Sex and fetal genome influence gene expression in pig endometrium at the end of gestation

BACKGROUND

A fine balance of feto-maternal resource allocation is required to support pregnancy, which depends on interactions between maternal and fetal genetic potential, maternal nutrition and environment, endometrial and placental functions. In particular, some imprinted genes have a role in regulating maternal-fetal nutrient exchange, but few have been documented in the endometrium. The aim of this study is to describe the expression of 42 genes, with parental expression, in the endometrium comparing two extreme breeds: Large White (LW); Meishan (MS) with contrasting neonatal mortality and maturity at two days of gestation (D90-D110). We investigated their potential contribution to fetal maturation exploring genes-fetal phenotypes relationships. Last, we hypothesized that the fetal genome and sex influence their endometrial expression. For this purpose, pure and reciprocally crossbred fetuses were produced using LW and MS breeds. Thus, in the same uterus, endometrial samples were associated with its purebred or crossbred fetuses.

RESULTS

Among the 22 differentially expressed genes (DEGs), 14 DEGs were differentially regulated between the two days of gestation. More gestational changes were described in LW (11 DEGs) than in MS (2 DEGs). Nine DEGs were differentially regulated between the two extreme breeds, highlighting differences in the regulation of endometrial angiogenesis, nutrient transport and energy metabolism. We identified DEGs that showed high correlations with indicators of fetal maturation, such as ponderal index at D90 and fetal blood fructose level and placental weight at D110. We pointed out for the first time the influence of fetal sex and genome on endometrial expression at D90, highlighting AMPD3, CITED1 and H19 genes. We demonstrated that fetal sex affects the expression of five imprinted genes in LW endometrium. Fetal genome influenced the expression of four genes in LW endometrium but not in MS endometrium. Interestingly, both fetal sex and fetal genome interact to influence endometrial gene expression.

CONCLUSIONS

These data provide evidence for some sexual dimorphism in the pregnant endometrium and for the contribution of the fetal genome to feto-maternal interactions at the end of gestation. They suggest that the paternal genome may contribute significantly to piglet survival, especially in crossbreeding production systems.

Analysis of image-based sow activity patterns reveals several associations with piglet survival and early growth

An activity pattern describes variations in activities over time. The objectives of this study are to automatically predict sow activity from computer vision over 11 days peripartum and estimate how sow behavior influences piglet's performance during early lactation. The analysis of video images used the convolutional neural network (CNN) YOLO for sow detection and posture classification of 21 Large White and 22 Meishan primiparous sows housed in individual farrowing pens. A longitudinal analysis and a clustering method were combined to identify groups of sows with a similar activity pattern. Traits under study are as follows: (i) the distribution of time spent daily in different postures and (ii) different activities while standing. Six postures were included along with three classes of standing activities, i.e., eating, drinking, and other, which can be in motion or not and root-pawing or not. They correspond to a postural budget and a standing-activity budget. Groups of sows with similar changes in their budget over the period (D-3 to D-1; D0 and D1-D7) were identified with the k-means clustering method. Next, behavioral traits (time spent daily in each posture, frequency of postural changes) were used as explanatory variables in the Cox proportional hazards model for survival and in the linear model for growth. Piglet survival was influenced by sow behavior on D-1 and during the period D1-D7. Piglets born from sows that were standing and doing an activity other than drinking and eating on D-1 had a 26% lower risk of dying than other piglets. Those born from sows that changed posture more frequently on D1-D7 had a 44% lower risk of dying. The number of postural changes, which illustrate sow restlessness, influenced piglet growth in the three periods. The average daily gain of piglets born from sows that were more restless on D1-D7 and that changed posture more frequently to hide their udder on D0 decreased by 22 and 45 g/d, respectively. Conversely, those born from sows that changed posture more frequently to hide their udder during the period of D1-D7 grew faster (+71 g/d) than the other piglets. Sow restlessness at different time periods influenced piglet performance.

Behavior Comparison During Chronic Heat Stress in Large White and Creole Pigs Using Image-Analysis

Behavior is a good indicator of animal welfare, especially in challenging environments. However, few studies have investigated how pig behavior changes during heat stress. The current study is a proof-of-concept using Convolutional Neural Network (CNN) models to monitor pig behavior in order to investigate the differences in behavioral response to heat stress of two contrasted breeds: Large White (LW), selected for high performance, and Creole (CR), adapted to tropical conditions. A total of 6 slaughter pigs (3 CR and 3 LW; 22 weeks of age) were monitored from 8:30 to 17:30 during 54 days. Two CNN architectures were used to detect the animal (Yolo v2) and to estimate animal's posture (GoogleNet). Pig postures estimated by the neural network showed that pigs spent more time lying on their side when temperature increased. When comparing the two breeds, as temperature increases, CR pigs spent more time lying on their side than LW pigs, suggesting that they use this posture to increase thermoregulation and dissipate heat more efficiently. This study demonstrates that neural network models are an efficient tool to monitor animal behavior in an automated way, which could be particularly relevant to characterize breed adaptation to challenging environments.

