse incidence.

Previous studies on genomics of antibody response, measured as sample-to-positive (S/P) ratio, to Porcine Reproductive and Respiratory Syndrome virus (PRRSV) have reported a major quantitative trait locus (QTL) on the major histocompatibility complex (MHC) on chromosome 7, explaining ~25% of the genetic variance of this trait. S/P ratio following modified live PRRSV vaccination in crossbred commercial gilts has been proposed as genetic indicator for reproductive performance in non-infected purebred sows and PRRSV-vaccinated crossbred sows. This motivated further genomic study for this trait by performing haplotype-based genome-wide association study (GWAS). 906 naïve F1 (Landrace x Large White) had blood samples taken at ~50d after vaccination for measuring PRRSV ELISA S/P ratio and genotyping. Haplotype-based GWAS identified 8 genomic regions on chromosomes 4 (108 Mb), 7 (15, 21, and 24–27 Mb), and 9 (33 Mb) that were associated (q-value < 0.07) with S/P ratio. From those, only the MHC region (chromosome 7; 24 – 26 Mb) had been identified in the SNP based GWAS. The main SNP identified in the SNP based GWAS (H3GA0020505) was not in LD with the haplotype; thus, we added this SNP to the haplotype model. We observed that the haplotype explained more of the genetic variance compared to the H3GA0020505 SNP, indicating that the MHC haplotype is in stronger LD with the QTL than the H3GA0020505 SNP. All the significant regions associated with S/P ratio included immune-related candidate genes, such as SLA-DOB, TAP2, TAPBP, TMIGD3, and ADORA. This study validated the QTL identified on the MHC region, narrowing the search for causal genes in this region, and identified new genomic regions, along with candidate genes associated with S/P ratio. Identifying novel genomic regions provides more resources for marker-assisted selection and genomic prediction of S/P ratio in purebred and commercial pig populations.

Previous studies proposed the use of antibody response to Porcine Reproductive and Respiratory Syndrome virus (PRRSV), measured as sample-to-positive (S/P) ratio, as a potential indicator trait to improve the reproductive performance of PRRSV-infected Landrace sows. However, this indicator trait has not yet been validated in Landrace sows or evaluated in a terminal sire line, such as Duroc. The main objective of this work was to perform host-genetic analyses of S/P ratio and reproductive traits during a PRRSV outbreak in maternal and terminal breeds. The data consisted of 690 Duroc and 541 Landrace multiparous sows (1.9±1.2 and 2.3±1.5, respectively) with S/P ratio collected at approximately 54 days after the predicted beginning of the outbreak. Of these, 644 Duroc and 528 Landrace sows also had reproductive data recorded during the PRRSV outbreak comprising number of piglets born alive (NBA), stillborn piglets (NSB), mummified piglets (NBM), number born dead (NBD; sum of NSB and NBM), total number born (TNB; sum of NBA and NBD), and number weaned (NW). All animals had genotype data on ~30K SNPs common across both breeds. Heritability estimates (± standard error) of S/P ratio during the PRRSV outbreak were moderate, with 0.33±0.06 for Duroc and 0.28±0.07 for Landrace. Reproductive traits during the PRRSV outbreak had overall low heritability estimates (≤0.18). Favorable genetic correlations of S/P ratio with NBA (0.65±0.33), in accordance with previous studies, and NBD (-0.33±0.28) were observed for Landrace sows only. Estimates of genetic correlation with other traits were -0.21±0.30 (NBM), -0.12±0.29 (NSB), 0.10±0.38 (NW), and 0.54±1.29 (TNB) for Landrace. For Duroc, these estimates were weaker: -0.33±0.40 (NBA), 0.26±0.27 (NBA), and 0.28±0.30 (NW), with convergence issues for mortality traits. These results further support the use of S/P ratio as an indicator trait for improved reproductive performance during a PRRSV outbreak in Landrace sows.

Studies on differences in resilience to porcine reproductive and respiratory syndrome (PRRS) virus (PRRSV) between breeds are scarce in the literature. Thus, the objective of this work was to assess PRRSV resilience in PRRSV wild-type infected sows from two breeds. Farrowing data included 2546 and 2522 litters from 894 Duroc and 813 Landrace sows, respectively, which were housed together and experienced the same PRRSV outbreak. Traits used for this study were number of piglets born alive (NBA), number born dead (NBD), total number born (TNB), and number weaned (NW). The impact of PRRSV infection was evaluated by comparing the reproductive performance of breeds between PRRS phases (pre-PRRS, PRRS, and post-PRRS). PRRS phases were defined based on the reproductive performance data. NBA, NBD, and NW were analyzed as a proportion of TNB using a Poisson mixed model. Pre-defined contrasts were used to evaluate the effect of breed on PRRSV resilience and on return to PRRSV-free performance, representing the differences between breeds for the difference between pre-PRRS and PRRS phases, and pre-PRRS and post-PRRS phases, respectively. There was a significant (P ≤ 0.003) interaction between PRRS phase and breed for all traits, as shown in Table 1. In general, reproductive performance reduced from pre-PRRS to PRRS, and then increased from PRRS to post-PRRS, as expected. The resilience contrast was significant for all traits (P ≤ 0.003). In all cases, the drop in percent reproductive performance from pre-PRRS to PRRS was lower for Duroc than for Landrace, indicating that Duroc sows have greater PRRSV resilience than Landrace sows. The return to PRRSV-free performance contrast had a trending effect for NBD (P = 0.055), and it was not significant for the other traits (P ≥ 0.515). These results indicate that Duroc sows have overall greater phenotypic PRRSV resilience for reproductive performance than Landrace sows.

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 as well as analyze the accuracy of prediction. Phenotypic information on carcass weight was available for 227,043 individuals from commercial farms in North Carolina and Missouri. Individuals were a commercial cross of a Duroc sire and a dam resulting from a Landrace and Large White cross. Genotypic information was available for 8,232 animals with 33,581 SNP. The pedigree file contained 553,448 animals. A 78 on the Temperature Humidity Index (THI) was used as a threshold for 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 trait 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 GEBV of complete data was used as the benchmark, 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 around 1% of the genetic variance were identified. The only difference between the two traits was a peak at chromosome 14. The genetic correlation suggests different mechanisms for growth under heat stress. The GWAS results show that both traits are highly polygenic, with few genomic regions explaining more than 1% of variance.

