Prediction and Experimental Verification of a Hierarchical Transcription Factor Regulatory Network of Porcine Myoglobin (Mb)

Myoglobin is a key chemical component that determines meat’s color and affects consumers’ purchase intentions. In this work, we firstly identified the promoter sequence of the Mb gene from the primary assembly of high-throughput genome sequencing in pigs, and predicted its potential transcription factors by LASAGNA. Through the data mining of the mRNA expression profile of longissimus dorsi muscle of different pig breeds, we constructed a hierarchical interplay network of Mb-TFs (Myoglobin-Transcription Factors), consisting of 16 adaptive transcription factors and 23 secondary transcription factors. The verification of gene expression in longissimus dorsi muscle showed that the Mb mRNA and encoded protein were significantly (p < 0.05) more abundant in Bama pigs than Yorkshire pigs. The qRT-PCR (Real-Time Quantitative Reverse Transcription PCR) validation on genes of the Mb-TFs network showed that FOS, STAT3, STAT1, NEFL21, NFE2L2 and MAFB were significant positive regulatory core transcription factors of Mb-TFs network in Bama pigs, whereas ATF3 was the secondary transcription factor most responsible for the activation of the above transcription factors. Our study provides a new strategy to unravel the mechanism of pork color formation, based on public transcriptome and genome data analysis.

Regulation of Swine Growth by Backfat Tissue during Growing and Finishing Stages

Recently, interest in the function of pig backfat (BF) has increased in the field of livestock animals, and many transcriptome-based studies using commercial pig breeds have been conducted. However, there is a lack of comprehensive studies regarding the biological mechanisms of Korean native pigs (KNPs) and Yorkshire pig crossbreeds. In this study, therefore, BF samples of F1 crossbreeds of KNPs and Yorkshire pigs were investigated to identify differentially expressed genes (DEGs) and their related terms using RNA-sequencing analysis. DEG analysis identified 611 DEGs, of which 182 were up-regulated and 429 were down-regulated. Lipid metabolism was identified in the up-regulated genes, whereas growth and maturation-related terminologies were identified in the down-regulated genes. LEP and ACTC1 were identified as highly connected core genes during functional gene network analysis. Fat tissue was observed to affect lipid metabolism and organ development due to hormonal changes driven by transcriptional alteration. This study provides a comprehensive understanding of BF contribution to crossbreeds of KNPs and Yorkshire pigs during growth periods.

Using Machine Learning To Improve the Accuracy of Genomic Prediction on Reproduction Traits in Pigs

Background Recently, machine learning (ML) is becoming attractive in genomic prediction, while its superiority in genomic prediction and the choosing of optimal ML methods are needed investigation. Results In this study, 2566 Chinese Yorkshire pigs with reproduction traits records were used, they were genotyped with GenoBaits Porcine SNP 50K and PorcineSNP50 panel. Four ML methods, including support vector regression (SVR), kernel ridge regression (KRR), random forest (RF) and Adaboost.R2 were implemented. Through 20 replicates of five-fold cross-validation, the genomic prediction abilities of ML methods were explored. Compared with genomic BLUP(GBLUP), single-step GBLUP (ssGBLUP) and Bayesian method BayesHE, our results indicated that ML methods significantly outperformed. The prediction accuracy of ML methods was improved by 19.3%, 15.0% and 20.8% on average over GBLUP, ssGBLUP and BayesHE, ranging from 8.9–24.0%, 7.6–17.5% and 11.1–24.6%, respectively. In addition, ML methods yielded smaller mean squared error (MSE) and mean absolute error (MAE) in all scenarios. ssGBLUP yielded improvement of 3.7% on average compared to GBLUP, and the performance of BayesHE was close to GBLUP. Among four ML methods, SVR and KRR had the most robust prediction abilities, which yielded higher accuracies, lower bias, lower MSE and MAE, and comparable computing efficiency as GBLUP. RF demonstrated the lowest prediction ability and computational efficiency among ML methods. Conclusion Our findings demonstrated that ML methods are more efficient than traditional genomic selection methods, and it could be new options for genomic prediction.

