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The Yorkshire breed uses white coat color as a selection requirement to indicate breed purity. In this study, genomic information from chromosomal regions, as well as the whole genome, was applied to estimate breed composition of purebred Yorkshire animals. Genotypes for approximately 60,000 SNP from the Illumina PorcineSNP60 BeadChip (60K) were available for reference animals for which the genetic background was known, study animals that included Yorkshire sires (Tes_York, n = 889), and known crossbred animals that had Yorkshire heritage (Tes_J, n = 12). Haplotypes of SNP flanking the KIT (Dominant white locus) and MC1R (Melanocortin receptor 1) genes were developed for reference animals for the Duroc, Hampshire, Landrace, Yorkshire, and Pietrain breeds. For the KIT region, haplotypes observed in Yorkshire reference animals were also found in 84 and 7% of the haplotypes in Landrace and Pietrain reference animals, respectively. They were not found in Duroc or Hampshire reference animals. The sensitivity and specificity of haplotype analysis was 0.93 and 0.75, respectively. In addition, whole genome SNP information was used in regression analyses to further differentiate breed composition. Using 60K, 90% of regression coefficients for Yorkshire, indicating relative Yorkshire composition, ranged from 0.791 to 1.073 and 0.524 to 1.06 in Tes_York and Tes_J, respectively. Regression coefficients for 90% of Hampshire ranged from -0.029 to 0.052 and -0.005 to 0.379 in Tes_York and Tes_J, respectively. Animals in Tes_J were likely of Yorkshire and Hampshire breed origin. The sensitivity and specificity of regression analysis was 0.96 and 0.58, respectively. Combining haplotype and regression analyses, 810 Tes_York animals were accepted as purebred Yorkshire. Genomic information can be used as a tool to describe an animal's breed composition and reduce the need for progeny testing for white coat color verification. [ABSTRACT FROM AUTHOR]

The length of adult sow life is now recognized as both an economic and a welfare concern. However, there are no consistent definitions to measure sow longevity. This study assessed 6 different descriptions of longevity and determined their relationship with developmental performance factors. Longevity definitions included stayability (probability of a sow producing 40 pigs or probability of her reaching 4 parities), lifespan (number of parities a female has accumulated before culling), lifetime prolificacy (number of pigs born alive during the productive lifetime of a female), herd life (time from first farrowing to culling), and pigs produced per day of life. Data consisted of 14,262 records of Yorkshire females from both nucleus and multiplication herds across 21 farms from 4 seedstock systems. Within a subset of the data, information was available on the litter birth record of the female and her growth and composition data. Therefore, data were subdivided into 2 data sets, consisting of 1) data A, data from the farrowing records of a female, and 2) data B, data A and information from the litter birth record of a female and the growth and backfat data from a female. A Cox proportional hazards model was used to determine the relationship of developmental factors and first farrowing record with longevity. Those factors that were significantly (P < 0.0001) associated with longevity, regardless of definition, were age at first farrowing, litter size at first farrowing and last farrowing, number of stillborn in the first litter, adjusted 21-d litter weight of the first litter, herd type, backfat, and growth. Within a contemporary group, fatter, slower growing gilts had a decreased risk of being culled. Additionally, sows that had more pigs born alive, fewer stillborn pigs, and heavier litters at 21 d of lactation in their first litter had a decreased risk of being culled. Furthermore, sows from nucleus herds experienced a greater risk of being culled. Many factors affected longevity, regardless of definition. Pork producers can implement management protocols that can extend the productive life of breeding females, resulting in improved profitability and animal welfare. [ABSTRACT FROM AUTHOR]

Focuses on a study of the effects of hormonal therapy on subsequent litter size of fall- and winter-weaned sows. Review of related literature; Materials and methods; Results of the study.