The objective of this study was to compare and determine the optimal validation method when comparing accuracy from single-step GBLUP (ssGBLUP) to traditional pedigree-based BLUP. Field data included six litter size traits. Simulated data included ten replicates designed to mimic the field data in order to determine the method that was closest to the true accuracy. Data were split into training and validation sets. The methods used were as follows: (i) theoretical accuracy derived from the prediction error variance (PEV) of the direct inverse (iLHS), (ii) approximated accuracies from the accf90(GS) program in the BLUPF90 family of programs (Approx), (iii) correlation between predictions and the single-step GEBVs from the full data set (GEBVFull ), (iv) correlation between predictions and the corrected phenotypes of females from the full data set (Yc ), (v) correlation from method iv divided by the square root of the heritability (Ych ) and (vi) correlation between sire predictions and the average of their daughters' corrected phenotypes (Ycs ). Accuracies from iLHS increased from 0.27 to 0.37 (37%) in the Large White. Approximation accuracies were very consistent and close in absolute value (0.41 to 0.43). Both iLHS and Approx were much less variable than the corrected phenotype methods (ranging from 0.04 to 0.27). On average, simulated data showed an increase in accuracy from 0.34 to 0.44 (29%) using ssGBLUP. Both iLHS and Ych approximated the increase well, 0.30 to 0.46 and 0.36 to 0.45, respectively. GEBVFull performed poorly in both data sets and is not recommended. Results suggest that for within-breed selection, theoretical accuracy using PEV was consistent and accurate. When direct inversion is infeasible to get the PEV, correlating predictions to the corrected phenotypes divided by the square root of heritability is adequate given a large enough validation data set.

Utilization of feed in livestock species consists of a wide range of biological processes, and therefore, its efficiency can be expressed in various ways, including direct measurement, such as daily feed intake, as well as indicator measures, such as feeding behavior. Measuring feed efficiency is important to the swine industry, and its accuracy can be enhanced by using automated feeding systems, which record feed intake and associated feeding behavior of individual animals. Each automated feeder space is often shared among several pigs and therefore raises concerns about social interactions among pen mates with regard to feeding behavior. The study herein used a data set of 14,901 Duroc boars with individual records on feed intake, feeding behavior, and other off-test traits. These traits were modeled with and without the random spatial effect of Pen_Room, a concatenation of room and pen, or random social interaction among pen mates. The nonheritable spatial effect of common Pen-Room was observed for traits directly measuring feed intake and accounted for up to 13% of the total phenotypic variance in the average daily feeding rate. The social interaction effect explained larger proportions of phenotypic variation in all the traits studied, with the highest being 59% for ADFI in the group of feeding behaviors, 73% for residual feed intake (RFI; RFI4 and RFI6) in the feed efficiency traits, and 69% for intramuscular fat percentage in the off-test traits. After accounting for the social interaction effect, residual BW gain and RFI and BW gain (RIG) were found to have the heritability of 0.38 and 0.18, respectively, and had strong genetic correlations with growth and off-test traits. Feeding behavior traits were found to be moderately heritable, ranging from 0.14 (ADFI) to 0.52 (average daily occupation time), and some of them were strongly correlated with feed efficiency measures; for example, there was a genetic correlation of 0.88 between ADFI and RFI6. Our work suggested that accounting for the social common pen effect was important for estimating genetic parameters of traits recorded by the automated feeding system. Residual BW gain and RIG appeared to be two robust measures of feed efficiency. Feeding behavior measures are worth further investigation as indicators of feed efficiency.

The vaginal microbiome of gilts vaccinated for porcine reproductive and respiratory syndrome (PRRS) has been previously associated with first-parity reproductive performance in absence of PRRS outbreak. However, associations using multiparous sows have not been investigated. The objective of this study was to associate the vaginal microbiota of PRRS-vaccinated gilts with longevity. Vaginal swabs from 251 commercial F1 gilts (Landrace/Large-White) were collected on days 4 and 52 after PRRS vaccination (dpv) for 16S rRNA sequencing. Sequences were clustered into operational taxonomic unit (OTU). Sows were assigned to one of four groups according to the maximum parity reached (number of animals): without parity (13), first-parity (45), second-parity (47), and 3 or more parities (146). A negative binomial mixed model including fixed effects of group, dpv, group*dpv, and collection age (covariate), and random effect of animal, was used to identify OTUs with differential abundance. The false discovery rate method was used for multiple test correction. Canonical discriminant analysis (CDA) was performed to classify animals into the four parity groups by including significant OTUs in stepwise selection (P < 0.05) using the whole data. A leave-one-out-cross-validation was used to assess the predictive ability of OTUs to correctly classify animals into the parity groups. Abundance of five and four OTUs was associated (q < 0.05) with group and with group*dpv (q < 0.05), respectively. In the CDA analysis, 220 OTUs identified in the vaginal microbiome were included (P < 0.05). The first and second canonical variables explained 96.6 and 3.4% of the variation, respectively. All animals were correctly classified into their respective parity groups. This study indicates that the vaginal microbiome composition of gilts collected after PRRS-vaccination may be used to predict longevity in commercial sow herds. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brazil (Capes) – Finance Code 001.

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.

