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Despite soybean's widespread recognition as a versatile and valuable crop due to many end-use purposes, breeders seek to develop varieties with improved nutritional and functional components that capture added-value for producers. Additionally, producers seek to maximize profits by utilizing field practices to augment crop value. Therefore, this dissertation had two main objectives of maximizing soybean value: 1) to evaluate accelerated selection methods by soybean breeders for methionine content and test weight, and 2) to identify sulfur fertilization impact on soybean seed composition including amino and fatty acid profiles. First, a genome-wide association study (GWAS) analyzed genomic influence on proteinogenic methionine in soybean seeds which identified 23 single nucleotide polymorphisms (SNPs). Utilizing a SNPs subset identified by GWAS, genomic selection (GS) exhibited average prediction accuracies ranging from 0.41-0.62. Secondly, a novel phenomic selection (PS) method using near-infrared reflectance spectroscopy (NIRS) was evaluated for predictive ability of soybean test weight. PS cross-validations exhibited average predictive accuracies of 0.75, 0.59, and 0.16 when incorporating all environments, between locations, and between years, respectively. Finally, sulfur fertilizer rates and sources were assessed across two years and six locations in relation to seed composition. Notably, ammonium sulfate (AMS) was found to have a significant impact (P < 0.05) on methionine content in soybean seed. These outcomes will have positive impacts on plant breeding and soybean production for seed composition and quality traits using contemporary breeding and fertilization. / Doctor of Philosophy / Despite soybean's widespread recognition as a versatile and valuable crop due to a myriad of end-use purposes, breeders seek to develop varieties with improved nutritional and functional components that captured value for producers. Additionally, producers seek to maximize their profits by utilizing field practices that increase crop value. Therefore, this dissertation had two main objectives of maximizing soybean value: 1) to evaluate accelerated selection methods by soybean breeders for methionine content and test weight, and 2) to identify sulfur fertilization impact on soybean seed protein and oil composition. The overall objective was to create a comprehensive toolset for soybean breeders to develop Mid-Atlantic soybean varieties with improved seed composition traits and to determine fertilization impacts for use by producers. Genetic controls for protein-bound methionine in soybean seed were identified and could be used for variety development. Additionally, a new prediction method that uses light reflectance to represent genetic information and environmental effects was shown to have high accuracy for soybean test weight. It was also found that sulfur fertilizer with high availability in the soil positively impacted methionine content. These outcomes will have positive impacts on plant breeding and soybean production for seed composition and quality traits using contemporary breeding and fertilization.

Les évaluations génomiques apportent une information précoce et suffisamment précise pour choisir les jeunes taureaux dans les schémas de sélection des bovins laitiers, incitant à remplacer le long processus de testage sur descendance par une étape de sélection génomique.Dès lors, seuls les candidats sélectionnés ont des filles avec performances et participent aux évaluations génétiques classiques. Cependant, toutes les informations ayant servi à la sélection ne sont plus incluses dans l’analyse et l’estimation des valeurs génétiques par la méthode du BLUP (Best Linear Unbiased Prediction) peut être incorrecte.Les évaluations génétiques classiques restent indispensables pour l’évaluation des animaux non génotypés, pour la comparaison des taureaux à l’échelle mondiale, et pour le calcul des futures prédictions génomiques. Compte-tenu de la rapide intégration de la génomique dans les schémas de sélection des bovins laitiers, il était important d’en étudier les conséquences sur les évaluations génétiques classiques.A l’échelle nationale, nos simulations ont montré que les valeurs génétiques des taureaux retenus sur information génomique étaient systématiquement sous-estimées et moins précises quand l’étape de sélection génomique n’était pas prise en compte dans le modèle statistique. Pour éviter ce biais, une méthode a été testée avec succès : pour l’ensemble des candidats à la sélection, des pseudo-performances sont calculées à partir des index génomiques et analysées par le BLUP. Suivant la prise en compte ou non de l’étape de sélection génomique, les pays participant aux évaluations internationales peuvent fournir des données biaisées et/ou incomplètes, au risque de pénaliser fortement leurs propres taureaux