Pigs housed in groups are remixed with unfamiliar individuals, which can trigger aggressive interactions, potentially compromising animal welfare. Skin lesions are a reliable indicator trait of aggression and are moderately heritable, suggesting that aggression may be reduced through selection. This study estimated genetic parameters of skin lesions of pigs at multiple life stages, explored genetic correlations of skin lesions between age groups and body location, and studied the relationship between skin lesions and production traits of commercial importance. A population of 1,079 Yorkshire pigs was strategically remixed into new groups of familiar and unfamiliar animals at 3 life stages (weaning, grow-finish, and mature gilts). Skin lesions (fresh, bright red cuts) were counted immediately prior to mixing and 24 h and 3 wk after mixing across 3 body regions: anterior, central, and caudal. Weights were recorded prior to each mixing event. Prior to slaughter, backfat thickness and loin muscle area were determined using ultrasound. Univariate analyses were performed to obtain heritability estimates of lesion scores. Bivariate analyses were performed with response variables being skin lesions, weight gain per life stage, backfat thickness, or loin muscle area, depending on the relationship of interest, to obtain correlations. Lesion score heritabilities ranged from 0.10 to 0.40 and were significant (P < 0.05). Heritability was highest for lesions on the anterior region of the body for 24 h and 3 wk after mixing. Lesions to the central and caudal areas showed the highest genetic correlation at each stage of production, whereas those to the anterior and caudal regions had the lowest correlation. The highest genetic correlation was found between the mature gilt and grow-finish stages, whereas the weaning and mature gilt stages had the lowest correlations. Genetic correlations between lesions and production traits were not significantly different from 0 for weight gain and backfat thickness, but loin muscle area was negatively correlated with lesions (P = 1.17 × 10-4, P = 2.30 × 10-5, and P = 6.08 × 10-4 for anterior, central, and caudal lesions, respectively). These results are promising for the industry because they suggest that pigs selected for reduced lesions will show increased loin muscle area without negative effects on growth. Alternatively, selection for these production traits would not increase lesions. [ABSTRACT FROM AUTHOR]

Genome-wide association ( GWA) studies based on GBLUP models are a common practice in animal breeding. However, effect sizes of GWA tests are small, requiring larger sample sizes to enhance power of detection of rare variants. Because of difficulties in increasing sample size in animal populations, one alternative is to implement a meta-analysis ( MA), combining information and results from independent GWA studies. Although this methodology has been used widely in human genetics, implementation in animal breeding has been limited. Thus, we present methods to implement a MA of GWA, describing the proper approach to compute weights derived from multiple genomic evaluations based on animal-centric GBLUP models. Application to real datasets shows that MA increases power of detection of associations in comparison with population-level GWA, allowing for population structure and heterogeneity of variance components across populations to be accounted for. Another advantage of MA is that it does not require access to genotype data that is required for a joint analysis. Scripts related to the implementation of this approach, which consider the strength of association as well as the sign, are distributed and thus account for heterogeneity in association phase between QTL and SNPs. Thus, MA of GWA is an attractive alternative to summarizing results from multiple genomic studies, avoiding restrictions with genotype data sharing, definition of fixed effects and different scales of measurement of evaluated traits. [ABSTRACT FROM AUTHOR]

Pork quality plays an important role in the meat processing industry. Thus, different methodologies have been implemented to elucidate the genetic architecture of traits affecting meat quality. One of the most common and widely used approaches is to perform genome-wide association (GWA) studies. However, a limitation of many GWA in animal breeding is the limited power due to small sample sizes in animal populations. One alternative is to implement a metaanalysis of GWA (MA-GWA) combining results from independent association studies. The objective of this study was to identify significant genomic regions associated with meat quality traits by performing MA-GWA for 8 different traits in 3 independent pig populations. Results from MA-GWA were used to search for genes possibly associated with the set of evaluated traits. Data from 3 pig data sets (U.S. Meat Animal Research Center, commercial, and Michigan State University Pig Resource Population) were used. A MA was implemented by combining z-scores derived for each SNP in every population and then weighting them using the inverse of estimated variance of SNP effects. A search for annotated genes retrieved genes previously reported as candidates for shear force (calpain-1 catalytic subunit [CAPN1] and calpastatin [CAST]), as well as for ultimate pH, purge loss, and cook loss (protein kinase, AMP-activated, ? 3 noncatalytic subunit [PRKAG3]). In addition, novel candidate genes were identified for intramuscular fat and cook loss (acyl-CoA synthetase family member 3 mitochondrial [ACSF3]) and for the objective measure of muscle redness, CIE a* (glycogen synthase 1, muscle [GYS1] and ferritin, light polypeptide [FTL]). Thus, implementation of MA-GWA allowed integration of results for economically relevant traits and identified novel genes to be tested as candidates for meat quality traits in pig populations. [ABSTRACT FROM AUTHOR]

