Genetic parameters of milk mid-infrared spectra and their genetic relationships with milk production and feed efficiency traits in French Lacaune dairy sheep.

In French dairy sheep, Fourier transform infrared (FTIR) milk spectral data routinely predict the major milk components used in national genetic evaluations. The direct influence of genetic and environmental factors on milk FTIR spectra has been widely studied in dairy cattle, and relatively little in dairy ewes. In this study, 36,873 milk test-day records were available for 4,712 French Lacaune ewes farmed on 8 commercial farms. Our main goals were to provide the first description of spectral data and estimate the genetic parameters of French Lacaune dairy sheep during lactation. Principal component analysis (PCA) results demonstrated the impact of the lactation period on specific wavenumbers, allowing the identification of FTIR spectra collected at early (mo 2-4) and late (mo 5-7) lactation stages. The average estimated heritability (±mean SE) of the FTIR milk spectra from 2,971 to 926 cm-1 (446 wavenumbers) was 0.29 ± 0.02, ranging from 0.13 ± 0.01 to 0.42 ± 0.02. Furthermore, the heritabilities of spectra collected at the beginning or end of lactation changed at each point of the spectrum. However, at each wavenumber, the genomic correlation of transmittance values between these 2 lactation periods was high (>0.77), indicating the absence of a genotype-environment interaction. The genomic correlations between spectral regions and milk production traits (i.e., daily milk yield, fat and protein content, somatic cell score) varied from moderate to high. The results suggested that the most heritable areas of the spectrum were also genetically associated with dairy traits. Finally, the genomic correlations observed between the ewes' feed efficiency traits and the FTIR spectrum were moderate to high, while the genomic correlations between the change in body condition score and spectral data were rather low to moderate. This study confirmed that spectral data from Lacaune ewe milk were heritable, evolved phenotypically and genetically during lactation and were genetically correlated with traits included in breeding goals or traits of interest to the dairy industry.

Validation of single-step genomic BLUP random regression test-day models and SNP effects analysis on milk yield in French Saanen goats.

The shape of the lactation curve is linked to an animal's health, feed requirements, and milk production throughout the year. Random regression models (RRM) are widely used for genetic evaluation of total milk production throughout the lactation and for milk yield persistency. Genomic information used with the single-step genomic BLUP method (ssGBLUP) substantially improves the accuracy of genomic prediction of breeding values in the main dairy cattle breeds. The aim of this study was to implement an RRM using ssGBLUP for milk yield in Saanen dairy goats in France. The data set consisted of 7,904,246 test-day records from 1,308,307 lactations of Saanen goats collected in France between 2000 and 2017. The performance of this type of evaluation was assessed by applying a validation step with data targeting candidate bucks. The model was compared with a nongenomic evaluation and a traditional evaluation that use cumulated performance throughout the lactation model (LM). The incorporation of genomic information increased correlations between daughter yield deviations (DYD) and estimated breeding values (EBV) obtained with a partial data set for candidate bucks. The LM and the RRM had similar correlation between DYD and EBV. However, the RRM reduced overestimation of EBV and improved the slope of the regression of DYD on EBV obtained at birth. This study shows that a genomic evaluation from a ssGBLUP RRM is possible in dairy goats in France and that RRM performance is comparable to a LM but with the additional benefit of a genomic evaluation of persistency. Variance of adjacent SNPs was studied with LM and RRM following the ssGBLUP. Both approaches converged on approximately the same regions explaining more than 1% of total variance. Regions associated with persistency were also found.

Estimation of genetic parameters for dairy traits and somatic cell score in the first 3 parities using a random regression test-day model in French Alpine goats.

Lactation curve shape can affect an animal's health, feed requirements, and milk production throughout the year. We implemented a random regression model for the genetic evaluation of lactation curve shapes of dairy traits in French Alpine goats for their first 3 parities. Milk, fat, and protein yields, fat and protein contents, somatic cell score, and fat/protein ratio were considered. The data consisted of test-day records from 49,849 first lactation Alpine goats during their first 3 lactations. The reference model used a Legendre polynomial of order 2 for each parity to describe the genetic and permanent environmental effects, and was compared with a model that combined the second and third parities. A rank reduction of the variance-covariance matrix was also performed using an eigenvalue decomposition for each parity from the 2 models. Genetic parameters were consistent between the models tested. With a reduction to rank 2 and combining the second and third parities, the first 2 principal components correctly summarized the genetic variability of milk yield level and persistency, with a near-nil correlation between the 2, and with a much shorter computation time than the reference model. A favorable correlation of +0.43 between milk yield persistency and fat/protein ratio persistency at the beginning of the lactation was found from buck estimated breeding values.

