Orientador: Lucia Galvão de Albuquerque / Resumo: A seleção de animais geneticamente superiores com base na informação genômica tem sido uma tendência crescente e promissora em programas de melhoramento. No entanto, os principais métodos de predição genômica envolvem modelos paramétricos, que em sua maioria, assumem somente variância aditiva para o efeito dos marcadores, ignorando-se possíveis relações não-lineares. A consideração de tais efeitos pode ser importante para melhorar a habilidade de predição em características com arquitetura genética complexa. Recentemente, tem crescido o interesse em métodos de predição semi e não paramétricos. Dentro desse contexto, os métodos de aprendizagem de máquina tais como Redes Neurais Artificiais (ANN), “Random Forest” (RF) e “Support Vector Machines” (SVM) são alternativas interessantes. Os objetivos do presente estudo foram: i) Comparar o desempenho preditivo do modelo “Genomic Best Linear Unbiased Predictor” (GBLUP) e de métodos de aprendizagem de máquina em populações simuladas de bovinos de corte, apresentando diferentes níveis para efeitos de dominância; ii) Investigar a habilidade de predição de diferentes métodos de aprendizagem de máquina para predição genômica de características reprodutivas em bovinos da raça Nelore; iii) Desenvolver um estudo de associação genômica ampla (GWAS) utilizando a metodologia “Random Forest”, a fim de buscar genes candidatos para idade ao primeiro parto em novilhas da raça Nelore. No primeiro estudo, o genoma simulado compreendeu um painel de SN... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: The selection of genetically superior animals based on genomic information has been an increasing and promising trend in breeding programs. However, the main methods used for genome-enabled prediction involve parametric models that mostly assume only additive variance for markers effects, ignoring possible nonlinear relationships. Accounting for such effects may be important to improve the predictive ability for traits with complex genetic architecture. The interest in semi and non-parametric prediction methods has recently increased. Within this context, machine learning methods such as Artificial Neural Networks (ANN), Random Forest (RF) and Support Vector Machines (SVM) are an interesting alternative. The aims of the present study were: i) To compare the predictive performance of Genomic Best Linear Unbiased Predictor (GBLUP) and machine learning methods in simulated beef cattle populations presenting different degrees of dominance; ii) To investigate the predictive ability of different machine learning for genome-enabled prediction of reproductive traits in Nellore cattle and compare their performance with parametric approaches (GBLUP and BLASSO); iii) To perform a genome-wide association study (GWAS) using the Random Forest approach for scanning candidate genes for age at first calving in Nellore heifers. In the first study, the simulated genome comprised 50k single nucleotide polymorphisms (SNPs) and 300 QTL (Quantitative Trait Loci), both biallelic and randomly distrib... (Complete abstract click electronic access below) / Doutor

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The inoculation with Azospirillum brasilense is one of the main strategies to supplement the inorganic inputs of nitrogen (N) and to increase the root development in maize. However, the beneficial inoculation effects are not always reached, which, in part, is due to genotypic variation in the plant host, resulting in different degrees of outcome. In this context, we aimed to study the genetic control and genomic prediction of maize traits related to the responsiveness to A. brasilense inoculation. For this, 118 maize hybrids were conducted under N stress and N stress plus A. brasilense treatments in controlled conditions over 2016 and 2017 seasons. We evaluated root and shoot traits and performed diallel analyses, association mapping, and genomic prediction methods considering 59,215 Single-Nucleotide Polymorphism (SNP) markers. Our results revealed a quantitative inheritance of the partnership-related maize traits, with both additive and non-additive genetic effects involved in the genetic control. Furthermore, several candidate genes were identified for the maize-A. brasilense association, especially with heterozygous (dis)advantage effects. In general, the prediction accuracies were higher mostly for the inoculated treatment compared to the non-inoculated. Finally, our findings enable a deeper understanding towards the genetic basis of the maize responsiveness to A. brasilense and may support plant breeders to establish selection strategies aiming the development of superior