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Animal behavior is indicative of health status and changes in behavior can indicate health issues (i.e., illness, stress, or injury). Currently, human observation (HO) is the only method for detecting behavior changes that may indicate problems in group-housed pigs. While HO is effective, limitations exist. Limitations include HO being time consuming, HO obfuscates natural behaviors, and it is not possible to maintain continuous HO. To address these limitations, a computer vision platform (NUtrack) was developed to identify (ID) and continuously monitor specific behaviors of group-housed pigs on an individual basis. The objectives of this study were to evaluate the capabilities of the NUtrack system and evaluate changes in behavior patterns over time of group-housed nursery pigs. The NUtrack system was installed above four nursery pens to monitor the behavior of 28 newly weaned pigs during a 42-d nursery period. Pigs were stratified by sex, litter, and randomly assigned to one of two pens (14 pigs/pen) for the first 22 d. On day 23, pigs were split into four pens (7 pigs/pen). To evaluate the NUtrack system’s capabilities, 800 video frames containing 11,200 individual observations were randomly selected across the nursery period. Each frame was visually evaluated to verify the NUtrack system’s accuracy for ID and classification of behavior. The NUtrack system achieved an overall accuracy for ID of 95.6%. This accuracy for ID was 93.5% during the first 22 d and increased (P < 0.001) to 98.2% for the final 20 d. Of the ID errors, 72.2% were due to mislabeled ID and 27.8% were due to loss of ID. The NUtrack system classified lying, standing, walking, at the feeder (ATF), and at the waterer (ATW) behaviors accurately at a rate of 98.7%, 89.7%, 88.5%, 95.6%, and 79.9%, respectively. Behavior data indicated that the time budget for lying, standing, and walking in nursery pigs was 77.7% ± 1.6%, 8.5% ± 1.1%, and 2.9% ± 0.4%, respectively. In addition, behavior data indicated that nursery pigs spent 9.9% ± 1.7% and 1.0% ± 0.3% time ATF and ATW, respectively. Results suggest that the NUtrack system can detect, identify, maintain ID, and classify specific behavior of group-housed nursery pigs for the duration of the 42-d nursery period. Overall, results suggest that, with continued research, the NUtrack system may provide a viable real-time precision livestock tool with the ability to assist producers in monitoring behaviors and potential changes in the behavior of group-housed pigs.

Sow longevity (sow productive lifetime) plays an important role in economically efficient piglet production. Direct selection for sow longevity is not commonly practiced in any pig-breeding program. In recent years, an increased number of peer reviewed articles addressing the economic impact, genetic parameter estimates, and genomic information (including markers and single nucleotide polymorphisms for sow longevity) have been published in the scientific literature. The studies in the literature indicate that sow longevity is a complex trait having economic value and is an animal well-being concern for commercial pork producers. Studies have concluded that sufficient genetic variation exists so that selection to improve sow longevity should be effective. Unlike the dairy industry, the primary parent animal used in the swine industry is a crossbred female, typically F1 (Landrace X Large White or Yorkshire). Sow longevity has shown to be genetically related with prolificacy and leg conformation traits. Sow longevity seems to be the ideal trait to utilize genomic selection when attempting to improve the trait. The genetic correlation between purebred and crossbred sow longevity is low. Since the crossbred sow is the breeding objective, phenotypic data from the crossbred females should ideally be used when estimating the breeding values for sow longevity that are used in the indexes to evaluate nucleus animals. Genomic selection is best suited for sex-limited traits, traits expressed later in life, and many animals do not reach some defined end-point parity, sow longevity seems ideally suited to be evaluated using the latest genome enabled selection technology. Keywords: heritability, leg conformation, selection, sow productive lifetime

Atypical porcine pestivirus (APPV), an RNA virus member of the Flaviviridae family, has been associated with congenital tremor in newborn piglets. Previously reported quantitative polymerase chain reaction (qPCR)–based assays were unable to detect APPV in novel cases of congenital tremor originated from multiple farms from U.S. Midwest (MW). These assays targeted the viral polyprotein coding genes, which were shown to display substantial variation, with sequence identity ranging from 58.2% to 70.7% among 15 global APPV strains. In contrast, the 5′-untranslated region (5′ UTR) was found to have a much higher degree of sequence conservation. In order to obtain the complete 5′ UTR of the APPV strains originated from MW, the 5′ end of the viral cDNA was obtained by using template switching approach followed by amplification and dideoxy sequencing. Eighty one percent of the 5′ UTR was identical across 14 global and 5 MW strains with complete or relatively complete 5′ UTR. Notably, some of the most highly conserved 5′ UTR segments overlapped with potentially important regions of an internal ribosome entry site (IRES), suggesting their functional role in viral protein translation. A newly designed single qPCR assay, targeting 100% conserved 5′ UTR regions across 19 strains, was able to detect APPV in samples of well documented cases of congenital tremor which originated from five MW farm sites (1–18 samples/site). As these fully conserved 5′ UTR sequences may have functional importance, we expect that assays targeting this region would broadly detect APPV strains that are diverse in space and time.

Selective genotyping of crossbred (CB) animals to include in traditionally purebred (PB) dominated genetic evaluations has been shown to provide an increase in the response to selection for CB performance. However, the inclusion of phenotypes from selectively genotyped CB animals, without the phenotypes of their non-genotyped cohorts, could cause bias in estimated variance components (VC) and subsequent estimated breeding values (EBV). The objective of the study was to determine the impact of selective CB genotyping on VC estimates and subsequent bias in EBV when non-genotyped CB animals are not included in genetic evaluations. A swine crossbreeding scheme producing 3-way CB animals was simulated to create selectively genotyped datasets. The breeding scheme consisted of three PB breeds each with 25 males and 450 females, F1 crosses with 1200 females and 12,000 CB progeny. Eighteen chromosomes each with 100 QTL and 4k SNP markers were simulated. Both PB and CB performance were considered to be moderately heritable (h2=0.4). Factors evaluated were, 1) CB phenotype and genotype inclusion of 15% (n=1800) or 35% (n=4200), 2) genetic correlation between PB and CB performance (rpc=0.1, 0.5 or 0.7) and 3) selective genotyping strategy. Genotyping strategies included: a) Random: random CB selection, b) Top: highest CB phenotype and c) Extreme: half highest and half lowest CB phenotypes. Top and Extreme selective genotyping strategies were considered by selecting animals in full-sib (FS) families or among the CB population (T). In each generation, 4320 PB selection candidates contributed phenotypic and genotypic records. Each scenario was replicated 15 times. VC were estimated for PB and CB performance utilizing bivariate models using pedigree relationships with dams of CB animals considered to be unknown. Estimated values of VC for PB performance were not statistically different from true values. Top selective genotyping strategies produced deflated estimates of phenotypic VC for CB performance compared to true values. When using estimated VC, Top_T and Extreme_T produced the most biased EBV, yet EBV of PB selection candidates for CB performance were most accurate when using Extreme_T. Results suggest that randomly selecting CB animals to genotype or selectively genotyping Top or Extreme CB animals within full-sib families can lead to accurate estimates of additive genetic VC for CB performance and unbiased EBV.

Feed cost accounts for two-thirds of the total cost in producing pork. The principle in reducing feed costs, without sacrificing pork output, is improving feed efficiency. To date, the two traits commonly associated with improving feed efficiency are growth and feed intake; however the impact they have on maintenance levels is unclear. Therefore, the objective of this study was to determine activity and behavior patterns of progeny using NUtrack from sires with different EBV combinations for growth and feed intake. A total of 175 pigs from four different sire groups (DNA Genetics Line 600; High Growth/High Feed Intake, HGHI; High Growth/Low Feed Intake, HGLI; Low Growth/High Feed Intake, LGHI; and Low Growth/Low Feed Intake, LGLI) were utilized at the UNL ENREC swine farm. Finisher pens were blocked by sire group and sex. Finisher exit weights were collected after 85 days, along with loin-eye area (LEA) and backfat (BF). NUtrack algorithms allowed for individual identification of pigs in a group housing setting to track daily activity behaviors including distance traveled (DIST) and time lying (LIE). Estimated marginal means were processed through RStudio. HIHG pigs traveled less DIST than those sired by LILG, HILG, and LIHG (P < 0.05; 929 vs. 1,066, 990, and 984m, respectively). The HIHG pigs had greater LIE time than LIHG and LILG pigs (P < 0.05; 1,119 vs. 1,188 and 1,175min, respectively). LILG pigs had greater LEA compared with HIHG, HILG, and LIHG sired pigs (P < 0.05; 52.85 vs. 49.39, 49.97, and 51.05cm2, respectively). The LILG pigs had less BF compared with HIHG, HILG, and LIHG sired pigs (P < 0.05; 1.42 vs. 1.48, 1.46, and 1.50cm, respectively). These results suggest that pigs from boars with high EBVs for growth travel less, spend more time lying and yield a fatter carcass.

