The objective of this study was to obtain a maturity EBV to improve longevity in dairy goat. Several multi-parities models were developed in this study: a multi-trait model (MULTI) that used lactational production and a test day model (TDM) that used directly test day data. They were compared to the current repeatability model routinely used in France. There were no major differences for heritabilities and EBVs. It was found that the ability to produce milk in first parity was not the same as the ability to produce milk in third lactation. The second eigenvector of the (co)variance matrix permitted to discriminate the first and the third parity and to obtain a maturity EBV. This EBV was linked positively with longevity EBV and could be used to predict longevity. The multi trait model could be used to modify the way to calculate milk yield EBV used routinely in France by putting more emphasis on third parity EBVs, which is less negatively linked to longevity than first parity EBVs.

Milk somatic cell count (SCC) is commonly higher in goats than in cattle and sheep. Furthermore, the ability of milk SCC to predict mastitis is considered lower in goats than in cattle and sheep, and the relevance of somatic cell score (SCS)-based selection in this species has been questioned. To address this issue, we created 2 divergent lines of Alpine goats using artificially inseminated bucks with extreme estimated breeding values for SCS. A total of 287 goats, 158 in high- and 129 in low-SCS lines, were scrutinized for mastitis infections. We subjected 2,688 milk samples to conventional bacteriological analyses on agarose and bacterial counts were estimated for positive samples. The SCS, milk yield, fat content, and protein content were recorded every 3 wk. Clinical mastitis was systematically noted. A subset of 40 goats (20 from each line) was subsequently challenged with Haemonchus contortus and monitored for anemia (blood packed cell volume) and fecal egg counts to see if SCS-based selection had an indirect effect on resistance to gastrointestinal nematodes. Milk production traits, including milk quantity, fat content, and protein content, were similar in both goat lines. In contrast, the raw milk SCC almost doubled between the lines, with 1,542,000 versus 855,000 cells/mL in the high- and low-SCS lines, respectively. The difference in breeding value for SCS between lines was 1.65 genetic standard deviation equivalents. The Staphylococcus spp. most frequently isolated from milk were S. xylosus, S. caprae, S. epidermidis, and S. aureus. The frequency of positive bacteriology samples was significantly higher in the high-SCS line (49%) than in the low-SCS line (33%). The highest odds ratio was 3.49 (95% confidence interval: 11.95-6.25) for S. aureus. The distribution of bacterial species in positive samples between lines was comparable. The average quantity of bacteria in positive samples was also significantly higher in high-SCS goats (69 ± 80 growing colonies) than in low-SCS goats (38 ± 62 growing colonies). Clinical cases were rare and equally distributed between high- (n = 4; 2.5%) and low-SCS (n = 3; 2.3%) lines. Furthermore, the larger the amounts of bacteria in milk the higher the SCS level. Conversely, goats with repeatedly culture-negative udders exhibited the lowest SCC levels, with an average of below 300,000 cells/mL. We therefore confirmed that SCS is a relevant predictor of intramammary infection and hygienic quality of milk in goats and can be used for prophylactic purposes. After challenge with H. contortus, goats were anemic with high fecal egg counts but we found no difference between the genetic lines. This result provides initial evidence that resistance to mastitis or to gastrointestinal nematodes infections is under independent genetic regulation. Altogether, this monitoring of the goat lines indicated that SCS-based selection helps to improve udder health by decreasing milk cell counts and reducing the incidence of infection and related bacterial shedding in milk. Selection for low SCC should not affect a goat's ability to cope with gastrointestinal nematodes.

The objective of this study was to evaluate genetic and non-genetic factors influencing artificial insemination (AI) success in French dairy goats. Data analysis, on a total of 584 676 and 386 517 AI records for Alpine and Saanen breed, respectively, collected from 1992 to 2009, was conducted separately on each breed. We used a linear simple repeatability animal model which combined male and female random effect and environmental fixed effects. The most important environmental factor identified was the period within year effect due to the European heat wave of 2003. The estimated values of the annual fertility exhibited a negative trend of 1% loss of AI success per 10 years for Alpine breed only. The range of variation for the flock×within years random effect was 70% and 65% for Alpine and Saanen breeds. The negative effect on AI success of antibody production after repetitive hormonal treatment was confirmed. We observed an important positive relationship between fertility and protein yield expressed as quartile within flock×years of protein 250-day yield for female with lactation number over 1, while this trend was negative for primiparous females. We detected a negative effect of the duration of conservation of semen with a difference of about 4% of AI success between extreme values (2 to 8+ or 9+ years). Heritability estimates for male fertility were 0.0037 and 0.0043 for Alpine and Saanen breed respectively, while estimates for female fertility was 0.040 and 0.049. Repeatability estimates for males were 0.008 and 0.010 for Alpine and Saanen, respectively, and 0.097 and 0.102 for females. With such low values of heritability, selection can hardly affect fertility.