genotypes for this association. / A inoculação com Azospirillum brasilense é uma das principais estratégias para suplementar os insumos inorgânicos de nitrogênio (N) e aumentar o desenvolvimento radicular do milho. No entanto, os efeitos benéficos da inoculação nem sempre são alcançados, o que, em parte, é devido à variação genotípica da planta hospedeira, que ocasiona diferentes graus de resultados. Neste contexto, nosso objetivo foi estudar o controle genético e a predição genômica de caracteres de milho relacionados à responsividade para a inoculação com A brasilense. Para isso, 118 híbridos de milho foram conduzidos sob estresse de N e estresse de N mais A brasilense em condições controladas nos anos de 2016 e 2017. Nós avaliamos características de raiz e parte aérea e realizamos análises dialélicas, mapeamento associativo e métodos de predição genômica considerando 59.215 marcadores Single-Nucleotide Polymorphism (SNP). Nossos resultados revelaram uma herança quantitativa das características do milho relacionadas à essa parceria, com efeitos genéticos aditivos e não-aditivos envolvidos no controle genético. Além disso, vários genes candidatos foram encontrados para a associação milho-A brasilense, especialmente com efeitos de (des)vantagens de heterozigotos. Em geral, as acurácias de predição foram mais maiores principalmente para o tratamento inoculado em comparação ao não inoculado. Finalmente, nossos resultados possibilitam uma compreensão mais aprofundada das bases genéticas da responsividade do milho à A. brasilense e podem auxiliar os melhoristas de plantas a estabelecerem estratégias de seleção visando o desenvolvimento de genótipos superiores para essa associação.

Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2017-04-18T14:36:39Z No. of bitstreams: 2 Tese - Odilon Peixoto de Morais Júnior - 2016.pdf: 4169553 bytes, checksum: 1841a99cece6656c30b62fbd8fda9da5 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2017-04-18T14:36:54Z (GMT) No. of bitstreams: 2 Tese - Odilon Peixoto de Morais Júnior - 2016.pdf: 4169553 bytes, checksum: 1841a99cece6656c30b62fbd8fda9da5 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2017-04-18T14:36:54Z (GMT). No. of bitstreams: 2 Tese - Odilon Peixoto de Morais Júnior - 2016.pdf: 4169553 bytes, checksum: 1841a99cece6656c30b62fbd8fda9da5 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2016-12-15 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Genetic gains for quantitative traits associated with the maintenance of genetic variability are important factors in recurrent selection programs. With advances in the area of statistical genomics, selection strategies potentially faster to achieve genetic gains are being developed, such as genomic selection. Using a subtropical population of irrigated rice (CNA12S), conducted during three cycles of recurrent selection, this study had as general objective to evaluate the potential of use of genomic recurrent selection (GRS) in a rice breeding program. Three specific studies were developed. In the first chapter, the efficiency of the genotypic recurrent selection (RS) used in the Embrapa’s rice breeding program was evaluated, in order to obtain genetic gains and maintain the population genetic variability. Ten yield trials of S1:3 progenies were used in the analyses. The evaluated traits were grain yield, plant height and days-to-flowering. Variance and covariance components were obtained using Bayesian approach. Using single nucleotide polymorphisms (SNP) markers, the population diversity and genetic structure also were estimated. Adjusted means of progenies in each cycle were computed and, genetic progress was estimated by generalized linear regression using frequentist approach. The magnitudes of effective population size and genetic variance indicated maintenance of genetic variability over selection cycles. The genetic progress achieved for grain yield was 760 kg ha-1 per cycle (1.95% per year), and for days-to-flowering, it was -6.3 days per cycle (-1.28% per year). It was concluded that the genetic progress already achieved and the genetic variability available in the population demonstrate the efficiency of RS in the improvement of rice populations. In the second chapter, in the context of genomic selection, the relative efficiency of GRS on RS was assessed, as well as the accuracy of different models of genomic prediction, in order to propose a GRS scheme for population breeding of self-pollinating species such as rice. In this study, the genetic material was the S1:3 progenies yield trial of the third selection cycle. From a group of 196 progenies that were phenotyped for eight traits with different heritabilities and genetic architectures, a