Accuracy of genomic evaluation with weighted single-step genomic best linear unbiased prediction for milk production traits, udder type traits, and somatic cell scores in French dairy goats.

Genomic evaluation of French dairy goats is routinely conducted using the single-step genomic BLUP (ssGBLUP) method. This method has the advantage of simultaneously using all phenotypes, pedigrees, and genotypes. However, ssGBLUP assumes that all SNP explain the same amount of genetic variance, which is unlikely in the case of traits whose major genes or QTL are segregating. In this study, we investigated the effect of weighted ssGBLUP and its alternatives, which give more weight to SNP associated with the trait, on the accuracy of genomic evaluation of milk production, udder type traits, and somatic cell scores. The data set included 2,955 genotyped animals and 2,543,680 pedigree animals. The number of phenotypes varied with the trait. The accuracy of genomic evaluation was assessed on 205 genotyped Alpine and 146 genotyped Saanen goats born between 2009 and 2012. For traits with unknown QTL, weighted ssGBLUP was less accurate than, or as accurate as, ssGBLUP. For traits with identified QTL (i.e., QTL only present in the Saanen breed), weighted ssGBLUP outperformed ssGBLUP by between 2 and 14%.

Diversity of dairy goat lactation curves in France.

A high level of production at the peak of lactation may be associated with animal health disorders, high feeding costs, and reduced milk supply throughout the year. The objective of this study was to typologize the lactation curves in French dairy goats and analyze the influence of environmental and genetic factors on these curves. The data set consisted of 2,231,720 monthly test-day records of 213,534 French Saanen and Alpine goats recorded between September 2008 and June 2012. First, principal component analysis classified the shape of the lactation curves into 3 principal components: the first component accounted for milk yield level throughout lactation, the second component accounted for lactation persistency, and the third component accounted for milk yield in mid-lactation. Then, from the principal component scores, the lactations were clustered into 5 different groups. Most lactations had a similar shape to the mean curve, except 30% of the lactations that fell into 3 clusters that had a high production level at the peak and then a different persistency according to cluster. Estimated breeding value for milk yield and home region of breeding were the factors most related to lactation production level. Month of kidding, breed, and gestation stage had the biggest effect on persistency. Month of kidding was the factor most strongly linked to mid-lactation production. A herd effect was observed on all 3 principal components.

Use of meta-analyses and joint analyses to select variants in whole genome sequences for genomic evaluation: An application in milk production of French dairy cattle breeds.

As a result of the 1000 Bull Genome Project, it has become possible to impute millions of variants, with many of these potentially causative for traits of interest, for thousands of animals that have been genotyped with medium-density chips. This enormous source of data opens up very interesting possibilities for the inclusion of these variants in genomic evaluations. However, for computational reasons, it is not possible to include all variants in genomic evaluation procedures. One potential approach could be to select the most relevant variants based on the results of genome-wide association studies (GWAS); however, the identification of causative mutations is still difficult with this method, partly because of weak imputation accuracy for rare variants. To address this problem, this study assesses the ability of different approaches based on multi-breed GWAS (joint and meta-analyses) to identify single-nucleotide polymorphisms (SNP) for use in genomic evaluation in the 3 main French dairy cattle breeds. A total of 6,262 Holstein bulls, 2,434 Montbéliarde bulls, and 2,175 Normande bulls with daughter yield deviations for 5 milk production traits were imputed for 27 million variants. Within-breed and joint (including all 3 breeds) GWAS were performed and 3 models of meta-analysis were tested: fixed effect, random effect, and Z-score. Comparison of the results of within- and multi-breed GWAS showed that most of the quantitative trait loci identified using within-breed approaches were also found with multi-breed methods. However, the most significant variants identified in each region differed depending on the method used. To determine which approach highlighted the most predictive SNP for each trait, we used a marker-assisted best unbiased linear prediction model to evaluate lists of SNP generated by the different GWAS methods; each list contained between 25 and 2,000 candidate variants per trait, which were identified using a single within- or multi-breed GWAS approach. Among all the multi-breed methods tested in this study, variant selection based on meta-analysis (fixed effect) resulted in the most-accurate genomic evaluation (+1 to +3 points compared with other multi-breed approaches). However, the accuracies of genomic evaluation were always better when variants were selected using the results of within-breed GWAS. As has generally been found in studies of quantitative trait loci, these results suggest that part of the genetic variance of milk production traits is breed specific in Holstein, Montbéliarde, and Normande cattle.

