Mapping splice QTLs reveals distinct transcriptional and post-transcriptional regulatory variation of gene expression and identifies putative alternative splicing variation mediating complex trait variation in pigs.

BACKGROUND: Alternative splicing is an important step in gene expression, generating multiple isoforms for the same genes and greatly expanding the diversity of proteomes. Genetic variation in alternative splicing contributes to phenotypic diversity in natural populations. However, the genetic basis of variation in alternative splicing in livestock including pigs remains poorly understood. RESULTS: In this study, using a Duroc x Pietrain F2 pig population, we performed genome-wide analysis of alternative splicing estimated from stranded RNA-Seq data in skeletal muscle. We characterized the genetic architecture of alternative splicing and compared its basic features with those of overall gene expression. We detected a large number of novel alternative splicing events that were not previously annotated. We found heritability of quantitative alternative splicing scores (percent spliced in or PSI) to be lower than that of overall gene expression. In addition, heritabilities showed little correlation between alternative splicing and overall gene expression. We mapped expression QTLs (eQTLs) and splice QTLs (sQTLs) and found them to be largely non-overlapping. Finally, we integrated sQTL mapping with phenotype QTL (pQTL mapping to identify potential mediator of pQTL effect by alternative splicing. CONCLUSIONS: Our results suggest that regulatory variation exists at multiple levels and that their genetic controls are distinct, offering opportunities for genetic improvement.

Transcriptome annotation of 17 porcine tissues using nanopore sequencing technology.

The annotation of animal genomes plays an important role in elucidating molecular mechanisms behind the genetic control of economically important traits. Here, we employed long-read sequencing technology, Oxford Nanopore Technology, to annotate the pig transcriptome across 17 tissues from two Yorkshire littermate pigs. More than 9.8 million reads were obtained from a single flow cell, and 69 781 unique transcripts at 50 108 loci were identified. Of these transcripts, 16 255 were found to be novel isoforms, and 22 344 were found at loci that were novel and unannotated in the Ensembl (release 102) and NCBI (release 106) annotations. Novel transcripts were mostly expressed in cerebellum, followed by lung, liver, spleen, and hypothalamus. By comparing the unannotated transcripts to existing databases, there were 21 285 (95.3%) transcripts matched to the NT database (v5) and 13 676 (61.2%) matched to the NR database (v5). Moreover, there were 4324 (19.4%) transcripts matched to the SwissProt database (v5), corresponding to 11 356 proteins. Tissue-specific gene expression analyses showed that 9749 transcripts were highly tissue-specific, and cerebellum contained the most tissue-specific transcripts. As the same samples were used for the annotation of cis-regulatory elements in the pig genome, the transcriptome annotation generated by this study provides an additional and complementary annotation resource for the Functional Annotation of Animal Genomes effort to comprehensively annotate the pig genome.

Assessment of DNA methylation in porcine immune cells reveals novel regulatory elements associated with cell-specific gene expression and immune capacity traits.

BACKGROUND: Genetics studies in the porcine immune system have enhanced selection practices for disease resistance phenotypes and increased the efficacy of porcine models in biomedical research; however limited functional annotation of the porcine immunome has hindered progress on both fronts. Among epigenetic mechanisms that regulate gene expression, DNA methylation is the most ubiquitous modification made to the DNA molecule and influences transcription factor binding as well as gene and phenotype expression. Human and mouse DNA methylation studies have improved mapping of regulatory elements in these species, but comparable studies in the pig have been limited in scope. RESULTS: We performed whole-genome bisulfite sequencing to assess DNA methylation patterns in nine pig immune cell populations: CD21+ and CD21- B cells, four T cell fractions (CD4+, CD8+, CD8+CD4+, and SWC6γδ+), natural killer and myeloid cells, and neutrophils. We identified 54,391 cell differentially methylated regions (cDMRs), and clustering by cDMR methylation rate grouped samples by cell lineage. 32,737 cDMRs were classified as cell lowly methylated regions (cLMRs) in at least one cell type, and cLMRs were broadly enriched in genes and regions of intermediate CpG density. We observed strong correlations between differential methylation and expression across immune cell populations, with cell-specific low methylation disproportionately impacting genes exhibiting enriched gene expression in the same cell type. Motif analysis of cLMRs revealed cell type-specific enrichment of transcription factor binding motifs, indicating that cell-specific methylation patterns may influence accessibility by trans-acting factors. Lastly, cDMRs were enriched for immune capacity GWAS SNPs, and many such overlaps occurred within genes known to influence immune cell development and function (CD8B, NDRG1). CONCLUSION: Our DNA methylation data improve functional annotation of the porcine genome through characterization of epigenomic regulatory patterns that contribute to immune cell identity and function, and increase the potential for identifying mechanistic links between genotype and phenotype.