Genetic parameters for 18 fatty acids or groups of fatty acids (FA), milk production traits, and somatic cell score (SCS) were estimated by restricted maximum likelihood with a repeatability animal model, using 45,259 test-day records from the first lactations of 13,677 Alpine and Saanen goats. Fatty acid data were collected as part of an extensive recording scheme (PhénoFinLait), and sample testing was based on mid-infrared spectra estimates. The total predicted FA content in milk was approximately 3.5% in Alpine and Saanen goats. Goat milk fat showed similar saturated FA to cattle and sheep, but higher contents of capric (C10:0) FA (~9.7g/100g of milk fat). Heritability estimates ranged from 0.18 to 0.49 for FA and estimates were generally higher when FA were expressed in g/100g of milk fat compared with g/100g of milk. In general, the 3 specific short- and medium-chain goat FA, caproic acid (C6:0), caprylic acid (C8:0), and especially capric (C10:0) acid, had among the highest heritability estimates (from 0.21 to 0.37; average of 0.30). Heritability estimates for milk yield, fat and protein contents, and SCS were 0.22, 0.23, 0.39, 0.09, and 0.24, 0.20, 0.40, and 0.15, in Alpine and Saanen goats, respectively. When FA were expressed in g/100g of milk, genetic correlations between fat content and all FA were high and positive. Genetic correlations between the fat content and FA groups expressed in g/100g of fat led to further investigation of the association between fat content and FA profile within milk fat. Accordingly, in both Saanen and Alpine breeds, no significant genetic correlations were found between fat content and C16:0, whereas the correlations between fat content and specific goat FA (C6:0 to C10:0) were positive (0.17 to 0.59). In addition, the genetic correlation between fat content and C14:0 was negative (−0.17 to −0.35). The values of the genetic correlations between protein content and individual FA were similar, although genetic correlations between protein content and FA groups were close to zero. Genetic correlations of milk yield or SCS with the FA profile were weak. Results for genetic parameters for FA, however, should be further validated, because the low predicting ability of certain FA using mid-infrared spectra and the limited calibration data set might have resulted in low accuracy. In conclusion, our results indicated substantial genetic variation in goat milk FA that supported their amenability for genetic selection. In addition, selection on protein and fat contents is not expected to have an undesirable effect on the FA profile in regard to specificity of goat products and human health.

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 50 kb, 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.

Pedigree analyses have been widely used to assess the genetic variability of livestock species, however, limited number of examples can be found for the goat species. France is one of the only countries in the world where collective goat selection is well implemented with successful breeding programs. This paper will briefly describe the French dairy goat and fiber goat selection schemes. It will present the results of a pedigree analysis, by using the PEDIG software, of two dairy goat breeds, the Alpine and Saanen, and a fiber goat breed, the Angora, in order to assess their genetic variability. The populations under study are large for the Saanen and Alpine (125,797 and 183,611 goats, respectively) and small (1271) for the Angora. The pedigree depth can be considered to be good (from 5.5 equivalent generations for the Angora to 7.8 for the Alpine). The effective number of ancestors is equal to 34, 46 and 51 for the Angora, Alpine and Saanen breeds respectively, while the effective population size varies between 76, 129 and 149 for the Angora, Saanen and Alpine breeds respectively. In the light of these results, in the case of the dairy breeds, there are reasons to believe that despite the fact that genetic variability has narrowed as an effect of 30 years of efficient selection, the specific management program implemented 10 years ago to maintain genetic diversity was successful. Meanwhile, for the Angora breed, the narrower genetic basis of the breed appears to be caused by its thin population basis as well as the small number of founders used at the start of the selection scheme. These results give us a good insight on the genetic variability of intensively selected populations as well as a direction on the programs that are efficient to keep a sustainable level of genetic variability.

In French goat breeding, milk recording relies on 3 official recording methods for dairy traits: A, AT and AZ as defined by ICAR rules. This study evaluated the adequacy of a simplified design based on spacing records. The first result was that such a recording system is difficult to implement in farm. Moreover, lactation yields and contents estimated in these conditions showed biases and losses of accuracy. Finally, using these performances estimated with a simplified recording method had consequences on EBVs, mainly resulting in rerankings of reproducers.

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.