dans les classements internationaux. La diversité des pratiques à l’échelle mondiale et l’interaction des possibles sources de biais dans les évaluations internationales rendent sa propagation incontrôlable et fortement dommageable.Il est donc nécessaire et urgent d’adapter les évaluations génétiques classiques pour prendre en compte l’information génomique et ses pratiques associées. Diverses approches récentes sont discutées afin de proposer des alternatives faciles à mettre en place dans les centres d’évaluation, permettant de maintenir des évaluations non biaisées mais aussi plus précises. / With the fast and wide development of genomic evaluations in dairy cattle, the design of breeding schemes has been modified and the long process of progeny testing is being replaced by an early and accurate genomic selection step.In the future, only selected candidates will get performances to be evaluated by the classical method of Best Linear Unbiased Prediction (BLUP). After a genomic selection step, information about the selection process is no longer complete, BLUP assumptions are violated and solutions, i.e., estimated breeding values, are feared to be incorrect.The aim of the thesis study was to consider the consequences of genomic selection on the classical genetic evaluations at the national and international levels.First, bias in national breeding values was assessed by repeated simulations. Estimated breeding values were systematically underestimated and less accurate after a genomic selection step not accounted for in genetic evaluation models.Secondly, a statistical procedure, a BLUP model with genomic pseudo-performances, was investigated to eliminate, with success, bias in estimated breeding values.In a third part, the consequences of genomic selection on international evaluations were studied by simulations. Bulls from the country sending incomplete and/or biased breeding values were the most penalized in international rankings.In conclusion, it is not only necessary but also urgent to prevent from bias in classical evaluations and therefore avoid harmful impacts on international comparisons, on future genomic evaluations, and more generally on selection efficiency. Alternative approaches were thus discussed to propose short and long term strategies for routine evaluations. Nevertheless, a main consequence of bias corrected breeding values is that all genetic predictions will include some genomic information in a near future: adaptations of evaluation methods are still required to optimally benefit from all types of information.

L'évolution rapide des techniques de séquençage et de génotypage soulèvent de nouveaux défis dans le développement des méthodes de sélection pour les animaux d’élevage. Par comparaison de séquences, il est à présent possible d'identifier des sites polymorphes dans chaque espèce afin de baliser le génome par des marqueurs moléculaires appelés SNP (Single Nucleotide Polymorphism). Les méthodes de sélection des animaux à partir de cette information moléculaire nécessitent une représentation complète des effets génétiques. Meuwissen et al. (2001) ont introduit le concept de sélection génomique en proposant de prédire simultanément tous les effets des régions marquées puis de construire un index "génomique" en sommant les effets de chaque région. Le challenge dans l’évaluation génomique est de disposer de la meilleure méthode de prédiction afin d’obtenir des valeurs génétiques précises pour une sélection efficace des animaux candidats. L’objectif général de cette thèse est d'explorer et d’évaluer de nouvelles approches génomiques capables de prédire des dizaines de milliers d'effets génétiques, sur la base des phénotypes de centaines d'individus. Elle s’inscrit dans le cadre du projet ANR AMASGEN dont le but est d’étendre la sélection assistée par marqueurs, utilisée jusqu’à lors chez les bovins laitiers français, et de développer une méthode de prédiction performante. Pour cela, un panel varié de méthodes est exploré en estimant leurs capacités prédictives. Les méthodes de régression PLS (Partial Least Squares) et sparse PLS, ainsi que des approches bayésiennes (LASSO bayésien et BayesCπ) sont comparées à deux méthodes usuelles en amélioration génétique : le BLUP basé sur l’information pedigree et le BLUP génomique basé sur l’information des SNP. Ces méthodologies fournissent des modèles de prédiction efficaces même lorsque le nombre d’observations est très inférieur au nombre de variables. Elles reposent sur la théorie des modèles linéaires mixtes gaussiens ou les méthodes de sélection de variables, en résumant l’information massive des SNP par la construction de nouvelles variables. Les données étudiées dans le cadre de ce travail proviennent de deux races de bovins laitiers français (1 172 taureaux de race Montbéliarde et 3 940 taureaux de race Holstein) génotypés sur environ 40 000 marqueurs SNP polymorphes. Toutes les