Individual responses of dairy cows to a 24-hour milking interval.

Some dairy farmers opt to omit one milking, either incidentally or weekly, without changing other milking times. This practice entails an extended milking interval of 24h (24h-MI), which is associated with a decrease in milk yield. This decrease varies among cows and could be partly due to factors such as stage of lactation and milk yield level. The aim of this study was to describe the average and individual responses in terms of loss and carryover effects of a 24h-MI on milk yield. The influence of factors such as parity, stage of lactation, and milk yield potential were investigated, together with response repeatability. Our trial used 292 Holstein-Friesian cows, and consisted of 3 successive periods: 1 wk of twice-daily milking (TDM) as a control, one 24h-MI, and then 13d of TDM. The number of observations per cow ranged from 1 to 9, with no more than three 24h-MI per lactation. The 24h-MI reduced milk yield by 23% (7.8 kg on average) and milk lactose content by 2.6g/kg on the 24h-MI day. Milk fat and protein content, and somatic cell score increased by 3.0 g/kg, 0.5 g/kg, and 0.4 units, respectively. No significant carryover effect was found of a 24h-MI on milk yield or milk composition 2 wk after resumption of TDM. Milk yield loss and recovery varied widely (coefficient of variation 62%), and the relationship between milk loss and milk recovery showed substantial variation (residual standard deviation 2.1 kg/d). Cows with a greater milk potential level lost more milk yield but recovered more milk, with no influence on recovery:loss ratio. Cows in early lactation recovered the lost milk yield faster. Repeatability of the responses to a 24h-MI was 44% for milk yield loss (kg/d), 57% for relative milk yield loss (%), 33% for milk yield recovery (kg/d), and 0% for milk recovery:loss ratio (%), suggesting a genetically determined ability to limit loss when one milking is omitted. To conclude, a 24h-MI caused higher milk yield losses than reported in previous studies. Stage of lactation, estimated potential milk yield level, and parity explained the cows' response to the 24h-MI, but did not account for all the individual variability.

Assessment of accuracy of genomic prediction for French Lacaune dairy sheep.

Genomic selection in Lacaune dairy sheep was investigated based on genotypes from the OvineSNP50 BeadChip (Illumina Inc., San Diego, CA). Historical artificial insemination progeny-tested rams formed a population of 2,892 genotyped rams. Additional ungenotyped rams and females were included by single-step genomic BLUP (ssGBLUP). Three prediction strategies were tried: pseudo-BLUP (using all rams and daughter yield deviations), pseudo-ssGBLUP (using all rams and daughter yield deviations), and regular ssGBLUP (using all phenotypes and pedigree in an animal model). The population linkage disequilibrium was determined, with an average squared correlation coefficient of 0.11 for markers closer than 0.1cM (lower than in dairy cattle). The estimated effective population is 370 individuals. Gain in accuracy of genomic selection over parent averages ranged from 0.10 to 0.20. Highest accuracies and lowest bias were found using regular ssGBLUP. Transition to a genomic breeding scheme is possible but costs need to be carefully evaluated.

Application of Bayesian least absolute shrinkage and selection operator (LASSO) and BayesCπ methods for genomic selection in French Holstein and Montbéliarde breeds.