Selection for increased litter size in swine has potentially resulted in a correlated increase in preweaning mortality. Additional selection criteria should be considered when selecting for increased litter size to account for associated decreases in piglet quality, specifically piglet survival, initial weight and growth. Traits such as gestation length (GL), which have been associated with piglet performance, could be utilized to improve piglet development and survivability. The objective of this study was to conduct a genome-wide association study to identify genomic regions associated with GL in differing parities in swine (n = 831) from the University of Nebraska-Lincoln reproductive longevity project. Gestation length was calculated as the number of days between last insemination administered and farrowing. Sows were genotyped with the Illumina SNP60 BeadArray, and the data were analyzed using Bayesian mixture models for GL at parity 1, 2, 3 and 4 (GL1, GL2, GL3 and GL4 respectively). Means (SD) for GL1-GL4 were 113 (1.4), 114 (1.2), 114 (1.3) and 115 (1.2) respectively. Posterior mean heritability estimates (PSD) for GL1, GL2, GL3 and GL4 were 0.33 (0.06), 0.34 (0.07), 0.32 (0.08) and 0.20 (0.08) respectively. Rank correlations between genomic estimated breeding values between GL1 and GL2, GL3 and GL4 respectively were moderate: 0.67, 0.65 and 0.60. The top SNP (ASGA0017859, SSC4, 7.8 Mb), located in the top common genomic region associated with GL1, GL2 and GL3, was associated with a difference of 1.1 days in GL1 between homozygote genotypes (P < 0.0001). The results of this study suggest that GL is a largely polygenic trait with relatively minor contributions from multiple genomic regions.

The goals of the study were to estimate the heritability and repeatability of boar sperm quality traits during the summer season, estimate phenotypic and genetic correlations between sperm quality traits, and assess the effect of these traits on total number born (TNB). Semen samples were collected and individually processed from Duroc boars (n = 363) from May through October 2017. Single sire litter information was available for 121 of the boars. Heritability, repeatability, genetic correlations, and phenotypic correlations were estimated for and among the following traits: total percentage of motile cells (MOT), total percentage of progressively motile cells (PROG), percentage of cells with a distal droplet (DIST), percentage of cells with a proximal droplet (PROX), percentage of cells with a bent tail (BENT), percentage of cells with a distal midpiece reflex (DMR), mean sperm head elongation (ELON), and total sperm cells per ejaculate (TOTSP). Heritability estimates ranged from 0.08 to 0.24 and repeatability estimates ranged from 0.21 to 0.62. The phenotypic and genetic correlations between sperm motility traits and morphological defects indicated a negative relationship. Sperm morphological defects had positive phenotypic and genetic correlations with each other. Total sperm per ejaculate showed positive genetic correlations with the motility traits. Positive phenotypic and genetic correlations existed between ELON and the motility traits. Total sperm motility (P 0.001) positively affected TNB while DIST (P 0.001), PROX (P 0.001), BENT (P = 0.05) and DMR (P = 0.002) negatively affected TNB. Results indicate genetic selection could enhance sperm quality during the summer season in boars.

Pre-weaning mortalities have become a pressing issue in modern swine production. Litter size at birth has greatly increased through direct genetic selection. Unfortunately, little emphasis was placed on improving functional teat number, resulting in a nutrient access shortage. Therefore, a total of 750 sows consisting of three genetic lines in a commercial barn in Nebraska, USA, were used to evaluate the impact of functional teat number on piglet survival. Teat traits recorded at farrowing included total teat number (TT), functional teat number (FT), and non-functional teat number (NFT); with population means of 14.84 (1.21), 14.55 (1.30), and 0.28 (0.56), respectively. Production traits recorded included total number born (TNB), wean number (WN), total pre-weaning mortality (PWM), and post-cross foster mortality (CFPWM). The lm function within RStudio was used to estimate regressions, with parity and piglets placed (PP) used as covariates for WN and CFPWM, and parity, PP, and TNB for PWM. One additional FT increased WN (P < 0.01; 0.33), and reduced PWM (P < 0.01; -3.04%) and reduced CFPWM (P < 0.01; -3.71%). A subset of 274 sows were used to determine the effects of increasing functional teats on sow and piglet efficiency. Additional traits recorded included sow average daily feed intake (ADFI), backfat loss (BF) and average piglet weaning weight (WW). Parity, ADFI, backfat-entry, and WN were used as covariates for BF; parity, backfat-entry, and WN for ADFI; and parity, PP, ADFI, and WN were used for estimating WW. Regression estimates showed that an additional functional teat had no significant impact (P > 0.05) on ADFI, BF, or WW. Taken together, these results suggest that improving functional teat number does not impact ADFI or BF for sows and does not influence average piglet weaning weight, but it does decrease PWM resulting in more pigs weaned per litter.

A consistent theme in swine production is to increase efficiency and reduce input costs. In this study, sow activity traits and lesions were analyzed to identify associations with lameness and gilt retention. Gilts (n = 73) were culled based on structural unsoundness as determined by an experienced herdsman. Females (n = 132) that had been retained for breeding, but either did not show estrus at an appropriate age or were excessed due to limited farrowing space were used as controls. Gilts were recorded with the NUtrack System for 1 week prior to selection. The NUtrack System records distance travelled (m), time standing (s), eating (s), and laying (s), angle rotated (degrees) and average speed (m/s). Animals were humanely harvested in a USDA inspected abattoir. Both ends of the humerus and the femur head were evaluated for osteochondrosis or osteoarthritis lesions. Joint lesions were categorized as Major or Minor lesions. Major lesions were severe osteochondrosis lesions where cartilage was severely fractured or cartilage exhibited a region of necrosis larger than 2 millimeters in diameter. Minor lesions were defined creases or indentations in the cartilage or where abnormal blood flow existed in bone tissue underneath cartilage. Ninety-eight animals were identified with minor or major lesions (28 cull and 70 control). Unexpectedly, chi-square analysis did identify control animals as having higher incidences of minor or major lesions than cull animals (p < 0.05). Data were analyzed using logistic regression (RStudio V1.2.5033) with farrowing group included in the model. No NUtrack trait was predictive of joint lesions (P > 0.1). However, time standing (P < 0.001) and average speed (P < 0.05) were significantly associated with being retained for breeding. These data suggest that animal behavior and movement, as analyzed by NUtrack, can enhance herdsman efforts in making culling decisions of breeding animals.

Overall activity and behavior are integral components of sows remaining productive in the herd. This investigation studied overall activity of group housed replacement gilts and the heritability of various activity traits. Beginning around 20 wk of age, video recorded data of approximately 75 gilts/group for a total of 2,378 gilts over 32 groups was collected for 7 consecutive d using the NUtrack System, which tracks distance travelled (m), avg speed (m/s), angle rotated (degrees), and time standing (s), sitting (s), eating (s), and laying (s). The recorded phenotypes were standardized to the distribution observed within a pen for each group. The final values used for analysis were the average daily standardized values. Data were analyzed using mixed models (RStudio V 1.2.5033) including effects of sire, dam, dam’s sire and dam, dam’s grandsire and granddam, farrowing group, barn, pen, and on-test date. Sire had an effect on every activity trait P < 0.001), and dam had an effect on average speed (P < 0.001). The dam’s sire had an effect on all activity traits (P < 0.001) and the dam’s grandsire had an effect on average speed (P < 0.001). Heritabilities and variance components of activity traits were estimated in ASReml 4 using an animal model with a two-generation pedigree. Genetic variances are 0.17 +/- 0.029, 0.19 +/- 0.034, and 0.11 +/- 0.024, residual variances are 0.37 +/- 0.023, 0.41 +/- 0.027, and 0.41 +/- 0.022, phenotypic variances are 0.54 +/- 0.018, 0.60 +/- 0.020, and 0.52 +/- 0.016, and heritabilities are 0.32 +/- 0.048, 0.32 +/- 0.049, and 0.21 +/- 0.044 for average speed, distance, and lie respectively. NUtrack offers potential to aid in selection decisions. Given the results presented herein, continued investigation into these activity traits and their association with sow longevity is warranted.