méthodes génomiques testées ici produisent des évaluations plus précises que la méthode basée sur la seule information pedigree. On observe un léger avantage prédictif des méthodes bayésiennes sur certains caractères mais elles sont cependant trop exigeantes en temps de calcul pour être appliquées en routine dans un schéma de sélection génomique. L’avantage des méthodes de sélection de variables est de pouvoir faire face au nombre toujours plus important de données SNP. De plus, elles sont capables de mettre en évidence des ensembles réduits de marqueurs, identifiés sur la base de leurs effets estimés, c’est-à-dire ayant un impact important sur les caractères étudiés. Il serait donc possible de développer une méthode de prédiction des valeurs génomiques sur la base de QTL détectés par ces approches. / The rapid evolution in sequencing and genotyping raises new challenges in the development of methods of selection for livestock. By sequence comparison, it is now possible to identify polymorphic regions in each species to mark the genome with molecular markers called SNPs (Single Nucleotide Polymorphism). Methods of selection of animals from genomic information require the representation of the molecular genetic effects. Meuwissen et al. (2001) introduced the concept of genomic selection by predicting simultaneously all the effects of the markers. Then a genomic index is built summing the effects of each region. The challenge in genomic evaluation is to find the best prediction method to obtain accurate genetic values of candidates. The overall objective of this thesis is to explore and evaluate new genomic approaches to predict tens of thousands of genetic effects, based on the phenotypes of hundreds of individuals. It is part of the ANR project AMASGEN whose aim is to extend the marker-assisted selection, used in French dairy cattle, and to develop an accurate method of prediction. A panel of methods is explored by estimating their predictive abilities. The PLS (Partial Least Squares) and sparse PLS regressions and Bayesian approaches (Bayesian LASSO and BayesCπ) are compared with two current methods in genetic improvement: the BLUP based on pedigree information and the genomic BLUP based on SNP markers. These methodologies are effective even when the number of observations is smaller than the number of variables. They are based on the theory of Gaussian linear mixed models or methods of variable selection, summarizing the massive information of SNP by new variables. The datasets come from two French dairy cattle breeds (1172 Montbéliarde bulls and 3940 Holstein bulls) genotyped with 40 000 polymorphic SNPs. All genomic methods give more accurate estimates than the method based on pedigree information only. There is a slight predictive advantage of Bayesian methods on some traits but they are still too demanding in computation time to be applied routinely in a genomic selection scheme. The advantage of variable selection methods is to cope with the increasing number of SNP data. In addition, they are able to extract reduced sets of markers based of their estimated effects, that is to say, with a significant impact on the trait studied. It would be possible to develop a method to predict genomic values on the basis of QTL detected by these approaches.

Genomic selection consists of using predicted effects of genetic markers to predict breeding values and/or genotypic values of genotyped individuals. With this approach, selection can be carried based only on those predicted breeding values, reducing the need for further phenotypic evaluations. This represents a great advance in terms of cost and effectiveness of selection in breeding programs of all kinds of crops. In the first chapter of this work, we explore one of the ways genomic selection can be used to increase efficiency when breeding clonally propagated crops for multiple traits. Using stochastic simulations, we show that an economic selection index should be preferred over independent culling. Our results show that the use of genomic selection may render the cost-efficiency benefit of independent culling obsolete when all early generation individuals are genotyped and accurate prediction of all traits becomes available simultaneously. Despite the potential benefits of selecting based on predicted breeding values, for some clonally propagated species the complexity of their genomes limits the implementation of genomic selection in breeding programs. Since including allele dosage information has been shown to improve performance of genomic selection models in autotetraploid species, our objective in the second chapter of this work was to assess the accuracy of genome-wide prediction in the highly complex polyploid sugarcane when incorporating allele dosage information. In this chapter, we expanded GBLUP genomic selection models developed for autotetraploids to include higher levels of ploidy. Two types