Recently, the amount of available single nucleotide polymorphism (SNP) marker data has considerably increased in dairy cattle breeds, both for research purposes and for application in commercial breeding and selection programs. Bayesian methods are currently used in the genomic evaluation of dairy cattle to handle very large sets of explanatory variables with a limited number of observations. In this study, we applied 2 bayesian methods, BayesCπ and bayesian least absolute shrinkage and selection operator (LASSO), to 2 genotyped and phenotyped reference populations consisting of 3,940 Holstein bulls and 1,172 Montbéliarde bulls with approximately 40,000 polymorphic SNP. We compared the accuracy of the bayesian methods for the prediction of 3 traits (milk yield, fat content, and conception rate) with pedigree-based BLUP, genomic BLUP, partial least squares (PLS) regression, and sparse PLS regression, a variable selection PLS variant. The results showed that the correlations between observed and predicted phenotypes were similar in BayesCπ (including or not pedigree information) and bayesian LASSO for most of the traits and whatever the breed. In the Holstein breed, bayesian methods led to higher correlations than other approaches for fat content and were similar to genomic BLUP for milk yield and to genomic BLUP and PLS regression for the conception rate. In the Montbéliarde breed, no method dominated the others, except BayesCπ for fat content. The better performances of the bayesian methods for fat content in Holstein and Montbéliarde breeds are probably due to the effect of the DGAT1 gene. The SNP identified by the BayesCπ, bayesian LASSO, and sparse PLS regression methods, based on their effect on the different traits of interest, were located at almost the same position on the genome. As the bayesian methods resulted in regressions of direct genomic values on daughter trait deviations closer to 1 than for the other methods tested in this study, bayesian methods are suggested for genomic evaluations of French dairy cattle.

A first step toward genomic selection in the multi-breed French dairy goat population.

The objectives of this study were to describe, using the goat SNP50 BeadChip (Illumina Inc., San Diego, CA), molecular data for the French dairy goat population and compare the effect of using genomic information on breeding value accuracy in different reference populations. Several multi-breed (Alpine and Saanen) reference population sizes, including or excluding female genotypes (from 67 males to 677 males, and 1,985 females), were used. Genomic evaluations were performed using genomic best linear unbiased predictor for milk production traits, somatic cell score, and some udder type traits. At a marker distance of 50kb, the average r(2) (squared correlation coefficient) value of linkage disequilibrium was 0.14, and persistence of linkage disequilibrium as correlation of r-values among Saanen and Alpine breeds was 0.56. Genomic evaluation accuracies obtained from cross validation ranged from 36 to 53%. Biases of these estimations assessed by regression coefficients (from 0.73 to 0.98) of phenotypes on genomic breeding values were higher for traits such as protein yield than for udder type traits. Using the reference population that included all males and females, accuracies of genomic breeding values derived from prediction error variances (model accuracy) obtained for young buck candidates without phenotypes ranged from 52 to 56%. This was lower than the average pedigree-derived breeding value accuracies obtained at birth for these males from the official genetic evaluation (62%). Adding females to the reference population of 677 males improved accuracy by 5 to 9% depending on the trait considered. Gains in model accuracies of genomic breeding values ranged from 1 to 7%, lower than reported in other studies. The gains in breeding value accuracy obtained using genomic information were not as good as expected because of the limited size (at most 677 males and 1,985 females) and the structure of the reference population.

Opportunities and limits of commercial farm data to study the genetic determinism of feed efficiency throughout lactation in dairy sheep.

The collective economic and environmental interest of the whole dairy sheep sector is to reduce feed costs and the negative impact of milk production on the environment. Thus, this study focused on the characterisation and genetic selection potential of feed efficiency in the Lacaune breed. Estimates for feed efficiency in dairy ewes are limited, mainly due to a lack of individual feed intake measurements in the sheepfold or in the pasture. We estimated the genetic parameters for two approximated (not entirely based on individual data) feed efficiency traits (lactation feed conversion ratio (LFCR) and residual energy intake (REI)) and daily milk yield (DMY) at different stages of lactation and throughout lactation. The accuracy of the efficiency traits was first evaluated on samples from Lacaune dairy ewes that were monitored individually, especially for their feed intake. Then, feed efficiency estimation methods were applied on eight commercial farms corresponding to 4 680 Lacaune dairy ewes over two milk lactations (30 854 records). Animals were collectively (for a large part of feed intake) or individually (for milk performance and dynamics of body fat reserves) monitored at different lactation stages. The heritabilities of LFCR and REI were estimated over lactations at 0.10 ± 0.01 and 0.11 ± 0.01, respectively. High genetic correlations were observed between the two efficiency traits and milk production traits, with a genetic correlation between LFCR and DMY of 0.74 ± 0.04 and between REI and DMY of -0.79 ± 0.04. A strong influence of environmental factors such as farm, year of milk production and lactation stage affected the genetic link between REI and milk production traits. Efficiency values observed in early lactation when animals were bred in the sheepfold were less genetically correlated with values obtained later in lactation when animals were grass-fed. However, individual characterisation of feed efficiency remains difficult due to the collective feeding context in dairy ewe farms.