Incorporation of precision livestock technology has the potential to provide swine producers with the means to rapidly and accurately identify immune-compromised pigs, allowing for accurate and timely interventions. The objective of this study was to utilize the NUtrack System (NUtrack) to identify changes in general (lying, standing and sitting) and spatial behaviors (at the feeder and meters/day) of newly weaned pigs exposed to a lipopolysaccharide (LPS) challenge. To achieve this objective, 12 nursery pens with 192 weaned pigs (16 pigs/pen) were randomly assigned to three treatments (4 pens/treatment): Control (saline injection), Mixed (8 pigs/pen received an LPS challenge and 8 pigs received saline injection) and 100% (all pigs received LPS). The LPS challenge consisted of a bolus subcutaneous injection at 300 µg/kg BW (E. coli O111:B4). Prior to placement, NUtrack was installed above the 12 nursery pens and initiated continuous data capture for the duration of the nursery phase (43 days). Ten days after placement in the nursery pens pigs received the assigned challenge (LPS or sham). Data were analyzed using the MIXED procedure of SAS specific for repeated measures (SAS Inst. Inc., Cary, NC). Regardless of treatment group, general special behaviors were similar (P = >0.28) prior to the LPS challenge (days 1–9). Following LPS challenge (day 10), spatial behaviors decreased (P =

Inclusion of crossbred (CB) data into traditionally purebred (PB) genetic evaluations has been shown to increase the response in CB performance. Currently, it is unrealistic to collect data on all CB animals in swine production systems, thus, a subset of CB animals must be selected to contribute genomic/phenotypic information. The aim of this study was to evaluate selective genotyping strategies in a simulated 3-way swine crossbreeding scheme. The swine crossbreeding scheme was simulated and produced 3-way CB animals for 6 generations with 3 distinct PB breeds each with 25 and 175 mating males and females, respectively. F1 crosses (400 mating females) produced 4,000 terminal CB progeny which were subjected to selective genotyping. The genome consisted of 18 chromosomes with 1,800 QTL and 72k SNP markers. Selection was performed using estimated breeding values (EBV) for CB performance. It was assumed that both PB and CB performance was moderately heritable (h2=0.4). Several scenarios altering the genetic correlation between PB and CB performance (rpc=0.1, 0.3, 0.5, 0.7,or 0.9) were considered. CB animals were chosen based on phenotypes to select 200, 400, or 800 CB animals to genotype per generation. Selection strategies included: (1) Random: random selection, (2) Top: highest phenotype, (3) Bottom: lowest phenotype, (4) Extreme: half highest and half lowest phenotypes, and (5) Middle: average phenotype. Each selective genotyping strategy, except for Random, was considered by selecting animals in half-sib (HS) or full-sib (FS) families. The number of PB animals with genotypes and phenotypes each generation was fixed at 1,680. Each unique genotyping strategy and rpc scenario was replicated 10 times. Selection of CB animals based on the Extreme strategy resulted in the highest (P < 0.05) rates of genetic gain in CB performance (ΔG) when rpc 0.05) ΔG. No differences (P > 0.05) were observed in ΔG between top, bottom, or middle when rpc>0.1. Higher correlations between true breeding values (TBV) and EBV were observed using Extreme when rpc

The aim of this study was to investigate different inclusion rates of purebred (PB) and CB phenotypes and genotypes in genetic evaluations. Assuming PB and CB traits with moderate heritabilities (h2 = 0.4), a three-way swine crossbreeding scheme was simulated, and selection was practiced for 6 generations. The goal was to increase the CB phenotype. Phenotypes, genotypes and pedigrees for three purebred breeds (each consisting of 25 males and 175 females), F1 crosses (400 females) and terminal cross progeny (2500) were simulated using AlphaSimR. The genome consisted of 18 chromosomes with 1,800 QTL and 59.4k SNP markers. Selection was performed using EBV produced by the BLUPf90 suite of programs for each phenotyping/genotyping strategy. Strategies investigated were 1) increasing the proportion of CB with genotypes, phenotypes and sire pedigree information, 2) decreasing the proportion of PB phenotypes and genotypes, and 3) altering the genetic correlation between PB and CB traits (rpc). Each strategy was replicated 15 times. Results showed that including CB performance improved the CB phenotype regardless of rpc or phenotyping/genotyping strategy. Compared to using only PB information, including 10% of possible CB animals per generation with sire pedigrees and phenotypes increased the response in CB phenotype when rpc was 0.1, 0.3, 0.5, 0.7, and 0.9 by 192, 64, 41, 25 and 21%, respectively. Including CB genotypes dramatically improved the previously mentioned increases in response. Minimal change was observed in the CB phenotype when PB phenotypes were included or removed, if CB phenotypes, genotypes and sire pedigrees were included. PB genotypes were more informative than phenotypes in enabling prediction for CB traits. In practice, the inclusion rates of CB and PB data depends upon the degree of connectedness between CB animals and PB selection candidates and the cost-benefit ratio of increased CB performance and genotyping/phenotyping costs.

Losses due to infectious diseases are one of the main factors affecting productivity in the swine industry, motivating the investigation of disease resilience-related traits for genetic selection. However, these traits are not expected to be expressed in the nucleus herds, where selection is performed. One alternative is to use information from the commercial level to identify and select nucleus animals genetically superior for coping with pathogen challenges. In this study, we analyzed the genetic basis of antibody (Ab) response to common infectious pathogens in health-challenged commercial swine herds as potential indicator traits for disease resilience, including Ab response to influenza A virus of swine (IAV), Mycoplasma hyopneumoniae (MH), porcine circovirus (PCV2), and Actinobacillus pleuropneumoniae (APP; different serotypes). Ab response was measured in blood at entry into gilt rearing, post-acclimation (∼40 days after entering the commercial herd), and parities 1 and 2. Heritability estimates for Ab response to IAV, MH, and PCV2 ranged from 0 to 0.76. Ab response to APP ranged from 0 to 0.40. The genetic correlation (rG) of Ab response to IAV with MH, PCV2, PRRSV, and APPmean (average Ab responses for all serotypes of APP) were positive (>0.29) at entry. APPmean was negatively correlated with PCV2 and MH at entry and parity 2 but positively correlated with MH at post-acclimation and parity 1. Genomic regions associated with Ab response to different APP serotypes were identified on 13 chromosomes. The region on chromosome 14 (2 Mb) was associated with several serotypes of APP, explaining up to 4.3% of the genetic variance of Ab to APP7 at entry. In general, genomic prediction accuracies for Ab response were low to moderate, except average Ab response to all infectious pathogens evaluated. These results suggest that genetic selection of Ab response in commercial sows is possible, but with variable success depending on the trait and the time-point of collection. Future work is needed to determine genetic correlations of Ab response with disease resilience, reproductive performance, and other production traits.

Tracking individual animals in a group setting is a exigent task for computer vision and animal science researchers. When the objective is months of uninterrupted tracking and the targeted animals lack discernible differences in their physical characteristics, this task introduces significant challenges. To address these challenges, a probabilistic tracking-by-detection method is proposed. The tracking method uses, as input, visible keypoints of individual animals provided by a fully-convolutional detector. Individual animals are also equipped with ear tags that are used by a classification network to assign unique identification to instances. The fixed cardinality of the targets is leveraged to create a continuous set of tracks and the forward-backward algorithm is used to assign ear-tag identification probabilities to each detected instance. Tracking achieves real-time performance on consumer-grade hardware, in part because it does not rely on complex, costly, graph-based optimizations. A publicly available, human-annotated dataset is introduced to evaluate tracking performance. This dataset contains 15 half-hour long videos of pigs with various ages/sizes, facility environments, and activity levels. Results demonstrate that the proposed method achieves an average precision and recall greater than 95% across the entire dataset. Analysis of the error events reveals environmental conditions and social interactions that are most likely to cause errors in real-world deployments.