A comparative analysis of chromatin accessibility in cattle, pig, and mouse tissues.

BACKGROUND: Although considerable progress has been made towards annotating the noncoding portion of the human and mouse genomes, regulatory elements in other species, such as livestock, remain poorly characterized. This lack of functional annotation poses a substantial roadblock to agricultural research and diminishes the value of these species as model organisms. As active regulatory elements are typically characterized by chromatin accessibility, we implemented the Assay for Transposase Accessible Chromatin (ATAC-seq) to annotate and characterize regulatory elements in pigs and cattle, given a set of eight adult tissues. RESULTS: Overall, 306,304 and 273,594 active regulatory elements were identified in pig and cattle, respectively. 71,478 porcine and 47,454 bovine regulatory elements were highly tissue-specific and were correspondingly enriched for binding motifs of known tissue-specific transcription factors. However, in every tissue the most prevalent accessible motif corresponded to the insulator CTCF, suggesting pervasive involvement in 3-D chromatin organization. Taking advantage of a similar dataset in mouse, open chromatin in pig, cattle, and mice were compared, revealing that the conservation of regulatory elements, in terms of sequence identity and accessibility, was consistent with evolutionary distance; whereas pig and cattle shared about 20% of accessible sites, mice and ungulates only had about 10% of accessible sites in common. Furthermore, conservation of accessibility was more prevalent at promoters than at intergenic regions. CONCLUSIONS: The lack of conserved accessibility at distal elements is consistent with rapid evolution of enhancers, and further emphasizes the need to annotate regulatory elements in individual species, rather than inferring elements based on homology. This atlas of chromatin accessibility in cattle and pig constitutes a substantial step towards annotating livestock genomes and dissecting the regulatory link between genome and phenome.

Genetic control of longissimus dorsi muscle gene expression variation and joint analysis with phenotypic quantitative trait loci in pigs.

BACKGROUND: Economically important growth and meat quality traits in pigs are controlled by cascading molecular events occurring during development and continuing throughout the conversion of muscle to meat. However, little is known about the genes and molecular mechanisms involved in this process. Evaluating transcriptomic profiles of skeletal muscle during the initial steps leading to the conversion of muscle to meat can identify key regulators of polygenic phenotypes. In addition, mapping transcript abundance through genome-wide association analysis using high-density marker genotypes allows identification of genomic regions that control gene expression, referred to as expression quantitative trait loci (eQTL). In this study, we perform eQTL analyses to identify potential candidate genes and molecular markers regulating growth and meat quality traits in pigs. RESULTS: Messenger RNA transcripts obtained with RNA-seq of longissimus dorsi muscle from 168 F2 animals from a Duroc x Pietrain pig resource population were used to estimate gene expression variation subject to genetic control by mapping eQTL. A total of 339 eQTL were mapped (FDR ≤ 0.01) with 191 exhibiting local-acting regulation. Joint analysis of eQTL with phenotypic QTL (pQTL) segregating in our population revealed 16 genes significantly associated with 21 pQTL for meat quality, carcass composition and growth traits. Ten of these pQTL were for meat quality phenotypes that co-localized with one eQTL on SSC2 (8.8-Mb region) and 11 eQTL on SSC15 (121-Mb region). Biological processes identified for co-localized eQTL genes include calcium signaling (FERM, MRLN, PKP2 and CHRNA9), energy metabolism (SUCLG2 and PFKFB3) and redox hemostasis (NQO1 and CEP128), and results support an important role for activation of the PI3K-Akt-mTOR signaling pathway during the initial conversion of muscle to meat. CONCLUSION: Co-localization of eQTL with pQTL identified molecular markers significantly associated with both economically important phenotypes and gene transcript abundance. This study reveals candidate genes contributing to variation in pig production traits, and provides new knowledge regarding the genetic architecture of meat quality phenotypes.

