Traits In Swine Research Articles

PSI-A-10 Correlations of NR3C1 Gene Expression with Pork Quality and Carcass Characteristics

Abstract Genetic control of carcass and meat quality traits is of great interest to the pork industry, and Hypothalamic-Pituitary-Adrenal (HPA) activation has been reported to have an inverse relationship with lean carcass growth traits in swine. The objective of this study was to determine if transcript abundance of the glucocorticoid receptor (NR3C1) in liver is associated with carcass trait variation. The liver is a key player in energy partitioning towards muscle or adipose accretion, and hepatic glucocorticoid receptors that bind cortisol are known to play a role in glucose metabolism. We hypothesized that NR3C1 expression levels are associated with differences in pork quality and carcass traits. We collected liver tissue from 18 gilts at slaughter (avg. BW =127 kg, age at slaughter = 164 d). Additionally, we collected measurements of carcass traits including dressing percentage, loin muscle area, 10th-rib backfat depth, subjective color and marbling scores, Minolta Colorimeter measures of L*, a*, b*, 45-min and ultimate pH, and driploss (Table 1). We extracted RNA from liver samples using a Qiagen miRNeasy kit and performed qRT-PCR using a TaqMan assay to quantify NR3C1 transcript abundance, with ACTB and HPRT1 as reference genes. Pearson correlation analysis was conducted between the carcass traits and NR3C1 expression. While liver NR3C1 transcript abundance was not significantly correlated with a majority of these traits, it was found to have a significant positive correlation with dressing percentage (r = 0.606, t = 3.05, P = 0.008). In conclusion, transcript abundance of NR3C1 in liver does not appear have a relationship with carcass merit and meat quality, but does have a positive relationship with dressing percentage. Further investigation into tissue-specific expression of NR3C1 may reveal the molecular mechanisms underlying the reported associations between HPA activation and differences in pork quality and carcass traits.

The role of gut archaea in the pig gut microbiome: a mini-review.

The gastrointestinal microbiota of swine harbors an essential but often overlooked component: the gut archaea. These enigmatic microorganisms play pivotal roles in swine growth, health, and yield quality. Recent insights indicate that the diversity of gut archaea is influenced by various factors including breed, age, and diet. Such factors orchestrate the metabolic interactions within the porcine gastrointestinal environment. Through symbiotic relationships with bacteria, these archaea modulate the host's energy metabolism and digestive processes. Contemporary research elucidates a strong association between the abundance of these archaea and economically significant traits in swine. This review elucidates the multifaceted roles of gut archaea in swine and underscores the imperative for strategic interventions to modulate their population and functionality. By exploring the probiotic potential of gut archaea, we envisage novel avenues to enhance swine growth, health, and product excellence. By spotlighting this crucial, yet under-investigated, facet of the swine gut microbiome, we aim to galvanize further scientific exploration into harnessing their myriad benefits.

Estimation of heritabilities and additive genetic correlations for reproduction traits in swine: insights for tropical commercial production systems using multiple trait animal models.

This study was to estimate heritabilities, additive genetic correlations, and phenotypic correlations between number of piglets born alive (NBA), litter birth weight (LTBW), number of piglets weaned (NPW) and litter weaning weight (LTWW) in different parities of Landrace (L), Yorkshire (Y), Landrace×Yorkshire (LY), and Yorkshire×Landrace (YL) sows in a commercial swine operation in Northern Thailand. Two models were utilized, a single trait repeatability model (RM) and a multiple trait animal model (MTM). The RM assumed reproductive records from different parities to be repeated values of the same trait, whereas the MTM assumed these records to be different traits. The two models accounted for the fixed effects of farrowing year-season, genetic group of the sow, heterosis, and age at first farrowing, and the random effects of sow, boar, and residual. Heritability estimates from RM were 0.02±0.01 for NBA, 0.10±0.01 for LTBW, 0.04±0.01 for NPW, and 0.11±0.01 for LTWW. Heritability estimates from MTM fluctuated across parities, ranging from 0.04±0.01 in parity 2 to 0.09±0.02 in parity 4 for NBA, 0.07±0.02 in parity 2 to 0.16±0.02 in parity 3 for LTBW, 0.04±0.02 in parity 4 to 0.08±0.01 in parity 1 for NPW, and 0.16±0.02 in parity 1 to 0.20±0.02 in parity 2 for LTWW. Additive genetic correlation estimates from MTM were also variable, ranging from 0.29±0.24 between NBA in parity 1 and NBA in parity 2 to 0.99±0.05 between LTWW in parity 3 and LTWW in parity 4. The findings of this study highlight the advantage of using MTM for the genetic improvement of reproductive traits in swine and contribute to the development of sustainable swine breeding programs in Thailand.