149 Integration of Selection Signatures Analyses and Weighted Single-step GWAS to Prioritize Candidate Genes for Body Conformation Traits in Pigs

Body conformation traits such as body height (BH) and body length (BL) have been included in the swine industry’s selection criteria. The objective of this study was to identify the quantitative trait loci (QTLs) and candidate genes for pig conformation traits using an integration of selection signatures analyses and weighted single-step GWAS (WssGWAS). Body measurement records of 5,593 Yorkshire pigs of which 598 animals were genotyped with Illumina 50K panel were used. Estimated breeding values (EBVs) for BH and BL were computed using univariate animal models. Genotyped animals were grouped into top 5% and bottom 5% based on their EBVs, and selection signatures analyses were performed using fixation index (Fst), FLK, hapFLK, and Rsb statistics, which were then combined as a Mahalanobis distance (Md) framework. The WssGWAS was conducted to detect the single nucleotide polymorphisms (SNPs) associated with the studied traits. The top 1% SNPs (n=530) from Md distribution that overlapped with the top 1% SNPs from WssGWAS (n = 530) were used to detect the candidate genes. A total of 31 and six overlapped SNPs were found to be associated with BH and BL, respectively. Several candidate genes were identified for BH (PARVA, DCDC1, SYT1, CASTOR2, RGSL1, RGS8, RBMS3, TGFBR2, and HS6ST1) and BL (SNTB1, AK7, PAPOLA, KSR1, CHODL, and BMP2), explaining 2.58% and 0.42% of the trait’s genetic variation, respectively. Our results indicated that integrating data from the signatures of selection tests with WssGWAS could help elucidate genomic regions underlying complex traits.

PSXII-12 Partitioning direct and maternal genetic effects into additive and non-additive components for growth and maternal traits in Yorkshire pigs

This study investigates how much direct and maternal non-additive genetic effects contribute to growth and maternal traits in swine. We analyzed a sample of 19,475 genotyped Yorkshire pigs from Acuity Ag Solutions, LLC (Carlyle, IL). Approximately 50K SNPs were kept after quality control, and missing genotypes were then imputed using findhap.f90. The genotypes were used to construct genomic relationship matrices (GRMs) corresponding to additive (A), dominance (D), and additive-by-additive epistasis (E) effects for both direct and maternal effects. The GRMs were subsequently employed as covariance structure matrices in a linear mixed model consisting of eight random components, namely three direct genetic effects (Ad, Dd, and Ed), three maternal genetic effects (Am, Dm, and Em), maternal environmental effect, and common litter environmental effect. We estimated these variance components (VCs) for six growth traits (birth weight, average daily gain, back fat, and loin area) and six maternal traits of a sow (total number of piglets born, number of piglets born alive, average weight of piglets at birth, average weight of piglets weaned) using REML in MMAP (https://mmap.github.io/). As shown in Table 1, we found significant (P&lt; 0.05) direct dominance and epistasis VCs for all six growth traits. Additionally, direct epistasis effects explained a larger proportion of phenotypic variation than direct dominance for all growth traits (0.04–0.12 vs. 0.01–0.04). In contrast, direct non-additive VCs were not significant for any maternal trait except for epistasis in average weight of piglets weaned. As for maternal non-additive effects, we only discovered significant additive VC in birth weight and average daily gain and significant epistasis VC in back fat (P&lt; 0.05). Other maternal genetic VCs were largely negligible. In summary, direct dominance and epistasis effects play a prominent role in growth traits of Yorkshire pigs.

Neuromodulation With Thoracic Dorsal Root Ganglion Stimulation Reduces Ventricular Arrhythmogenicity

