Large scale sequence-based screen for recessive variants allows for identification and monitoring of rare deleterious variants in pigs.

Most deleterious variants are recessive and segregate at relatively low frequency. Therefore, high sample sizes are required to identify these variants. In this study we report a large-scale sequence based genome-wide association study (GWAS) in pigs, with a total of 120,000 Large White and 80,000 Synthetic breed animals imputed to sequence using a reference population of approximately 1,100 whole genome sequenced pigs. We imputed over 20 million variants with high accuracies (R2>0.9) even for low frequency variants (1-5% minor allele frequency). This sequence-based analysis revealed a total of 14 additive and 9 non-additive significant quantitative trait loci (QTLs) for growth rate and backfat thickness. With the non-additive (recessive) model, we identified a deleterious missense SNP in the CDHR2 gene reducing growth rate and backfat in homozygous Large White animals. For the Synthetic breed, we revealed a QTL on chromosome 15 with a frameshift variant in the OBSL1 gene. This QTL has a major impact on both growth rate and backfat, resembling human 3M-syndrome 2 which is related to the same gene. With the additive model, we confirmed known QTLs on chromosomes 1 and 5 for both breeds, including variants in the MC4R and CCND2 genes. On chromosome 1, we disentangled a complex QTL region with multiple variants affecting both traits, harboring 4 independent QTLs in the span of 5 Mb. Together we present a large scale sequence-based association study that provides a key resource to scan for novel variants at high resolution for breeding and to further reduce the frequency of deleterious alleles at an early stage in the breeding program.

Using social interaction models for genetic analysis of skin damage in gilts.

BACKGROUND: Skin damage is a trait of economic and welfare importance that results from social interactions between animals. These interactions may produce wound signs on the gilt's skin as a result of damage behavior (i.e., fighting), biting syndromes (i.e., tail, vulva, or ear biting), and swine inflammation and necrosis syndrome. Although current selection for traits that are affected by social interactions primarily focuses on improving direct genetic effects, combined selection on direct and social genetic effects could increase genetic gain and avoid a negative response to selection in cases of competitive behavior. The objectives of this study were to (1) estimate variance components for combined skin damage (CSD), with or without accounting for social genetic effects, (2) investigate the impact of including genomic information on the prediction accuracy, bias, and dispersion of CSD estimated breeding values, and (3) perform a single-step genome-wide association study (ssGWAS) of CSD under a classical and a social interaction model. RESULTS: Our results show that CSD is heritable and affected by social genetic effects. Modeling CSD with social interaction models increased the total heritable variance relative to the phenotypic variance by three-fold compared to the classical model. Including genomic information increased the prediction accuracy of direct, social, and total estimated breeding values for purebred sires by at least 21.2%. Bias and dispersion of estimated breeding values were reduced by including genomic information in classical and social interaction models but remained present. The ssGWAS did not identify any single nucleotide polymorphism that was significantly associated with social or direct genetic effects for CSD. CONCLUSIONS: Combined skin damage is heritable, and genetic selection against this trait will increase the welfare of animals in the long term. Combined skin damage is affected by social genetic effects, and modeling this trait with a social interaction model increases the potential for genetic improvement. Including genomic information increases the prediction accuracy of estimated breeding values and reduces their bias and dispersion, although some biases persist. The results of the genome-wide association study indicate that CSD has a polygenic architecture and no major quantitative trait locus was detected.

Identification of the genetic basis of sow pelvic organ prolapse.

