Search

International audience; Fertility is of primary economic importance in dairy cattle and the most common reason for involuntary culling. However, standard fertility traits have very low heritability that renders genetic selection slow and difficult. In this study, we explored fertility from an endocrine standpoint. A total of 1,163 crossbred Holstein-Normande females in a 3-generation familial design were studied for progesterone level measured every 10 d to determine age at puberty (PUB) and commencement of postpartum luteal activity (CPLA). Genetic parameters were estimated using REML with WOMBAT software. The heritability estimates were 0.38 ± 0.10 and 0.16 ± 0.07 for PUB and CPLA, respectively. Moreover, the 2 traits were genetically correlated (0.45 ± 0.23), suggesting a partially common determinism. Because of the family structure, a linkage disequilibrium and linkage analysis approach was preferred over standard genome-wide association study to map genomic regions associated with these traits. Ten quantitative trait loci (QTL) were detected for PUB on chromosomes 1, 3, 11, 13, 14, 21, and 29, whereas 3 QTL were associated with CPLA on chromosomes 21 and 26. Only the QTL on chromosome 21 was common to both traits. Four functional candidate genes (NCOA2, GAS2, OVOL1, and FOSL1) were identified in the detected regions. These findings will contribute to a clearer understanding of fertility determinism and enhance the value of introducing endocrinological data in fertility studies.

SMARTER publication

MicroRNAs are small noncoding RNAs that have important roles in the lactation process and milk biosynthesis. Some polymorphisms have been studied in various livestock species from the perspective of pathology or production traits. To target variants that could be the causal variants of dairy traits, genetic variants of microRNAs expressed in the mammary gland or present in milk and localized in dairy quantitative trait loci (QTLs) were investigated in bovine, caprine, and ovine species. In this study, a total of 59,124 (out of 28 millions), 13,427 (out of 87 millions), and 4761 (out of 38 millions) genetic variants in microRNAs expressed in the mammary gland or present in milk were identified in bovine, caprine, and ovine species, respectively. A total of 4679 of these detected bovine genetic variants are located in dairy QTLs. In caprine species, 127 genetic variants are localized in dairy QTLs. In ovine species, no genetic variant was identified in dairy QTLs. This study leads to the detection of microRNA genetic variants of interest in the context of dairy production, taking advantage of whole genome data to identify microRNA genetic variants expressed in the mammary gland and localized in dairy QTLs.

International audience; 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.

Background Random regression models (RRM) are widely used to analyze longitudinal data in genetic evaluation systems because they can better account for time-course changes in environmental effects and additive genetic values of animals by fitting the test-day (TD) specific effects. Our objective was to implement a random regression model for the evaluation of dairy production traits in French goats. Results The data consisted of milk TD records from 30,186 and 32,256 first lactations of Saanen and Alpine goats. Milk yield, fat yield, protein yield, fat content and protein content were considered. Splines were used to model the environmental factors. The genetic and permanent environmental effects were modeled by the same Legendre polynomials. The goodness-of-fit and the genetic parameters derived from functions of the polynomials of orders 0 to 4 were tested. Results were also compared to those from a lactation model with total milk yield calculated over 250 days and to those of a multiple-trait model that considers performance in six periods throughout lactation as different traits. Genetic parameters were consistent between models. Models with fourth-order Legendre polynomials led to the best fit of the data. In order to reduce complexity, computing time, and interpretation, a rank reduction of the variance covariance matrix was performed using eigenvalue decomposition. With a reduction to rank 2, the first two principal components correctly summarized the genetic variability of milk yield level and persistency, with a correlation close to 0 between them. Conclusions A random regression model was implemented in France to evaluate and select goats for yield traits and persistency, which are independent i.e. no genetic correlation between them, in first lactation. Electronic supplementary material The online version of this article (10.1186/s12711-019-0485-3) contains supplementary material, which is available to authorized users.

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.