Improving swine climatic resilience through genomic selection has the potential to minimize welfare issues and increase the industry profitability. The main objective of this study was to investigate the genetic and genomic determinism of tolerance to heat stress in four independent purebred populations of swine. Three female reproductive traits were investigated: total number of piglets born (TNB), number of piglets born alive (NBA) and average birth weight (ABW). More than 80,000 phenotypic and 12,000 genotyped individuals were included in this study. Genomic random-regression models were fitted regressing the phenotypes of interest on a set of 95 environmental covariates extracted from public weather station records. The models yielded estimates of (genomic) reactions norms for individual pigs, as indicator of heat tolerance. Heat tolerance is a heritable trait, although the heritabilities are larger under comfortable than heat-stress conditions (larger than 0.05 vs. 0.02 for TNB; 0.10 vs. 0.05 for NBA; larger than 0.20 vs. 0.10 for ABW). TNB showed the lowest genetic correlation (-38%) between divergent climatic conditions, being the trait with the strongest impact of genotype by environment interaction, while NBA and ABW showed values slightly negative or equal to zero reporting a milder impact of the genotype by environment interaction. After estimating genetic parameters, a genome-wide association study was performed based on the single-step GBLUP method. Heat tolerance was observed to be a highly polygenic trait. Multiple and non-overlapping genomic regions were identified for each trait based on the genomic breeding values for reproductive performance under comfortable or heat stress conditions. Relevant regions were found on chromosomes (SSC) 1, 3, 5, 6, 9, 11, and 12, although there were important regions across all autosomal chromosomes. The genomic region located on SSC9 appears to be of particular interest since it was identified for two traits (TNB and NBA) and in two independent populations. Heat tolerance based on reproductive performance indicators is a heritable trait and genetic progress for heat tolerance can be achieved through genetic or genomic selection. Various genomic regions and candidate genes with important biological functions were identified, which will be of great value for future functional genomic studies.

Genomic selection increases intensity of selection and decreases generation interval. However, intensive selection reduces genetic variation and can strengthen undesirable genetic correlations. The purpose of this study was to investigate changes in genetic parameters of litter size (LS), number born alive (NBA), number born dead (NBD) and average daily gain (ADG) and weight at off-test (WT) in pigs over time. The data set contained 20,086 (LS), 21,230 (NBA), 21,230 (NBD), 144,717 (ADG) and 144,718 (WT) phenotypic records. Pedigree file included 369,776 animals born between 2001 and 2018, of which 39,038 were genotyped. Two trait models were evaluated (LS-ADG, LS-WT, NBA-ADG, NBA-WT, NBD-ADG and NBD-WT) using 3-year sliding subsets starting from 2010. Variance components and genetic parameters were estimated using GIBBS2F90 program. Computations were performed with (BLUP) or without (ssGBLUP) genotypes. For BLUP (ssGBLUP), the changes in heritability from 2010–2012 to 2015–2018 were 0.08 to 0.09 (0.08 to 0.06) for LS, 0.33 to 0.24 (0.37 to 0.16) for ADG, 0.11 to 0.07 (0.10 to 0.07) for NBD, and 0.32 to 0.24 (0.38 to 0.17) for WT. Differences were also observed for genetic correlations as they were -0.23 to -0.73 (-0.31 to -0.58) for LS-ADG, -0.24 to -0.74 (-0.31 to -0.50) for LS-WT, -0.33 to -0.65 (-0.41 to -0.53) for NBA-ADG, -0.35 to -0.66 (-0.42 to -0.45) for NBA-WT, 0.12 to 0.04 (0.12 to 0.16) for NBD-ADG, and 0.11 to 0.05 (0.11 to 0.22) for NBD-WT. Genomic selection in pigs reduced heritabilities and emphasized the antagonistic relationship that are under strong selection. Heritabilities estimated from ssGBLUP declined more than those by BLUP while changes in the genetic correlations were smaller and more gradual by ssGBLUP. Differences between ssGBLUP and BLUP could be caused by genomic pre-selection unaccounted for by BLUP.

Vaginal and rectal prolapse incidence has been increasing in the commercial breeding sow population in the U.S. This has resulted in increased sow breeding mortality rates in the U.S. in recent years. The objective of the present study was to estimate the potential role that genetics plays in the prolapse incidence of the U.S. commercial sow population. Data from 22,577 LW x LR crossbred sows from commercial breeding herds were used in this study. Genetic parameters for prolapse incidence were estimated using AIREMLF90 and THRGIBBS1F90 where an animal and threshold models were implemented. The fixed effects in the models included: contemporary group as farm and year-month of removal and removal parity. A random effect for sow and pedigree based relationship matrix was also included in the model. The highest rectal and reproductive tract prolapse incidence were observed in the 1st and 2nd parities. Moreover, sows born in 2013 were investigated greatest incidence of both prolapse incidence. Heritability estimates for prolapse incidence were 0.03 and 0.003 by using the animal and threshold models. The results from this study indicated that there is very little of any genetic impact on the prolapse incidence. Pork producers identify the environmental effects contributing to prolapse incidence.

Our objectives were to evaluate the interaction between host genetics and vaginal microbiota and their relationships with antibody (Ab) response to porcine reproductive and respiratory syndrome virus (PRRSV) vaccination and farrowing performance in commercial gilts. The farrowing performance traits were number born alive, number weaning (NW), total number born, number born dead, stillborn, mummies and preweaning mortality (PWM). The vaginal microbiota was collected on days 4 (D4) and 52 (D52) after vaccination for PRRSV. Blood samples were collected on D52 for Ab measurement. Actinobacteria, Bacterioidetes, Firmicutes, Proteobacteria and Tenericutes were the most abundant Phyla identified in the vaginal microbiota. Heritability ranged from ~0 to 0.60 (Fusobacterium) on D4 and from ~0 to 0.63 (Terrisporobacter) on D52, with 43 operational taxonomic units (OTUs) presenting moderate to high heritability. One major QTL on chromosome 12 was identified for 5 OTUs (Clostridiales, Acinetobacter, Ruminococcaceae, Campylobacter and Anaerococcus), among other 19 QTL. The microbiability for Ab response to PRRSV vaccination was low for both days (