Genome-wide identification of tissue-specific long non-coding RNA in three farm animal species.

BACKGROUND: Numerous long non-coding RNAs (lncRNAs) have been identified and their roles in gene regulation in humans, mice, and other model organisms studied; however, far less research has been focused on lncRNAs in farm animal species. While previous studies in chickens, cattle, and pigs identified lncRNAs in specific developmental stages or differentially expressed under specific conditions in a limited number of tissues, more comprehensive identification of lncRNAs in these species is needed. The goal of the FAANG Consortium (Functional Annotation of Animal Genomes) is to functionally annotate animal genomes, including the annotation of lncRNAs. As one of the FAANG pilot projects, lncRNAs were identified across eight tissues in two adult male biological replicates from chickens, cattle, and pigs. RESULTS: Comprehensive lncRNA annotations for the chicken, cattle, and pig genomes were generated by utilizing RNA-seq from eight tissue types from two biological replicates per species at the adult developmental stage. A total of 9393 lncRNAs in chickens, 7235 lncRNAs in cattle, and 14,429 lncRNAs in pigs were identified. Including novel isoforms and lncRNAs from novel loci, 5288 novel lncRNAs were identified in chickens, 3732 in cattle, and 4870 in pigs. These transcripts match previously known patterns of lncRNAs, such as generally lower expression levels than mRNAs and higher tissue specificity. An analysis of lncRNA conservation across species identified a set of conserved lncRNAs with potential functions associated with chromatin structure and gene regulation. Tissue-specific lncRNAs were identified. Genes proximal to tissue-specific lncRNAs were enriched for GO terms associated with the tissue of origin, such as leukocyte activation in spleen. CONCLUSIONS: LncRNAs were identified in three important farm animal species using eight tissues from adult individuals. About half of the identified lncRNAs were not previously reported in the NCBI annotations for these species. While lncRNAs are less conserved than protein-coding genes, a set of positionally conserved lncRNAs were identified among chickens, cattle, and pigs with potential functions related to chromatin structure and gene regulation. Tissue-specific lncRNAs have potential regulatory functions on genes enriched for tissue-specific GO terms. Future work will include epigenetic data from ChIP-seq experiments to further refine these annotations.

Evidence for transcriptome-wide RNA editing among Sus scrofa PRE-1 SINE elements.

BACKGROUND: RNA editing by ADAR (adenosine deaminase acting on RNA) proteins is a form of transcriptional regulation that is widespread among humans and other primates. Based on high-throughput scans used to identify putative RNA editing sites, ADAR appears to catalyze a substantial number of adenosine to inosine transitions within repetitive regions of the primate transcriptome, thereby dramatically enhancing genetic variation beyond what is encoded in the genome. RESULTS: Here, we demonstrate the editing potential of the pig transcriptome by utilizing DNA and RNA sequence data from the same pig. We identified a total of 8550 mismatches between DNA and RNA sequences across three tissues, with 75% of these exhibiting an A-to-G (DNA to RNA) discrepancy, indicative of a canonical ADAR-catalyzed RNA editing event. When we consider only mismatches within repetitive regions of the genome, the A-to-G percentage increases to 94%, with the majority of these located within the swine specific SINE retrotransposon PRE-1. We also observe evidence of A-to-G editing within coding regions that were previously verified in primates. CONCLUSIONS: Thus, our high-throughput evidence suggests that pervasive RNA editing by ADAR can exist outside of the primate lineage to dramatically enhance genetic variation in pigs.

Molecular advances in QTL discovery and application in pig breeding.

Thousands of quantitative trait loci (QTL) have been identified for a wide range of economically important phenotypes in pigs. Recently, QTL analyses have begun to use high-density single nucleotide polymorphism (SNP) panels and applications have extended beyond experimental intercrosses to outbred populations by exploiting long-range linkage disequilibrium that results in higher resolution QTL mapping. Relevant phenotypes generally fall under categories of growth and body composition, carcass and meat quality, reproduction, and disease resistance. A few expression QTL (eQTL) studies have been performed that integrate transcriptional profiles with genotype data by considering expression levels as response variables in QTL analyses for identifying genes controlling important trait phenotypes. Rapidly evolving genomics technologies, including RNAseq, provide tremendous opportunities for QTL and eQTL discovery. In this review, we discuss recent progress in pig QTL and eQTL discovery, including approaches for allele-specific expression, and implications of these discoveries for pig breeding and genetics.