International audience; An extended milking interval of 24 h (24-h milking interval (24h-MI)) constitutes the acute phase of cow adaptation to once-daily milking (ODM). A recent trial including 724 24h-MI challenges demonstrated that milk yield responses to this acute phase of ODM are highly variable (from+22% to -52% of milk yield when switching to the 24h-MI, mean=-25.3%) and that factors such as stage of lactation parity and milk yield level influenced cows' responses but did not account for all individual variability. Additional traits related to physiological, immune and behavioural adaptation were measured on a subset (96 observations) of this data set. This study aimed to determine (1) the relationship of these traits with cows' milk yield responses, (2) their ability - combined with previously identified traits - to help predict milk yield responses to 24h-MI (adaptive profiles). The 24h-MI challenge consisted of three successive periods: one control week of twice-daily milking (cTDM), one single day of 24h-MI and then 13 days of TDM (pTDM). Milk yield responses to the 24h-MI (corrected for effects of stage of lactation, parity, milk yield level and milk yield) were related to physiological traits measured during cTDM (milk flow rate, presence or absence of interleukin-8) and to their changes during the 24h-MI (absolute increase in milk flow rate and relative udder distension). Analysis of associations between milk yield responses, stage of lactation, parity, milk yield level, proteolysis, udder expansion and immune traits found three adaptive cow profile clusters. Cows in cluster 1 had a less compliant udder than cows in cluster 2, and they lost more milk during the 24h-MI than cluster-2 and cluster-3 cows. After resuming twice daily-milking (TDM), cluster-2 cows fully recovered the milk they had lost during the 24h-MI. On the opposite, cluster-3 cows did not recover the milk they lost, likely due to udder inflammation during cTDM, as suggested by elevated concentrations of interleukin-8 in their milk. These results combining new traits with stage of lactation, parity and milk yield level constitute a first step towards predicting individual cow responses to a 24h-MI.

International audience; 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 arid 14%.

In 2017, genomic selection was implemented in French dairy goats using the single-step genomic best linear unbiased prediction (ssGBLUP) method, which assumes that all single nucleotide polymorphisms explain the same fraction of genetic variance. However, ssGBLUP is not suitable for protein content, which is controlled by a major gene, i.e. α s 1 casein. This gene explains about 40% of the genetic variation in protein content. In this study, we evaluated the accuracy of genomic prediction using different genomic methods to include the effect of the α s 1 casein gene. Genomic evaluation for protein content was performed with data from the official genetic evaluation on 2955 animals genotyped with the Illumina goat SNP50 BeadChip, 7202 animals genotyped at the α s 1 casein gene and 6,767,490 phenotyped females. Pedigree-based BLUP was compared with regular unweighted ssGBLUP and with three weighted ssGBLUP methods (WssGBLUP, WssGBLUPMax and WssGBLUPSum), which give weights to SNPs according to their effect on protein content. Two other methods were also used: trait-specific marker-derived relationship matrix (TABLUP) using pre-selected SNPs associated with protein content and gene content based on a multiple-trait genomic model that includes α s 1 casein genotypes. We estimated accuracies of predicted genomic estimated breeding values (GEBV) in two populations of goats (Alpine and Saanen). Accuracies of GEBV with ssGBLUP improved by + 5 to + 7 percent points over accuracies from the pedigree-based BLUP model. With the WssGBLUP methods, SNPs that are located close to the α s 1 casein gene had the biggest weights and contributed substantially to the capture of signals from quantitative trait loci. Improvement in accuracy of genomic predictions using the three weighted ssGBLUP methods delivered up to + 6 percent points of accuracy over ssGBLUP. A similar accuracy was obtained for ssGBLUP and TABLUP considering the 20,000 most important SNPs. Incorporating information on the α s 1 casein genotypes based on the gene content method gave similar results as ssGBLUP. The three weighted ssGBLUP methods were efficient for detecting SNPs associated with protein content and for a better prediction of genomic breeding values than ssGBLUP. They also combined fast computing, simplicity and required ssGBLUP to be run only twice.