The purposes of this study were to analyze the impact of seasonal losses due to heat stress in pigs from different breeds raised in different environments and to evaluate the accuracy improvement from adding genomic information to genetic evaluations. Data were available for 2 different swine populations: purebred Duroc animals raised in Texas and North Carolina and commercial crosses of Duroc and F females (Landrace × Large White) raised in Missouri and North Carolina; pedigrees provided links for animals from different states. Pedigree information was available for 553,442 animals, of which 8,232 pure breeds were genotyped. Traits were BW at 170 d for purebred animals and HCW for crossbred animals. Analyses were done with an animal model as either single- or 2-trait models using phenotypes measured in different states as separate traits. Additionally, reaction norm models were fitted for 1 or 2 traits using heat load index as a covariable. Heat load was calculated as temperature-humidity index greater than 70 and was averaged over 30 d prior to data collection. Variance components were estimated with average information REML, and EBV and genomic EBV (GEBV) with BLUP or single-step genomic BLUP (ssGBLUP). Validation was assessed for 146 genotyped sires with progeny in the last generation. Accuracy was calculated as a correlation between EBV and GEBV using reduced data (all animals, except the last generation) and using complete data. Heritability estimates for purebred animals were similar across states (varying from 0.23 to 0.26), and reaction norm models did not show evidence of a heat stress effect. Genetic correlations between states for heat loads were always strong (>0.91). For crossbred animals, no differences in heritability were found in single- or 2-trait analysis (from 0.17 to 0.18), and genetic correlations between states were moderate (0.43). In the reaction norm for crossbreeds, heritabilities ranged from 0.15 to 0.30 and genetic correlations between heat loads were as weak as 0.36, with heat load ranging from 0 to 12. Accuracies with ssGBLUP were, on average, 25% greater than with BLUP. Accuracies were greater in 2-trait reaction norm models and at extreme heat load values. Impacts of seasonality are evident only for crossbred animals. Genomic information can help producers mitigate heat stress in swine by identifying superior sires that are more resistant to heat stress.

The purpose of this study was to analyze the impact of seasonal losses due to heat stress in different environments and genetic group combinations. Data were available for 2 different swine populations: purebred Duroc animals raised in nucleus farms in Texas and North Carolina and crosses of Duroc and F females (Landrace × Large White) raised in commercial farms in Missouri and North Carolina; pedigrees provided links between animals from different states. Traits included BW at harvest age for purebred animals and HCW for crossbred animals. Weather data were collected at airports located close to the farms. Heat stress was quantified by a heat load function, defined by the units of temperature-humidity of temperature-humidity index (THI) greater than a certain threshold for 30 to 70 d before phenotype collection. Heat stress responses were quantified by a linear regression of phenotype on heat load. The greatest coefficient of determination occurred with a length of 30 d before phenotype measurements for all states and genetic groups. In the crossbreed data, THI thresholds were 67 in Missouri and 72 in North Carolina. For pure breeds, heat load had the best fit for THI thresholds greater than 70 in North Carolina, although differences in coefficient of determinations were negligible. On the other hand, no optimal THI threshold existed in Texas. In this study, heat stress had a greater impact in commercial farms than in nucleus farms and the effect of heat stress on weight varied by year and state.

Obtaining accurate individual feed intake records is the key first step in achieving genetic progress toward more efficient nutrient utilization in pigs. Feed intake records collected by electronic feeding systems contain errors (erroneous and abnormal values exceeding certain cutoff criteria), which are due to feeder malfunction or animal-feeder interaction. In this study, we examined the use of a novel data-editing strategy involving multiple imputation to minimize the impact of errors and missing values on the quality of feed intake data collected by an electronic feeding system. Accuracy of feed intake data adjustment obtained from the conventional linear mixed model (LMM) approach was compared with 2 alternative implementations of multiple imputation by chained equation, denoted as MI (multiple imputation) and MICE (multiple imputation by chained equation). The 3 methods were compared under 3 scenarios, where 5, 10, and 20% feed intake error rates were simulated. Each of the scenarios was replicated 5 times. Accuracy of the alternative error adjustment was measured as the correlation between the true daily feed intake (DFI; daily feed intake in the testing period) or true ADFI (the mean DFI across testing period) and the adjusted DFI or adjusted ADFI. In the editing process, error cutoff criteria are used to define if a feed intake visit contains errors. To investigate the possibility that the error cutoff criteria may affect any of the 3 methods, the simulation was repeated with 2 alternative error cutoff values. Multiple imputation methods outperformed the LMM approach in all scenarios with mean accuracies of 96.7, 93.5, and 90.2% obtained with MI and 96.8, 94.4, and 90.1% obtained with MICE compared with 91.0, 82.6, and 68.7% using LMM for DFI. Similar results were obtained for ADFI. Furthermore, multiple imputation methods consistently performed better than LMM regardless of the cutoff criteria applied to define errors. In conclusion, multiple imputation is proposed as a more accurate and flexible method for error adjustments in feed intake data collected by electronic feeders.

Antibody (Ab) response to natural infection with porcine reproductive and respiratory syndrome (PRRS) virus has been shown to be highly heritable (~0.40), to be genetically correlated with farrowing performance in PRRSV-infected sows, be controlled by two major QTL on chromosome 7, among others, and have moderate genomic prediction accuracy. However, waiting for PRRS outbreaks to occur to collect data limits the use of Ab response to select for increased PRRS resilience. Thus, we investigated the genomic basis of Ab response to PRRS vaccination with a modified live PRRSV vaccine as a strategy to generate data for this purpose. Nine hundred and six commercial F1 replacement gilts (189±16 days old) were vaccinated with a commercial PRRS modified live virus vaccine. Blood samples were collected 52 days after vaccination to measure Ab response, as sample-to-positive (S/P) ratio using a commercial ELISA, and for SNP genotyping (~50K). BayesC0 was used to estimate heritability for S/P ratio using in a model with contemporary group (CG) as fixed effect and SNP effects as random. Genome-wide association study and genomic prediction for S/P ratio were performed with BayesB (Pi=0.99). For genomic prediction, a three-fold cross-validation was used, in which each CG (n=3) was used as validation dataset. Accuracy of genomic prediction was defined as the correlation between genomic estimated breeding values and phenotypes adjusted for estimates of fixed effects, weighed by the number of individuals in the validation dataset. Heritability of S/P was moderate (0.35±0.04). A QTL was identified on chromosome 7 (25 Mb) explaining ~28% of the genetic variance. Accuracy of genomic prediction was fairly high (0.60±0.15). This is the first study describing the genomic basis of Ab response to PRRS vaccination with modified-live virus vaccine. Additional work is needed to evaluate the genetic correlation of Ab response to vaccination with resilience traits in pigs.