Whole blood microarray analysis of pigs showing extreme phenotypes after a porcine reproductive and respiratory syndrome virus infection.

BACKGROUND: The presence of variability in the response of pigs to Porcine Reproductive and Respiratory Syndrome virus (PRRSv) infection, and recent demonstration of significant genetic control of such responses, leads us to believe that selection towards more disease resistant pigs could be a valid strategy to reduce its economic impact on the swine industry. To find underlying molecular differences in PRRS susceptible versus more resistant pigs, 100 animals with extremely different growth rates and viremia levels after PRRSv infection were selected from a total of 600 infected pigs. A microarray experiment was conducted on whole blood RNA samples taken at 0, 4 and 7 days post infection (dpi) from these pigs. From these data, we examined associations of gene expression with weight gain and viral load phenotypes. The single nucleotide polymorphism (SNP) marker WUR10000125 (WUR) on the porcine 60 K SNP chip was shown to be associated with viral load and weight gain after PRRSv infection, and so the effect of the WUR10000125 (WUR) genotype on expression in whole blood was also examined. RESULTS: Limited information was obtained through linear modeling of blood gene differential expression (DE) that contrasted pigs with extreme phenotypes, for growth or viral load or between animals with different WUR genotype. However, using network-based approaches, molecular pathway differences between extreme phenotypic classes could be identified. Several gene clusters of interest were found when Weighted Gene Co-expression Network Analysis (WGCNA) was applied to 4 dpi contrasted with 0 dpi data. The expression pattern of one such cluster of genes correlated with weight gain and WUR genotype, contained numerous immune response genes such as cytokines, chemokines, interferon type I stimulated genes, apoptotic genes and genes regulating complement activation. In addition, Partial Correlation and Information Theory (PCIT) identified differentially hubbed (DH) genes between the phenotypically divergent groups. GO enrichment revealed that the target genes of these DH genes are enriched in adaptive immune pathways. CONCLUSION: There are molecular differences in blood RNA patterns between pigs with extreme phenotypes or with a different WUR genotype in early responses to PRRSv infection, though they can be quite subtle and more difficult to discover with conventional DE expression analyses. Co-expression analyses such as WGCNA and PCIT can be used to reveal network differences between such extreme response groups.

Rapid screening for phenotype-genotype associations by linear transformations of genomic evaluations.

BACKGROUND: Currently, association studies are analysed using statistical mixed models, with marker effects estimated by a linear transformation of genomic breeding values. The variances of marker effects are needed when performing the tests of association. However, approaches used to estimate the parameters rely on a prior variance or on a constant estimate of the additive variance. Alternatively, we propose a standardized test of association using the variance of each marker effect, which generally differ among each other. Random breeding values from a mixed model including fixed effects and a genomic covariance matrix are linearly transformed to estimate the marker effects. RESULTS: The standardized test was neither conservative nor liberal with respect to type I error rate (false-positives), compared to a similar test using Predictor Error Variance, a method that was too conservative. Furthermore, genomic predictions are solved efficiently by the procedure, and the p-values are virtually identical to those calculated from tests for one marker effect at a time. Moreover, the standardized test reduces computing time and memory requirements.The following steps are used to locate genome segments displaying strong association. The marker with the highest - log(p-value) in each chromosome is selected, and the segment is expanded one Mb upstream and one Mb downstream of the marker. A genomic matrix is calculated using the information from those markers only, which is used as the variance-covariance of the segment effects in a model that also includes fixed effects and random genomic breeding values. The likelihood ratio is then calculated to test for the effect in every chromosome against a reduced model with fixed effects and genomic breeding values. In a case study with pigs, a significant segment from chromosome 6 explained 11% of total genetic variance. CONCLUSIONS: The standardized test of marker effects using their own variance helps in detecting specific genomic regions involved in the additive variance, and in reducing false positives. Moreover, genome scanning of candidate segments can be used in meta-analyses of genome-wide association studies, as it enables the detection of specific genome regions that affect an economically relevant trait when using multiple populations.

Methods of tagSNP selection and other variables affecting imputation accuracy in swine.