Accurate evaluations of growth and composition traits enable better management decisions regarding genetic merit, feeding, and marketing. Sires from Duroc and Pietrain populations were used to produce crossbred pigs, which were evaluated for growth and composition traits. All parents were normal for the ryanodine receptor gene. Boars from each breed were mated to either Yorkshire or F1 Yorkshire-Landrace females with 307 offspring evaluated from birth through 26 wk of age. No significant differences between sire breeds were seen for pig BW from birth through 10 wk of age. Body weight, 10th rib backfat (BF10), last rib backfat (LRF), and loin muscle area (LMA) were serially measured at 10, 13, 16, 19, 22, 24, and 26 wk of age. At 26 wk of age, Duroc-sired progeny were heavier (143.4 vs. 132.7 kg, P < 0.001), had more BF10 (27.1 vs. 23.7 mm, P < 0.001) and LRF (21.2 vs. 19.2 mm, P < 0.001), but had similar LMA (46.4 vs. 47.1 cm²) compared with Pietrain-sired progeny. Mean feed efficiency did not differ between breed of sire in any period of the study. Duroc progeny had a greater ADG (980.1 vs. 892.3 g/d, P < 0.001) from 10 to 26 wk of age than Pietrain-sired pigs. Composition traits of fat-free total lean (FFTOLN), total fat tissue (TOFAT), empty body protein (EBPRO), and empty body lipid (EBLIPID) were calculated. Random regression animal models with polynomial regression on week on-test were fitted to BW, BF10, LRF, LMA, FFTOLN, TOFAT, EBPRO, and EBLIPID from 10 to 26 wk of age. Duroc-sired harrows tended to grow faster but with more fat tissue, and Pietrain-sired guts were slower growing but leaner, whereas Duroc-sired guts and Pietrain-sired barrows were intermediate for growth and backfat measures. Serial heritability estimates generally increased from 10 to 26 wk of age with ranges as follows: BW (0.05 to 0.39), BF1O (0.13 to 0.76), LRF (0.11 to 0.79), LMA (0.05 to 0.73), FFTOLN (0.07 to 0.16), TOFAT (0.19 to 0.45), EBPRO (0.02 to 0.55), and EBLIPID (0.12 to 0.60). Pigs sired by Duroc and Pietrain boars had similar lean tissue growth but achieved it through different mechanisms. [ABSTRACT FROM AUTHOR]

Meat quality is essential for consumer acceptance and ultimately impacts pork production profitability, and it is subject to genetic control. We performed genomewide association analysis of pork quality traits to increase our understanding of die genetic basis of variation in these traits. Thirty-eight meat quality and carcass traits were recorded in 948 F2 pigs from the Michigan State University Duroc x Pietrain F2 resource population. The F0 Fp and 336 F2 pigs were genotyped with the Illumina Porcine SNP60 beadchip, and the remaining F2 animals were genotyped with the GeneSeek Genomic Profiler for Porcine LD chip. Monomorphic SNP and low-quality genotypes where discarded, and the remaining missing genotypes where imputed (accuracy = 0.968). A total of 44,911 SNP remained for analysis. Analysis was performed using linear mixed effects models using the R gwaR package. Fixed effects of sex, contemporary group, and age at slaughter and a random polygenic effect with variance proportional to marker relationship matrix G were included. Type I error rate was controlled at false discover rate = 5%. Ten putative QTL regions were found for 16 meat quality and carcass traits. Table 030 shows for each trait the chromosomal location for the QTL, physical peak position, the g-value, and the total number of annotated genes found in the region. These regions can now be further evaluated to search for functional candidate genes. For instance, a QTL associated with backfat thickness was identified on chromosome 6. This region includes genes that encode the enzymes phosphoglucomutase 1 and acyl-CoA dehydrogenase C4 to C12 straight chain, which are involved in pathways for glycogenolysis and lipolysis, respectively. The QTL and genes in the regions should be further investigated to assess their potential to improve pork quality. [ABSTRACT FROM AUTHOR]