International audience; Despite its potential utility for predicting cows' milk yield responses to once-daily milking (ODM), the genetic basis of cow milk trait responses to ODM has been scarcely if ever described in the literature, especially for short ODM periods. This study set out to (1) estimate the genetic determinism of milk yield and composition during a 3-wk ODM period, (2) estimate the genetic determinism of milk yield responses (i.e., milk yield loss upon switching cows to ODM and milk yield recovery upon switching them back to twice-daily milking; TDM), and (3) seek predictors of milk yield responses to ODM, in particular using the first day of ODM. Our trial used 430 crossbred Holstein × Normande cows and comprised 3 successive periods: 1 wk of TDM (control), 3 wk of ODM, and 2 wk of TDM. Implementing ODM for 3 wk reduced milk yield by 27.5% on average, and after resuming TDM cows recovered on average 57% of the milk lost. Heritability estimates in the TDM control period and 3-wk ODM period were, respectively, 0.41 and 0.35 for milk yield, 0.66 and 0.61 for milk fat content, 0.60 and 0.80 for milk protein content, 0.66 and 0.36 for milk lactose content, and 0.20 and 0.15 for milk somatic cell score content. Milk yield and composition during 3-wk ODM and TDM periods were genetically close (within-trait genetic correlations between experimental periods all exceeding 0.80) but were genetically closer within the same milking frequency. Heritabilities of milk yield loss observed upon switching cows to ODM (0.39 and 0.34 for milk yield loss in kg/d and %, respectively) were moderate and similar to milk yield heritabilities. Milk yield recovery (kg/d) upon resuming TDM was a trait of high heritability (0.63). Because they are easy to measure, TDM milk yield and composition and milk yield responses on the first day of ODM were investigated as predictors of milk yield responses to a 3-wk ODM to easily detect animals that are well adapted to ODM. Twice-daily milking milk yield and composition were found to be partly genetically correlated with milk yield responses but not closely enough for practical application. With genetic correlations of 0.98 and 0.96 with 3-wk ODM milk yield losses (in kg/d and %, respectively), milk yield losses on the first day of ODM proved to be more accurate in predicting milk yield responses on longer term ODM than TDM milk yield.

La sélection génomique, qui a révolutionné la sélection génétique des bovins laitiers notamment, est désormais envisagée dans d’autres filières animales. Chez les caprins laitiers français, le gain de précision attendu des valeurs génomiques était un des questionnements de la filière en raison de la petite taille de la population de référence disponible (825 mâles et 1945 femelles génotypés sur une puce SNP 50K). Le but de cette étude est de tester différentes techniques d’évaluation génomique afin d’obtenir les évaluations génomiques les plus précises possibles. Une étude de la structure génétique de la population de référence caprine constituée d’animaux de races Saanen et Alpine, a révélé de faibles niveaux de déséquilibre de liaison (0,17 entre deux SNP consécutifs), de consanguinité et de parenté au sein de la population, ce qui n’est pas favorable à une bonne précision des évaluations génomiques. Les méthodes d’évaluations génomiques (GBLUP ou Bayésiennes), basées sur des performances pré-corrigées n’ont pas permis une amélioration significative des précisions des évaluations génomiques pour les caractères évalués en routine (caractères de production, de morphologie et comptages de cellules somatiques). Cependant les évaluations génomiques basées sur les performances propres des femelles ont permis d’obtenir des précisions supérieures à celles obtenues sur ascendance. La sélection génomique est donc envisageable chez les caprins laitiers français. Ces précisions peuvent également être légèrement augmentées par l’inclusion de gènes majeurs tels que celui de la caséine αs1., Genomic selection, which is revolutionizing genetic selection in dairy cattle is now considered in the breeding of other animals. In French dairy goats, gain in accuracy using genomic selection has been questionned due to the small size of the reference population (825 males and 1 945 females genotyped). The aim of this study was to investigate how to reach adequate genomic evaluation accuracy in the French dairy goat population. The study of a reference population structure (Alpine and Saanen breeds) showed that the level of linkage disequilibrium (0.17 between two consecutive SNP), inbreeding and the relationship between the reference and candidate population were not ideal to maximize genomic evaluation accuracy. Two steps genomic evaluation (GBLUP, Bayesian) based on performances corrected for fixed effects did not improve genetic evaluation accuracy compared to classical evaluations for milk production traits, udder type traits and somatic cells scores. When using genomic evaluation directly based on female performances (single step), accuracy of genomic evaluation is higher than the accuracy level obtained from the ascendance. Genomic selection is feasible in French dairy goats. Accuracies could be slightly improved integrating a major gene such as αs1 casein.

Genomic best linear unbiased prediction methods assume that all markers explain the same fraction of the genetic variance and do not account effectively for genes with major effects such as the α s1 casein polymorphism in dairy goats. In this study, we investigated methods to include the available α s1 casein genotype effect in genomic evaluations of French dairy goats. First, the α s1 casein genotype was included as a fixed effect in genomic evaluation models based only on bucks that were genotyped at the α s1 casein locus. Less than 1 % of the females with phenotypes were genotyped at the α s1 casein gene. Thus, to incorporate these female phenotypes in the genomic evaluation, two methods that allowed for this large number of missing α s1 casein genotypes were investigated. Probabilities for each possible α s1 casein genotype were first estimated for each female of unknown genotype based on iterative peeling equations. The second method is based on a multiallelic gene content approach. For each model tested, we used three datasets each divided into a training and a validation set: (1) two-breed population (Alpine + Saanen), (2) Alpine population, and (3) Saanen population. The α s1 casein genotype had a significant effect on milk yield, fat content and protein content. Including an α s1 casein effect in genetic and genomic evaluations based only on male known α s1 casein genotypes improved accuracies (from 6 to 27 %). In genomic evaluations based on all female phenotypes, the gene content approach performed better than the other tested methods but the improvement in accuracy was only slightly better (from 1 to 14 %) than that of a genomic model without the α s1 casein effect. Including the α s1 casein effect in a genomic evaluation model for French dairy goats is possible and useful to improve accuracy. Difficulties in predicting the genotypes for ungenotyped animals limited the improvement in accuracy of the obtained estimated breeding values.