Thermoregulation at birth differs between piglets from two genetic lines divergent for residual feed intake

Thermoregulation is essential to piglets' neonatal survival. This study used infrared thermography (IRT) to assess thermoregulation abilities of piglets from two lines divergent for residual feed intake (RFI). At birth, morphology (weight, length, width and circumference), vigour (respiration, mobility and vocalisation), and rectal temperature were recorded from piglets of the 11th generation of the low RFI (LRFI, more efficient; n = 34) and the high RFI (HRFI, less efficient; n = 28) lines. Infrared thermography images were taken at 8, 15, 30 and 60 min post partum. Temperatures of the ear base and tip, and of the back (i.e. shoulders to rumps) were extracted (Thermacam Researcher Pro 2.0) and analysed with linear mixed models (SAS 9.4). Piglets had different average hourly weight gain (HRFI = 7.1 ± 1.3 g/h, LRFI = 3.6 ± 1.3 g/h; P < 0,001) but did not differ in morphology or vigour. All temperatures increased overtime. At birth, piglets' rectal temperature was correlated with the initial temperature of the ear base and the maximum back temperature (0.37 and 0.33, respectively; P < 0.05). High residual feed intake piglets had lower ear tip temperatures than LRFI piglets at 15 (24.7 ± 0.37 °C vs. 26.3 ± 0.36 °C, respectively; F1, 63.5 = 9.11, P < 0.005) and 30 min post partum (26.2 ± 0.47 °C vs. 27.6 ± 0.44 °C, respectively; F1, 66.9 = 4.52, P < 0.05). Moreover, thermal pattern of the ear tip differed between the two genetic lines. In conclusion, IRT allowed non-invasive assessment of piglets' thermoregulation abilities and indicated an influence of genetic selection for RFI on neonatal thermoregulation abilities.

Prospects for the Analysis and Reduction of Damaging Behaviour in Group-Housed Livestock, With Application to Pig Breeding

Innovations in the breeding and management of pigs are needed to improve the performance and welfare of animals raised in social groups, and in particular to minimise biting and damage to group mates. Depending on the context, social interactions between pigs can be frequent or infrequent, aggressive, or non-aggressive. Injuries or emotional distress may follow. The behaviours leading to damage to conspecifics include progeny savaging, tail, ear or vulva biting, and excessive aggression. In combination with changes in husbandry practices designed to improve living conditions, refined methods of genetic selection may be a solution reducing these behaviours. Knowledge gaps relating to lack of data and limits in statistical analyses have been identified. The originality of this paper lies in its proposal of several statistical methods for common use in analysing and predicting unwanted behaviours, and for genetic use in the breeding context. We focus on models of interaction reflecting the identity and behaviour of group mates which can be applied directly to damaging traits, social network analysis to define new and more integrative traits, and capture-recapture analysis to replace missing data by estimating the probability of behaviours. We provide the rationale for each method and suggest they should be combined for a more accurate estimation of the variation underlying damaging behaviours.

Intergenerational Transmission of Characters Through Genetics, Epigenetics, Microbiota, and Learning in Livestock

Evolutionary biologists studying wild species have demonstrated that genetic and non-genetic sources of information are inherited across generations and are therefore responsible for phenotypic resemblance between relatives. Although it has been postulated that non-genetic sources of inheritance are important in natural selection, they are not taken into account for livestock selection that is based on genetic inheritance only. According to the natural selection theory, the contribution of non-genetic inheritance may be significant for the transmission of characters. If this theory is confirmed in livestock, not considering non-genetic means of transmission in selection schemes might prevent achieving maximum progress in the livestock populations being selected. The present discussion paper reviews the different mechanisms of genetic and non-genetic inheritance reported in the literature as occurring in livestock species. Non-genetic sources of inheritance comprise information transmitted via physical means, such as epigenetic and microbiota inheritance, and those transmitted via learning mechanisms: behavioral, cultural and ecological inheritance. In the first part of this paper we review the evidence that suggests that both genetic and non-genetic information contribute to inheritance in livestock (i.e. transmitted from one generation to the next and causing phenotypic differences between individuals) and discuss how the environment may influence non-genetic inherited factors. Then, in a second step, we consider methods for favoring the transmission of non-genetic inherited factors by estimating and selecting animals on their extended transmissible value and/or introducing favorable non-genetic factors via the animals’ environment.

Proteomic analysis of adipose tissue during the last weeks of gestation in pure and crossbred Large White or Meishan fetuses gestated by sows of either breed

Background

The degree of adipose tissue development at birth may influence neonatal survival and subsequent health outcomes. Despite their lower birth weights, piglets from Meishan sows (a fat breed with excellent maternal ability) have a higher survival rate than piglets from Large White sows (a lean breed). To identify the main pathways involved in subcutaneous adipose tissue maturation during the last month of gestation, we compared the proteome and the expression levels of some genes at d 90 and d 110 of gestation in purebred and crossbred Large White or Meishan fetuses gestated by sows of either breed.