Summary: Aggression in group‐housed pigs is a welfare concern and can negatively affect production. Skin lesions are reliable indicators of aggression and are moderately heritable, suggesting that selective breeding may reduce aggression. To further understand the genetic control of behavioral traits, such as the aggressive response to regrouping, associated single nucleotide polymorphisms (SNPs) can be identified within the genome, and the region in which these SNPs are located can be related to known genes. To investigate SNPs associated with aggression, 1093 purebred Yorkshire pigs were strategically remixed into new groups of familiar and unfamiliar animals at three life stages and lesion counts were recorded. Genomic best linear unbiased prediction (GBLUP) models were fitted for each trait. The genetic additive effect was obtained from a genetic relationship matrix constructed from the 50 924 SNPs. SNP effects and their variances were estimated from the GBLUP objects. SNPs that were associated with a significant portion of the trait variance were identified for lesions to the anterior (three SNPs, FDR <5%) and central (one SNP, FDR <5%) portions of the body in grow‐finish pigs. These SNPs were located on chromosome 11, suggesting that chromosome 11 contains a region explaining variation in lesion counts that should be further explored to identify genes underlying biological control of aggression. [ABSTRACT FROM AUTHOR]

Meat quality is essential for consumer acceptance, it ultimately impacts pork production profitability and it is subject to genetic control. The objective of this study was to map genomic regions associated with economically important meat quality and carcass traits. We performed a genome-wide association (GWA) analysis to map regions associated with 38 meat quality and carcass traits recorded for 948 F2 pigs from the Michigan State University Duroc × Pietrain resource population. The F0, F1, and 336 F2 pigs were genotyped with the Illumina Porcine SNP60 BeadChip, while the remaining F2 pigs were genotyped with the GeneSeek Genomic Profiler for Porcine Low Desnisty (LD) chip, and imputed with high accuracy (r2 = 0.97). Altogether the genomic dataset comprised 1,019 animals and 44,911 SNP. A Gaussian linear mixed model was fitted to estimate the breeding values and the variance components. A linear transformation was performed to estimate the marker effects and variances. Type I error rate was controlled at a False Discovery Rate of 5%. Seven putative QTL found in this study were previously reported in other studies. Two novel QTL associated with tenderness (TEN) were located on SSC3 [135.6:137.5Mb; False Discovery rate (FDR) < 0.03] and SSC5 (67.3:69.1Mb; FDR < 0.02). The QTL region identified on SSC15 includes Protein Kinase AMP-activated γ 3-subunit gene (PRKAG3), which has been associated with 24-h pH (pH24), drip loss (DL) and cook yield (CY). Also, novel candidate genes were identified for TEN in the region on SSC5 [A Kinase (PRKA) Anchor Protein 3 (AKAP3)], and for tenth rib backfat thickness (BF10) [Carnitine O-Acetyltransferase (CRAT)] on SSC1. The association of PRKAG3 gene polymorphisms with pork quality traits has been reported for several pig populations. However, there are no SNP for this gene on the chip used, thus we genotyped the animals for 2 non-synonymous variants (I199V and T30N). We then performed a GWA conditioning on the genotype of both SNP and I199V was associated with pH24, DL, protein content (PRO) and CY (P < 0.004) and T30N with Juiciness, TEN, shear force, pH24, PRO, and CY (P < 0.04). Finally, we performed a GWA conditioning on the genotype of the SNP peak detected in this study, and T30N remained associated only with PRO (P < 0.02). Therefore, in this study we identified 2 novel QTL regions, suggest 2 novel candidate genes, and conclude that other SNP in PRKAG3 or nearby gene(s) explain the observed associations on SSC15 in this population. [ABSTRACT FROM AUTHOR]