Introduction: Pelvic organ prolapse (POP) is one contributor to recent increases in sow mortality that have been observed in some populations and environments, leading to financial losses and welfare concerns. Methods: With inconsistent previous reports, the objective here was to investigate the role of genetics on susceptibility to POP, using data on 30,429 purebred sows, of which 14,186 were genotyped (25K), collected from 2012 to 2022 in two US multiplier farms with a high POP incidence of 7.1% among culled and dead sows and ranging from 2% to 4% of all sows present by parity. Given the low incidence of POP for parities 1 and >6, only data from parities 2 to 6 were retained for analyses. Genetic analyses were conducted both across parities, using cull data (culled for POP versus another reason), and by parity, using farrowing data. (culled for POP versus culled for another reason or not culled). Results and Discussion: Estimates of heritability from univariate logit models on the underlying scale were 0.35 ± 0.02 for the across-parity analysis and ranged from 0.41 ± 0.03 in parity 2 to 0.15 ± 0.07 in parity 6 for the by-parity analyses. Estimates of genetic correlations of POP between parities based on bivariate linear models indicated a similar genetic basis of POP across parities but less similar with increasing distance between parities. Genome wide association analyses revealed six 1 Mb windows that explained more than 1% of the genetic variance in the across-parity data. Most regions were confirmed in several by-parity analyses. Functional analyses of the identified genomic regions showed a potential role of several genes on chromosomes 1, 3, 7, 10, 12, and 14 in susceptibility to POP, including the Estrogen Receptor gene. Gene set enrichment analyses showed that genomic regions that explained more variation for POP were enriched for several terms from custom transcriptome and gene ontology libraries. Conclusion: The influence of genetics on susceptibility to POP in this population and environment was confirmed and several candidate genes and biological processes were identified that can be targeted to better understand and mitigate the incidence of POP.

The genetic basis of swine inflammation and necrosis syndrome and its genetic association with post-weaning skin damage and production traits.

The swine inflammation and necrosis syndrome (SINS) is a syndrome visually characterized by the presence of inflamed and necrotic skin at extreme body parts, such as the teats, tail, ears, and claw coronary bands. This syndrome is associated with several environmental causes, but knowledge of the role of genetics is still limited. Moreover, piglets affected by SINS are believed to be phenotypically more susceptible to chewing and biting behaviors from pen mates, which could cause a chronic reduction in their welfare throughout the production process. Our objectives were to 1) investigate the genetic basis of SINS expressed on piglets' different body parts and 2) estimate SINS genetic relationship with post-weaning skin damage and pre and post-weaning production traits. A total of 5,960 two to three-day-old piglets were scored for SINS on the teats, claws, tails, and ears as a binary phenotype. Later, those binary records were combined into a trait defined as TOTAL_SINS. For TOTAL_SINS, animals presenting no signs of SINS were scored as 1, whereas animals showing at least one affected part were scored as 2. Apart from SINS traits, piglets had their birth weight (BW) and weaning weight (WW) recorded, and up to 4,132 piglets were later evaluated for combined skin damage (CSD), carcass backfat (BF), and loin depth (LOD). In the first set of analyses, the heritability of SINS on different body parts was estimated with single-trait animal-maternal models, and pairwise genetic correlations between body parts were obtained from two-trait models. Later, we used four three-trait animal models with TOTAL_SINS, CSD, and an alternative production trait (i.e., BW, WW, LOD, BF) to access trait heritabilities and genetic correlations between SINS and production traits. The maternal effect was included in the BW, WW, and TOTAL_SINS models. The direct heritability of SINS on different body parts ranged from 0.08 to 0.34, indicating that reducing SINS incidence through genetic selection is feasible. The direct genetic correlation between TOTAL_SINS and pre-weaning growth traits (BW and WW) was favorable and negative (from -0.40 to -0.30), indicating that selection for animals genetically less prone to present signs of SINS will positively affect the piglet's genetics for heavier weight at birth and weaning. The genetic correlations between TOTAL_SINS and BF and between TOTAL_SINS and LOD were weak or not significant (-0.16 to 0.05). However, the selection against SINS was shown to be genetically correlated with CSD, with estimates ranging from 0.19 to 0.50. That means that piglets genetically less likely to present SINS signs are also more unlikely to suffer CSD after weaning, having a long-term increase in their welfare throughout the production system.

The swine inflammation and necrosis syndrome (SINS) is visually characterized by the presence of inflamed and necrotic skin at extreme body parts, such as the teats, tail, ears, and claw coronary bands. Piglets affected by this syndrome are considered phenotypically more susceptible to chewing and biting behaviors from pen mates. However, the genetic relationship between SINS and post-weaning skin damage is still unclear. In this study, we aimed to investigate the genetic basis of SINS expressed on piglets' different body parts and to estimate the SINS genetic relationship with skin damage and pre and post-weaning production traits. We showed that SINS on different body parts is heritable and that the direct selection against a combined score of SINS in different body parts (TOTAL_SINS) will favor the piglet's genetics for heavier weight at birth and weaning. However, TOTAL_SINS is not significantly correlated with carcass backfat thickness and loin depth at the piglet genetic level. The direct selection against SINS is genetically correlated with skin damage after weaning, meaning that piglets genetically more prone to present signs of SINS are more likely to receive skin damage later in life.