Recent studies on host-genetics of response to porcine reproductive and respiratory syndrome virus (PRRSV) infection have shown that genetic markers can be used to predict performance in growing pigs. On the other hand, no work has been done on the genomic prediction of reproductive performance in PRRSV-infected sows. Thus, the objective of this work was to assess the accuracies of genomic prediction for reproductive traits in PRRSV-infected sows. A total of 475 Duroc and 405 Landrace sows with ~30K SNP genotypes had farrowing performance data during the outbreak on: number of piglets born alive (NBA), stillborn piglets (NSB), mummified piglets (NM), piglets born dead (NBD; NSB+NM), total number of piglets born (TNB; NBA+NBD), and piglets weaned (NW). Genomic prediction was performed by Bayes-B with a model fitting parity as fixed-effect and SNP effects as random. Analyses were performed for two scenarios: within-breed and between-breed. For the within-breed prediction, 4-fold cross-validation was used. For the between-breed prediction, all data for each breed were used. Accuracy of genomic prediction (AGP) was calculated as the correlation between genomic estimated breeding values and pre-adjusted phenotype, divided by square-root of heritability. For the within-breed prediction, AGP (± standard deviation) for NBA, NBD, NM, NSB, NW, and TNB were 0.24±0.05, 0.07±0.10, -0.27±0.06, -0.24±0.09, -0.05±0.05, 0.19±0.03, respectively, for Duroc, and 0.37±0.02, 0.38±0.11, 0.30±0.07, 0.60±0.10, 0.09±0.11, 0.15±0.10, respectively, for Landrace. For the between-breed prediction, AGP for NBA, NBD, NM, NSB, NW, and TB were 0.28, -0.17, -0.01, 0.37, 0.27, and 0.29, respectively, when training on Landrace and validating on Duroc, and 0.28, -0.14, 0.01, 0.15, 0.23, and -0.01, respectively, when training on Duroc and validating on Landrace. Results show that the accuracies of genomic prediction for reproductive traits during a PRRSV outbreak are low to moderate.

The objective of this study was to estimate the heritabilities and genetic correlations of meat quality and carcass composition traits in 2 commercial crossbred swine populations: The Maschhoffs (TML) and Smithfield Premium Genetics (SPG). The TML dataset consisted of 1,255 crossbred individuals genotyped and phenotyped for meat quality (MQ), carcass yield (CY) and carcass weight (CW) traits. The SPG population included over 30,000 crossbred individuals phenotyped for a subset of MQ, CY and CW traits, and 1,156 sires genotyped. The two populations were analyzed separately with the use of multiple-trait genomic models. For the TML dataset, models included fixed effects of dam line, contemporary group (CG), gender, as well as a random additive genetic effect and pen nested within CG. For the SPG dataset, fixed effects included parity, gender and CG, as well as a random additive genetic effect and harvest group. Analyses were conducted using the BLUPf90 suite of programs. Bivariate analyses were used to estimate correlations among traits. Heritabilities [confidence interval] for CY traits (0.17[0.09, 0.25] to 0.45[0.36, 0.55]) were higher than CW (0.14[0.06, 0.23] to 0.30[0.20,0.41]). For MQ traits, heritabilities ranged from low to moderate having highest estimate for intramuscular fat: 0.52[0.40, 0.62]. Most of the genetic correlations were significant and ranged from -0.07[-0.14, -0.02]) to -0.70[-0.85, -0.84], 0.50[0.32, 0.79] to 0.81[0.78, 0.82] and -0.10[-0.02, -0.04] to -0.96 [-1.00, -0.83] respectively among CY, CW and MQ traits. The genetic correlations of MQ and carcass composition traits ranged from moderate to high in both directions. The genetic parameter estimates indicate that a multi-trait approach should be considered for selection programs aimed at carcass quality and composition in commercial crossbred swine population.

The objective of this study was to investigate host-genetic contributions to the vaginal microbiome of commercial gilts vaccinated for Porcine Reproductive and Respiratory Syndrome (PRRS). Vaginal swab samples (n = 576) from 308 F1 gilts (183±12 days old) were collected on day 4 (D4) and 52 (D52) post-vaccination with a commercial modified live virus PRRS vaccine. Samples were used to profile the vaginal microbiome by 16S rRNA gene sequencing, with sequences clustered into operational taxonomic units (OTUs) and taxonomically classified. All animals were genotyped for 45,536 SNPs. Arcsine of the square root-transformed OTUs abundance data were analyzed using a linear mixed animal model with age at vaccination as a covariate and animal (random) for estimation of genetic parameters. The same model was used for GWAS for the 100 most abundant OTUs but with addition of genotype of SNPs as a covariate, one at a time. For D4, heritability estimates ranged from < 0.001±0.01(13 OTUs) to 0.60±0.13 (Fusobacterium), with OTUs corresponding to the genera Fusobacterium, Pasteurellaceae, Clostridiales, Prevotellaceae, and Lactobacillus having high estimates (0.41±0.13 to 0.60±0.13). For D52, heritability estimates ranged from < 0.001±0.01 (10 OTUs) to 0.63±0.12 (Terrisporobacter), with OTUs corresponding to Clostridium, Terrisporobacter, Romboutsia, Turicibacter, Phascolarctobacterium, Muribaculaceae, and Ruminococcaceae having high estimates (0.42±0.14 to 0.63±0.12). Forty-six QTLs were significantly (P < 0.00001) associated with OTU across days. Among these, one main QTL on chromosome 12 (20–23Mb), a gene-rich region with previously identified QTL for immune-related traits, was identified for 5 and 6 OTUs on D4 and D52, respectively. These OTUs were mainly of the phyla Proteobacteria and Firmicutes on D4 and D52, respectively. In conclusion, there is evidence of substantial host genetic variation for vaginal microbiome in commercial PRRS-vaccinated gilts, including the identification of many QTLs. Additional research is needed to investigate the genetic relationship between vaginal microbiome, health, and production in pigs.