Results

A total of 52 proteins in fetal subcutaneous adipose tissue were identified as differentially expressed over the course of gestation. Many proteins involved in energy metabolism were more abundant, whereas some proteins participating in cytoskeleton organization were reduced in abundance on d 110 compared with d 90. Irrespective of age, 24 proteins differed in abundance between fetal genotypes, and an interaction effect between fetal age and genotype was observed for 13 proteins. The abundance levels of proteins known to be responsive to nutrient levels such as aldolase and fatty acid binding proteins, as well as the expression levels of FASN, a key lipogenic enzyme, and MLXIPL, a pivotal transcriptional mediator of glucose-related stimulation of lipogenic genes, were elevated in the adipose tissue of pure and crossbred fetuses from Meishan sows. These data suggested that the adipose tissue of these fetuses had superior metabolic functionality, whatever their paternal genes. Conversely, proteins participating in redox homeostasis and apoptotic cell clearance had a lower abundance in Meishan than in Large White fetuses. Time-course differences in adipose tissue protein abundance were revealed between fetal genotypes for a few secreted proteins participating in responses to organic substances, such as alpha-2-HS-glycoprotein, transferrin and albumin.

Conclusions

These results underline the importance of not only fetal age but also maternal intrauterine environment in the regulation of several proteins in subcutaneous adipose tissue. These proteins may be used to estimate the maturity grade of piglet neonates.

Electronic supplementary material

The online version of this article (10.1186/s40104-018-0244-2) contains supplementary material, which is available to authorized users.

Integrated Analysis of Proteomic and Transcriptomic Data Highlights Late Fetal Muscle Maturation Process

In pigs, the perinatal period is the most critical time for survival. Piglet maturation, which occurs at the end of gestation, is an important determinant of early survival. Skeletal muscle plays a key role in adaptation to extra-uterine life, e.g. motor function and thermoregulation. Progeny from two breeds with extreme neonatal mortality rates were analyzed at 90 and 110 days of gestation (dg). The Large White breed is a highly selected breed for lean growth and exhibits a high rate of neonatal mortality, whereas the Meishan breed is fatter and more robust and has a low neonatal mortality. Our aim was to identify molecular signatures underlying late fetal longissimus muscle development. First, integrated analysis was used to explore relationships between co-expression network models built from a proteomic data set (bi-dimensional electrophoresis) and biological phenotypes. Second, correlations with a transcriptomic data set (microarrays) were investigated to combine different layers of expression with a focus on transcriptional regulation. Muscle glycogen content and myosin heavy chain polymorphism were good descriptors of muscle maturity and were used for further data integration analysis. Using 89 identified unique proteins, network inference, correlation with biological phenotypes and functional enrichment revealed that mitochondrial oxidative metabolism was a key determinant of neonatal muscle maturity. Some proteins, including ATP5A1 and CKMT2, were important nodes in the network related to muscle metabolism. Transcriptomic data suggest that overexpression of mitochondrial PCK2 was involved in the greater glycogen content of Meishan fetuses at 110 dg. GPD1, an enzyme involved in the mitochondrial oxidation of cytosolic NADH, was overexpressed in Meishan. Thirty-one proteins exhibited a positive correlation between mRNA and protein levels in both extreme fetal genotypes, suggesting transcriptional regulation. Gene ontology enrichment and Ingenuity analyses identified PPARGC1A and ESR1 as possible transcriptional factors positively involved in late fetal muscle maturation.

Comparing the intestinal transcriptome of Meishan and Large White piglets during late fetal development reveals genes involved in glucose and lipid metabolism and immunity as valuable clues of intestinal maturity

Background

Maturity of intestinal functions is critical for neonatal health and survival, but comprehensive description of mechanisms underlying intestinal maturation that occur during late gestation still remain poorly characterized. The aim of this study was to investigate biological processes specifically involved in intestinal maturation by comparing fetal jejunal transcriptomes of two representative porcine breeds (Large White, LW; Meishan, MS) with contrasting neonatal vitality and maturity, at two key time points during late gestation (gestational days 90 and 110). MS and LW sows inseminated with mixed semen (from breed LW and MS) gave birth to both purebred and crossbred fetuses. We hypothesized that part of the differences in neonatal maturity between the two breeds results from distinct developmental profiles of the fetal intestine during late gestation. Reciprocal crossed fetuses were used to analyze the effect of parental genome. Transcriptomic data and 23 phenotypic variables known to be associated with maturity trait were integrated using multivariate analysis with expectation of identifying relevant genes-phenotypic variable relationships involved in intestinal maturation.

Results

A moderate maternal genotype effect, but no paternal genotype effect, was observed on offspring intestinal maturation. Four hundred and four differentially expressed probes, corresponding to 274 differentially expressed genes (DEGs), more specifically involved in the maturation process were further studied. In day 110-MS fetuses, Ingenuity® functional enrichment analysis revealed that 46% of DEGs were involved in glucose and lipid metabolism, cell proliferation, vasculogenesis and hormone synthesis compared to day 90-MS fetuses. Expression of genes involved in immune pathways including phagocytosis, inflammation and defense processes was changed in day 110-LW compared to day 90-LW fetuses (corresponding to 13% of DEGs). The transcriptional regulator PPARGC1A was predicted to be an important regulator of differentially expressed genes in MS. Fetal blood fructose level, intestinal lactase activity and villous height were the best predicted phenotypic variables with probes mostly involved in lipid metabolism, carbohydrate metabolism and cellular movement biological pathways.