Genome-Wide Association Analysis Identifies Genomic Regions and Candidate Genes for Growth and Fatness Traits in Diannan Small-Ear (DSE) Pigs.

In the livestock industry, the growth and fatness traits are directly related to production efficiency and economic profits. As for Diannan small-ear (DSE) pigs, a unique indigenous breed, the genetic architecture of growth and fatness traits is still elusive. The aim of this study was to search the genetic loci and candidate genes associated with phenotypic traits in DSE pigs using GWAS based on the Geneseek Porcine 50K SNP Chip data. A total of 22,146 single nucleotide polymorphisms (SNPs) were detected in 265 DSE pigs and used for Genome-wide association studies (GWAS) analysis. Seven SNPs were found to be associated with back height, chest circumference, cannon bone circumference, and backfat thickness at the suggestive significance level. Based on gene annotation results, these seven SNPs were, respectively, mapped to the following candidate genes, VIPR2, SLC10A2, NUCKS1, MCT1, CHCHD3, SMOX, and GPR1, which are mainly involved with adipocyte differentiation, lipid metabolism, skeletal muscle development, and average daily weight gain. Our work offers novel insights into the genetic architecture of economically important traits in swine and may play an important role in breeding using molecular markers in the DSE breed.

Use of Host Feeding Behavior and Gut Microbiome Data in Estimating Variance Components and Predicting Growth and Body Composition Traits in Swine.

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

Genetic correlation between biopsied and post-mortem muscle fibre characteristics and meat quality traits in swine

Muscle fibre characteristics (MFCs) are related to lean meat production and meat quality. We newly estimated the heritability and genetic correlation of MFCs and meat quality in biopsied and post-mortem meat samples of 595 Jeju black pigs (JBPs). The cross-sectional area (CSA) (Type I: 0.76 and Type IIA: 0.89), % MF area (MFA) (Type I: 0.82), and % MF number (MFN) between the biopsied and post-mortem samples had high absolute genetic correlation coefficients. Moreover, the genetic correlation between the biopsied samples and drip loss (MFN of Type I: −0.77), lightness (CSA of Type I: 0.56), colour score (MFA of Type IIB: −0.79), and marbling score (CSA of Type IIB: 0.84 and MFA of Type IIA: −0.42) were moderate to high. Therefore, the MFCs of biopsied pre-mortem samples could predict the post-mortem MFCs and quality of the meat. Consequently, the biopsy method could be useful for the estimation of MFCs and meat quality in live JBPs.

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

Abstract 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< 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< 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.

Maternal Supplementation with Polyphenols and Omega-3 Fatty Acids during Pregnancy: Prenatal Effects on Growth and Metabolism.

Simple SummaryThe present study aimed to determine benefits and risks of a dietary supplementation combining hydroxytyrosol and n-3 polyunsaturated fatty acids (PUFA) on prenatal development and metabolic traits in swine, a model of intrauterine growth restricted (IUGR) pregnancies. No effects were found regarding sows’ weight and adiposity. Treated sows had larger litters, with smaller fetuses. However, these animals had better development of some major organs. Fetuses from the treated group had better glycemic and lipidic indexes, but no effects on anti/prooxidant profiles were found.Maternal supplementation with antioxidants and n-3 PUFAs may be a promising strategy to reduce the risk of intrauterine growth restriction and preterm delivery, which may diminish the appearance of low-birth-neonates. A previous studies showed beneficial outcomes of the combination of hydroxytyrosol and linoleic acid, but there is no data of its prenatal effects. The present study aimed to determine the possible prenatal implications of such maternal supplementation at prenatal stages in swine, a model of IUGR pregnancies. Results showed effects on litter size, with treated sows having larger litters and, therefore, smaller fetuses. However, the brain/head weight ratio showed a positive effect of the treatment in development, as well as in some other major organs like lungs, spleen, or kidneys. On the other hand, treated piglets showed better glycemic and lipidemic profiles, which could explain postnatal effects. However, further research on the implications of the treatment on litter size and prenatal and postnatal development must be done before practical recommendation can be given.