Summary Sow longevity is a key component for efficient and profitable pig farming; however, approximately 50% of sows are removed annually from a breeding herd. There is no consensus in the scientific literature regarding a definition for sow longevity; however, it has been suggested that it can be measured using several methods such as stayability and economic indicators such as lifetime piglets produced. Sow longevity can be improved by genetic selection; however, it is rarely included in genetic evaluations. One reason is elongated time intervals required to collect complete lifetime data. The effect of genetic parameter estimation software in handling incomplete data (censoring) and possible early indicator traits were evaluated analysing a 30% censored data set (12 725 pedigreed Landrace × Large White sows that included approximately 30% censored data) with DMU6, THRGIBBS1F90 and GIBBS2CEN. Heritability estimates were low for all the traits evaluated. The results show that the binary stayability traits benefited from being analysed with a threshold model compared to analysing with a linear model. Sires were ranked very similarly regardless if the program handled censoring when all available data were included. Accumulated born alive and stayability were good indicators for lifetime born alive traits. Number of piglets born alive within each parity could be used as an early indicator trait for sow longevity.

While for the most part genome-wide metrics are currently employed in managing livestock inbreeding, genomic data offer, in principle, the ability to identify functional inbreeding. Here we present a heuristic method to identify haplotypes contained within a run of homozygosity (ROH) associated with reduced performance. Results are presented for simulated and swine data. The algorithm comprises 3 steps. Step 1 scans the genome based on marker windows of decreasing size and identifies ROH genotypes associated with an unfavorable phenotype. Within this stage, multiple aggregation steps reduce the haplotype to the smallest possible length. In step 2, the resulting regions are formally tested for significance with the use of a linear mixed model. Lastly, step 3 removes nested windows. The effect of the unfavorable haplotypes identified and their associated haplotype probabilities for a progeny of a given mating pair or an individual can be used to generate an inbreeding load matrix (ILM). Diagonals of ILM characterize the functional inbreeding load of individual (IIL). We estimated the accuracy of predicting the phenotype based on ILL. We further compared the significance of the regression coefficient for IIL on phenotypes to genome-wide inbreeding metrics. We tested the algorithm using simulated scenarios (n =12) combining different levels of linkage disequilibrium (LD) and number of loci impacting a quantitative trait. Additionally, we investigated 9 traits from two maternal purebred swine lines. In simulated data, as the LD in the population increased the algorithm identified a greater proportion of the true unfavorable ROH effects. For example, the proportion of highly unfavorable true ROH effects identified raised from 32 to 41 % for the low to the high LD scenario. In both simulated and real data the haplotypes identified were contained within a much larger ROH (9.12-12.1 Mb). The IIL prediction accuracy was greater than zero across all scenarios for simulated data (high LD scenario mean (95% confidence interval): 0.49 (0.47-0.52)) and for nearly all swine traits (mean ± SD: 0.17±0.10). On average across simulated and swine datasets the IIL regression coefficient was more closely related to progeny performance than any genome-wide inbreeding metric. A heuristic method was developed that identified ROH genotypes with reduced performance and characterized the combined effects of ROH genotypes within and across individuals.

Records from 99,384 crossbred pigs from Duroc sires and Large White×Landrace dams were used to estimate genetic parameters for survival traits at different stages of the fattening period, and their relations with final weight. Traits analyzed were preweaning mortality (PWM), culling between weaning and harvesting (Call), culling during the farrowing period (Cfar), in the nursery site (Cnur), during the finishing phase (Cfin), and hot carcass weight (HCW). Because of the binary nature of PWM and culling traits, threshold-linear models were used: Model 1, including PWM, Call, and HCW; Model 2, including PWM, Cfar, Cnur, Cfin, and HCW. Both models included sex and parity number as fixed effects for all traits. Contemporary groups were considered as fixed effect for HCW and as random effects for the binary traits. Random effects were sire additive genetic, common litter, and residual effects for all traits and models. Heritability estimates were 0.03 for PWM, and 0.15 for HCW with both models, 0.06 for Call with Model 1, and 0.06 for Cfar, 0.14 for Cnur, and 0.10 for Cfin with Model 2. Litter variance explained a large part of the total variance and its influence declined slightly with age. For Model 1, genetic correlations were −0.36 between PWM and Call, −0.02 between PWM and HCW, and −0.25 between Call and HCW; correlations for litter effect were −0.15 between PWM and Call, −0.19 between PWM and HCW, and −0.21 between Call and HCW. For Model 2, genetic correlations were all positive between PWM and culling traits, except between PWM and Cnur (−0.61). Genetic correlations between HCW and the other traits were moderate and negative to null. Correlations for common litter effect were all negative between traits, except between Cfar and Cfin, and between Cnur and Cfin. Heritability of PWM and culling traits increased with age period. Therefore, selection for survival after weaning may be more efficient. The low genetic correlations between PWM and culling traits suggest that different genes influence pre and postweaning mortality. The HCW was not correlated with the other traits. However, relationships are not strongly unfavorable, therefore selection for survival and high final weight is possible.

The efficiency of producing salable products in the pork industry is largely determined by costs associated with feed and by the amount and quality of lean meat produced. The objectives of this paper were 1) to explore heritability and genetic correlations for growth, feed efficiency, and real-time ultrasound traits using both pedigree and marker information and 2) to assess accuracy of genomic prediction for those traits using Bayes A prediction models in a Duroc terminal sire population. Body weight at birth (BW at birth) and weaning (BW at weaning) and real-time ultrasound traits, including back fat thickness (BF), muscle depth (MD), and intramuscular fat content (IMF), were collected on the basis of farm protocol. Individual feed intake and serial BW records of 1,563 boars obtained from feed intake recording equipment (FIRE; Osborne Industries Inc., Osborne, KS) were edited to obtain growth, feed intake, and feed efficiency traits, including ADG, ADFI, feed conversion ratio (FCR), and residual feed intake (RFI). Correspondingly, 1,047 boars were genotyped using the Illumina PorcineSNP60 BeadChip. The remaining 516 boars, as an independent sample, were genotyped with a low-density GGP-Porcine BeadChip and imputed to 60K. Magnitudes of heritability from pedigree analysis were moderate for growth, feed intake, and ultrasound traits (ranging from 0.44 ± 0.11 for ADG to 0.58 ± 0.09 for BF); heritability estimates were 0.32 ± 0.09 for FCR but only 0.10 ± 0.05 for RFI. Comparatively, heritability estimates using marker information by Bayes A models were about half of those from pedigree analysis, suggesting "missing heritability." Moderate positive genetic correlations between growth and feed intake (0.32 ± 0.05) and back fat (0.22 ± 0.04), as well as negative genetic correlations between growth and feed efficiency traits (-0.21 ± 0.08, -0.05 ± 0.07), indicate selection solely on growth traits may lead to an undesirable increase in feed intake, back fat, and reduced feed efficiency. Genetic correlations among growth, feed intake, and FCR assessed by a multiple-trait Bayes A model resulted in increased genetic correlation between ADG and ADFI, a negative correlation between ADFI and FCR, and a positive correlation between ADG and FCR. Accuracies of genomic prediction for the traits investigated, ranging from 9.4% for RFI to 36.5% for BF, were reported that might provide new insight into pig breeding and future selection programs using genomic information.