Numerous methods have been suggested to incorporate crossbred (CB) phenotypes and genotypes into swine selection programs, yet little research has focused on the implicit trade-off decisions between generating data at the nucleus or commercial level. The aim of this study was to investigate the impact of altering the proportion of purebred (PB) and CB phenotypes and genotypes in genetic evaluations on the response to selection of CB performance. Assuming CB and PB performance with moderate heritabilities ([Formula: see text]), a three-breed swine crossbreeding scheme was simulated and selection was practiced for six generations, where the goal was to increase CB performance. Phenotypes, genotypes, and pedigrees for three PB breeds (25 and 175 mating males and females for each breed, respectively), F(1) crosses (400 mating females), and terminal cross progeny (2,500) were simulated. The genome consisted of 18 chromosomes with 1,800 quantitative trait loci and 72k single nucleotide polymorphism (SNP) markers. Selection was performed in PB breeds using estimated breeding value for each phenotyping/genotyping strategy. Strategies investigated were: 1) increasing the proportion of CB with genotypes, phenotypes, and sire pedigree relationships, 2) decreasing the proportion of PB phenotypes and genotypes, and 3) altering the genetic correlation between PB and CB performance ([Formula: see text]). Each unique [Formula: see text] scenario and data collection strategy was replicated 10 times. Results showed that including CB data improved the CB performance regardless of [Formula: see text] or data collection strategy compared with when no CB data were included. Compared with using only PB information, including 10% of CB progeny per generation with sire pedigrees and phenotypes increased the response in CB phenotype by 134%, 55%, 33%, 23%, and 21% when [Formula: see text] was 0.1, 0.3, 0.5, 0.7, and 0.9, respectively. When the same 10% of CB progeny were also genotyped, CB performance increased by 243%, 54%, 38%, 23%, and 20% when the [Formula: see text] was 0.1, 0.3, 0.5, 0.7, and 0.9, respectively, compared with when no CB data were utilized. Minimal change was observed in the average CB phenotype when PB phenotypes were included or proportionally removed when CB were genotyped. Removal of both PB phenotypes and genotypes when CB were genotyped greatly reduced the response in CB performance. In practice, the optimal inclusion rate of CB and PB data depends upon the genetic correlation between CB and PB animals and the expense of additional CB data collection compared with the economic benefit associated with increased CB performance.

Modern pork production is an important source of animal protein with pork accounting for nearly 43% of all red meat consumed worldwide. Despite environmental concerns pertaining to livestock production, growing consumer demand especially in the developing world will push meat consumption much higher worldwide in places such as Asia and Africa. To meet such market demands, pig producers must use all genetic and genomic technology available. During the past 50 years, swine genetics have moved from evaluating pigs based on visual appraisal to more advanced methods of measuring growth, backfat and feed efficiency. With the beginning of the age of genomics, swine geneticists first used genetic markers to remove defects and to select for improved feed efficiency, growth, meat quality and increased litter size. More recently, sequencing of the pig genome has led to the ability to genotype animals for thousands of genes to identify those genes responsible for a number of specific traits, to use genomic selection to improve rates of genetic improvement and to reduce levels of inbreeding. Recent discoveries to find pigs less susceptible to disease and heat stress are also being made. It is expected that such genetic improvements will lead to more sustainable production and improved pork products for the consumer.

Feet and legs of replacement females were objectively evaluated at selection, i.e., approximately 150 d of age (n = 319) and post first parity, i.e., any time after weaning of first litter and before second parturition (n = 277) to 1) compare feet and leg joint angle ranges between selection and post first parity; 2) identify feet and leg joint angle differences between selection and first 3 wk of second gestation; 3) identify feet and leg joint angle differences between farms and gestation days during second gestation; and 4) obtain genetic variance components for conformation angles for the two time points measured. Angles for carpal joint (knee), metacarpophalangeal joint (front pastern), metatarsophalangeal joint (rear pastern), tarsal joint (hock), and rear stance were measured using image analysis software. Between selection and post first parity, significant differences were observed for all joints measured (P < 0.05). Knee, front and rear pastern angles were less (more flexion), and hock angles were greater (less flexion) as age progressed (P < 0.05), while the rear stance pattern was less (feet further under center) at selection than post first parity (only including measures during first 3 wk of second gestation). Only using post first parity leg conformation information, farm was a significant source of variation for front and rear pasterns and rear stance angle measurements (P < 0.05). Knee angle was less (more flexion; P < 0.05) as gestation age progressed. Heritability estimates were low to moderate (0.04–0.35) for all traits measured across time points. Genetic correlations between the same joints at different time points were high (>0.8) between the front leg joints and low (

Conformation-based sow selection is performed prior to reaching mature size, yet little is known about how conformation changes as growth continues. To assess conformation changes, 9 conformational traits were objectively measured at 12 discrete time points between 112 d of age and parity 3 weaning on 622 sows in 5 cohorts. The 9 traits included 5 body size traits (body length, body depth at the shoulder and flank, and height at the shoulder and flank) and 4 joint angles (knee, hock, and front and rear pastern). Data were analyzed with a repeated measures model (SAS V 9.4) including cohort and time point as fixed effects, sire as a random effect, and heterogeneous compound symmetry as the covariance structure. Sire variance ranged from 0.16 (body depth shoulder) to 2.00 (body length) cm2 for body size traits and 2.28 (rear pastern) to 4.22 (front pastern) degrees2 for joint angles. Cohort had an effect on all traits (P < 0.05). All traits displayed changes over time (P < 0.001). Size traits increased between 112 d of age and parity 3 weaning (64.16 vs. 107.57 cm, 26.62 vs. 44.14 cm, 23.32 vs. 36.92 cm, 46.10 vs. 73.55 cm, 49.36 vs. 77.47 cm for body length, body depth shoulder and flank, and height shoulder and flank, respectively); however, they fluctuated within parity by increasing during gestation and decreasing at weaning. Knee angle decreased (164.12 vs. 150.72 degrees) while fluctuating within parity by decreasing in the second half of gestation and increasing after weaning. Front and rear pastern angles decreased over time (60.89 vs. 53.74 degrees and 64.64 vs. 55.50 degrees for front and rear pastern, respectively), while biologically negligible change was observed in hock angle (148.63 vs. 147.48 degrees). Sow conformation changes throughout life, and these changes may require consideration when making selection decisions.

Summary Porcine epidemic diarrhea virus (PEDV) belongs to the Coronaviridae family and causes malabsorptive watery diarrhea, vomiting, dehydration and imbalanced blood electrolytes in pigs. Since the 1970s, PED outbreaks have become a source of problems in pig producing countries all over the world, causing large economic losses for pig producers. Although the infection in adults is not fatal, in naïve suckling piglets mortality is close to 100%. In this study, we investigated genome‐wide differences between dead and recovered suckling piglets from commercial farms after PED outbreaks. Samples from 262 animals (156 dead and 106 recovered) belonging to several commercial lines were collected from five different farms in three different countries (USA, Canada and Germany) and genotyped with the porcine 80K SNP chip. Mean F st value was calculated in 1‐Mb non‐overlapping windows between dead and recovered individuals, and the results were normalized to find differences within the comparison. Seven windows with high divergence between dead and recovered were detected—five on chromosome 2, one on chromosome 4 and one on chromosome 15—in total encompassing 152 genes. Several of these genes are either under‐ or overexpressed in many virus infections, including Coronaviridae (such as SARS‐CoV). A total of 32 genes are included in one or more Gene Ontology terms that can be related to PED development, such as Golgi apparatus, as well as mechanisms generally linked to resilience or diarrhea development (cell proliferation, ion transport, ATPase activity). Taken together this information provides a first genomic picture of PEDV resilience in suckling piglets.