BACKGROUND: Genotype imputation is a cost efficient alternative to use of high density genotypes for implementing genomic selection. The objective of this study was to investigate variables affecting imputation accuracy from low density tagSNP (average distance between tagSNP from 100kb to 1Mb) sets in swine, selected using LD information, physical location, or accuracy for genotype imputation. We compared results of imputation accuracy based on several sets of low density tagSNP of varying densities and selected using three different methods. In addition, we assessed the effect of varying size and composition of the reference panel of haplotypes used for imputation. RESULTS: TagSNP density of at least 1 tagSNP per 340kb (~7000 tagSNP) selected using pairwise LD information was necessary to achieve average imputation accuracy higher than 0.95. A commercial low density (9K) tagSNP set for swine was developed concurrent to this study and an average accuracy of imputation of 0.951 based on these tagSNP was estimated. Construction of a haplotype reference panel was most efficient when these haplotypes were obtained from randomly sampled individuals. Increasing the size of the original reference haplotype panel (128 haplotypes sampled from 32 sire/dam/offspring trios phased in a previous study) led to an overall increase in imputation accuracy (IA = 0.97 with 512 haplotypes), but was especially useful in increasing imputation accuracy of SNP with MAF below 0.1 and for SNP located in the chromosomal extremes (within 5% of chromosome end). CONCLUSION: The new commercially available 9K tagSNP set can be used to obtain imputed genotypes with high accuracy, even when imputation is based on a comparably small panel of reference haplotypes (128 haplotypes). Average imputation accuracy can be further increased by adding haplotypes to the reference panel. In addition, our results show that randomly sampling individuals to genotype for the construction of a reference haplotype panel is more cost efficient than specifically sampling older animals or trios with no observed loss in imputation accuracy. We expect that the use of imputed genotypes in swine breeding will yield highly accurate predictions of GEBV, based on the observed accuracy and reported results in dairy cattle, where genomic evaluation of some individuals is based on genotypes imputed with the same accuracy as our Yorkshire population.

Genotype imputation accuracy in a F2 pig population using high density and low density SNP panels.

BACKGROUND: F(2) resource populations have been used extensively to map QTL segregating between pig breeds. A limitation associated with the use of these resource populations for fine mapping of QTL is the reduced number of founding individuals and recombinations of founding haplotypes occurring in the population. These limitations, however, become advantageous when attempting to impute unobserved genotypes using within family segregation information. A trade-off would be to re-type F(2) populations using high density SNP panels for founding individuals and low density panels (tagSNP) in F(2) individuals followed by imputation. Subsequently a combined meta-analysis of several populations would provide adequate power and resolution for QTL mapping, and could be achieved at relatively low cost. Such a strategy allows the wealth of phenotypic information that has previously been obtained on experimental resource populations to be further mined for QTL identification. In this study we used experimental and simulated high density genotypes (HD-60K) from an F(2) cross to estimate imputation accuracy under several genotyping scenarios. RESULTS: Selection of tagSNP using physical distance or linkage disequilibrium information produced similar imputation accuracies. In particular, tagSNP sets averaging 1 SNP every 2.1 Mb (1,200 SNP genome-wide) yielded imputation accuracies (IA) close to 0.97. If instead of using custom panels, the commercially available 9K chip is used in the F(2), IA reaches 0.99. In order to attain such high imputation accuracy the F(0) and F(1) generations should be genotyped at high density. Alternatively, when only the F(0) is genotyped at HD, while F(1) and F(2) are genotyped with a 9K panel, IA drops to 0.90. CONCLUSIONS: Combining 60K and 9K panels with imputation in F(2) populations is an appealing strategy to re-genotype existing populations at a fraction of the cost.

Estimation of linkage disequilibrium in four US pig breeds.