Conclusions

Collectively, our findings indicate that the neonatal maturity of pig intestine may rely on functional development of glucose and lipid metabolisms, immune phagocyte differentiation and inflammatory pathways. This process may partially be governed by PPARGC1A.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-4001-2) contains supplementary material, which is available to authorized users.

Interaction of direct and social genetic effects with feeding regime in growing rabbits

Background

Most rabbit production farms apply feed restriction at fattening because of its protective effect against digestive diseases that affect growing rabbits. However, it leads to competitive behaviour between cage mates, which is not observed when animals are fed ad libitum. Our aim was to estimate the contribution of direct (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d$$\end{document}d) and social (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s$$\end{document}s) genetic effects (also known as indirect genetic effects) to total heritable variance of average daily gain (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{ADG}}$$\end{document}ADG) in rabbits on different feeding regimens (FR), and the magnitude of the interaction between genotype and FR (G × FR).

Methods

A total of 6264 contemporary kits were housed in cages of eight individuals and raised on full (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F$$\end{document}F) or restricted (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R$$\end{document}R) feeding to 75% of the ad libitum intake. A Bayesian analysis of weekly records of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{ADG}}$$\end{document}ADG (from 32 to 60 days of age) in rabbits on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F$$\end{document}F and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R$$\end{document}R was performed with a two-trait model including \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d$$\end{document}d and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s$$\end{document}s.

Results

The ratio between total heritable variance and phenotypic variance (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T^{2}$$\end{document}T2) was low (<0.10) and did not differ significantly between FR. However, the ratio between \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$h^{2}$$\end{document}h2 (i.e. variance of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d$$\end{document}d relative to phenotypic variance) and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T^{2}$$\end{document}T2 was ~0.52 and 0.86 for animals on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R$$\end{document}R and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F$$\end{document}F, respectively, thus \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s$$\end{document}s contributed more to the heritable variance of animals on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R$$\end{document}R than on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F$$\end{document}F. Feeding regimen also affected the sign and magnitude of the correlation between \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d$$\end{document}d and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s$$\end{document}s, i.e. −0.5 and ~0 for animals on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R$$\end{document}R and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F$$\end{document}F, respectively. The posterior mean (posterior sd) of the correlation between estimated total breeding values (ETBV) of animals on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R$$\end{document}R and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F$$\end{document}F was 0.26 (0.20), indicating very strong G × FR interactions. The correlations between \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d$$\end{document}d and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s$$\end{document}s in rabbits on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F$$\end{document}F and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R$$\end{document}R ranged from −0.47 (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d$$\end{document}d on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F$$\end{document}F and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s$$\end{document}s on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R$$\end{document}R) to 0.64.

Conclusions

Our results suggest that selection of rabbits for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{ADG}}$$\end{document}ADG under \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F$$\end{document}F may completely fail to improve \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{ADG}}$$\end{document}ADG in rabbits on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R$$\end{document}R. Social genetic effects contribute substantially to ETBV of rabbits on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R$$\end{document}R but not on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F$$\end{document}F. Selection for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{ADG}}$$\end{document}ADG should be performed under production conditions regarding the FR, by accounting for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s$$\end{document}s if the amount of food is limited.

Multiple-trait structured antedependence model to study the relationship between litter size and birth weight in pigs and rabbits

BACKGROUND

Some genetic studies need to take into account correlations between traits that are repeatedly measured over time. Multiple-trait random regression models are commonly used to analyze repeated traits but suffer from several major drawbacks. In the present study, we developed a multiple-trait extension of the structured antedependence model (SAD) to overcome this issue and validated its usefulness by modeling the association between litter size (LS) and average birth weight (ABW) over parities in pigs and rabbits.

METHODS

The single-trait SAD model assumes that a random effect at time [Formula: see text] can be explained by the previous values of the random effect (i.e. at previous times). The proposed multiple-trait extension of the SAD model consists in adding a cross-antedependence parameter to the single-trait SAD model. This model can be easily fitted using ASReml and the OWN Fortran program that we have developed. In comparison with the random regression model, we used our multiple-trait SAD model to analyze the LS and ABW of 4345 litters from 1817 Large White sows and 8706 litters from 2286 L-1777 does over a maximum of five successive parities.

RESULTS

For both species, the multiple-trait SAD fitted the data better than the random regression model. The difference between AIC of the two models (AIC_random regression-AIC_SAD) were equal to 7 and 227 for pigs and rabbits, respectively. A similar pattern of heritability and correlation estimates was obtained for both species. Heritabilities were lower for LS (ranging from 0.09 to 0.29) than for ABW (ranging from 0.23 to 0.39). The general trend was a decrease of the genetic correlation for a given trait between more distant parities. Estimates of genetic correlations between LS and ABW were negative and ranged from -0.03 to -0.52 across parities. No correlation was observed between the permanent environmental effects, except between the permanent environmental effects of LS and ABW of the same parity, for which the estimate of the correlation was strongly negative (ranging from -0.57 to -0.67).

CONCLUSIONS

We demonstrated that application of our multiple-trait SAD model is feasible for studying several traits with repeated measurements and showed that it provided a better fit to the data than the random regression model.