A functional genomic approach to the study of the milking ability in dairy sheep.

To understand the mechanisms underlying milk ability and more precisely the kinetics of milk emission, we compared teat transcriptome profiles from Lacaune ewes in the tails of the milk flow phenotypic distribution. Two different arrays containing respectively 1896 and 13 168 PCR products selected from several tissue-specific cDNA libraries, including mammary gland, allowed the identification of 73 differentially expressed genes between teats from high and low milk flow ewes. Genes involved in muscle contraction were identified as over-expressed, and genes encoding collagen were found to be under-expressed in teats from low milk flow ewes. We confirmed this underexpression of COL1A1 and COL1A2 in low-milk flow ewes using RT-qPCR. These results suggest that milking ability may be due to the capacity of the teat sphincter to relax during mechanical milking. We propose that an optimal condition for mechanical milking may require proper relaxation of the teats. To our knowledge, this is the first transcriptomic analysis studying milking ability, using udder tissue for gene expression profiling, which demonstrates that mechanical milking ability is not only determined by morphological features but also by tissue composition.

A comparison of partial least squares (PLS) and sparse PLS regressions in genomic selection in French dairy cattle.

Genomic selection involves computing a prediction equation from the estimated effects of a large number of DNA markers based on a limited number of genotyped animals with phenotypes. The number of observations is much smaller than the number of independent variables, and the challenge is to find methods that perform well in this context. Partial least squares regression (PLS) and sparse PLS were used with a reference population of 3,940 genotyped and phenotyped French Holstein bulls and 39,738 polymorphic single nucleotide polymorphism markers. Partial least squares regression reduces the number of variables by projecting independent variables onto latent structures. Sparse PLS combines variable selection and modeling in a one-step procedure. Correlations between observed phenotypes and phenotypes predicted by PLS and sparse PLS were similar, but sparse PLS highlighted some genome regions more clearly. Both PLS and sparse PLS were more accurate than pedigree-based BLUP and generally provided lower correlations between observed and predicted phenotypes than did genomic BLUP. Furthermore, PLS and sparse PLS required similar computing time to genomic BLUP for the study of 6 traits.

Genomic selection in the French Lacaune dairy sheep breed.

Genomic selection aims to increase accuracy and to decrease generation intervals, thus increasing genetic gains in animal breeding. Using real data of the French Lacaune dairy sheep breed, the purpose of this study was to compare the observed accuracies of genomic estimated breeding values using different models (infinitesimal only, markers only, and joint estimation of infinitesimal and marker effects) and methods [BLUP, Bayes Cπ, partial least squares (PLS), and sparse PLS]. The training data set included results of progeny tests of 1,886 rams born from 1998 to 2006, whereas the validation set had results of 681 rams born in 2007 and 2008. The 3 lactation traits studied (milk yield, fat content, and somatic cell scores) had heritabilities varying from 0.14 to 0.41. The inclusion of molecular information, as compared with traditional schemes, increased accuracies of estimated breeding values of young males at birth from 18 up to 25%, according to the trait. Accuracies of genomic methods varied from 0.4 to 0.6, according to the traits, with minor differences among genomic approaches. In Bayes Cπ, the joint estimation of marker and infinitesimal effects had a slightly favorable effect on the accuracies of genomic estimated breeding values, and were especially beneficial for somatic cell counts, the less heritable trait. Inclusion of infinitesimal effects also improved slopes of predictive regression equations. Methods that select markers implicitly (Bayes Cπ and sparse PLS) were advantageous for some models and traits, and are of interest for further quantitative trait loci studies.

Comparison of isogenic lines provides evidence that phenotypic plasticity is under genetic control in rainbow trout Oncorhynchus mykiss.