BACKGROUND: The success of marker assisted selection depends on the amount of linkage disequilibrium (LD) across the genome. To implement marker assisted selection in the swine breeding industry, information about extent and degree of LD is essential. The objective of this study is to estimate LD in four US breeds of pigs (Duroc, Hampshire, Landrace, and Yorkshire) and subsequently calculate persistence of phase among them using a 60 k SNP panel. In addition, we report LD when using only a fraction of the available markers, to estimate persistence of LD over distance. RESULTS: Average r2 between adjacent SNP across all chromosomes was 0.36 for Landrace, 0.39 for Yorkshire, 0.44 for Hampshire and 0.46 for Duroc. For markers 1 Mb apart, r2 ranged from 0.15 for Landrace to 0.20 for Hampshire. Reducing the marker panel to 10% of its original density, average r2 ranged between 0.20 for Landrace to 0.25 for Duroc. We also estimated persistence of phase as a measure of prediction reliability of markers in one breed by those in another and found that markers less than 10 kb apart could be predicted with a maximal accuracy of 0.92 for Landrace with Yorkshire. CONCLUSIONS: Our estimates of LD, although in good agreement with previous reports, are more comprehensive and based on a larger panel of markers. Our estimates also confirmed earlier findings reporting higher LD in pigs than in American Holstein cattle, especially at increasing marker distances (> 1 Mb). High average LD (r2 > 0.4) between adjacent SNP found in this study is an important precursor for the implementation of marker assisted selection within a livestock species.Results of this study are relevant to the US purebred pig industry and critical for the design of programs of whole genome marker assisted evaluation and selection. In addition, results indicate that a more cost efficient implementation of marker assisted selection using low density panels with genotype imputation, would be feasible for these breeds.

The Social Life of Pigs: Changes in Affiliative and Agonistic Behaviors following Mixing.

This study investigated potentially affiliative behaviors in grow-finish pigs, how these behaviors changed over time and their relationship to agonistic behaviors. A total of 257 Yorkshire barrows were observed for agonistic (reciprocal fights, attacks) and affiliative (nosing, play, non-agonistic contact) behaviors after mixing (at 10 weeks of age), and weeks 3, 6, and 9 after mix. The least square means of affiliative behaviors were compared across time points. Relationships among affiliative and agonistic behaviors were assessed using generalized linear mixed models. Non-agonistic contact with conspecifics increased until week 6 then remained stable between weeks 6 and 9. Nosing was highest at mix, then decreased in the following weeks. Play was lowest at mix and highest at week 3. Affiliative behaviors were negatively related with aggression at mix (p < 0.001). Pigs who engaged in play and nosing behaviors were more likely to be involved in agonistic interactions in the weeks after mixing (p < 0.05), while pigs engaging in non-agonistic contact were less likely to be involved in agonistic interactions (p < 0.001). There appear to be relationships between affiliative and agonistic behaviors in pigs, with contact being the most predictive of less aggression. Future studies could focus on promoting positive non-agonistic contact in unfamiliar pigs as a way to mitigate aggressive interactions.

Transcriptional profiling identifies differentially expressed genes in developing turkey skeletal muscle.

BACKGROUND: Skeletal muscle growth and development from embryo to adult consists of a series of carefully regulated changes in gene expression. Understanding these developmental changes in agriculturally important species is essential to the production of high quality meat products. For example, consumer demand for lean, inexpensive meat products has driven the turkey industry to unprecedented production through intensive genetic selection. However, achievements of increased body weight and muscle mass have been countered by an increased incidence of myopathies and meat quality defects. In a previous study, we developed and validated a turkey skeletal muscle-specific microarray as a tool for functional genomics studies. The goals of the current study were to utilize this microarray to elucidate functional pathways of genes responsible for key events in turkey skeletal muscle development and to compare differences in gene expression between two genetic lines of turkeys. To achieve these goals, skeletal muscle samples were collected at three critical stages in muscle development: 18d embryo (hyperplasia), 1d post-hatch (shift from myoblast-mediated growth to satellite cell-modulated growth by hypertrophy), and 16 wk (market age) from two genetic lines: a randombred control line (RBC2) maintained without selection pressure, and a line (F) selected from the RBC2 line for increased 16 wk body weight. Array hybridizations were performed in two experiments: Experiment 1 directly compared the developmental stages within genetic line, while Experiment 2 directly compared the two lines within each developmental stage. RESULTS: A total of 3474 genes were differentially expressed (false discovery rate; FDR < 0.001) by overall effect of development, while 16 genes were differentially expressed (FDR < 0.10) by overall effect of genetic line. Ingenuity Pathways Analysis was used to group annotated genes into networks, functions, and canonical pathways. The expression of 28 genes involved in extracellular matrix regulation, cell death/apoptosis, and calcium signaling/muscle function, as well as genes with miscellaneous function was confirmed by qPCR. CONCLUSIONS: The current study identified gene pathways and uncovered novel genes important in turkey muscle growth and development. Future experiments will focus further on several of these candidate genes and the expression and mechanism of action of their protein products.