Recently, piglets from a high-health status farm began exhibiting congenital tremors, high preweaning mortality and incidence of splayed legs. Postmortem histological examination identified a small number of scattered white matter vacuoles in the cerebellum and underlying brainstem of affected piglets. Presence of potential viral sources associated with this neurologic condition was initially infirmed using quantitative PCR for atypical porcine pestivirus (APPV), porcine teschovirus, and porcine sapelovirus. Using metagenomic analysis, APPV was identified as the main microbial species in serum obtained from piglets affected by congenital tremor. These piglets had higher preweaning mortality rates (46.4% vs. 15.3%) and incidence of splayed legs (33.0% vs. 0.8 %) compared to unaffected piglets. Piglets affected by congenital tremor had higher viral titer (P < 0.15) and larger birth weights (P < 0.05) compared to normal litter mates. Whole-genome sequencing and genome assembly of the novel APPV strain (MK728876) was carried out using Oxford Nanopore and related bioinformatics pipelines. Phylogenic analysis demonstrated that this strain along with other completely sequenced APPV strains were grouped into 2 clades, both including strains-inducing congenital tremor. Strains appear to cluster based on region but there were still significant differences within regions. Future research needs to address potential underdiagnosis due to genetic diversity but also to understand mode of transmission, variation in virulence, and the role of host genetics in APPV susceptibility.

Semen quality has a dramatic impact on reproductive efficiency in the swine industry, influencing both conception rate and litter size. The objective of this study was to assess whether the presence of varicocele hinders semen quality in both thermoneutral and heat stress (HS) conditions. At approximately 6 mo of age, ultrasonography was used to measure left and right pampiniform plexus area in order to detect varicocele in maternal line boars at the University of Nebraska–Lincoln. Between 10 and 12 mo of age, semen was collected from each boar (n = 28) twice weekly. Boars were collected under thermoneutral conditions, were then heat stressed for 7 d to exacerbate any semen quality issues, and semen was collected post-HS for 6 wk. Sperm characteristics were determined by computer-assisted semen analysis. The presence of varicocele had a significant effect on sperm concentration (P = 0.04) and trended toward significance for mean sperm head area (P = 0.06) throughout the duration of the study. An interaction existed between varicocele and collection time point at weeks 2–5 post-HS for distal droplet percentage, suggesting that boars with varicocele were possibly more susceptible to heat-stress-induced semen quality issues than boars without varicocele. Moreover, semen quality was reduced in boars with versus without varicocele under both thermoneutral and HS conditions. Therefore, detection of varicocele by ultrasound could represent a potential marker of fertility in young boars or as a component trait in selection indices for fertility.

Previous results indicated that antibody response to PRRSV has moderate genomic prediction accuracy; however, little is known about this for other common infectious diseases. Therefore, the objective of this study was to estimate the accuracy of genomic prediction for antibody response to infectious diseases in commercial sows. A total of 2,848 Large White x Landrace replacement gilts were sourced from 17 high-health multipliers (7 breeding companies; BC) and introduced to 23 commercial farms with a history of common diseases, following standard acclimation procedures. Serum was used to quantify antibody response to swine influenza virus (SIV), Mycoplasma hyopneumoniae (MH), porcine circovirus type 2 (PCV2), and 8 serotypes of Actinobacilluspleuropneumoniae(APP1-3, 5, 7, 10, 12, and 13) at entry (S/PEntry), following acclimation (S/PAcclimation), and during parities 1 (S/PParity1) and 2 (S/PParity2). All animals were genotyped for 38,191 SNPs. Genomic prediction was performed using BayesB (pi=0.99), with the fixed effect of CG and random effects of SNPs included in the model. Training and validation were performed using 7-fold cross-validation, with data from each BC used as the validation dataset in one-fold. In general, prediction accuracies were low: SIV, from 0.13 (S/PAcclimation) to 0.26 (S/PParity1); MH, -0.07 (S/PAcclimation) to 0.13 (S/PParity2); PCV2, 0.04 (S/PParity1) to 0.32 (S/PAcclimation); APP, -0.08 (S/PEntry, APP10) to 0.26 (S/PAcclimation, APP7). At each point, average accuracies were 0.06 for S/PEntry, 0.09 for S/PAcclimationand S/PParity1, and 0.08 for S/PParity2, showing small increases in accuracy after the acclimation period. Among diseases, average accuracies ranged from 0.01 (APP1) to 0.22 (PCV2). Results show that, overall, the accuracy of genomic prediction of antibody response to common infectious diseases in commercial gilts is limited. The authors thank PigGen Canada, Genome Canada, and the Canadian Swine Health Board for financial support, and the late Dr. Stephen Bishop for his scientific contributions.

The objective of this study was to perform genomic predictions for reproductive performance of sows under natural health challenge. Reproductive performance (1 to 4 parities) and genotype (~40K SNPs) were available for 2,604 crossbred sows, for a total of 7,635 farrowing records. Animals from 17 high-health multipliers from 7 breeding companies (PigGen Canada) were shipped to 23 commercial farms with recent history of common infectious diseases. Gilts entered farms with an average of 53 animals per contemporary group (CG). Traits included total number of piglets: born (TB), born alive (NBA), stillborn (SB), mummified (MUM), born dead (NBD), and weaned (NW). Genomic predictions were performed using Bayes-B (pi=0.995) with a seven-fold cross-validation using each company in turn for validation and the others for training. The model included the effects of CG (fixed) and SNP (random), and net number of fosters (covariate) for NW. Genomic predictions were done for animal lifetime performance (sum performance of parities) for each trait and using first parity performance as the training set to predict subsequent parity performance. Accuracy was calculated as the weighted average correlation between GEBV and adjusted phenotype across validation sets divided by the square root of heritability. Lifetime performance accuracies were low to moderate, ranging from 0.11 (TB) to 0.45 (NBD). Accuracies using parity 1 to predict subsequent performance were low, ranging from -0.07 (SB in parity 3) to 0.19 (NBD in parity 2), with average accuracies per trait ranging from 0.04 (SB) to 0.16 (NBD).Although most accuracies were low, the moderately high accuracies for some lifetime performance shows that genomic prediction can be used to improve performance under natural health challenge in sows. We appreciate the financial support of PigGen Canada, Canadian Swine Health Board, Genome Alberta and Swine Innovation Porc, and the late Dr. Stephen Bishop for his scientific contributions.

Computer vision systems have the potential to provide automated, non-invasive monitoring of livestock animals, however, the lack of public datasets with well-defined targets and evaluation metrics presents a significant challenge for researchers. Consequently, existing solutions often focus on achieving task-specific objectives using relatively small, private datasets. This work introduces a new dataset and method for instance-level detection of multiple pigs in group-housed environments. The method uses a single fully-convolutional neural network to detect the location and orientation of each animal, where both body part locations and pairwise associations are represented in the image space. Accompanying this method is a new dataset containing 2000 annotated images with 24,842 individually annotated pigs from 17 different locations. The proposed method achieves over 99% precision and over 96% recall when detecting pigs in environments previously seen by the network during training. To evaluate the robustness of the trained network, it is also tested on environments and lighting conditions unseen in the training set, where it achieves 91% precision and 67% recall. The dataset is publicly available for download.

Structural conformation and behavior are integral components in sows remaining productive in the herd. This initial investigation studied the impact of activity data on weight and the impact of sires on overall activity of group housed replacement gilts. Beginning at 19 wk of age, video recorded data of approximately 75 gilts/wk for a total of 230 gilts over 3 wk was collected for 7 consecutive d using the NUtrack system, which tracks distance travelled (m), time standing (s), eating (s), and laying (s), and angle rotated (degrees). Any d that logged for less than 24 hr were dropped from analysis. Data were analyzed using mixed models (SAS V 9.4) including random effects of dam, barn, pen, and on-test date, and fixed effects including total laying time, angle rotated, distance travelled, time standing, and sire. For 20 wk wt, the mixed model included distance, laying, angle, and standing. Sire had an effect on distance traveled (P < 0.05). The following traits also had an effect on distance: angle (P < 0.01), laying (P < 0.001), standing (P < 0.001), sitting (P < 0.01), and eating (P < 0.01). Pearson correlation coefficient showed a positive correlation between wt and laying (0.26), and negative correlations between wt and distance (-0.34), standing (-0.28) and angle (-0.42). The regression of distance on 20 wk wt yielded a regression coefficient of -0.027 (P < 0.05) m/d per lb and the model explained 11.85% of the variation in 20 wk wt. NUtrack can be utilized to track activities and distance travelled of swine in group pens. Given the results presented herein, continued investigation into the heritability of these activity traits and their association with sow longevity is warranted.