Comparison of nine isogenic lines of rainbow trout Oncorhynchus mykiss kept in the same environment showed significant genetic determinism of phenotypic plasticity assessed through body mass measurements. Ranking of lines differed between two tested environments.

Genetic parameters for milk somatic cell score and relationship with production and udder type traits in dairy Alpine and Saanen primiparous goats.

Goat milk somatic cell counts have been collected for several years in France by the national milk recording organization. Information is used for health management, because repeatedly elevated somatic cell counts are a good indirect predictor of intramammary infection. Genetic parameters were estimated for 67,882 and 49,709 primiparous goats of the dairy Alpine and Saanen breeds, respectively, with complete information for milk somatic cell counts and milk production traits. About 40% of the goats had additional information for 11 udder type traits scored by official classifiers of the breeders' association CAPGENES. Estimates were obtained by REML with an animal model. The studied trait was lactation somatic cell score (LSCS), the weighted mean of somatic cell score (log-transformed SCC) adjusted for lactation stage. Heritability of LSCS was 0.20 and 0.24 in the Alpine and Saanen breeds, respectively. Relationships with milk production and udder type traits were additionally estimated by using multitrait analyses. Heritability estimates in first lactation ranged from 0.30 to 0.35 for lactation milk, fat, and protein yields; from 0.60 to 0.67 for fat and protein contents; and from 0.22 to 0.50 for udder type traits. Genetic correlations of somatic cell score with milk production traits were generally low, ranging from -0.13 to 0.12. Slightly more negative correlations were estimated for fat content: -0.18 and -0.20 in Saanen and Alpine breeds, respectively. Lactation somatic cell score was genetically correlated with udder floor position (r(g)=-0.24 and -0.19 in the Alpine and Saanen breeds, respectively), and, in Saanen, teat length, teat width, and teat form (r(g)=0.29, 0.34 and -0.27, respectively). These results suggest that a reduction in somatic cell count can be achieved by selection while still improving milk production and udder type and teat traits.

Comparison of nonlinear and spline regression models for describing mule duck growth curves.

This study compared models for growth (BW) before overfeeding period for male mule duck data from 7 families of a QTL experimental design. Four nonlinear models (Gompertz, logistic, Richards, and Weibull) and a spline linear regression model were used. This study compared fixed and mixed effects models to analyze growth. The Akaike information criterion was used to evaluate these alternative models. Among the nonlinear models, the mixed effects Weibull model had the best overall fit. Two parameters, the asymptotic weight and the inflexion point age, were considered random variables associated with individuals in the mixed models. In our study, asymptotic weight had a greater effect in Akaike's information criterion reduction than inflexion point age. In this data set, the between-ducks variability was mostly explained by asymptotic BW. Comparing fixed with mixed effects models, the residual SD was reduced in about 55% in the latter, pointing out the improvement in the accuracy of estimated parameters. The mixed effects spline regression model was the second best model. Given the piecewise nature of growth, this model is able to capture different growth patterns, even with data collected beyond the asymptotic BW.

Response to somatic cell count-based selection for mastitis resistance in a divergent selection experiment in sheep.

A divergent selection experiment in sheep was implemented to study the consequences of log-transformed somatic cell score (SCS)-based selection on resistance to natural intramammary infections. Using dams and progeny-tested rams selected for extreme breeding values for SCS, we created 2 groups of ewes with a strong divergence in SCS of approximately 3 genetic standard deviations. A survey of 84 first-lactation ewes of both the High and Low SCS lines indicated favorable responses to SCS-based selection on resistance to both clinical and subclinical mastitis. All clinical cases (n = 5) occurred in the High SCS line. Additionally, the frequency of chronic clinical mastitis, as detected by the presence of parenchymal abscesses, was much greater in the High SCS line (n = 21) than in the Low SCS line (n = 1). According to monthly milk bacteriological examinations of udder halves, the prevalence of infection was significantly greater (odds ratio = 3.1) in the High SCS line than in the Low SCS line, with predicted probabilities of 37 and 16%, respectively. The most frequently isolated bacteria responsible for mastitis were staphylococci: Staphylococcus auricularis (42.6% of positive samples), Staphylococcus simulans, Staphylococcus haemoliticus, Staphylococcus xylosus, Staphylococcus chromogenes, Staphylococcus lentus, Staphylococcus warneri, and Staphylococcus aureus. The incidence of positive bacteriology was greater in the High SCS line (39%) than in the Low SCS line (12%) at lambing, indicating that High SCS line ewes were especially susceptible to postpartum subclinical mastitis. Negativation of bacteriological results from one sampling time point to the next was markedly different between lines after weaning (e.g., 41 and 84% in the High and Low SCS lines, respectively). This result was consistent with differences in the duration of infection, which was much greater in the High SCS line compared with the Low SCS line. Finally, ewes from the High SCS line consistently had greater SCS in positive milk samples than did ewes from the Low SCS line (+2.04 SCS, on average), with an especially large difference between lines during the suckling period (+3.42 SCS). Altogether, the preliminary results suggest that the better resistance of Low SCS line ewes, compared with High SCS line ewes, was principally characterized by a better ability to limit infections during the peripartum period, to eliminate infections during lactation, and quantitatively to limit the inflammation process and its clinical consequences.