Introduction: Sympathetic hyperactivity is strongly associated with ventricular arrhythmias and sudden cardiac death. Neuromodulation provides therapeutic options for ventricular arrhythmias by modulating cardiospinal reflexes and reducing sympathetic output at the level of the spinal cord. Dorsal root ganglion stimulation (DRGS) is a recent neuromodulatory approach; however, its role in reducing ventricular arrhythmias has not been evaluated. The aim of this study was to determine if DRGS can reduce cardiac sympathoexcitation and the indices for ventricular arrhythmogenicity induced by programmed ventricular extrastimulation. We evaluated the efficacy of thoracic DRGS at both low (20 Hz) and high (1 kHz) stimulation frequencies.Methods: Cardiac sympathoexcitation was induced in Yorkshire pigs (n = 8) with ventricular extrastimulation (S1/S2 pacing), before and after DRGS. A DRG-stimulating catheter was placed at the left T2 spinal level, and animals were randomized to receive low-frequency (20 Hz and 0.4 ms) or high-frequency (1 kHz and 0.03 ms) DRGS for 30 min. High-fidelity cardiac electrophysiological recordings were performed with an epicardial electrode array measuring the indices of ventricular arrhythmogenicity—activation recovery intervals (ARIs), electrical restitution curve (Smax), and Tpeak–Tend interval (Tp-Te interval).Results: Dorsal root ganglion stimulation, at both 20 Hz and 1 kHz, decreased S1/S2 pacing-induced ARI shortening (20 Hz DRGS −21±7 ms, Control −50±9 ms, P = 0.007; 1 kHz DRGS −13 ± 2 ms, Control −46 ± 8 ms, P = 0.001). DRGS also reduced arrhythmogenicity as measured by a decrease in Smax (20 Hz DRGS 0.5 ± 0.07, Control 0.7 ± 0.04, P = 0.006; 1 kHz DRGS 0.5 ± 0.04, Control 0.7 ± 0.03, P = 0.007), and a decrease in Tp-Te interval/QTc (20 Hz DRGS 2.7 ± 0.13, Control 3.3 ± 0.12, P = 0.001; 1 kHz DRGS 2.8 ± 0.08, Control; 3.1 ± 0.03, P = 0.007).Conclusions: In a porcine model, we show that thoracic DRGS decreased cardiac sympathoexcitation and indices associated with ventricular arrhythmogenicity during programmed ventricular extrastimulation. In addition, we demonstrate that both low-frequency and high-frequency DRGS can be effective neuromodulatory approaches for reducing cardiac excitability during sympathetic hyperactivity.

Single-step genomic evaluation with metafounders for feed conversion ratio and average daily gain in Danish Landrace and Yorkshire pigs

Background The single-step genomic best linear unbiased prediction (SSGBLUP) method is a popular approach for genetic evaluation with high-density genotype data. To solve the problem that pedigree and genomic relationship matrices refer to different base populations, a single-step genomic method with metafounders (MF-SSGBLUP) was put forward. The aim of this study was to compare the predictive ability and bias of genomic evaluations obtained with MF-SSGBLUP and standard SSGBLUP. We examined feed conversion ratio (FCR) and average daily gain (ADG) in DanBred Landrace (LL) and Yorkshire (YY) pigs using both univariate and bivariate models, as well as the optimal weighting factors (ω), which represent the proportions of the genetic variance not captured by markers, for ADG and FCR in SSGBLUP and MF-SSGBLUP. Results In general, SSGBLUP and MF-SSGBLUP showed similar predictive abilities and bias of genomic estimated breeding values (GEBV). In the LL population, the predictive ability for ADG reached 0.36 using uni- or bi-variate SSGBLUP or MF-SSGBLUP, while the predictive ability for FCR was highest (0.20) for the bivariate model using MF-SSGBLUP, but differences between analyses were very small. In the YY population, predictive ability for ADG was similar for the four analyses (up to 0.35), while the predictive ability for FCR was highest (0.36) for the uni- and bi-variate MF-SSGBLUP analyses. SSGBLUP and MF-SSGBLUP exhibited nearly the same bias. In general, the bivariate models had lower bias than the univariate models. In the LL population, the optimal ω for ADG was ~ 0.2 in the univariate or bivariate models using SSGBLUP or MF-SSGBLUP, and the optimal ω for FCR was 0.70 and 0.55 for SSGBLUP and MF-SSGBLUP, respectively. In the YY population, the optimal ω ranged from 0.25 to 0. 35 for ADG across the four analyses and from 0.10 to 0.30 for FCR. Conclusions Our results indicate that MF-SSGBLUP performed slightly better than SSGBLUP for genomic evaluation. There was little difference in the optimal weighting factors (ω) between SSGBLUP and MF-SSGBLUP. Overall, the bivariate model using MF-SSGBLUP is recommended for single-step genomic evaluation of ADG and FCR in DanBred Landrace and Yorkshire pigs.