Ultrasonography was used to capture a coronal-sagittal image of the veins of the pampiniform plexus (PP) and the testicular artery of 327 maternal-line boars at approximately 6 months of age at the University of Nebraska-Lincoln. Varicocoele was diagnosed by two methods. Method 1 diagnosed varicocoele when the average vessel area on one side of the scrotum was 1.5 times larger than the average vessel area on the other side of the scrotum. Method 2 diagnosed varicocoele when the average vessel area on one side of the scrotum of a boar was 1.5 times larger than the average vessel on the same side of the scrotum of the boar's cohorts (same population and year). Varicocoele was diagnosed in 23.17% and 15.1% of boars measured using method 1 and method 2, respectively. Ultrasonography showed to be an effective means to measure PP vessel size in boars and may even allow for earlier detection of varicocoele than by using palpation. Animal models were employed to estimate the heritability for: average area of right PP vessels (0.52), average area of the left PP vessels (0.46), varicocoele presence using method 1 (0.26) and varicocoele presence using method 2 (0.25). These heritability estimates suggest that vessel size and varicocoele could be selected against in breeding programmes to potentially improve boar semen quality.

The objective of this study was to perform genome-wide association studies (GWAS) to identify Quantitative Trait Loci (QTL) associated with antibody response to infectious diseases in commercial sows. A total of 2,848 Large White x Landrace replacement gilts were sourced from 17 high-health multipliers (7 breeding companies; BC) and introduced to 23 commercial farms with a history of common pig diseases, following the standard acclimation procedures with an average of 53 animals per entry group (CG). Serum was used to quantify antibody response to swine influenza virus (SIV), Mycoplasma hyopneumoniae (MH), porcine circovirus type-2 (PCV2), and 8 serotypes of Actinobacillus pleuropneumoniae (APP1-3, 5, 7, 10, 12, and 13) at entry (S/PEntry), following acclimation (S/PAcclimation), and during parities 1 (S/PParity1) and 2 (S/PParity2). All animals were genotyped for 38,191 SNPs. GWAS was performed using BayesB (pi=0.99), with the fixed effect of CG and the random effects of SNPs in the model. For APP, QTL were only identified at S/PAcclimation; on SSC14 (2Mb) for APP3, APP7, APP10, and APP13 that explained 5.6, 4.7, 2.8, and 3.6% of the genetic variance, respectively. A gene within this QTL region is SYK, involved in the control of immune-receptors. For APP5, a QTL that explained 4.2% of the genetic variance was identified on SSC4 (105Mb), which co-localizes with two genes associated with immune-response: SIKE1and NRAS. For SIV, no QTL was identified. A QTL on SSC7 (130-131Mb) was identified for MH (S/PParity1, 5.1%) and PCV2 (S/PEntry, 34%; S/PAcclimation, 43.4%). These results provide new information on the genetic basis of response to infectious diseases in sows. The identified QTL have the potential to be used to select for improved immune response. The authors thanks PigGen Canada, Genome Canada, and the Canadian Swine Health Board for financial support, and the late Dr. Stephen Bishop for his scientific contributions.

Background: The objectives of this study were to create an objective measurement method of joint angles for knee, hock, front and rear pasterns and a rear stance position in swine using digital imaging technology and to assess the repeatability of the objective measurement process. Methods and Findings: Forty-five multiparous sows (average parity 6.7 ± 2.5; parity range 5 to 14) from two commercial farms (n=21 farm 1 and n=24 farm 2) were used. Sows were moved to a pen where digital images of the profile and rear stance were captured. On average, 5.2 (± 2.6) profile and 2.6 (± 1.0) rear stance high quality images were used per sow. A joint angle measuring system was devised to collect angle measurements on the four feet and leg joints previously mentioned and the rear stance. Joint measurements were analyzed using repeated measure mixed model methods, including farm and parity (as 5, 6, and 7+) as fixed effects. Intraclass correlation coefficients were calculated to evaluate process repeatability. Joint angle measurement repeatability ranged from 0.63 to 0.82. Lowest and highest repeatabilities were observed for the front pastern and hock angle measurements, respectively. No significant farm or parity differences were observed for joint angles measured except for the knee angle between farms (P

Background Our recent research showed that antibody response to porcine reproductive and respiratory syndrome (PRRS), measured as sample-to-positive (S/P) ratio, is highly heritable and has a high genetic correlation with reproductive performance during a PRRS outbreak. Two major quantitative trait loci (QTL) on Sus scrofa chromosome 7 (SSC7; QTLMHC and QTL130) accounted for ~40 % of the genetic variance for S/P. Objectives of this study were to estimate genetic parameters for PRRS S/P in gilts during acclimation, identify regions associated with S/P, and evaluate the accuracy of genomic prediction of S/P across populations with different prevalences of PRRS and using different single nucleotide polymorphism (SNP) sets. Methods Phenotypes and high-density SNP genotypes of female pigs from two datasets were used. The outbreak dataset included 607 animals from one multiplier herd, whereas the gilt acclimation (GA) dataset included data on 2364 replacement gilts from seven breeding companies placed on health-challenged farms. Genomic prediction was evaluated using GA for training and validation, and using GA for training and outbreak for validation. Predictions were based on SNPs across the genome (SNPAll), SNPs in one (SNPMHC and SNP130) or both (SNPSSC7) QTL, or SNPs outside the QTL (SNPRest). Results Heritability of S/P in the GA dataset increased with the proportion of PRRS-positive animals in the herd (from 0.28 to 0.47). Genomic prediction accuracies ranged from low to moderate. Average accuracies were highest when using only the 269 SNPs in both QTL regions (SNPSSC7, with accuracies of 0.39 and 0.31 for outbreak and GA validation datasets, respectively. Average accuracies for SNPALL, SNPMHC, SNP130, and SNPRest were, respectively, 0.26, 0.39, 0.21, and 0.05 for the outbreak, and 0.28, 0.25, 0.22, and 0.12, for the GA validation datasets. Conclusions Moderate genomic prediction accuracies can be obtained for PRRS antibody response using SNPs located within two major QTL on SSC7, while the rest of the genome showed limited predictive ability. Results were obtained using data from multiple genetic sources and farms, which further strengthens these findings. Further research is needed to validate the use of S/P ratio as an indicator trait for reproductive performance during PRRS outbreaks. Electronic supplementary material The online version of this article (doi:10.1186/s12711-016-0230-0) contains supplementary material, which is available to authorized users.

Rapid identification of morbid/injured pigs is essential for swine producers to ensure the health and well-being of each individual pig and ensure production efficiency. As such, there is a need to develop advanced technology as a means to further ensure the health and well-being of pigs. The objective of this study was to evaluate a novel computer vision, Deep-Frame – Detection and Tracking Platform (DF–DTP), for the ability to automatically identify/maintain identity and continuously track the activities of group-housed pigs. Utilizing a depth-enabled camera and multi-ellipsoid expectation maximization technology, 28 nursery pigs were continuously evaluated during the first 42 d of the nursery phase. Over the 42-d nursery period, the DF–DTP was capable of achieving a 93.7% accuracy for identifying and maintaining the identity of individual pigs. Through visual validation (10,544 observations), 642 identification errors were detected. Of the identification errors, 82% occurred when pigs were lying, 7.8% standing, 2.1% walking, 7.2% at the feeder (at the feeder), and 1.0% at the waterer. The DF–DTP was capable of a 96.2% accuracy rate for classification of an individual pig’s activity. Accuracy for classification of activities was 96.3% for walking, 96.3 for standing, 99.1 for lying, 86.4% for at the feeder, and 73.6% for at the waterer. Across the 42-d trial, the average time pigs spent 77.8% (+ 0.02) lying, 8.6% (+ 0.32) standing, 2.9% (+ 0.09) walking and traveled 943.1 meters/d (+ 195.9). Over time (d 1 – d 42), the time pigs spent lying, standing, walking, and meters/d decreased (P 0.001). As d within the nursery phase increased, time at the feeder increased (P < 0.001), there was no change (P = 0.11) for time at the waterer. Results of the study indicate that the DF–DTP is capable of accurately identifying, maintaining the identity of and continuously tracking the activity of group-housed nursery pigs.