Après une présentation du contexte de la sélection ovine laitière française et de la stratégie de sélection génomique qui en découle, cet article retrace l’ensemble des travaux de recherche et développement entrepris depuis 2009 pour mettre en œuvre cette stratégie et qui débouchent sur le déploiement de la sélection génomique en 2015. Sont abordés successivement la constitution des populations de référence, les investissements en méthodes et stratégies de calcul d’index génomique, les résultats de précision des index génomiques en ovins laitiers, l’expérimentation génomique originale mise en œuvre en race Lacaune, la conception, la modélisation et l’optimisation de schémas de sélection génomique adaptés aux spécificités des races ovines laitières, les choix opérés par les entreprises et organismes de sélection et leurs conséquences organisationnelles et contractuelles. Les perspectives ouvertes par la sélection génomique sont évoquées : inclusion de nouveaux caractères, plus grande flexibilité, meilleure gestion de la variabilité génétique. La sélection génomique est effective depuis 2015 en race Lacaune et devrait être déployée en 2017 dans les races pyrénéennes., After a presentation of the background of French dairy sheep selection and the resulting strategy of genomic selection, this article considers the different research x development achievements undertaken since 2009 in order to assess the feasibility of this strategy and which results in the implementation of genomic selection in 2015. The following topics are successively reviewed: the constitution of the reference populations, the methods and strategies of genomic evaluation, the accuracy obtained for genomic prediction in the different dairy sheep breeds, the original experiment conducted in the Lacaune breed, the conception, modeling and optimization of dairy-sheep-customized genomic breeding schemes, the decisions made by the breeding societies and their organizational and contractual implications. The main expectations opened by this new field of genomic selection are highlighted: selection for new traits, increased flexibility in the breeding programs, more efficient management of genetic variability. The Lacaune breed has been switched to genomic selection in 2015, whereas in the Pyrenean breeds, breeders intend to do it by 2017.

Une série d’expérimentations a été conduite pour mesurer l’impact de la monotraite à l’échelle de la lactation de brebis laitières de race Lacaune. La production de lait et sa composition, la physiologie et la morphologie de la mamelle, l’ingestion et le métabolisme énergétique ont été mesurés tout au long de la période de traite, du sevrage au tarissement. L’impact de la monotraite a été modélisé à l’échelle de l’exploitation et du bassin de production. La perte de lait a été de 14% chez les multipares comme les primipares. Selon la ration offerte, le taux butyreux a diminué ou a eu tendance à augmenter, alors que le taux protéique n’a pas été modifié ou a augmenté en lien avec une augmentation des protéines solubles. Les quantités ingérées n’étaient pas différentes entre brebis en traite biquotidienne et monotraite, ce qui a entraîné un bilan énergétique positif supérieur pour ces dernières. Cela s’est traduit par des taux d’acides gras non estérifiés rapidement plus bas et de leptine supérieurs. La mamelle a été perturbée dans les premiers jours de monotraite, mais a rapidement retrouvé son intégrité du fait d’une bonne plasticité sans aucune conséquence sur sa santé. Au niveau de l’exploitation, la monotraite entraîne une perte de revenu qui peut être compensée en tout ou partie par un passage en monotraite à un stade plus tardif ou un allongement de la période de traite. Des travaux complémentaires conduits à l’échelle de l’exploitation permettraient de mieux préciser les répercussions de cette pratique à ce niveau et à celui du bassin de production., Several experiments were conducted for evaluating the effects of once daily milking (ODM) in Lacaune dairy ewes throughout the milking period. Milk production and composition, udder health, daily feed intake and energy metabolism were individually monitored. The effects of adopting ODM at the farm and the production region levels were also modelled. Individual milk yield was 14% lower either in adult or primiparous ewes. Milk fat and protein contents were lower and higher, respectively, when ewes were fed ad libitum; however, these parameters were almost not modified when diets were adjusted to the actual milk yield. Dry matter intake was not affected by ODM, which led to a marked positive energy balance in ODM ewes. The last was well illustrated by the metabolic profile of the ewes, showing lower body reserves mobilization in ODM ewes. The udder morphology and functioning was only disturbed during the first week of ODM, but it was rapidly recovered without associated further health constraints. At the farm level, adopting the ODM regime showed a decrease in the global farm incomes, possibly to be recovered by starting ODM in the mid-lactation or prolonging the milking period length. Further researches will be required for characterizing in a deeper way the related effects of applying ODM under different feeding and management situations at the flock, farm and regional production scales.