The objective of this study was to implement a multi-trait genomic evaluation for maternal and growth traits in a swine population. Phenotypes for preweaning mortality, litter size, weaning weight, and average daily gain were available for 282K Large White pigs. The pedigree included 314k individuals, of which 35,731 were genotyped for 45K SNPs. Variance components were estimated in a multi-trait animal model without genomic information by AIREMLF90. Genomic breeding values were estimated using the genomic information by single-step GBLUP. The algorithm for proven and young (APY) was used to reduce computing time. Genetic correlation between proportion and the total number of preweaning deaths was 0.95. A strong, positive genetic correlation was also observed between weaning weight and average daily gain (r = 0.94). Conversely, the genetic correlations between mortality and growth traits were negative, with an average of -0.7. To avoid computations by expensive threshold models, preweaning mortality was transformed from a binary trait to two linear dam traits: proportion and a total number of piglets dead before weaning. Because of the high genetic correlations within groups of traits, inclusion of only one growth and one mortality trait in the model decreases computing time and allows for the inclusion of other traits. Reduction in computing time for the evaluation using APY was up to 20x, and no differences in EPD ranking were observed. The algorithm for proven and young improves the efficiency of genomic evaluation in swine without harming the quality of predictions. For this population, a binary trait of mortality can be replaced by a linear trait of the dam, resulting in a similar ranking for the selection candidates.

Genetic parameters for birth weight (BWT), preweaning mortality (PWM), and HCW were estimated for a crossbred pig population to determine if BWT could be used as an early predictor for later performances. Sire genetic effects for those traits were estimated to determine if early selection of purebred sires used in crossbreeding could be improved. Data were recorded from 1 commer- cial farm between 2008 and 2010. Data were from 24,376 crossbred pigs from Duroc sires and crossbred Large White × Landrace dams and included 24,376 BWT and PWM records and 13,029 HCW records. For the analy- sis, PWM was considered as a binary trait (0 for live or 1 for dead piglet at weaning). A multitrait threshold-linear animal model was used, with animal effect divided into sire genetic and dam effects; the dam effects included both genetic and environmental variation due to the absence of pedigree information for crossbred dams. Fixed effects were sex and parity for all traits, contemporary groups for BWT and HCW, and age at slaughter as a linear covariable for HCW. Random effects were sire additive genetic, dam, litter, and residual effects for all traits and contemporary group for PWM. Heritability estimates were 0.04 for BWT, 0.02 for PWM, and 0.12 for HCW. The ratio between sire genetic and total estimated variances was 0.01 for BWT and PWM and 0.03 for HCW. Dam and litter variances explained, respectively, 14% and 15% of total variance for BWT, 2% and 10% for PWM, and 3% and 8% for HCW. Genetic correlations were −0.52 between BWT and PWM, 0.55 between BWT and HCW, and −0.13 between PWM and HCW. Selection of purebred sires for higher BWT of crossbreds may slightly improve survival until weaning and final market weight at the commercial level.

Background In nucleus populations, regions of the genome that have a high frequency of runs of homozygosity (ROH) occur and are associated with a reduction in genetic diversity, as well as adverse effects on fitness. It is currently unclear whether, and to what extent, ROH stretches persist in the crossbred genome and how genomic management in the nucleus population might impact low diversity regions and its implications on the crossbred genome. Methods We calculated a ROH statistic based on lengths of 5 (ROH5) or 10 (ROH10) Mb across the genome for genotyped Landrace (LA), Large White (LW) and Duroc (DU) dams. We simulated crossbred dam (LA × LW) and market [DU × (LA × LW)] animal genotypes based on observed parental genotypes and the ROH frequency was tabulated. We conducted a simulation using observed genotypes to determine the impact of minimizing parental relationships on multiple diversity metrics within nucleus herds, i.e. pedigree-(A), SNP-by-SNP relationship matrix or ROH relationship matrix. Genome-wide metrics included, pedigree inbreeding, heterozygosity and proportion of the genome in ROH of at least 5 Mb. Lastly, the genome was split into bins of increasing ROH5 frequency and, within each bin, heterozygosity, ROH5 and length (Mb) of ROH were evaluated. Results We detected regions showing high frequencies of either ROH5 and/or ROH10 across both LW and LA on SSC1, SSC4, and SSC14, and across all breeds on SSC9. Long haplotypes were shared across parental breeds and thus, regions of ROH persisted in crossbred animals. Averaged across replicates and breeds, progeny had higher levels of heterozygosity (0.0056 ± 0.002%) and lower proportion of the genome in a ROH of at least 5 Mb (−0.015 ± 0.003%) than their parental genomes when genomic relationships were constrained, while pedigree relationships resulted in negligible differences at the genomic level. Across all breeds, only genomic data was able to target low diversity regions. Conclusions We show that long stretches of ROH present in the parents persist in crossbred animals. Furthermore, compared to using pedigree relationships, using genomic information to constrain parental relationships resulted in maintaining more genetic diversity and more effectively targeted low diversity regions. Electronic supplementary material The online version of this article (doi:10.1186/s12711-016-0269-y) contains supplementary material, which is available to authorized users.