of model were used, one with additive effects only and one with additive and digenic dominance effects. We observed a modest improvement in the performance of the prediction model when ploidy and allele dosage estimates were included, indicating that this is a possible way of improving genomic selection in sugarcane. The results obtained in both studies can assist researchers and breeders of clonally propagated crops, opening new research opportunities and indicating the most efficient ways to implement genomic selection. / A seleção genômica consiste no uso de efeitos preditos de marcadores genéticos para predizer os valores genéticos e/ou genotípicos de indivíduos genotipados. Desta forma, a seleção de genótipos superiores pode ser feita baseada apenas em valores genéticos preditos, reduzindo a necessidade de avaliações fenotípicas subsequentes. Isto representa um grande avanço em termos de custos e eficiência da seleção em programas de melhoramento de todos os tipos de culturas. No primeiro capítulo deste trabalho, nós exploramos uma das maneiras com que a seleção genômica pode ser utilizada para aumentar a eficiência no melhoramento simultâneo para múltiplos caráteres em espécies de propagação vegetativa. Utilizando simulações estocásticas, nós mostramos que um índice de seleção econômico deve ser utilizado no lugar da eliminação independente (independent culling). Os resultados mostram que o uso da seleção genômica pode tornar o custo-benefício da eliminação independente obsoleto se indivíduos em gerações iniciais forem genotipados e predições acuradas para todos os caráteres estiverem disponíveis desde o início. Apesar dos potenciais benefícios de realizar a seleção com base em valores genéticos preditos, para algumas espécies de propagação vegetativa a complexidade de seus genomas é um fator limitante para a efetiva implementação da seleção genômica em programas de melhoramento. Considerando que incluir a informação de dosagem alélica melhorou a performance de modelos de seleção genômica em espécies autotetraploides, nosso objetivo no segundo capítulo deste trabalho foi avaliar a acurácia da predição genômica com informação de dosagem alélica em cana-de-açúcar, que é uma complexa espécie poliploide. Neste capítulo, nós expandimos modelos GBLUP de seleção genômica desenvolvidos para autotetraploides para incluir níveis mais altos de ploidia. Dois modelos foram utilizados, um modelo com somente efeitos aditivos e um modelo com efeitos aditivos e efeitos de dominância digênica. Nós observamos uma modesta melhora na performance do modelo preditivo quando estimativas de ploidia e dosagem alélica foram incluídas, indicando que esta é uma possível maneira de aprimorar a seleção genômica em cana-de-açúcar. Os resultados obtidos nos dois estudos podem auxiliar pesquisadores e melhoristas de espécies de propagação vegetativa, abrindo portas para novas pesquisas e indicando as maneiras mais eficientes para implementação da seleção genômica.

A seleção genômica é a mais moderna tecnologia no que tange a utilização de marcadores genéticos para a seleção e melhoramento genético de animais domésticos. Em síntese a metodologia consiste em uma seleção assistida por marcadores em uma escala ampla do Genoma e foi proposta por Meuwissen et al. (2001). O desequilíbrio de ligação (DL) entre os marcadores e loci de características quantitativas (QTL) e a composição da população referência são pontos chave para a confiabilidade da seleção genômica. À medida que os indivíduos se distanciam geneticamente, o DL entre marcadores e QTL diminui, o que dificulta a aplicação da seleção genômica em populações de animais multirraciais e explica o fato de até hoje a maior parte das pesquisas se concentrarem na estimação de valores genômicos apenas para animais de raças puras. A aplicação dessa metodologia em populações multirraciais de bovinos constitui uma possibilidade real de grandes avanços no melhoramento genético do rebanho brasileiro de bovinos de corte. / Genomic Selection is the newest technology into the use of genetic markers at the animal breeding. In sinthesys the metodology consists in a marker assisted selection in a genome wide scale, it was pourpused by Mewissent et al. (2001). The linkage disequilibrium between the marker and quantitative trait loci (QTL) and the composition of the reference population are key points to the reablility of the genomic selection. Once the individuals get genetically distants, the DL between markers and QTLs decays, what turns hard the aplication of genomic selection in multirracial populations of animals and explains the fact of untill today the major part of researchs are dedicated to the estimation of genomic breeding values only to purebreed animals. The aplication of this methodology in multibreed cattle populations consists in a real possibility to reach greats advances in genetic improvement of the brazilian beef cattle heard.