Analysis of Carcass Quality Grades According to Marketing Year and Carcass Traits in Swine

Analysis of Carcass Quality Grades According to Marketing Year and Carcass Traits in Swine

Characterisation of sperm piRNAs and their correlation with semen quality traits in swine.

Piwi-interacting RNAs (piRNAs) are a class of non-coding RNAs that are essential in the transcriptional silencing of transposable elements and warrant genome stability in the mammalian germline. In this study, we have identified piRNAs in porcine sperm using male germline and zygote datasets from human, mice, cow and pig, and evaluated the relation between their abundances and sperm quality traits. In our analysis, we identified 283382 piRNAs, 1355 of which correlated with P≤0.01 to at least one semen quality trait. Fifty-seven percent of the correlated piRNAs mapped less than 50kb apart from any other piRNA in the pig genome. Furthermore, piRNA location was significantly enriched near long interspersed nuclear elements. Moreover, some of the significant piRNAs mapped within or close to genes relevant for fertility or spermatogenesis such as CSNK1G2 and PSMF1.

Microbiability of meat quality and carcass composition traits in swine.

The impact of gut microbiome composition was investigated at different stages of production (weaning, Mid‐test and Off‐test) on meat quality and carcass composition traits of 1,123 three‐way crossbred pigs. Data were analysed using linear mixed models which included the fixed effects of dam line, contemporary group and gender as well as the random effects of pen, animal and microbiome information at different stages. The contribution of the microbiome to all traits was prominent although it varied over time, increasing from weaning to Off‐test for most traits. Microbiability estimates of carcass composition traits were greater than that of meat quality traits. Among all of the traits analysed, belly weight (BEL) had a higher microbiability estimate (0.29 ± 0.04). Adding microbiome information did not affect the estimates of genomic heritability of meat quality traits but affected the estimates of carcass composition traits. Fat depth had a greater decrease (10%) in genomic heritability at Off‐test. High microbial correlations were found among different traits, particularly with traits related to fat deposition with a decrease in the genomic correlation up to 20% for loin weight and BEL. This suggested that genomic correlation was partially contributed by genetic similarity of microbiome composition. The results indicated that better understanding of microbial composition could aid the improvement of complex traits, particularly the carcass composition traits in swine by inclusion of microbiome information in the genetic evaluation process.

Modeling host-microbiome interactions for the prediction of meat quality and carcass composition traits in swine

BackgroundThe objectives of this study were to evaluate genomic and microbial predictions of phenotypes for meat quality and carcass traits in swine, and to evaluate the contribution of host-microbiome interactions to the prediction. Data were collected from Duroc-sired three-way crossbred individuals (n = 1123) that were genotyped with a 60 k SNP chip. Phenotypic information and fecal 16S rRNA microbial sequences at three stages of growth (Wean, Mid-test, and Off-test) were available for all these individuals. We used fourfold cross-validation with animals grouped based on sire relatedness. Five models with three sets of predictors (full, informatively reduced, and randomly reduced) were evaluated. ‘Full’ included information from all genetic markers and all operational taxonomic units (OTU), while ‘informatively reduced’ and ‘randomly reduced’ represented a reduced number of markers and OTU based on significance preselection and random sampling, respectively. The baseline model included the fixed effects of dam line, sex and contemporary group and the random effect of pen. The other four models were constructed by including only genomic information, only microbiome information, both genomic and microbiome information, and microbiome and genomic information and their interaction.ResultsInclusion of microbiome information increased predictive ability of phenotype for most traits, in particular when microbiome information collected at a later growth stage was used. Inclusion of microbiome information resulted in higher accuracies and lower mean squared errors for fat-related traits (fat depth, belly weight, intramuscular fat and subjective marbling), objective color measures (Minolta a*, Minolta b* and Minolta L*) and carcass daily gain. Informative selection of markers increased predictive ability but decreasing the number of informatively reduced OTU did not improve model performance. The proportion of variation explained by the host-genome-by-microbiome interaction was highest for fat depth (~ 20% at Mid-test and Off-test) and shearing force (~ 20% consistently at Wean, Mid-test and Off-test), although the inclusion of the interaction term did not increase the accuracy of predictions significantly.ConclusionsThis study provides novel insight on the use of microbiome information for the phenotypic prediction of meat quality and carcass traits in swine. Inclusion of microbiome information in the model improved predictive ability of phenotypes for fat deposition and color traits whereas including a genome-by-microbiome term did not improve prediction accuracy significantly.