group of 174 progenies was genotyped for SNP markers. Ten predictive models were fitted to the data set. The proposed GRS scheme, when compared to the RS method, showed higher efficiency, especially in genetic gain per unit of time. From the predictive models assessed, HBLUP (hybrid best linear unbiased prediction, using hybrid relationship matrix based in pedigree and SNP markers) and RForest (random forest) have greater potential for genomic prediction in irrigated rice, given the high accuracy of their predictions for a number of traits. The HBLUP model was notoriously superior for more complex traits, such as grain yield, while RForest stood out for less complex traits. The high extent of linkage disequilibrium in the population suggests that the marker density employed (approximately one SNP per 60 kb) is enough for the practice of genomic selection in populations with similar genetic structure. In the third chapter, the objective was to extend a class of HBLUP models based on reaction norm, in context of multi-environmental trials with genotype x environment interaction, for accommodation of hybrid genetic relationship and information of the assessed environments. The accuracy of alternative models for multi-environmental predictions was evaluated, as well as the relative importance of structures of additive and multiplicative components, using genetic relationship information and environmental covariates. This strategy allowed to evaluate the influence of different approaches to group the genetic-environmental information on the accuracy of models for prediction of breeding value of progenies for agronomic traits. The data consisted of the same ten trial of S1:3 progenies, carried out during three recurrent selection cycles. Six predictive HBLUP models of reaction norm were considered, using genetic and environmental covariates, as well as interactions between these effects. Genomic information was derived from SNP markers obtained for the 174 progenies of the third selection cycle. The 401 environmental covariates, the genetic information (hybrid genetic relationship) and the interactions among these effects explained an important portion of the phenotypic variance, allowing an increase in the predictive accuracy of models. The use of genetic information and environmental covariates only from the respective selection cycle is enough for accurate predictions of unphenotyped progenies, even in non-sampled environments. This is the first study to take into account simultaneously hybrid genetic relationship, stemming from pedigree information plus SNP markers, and environmental covariates in multi-environmental models based on reaction norm for breeding value prediction in target environments of a recurrent selection program. / A obtenção de ganhos genéticos para caracteres quantitativos associada à manutenção da variabilidade genética são fatores importantes em programas de seleção recorrente. Com os avanços no campo da estatística genômica, estratégias de seleção potencialmente mais rápidas para alcance de ganhos genéticos estão sendo desenvolvidas, como a seleção genômica. Partindo-se de uma população subtropical de arroz irrigado (CNA12S), conduzida durante três ciclos de seleção recorrente, este estudo teve como objetivo geral avaliar o potencial de emprego do esquema de seleção recorrente genômica (GRS) em programas de melhoramento genético de arroz. Três estudos específicos foram desenvolvidos. No primeiro deles, avaliou-se a eficiência do esquema de seleção recorrente genotípica (RS) utilizado no programa de melhoramento de arroz da Embrapa, na obtenção de ganhos genéticos e manutenção da variabilidade genética populacional. O material experimental utilizado constituiu-se de dez ensaios de rendimento de progênies S1:3 associadas a cada ciclo de seleção. Os caracteres avaliados foram produtividade de grãos, altura de planta e número de dias até o florescimento. Componentes de variância e covariância foram obtidos via abordagem Bayesiana e, com uso de marcadores SNP (single nucleotide polymorphisms) associados às progênies, também a diversidade e a estrutura genética populacional. Médias ajustadas de progênies em cada ciclo foram computadas e, por regressão linear generalizada, estimou-se o progresso genético, via abordagem frequentista. As magnitudes do tamanho efetivo populacional e da variância genética indicaram manutenção da variabilidade genética ao longo dos ciclos de seleção. O progresso genético alcançado para produtividade de grãos foi de 760 kg ha-1 por ciclo (1,95 % ao ano) e para dias para florescimento, -6,3 dias por ciclo (-1,28 % ao ano). Concluiu-se que, o progresso genético já alcançado e a variabilidade genética