In plant breeding, genomic prediction (GP) may be an efficient tool to increase the accuracy of selecting genotypes, mainly, under multi-environments trials. This approach has the advantage to increase genetic gains of complex traits and reduce costs. However, strategies are needed to increase the accuracy and reduce the bias of genomic estimated breeding values. In this context, the objectives were: i) to compare two strategies to obtain markers subsets based on marker effect regarding their impact on the prediction accuracy of genome selection; and, ii) to compare the accuracy of four GP methods including genotype × environment interaction and two kernels (GBLUP and Gaussian). We used a rice diversity panel (RICE) and two maize datasets (HEL and USP). These were evaluated for grain yield and plant height. Overall, the prediction accuracy and relative efficiency of genomic selection were increased using markers subsets, which has the potential for build fixed arrays and reduce costs with genotyping. Furthermore, using Gaussian kernel and the including G×E effect, there is an increase in the accuracy of the genomic prediction models. / No melhoramento de plantas, a predição genômica (PG) é uma eficiente ferramenta para aumentar a eficiência seletiva de genótipos, principalmente, considerando múltiplos ambientes. Esta técnica tem como vantagem incrementar o ganho genético para características complexas e reduzir os custos. Entretanto, ainda são necessárias estratégias que aumentem a acurácia e reduzam o viés dos valores genéticos genotípicos. Nesse contexto, os objetivos foram: i) comparar duas estratégias para obtenção de subconjuntos de marcadores baseado em seus efeitos em relação ao seu impacto na acurácia da seleção genômica; ii) comparar a acurácia seletiva de quatro modelos de PG incluindo o efeito de interação genótipo × ambiente (G×A) e dois kernels (GBLUP e Gaussiano). Para isso, foram usados dados de um painel de diversidade de arroz (RICE) e dois conjuntos de dados de milho (HEL e USP). Estes foram avaliados para produtividade de grãos e altura de plantas. Em geral, houve incremento da acurácia de predição e na eficiência da seleção genômica usando subconjuntos de marcadores. Estes poderiam ser utilizados para construção de arrays e, consequentemente, reduzir os custos com genotipagem. Além disso, utilizando o kernel Gaussiano e incluindo o efeito de interação G×A há aumento na acurácia dos modelos de predição genômica.

Phenotyping in multi-environment trials (MET) plays an important role to access the differential response of maize hybrids across target breeding regions due to genotype by environment (GxE) interaction. In this context, an effective model of genomic selection (GS) to predict the performance of untested hybrids in MET is essential to maximize genetic gains and to efficiently allocated the breeding programs\' budget. Therefore, the goals of this study were (i) to evaluate the predictive accuracies of GBLUP (Genomic Best Linear Unbiased Prediction) models to predict grain yield performance of unobserved tropical maize single-cross hybrids, using models that consider GxE interaction by fitting a factor analytic (FA) variance-covariance (VCOV) structure, and (ii) to investigate the usefulness of genomic relationship information in combination with different VCOV for genetics and residuals effects, under different levels of unbalanced environments. Predictions were performed for two situations: (CV1) untested hybrids, and (CV2) hybrids evaluated in some environments but missing in others. Phenotypic data of grain yield was measured in 156 maize single-cross hybrids at 12 environments. Hybrids genotypes were inferred based on their parents (inbred lines) via SNP (single nucleotide polymorphism) markers obtained from GBS (genotypingby- sequencing). The procedures and models applied in this study can be easily extended to other crops in which MET plays an important role in the breeding process. / A fenotipagem em ensaios de múltiplos ambientes (MET) tem papel importante para acessar a resposta diferencial de híbridos de milho em diferentes regiões alvo de melhoramento, o que se deve a interação genótipos por ambientes (GxE). Neste contexto, um modelo efetivo de seleção genômica (GS) para predição do desempenho de híbridos não avaliados em MET é essencial para maximizar os ganhos genéticos e alocar eficientemente o orçamento dos programas de melhoramento. Desta forma, os objetivos deste estudo foram (i) avaliar as acurácias preditivas de modelos GBLUP (do inglês, Genomic Best Linear Unbiased Prediction) na predição da produtividade de grãos de híbridos simples de milho tropical não avaliados, usando modelos genético-estatísticos que levam em consideração a interação GxE através de uma estrutura de variância-covariância (VCOV) do tipo fator analítico (FA) e (ii) investigar a utilidade da matriz de parentesco realizada em combinação com diferentes estruturas de VCOV para efeitos genéticos e de resíduos em diferentes níveis de ambientes em desbalanceamento. As predições foram realizadas em duas situações: (CV1) híbridos não avaliados em nenhum ambiente e (CV2) híbridos avaliados em alguns ambientes e em outros não. Foram fenotipados 156 híbridos simples de milho em 12 ambientes para a característica produtividade de grãos. O genótipo dos híbridos foi inferido com base nas informações de marcadores SNP (do inglês, single nucleotide polymorphism) das linhagens parentais, obtidos via GBS (do inglês, genotyping-by-sequencing). Os procedimentos e modelos utilizados neste estudo podem ser facilmente estendidos a outras culturas em que MET desempenha um papel importante no processo de melhoramento.