Impact of selection for residual feed intake on production traits and behavior of mule ducks

A divergent selection experiment of Muscovy sires based on the residual feed intake (RFI) of their male mule progeny was initiated in 2009. Using electronic feeders, the aim of this study was to establish whether 3 generations of selection for RFI had an impact on feeding behavior traits and general behavior, and to examine its effect on liver and meat quality. Eighty mule ducks, issued from 8 Muscovy drakes per line with extreme RFI, were tested in a pen equipped with 4 electronic feeders. Feeding behaviors were recorded from 3 to 7 wk after hatching under ad libitum feeding conditions. Then animals were prepared for overfeeding with a 3-week period of restricted feeding, and overfed during 12 d before slaughter. The RFI was significantly lower in the low RFI line than in the high RFI line (−5.4 g/d, P = 0.0005) and daily feed intake was reduced both over the entire test period (−5 g/d, P = 0.049) and on a weekly basis (P = 0.006). Weekly and total feed conversion ratios were also significantly lower (−0.08, P = 0.03 and −0.06, P = 0.01, respectively). Low RFI ducks had more frequent meals, spent as much time eating as high RFI ducks, and their feeding rate was lower when analyzed at the wk level only. Additionally no significant correlation between feed efficiency and feeding behavior traits was evidenced, indicating only limited relationships between RFI and feeding patterns. Some differences in behavioral responses to stressors (open field test combined with a test measuring the response to human presence) suggested that a lower RFI is associated with less fearfulness. Selection for RFI had no effect on liver weight and quality and a slightly deleterious impact on meat quality (decreased drip loss and L*). Finally, low RFI animals had higher body weights after restricted feeding from wk 10 to wk 12 and after overfeeding than high RFI ducks. This suggests that selection for reduced RFI until 7 wk of age increases the feed efficiency up to slaughter.

Genetic trends in maternal and neonatal behaviors and their association with perinatal survival in French Large White swine

Genetic trends in maternal abilities were studied in French Large White sows. Two lines representing old-type and modern-type pigs were obtained by inseminating modern sows with semen from boars born in 1977 or 1998. Successive generations were produced by inter-se mating. The maternal performance of sows from the second generation was compared in farrowing crates. Video analysis was performed for the 1st h after the onset of 43 and 36 farrowing events, and for the 6 first hours for 23 and 21 events, in old-type and modern-type sows, respectively. Genetic trends were estimated as twice the difference in estimates between the 2 lines. The contribution of behavior to the probability of stillbirth and piglet death in the first 2 days was estimated as the percentage of deviance reduction (DR) due to the addition of behavior traits as factors in the mortality model. Sow activity decreased strongly from the 1st to the 2nd h in both lines (P < 0.001). In the first 6 h, old-type sows sat (1st parity), stood (2nd parity) and rooted (both parities) for longer than modern-type sows, which were less active, especially in 2nd parity. In modern-type sows, stillbirth was associated positively with lying laterally in the first 6 h (4.6% DR) and negatively in the 1st h (9.1% DR). First-parity old-type sows were more attentive to piglets (P = 0.003) than modern-type sows which responded more to nose contacts at 2nd parity (P = 0.01). Maternal reactivity of modern-type sows was associated with a higher risk of piglet death (4.6% DR). Respiratory distress at birth tended to be higher in modern-type piglets than in old-type piglets (P < 0.10) and was associated with a higher risk of piglet death in both lines (2.7–3.1% DR). Mobility at birth was lower in modern-type than old-type piglets (P < 0.0001). Genetic trends show that sow and piglet behaviors at farrowing have changed. Our results indicate reduced welfare in parturient modern-type sows and their newborn piglets.

Genetic parameters for litter size, piglet growth and sow's early growth and body composition in the Chinese-European line Tai Zumu

Genetics of piglet growth in association with sow's early growth and body composition were estimated in the Tai Zumu line. Piglet and sow's litter growth traits were calculated from individual weights collected at birth and at 3 weeks of age. Sow's litter traits included the number of piglets born alive (NBA), the mean piglet weight (MW) and the standard deviation of weights within the litter (SDW). Sow's early growth was measured by the age at 100 kg (A100), and body composition included backfat thickness (BF100). A main objective of this study was to estimate separately the direct genetic effect (d) and the maternal genetic effect (m) on piglet weight and daily weight gain during lactation. Variance components were estimated using the restricted maximum likelihood methodology based on animal models. The heritability estimates were 0.19 for NBA, 0.15 and 0.26 for SDW and MW at 3 weeks and 0.42 and 0.70 for A100 and BF100. The NBA was almost independent from SDW. Conversely, the A100 and BF100 were correlated unfavourably with SDW (rg <-0.24, SE<0.12). A stronger selection for litter size should have little effect on litter homogeneity in weights. Selection for lean growth rate tends to favour heterogeneity in weights. The direct effect on piglet weight at birth and daily weight gain accounted for 12% (h(²) (d) = 0.02) and 50% (h(²) (d) = 0.11) of the genetic variance, respectively. The association between d and m for piglet weight was not different from zero at birth (rg = 0.19, SE = 0.27), but a strong antagonism between d and m for daily weight gain from birth to 3 weeks was found (rg = -0.41, SE = 0.17). Substantial direct and maternal genetic effects influenced piglet growth until weaning in opposite way.