Preimplantation Genetic Testing for Aneuploidy (PGT-A) Reveals High Levels of Chromosomal Errors in In Vivo-Derived Pig Embryos, with an Increased Incidence When Produced In Vitro.

Preimplantation genetic testing for aneuploidy (PGT-A) is widespread, but controversial, in humans and improves pregnancy and live birth rates in cattle. In pigs, it presents a possible solution to improve in vitro embryo production (IVP), however, the incidence and origin of chromosomal errors remains under-explored. To address this, we used single nucleotide polymorphism (SNP)-based PGT-A algorithms in 101 in vivo-derived (IVD) and 64 IVP porcine embryos. More errors were observed in IVP vs. IVD blastocysts (79.7% vs. 13.6% p < 0.001). In IVD embryos, fewer errors were found at blastocyst stage compared to cleavage (4-cell) stage (13.6% vs. 40%, p = 0.056). One androgenetic and two parthenogenetic embryos were also identified. Triploidy was the most common error in IVD embryos (15.8%), but only observed at cleavage, not blastocyst stage, followed by whole chromosome aneuploidy (9.9%). In IVP blastocysts, 32.8% were parthenogenetic, 25.0% (hypo-)triploid, 12.5% aneuploid, and 9.4% haploid. Parthenogenetic blastocysts arose from just three out of ten sows, suggesting a possible donor effect. The high incidence of chromosomal abnormalities in general, but in IVP embryos in particular, suggests an explanation for the low success of porcine IVP. The approaches described provide a means of monitoring technical improvements and suggest future application of PGT-A might improve embryo transfer success.

Genetic aspects of piglet survival and related traits: a review.

In livestock, mortality in general, and mortality of the young, is societal worries and is economically relevant for farm efficiency. Genetic change is cumulative; if it exists for survival of the young and genetic merit can be estimated with sufficient accuracy, it can help alleviate the pressure of mortality. Lack of survival is a moving target; livestock production is in continuous change and labor shortage is a given. There is now ample evidence of clear genetic variance and of models able to provide genomic predictions with enough accuracy for selection response. Underlying traits such as birth weight, uniformity in birth weight, gestation length, number of teats, and farrowing duration all show genetic variation and support selection for survival or, alternatively, be selected for on their own merit.

Piglet survival is under genetic control and there are clear differences between individuals in their ability to live. Animals that do not survive their first weeks will obviously not reproduce as this is natural selection. Animals that survive still harbor relevant genetic differences. The genomic toolset, the use of genetic markers, makes it possible to link each animal to all others in the population, alive or dead, creating good opportunities for selection. Piglet survival depends on the genetic make-up of 1) the piglet itself, is it vital and heavy enough, 2) of the mother, are the piglets born at term, with low variation in birth weight, and 3) of the sow nursing the piglets, often the mother, does she allow the piglets to drink enough colostrum and milk of enough quality? This review explores the black box approach, complex statistical analysis of very large scale genomic recording of survival data, and it explores the biological approach, the influences of gestation length, birth weight, uniformity, number of teats, colostrum, etc., on birth weight. There is little doubt that genetic selection can increase survival of piglets. The challenge is to do this selection in balance with other production traits, such as litter size and body composition.

Effects of a Multi-Suckling System Combined With Enriched Housing Post-Weaning on Response and Cognitive Resilience to Isolation.