A comparative evaluation of the protective efficacy of rMd5deltaMeq and CVI988/ Rispens against a vv+ strain of Marek's disease virus infection in a series of recombinant congenic strains of White Leghorn chickens.

Marek's disease (MD) is a lymphoproliferative disease of domestic chickens caused by a highly infectious, oncogenic alpha-herpesvirus known as Marek's disease virus (MDV). MD is presently controlled by vaccination. Current MD vaccines include attenuated serotype 1 strains (e.g., CVI988/Rispens), avirulent serotype 2 (SB-1), and serotype 3 (HVT) MDV strains. In addition, recombinant MDV strains have been developed as potential new and more efficient vaccines to sustain the success of MD control in poultry. One of the candidate recombinant MDV strains, named rMd5deltaMeq, was derived from Md5, a very virulent strain of MDV lacking the MDV oncogene Meq. Our earlier reports suggest that rMd5deltaMeq provided protection equally well or better than commonly used MD vaccines in experimental and commercial lines of chickens challenged with very virulent plus (vv+) strains of MDV. In this study, maternal antibody-positive (trial 1) and negative (trial 2) chickens from a series of relatively MD resistant lines were either vaccinated with the rMd5deltaMeq or CVI988/Rispens followed by infection of a vv+ strain of MDV, 648A, passage 10. This report presents experimental evidence that the rMd5deltaMeq protected significantly better than the CVI988/Rispens (P < 0.01) in the relatively resistant experimental lines of chickens challenged with the vv+ strain of MDV. Together with early reports, the rMd5deltaMeq appeared to provide better protection, comparing with the most efficacious commercially available vaccine, CVI988/Rispens, for control of MD in lines of chickens regardless of their genetic background.

Weaning Induces Stress-Dependent DNA Methylation and Transcriptional Changes in Piglet PBMCs.

Changes to the epigenome, including those to DNA methylation, have been proposed as mechanisms by which stress can induce long-term physiological changes in livestock species. Pig weaning is associated with dietary and social stress, both of which elicit an immune response and changes to the hypothalamic-pituitary-adrenal (HPA) axis. While differential methylation following stress has been assessed in model organisms, it remains poorly understood how the pig methylome is altered by stressors in production settings. We quantified changes in CpG methylation and transcript abundance in piglet peripheral blood mononuclear cells (PBMCs) following weaning and also assessed differential patterns in pigs exhibiting high and low stress response as measured by cortisol concentration and lesion scores. Blood was collected from nine gilt piglets 24 h before and after weaning, and whole-genome bisulfite sequencing (WGBS) and RNA-sequencing were performed on six and nine animals, respectively, at both time points. We identified 2,674 differentially methylated regions (DMRs) that were enriched within promoters of genes associated with lymphocyte stimulation and transcriptional regulation. Stress groups displayed unique differential methylation and expression patterns associated with activation and suppression of T cell immunity in low and high stress animals, respectively. Differential methylation was strongly associated with differential expression; specifically, upregulated genes were enriched among hypomethylated genes. We observed post-weaning hypermethylation of the glucocorticoid receptor (NR3C1) promoter and a significant decrease in NR3C1 expression (n = 9, p = 6.1 × 10-3). Our results indicate that weaning-associated stress elicits genome-wide methylation changes associated with differential gene expression, reduced T cell activation, and an altered HPA axis response.

Integrated Genome-Wide Analysis of MicroRNA Expression Quantitative Trait Loci in Pig Longissimus Dorsi Muscle.

Determining mechanisms regulating complex traits in pigs is essential to improve the production efficiency of this globally important protein source. MicroRNAs (miRNAs) are a class of non-coding RNAs known to post-transcriptionally regulate gene expression affecting numerous phenotypes, including those important to the pig industry. To facilitate a more comprehensive understanding of the regulatory mechanisms controlling growth, carcass composition, and meat quality phenotypes in pigs, we integrated miRNA and gene expression data from longissimus dorsi muscle samples with genotypic and phenotypic data from the same animals. We identified 23 miRNA expression Quantitative Trait Loci (miR-eQTL) at the genome-wide level and examined their potential effects on these important production phenotypes through miRNA target prediction, correlation, and colocalization analyses. One miR-eQTL miRNA, miR-874, has target genes that colocalize with phenotypic QTL for 12 production traits across the genome including backfat thickness, dressing percentage, muscle pH at 24 h post-mortem, and cook yield. The results of our study reveal genomic regions underlying variation in miRNA expression and identify miRNAs and genes for future validation of their regulatory effects on traits of economic importance to the global pig industry.