Heritability and genome-wide association of swine gut microbiome features with growth and fatness parameters

Despite recent efforts to characterize longitudinal variation in the swine gut microbiome, the extent to which a host’s genome impacts the composition of its gut microbiome is not yet well understood in pigs. The objectives of this study were: i) to identify pig gut microbiome features associated with growth and fatness, ii) to estimate the heritability of those features, and, iii) to conduct a genome-wide association study exploring the relationship between those features and single nucleotide polymorphisms (SNP) in the pig genome. A total of 1,028 pigs were characterized. Animals were genotyped with the Illumina PorcineSNP60 Beadchip. Microbiome samples from fecal swabs were obtained at weaning (Wean), at mid-test during the growth trial (MidTest), and at the end of the growth trial (OffTest). Average daily gain was calculated from birth to week 14 of the growth trial, from weaning to week 14, from week 14 to week 22, and from week 14 to harvest. Backfat and loin depth were also measured at weeks 14 and 22. Heritability estimates (±SE) of Operational Taxonomic Units ranged from 0.025 (±0.0002) to 0.139 (±0.003), from 0.029 (±0.003) to 0.289 (±0.004), and from 0.025 (±0.003) to 0.545 (±0.034) at Wean, MidTest, and OffTest, respectively. Several SNP were significantly associated with taxa at the three time points. These SNP were located in genomic regions containing a total of 68 genes. This study provides new evidence linking gut microbiome composition with growth and carcass traits in swine, while also identifying putative host genetic markers associated with significant differences in the abundance of several prevalent microbiome features.

Identification of a novel premature stop codon and other recurrent variations in the porcine gelsolin gene.

Gelsolin is an actin-binding protein that plays a significant role in sustaining cell motility and cell metabolism. Investigations of the mutations present in the key regions of gelsolin provide extensive information to further understand the mechanism by which gelsolin causes variation in the phenotype [e.g., residual feed intake (RFI) or feed efficiency ]of pigs. However, there have been no investigations of the variation in functional binding regions or research on Chinese native pigs. In this study, three key regions of gelsolin were investigated in 144 pigs from six breeds using a sequencing method. The results revealed 16 nucleotide substitutions, eight of which (c.42-13G/T, c.59T/C, c.86C/T, c.87G/T, c.104C/T, c.144T/C, c.206G/C, and c.237+21A/G) were novel and identified in intron 1, exon 2, and intron 2. Two variants (c.87G/T and c.144T/C) resulted in a premature stop codon (p.Gly16Uga(Stop)) and an amino acid change (p.Tyr35His), respectively. In region 1, c.144T/C was the most common (at a total frequency of 46.5%), followed by c.42-13G/T (at a total frequency of 41.7%). In region 2, two variants (c.350A/G and c.374A/G) were most common (both at a total frequency of 36.1%). There were significant differences (P<0.05) in variant frequencies between Chinese indigenous pigs and overseas pigs. Our findings revealed one novel premature stop codon and eight novel variations in re-sequencing regions, which suggest that these variations of gelsolin may influence its mRNA expression and consequently affect production traits in swine.