The objective of this study was to examine relationships between behaviors in three different standard tests of temperament in pigs, Sus scrofa. For the backtest, pigs (n = 575) were placed in the supine position for 60 s. Each pig was tested twice. Amount of time struggling and number of struggle attempts during each test was recorded and combined to give total time struggling and total struggle attempts. Increased total time struggling was weakly associated with low preweaning average daily gain (r = −0.24; P 0.1). Pigs grouped based on total time struggling did not differ consistently during resident intruder test or novel object test trials. In conclusion, results of the present study do not support the theory that backtest performance is indicative of behavior in a test of aggression (resident intruder test) or a test of fear/curiosity in response to novelty (novel object test).

The objective of the study was to assess variation in hair coat shedding of Angus cows, and its effect on adjusted weaning weight ( d205wt ) and BCS. Data were available from 532 Angus cows over 3 years of age. Beginning in March and for 5 months at 30-d intervals, trained technicians scored cows on a scale from 1 to 5, with 1 representing slick coats and 5 winter coats. For each cow, the first month with a score of 3 or less ( MFS , 5 levels) was considered the beginning of winter coat shedding and used in the analyses. Association between MFS and d205WT or BCS, was investigated using the mixed procedure of SAS. Data were further analyzed by dividing cows into two groups, group one (Group 1) were cows with a shedding score of 3 or less by June 1st and group two (Group 2) consisted of cows with a shedding score of 4 or 5 on June 1st ( AS, 2 levels). Calves from Group1 dams were 11.1 ± 2.8 kg heavier at weaning (P 2 = 0.35) with a moderate genetic correlation with d205WT (r g = − 0.58). Hair coat shedding is a heritable trait and could be altered by selection. Producers within the Southeastern or Southern United States who are concerned about heat stress may want to select for cattle that shed their winter hair coat earlier in the season. In conclusion, cows who shed their winter coat by June 1st will wean heavier calves on average.

Summary Direct gestation length influences economically important traits in dairy cattle that are related to birth and peri-natal survival of the calf. The objective of this study was to identify single nucleotide polymorphisms (SNPs) that are significantly associated with direct gestation length through a genome-wide association study. Data used in the analysis included 7 308 194 cow gestation lengths from daughters of 4743 United States Holstein sires in the Cooperative Dairy DNA Repository population and 580 157 gestation lengths from 749 sires in the Italian Brown population. Association analysis included 36 768 and 35 082 SNPs spanning all autosomes for Holstein and Brown Swiss, respectively. Multiple shrinkage Bayesian was employed. Estimates of heritability for both populations were moderate, with values of 0.32 (±0.03) and 0.29 (±0.02) for Holstein and Brown Swiss, respectively. A panel of SNPs was identified, which included SNPs that have significant effects on direct gestation length, of which the strongest candidate region is located on chromosome 18. Two regions not previously linked to direct calving ease and calf survival were identified on chromosome 7 and 28, corresponding to regions that contain genes related to embryonic development and foetal development. SNPs were also identified in regions that have been previously mapped for calving difficulty and longevity. This study identifies target regions for the investigation of direct foetal effects, which are a significant factor in determining the ease of calving.

The objective of this study was to estimate heritabilities and genetic correlations between milk-release parameters, somatic cell score, milk yield, and udder functional traits in the Italian Brown Swiss population. Data were available from 37,511 cows over a span of 12 yr (1997-2008) from 1,592 herds. Milking flows were recorded for each individual once during lactation. Three different analyses were performed to estimate variance components for all the traits of interest. The first analysis included single control data milk yield, somatic cell score, maximum milk flow, average milk flow, time of plateau, decreasing time, and total milking time, whereas the second analysis included milk-release parameters as well as total udder score, udder depth, and 305-d milk yield and somatic cell score as dependent variables. The third analysis included total milking time, 305-d milk yield and somatic cell score, total udder score, udder depth, and ratios of maximum milk flow over total milking time (R1), time of plateau (R2), and decreasing time (R3) to estimate the relationship between the shape of the milk-release curves and important milking traits. Results from the first and second analysis found similar heritabilities for milkability traits ranging from 0.05 to 0.41 with genetic correlations between production traits and flow traits ranging from low to moderate values. Positive genetic correlations were found among production, somatic cell score, and milkability traits. The third analysis showed that R1 had the greatest heritability of the ratio traits (0.37) with large genetic correlations with R2 and R3, a low correlation with 305-d somatic cell score, and no correlation with 305-d milk yield. Estimated responses to selection over 5 generations were also calculated using different indexes, which included either flow or ratio traits. The results of this study show that it is possible to use information collected through portable flowmeters to improve milkability traits. Using a set of variables or traits to describe the overall release of milk can be an advantageous selection strategy to decrease management costs while maintaining milk production.

Background The overall breeding objective for a nucleus swine selection program is to improve crossbred commercial performance. Most genetic improvement programs are based on an assumed high degree of positive relationship between purebred performance in a nucleus herd and their relatives’ crossbred performance in a commercial herd. The objective of this study was to examine the relationship between purebred and crossbred sow longevity performance. Sow longevity was defined as a binary trait with a success occurring if a sow remained in the herd for a certain number of parities and including the cumulative number born alive as a measure of reproductive success. Heritabilities, genetic correlations, and phenotypic correlations were estimated using THRGIBBS1F90. Results Results indicated little to no genetic correlations between crossbred and purebred reproductive traits. This indicates that selection for longevity or lifetime performance at the nucleus level may not result in improved longevity and lifetime performance at the crossbred level. Early parity performance was highly correlated with lifetime performance indicating that an indicator trait at an early parity could be used to predict lifetime performance. This would allow a sow to have her own record for the selection trait before she has been removed from the herd. Conclusions Results from this study aid in quantifying the relationship between purebred and crossbred performance and provide information for genetic companies to consider when developing a selection program where the objective is to improve crossbred sow performance. Utilizing crossbred records in a selection program would be the best way to improve crossbred sow productivity.