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.

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 muscle samples with genotypic and phenotypic data from the same animals.

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.

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.

Profiling and characterization of a muscle microRNA dataset from an F Duroc × Pietrain pig resource population.

To elucidate the effects of microRNA (miRNA) regulation in skeletal muscle of adult pigs, miRNA expression profiling was performed with RNA extracted from (LD) muscle samples from 174 F pigs (~ 5.5 months of age) from a Duroc × Pietrain resource population. Total RNA was extracted from LD samples, and libraries were sequenced on an Illumina HiSeq 2500 platform in 1 × 50 bp format.

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.

Estimation of genome-wide and locus-specific breed composition in pigs.

Advances in pig genomic technologies enable implementation of new methods to estimate breed composition, allowing innovative and efficient ways to evaluate and ensure breed and line background. Existing methods to test for homozygosity at key loci involve test mating the animal in question and observing phenotypic patterns among offspring, requiring extensive resources. In this study, whole-genome pig DNA microarray data from over 8,000 SNP was used to profile the composition of U.

Genomic Prediction Accounting for Residual Heteroskedasticity.

Whole-genome prediction (WGP) models that use single-nucleotide polymorphism marker information to predict genetic merit of animals and plants typically assume homogeneous residual variance. However, variability is often heterogeneous across agricultural production systems and may subsequently bias WGP-based inferences. This study extends classical WGP models based on normality, heavy-tailed specifications and variable selection to explicitly account for environmentally-driven residual heteroskedasticity under a hierarchical Bayesian mixed-models framework.

Exploring causal networks underlying fat deposition and muscularity in pigs through the integration of phenotypic, genotypic and transcriptomic data.

Background: Joint modeling and analysis of phenotypic, genotypic and transcriptomic data have the potential to uncover the genetic control of gene activity and phenotypic variation, as well as shed light on the manner and extent of connectedness among these variables. Current studies mainly report associations, i.e.

Association of Insulin-like growth factor binding protein 2 genotypes with growth, carcass and meat quality traits in pigs.

Background: This study was conducted to investigate the potential association of variation in the insulin-like growth factor binding protein 2 (IGFBP2) gene with growth, carcass and meat quality traits in pigs. IGFBP2 is a member of the insulin-like growth factor binding protein family that is involved in regulating growth, and it maps to a region of pig chromosome 15 containing significant quantitative trait loci that affect economically important trait phenotypes.

Results: An IGFBP2 polymorphism was identified in the Michigan State University (MSU) Duroc × Pietrain F2 resource population (n = 408), and pigs were genotyped by MspI PCR-RFLP.

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.

Accuracy of estimation of genomic breeding values in pigs using low-density genotypes and imputation.

Genomic selection has the potential to increase genetic progress. Genotype imputation of high-density single-nucleotide polymorphism (SNP) genotypes can improve the cost efficiency of genomic breeding value (GEBV) prediction for pig breeding. Consequently, the objectives of this work were to: (1) estimate accuracy of genomic evaluation and GEBV for three traits in a Yorkshire population and (2) quantify the loss of accuracy of genomic evaluation and GEBV when genotypes were imputed under two scenarios: a high-cost, high-accuracy scenario in which only selection candidates were imputed from a low-density platform and a low-cost, low-accuracy scenario in which all animals were imputed using a small reference panel of haplotypes.

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.

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.

Evaluation of QTL for carcass merit and meat quality traits in a US commercial Duroc population.

Putative quantitative trait loci (QTL) regions on 5 chromosomes (SSC3, 6, 12, 15, and 18) were selected from our previous genome scans of a Duroc×Pietrain F(2) resource population for further evaluation in a US commercial Duroc population (n=331). A total of 81 gene-specific single nucleotide polymorphism (SNP) markers were genotyped and 33 markers were segregating. The MDH1 SNP on SSC3 was associated with 45-min and ultimate pH (pHu), and pH decline.

Identification of Carcass and Meat Quality QTL in an F(2) Duroc × Pietrain Pig Resource Population Using Different Least-Squares Analysis Models.

A three-generation resource population was constructed by crossing pigs from the Duroc and Pietrain breeds. In this study, 954 F(2) animals were used to identify quantitative trait loci (QTL) affecting carcass and meat quality traits. Based on results of the first scan analyzed with a line-cross (LC) model using 124 microsatellite markers and 510 F(2) animals, 9 chromosomes were selected for genotyping of additional markers.

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.

Genome-wide linkage analysis of global gene expression in loin muscle tissue identifies candidate genes in pigs.

Background: Nearly 6,000 QTL have been reported for 588 different traits in pigs, more than in any other livestock species. However, this effort has translated into only a few confirmed causative variants. A powerful strategy for revealing candidate genes involves expression QTL (eQTL) mapping, where the mRNA abundance of a set of transcripts is used as the response variable for a QTL scan.

Application of alternative models to identify QTL for growth traits in an F2 Duroc x Pietrain pig resource population.

Background: A variety of analysis approaches have been applied to detect quantitative trait loci (QTL) in experimental populations. The initial genome scan of our Duroc x Pietrain F2 resource population included 510 F2 animals genotyped with 124 microsatellite markers and analyzed using a line-cross model. For the second scan, 20 additional markers on 9 chromosomes were genotyped for 954 F2 animals and 20 markers used in the first scan were genotyped for 444 additional F2 animals.

Selective transcriptional profiling and data analysis strategies for expression quantitative trait loci mapping in outbred F2 populations.

Genetic analysis of transcriptional profiling experiments is emerging as a promising approach for unraveling genes and pathways that underlie variation of complex biological traits. However, these genetical genomics approaches are currently limited by the high cost of microarrays. We studied five different strategies to optimally select subsets of individuals for transcriptional profiling, including (1) maximizing genetic dissimilarity between selected individuals, (2) maximizing the number of recombination events in selected individuals, (3) selecting phenotypic extremes within inferred genotypes of a previously identified quantitative trait locus (QTL), (4) purely random selection, and (5) profiling animals with the highest and lowest phenotypic values within each family-gender subclass.