Intra-flock variability in the body reserve dynamics of meat sheep by analyzing BW and body condition score variations over multiple production cycles.

Breeding for resilience requires a better understanding of intra-flock variability and the related mechanisms responsible for robustness traits. Among such traits, the animals' ability to cope with feed fluctuations by mobilizing or restoring body reserves (BR) is a key mechanism in ruminants. The objective of this work was to characterize individual variability in BR dynamics in productive Romane ewes reared in extensive conditions. The BR dynamics profiles were characterized by combining individual longitudinal measurements of BW and body condition scores (BCS) over several production cycles. Historical data, including up to 2628 records per trait distributed in 1146 ewes, underwent cluster analysis. Two to four trajectories were observed for BW depending on the cycle, while three trajectories were found for BCS, whatever the cycle. Most trajectories suggested that BR dynamics were similar but the level of BR may differ between ewes. Nevertheless, some trajectories suggested that both BR dynamics and levels were different for a proportion of ewes. Clustering on BW and BCS profiles adjusted for individual level trends, resulted in differences only in the level of BW or BCS, rather than differences in trajectories. Thus, the overall shape of trajectories was not changed considering or not the individual level trend across cycles. In addition to individual variability, the ewe's age at first lambing and litter size contributed to the distribution of the ewes between the trajectories. Regarding the entire productive life, three trajectories were observed for BW and BCS changes over three productive cycles. Increase in BW at each cycle suggested that ewes kept growing up until 3 to 4 years old in our conditions. Similar alternation of BCS gains and losses across cycles suggested BR dynamics might be repeatable. Many individual trajectories remained the same throughout a ewe's life, whatever the age at first lambing, parity or litter size. Our results demonstrate the relevance of using BW and BCS changes for characterizing the diversity of BR mobilization-accretion profiles in sheep in a long timespan perspective.

In vivo gene expression in granulosa cells during pig terminal follicular development.

Ovarian antral follicular development is clearly dependent on pituitary gonadotrophins FSH and LH. Although the endocrine mechanism that controls ovarian folliculogenesis leading to ovulation is quite well understood, the detailed mechanisms and molecular determinants in the different follicular compartments remain to be clarified. The aim of this study was to identify the genes differentially expressed in pig granulosa cells along the terminal ovarian follicle growth, to gain a comprehensive view of these molecular mechanisms. First, we developed a specific micro-array using cDNAs from suppression subtractive hybridization libraries (345 contigs) obtained by comparison of three follicle size classes: small, medium and large antral healthy follicles. In a second step, a transcriptomic analysis using cDNA probes from these three follicle classes identified 79 differentially expressed transcripts along the terminal follicular growth and 26 predictive genes of size classes. The differential expression of 18 genes has been controlled using real-time PCR experiments validating the micro-array analysis. Finally, the integration of the data using Ingenuity Pathways Analysis identified five gene networks providing descriptive elements of the terminal follicular development. Specifically, we observed: (1) the down-expression of ribosomal protein genes, (2) the genes involved in lipid metabolism and (3) the down-expression of cell morphology and ion-binding genes. In conclusion, this study gives new insight into the gene expression during pig terminal follicular growth in vivo and suggested, in particular, a morphological change in pig granulosa cells accompanying terminal follicular growth.