The identification of genomic regions that affect additive genetic variation and contain genes involved in controlling growth and fat deposition has enormous impact in the farm animal industry (e.g., carcass merit and meat quality). Therefore, a genomewide association study was implemented in an F2 pig population using a 60,000 SNP marker panel for traits related to growth and fat deposition. Estimated genomic EBV were linearly transformed to calculate SNP effects and to identify genomic positions possibly associated with the genetic variability of each trait. Genomic segments were then defined considering the markers included in a region 1 Mb up- and downstream from the SNP with the smallest P-value and a false discovery rate < 0.05 for each trait. The significance for each 2-Mb segment was tested using the Bonferroni correction. Significant SNP were detected on SSC2, SSC3, SSC5, and SSC6, but 2-Mb segment significant effects were observed on SSC3 for weight at birth (wt_birth) and on SSC6 for 10th-rib backfat and last-rib backfat measured by ultrasound at different ages. Furthermore, a 6-Mb segment on SSC6 was also considered because the 2-Mb segments for 10 different fat deposition traits were overlapped. Although the segment effects for each trait remain significant, the proportion of additive variance explained by this larger segment was slightly smaller in some traits. In general, the results confirm the presence of genetic variability for wt_birth on SSC3 (18.0-20.2 Mb) and for fat deposition traits on SSC6 (133.8-136.0 Mb). Within these regions, fibrosin (FBRS) and myosin light chain, phosphorylatable, fast skeletal muscle (MYLPF) genes could be considered as candidates for the wt_birth signal on SSC3, and the SERPINE1 mRNAbinding protein 1 gene (SERBP1) may be a candidate for the fat deposition trait signals on SSC6. [ABSTRACT FROM AUTHOR]

Structural equation models (SEQM) can be used to model causal relationships between multiple variables in multivariate systems. Among the strengths of SEQM is its ability to consider causal links between latent variables. The use of latent variables allows modeling complex phenomena while reducing at the same time the dimensionality of the data. One relevant aspect in the quantitative genetics context is the possibility of correlated genetic effects influencing sets of variables under study. Under this scenario, if one aims at inferring causality among latent variables, genetic covariances act as confounders if ignored. Here we describe a methodology for assessing causal networks involving latent variables underlying complex phenotypic traits. The first step of the method consists of the construction of latent variables defined on the basis of prior knowledge and biological interest. These latent variables are jointly evaluated using confirmatory factor analysis. The estimated factor scores are then used as phenotypes for fitting a multivariate mixed model to obtain the covariance matrix of latent variables conditional on the genetic effects. Finally, causal relationships between the adjusted latent variables are evaluated using different SEQM with alternative causal specifications. We have applied this method to a data set with pigs for which several phenotypes were recorded over time. Five different latent variables were evaluated to explore causal links between growth, carcass, and meat quality traits. The measurement model, which included 5 latent variables capturing the information conveyed by 19 different phenotypic traits, showed an acceptable fit to data (e.g., χ2/df = 1.3, root-mean-square error of approximation = 0.028, standardized root-mean-square residual = 0.041). Causal links between latent variables were explored after removing genetic confounders. Interestingly, we found that both growth (-0.160) and carcass traits (-0.500) have a significant negative causal effect on quality traits (P-value ≤ 0.001). This result may have important implications for strategies for pig production improvement. More generally, the proposed method allows further learning regarding phenotypic causal structures underlying complex traits in farm species. [ABSTRACT FROM AUTHOR]

Whole genome prediction (WGP) based on high density SNP marker panels is known to improve the accuracy of breeding value (BV) prediction in livestock. However, these accuracies can be compromised when genotype by environment interaction (G×E) exists but is not accounted for. Reaction norm (RN) and random regression (RR) models have proven to be useful in accounting for G×E in pre-WGP evaluations by modeling BV as linear or higher order functions of environmental or temporal covariates. We extend these RR/RN models based on several alternative specifications for SNP-specific intercepts and linear slopes on environmental covariates. One specification is based on bivariate normality (BVN) of SNP-specific intercepts and slopes, whereas 2 others, IW-BayesA and based on inverted Wishart (IW) extensions IW-BayesB, are, respectively, bivariate Student t extensions of currently popular models without (BayesA) or with (BayesB) variable selection. We highlight alternative specifications based on the square root free Cholesky decomposition (CD) of SNP-specific variance-covariance (VCV) matrices in an attempt to better differentially model environmentally sensitive from environmentally robust QTL. Two CD specifications were considered with (CD-BayesB) or without (CD-BayesA) any variable selection on intercept and slope effects. We compared each of the 5 models based on an RN simulation study. Six scenarios were considered based on differences in overall genetic correlations between SNP-specific intercept and slope effects as well as on heritabilities and numbers of environmentally robust versus sensitive QTL. In most scenarios, IW-BayesA had the greatest accuracy, whereas CD-BayesB exhibited the greatest accuracy in low complexity architectures (i.e., low number of QTL). In an RR application of a Duroc × Pietrain resource population at Michigan State University, 5,271 SNP markers and 928 F2 animals with known pedigree were analyzed for backfat thickness at wk 10, 13, 16, 19, and 22. SNP-based RR methods had a 2.5% greater (P < 0.0001) cross-validation accuracy for predicting phenotypes than the SNP-based conventional BayesA/ BayesB and/or pedigree based RR BLUP; however, none of the proposed RR models had performances that were different from each other. [ABSTRACT FROM AUTHOR]