Muscle transcriptomic investigation of late fetal development identifies candidate genes for piglet maturity

Background

In pigs, the perinatal period is the most critical time for survival. Piglet maturation, which occurs at the end of gestation, leads to a state of full development after birth. Therefore, maturity is an important determinant of early survival. Skeletal muscle plays a key role in adaptation to extra-uterine life, e.g. glycogen storage and thermoregulation. In this study, we performed microarray analysis to identify the genes and biological processes involved in piglet muscle maturity. Progeny from two breeds with extreme muscle maturity phenotypes were analyzed at two time points during gestation (gestational days 90 and 110). The Large White (LW) breed is a selected breed with an increased rate of mortality at birth, whereas the Meishan (MS) breed produces piglets with extremely low mortality at birth. The impact of the parental genome was analyzed with reciprocal crossed fetuses.

Results

Microarray analysis identified 12,326 differentially expressed probes for gestational age and genotype. Such a high number reflects an important transcriptomic change that occurs between 90 and 110 days of gestation. 2,000 probes, corresponding to 1,120 unique annotated genes, involved more particularly in the maturation process were further studied. Functional enrichment and graph inference studies underlined genes involved in muscular development around 90 days of gestation, and genes involved in metabolic functions, such as gluconeogenesis, around 110 days of gestation. Moreover, a difference in the expression of key genes, e.g. PCK2, LDHA or PGK1, was detected between MS and LW just before birth. Reciprocal crossing analysis resulted in the identification of 472 genes with an expression preferentially regulated by one parental genome. Most of these genes (366) were regulated by the paternal genome. Among these paternally regulated genes, some known imprinted genes, such as MAGEL2 or IGF2, were identified and could have a key role in the maturation process.

Conclusion

These results reveal the biological mechanisms that regulate muscle maturity in piglets. Maturity is also under the conflicting regulation of the parental genomes. Crucial genes, which could explain the biological differences in maturity observed between LW and MS breeds, were identified. These genes could be excellent candidates for a key role in the maturity.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-797) contains supplementary material, which is available to authorized users.

Genetic associations between behavioral traits and direct-social effects of growth rate in pigs

This study examined the behavioral consequences of selecting pigs using a social genetic model for growth. Calculations enable each member of a group of pigs to be given a direct breeding value (DBV) and a social breeding value (SBV), which can be summarized into a total breeding value (TBV) for growth. Selection for growth TBV could affect animal behavior because social effects account for within-group interactions. Data were recorded from 96 groups of Yorkshire and Yorkshire × Landrace pigs in a nucleus herd. Each group contained 15 pigs fed ad libitum from 2 feeders; the space allowance was 0.85 m2/pig. Average daily gain was quantified from 35 to 100 kg of BW. Fighting and bullying activity at mixing (period 1), lying frequency 3 wk after mixing (period 2), and counts of skin lesions in periods 1 and 2 were recorded. The DBV for these traits were estimated with a classic animal model. We simulated different correlations between the direct genetic effect and the social genetic effect on growth rate (r(DS)), 2 components that respectively determine a pig's genetic capacity to grow and its genetic influence on growth of group mates: r(DS) was successively assumed to be 0 and ±0.12, ±0.20, ±0.29, and ±0.58. Finally, the correlations between DBV, SBV, and TBV for ADG, as well as the DBV for behavior and skin lesions, were calculated and tested for a level of significance at P < 0.05. The gradient from negative to positive values of r(DS) refers to a progressive path running from genetic antagonism to genetic mutualism for growth. If rDS in the population truly ranged between -0.58 and -0.20, correlations for TBV for ADG with DBV for fighting and bullying progressively increased with rDS. Consequently, if rDS was low (between -0.12 and +0.12) or positive (>+0.12), pigs with high TBV for ADG had higher DBV for bullying other pigs in the group and for fighting than pigs with lower TBV for ADG. Pigs with high TBV for ADG did not differ from other pigs in their DBV for lesions to the anterior part of the body, but they had a lower DBV for posterior lesions, whereas in period 2, they had higher DBV for posterior lesions and lower DBV for lying. Under genetic mutualism for growth and in housing conditions similar to those in the present study, selection for growth TBV would promote the rapid establishment of the dominance relationships, with more aggressive contests among group mates at mixing. Pigs would subsequently be more active but, judging by skin lesions, less willing to fight in a more stable social situation.

Genetics of growth in piglets and the association with homogeneity of body weight within litters