216 Contribution of host gut microbiome in prediction of meat quality and carcass composition traits in swine

Abstract The objective of this study was to assess the improvement in predictive ability (PA) for meat quality and carcass composition traits by including the effect of host gut microbiome. Data were collected from Duroc sired three-way crossbred individuals (n = 1,123) genotyped from 60k SNP chips. Fecal 16S microbial sequences for all individuals were obtained at three different stages: weaning (WEAN: 18.64±1.09 days); week 15 (W_15: 118.2±1.18 days); and off test (OT: 196.4±7.80 days). A 4-fold cross validation was used, with animals grouped based on sire relatedness. Analysis was conducted in “BGLR” package in R. The first model included the fixed effect of dam line, contemporary group, and sex, as well as random pen and additive genetic effect (with genomic relationship matrix). The second model included same fixed and random effects with the addition of the microbiome effect (with microbiome relationship matrix). With the inclusion of microbiome, the PA increased for majority of traits. For carcass traits, the increase in PA was greatest for belly weight which changed from 0.22±0.06 to 0.32±0.06 at OT and lowest for loin depth and ham weight which changed from 0.14±0.06 to 0.15 ±0.06 and 0.12±0.02 to 0.13 ±0.03 at WEAN respectively. For meat quality traits, the increase in PA was greatest for subjective firmness score which changed from 0.16±0.04 to 0.19±0.04 at OT and lowest for marbling score and intramuscular fat which changed from 0.27±0.09 to 0.28±0.08 and 0.39±0.05 to 0.40±0.05 at WEAN respectively. Higher predictive ability was observed at OT rather than at WEAN and W_15 microbiome information. The results may lead to a newer approach to the genetic evaluation program of swine.

Predicting Growth and Carcass Traits in Swine Using Microbiome Data and Machine Learning Algorithms

In this paper, we evaluated the power of microbiome measures taken at three time points over the growth test period (weaning, 15 and 22 weeks) to foretell growth and carcass traits in 1039 individuals of a line of crossbred pigs. We measured prediction accuracy as the correlation between actual and predicted phenotypes in a five-fold cross-validation setting. Phenotypic traits measured included live weight measures and carcass composition obtained during the trial as well as at slaughter. We employed a null model excluding microbiome information as a baseline to assess the increase in prediction accuracy stemming from the inclusion of operational taxonomic units (OTU) as predictors. We further contrasted performance of models from the Bayesian alphabet (Bayesian Lasso) as well machine learning approaches (Random Forest and Gradient Boosting) and semi-parametric kernel models (Reproducing Kernel Hilbert space). In most cases, prediction accuracy increased significantly with the inclusion of microbiome data. Accuracy was more substantial with the inclusion of microbiome information taken at weeks 15 and 22, with values ranging from approximately 0.30 for loin traits to more than 0.50 for back fat. Conversely, microbiome composition at weaning resulted in most cases in marginal gains of prediction accuracy, suggesting that later measures might be more useful to include in predictive models. Model choice affected predictions marginally with no clear winner for any model/trait/time point. We, therefore, suggest average prediction across models as a robust strategy in fitting microbiome information. In conclusion, microbiome composition can effectively be used as a predictor of growth and composition traits, particularly for fatness traits. The inclusion of OTU predictors could potentially be used to promote fast growth of individuals while limiting fat accumulation. Early microbiome measures might not be good predictors of growth and OTU information might be best collected at later life stages. Future research should focus on the inclusion of both microbiome as well as host genome information in predictions, as well as the interaction between the two. Furthermore, the influence of the microbiome on feed efficiency as well as carcass and meat quality should be investigated.

Genome-wide selection for reproductive traits in swine

Genome-wide selection for reproductive traits in swine

Genome-wide selection for reproductive traits in swine

The introduction of high-density SNP arrays in livestock species has enabled genomic evaluations on a scale not possible just a few years ago. Faster genetic gains are realized from application of genomics in pigs by increasing the accuracy of selection. This is especially important for lowly heritable reproductive traits, where female selection candidates have not yet expressed a phenotype at the point of selection and male candidates have no phenotype at all. Litter size was one of the first traits for which the breeding company PIC implemented genome assisted selection, using a 196 SNP panel in 2010. Significant improvements in accuracy for all selection index traits are now achieved using single step genomic evaluation incorporating genomic information into multivariate mixed model evaluations. Genotyping costs have been a barrier to fullscale implementation, but this challenge has been overcome by imputing high density genotypes from low density panels on selection candidates. Next generation sequencing technology is revolutionizing genomics research. The recently published draft pig genome sequence, along with the availability of phenotypes and tissue samples on thousands of animals, often with complete pedigree, facilitate discovery of genomic regions, genes and causative mutations for traits such as disease resistance. In the future, genome editing has the potential to introgress beneficial alleles from rare or indigenous breeds not present in improved commercial lines, as well as increasing the expression of beneficial genes through directed gene duplication. Additionally, nonlinear and nonparametric methods may be applied to further improve statistical predictions of genetic merit and performance.