Improving welfare is still a critical issue in pig husbandry. Upgrades of the housing environment seem to be a promising solution to optimise resilience as a whole, and therefore improve animal welfare. The objective of this study was to evaluate the effect of an alternative housing system to enhance cognitive resilience and also to promote the pigs' welfare. A total of 96 piglets from two contrasted housing systems [alternative housing system (AHS) vs. conventional system (CONV)] was used. The major upgrades of the alternative system were multi-litter housing during lactation, delayed weaning, extra space allowance, and environmental enrichment from birth onwards. To estimate welfare, weight, and feed intake (as a general indicator of performances), the tear staining area (as a chronic stress indicator), behavioural postures, heart rate traits, and saliva cortisol concentration were measured over a 21 h-isolation. To assess cognitive resilience, the pigs were subjected to a maze with a social reward both before and after the isolation challenge and indicators of cognitive abilities were followed. The AHS pigs showed lower cortisol levels and tear staining area before the challenge, demonstrating overall better welfare due to the alternative housing conditions. During the challenge, AHS pigs had a lower heart rate, higher heart rate variability, and higher vagal activity than the CONV pigs, which might indicate a reduced sensitivity to the stressor. AHS pigs appeared to have a better long-term memory tested in a maze. Providing social and environmental enrichments, that fit the satisfaction of the essential needs of the pigs better, appears to be beneficial for pig welfare as a whole. Its effects on cognitive resilience still need to be proven.

Impact of Enrichment and Repeated Mixing on Resilience in Pigs.

Resilience, the capacity of animals to be minimally affected by a disturbance or to rapidly bounce back to the state before the challenge, may be improved by enrichment, but negatively impacted by a high allostatic load from stressful management procedures in pigs. We investigated the combined effects of diverging environmental conditions from weaning and repeated mixing to create high allostatic load on resilience of pigs. Pigs were either exposed to barren housing conditions (B) from weaning onwards or provided with sawdust, extra toys, regular access to a "play arena" and daily positive human contact (E). Half of the pigs were exposed to repeated mixing (RM) and the other half to one mixing only at weaning (minimal mixing, MM). To assess their resilience, the response to and recovery from a lipopolysaccharide (LPS) sickness challenge and a Frustration challenge were studied. In addition, potential long-term resilience indicators, i.e. natural antibodies, hair cortisol and growth were measured. Some indications of more favorable responses to the challenges in E pigs were found, such as lower serum reactive oxygen metabolite (dROM) concentrations and a smaller area under the curve of dROM after LPS injection. In the Frustration challenge, E pigs showed less standing alert, escape behaviors and other negative behaviors, a tendency for a smaller area under the curve of salivary cortisol and a lower plasma cortisol level at 1 h after the challenge. Aggression did not decrease over mixings in RM pigs and was higher in B pigs than in E pigs. Repeated mixing did not seem to reduce resilience. Contrary to expectations, RM pigs showed a higher relative growth than MM pigs during the experiment, especially in the week of the challenges. Barren RM pigs showed a lower plasma cortisol concentration than barren MM pigs after the LPS challenge, which may suggest that those RM pigs responded less detrimentally than MM pigs. Enriched RM pigs showed a higher level of IgM antibodies binding keyhole limpet hemocyanin (KLH) than enriched MM and barren RM pigs, and RM pigs showed a sharper decline in IgG antibodies binding Bovine Serum Albumin (PC-BSA) over time than MM pigs. Hair cortisol concentrations were not affected by enrichment or mixing. To conclude, enrichment did not enhance the speed of recovery from challenges in pigs, although there were indications of reduced stress. Repeated as opposed to single mixing did not seem to aggravate the negative effects of barren housing on resilience and for some parameters even seemed to reduce the negative effects of barren housing.

Evaluation of the phenotypic and genomic background of variability based on litter size of Large White pigs.