The objective of this study was to examine the genetic basis of homogeneity in piglets and the genetic correlations with litter size and growth during lactation. Genetic parameters for variation in piglet BW within litters at birth and at 3 wk of age, and in the BW of individual piglets at 3 wk (BW3) were estimated from the Norwegian Landrace nucleus population. Data on BW3 were collected from 146,572 piglets from 14,045 litters in 58 herds. Body weight at birth and at 3 wk of age was recorded for 13,318 piglets from 5 nucleus herds. Litter data were evaluated using multivariate trait models. The heritability estimates for the SD of BW at birth and at 3 wk (SDBW3) were in agreement with the literature (0.10 and 0.08, respectively). The genetic correlation for the number of piglets born alive and the mean BW at 3 wk was negative (-0.40 +/- 0.07), and the correlation of number of piglets born alive with SDBW3 was close to zero (-0.03 +/- 0.11). The genetic correlation between the SD of BW at birth and SDBW3 was moderate (0.51 +/- 0.31). The mean BW at birth was genetically correlated with mean BW at 3 wk (0.59 +/- 0.16) but was independent of SDBW3 (0.08 +/- 0.27). The estimates of direct and maternal heritability for BW3 were 0.03 and 0.07, respectively, and the genetic correlation between the 2 components was negative (-0.43 +/- 0.10). The genetic correlation of SDBW3 with the maternal effect on BW3 was positive and strong (0.66 +/- 0.08), whereas a negative correlation was found with the direct effect on BW3 (-0.18 +/- 0.14). These results suggest that it is possible to select for mean BW at birth without an increase in within-litter heterogeneity at 3 wk of age. A more efficient strategy would be to consider both the direct and the maternal effects on BW3 in the genetic evaluation, together with SDBW3. Thus, it is possible to avoid the increase in within-litter heterogeneity that would occur as a result of selection performed at 3 wk on a litter trait such as mean BW.

Correlated responses of pre- and postweaning growth and backfat thickness to six generations of selection for ovulation rate or prenatal survival in French Large White pigs

Correlated effects of selection for components of litter size on growth and backfat thickness were estimated using data from 3 pig lines derived from the same base population of Large White. Two lines were selected for 6 generations on either high ovulation rate at puberty (OR) or high prenatal survival corrected for ovulation rate in the first 2 parities (PS). The third line was an unselected control (C). Genetic parameters for individual piglet BW at birth (IWB); at 3 wk of age (IW3W); and at weaning (IWW); ADG from birth to weaning (ADGBW), from weaning to 10 wk of age (ADGPW), and from 25 to 90 kg of BW (ADGT); and age (AGET) and average backfat thickness (ABT) at 90 kg of BW were estimated using REML methodology applied to a multivariate animal model. In addition to fixed effects, the model included the common environment of birth litter, as well as direct and maternal additive genetic effects as random effects. Genetic trends were estimated by computing differences between OR or PS and C lines at each generation using both least squares (LS) and mixed model (MM) methodology. Average genetic trends for direct and maternal effects were computed by regressing line differences on generation number. Estimates of direct and maternal heritabilities were, respectively, 0.10, 0.12, 0.20, 0.24, and 0.41, and 0.17, 0.33, 0.32, 0.41, and 0.21 (SE = 0.03 to 0.04) for IWB, IW3W, IWW, ADGBW, and ADGPW. Genetic correlations between direct and maternal effects were moderately negative for IWB (-0.21 +/- 0.18), but larger for the 4 other traits (-0.59 to -0.74). Maternal effects were nonsignificant and were removed from the final analyses of ADGT, AGET, and ABT. Direct heritability estimates were 0.34, 0.46, and 0.21 (SE = 0.03 to 0.05) for ADGT, AGET, and ABT, respectively. Direct and maternal genetic correlations of OR with performance traits were nonsignificant, with the exception of maternal correlations with IWB (-0.28 +/- 0.13) and ADGPW (0.23 +/- 0.11) and direct correlation with AGET (-0.23 +/- 0.09). Prenatal survival also had low direct but moderate to strong maternal genetic correlations (-0.34 to -0.65) with performance traits. The only significant genetic trends were a negative maternal trend for IBW in the OR line and favorable direct trends for postweaning growth (ADGT and AGET) in both lines. Selection for components of litter size has limited effects on growth and backfat thickness, although it slightly reduces birth weight and improves postweaning growth.

Correlated responses for litter traits to six generations of selection for ovulation rate or prenatal survival in French Large White pigs

Effects of selection for reproductive traits were estimated using data from 3 pig lines derived from the same Large White population base. Two lines were selected for 6 generations on high ovulation rate at puberty (OR line) or high prenatal survival corrected for ovulation rate in the first 2 parities (PS line). The third line was an unselected control line. Genetic parameters for age and BW at puberty (AP and WP); number of piglets born alive, weaned, and nurtured (NBA, NW, and NN, respectively); proportions of stillbirth (PSB) and survival from birth to weaning (PSW); litter and average piglet BW at birth (LWB and AWB), at 21 d (LW21 and AW21), and at weaning (LWW and AWW) were estimated using REML methodology. Heritability estimates were 0.38 +/- 0.03, 0.46 +/- 0.03, 0.16 +/- 0.01, 0.08 +/- 0.01, 0.09 +/- 0.01, 0.04 +/- 0.01, 0.04 +/- 0.02, 0.19 +/- 0.02, 0.10 +/- 0.02, 0.10 +/- 0.02, 0.36 +/- 0.02, 0.27 +/- 0.01, and 0.24 +/- 0.01 for AP, WP, NBA, PSB, NW, NN, PSW, LWB, LW21, LWW, AWB, AW21, and AWW, respectively. The measures of litter size showed strong genetic correlations (r(a) >/= 0.95) and had antagonistic relations with PSB (r(a) = -0.59 to -0.75) and average piglet BW (r(a) = -0.19 to -0.46). They also had strong positive genetic correlations with prenatal survival (r(a) = 0.67 to 0.78) and moderate ones with ovulation rate (r(a) = 0.36 to 0.42). Correlations of litter size with PSW were negative at birth but positive at weaning. The OR and PS lines were negatively related to PSW and average piglet BW. Puberty traits had positive genetic correlations with OR and negative ones with PS. Genetic trends were estimated by computing differences between OR or PS and control lines at each generation using least squares and mixed model methodologies. Average genetic trends were computed by regressing line differences on generation number. Significant (P < 0.05) average genetic trends were obtained in OR and PS lines for AP (respectively, 2.1 +/- 0.9 and 3.2 +/- 1.0 d/generation) and WP (respectively, 2.0 +/- 0.5 and 1.8 +/- 0.5 d/generation) and in the PS line for NBA (0.22 +/- 0.10 piglet/generation). Tendencies (P < 0.10) were also observed for LWB (0.21 +/- 0.12 kg/generation) and AWW (-0.25 +/- 0.14 kg/generation) in the PS line. Selection on components of litter size can be used to improve litter size at birth, but result in undesirable trends for preweaning survival.