The objective of the current study was to conduct a genome-wide association on gestation length (GL) in different parities in swine. Sows (n = 831) belonging to the University of Nebraska – Lincoln resource population (Landrace X Nebraska Index Line) were utilized. GL was defined as the number of days between the final insemination and farrowing. Four traits, GL at parity 1, 2, 3 and 4 (GL1, GL2, GL3 and GL4, respectively) were investigated. Animals which were induced 24h prior to a farrowing event were removed from the analysis. Sows were genotyped with the Illumina SNP60 BeadArray. A Bayes C model with π=0.995 was implemented with fixed effects of contemporary group, development pen, diet, linear and quadratic terms for age at puberty (GL1; P < 0.01), and linear and quadratic terms for farrowing age (GL2; P < 0.01). Results are posterior means of 55,000 samples.Single marker association analysis (SMA) was performed in R utilizing a linear model on SNP from 1-Mb windows (n = 10) which explained the largest proportion of genetic variation in GL1. Top 10 (0.5% of all windows) 1-Mb windows accounted for a limited proportion of genetic variance, 7.75, 4.66, 3.45 and 2.05% in GL1, GL2, GL3 and GL4, respectively. Posterior mean heritability estimates (posterior SD) for GL1, GL2, GL3 and GL4 were 0.33 (0.06), 0.34 (0.07), 0.32 (0.08) and 0.20 (0.08), respectively. The top SNP (ASGA0017859, SSC4, 7.8 Mb) located in one of the two top common genomic regions associated with GL1, GL2 and GL3 displayed a difference of 1.1d in GL1 between alternate homozygotes (P < 0.01). The top SNP from nine of the ten regions were significant (P < 0.05) in the SMA. Two of these regions were in common with GL2 and GL3 where SNP with potential functional effects were found in ZFAT, MAML2 and CCDC82. Results suggest GL is a largely polygenic trait.

There are numerous benefit claims for the supplementation of l-arginine in animal production systems. Its positive effect on fertility has been widely characterised in females of various species; however, the effects on the male reproductive system remain debatable with still unfolding molecular mechanisms. Here we investigated the effects of a dietary l-arginine supplementation on boar semen production outcomes, sperm characteristics, and seminal plasma proteome. Nine mature (20 months of age) Nebraska Index Line boars were individually housed and randomly assigned to a soybean meal-based diet containing 0.77% standard l-arginine (control, n=4) or 1.77% high l-arginine (n=5). Boar semen was collected weekly during the 6-week dietary arginine trial. Semen production outcomes (volume and total spermatozoa) and sperm motion (total and progressive motility) and morphology characteristics were subsequently analysed using a computer-assisted sperm analyzer (CEROS II). On Week 6, the seminal plasma of each boar was obtained by centrifugation at 4°C for proteome analysis. The clarified seminal plasma was precipitated; the purified proteins were resuspended in an appropriate buffer and loaded (300μg) onto an immobilized pH gradient strip (isoelectric point 3-10) for the first-dimension electrophoresis. The second-dimension was subsequently performed (4-20% gradient gel), and all gels were stained with Coomassie R250 dye. The PDQuest software (Bio-Rad, Hercules, CA, USA) was used to detect all significantly differentially expressed protein spots (Student’s t-test with P0.05). Similarly, various sperm motility (total and progressive motility) and velocity characteristics were not affected (P>0.05). By the end of the feed trial, the average (mean±standard error of the mean) semen volume (389±40mL), the total spermatozoa per ejaculate (39±8×109), the total (82±2%) and progressive (62±3%) sperm motility, the sperm velocity (e.g. average path: 98±7μm s−1), and sperm morphology (e.g. distal droplet: 5±1%) parameters of the control group were not significantly different from the group supplemented with high l-arginine: 393±35mL, 47±4×109, 82±2%, 61±3%, 92±8μm s−1, and 5±0%, respectively (P>0.05). In contrast, the proteome profiles of seminal plasma harvested on the sixth week of diet revealed various changes, characterised by the significantly increased expression of 8 protein spots in seminal plasma samples derived from arginine-fed boars (P

The objective of this study was to estimate genetic parameters for growth, body composition, and structural soundness traits in commercial gilt lines. The data included 1,449 gilts: 462 females from a grandparent maternal line and 987 from a parent maternal line. Growth was expressed as number of days to a constant 113.5 kg BW (DAYS) and compositional traits included loin muscle area (LMA), 10th rib backfat (BF10), and last rib backfat (LRF). Subjective structural soundness evaluation was completed using a 9-point scale and included: body length (BL), body depth (BD), body width (BWD), rib shape (BRS), top line (BTL), and hip structure (BHS); front legs: legs turned (FLT), buck knees (FBK), pastern posture (FPP), foot size (FFS), and uneven toes (FUT); rear legs: legs turned (RLT), leg posture (RLP), pastern posture (RPP), foot size (RFS), and uneven toes (RUT); and overall leg action (OLA). Genetic parameters were estimated with multivariate linear animal models, using the average information REML algorithm. Heritability estimates for growth and body composition traits ranged from 0.50 to 0.70, for body structure traits from 0.15 to 0.31, for leg structure traits from 0.07 to 0.31, and the estimate for OLA was 0.12. Several moderate to high genetic correlations were obtained among body structure traits, whereas correlations among leg structure traits were mainly low and nonsignifi cant. A strong correlation was found between FPP and OLA (P < 0.001); more upright FPP coincided with inferior OLA. Furthermore, FBK and FFS appeared to be favorably associated with OLA (0.05 < P < 0.10). Body structure trait correlations among each other and with leg soundness traits were primarily favorable. Correlations indicated that great BL and high BTL coincided with each other and deterioration of other structural soundness traits. Although genetic correlations obtained for DAYS and backfat measurements with structural soundness traits had an unfavorable trend, they were mainly low to moderate (i.e., simultaneous genetic improvement would be possible, including adversely associated traits). Due to greater heritabilities, faster genetic change could be expected for compositional and body structure traits than leg structure traits. Because of the genetic relationship among the trait groups, using information across traits when making selection decisions could result in genetic improvement among leg soundness traits.

The objective of this study was to estimate genetic associations for gilt growth, compositional, and structural soundness with sow longevity and lifetime reproduction. Performance and pedigree information from 1,447 commercial females from 2 genetic lines were included in the data analyzed. Growth was expressed as days to 113.5 kg BW (DAYS) and compositional traits included loin muscle area (LMA), 10th rib backfat (BF10), and last rib backfat (LRF). Structural soundness traits included body structure traits [length (BL), depth (BD), width (BWD), rib shape (BRS), top line (BTL), and hip structure (BHS)], leg structure traits [front legs: legs turned (FLT), buck knees (FBK), pastern posture (FPP), foot size (FFS), and uneven toes (FUT); rear legs: legs turned (RLT), leg posture (RLP), pastern posture (RPP), foot size (RFS), and uneven toes (RUT)], and overall leg action (OLA). Lifetime (LT) and removal parity (RP) were considered as longevity traits whereas lifetime reproductive traits included lifetime total number born (LNB), lifetime number born alive (LBA), number born alive per lifetime day (LBA/LT), and percentage productive days from total herd days (PD%). Genetic parameters were estimated with linear animal models using the average information REML algorithm. Second, to account for censored longevity and lifetime reproduction records, genetic parameters were estimated using Markov Chain Monte Carlo and Gibbs sampling methods. Similar estimates were obtained across the analysis methods. Heritability estimates for growth and compositional traits ranged from 0.50 to 0.70 and for structural soundness traits from 0.07 to 0.31. Longevity and lifetime reproductive trait heritability estimates ranged from 0.14 to 0.17 when REML was used. Unfavorable genetic correlations were obtained for DAYS with LT, RP, LNB, LBA, and PD% and for LRF with PD%. However, LMA was favorably associated with LT, RP, and LNB. Moderate to high correlations were obtained for BL and BRS with all longevity and lifetime reproductive traits. Correlations of BWD with LT and RP were moderate. Associations for leg soundness traits with longevity and lifetime reproductive traits were mainly low and nonsignificant (P ≥ 0.10). However, RLP was moderately correlated with LBA/LT and PD%. Current results indicate that selection for fewer DAYS has an antagonistic effect on lifetime performance. Furthermore, great BL, flat BRS, narrow BWD, and upright RLP seem detrimental to sow longevity and lifetime reproduction.