BACKGROUND: The genetic background of trait variability has captured the interest of ecologists and animal breeders because the genes that control it could be involved in buffering various environmental effects. Phenotypic variability of a given trait can be assessed by studying the heterogeneity of the residual variance, and the quantitative trait loci (QTL) that are involved in the control of this variability are described as variance QTL (vQTL). This study focuses on litter size (total number born, TNB) and its variability in a Large White pig population. The variability of TNB was evaluated either using a simple method, i.e. analysis of the log-transformed variance of residuals (LnVar), or the more complex double hierarchical generalized linear model (DHGLM). We also performed a single-SNP (single nucleotide polymorphism) genome-wide association study (GWAS). To our knowledge, this is only the second study that reports vQTL for litter size in pigs and the first one that shows GWAS results when using two methods to evaluate variability of TNB: LnVar and DHGLM. RESULTS: Based on LnVar, three candidate vQTL regions were detected, on Sus scrofa chromosomes (SSC) 1, 7, and 18, which comprised 18 SNPs. Based on the DHGLM, three candidate vQTL regions were detected, i.e. two on SSC7 and one on SSC11, which comprised 32 SNPs. Only one candidate vQTL region overlapped between the two methods, on SSC7, which also contained the most significant SNP. Within this vQTL region, two candidate genes were identified, ADGRF1, which is involved in neurodevelopment of the brain, and ADGRF5, which is involved in the function of the respiratory system and in vascularization. The correlation between estimated breeding values based on the two methods was 0.86. Three-fold cross-validation indicated that DHGLM yielded EBV that were much more accurate and had better prediction of missing observations than LnVar. CONCLUSIONS: The results indicated that the LnVar and DHGLM methods resulted in genetically different traits. Based on their validation, we recommend the use of DHGLM over the simpler method of log-transformed variance of residuals. These conclusions can be useful for future studies on the evaluation of the variability of any trait in any species.

Relationship between indirect genetic effects for growth, environmental enrichment, coping style and sex with the serum metabolome profile of pigs.

Including Indirect Genetic Effects (IGE) in breeding programs to reduce aggression in group housed animals has been proposed. However, the effect of selection for IGE for growth on animal metabolism and physiology is unknown. The purpose of this study was twofold: (1) To investigate the effects of this new breeding method along with two housing (barren and straw), coping style (high and low resisters) and sex (female and castrated males) options on the metabolome profile of pigs. (2) To identify and map biological processes associated with a regrouping test at 9 weeks of age. We used Nuclear Magnetic Resonance to quantify 49 serum metabolites at week 8, 9 and 22. Also, we quantified 3 catecholamines (tyramine, epinephrine, phenylethylamine) and serotonin and three water soluble vitamins (B2, B5 and B7). Overall, no significant differences were observed between negative and positive IGE animals. The magnitude of change (delta) of many metabolites as a response to the regrouping test was significantly affected by IGE, especially that of the amino acids (P < 0.05), being greater in positive IGE pigs. The regrouping test was associated with alteration in glycine, serine and threonine metabolism. In conclusion positive and negative IGE animals respond differently to the regrouping test.

Investigating pig survival in different production phases using genomic models.

Pig survival is an economically important trait with relevant social welfare implications, thus standing out as an important selection criterion for the current pig farming system. We aimed to estimate (co)variance components for survival in different production phases in a crossbred pig population as well as to investigate the benefit of including genomic information through single-step genomic best linear unbiased prediction (ssGBLUP) on the prediction accuracy of survival traits compared with results from traditional BLUP. Individual survival records on, at most, 64,894 crossbred piglets were evaluated under two multi-trait threshold models. The first model included farrowing, lactation, and combined postweaning survival, whereas the second model included nursery and finishing survival. Direct and maternal breeding values were estimated using BLUP and ssGBLUP methods. Furthermore, prediction accuracy, bias, and dispersion were accessed using the linear regression validation method. Direct heritability estimates for survival in all studied phases were low (from 0.02 to 0.08). Survival in preweaning phases (farrowing and lactation) was controlled by the dam and piglet additive genetic effects, although the maternal side was more important. Postweaning phases (nursery, finishing, and the combination of both) showed the same or higher direct heritabilities compared with preweaning phases. The genetic correlations between survival traits within preweaning and postweaning phases were favorable and strong, but correlations between preweaning and postweaning phases were moderate. The prediction accuracy of survival traits was low, although it increased by including genomic information through ssGBLUP compared with the prediction accuracy from BLUP. Direct and maternal breeding values were similarly accurate with BLUP, but direct breeding values benefited more from genomic information. Overall, a slight increase in bias was observed when genomic information was included, whereas dispersion of breeding values was greatly reduced. Combined postweaning survival presented higher direct heritability than in the preweaning phases and the highest prediction accuracy among all evaluated production phases, therefore standing out as a candidate trait for improving survival. Survival is a complex trait with low heritability; however, important genetic gains can still be obtained, especially under a genomic prediction framework.