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 24 h (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 13 d 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 2.6 g/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.

L’un des facteurs limitants identifiés dans les années 1950 était le potentiel laitier insuffisant des races de brebis traites en France, qui a initié la collaboration entre profession et recherche qui perdure depuis. Après une phase initiale où les deux stratégies génétiques possibles, croisement ou race pure, ont été explorées, la profession a fait le choix dans les années 1970 de la sélection de chaque race locale dans son terroir de production avec son fromage d'appellation d’Origine Protégée. La réussite de la sélection des cinq races locales concernées est le fruit d’une démarche raisonnée et optimisée dans l’espace (gestion pyramidale) et dans le temps (la quantité de lait, puis les aptitudes fromagères du lait, puis les caractères fonctionnels de la sphère mammaire), et de la mise en œuvre d’objectifs de sélection cumulatifs à l’échelle de chacune des populations concernées. Cette démarche est validée par des va-et-vient permanents entre élevages privés et expérimentaux, ou des expérimentations dans les élevages privés : elle a permis de proposer des solutions inédites comme le contrôle laitier qualitatif ponctuel pour les taux butyreux et protéiques, puis pour les comptages de cellules somatiques, tout en permettant de comprendre l’originalité de milieux de sélection induits par le pilotage global au troupeau, susceptible de favoriser des réponses génétiques indirectes durables (capacité d’ingestion, vitesse de traite et aptitude à la mono-traite des brebis) bien exploitées par les éleveurs. Cette démarche balisée n’interdit pas de sortir du cadre quand il s’agit de répondre à un objectif stratégique et imprévu, comme la résistance à la tremblante dans les années 1990. La diversification pour chacune des cinq races locales de nouveaux objectifs de sélection (aptitude à la mono-traite quotidienne, longévité fonctionnelle, persistance laitière, résistance aux strongles gastro-intestinaux) est susceptible d’intervenir avec le démarrage depuis 2015 de la sélection génomique des brebis laitières françaises., One of the limiting factors identified in the 1950’s was low milk yield level of the French dairy ewes, which initiated the collaboration between breeder organizations and researchers that has been developed. Initially, the two possible genetic strategies, crossbreeding or purebred selection, were investigated. Finally the breeders chose in the 1970’s the selection of each local French dairy sheep breed in its specific area of production related to its PDO cheese. The success of the selection of the five involved local breeds is the result of a rationalized and optimized approach, in space (pyramidal management of the population), and in time (progressiveness of the breeding goals from milk yield only to global merit giving the same weights to milk traits and udder functional traits). This approach was validated by permanent exchanges between private and experimental flocks: it permits, on the one hand to conceive and validate simplified tools such as lactation sampling for fat and protein contents, and SCC, and on the other hand to understand the originality of the dairy selection environment when management decisions are taken at the flock level and not individually, which may induce sustainable indirect genetic responses (feed intake ability, milking speed, once daily milking ability) used by the breeders. This rationalized approach does not preclude the implementation of other solutions to address a strategic and unforeseen new goal, such as scrapie resistance in the 1990’s. For each of the five local breeds, diversification of new breeding objectives (once daily milking ability, functional longevity, milk persistency, resistance to gastrointestinal strongyles) is likely to occur since the 2015 starting of genomic selection in French dairy sheep breeds.

