Soutenance de Thèse - Développement d'une évaluation génomique pour l'analyse de données longitudinales : application aux contrôles élémentaires chez les caprins laitiers.

Genetic improvement of dairy goats is based on the measurement of the quantity and quality of milk production of females on farms, at intervals of 4 to 5 weeks during lactation, according to strict protocols. The genetic evaluation of the quantity of milk is based on the estimation of the total quantity of milk produced per lactation. This selection based on the total quantity of milk in lactation tends to select animals with increasingly high peak lactation production. High production at the beginning of lactation can cause metabolic problems for females. In addition, in the case of dairy goats, in a context of seasonal production, a milk production that is maintained after the peak, i.e. persistent, would allow a more spread out milk production, in line with market expectations. There is therefore a zootechnical and economic interest in wanting to select more persistent dairy goats. In our study, the approach consists in modelling the shape of the lactation curve based on the information collected during each farm test. Models allowing the analysis of such longitudinal data are generally called test-day models. One of the main interests is to take better account of environmental effects, affecting production on test-day, with a herd-test-day effect depending only on the animals present during the test. The second advantage of this type of model is that most genetic and environmental effects are modelled as curves, so it would be possible to select animals with the best genetic value for persistence. The development of these models requires the prior study of the environmental effects affecting milk production over time. Following a detailed descriptive analysis of the lactation curves of the two main French goat breeds (Alpine and Saanen), we showed that there was a variability in the shape of the lactation curves, and in particular the month of calving was involved in the different curve shapes. We then proposed a random regression model, similar to that developed in French dairy cattle. The proposed modeling makes it possible to obtain two genetic indexes directly: one corresponding to the genetic value of the animal for the total quantity of milk during lactation and a second corresponding to a genetic value of the animal's milk persistency, without correlation between the two. The model proposed is more relevant than the current one because it takes into account in a disjointed way the goats in primiparous and the goats in multiparous. We also studied correlations between indexes of different traits during lactation and correlations between persistence and AI fertility or between persistence and longevity. In the last part of the thesis, we extended the genetic evaluation of test-day to a genomic evaluation model (Single Step GBLUP) allowing to exploit all available molecular information (genotyping SNP 50K). A validation of this model and a comparison with the current model was carried out. The main difference between the Single Step GBLUP RRM and the Single Step GBLUP lactation model currently in use was the differences in the averages of the estimated indexes per year of birth of the bucks. Finally, from the Single-Step GBLUP model we have identified some interested regions of the genome linked to milk persistence.