Unraveling the actual background of second litter syndrome in pigs: based on Large White data.

Second litter syndrome (SLS) in sows is when fertility performance is lower in the second parity than in the first parity. The causes of SLS have been associated with lactation weight loss, premature first insemination, short lactation length, short weaning to insemination interval, season, and farm of farrowing. There is little known about the genetic background of SLS or if it is a real biological problem or just a statistical issue. Thus, we aimed to evaluate risk factors, investigate genetic background of SLS, and estimate the probability of SLS existing due to the statistical properties of the trait. The records of 246 799 litters (total number born, TNB) from 46 218 Large White sows were used. A total of 15 398 sows had SLS. Two traits were defined: first a binominal trait if a sow had SLS or not (biSLS) and second a continuous trait (Range) created by subtracting the total number of piglets born in the first parity (TNB1) from the piglets born in the second parity (TNB2). Lactation length, farm, and season of the farrowing had significant effects on SLS traits when tested as fixed effects in the genetic model. These effects are farm management-related factors. The age at first insemination and weaning to insemination interval were significant only for other reproduction traits (e.g., TNB1, TNB2, litter weight in parity 1 and 2). The heritability of biSLS was 0.05 (on observed scale), whereas heritability of Range was 0.03. To verify the existence of SLS data with records of 50 000 sows and 9 parities was simulated. The simulations showed that the average expected frequency of SLS across all the parities was 0.49 (±0.05) while the observed frequency in the actual data was 0.46 (±0.04). We compared this to SLS frequencies in 67 farms and only 2 farms had more piglets born in the first parity compared to the second. Therefore, on the individual sow level SLS is likely due to statistical properties of the trait, whereas on the farm level SLS is likely due to farm management. Thus, SLS should not be considered an abnormality nor a syndrome if on average the herd litter size in parity 2 is larger than in parity 1.

Applying an association weight matrix in weighted genomic prediction of boar taint compounds.

Biological information regarding markers and gene association may be used to attribute different weights for single nucleotide polymorphism (SNP) in genome-wide selection. Therefore, we aimed to evaluate the predictive ability and the bias of genomic prediction using models that allow SNP weighting in the genomic relationship matrix (G) building, with and without incorporating biological information to obtain the weights. Firstly, we performed a genome-wide association studies (GWAS) in data set containing single- (SL) or a multi-line (ML) pig population for androstenone, skatole and indole levels. Secondly, 1%, 2%, 5%, 10%, 30% and 50% of the markers explaining the highest proportions of the genetic variance for each trait were selected to build gene networks through the association weight matrix (AWM) approach. The number of edges in the network was computed and used to derive weights for G (AWM-WssGBLUP). The single-step GBLUP (ssGBLUP) and weighted ssGBLUP (WssGBLUP) were used as standard scenarios. All scenarios presented predictive abilities different from zero; however, the great overlap in their confidences interval suggests no differences among scenarios. Most of scenarios of based on AWM provide overestimations for skatole in both SL and ML populations. On the other hand, the skatole and indole prediction were no biased in the ssGBLUP (S1) in both SL and ML populations. Most of scenarios based on AWM provide no biased predictions for indole in both SL and ML populations. In summary, using biological information through AWM matrix and gene networks to derive weights for genomic prediction resulted in no increase in predictive ability for boar taint compounds. In addition, this approach increased the number of analyses steps. Thus, we can conclude that ssGBLUP is most appropriate for the analysis of boar taint compounds in comparison with the weighted strategies used in the present work.

Quantifying Individual Response to PRRSV Using Dynamic Indicators of Resilience Based on Activity.

Pigs are faced with various perturbations throughout their lives, some of which are induced by management practices, others by natural causes. Resilience is described as the ability to recover from or cope with a perturbation. Using these data, activity patterns of an individual, as well as deviations from these patterns, can potentially be used to quantify resilience. Dynamic indicators of resilience (DIORs) may measure resilience on a different dimension by calculating variation, autocorrelation and skewness of activity from the absolute activity data. The aim of this study was to investigate the potential of using DIORs of activity, such as average, root mean square error (RMSE), autocorrelation or skewness as indicators of resilience to infection with the Porcine Reproductive and Respiratory Syndrome Virus (PRRSV). For this study, individual activity was obtained from 232 pigs equipped with ear tag accelerometers and inoculated with PRRSV between seven and 9 weeks of age. Clinical scores were assigned to each individual at 13 days post-challenge and used to distinguish between a resilient and non-resilient group. Mortality post-challenge was also recorded. Average, RMSE, autocorrelation and skewness of activity were calculated for the pre- and post-challenge phases, as well as the change in activity level pre- vs. post-challenge (i.e., delta). DIORs pre-challenge were expected to predict resilience to PRRSV in the absence of PRRSV infection, whereas DIORs post-challenge and delta were expected to reflect the effect of the PRRSV challenge. None of the pre-challenge DIORs predicted morbidity or mortality post-challenge. However, a higher RMSE in the 3 days post-challenge and larger change in level and RMSE of activity from pre- to post-challenge tended to increase the probability of clinical signs at day 13 post-infection (poor resilience). A higher skewness post-challenge (tendency) and a larger change in skewness from pre- to post-challenge increased the probability of mortality. A decrease in skewness post-challenge lowered the risk of mortality. The post-challenge DIOR autocorrelation was neither linked to morbidity nor to mortality. In conclusion, results from this study showed that post-challenge DIORs of activity can be used to quantify resilience to PRRSV challenge.

Estimation of litter size variability phenotypes in Large White sows.

The objective of this study was to obtain new phenotypes of phenotypic variability for the total number born (TNB) in pigs using the residual variance of TNB. The analysis was based on 246,799 Large White litter observations provided by Topigs Norsvin. Three animal models were used to obtain estimates of residual variance for TNB: the basic model (BM) containing fixed effects of farm-year and season and random effects of animal and permanent environmental sow, the basic model with an additional fixed effect of parity (BMP) and a random regression model (RRM). The within-individual variance of the residuals was calculated and log-transformed to obtain three new variability traits: LnVarBM, LnVarBMP and LnVarRRM. Then, (co)variance components, heritability, the genetic coefficient of variation at the standard deviation level (GCVSDe ) and genetic correlations between the three LnVar's and between the LnVar's and mean total number born (mTNB) were estimated with uni-, bi- and trivariate models. Results indicated that genetically LnVar's are the same trait and are positively correlated with the mTNB (~0.60). Thus, both traits should be included in breeding programmes to avoid an increase in TNB variability while selecting for increased TNB. Heritability of the LnVar's was estimated at 0.021. The GCVSDe for LnVar's showed that a change of 8% in residual standard deviation of TNB could be obtained per generation. Those results indicate that phenotypic variability of litter size is under genetic control, thus it may be improved by selection.

Genetic correlations between growth performance and carcass traits of purebred and crossbred pigs raised in tropical and temperate climates1.

In pig breeding, selection commonly takes place in purebred (PB) pigs raised mainly in temperate climates (TEMP) under optimal environmental conditions in nucleus farms. However, pork production typically makes use of crossbred (CB) animals raised in nonstandardized commercial farms, which are located not only in TEMP regions but also in tropical and subtropical regions (TROP). Besides the differences in the genetic background of PB and CB, differences in climate conditions, and differences between nucleus and commercial farms can lower the genetic correlation between the performance of PB in the TEMP (PBTEMP) and CB in the TROP (CBTROP). Genetic correlations (rg) between the performance of PB and CB growing-finishing pigs in TROP and TEMP environments have not been reported yet, due to the scarcity of data in both CB and TROP. Therefore, the present study aimed 1) to verify the presence of genotype × environment interaction (G × E) and 2) to estimate the rg for carcass and growth performance traits when PB and 3-way CB pigs are raised in 2 different climatic environments (TROP and TEMP). Phenotypic records of 217,332 PB and 195,978 CB, representing 2 climatic environments: TROP (Brazil) and TEMP (Canada, France, and the Netherlands) were available for this study. The PB population consisted of 2 sire lines, and the CB population consisted of terminal 3-way cross progeny generated by crossing sires from one of the PB sire lines with commercially available 2-way maternal sow crosses. G × E appears to be present for average daily gain, protein deposition, and muscle depth given the rg estimates between PB in both environments (0.64 to 0.79). With the presence of G × E, phenotypes should be collected in TROP when the objective is to improve the performance of CB in the TROP. Also, based on the rg estimates between PBTEMP and CBTROP (0.22 to 0.25), and on the expected responses to selection, selecting based only on the performance of PBTEMP would give limited genetic progress in the CBTROP. The rg estimates between PBTROP and CBTROP are high (0.80 to 0.99), suggesting that combined crossbred-purebred selection schemes would probably not be necessary to increase genetic progress in CBTROP. However, the calculated responses to selection show that when the objective is the improvement of CBTROP, direct selection based on the performance of CBTROP has the potential to lead to the higher genetic progress compared with indirect selection on the performance of PBTROP.