Time budgets of group-housed pigs in relation to social aggression and production.

Commercial producers house growing pigs by sex and weight to allow for efficient use of resources and provide pigs the welfare benefits of interacting with their conspecifics and more freedom of movement. However, the introduction of unfamiliar pigs can cause increased aggression for 24 to 48 h as pigs establish social relationships. To address this issue, a better understanding of pig behavior is needed. The objectives of this study were to quantify time budgets of pigs following introduction into a new social group and how these changed over time and to investigate how social aggression influences the overall time budgets and production parameters. A total of 257 grow-finish Yorkshire barrows across 20 pens were introduced into new social groups at 10 wk of age (~23 kg) and observed for aggression and time budgets of behavior at four periods: immediately after introduction and 3, 6, and 9 wk later. Pigs were observed for the duration of total aggression and initiated aggression (s) for 9 h after introduction and for 4 h at 3, 6, and 9 wk later. Time budgets were created by scan sampling inactive, movement, ingestion, social, and exploration behaviors every 2 min for 4 h in the afternoon and summarizing the proportion of time each behavior was performed by period. The least square means of each behavior were compared across time points. Pigs spent most of their time inactive. In general, the greatest change in pig behavior was observed between introduction and week 3 (P < 0.003), with gradual changes throughout the study period as pigs became more inactive (week 3 vs. week 6: P = 0.209; week 6 vs. week 9: P = 0.007) and spent less time on other behaviors. Pigs' nonaggressive behavior and production parameters were compared with aggression using generalized linear mixed models. The time pigs spent on nonaggressive behaviors was negatively related to aggression (P < 0.045) with few exceptions. Initiated aggression after introduction was negatively related to loin muscle area (P = 0.003). These results show how finishing pigs spend their time in commercial facilities and indicate that behavior continues to change for up to 9 wk after introduction into a new social group. Efforts to reduce chronic levels of aggression should focus on promoting nonaggressive behaviors, such as exploration and movement, after the initial fighting that occurs immediately after introduction has waned, and should be implemented for up to 9 wk after introduction into new social groups.

Tissue Resources for the Functional Annotation of Animal Genomes.

In order to generate an atlas of the functional elements driving genome expression in domestic animals, the Functional Annotation of Animal Genome (FAANG) strategy was to sample many tissues from a few animals of different species, sexes, ages, and production stages. This article presents the collection of tissue samples for four species produced by two pilot projects, at INRAE (National Research Institute for Agriculture, Food and Environment) and the University of California, Davis. There were three mammals (cattle, goat, and pig) and one bird (chicken). It describes the metadata characterizing these reference sets (1) for animals with origin and selection history, physiological status, and environmental conditions; (2) for samples with collection site and tissue/cell processing; (3) for quality control; and (4) for storage and further distribution. Three sets are identified: set 1 comprises tissues for which collection can be standardized and for which representative aliquots can be easily distributed (liver, spleen, lung, heart, fat depot, skin, muscle, and peripheral blood mononuclear cells); set 2 comprises tissues requiring special protocols because of their cellular heterogeneity (brain, digestive tract, secretory organs, gonads and gametes, reproductive tract, immune tissues, cartilage); set 3 comprises specific cell preparations (immune cells, tracheal epithelial cells). Dedicated sampling protocols were established and uploaded in https://data.faang.org/protocol/samples. Specificities between mammals and chicken are described when relevant. A total of 73 different tissues or tissue sections were collected, and 21 are common to the four species. Having a common set of tissues will facilitate the transfer of knowledge within and between species and will contribute to decrease animal experimentation. Combining data on the same samples will facilitate data integration. Quality control was performed on some tissues with RNA extraction and RNA quality control. More than 5,000 samples have been stored with unique identifiers, and more than 4,000 were uploaded onto the Biosamples database, provided that standard ontologies were available to describe the sample. Many tissues have already been used to implement FAANG assays, with published results. All samples are available without restriction for further assays. The requesting procedure is described. Members of FAANG are encouraged to apply a range of molecular assays to characterize the functional status of collected samples and share their results, in line with the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles.