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The largest gains in accuracy in a genomic selection program come from genotyping young selection candidates who have not yet produced progeny and who might, or might not, have a phenotypic record recorded. To reduce genotyping costs and to allow for an increased amount of genomic data to be available in a population, young selection candidates may be genotyped with low-density (LD) panels and imputed to a higher density. However, to ensure that a reasonable imputation accuracy persists overtime, some parent animals originally genotyped at LD must be re-genotyped at a higher density. This study investigated the long-term impact of selectively re-genotyping parents with a medium-density (MD) SNP panel on the accuracy of imputation and on the genetic predictions using ssGBLUP in a simulated beef cattle population. Assuming a moderately heritable trait (0.25) and a population undergoing selection, the simulation generated sequence data for a founder population (100 male and 500 female individuals) and 9,000 neutral markers, considered as the MD panel. All selection candidates from generation 8 to 15 were genotyped with LD panels corresponding to a density of 0.5% (LD_0.5), 2% (LD_2), and 5% (LD_5) of the MD. Re-genotyping scenarios chose parents at random or based on EBV and ranged from 10% of male parents to re-genotyping all male and female parents with MD. Ranges in average imputation accuracy at generation 15 were 0.567 to 0.936, 0.795 to 0.985, and 0.931 to 0.995 for the LD_0.5, LD_2, and LD_5, respectively, and the average EBV accuracies ranged from 0.453 to 0.735, 0.631 to 0.784, and 0.748 to 0.807 for LD_0.5, LD_2, and LD_5, respectively. Re-genotyping parents based on their EBV resulted in higher imputation and EBV accuracies compared to selecting parents at random and these values increased with the size of LD panels. Differences between re-genotyping scenarios decreased when the density of the LD panel increased, suggesting fewer animals needed to be re-genotyped to achieve higher accuracies. In general, imputation and EBV accuracies were greater when more parents were re-genotyped, independent of the proportion of males and females. In practice, the relationship between the density of the LD panel used and the target panel must be considered to determine the number (proportion) of animals that would need to be re-genotyped to enable sufficient imputation accuracy. [ABSTRACT FROM AUTHOR]

Modeling epigenetic factors impacting phenotypic expression of economically important traits has become a hot-topic in the field of animal breeding due to the variability in genetic expression caused by environmental stressors (e.g., heat stress). This variability may be due, in part, to in-utero epigenomic remodeling, which has been reported to be passed from parent to offspring. We aimed to estimate transgenerational epigenetic variance for various production and reproduction traits measured in a maternal-line pig population, using a Bayesian approach. The phenotypes for production [n = 10,862; i.e., weaning weight (WW), birth weight (BW) and ultrasound-backfat thickness (BF)] and reproduction [n = 5,235, i.e., number of piglets born alive (NBA) and total number of piglets born (TB)] traits from a purebred Landrace population were provided by Smithfield Premium Genetics (NC, USA). The pedigree information traced back to 10 generations. Single-trait genetic analyses were performed using mixed models that included additive genetic, common environmental, and epigenetic random effects. The Gibbs sampler algorithm based on Markov chain Monte Carlo was used to estimate the variance components. The epigenetic relationship matrix was constructed using a recursive parameter (?) related to the transmissibility coefficient of epigenetic markers. A grid search approach was used to define the optimal ? value (? values ranged from 0.1 to 0.5, with an interval of 0.1). The optimal ? value was determined based on the deviance information criterion, and it was used to estimate the additive and epigenetic variances. For instance, based on preliminary results, the optimal ? value estimated for TB was 0.3 with an additive genetic variance of 0.94 (0.19 PSD) and epigenetic variance of 0.67 (0.18 PSD). The additive genetic heritability was 0.076 (0.015 PSD) and the estimated epigenetic heritability was 0.053 (0.015 PSD). This preliminary result suggests that epigenetics contribute to the non-Mendelian variability in pigs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Sow functional teat number (FTEAT) is positively associated with piglet preweaning survival and litter throughput. The objective was to estimate the value of FTEAT in relationship to litter size to optimize the number of pigs weaned. Number of pigs born alive (NBA) and total teat number (TTEAT) were counted at farrowing on 836 multiparous purebred sows between March and September, 2020. Teats were evaluated by trained staff at farrowing and considered functional based on visual appraisal of teat morphology. Litter size at weaning (LSW) was recorded after a 26.5 d lactation length (LL). Sow was the experimental unit and all data were analyzed as a function of the biological sow. Number born alive was categorized by quartile: Q1 ≤ 10 NBA (n=185; μ=8.2); Q2 = 11 to 12 NBA (n=194; μ=11.6); Q3 = 13 to 14 NBA (n=238; μ=13.5); Q4 ≥ 15 NBA (n=219; μ=16.3). Data were analyzed in PROC GLM of SAS with farm, breed, and NBA quartile as categorical effects and LL and FTEAT as linear terms. The interaction of NBA quartile and FTEAT was also included. Mean TTEAT, FTEAT, LSW and preweaning survival were 15.4, 14.5, 11.3 and 89.4%, respectively. As a linear term, a one teat increase in FTEAT improved (P< 0.01) LSW by 0.3±0.1 pigs. Yet the value of an additional functional teat increased with increasing NBA. A one teat increase in FTEAT improved (P< 0.01) LSW in Q1, Q2, Q3, and Q4 by 0.12, 0.27, 0.33, and 0.38 pigs, respectively. The analysis demonstrates the impact of FTEAT on sow performance increases with increasing litter size, and highlights the importance of functional teats to optimize litter throughput and maximize the genetic potential of a maternal line. [ABSTRACT FROM AUTHOR]