The availability of molecular markers covering the entire genome, such as single nucleotide polymorphism (SNP) markers, allied to the computational resources for processing large amounts of data, enabled the development of an approach for marker assisted selection for quantitative traits, known as genomic selection. In the last decade, genomic selection has been successfully implemented in a wide variety of animal and plant species, showing its benefits over traditional marker assisted selection and selection based only on pedigree information. However, some practical challenges may still limit the wide implementation of this method in a plant breeding program. For example, we cite the cost of high-density genotyping of a large number of individuals and the application of more complex models that take into account multiple traits and environments. Thus, this study aimed to i) investigate SNP calling and imputation strategies that allow cost-effective high-density genotyping, as well as ii) evaluating the application of multivariate genomic selection models to data from multiple traits and environments. This work was divided into two chapters. In the first chapter, we compared the accuracy of four imputation methods: NPUTE, Beagle, KNNI and FILLIN, using genotyping-by-sequencing (GBS) data from 1060 maize inbred lines, which were genotyped using different depths of coverage. In addition, two SNP calling and imputation strategies were evaluated. Our results indicated that combining SNP-calling and imputation strategies can enhance cost-effective genotyping, resulting in higher imputation accuracies. In the second chapter, multivariate genomic selection models, for multiple traits and environments, were compared with their univariate versions. We used data from 415 hybrids evaluated in the second season in four years (2006-2009) for grain yield, number of ears and grain moisture. Hybrid genotypes were inferred in silico based on their parental inbred lines using SNP markers obtained via GBS. However, genotypic information was available only for 257 hybrids, motivating the use of the H matrix, which combines genetic information based on pedigree and molecular markers. Our results demonstrated that the use of multi-trait multi-environment models can improve predictive abilities, especially to predict the performance of hybrids that have not yet been evaluated in any environment. / A disponibilidade de marcadores moleculares cobrindo todo o genoma, como os polimorfismos de nucleotídeos individuais (single nucleotide polymorphism - SNP), aliada aos recursos computacionais para o processamento de grande volume de dados, tornou possível o desenvolvimento de uma abordagem de melhoramento assistido para caracteres de herança quantitativa, conhecida como seleção genômica. Na última década a seleção genômica tem sido implementada com sucesso em uma enorme variedade de espécies animais e vegetais, comprovando suas vantagens sobre a seleção assistida por marcadores tradicional e a seleção baseada apenas em informações de parentesco. No entanto, alguns desafios práticos ainda podem limitar a implementação deste método em um programa de melhoramento de plantas. Como exemplos, citam-se o custo da genotipagem de alta densidade de um grande número de indivíduos e a aplicação de modelos mais complexos, que consideram múltiplos caracteres e ambientes. Dessa forma, este estudo teve como objetivos: i) investigar estratégias de identificação de SNPs e imputação que possibilitem uma genotipagem de alta densidade economicamente viável; e ii) avaliar a aplicação de modelos multivariados de seleção genômica para múltiplos caracteres e ambientes. Este trabalho foi divido em dois capítulos. No primeiro capítulo, comparou-se a acurácia de quatro métodos de imputação: NPUTE, Beagle, KNNI e FILLIN, usando dados de genotipagem por sequenciamento (genotyping-by-sequencing - GBS) de 1.060 linhagens de milho, que foram genotipadas usando diferentes profundidades de cobertura. Além disso, duas estratégias de identificação de SNPs e imputação foram avaliadas. Os resultados indicaram que a combinação de estratégias de detecção de polimorfismos e imputação