Selecting for changes in average "parity curve" pattern of litter size in Large White pigs.

This study aimed to analyse genetic background of variation in reproductive performance between parities of a sow and to investigate selection strategies to change the "parity curve". Total number born (TNB) recorded in Large White sows was provided by Topigs Norsvin. Analysis with basic (BM) and random regression (RRM) models was done in ASReml 4.1. The BM included only a fixed "parity curve", while RRM included 3rd order polynomials for additive genetic and permanent sow effects. Parameters from RRM were used in simulations in SelAction 2.1. Based on Akaike information criterion, RRM was a better model for TNB data. Genetic variance and heritability estimates of TNB from BM and RRM were increasing with parity from parity 2. Genetically, parity 1 is the most different from parities 7 to 10, whereas most similar to parities 2 and 3. This indicates presence of genetic variation to change the "parity curve". Based on simulations, the selection to increase litter size in parity 1 only increases TNB in all parities, but does not change the observed shape of "parity curve", whereas selection for increased TNB in parity 1 and reduced TNB in parity 5 decreases differences between parities, but also reduces overall TNB in all parities. Changing the "parity curve" will be difficult as the genetic and phenotypic relationships between the parities are hard to overcome even when selecting for one parity.

Weighted single-step GWAS and gene network analysis reveal new candidate genes for semen traits in pigs.

BACKGROUND: In recent years, there has been increased interest in the study of the molecular processes that affect semen traits. In this study, our aim was to identify quantitative trait loci (QTL) regions associated with four semen traits (motility, progressive motility, number of sperm cells per ejaculate and total morphological defects) in two commercial pig lines (L1: Large White type and L2: Landrace type). Since the number of animals with both phenotypes and genotypes was relatively small in our dataset, we conducted a weighted single-step genome-wide association study, which also allows unequal variances for single nucleotide polymorphisms. In addition, our aim was also to identify candidate genes within QTL regions that explained the highest proportions of genetic variance. Subsequently, we performed gene network analyses to investigate the biological processes shared by genes that were identified for the same semen traits across lines. RESULTS: We identified QTL regions that explained up to 10.8% of the genetic variance of the semen traits on 12 chromosomes in L1 and 11 chromosomes in L2. Sixteen QTL regions in L1 and six QTL regions in L2 were associated with two or more traits within the population. Candidate genes SCN8A, PTGS2, PLA2G4A, DNAI2, IQCG and LOC102167830 were identified in L1 and NME5, AZIN2, SPATA7, METTL3 and HPGDS in L2. No regions overlapped between these two lines. However, the gene network analysis for progressive motility revealed two genes in L1 (PLA2G4A and PTGS2) and one gene in L2 (HPGDS) that were involved in two biological processes i.e. eicosanoid biosynthesis and arachidonic acid metabolism. PTGS2 and HPGDS were also involved in the cyclooxygenase pathway. CONCLUSIONS: We identified several QTL regions associated with semen traits in two pig lines, which confirms the assumption of a complex genetic determinism for these traits. A large part of the genetic variance of the semen traits under study was explained by different genes in the two evaluated lines. Nevertheless, the gene network analysis revealed candidate genes that are involved in shared biological pathways that occur in mammalian testes, in both lines.