Joint linkage and linkage disequilibrium mapping reveals association of BRMS1L with total teat number in a large intercross between Landrace and Korean native pigs

Previously, we reported a quantitative trait locus (QTL) that affects total teat number (TTN) on pig chromosome 7 (SSC7) in a large F2 intercross population between Landrace and Korean native pigs. The aim of this study was to refine the QTL associated with TTN and to identify positional candidate gene(s) within the refined genomic region. TTN was recorded in 1105 F2 progeny. All experimental animals were genotyped using 980 informative single nucleotide polymorphism (SNP) markers located on SSC7. A haplotype-based linkage and association analysis using the PHASEBOOK programme was applied to refine the QTL region. Additionally, linear mixed-effect models were used to assess the effect of a positional candidate gene on TTN and other economically important traits [i.e., thoracic vertebrae number (THO), carcass body length (CBL) and weight (CW), back fat thickness (BFT) and intramuscular fat content (IMF) in loin muscles]. Joint linkage and association analysis refined the critical region to a 4.50 Mb region that included a novel positional candidate gene, BRMS1L that encodes the breast cancer metastasis-suppressor 1-like protein, which could possibly be implicated in normal mammary gland development. Significant association of an SNP marker (g. −1087 G > A) in the 5′-flanking region of BRMS1L with TTN (P = 1.10 × 10−8), THO (P = 5.80 × 10−4), and CBL (P = 0.038) was observed. Based on these data, we propose BRMS1L as a positional candidate gene for TTN in pigs. After validation of the association in other independent populations, these results could be useful in optimizing breeding programmes that improve TTN and other economically important traits in swine.

The Effects of Antibiotic Alternatives on Feed Efficiency, Growth Performance, and Carcass Traits in Swine

ObjectivesThe U.S. swine producers have been challenged to find antibiotic alternatives to use in their swine heard. With feed expenses accounting for the majority of their costs, the lack of an antibiotic could lead to increased expenses. Scours is a common occurrence that will cause problems with not only the animals, but producers as well; therefore, the need to investigate alternative sources is a necessity. The objectives of this study were to: 1. Determine the effects of antibiotic alternatives (lysozyme and show feed additive) on feed efficiency in market swine and 2. Determine effects on carcass characteristics.Materials and MethodsThirty pigs (15 barrows/15 gilts) were assigned to 1 of 3 treatments (TRT). Treatments included: TRT C: control (commercial diet with no additives, n = 10), TRT L: lysozyme alternative (commercial diet with lysozyme additive, n = 10), TRT S: Ohio show feed alternative (commercial diet with show-feed additive, n = 10). Animals used in the current study were approved by IACUC (2017A00000075). On a weekly basis, pig weights and feed consumption were recorded. Carcass characteristics were measured on a 48 h. chill where carcass traits (back fat, LEA, carcass weight, subjective and objective color scores, and marbling, firmness, and wetness scores) were recorded, at the 10th/11th rib interface (left side of carcasses).ResultsWith little to no variation between treatments, results revealed no significant (P > 0.05) differences in feed efficiency or carcass characteristics. Treatments L and S revealed higher feed efficiency levels than control between d 42 and 77; however, by the end of the trial no differences (P > 0.05) were noted.ConclusionOverall, Trt S resulted in less mortalities when compared to other treatments. Additionally, TRT S was the most cost effective overall when viewed on an economic basis; which will be beneficial to the producer. However, with the combination of number of observations and the significant reduction in numbers due to a Scours outbreak (E. coli), authors recognize the need to repeat the study with an increase number of animals per treatment. On a side note, Trt L proved to not be suitable for E. coli.