La race bovine Bretonne Pie-Noire est l’une des douze races bovines actuellement en conservation en France. Son programme de sauvegarde remonte à 1976. Après avoir rappelé le contexte socio-économique correspondant à cette race et l’évolution des effectifs bovins impliqués, l’article présente les principes de la gestion génétique du plan de sauvegarde appliqués jusqu’en 2000 et les évolutions des critères habituels de variabilité génétique (parenté, consanguinité, nombre de lignées génétiques représentées). On présente les motivations et principes d’un nouveau plan de sauvegarde, qui devrait être plus efficace que l’ancien. Une extrapolation des tendances passées montre que ce dernier aurait généré en 2030 des coefficients de consanguinité moyens de l’ordre de 10-11 %. Des simulations du nouveau plan montrent qu’à la même date ces coefficients moyens seraient seulement de l’ordre de 4,5 à 5 %.

International audience; Background: All progeny-tested bucks from the two main French dairy goat breeds (Alpine and Saanen) were genotyped with the Illumina goat SNP50 BeadChip. The reference population consisted of 677 bucks and 148 selection candidates. With the two-step approach based on genomic best linear unbiased prediction (GBLUP), prediction accuracy of candidates did not outperform that of the parental average. We investigated a GBLUP method based on a single-step approach, with or without blending of the two breeds in the reference population.Methods: Three models were used: (1) a multi-breed model, in which Alpine and Saanen breeds were considered as a single breed; (2) a within-breed model, with separate genomic evaluation per breed; and (3) a multiple-trait model, in which a trait in the Alpine was assumed to be correlated to the same trait in the Saanen breed, using three levels of between-breed genetic correlations (ρ): ρ = 0, ρ = 0.99, or estimated ρ. Quality of genomic predictions was assessed on progeny-tested bucks, by cross-validation of the Pearson correlation coefficients for validation accuracy and the regression coefficients of daughter yield deviations (DYD) on genomic breeding values (GEBV). Model-based estimates of average accuracy were calculated on the 148 candidates.Results: The genetic correlations between Alpine and Saanen breeds were highest for udder type traits, ranging from 0.45 to 0.76. Pearson correlations with the single-step approach were higher than previously reported with a two-step approach. Correlations between GEBV and DYD were similar for the three models (within-breed, multi-breed and multiple traits). Regression coefficients of DYD on GEBV were greater with the within-breed model and multiple-trait model with ρ = 0.99 than with the other models. The single-step approach improved prediction accuracy of candidates from 22 to 37% for both breeds compared to the two-step method.Conclusions: Using a single-step approach with GBLUP, prediction accuracy of candidates was greater than that based on parent average of official evaluations and accuracies obtained with a two-step approach. Except for regression coefficients of DYD on GEBV, there were no significant differences between the three models.

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.

International audience; The new challenges of the dairy industry require an accurate estimation of fine milk composition. The mid-infrared (MIR) spectrometry method appears to be a good, fast and cheap method for assessing milk fatty acid profile. Although partial least squares (PLS) regression is a very useful and powerful method to determine fine milk composition from the spectra, the estimations are not always very accurate and stable over time. Therefore a genetic algorithm (GA) combined with a PLS regression was used to produce models with a reduced number of wavelengths and a better accuracy. The results are a little sensitive to the choice of parameters in the algorithm. The number of wavelengths to consider is reduced substantially by 4 and accuracy is increased on average by 15%.

Radiological and clinical findings were compared in 440 patients with the iliolumbar syndrome, a frequently observed but often unrecognized affection. Anatomical and clinical features relating to the iliolumbar ligament, this illustrious unknown part of the body, are discussed, together with the pain syndromes that arise from it and which are often dissimulated under more familiar terms such as lumbosciatica, too frequently employed as an alibi. Original data concerning the radiological appearances in patients with this syndrome are presented.

Master; AbstractReference population is the main point to succeed in genomic selection, improving genetic breeding value accuracies. The aim of this study was to characterize the structure ofthe multi-breed (Alpine and Saanen) dairy goat reference population genotyped with a 50kchip, studying level of relationship and linkage disequilibrium extent within population. Several reference populations (from 20 males to 705 males and 1 946 females) were designedto examine effect of population size and interest of female genotypes on genomic estimated breeding values (GEBV) using GBLUP. Feasibility of genomic selection in goats wasconsidered comparing GEBV accuracies and ascendance accuracy calculated for youngmales. The reference population considered in this study with 250 effective founders and 0.115 level of LD extent between two consecutive markers was not perfect for genomicselection. Increase population size induced proportional increase in GEBV accuracies from + 2% to + 31 % according to the case studied and the trait. Adding female genotypes improved accuracy from + 0.28 to + 0.01 depending on the reference population and trait studied, more relevant considering small population size. Genomic selection did not improve GEBV accuracies for young males except for those with sires genotyped.