Pigs from the F2 generation of a Duroc x Pietrain resource population were evaluated to discover QTL affecting carcass composition and meat quality traits. Carcass composition phenotypes included primal cut weights, skeletal characteristics, backfat thickness, and LM area. Meat quality data included LM pH, temperature, objective and subjective color information, marbling and firmness scores, and drip loss. Addition- ally, chops were analyzed for moisture, protein, and fat composition as well as cook yield and Warner-Bratzler shear force measurements. Palatability of chops was determined by a trained sensory panel. A total of 510 F2 animals were genotyped for 124 microsatellite markers evenly spaced across the genome. Data were analyzed with line cross, least squares regression interval, mapping methods using sex and litter as fixed effects and carcass weight or slaughter age as covariates. Significance thresholds of the F-statistic for single QTL with additive, dominance, or imprinted effects were determined on chromosome- and genome-wise levels by permutation tests. A total of 94 QTL for 35 of the 38 traits analyzed were found to be significant at the 5% chromo- some-wise level. Of these 94 QTL, 44 were significant at the 1% chromosome-wise, 28 of these 44 were also significant at the 5% genome-wise, and 14 of these 28 were also significant at the 1% genome-wise significance thresholds. Putative QTL were discovered for 45-min pH and pH decline from 45 mm to 24 h on SSC 3, marbling score and carcass backfat on SSC 6, carcass length and number of ribs on SSC 7, marbling score on SSC 12, and color measurements and tenderness score on SSC 15. These results will facilitate fine mapping efforts to identify genes controlling carcass composition and meat quality traits that can be incorporated into marker-assisted selection programs to accelerate genetic improvement in pig populations. [ABSTRACT FROM AUTHOR]

Pigs from the F2 generation of a Duroc x Pietrain resource population were evaluated to discover QTL affecting growth and composition traits. Body weight and ultrasound estimates of 10th-rib backfat, last-rib backfat, and LM area were serially measured throughout development. Estimates of fat-free total lean, total body fat, empty body protein, empty body lipid, and ADG from 10 to 22 wk of age were calculated, and random regression analyses were performed to estimate individual animal phenotypes representing intercept and linear rates of increase in these serial traits. A total of 510 F2 animals were genotyped for 124 micro-satellite markers evenly spaced across the genome. Data were analyzed with line cross, least squares regression, interval mapping methods using sex and litter as fixed effects. Significance thresholds of the F-statistic for single QTL with additive, dominance, or imprinted effects were determined at the chromosome- and genome-wise levels by permutation tests. A total of 43 QTL for 22 of the 29 measured traits were found to be significant at the 5% chromosome-wise level. Of these 43 QTL, 20 were significant at the 1% chromosome-wise significance threshold, 14 of these 20 were also significant at the 5% genome-wise significance threshold, and 10 of these 14 were also significant at the 1% genome-wise significance threshold. A total of 22 QTL for the animal random regression terms were found to be significant at the 5% chromosome-wise level. Of these 22 QTL, 6 were significant at the 1% chromosome-wise significance threshold, 4 of these 6 were also significant at the 5% genome-wise significance threshold, and 3 of these 4 were also significant at the 1% genome-wise significance threshold. Putative QTL were discovered for 10th-rib and last-rib backfat on SSC 6, body composition traits on SSC 9, backfat and lipid composition traits on SSC 11, 10th-rib backfat and total body fat tissue on SSC 12, and linear regression of last-rib backfat and total body fat tissue on SSC 8. These results will facilitate fine-mapping efforts to identify genes controlling growth and body composition of pigs that can be incorporated into marker-assisted selection programs to accelerate genetic improvement in pig populations. [ABSTRACT FROM AUTHOR]