pode possibilitar uma genotipagem economicamente viável, resultando em maiores acurácias de imputação. No segundo capítulo, modelos multivariados de seleção genômica, para múltiplos caracteres e ambientes, foram comparados com suas versões univariadas. Dados de 415 híbridos avaliados na segunda safra em quatro anos (2006-2009) para os caracteres produtividade de grãos, número de espigas e umidade foram utilizados. Os genótipos dos híbridos foram inferidos in silico com base nos genótipos das linhagens parentais usando marcadores SNPs obtidos via GBS. No entanto, informações genotípicas estavam disponíveis para apenas 257 híbridos, de modo que foi necessário fazer uso da matriz H, a qual combina informações de parentesco genético baseadas em pedigree e marcadores. Os resultados obtidos demonstraram que o uso de modelos de seleção genômica para múltiplos caracteres e ambientes pode aumentar a capacidade preditiva, especialmente para predizer a performance de híbridos nunca avaliados em qualquer ambiente.

La paratuberculosis (PTB) est une maladie endémique des ruminants causée par Mycobacterium avium subsp. paratuberculosis (Map). Les stratégies de maîtrise actuelles ne sont pas suffisamment efficaces. La réponse à l'exposition à Map varie entre les animaux avec une part de déterminisme génétique. Des marqueurs génétiques pourraient permettre une sélection. L'objectif était d'évaluer par modélisation l'efficacité potentielle attendue de stratégies de maîtrise utilisant la sélection génétique ou la réduction de l'exposition en élevage. Nous avons identifié quatre traits phénotypiques de résistance influençant principalement la propagation de Map à l'échelle du troupeau et montré la valeur ajoutée de leur amélioration simultanée. Nous avons évalué l'effet de l'environnement du troupeau et du système d’élevage sur la propagation et la maîtrise de Map. Nous avons montré une différence d’efficacité des stratégies de maîtrise les plus pertinentes entre deux systèmes d'élevage bovins laitiers contrastés d'Europe: l'ouest de la France et l'Irlande. Nous avons évalué l'efficacité que pourrait apporter la sélection génomique en évaluant le temps nécessaire pour atteindre des niveaux de variation des traits sélectionnés permettant un bon contrôle de l‘infection sous l’hypothèse que des marqueurs de sélection soient disponibles. Nous avons identifié 2 paramètres du modèle de sélection génomique influents sur l’efficacité de la sélection. Notre modèle permet d’intégrer de nouvelles connaissances biologiques sur le déterminisme génétique de la résistance à Map pour évaluer des stratégies de maîtrise complexes comprenant une composante de sélection génomique. / Paratuberculosis (PTB) is an endemic disease of ruminants caused by Mycobacterium avium subsp. paratuberculosis (Map). Current control strategies are not effective enough. The response to Map exposure varies between animals with evidence of a partial genetic determinism. Genetic markers could allow selection. The objective was to assess the potential expected effectiveness of control strategies relying on genetic selection or reduction of exposure in herds, using a modelling approach. We identified four phenotypic traits of resistance mainly influencing the spread of Map at the herd scale and showed the added value of their simultaneous improvement. We evaluated the effect of the herd environment and management on the spread and control of Map. We showed a difference in effectiveness of the most relevant control strategies between two contrasting dairy cattle systems in Europe: western France and Ireland. We evaluated the effectiveness of genomic selection by assessing the time required to reach levels of variation in the selected traits allowing to achieve a good control of infection, assuming that associated genomic markers could be available. Effectiveness of selection was mainly influenced by 2 of the parameters of the developed genomic selection model. Our model allows to account for future knowledge about the genetic determinism of cattle resistance to Map in order to assess the effectiveness of complex control strategies including a genomic selection component.