Between-breed variability of stillbirth and its relationship with sow and piglet characteristics

Litter characteristics at birth were recorded in 4 genetic types of sows with differing maternal abilities. Eighty-two litters from F(1) Duroc x Large White sows, 651 litters from Large White sows, 63 litters from Meishan sows, and 173 litters from Laconie sows were considered. Statistical models included random effects of sow, litter, or both; fixed effects of sow genetic type, parity, birth assistance, and piglet sex, as well as gestation length, farrowing duration, piglet birth weight, and litter size as linear covariates. The quadratic components of the last 2 factors were also considered. For statistical analyses, GLM were first considered, assuming a binomial distribution of stillbirth. Hierarchical models were also fitted to the data to take into account correlations among piglets from the same litter. Model selection was performed based on deviance and deviance information criterion. Finally, standard and robust generalized estimating equations (GEE) procedures were applied to quantify the importance of each effect on a piglet's probability of stillbirth. The 5 most important factors involved were, in decreasing order (contribution of each effect to variance reduction): difference between piglet birth weight and the litter mean (2.36%), individual birth weight (2.25%), piglet sex (1.01%), farrowing duration (0.99%), and sow genetic type (0.94%). Probability of stillbirth was greater for lighter piglets, for male piglets, and for piglets from small or very large litters. Probability of stillbirth increased with sow parity number and with farrowing duration. Piglets born from Meishan sows had a lower risk of stillbirth (P < 0.0001) and were little affected by the sources of variation mentioned above compared with the 3 other sow genetic types. Standard and robust GEE approaches gave similar results despite some disequilibrium in the data set structure highlighted with the robust GEE approach.

Genetic variation of farrowing kinetics traits and their relationships with litter size and perinatal mortality in French Large White sows

Genetic parameters of litter traits and their relationships with farrowing kinetics traits were estimated in a Large White population to examine the impact of selection for litter size on perinatal mortality and one of its main determinants, farrowing kinetics. Data were collected on 2,947 farrowings from 1,267 sows between 1996 and 2004. Litter traits included the number born in total (NBT), number born alive (NBA), and the number (NSB) and proportion (PSB) of stillborn piglets. Four farrowing kinetics traits were considered: farrowing duration (FD), birth interval (BI = FD/NBT), heterogeneity of birth intervals (SDNB = SD of the number of piglets born each one-half hour), and birth assistance (BA) during the farrowing process. Genetic parameters were estimated using restricted maximum likelihood methodology. All traits were analyzed using a mixed linear animal model including year x month and parity as fixed effects; the additive genetic value of each animal and the sow permanent environment were treated as random effects. To normalize their distribution, kinetics traits were Box-Cox-transformed. Low heritability estimates were obtained for litter size and mortality traits, which was in agreement with literature results (i.e., 0.10 +/- 0.02, 0.08 +/- 0.02, 0.19 +/- 0.02, and 0.14 +/- 0.02 for NBT, NBA, NSB, and PSB, respectively). Heritability values were also low for kinetics traits: 0.10 +/- 0.02, 0.08 +/- 0.02, 0.01 +/- 0.01, and 0.05 +/- 0.03 for FD, BI, SDNB, and BA, respectively. The genetic correlation between NBT and NBA was strongly positive (ra = 0.90). On both phenotypic and genetic scales, NBT was positively associated with stillbirth (ra = 0.45 +/- 0.11, rp = 0.38 for NSB; ra = 0.46 +/- 0.13, rp = 0.17 for PSB). Conversely, NBA had low correlations with SB and PSB. Number born in total was moderately correlated to FD (ra = 0.34 +/- 0.15) and BI (ra = -0.37 +/- 0.15). A stronger relationship was found between NBA and BI (ra = -0.49 +/- 0.13), whereas the relationship with FD was lower (ra = 0.16 +/- 0.17). Moreover, FD was strongly correlated with stillbirth (ra = 0.42 +/- 0.12 with NSB), whereas BI was nearly independent of stillbirth. Contrary to selection on NBT, selection on NBA appears to be a good way to limit the negative side effects on stillbirth. Moreover, selection on NBA would lead to a small increase in FD and a faster and more regular birth process than would be obtained by selecting on NBT.