La saisonnalité de la reproduction chez les chèvres originaires des latitudes tempérées ou subtropicales peut maintenant être contrôlée par des changements artificiels de la photopériode. Les jours courts stimulent l’activité sexuelle tandis que les jours longs l’inhibent. Ces connaissances ont permis le développement de traitements photopériodiques pour le contrôle de l’activité sexuelle des chèvres et des boucs. En France, l’Insémination Artificielle (IA) des chèvres joue un rôle central pour le contrôle des appariements et l’organisation du schéma de sélection. La plupart des chèvres sont inséminées en dehors de la saison sexuelle avec de la semence cryoconservée, après induction hormonale de l’ovulation seule ou en combinaison avec des traitements photopériodiques. Les taux de fertilité sont en moyenne de 65%. De nouvelles stratégies sont en cours d’expérimentation. Elles sont basées sur l’IA après un effet mâle pour réduire l’utilisation des hormones. Le schéma de sélection s’est développé grâce aux progrès de l’IA. Ce schéma repose sur des plans d’accouplements entre reproducteurs d’élite, le testage sur descendance en fermes et la diffusion des semences de boucs améliorateurs. Après les caractères laitiers, les caractères fonctionnels sont désormais pris en compte. Actuellement, l’accent est mis sur la morphologie de la mamelle. La résistance à certaines maladies est à l’étude. Outre cette approche de génétique quantitative, de nouvelles perspectives basées sur une approche moléculaire permettront de détecter des gènes économiquement intéressants pour l’élevage caprin.

Reproductive seasonality observed in all breeds of goats originating from temperate latitudes and in some breeds from subtropical latitudes can now be controlled by artificial changes in photoperiod. Short days stimulate sexual activity, while long days inhibit it. This knowledge has allowed the development of photoperiodic treatments to control sexual activity in goats, for both the buck and doe. In the French intensive milk production system, goat AI plays an important role to control reproduction and, in conjunction with progeny testing, to improve milk production. Most dairy goats are inseminated out of the breeding season with deep frozen semen, after induction of oestrus and ovulation by hormonal treatments. This protocol provides a kidding rate of approximately 65%. New breeding strategies have been developed, based on the buck effect associated with AI, to reduce the use of hormones. With the development of insemination with frozen semen, a classical selection programme was set up, including planned mating, progeny testing and the diffusion of proved sires by inseminations in herds. Functional traits have become important for efficient breeding schemes in the dairy goat industries. Based on knowledge gained over the past decade, the emphasis in selective breeding has been placed on functional traits related to udder morphology and health. New windows have been opened based on new molecular tools, allowing the detection and mapping of genes of economic importance.

Reproductive seasonality observed in all breeds of goats originating from temperate latitudes and in some breeds from subtropical latitudes can now be controlled by artificial changes in photoperiod. Short days stimulate sexual activity, while long days inhibit it. This knowledge has allowed the development of photoperiodic treatments to control sexual activity in goats, in both the buck and doe. In the French intensive milk production system, goat artificial insemination plays an important role in controlling reproduction and, in conjunction with progeny testing, in improving milk production. Most dairy goats are inseminated out of the breeding season with deep frozen semen, after induction of oestrus and ovulation by hormonal treatment. This protocol provides a kidding rate of about 65%. New breeding strategies based on the buck effect associated with artificial insemination are being developed to reduce the use of hormones. With the development of insemination with frozen semen, a classical selection program was set up, including planned mating, progeny testing and the diffusion of proven sires by insemination in herds. Functional traits have become important for efficient breeding schemes in the dairy goat industries. Emphasis on functional traits related to udder morphology and health resulted from the knowledge established during the last decade. New windows have been opened based on new molecular tools allowing the detection and mapping of genes of economic importance in farm animals.

Genetic parameters of dairy traits for dairy sheep and goats follow the same patterns as in cattle: heritabilities for milk, fat and protein lactation yields are moderate (∼ 0.30) and smaller than those for fat and protein contents (∼ 0.50 to 0.60). Negative genetic correlations between milk yield and contents indicate that a compromise must be found if selection is oriented toward milk production and contents for cheese production for instance. Furthermore, if the genetic polymorphism of sheep milk for alpha sl -casein and β-lactoglobulin loci exhibit inconsistent results, on the opposite the effects of the major gene of the caprine alpha sl -casein on goat milk traits and properties of dairy products are more and more documented. These well-known genetic variations of dairy traits for small ruminants indicate the possibility of improving significantly dairy sheep and goat populations in a few decades, using either a classical polygenic approach (sheep) or taking profit also of major genes (goat). But in practice, the best breeding strategy has been an ongoing debate for these two species: crossing for absorption of low producing local breeds is often used when the implementation of a cooperative breeding scheme at the population level appears a too difficult process to be achieved in a realistic time. Indeed such a programme is based on on-farm milk recording, the joint use of artificial insemination and controlled natural matings, combined with an accurate genetic evaluation of the animals. In practice, operating breeding programmes are concentrated in Europe (especially Mediterranean countries for dairy sheep) and North America. Therefore a present Mexican experiment, using a basic recording system and selection only on the female side, has to be promoted, since it appears as a sound strategy to create genetic progress in low to medium milk yield level sheep and goat populations which are numerous in developing countries. To end the presentation, new perspectives based on QTL detection programmes in dairy sheep and selection on somatic cell count to face the improvement of hygienic quality of sheep and goat milks are presented.