Porcine Epidemic Diarrhea (PED) is a serious and potentially devastating disease in the US and Canadian pig industries. To investigate if there is genetic resistance to PEDV a total of 156 dead and 106 surviving neonatal piglets infected with PEDV were genotyped with over 60,000 genetic markers to investigate genetic differences between the two groups. This allows for the identification of genomic regions that could be linked to susceptibility/resistance to virus infection and in cellular components that are involved in the PED virus infection cycle, providing new insights on the genetic resistance to this disease.

This study is a portion from the validation process of an objective methodology to measure feet and leg joint angles and to evaluate their relationship with sow longevity. Joint angle measurements from five feet and leg conformation traits (knee, hock, front and rear pasterns and rear stance) that have been previously described in the literature as being related with sow longevity were obtained from maternal gilts at selection and during their second gestation and compared with those obtained from 45 crossbred sows parity 5 and above. Significant differences (P < 0.05) between parities were identified in the knee, both pasterns, hock and rear stance joint angle measurements. These results suggest that as pigs age, structural changes occur. The range of which these changes occur could carry implications for the use of objective feet and leg angles towards future selection programs and protocols.

Reproduction traits play an important role in economically viable piglet production and are closely related to the quality and length of the produc- tive life of the sow. A increased removal rate of young sows is undesirable not only because of the associ- ated financial penalties incurred, but also because of ethical concerns. Candidate genes and gene pathways have been identified for fertility in model species, and recent studies have provided evidence that polymor- phisms within these genes are associated with repro- duction traits in American Yorkshire/Large White and Landrace populations. In this study we evaluated the impact of single polymorphisms (n = 7) in 7 candi- date genes on reproductive efficiency in Finnish York- shire (n = 280) and Landrace (n = 271) populations: IGFBP1, IGFBP2, IGFBP3, IGFBP5, CPTIA (carni- tine O-palmitoyltransferase I), COX2 (PG-endoperox- ide synthase 2, also known as cyclooxgenase-2), and SLC22A5 (organic cation/carnitine transporter 2 (sol- ute carrier family member I), OCTN2). In the Finnish Yorkshire population, only 4 of the analyzed markers were polymorphic. Significant effects on farrowing time were detected from the Yorkshire data, with polymor- phisms within the genes CPT1A (a (allele substitution effect of allele A) = 2.97 d for age at first farrowing)), IGFBP3 (a = 0.54 d for farrowing interval of parities >1), and IGFBP5 (a = 3.22, 1.27, and 0.85 d for age at first farrowing and farrowing interval in the first and later parities, respectively). For the Landrace popula- tion, 6 markers were polymorphic, and significant ef- fects were detected for traits affecting litter size. The polymorphism within the COX2 gene had an additive effect of 0.3 piglets for litter size in parities >1, and the IGFBP1 gene had an additive effect of 0.21, 0.26, and 0.11 piglets for litter size in the first parity, pari- ties >1, and stillborn in parities >1, respectively. The additive effect of the SNP within the IGFBP2 gene was 0.16, 0.09, and 0.09 piglets for litter size in parities >1 and stillborn in the first and later parities, respec- tively. Finally, the IGFBP5 gene had an additive effect of 0.18, 0.07, and 0.07 piglets for litter size in the first parity, stillborn in parities >1, and mortality between farrowing and weaning in the first parity, respectively. These results support the suitability of the candidate gene approach for identification of markers to improve the reproductive performance of sows and to provide potential markers for marker-assisted selection.

Escalating replacement rates and production costs warrant attention on sow productive life (SPL). Increasing average SPL by one-tenth of 1 parity would result in an annual revenue increase of over $15 million in the United States. Research in model organisms has revealed conserved genes and gene pathways that lead to longer lifespan. The most prominent gene pathways are those involved in growth, most notably genes in the IGF pathway that serve to mimic the response of caloric restriction. The objective of this research was to test the hypothesis that these well conserved genes and gene pathways could also play a role in SPL, even though the productive life of sows is both a measure of longevity and their reproductive performance. Preliminary research on 3 distinct populations of over 2,000 animals suggested that several genes were associated with components of SPL. Genetic markers were then analyzed against the corresponding records of the sows for reproductive and longevity traits using a validation population of 2,000 commercial females. Right censored data were used to test associations of genetic markers with survival to defined time points. Three distinct models of survival analysis were implemented using nonparametric estimates of the survival distribution in a sequential order, using a parametric accelerated failure time model with a Weibull distribution of the error term, and a Cox proportional hazards model, which is a semiparametric model that uses an unspecified baseline hazard function. The genetic markers CCR7 and CPT1A were significantly associated (P < 0.05) with survival using the nonparametric model and tended (P < 0.1) toward significance using the parametric and semiparametric models with significantly different effects (P < 0.05) between some genotype classes. Genetic markers for MBL2, IGFBP3, and WARS2 also tended (P < 0.1) toward significance for survival traits, but were not consistent. Mixed model analyses were used to determine the associations of these genetic markers with reproductive traits. The genetic markers for IGFBP1, MBL2, CPT1A, CCR7, SLC22A5, and ACE were significantly (P < 0.05) associated with at least 1 reproductive trait. These results show that molecular markers should be considered for use in marker-assisted selection to improve SPL.

A microsatellite-based genome scan of a Wagyu × Limousin F2 cross population previously demonstrated QTL affecting LM area and fatty acid composition were present in regions near the centrom- ere of BTA2. In this study, we used 70 SNP markers to examine the centromeric 24 megabases (Mb) of BTA2, including the Limousin-specific F94L myostatin allele (AB076403.1; 415C > A) located at approximately 6 Mb on the draft genome sequence of BTA2. A signifi- cant effect of the F94L marker was observed (F = 60.17) for LM area, which indicated that myostatin is most likely responsible for the effect. This is consistent with previous reports that the substitution of Leu for Phe at AA 94 of myostatin (caused by the 415C > A transver- sion) is associated with increased muscle growth. Sur- prisingly, several fatty acid trait QTL, which affected the amount of unsaturated fats, also mapped to or very near the myostatin marker, including the ratio of C16:1 MUFA to C16:0 saturated fat (F = 16.72), C18:1 to C18:0 (F = 18.88), and total content of MUFA (F = 17.12). In addition, QTL for extent of marbling (F = 14.73) approached significance (P = 0.05), and CLA concentration (F = 9.22) was marginally significant (P = 0.18). We also observed associations of SNP located at 16.3 Mb with KPH (F = 15.00) and for the amount of SFA (F = 12.01). These results provide insight into genetic differences between the Wagyu and Limousin breeds and may lead to a better tasting and healthier product for consumers through improved selection for lipid content of beef.

Sow longevity is a large and often overlooked component of profitability and efficiency for commercial swine operations. Culling and mortality rates average near 50 and 9%, respectively, leaving the responsibility of profitability on sows that can remain productive past parity 3. A new study was conducted using 2,000 commercial sows, with one-half of the females being gilts and the remaining half consisting of sows that produced at least 5 litters in the same breeding herd. Although culling reasons for young sows have not changed, this study revealed the primary reason that sows over parity 5 were being culled was because of old age, regardless of their production. Sows from the parity 5 and older group had a greater number of pigs born alive through 3 parities (P < 0.05) and had a reduced wean-to-first-service interval (P < 0.05) after their first parity when compared with the females that had just entered the farm at the inception of the study. Comparisons within the young group revealed that sows that were removed from production after a single litter were inferior for the number of pigs born alive (P < 0.05) and wean-to-first-service interval (P < 0.05) compared with sows that remained in the herd for 4 parities. This study revealed that sows can be selected for longevity without detrimental effects on reproductive performance, because sows that remained in production to more advanced parities also reproductively outperformed their contemporaries that were removed early from the breeding herd.