disponível na população demonstram a eficiência de RS no melhoramento de populações de arroz. Num segundo estudo, no contexto de seleção genômica, avaliou-se a eficiência relativa de GRS sobre o esquema de RS; além da acurácia de diferentes modelos de predição genômica, buscando-se propor um esquema de GRS para melhoramento populacional de espécies autógamas como o arroz. Nesse estudo, o material genético foi composto por um ensaio de rendimento de progênies S1:3 do terceiro ciclo de seleção. Do grupo de 196 progênies fenotipadas para oito caracteres, com herdabilidades e arquiteturas genéticas diferentes, um grupo de 174 progênies foi genotipado para marcadores SNP. Dez modelos preditivos foram ajustados ao conjunto de dados. O esquema de GRS, quando comparado ao de RS, apresentou maior eficiência, sobretudo em ganho genético por unidade de tempo. Dos modelos preditivos avaliados, HBLUP (hybrid best linear unbiased prediction, com uso de matriz híbrida de parentesco baseada em pedigree e marcadores SNP) e RForest (random forest) apresentaram maior potencial para predição genômica, haja vista a elevada acurácia de suas predições para maior número de caracteres. O modelo HBLUP foi notoriamente superior para caracteres mais complexos, como produtividade de grãos, enquanto RForest destacou-se para caracteres menos complexos. A alta extensão do desequilíbrio de ligação na população sugere que a densidade de marcadores empregada (aproximadamente um SNP por 60 kb) é suficiente para a prática de predição genômica em populações com estrutura genética similar. No terceiro estudo buscou-se estender uma classe de modelos preditivos HBLUP baseados em norma de reação (contexto de ensaios multiambientais com interação genótipos × ambientes), para acomodar informações de parentesco e de covariáveis associadas aos ambientes de avaliação. Assim, avaliouse a acurácia preditiva de modelos alternativos para predições multiambientais, bem como a importância relativa de estruturas de componentes aditivos e multiplicativos; além da influência de diferentes abordagens de agrupamento de informações genético-ambientais sobre a acurácia dos modelos. O material genético constituiu-se nos mesmos dez ensaios de rendimento de progênies S1:3, conduzidos durante três ciclos de seleção recorrente. Foi considerada uma sequência de seis modelos preditivos de norma de reação, do tipo HBLUP, com uso de covariáveis genéticas e ambientais, além de interações entre esses efeitos. A informação genômica foi proveniente de marcadores SNP obtidos por genotipagem de 173 progênies do terceiro ciclo de seleção. As covariáveis ambientais (num total de 401), informações genéticas (parentesco híbrido) e as interações entre esses efeitos explicaram importante porção da variância fenotípica, o que possibilitou aumento da acurácia preditiva dos modelos. O emprego de informações genéticas e de covariáveis ambientais apenas do respectivo ciclo de seleção mostrou-se suficiente para predições acuradas do desempenho de progênies não fenotipadas, mesmo em ambientes não amostrados. Este estudo é pioneiro em considerar conjuntamente parentesco híbrido, oriundo de informações de pedigree mais marcadores SNP, e covariáveis ambientais em modelos multiambientais baseados em norma de reação, para predição de valor genético em ambientes-alvo de programas de seleção recorrente.

Un des principaux enjeux de l’amélioration des plantes consiste aujourd’hui à faire face au changement climatique, en assurant un rendement élevé et plus stable dans des systèmes agricoles économes en intrants (eau, fertilisants) et respectueux de l’environnement. Les nouvelles variétés de blé devront non seulement être tolérantes aux stress hydriques et aux fortes températures, mais aussi continuer à être productives avec des apports limités en fertilisation, tout en maintenant une qualité du grain adaptés aux différents usages. De nouvelles méthodes de prédiction des réponses des blés à ces stress sont indispensables pour avancer dans cette direction. Dans ce travail, nous avons tout d’abord identifié les stress qui régissaient les interactions entre génotypes et les environnements (GxE) dans les essais considérés, puis développé un modèle génomique de l’adaptation à un stress environnemental (Factorial Regression genomic Best Linear Unbiased Prediction ou FR-gBLUP), en particulier pour le stress hydrique. En émettant l’hypothèse que plus des variétés de blés sont génétiquement proches, plus elles répondront de façon similaire à un stress environnemental donné, nous avons mesuré par validation croisée des gains de précision de prédiction par rapport à un modèle additif variant entre 3.5% et 15.4%. Des simulations complètent l’étude en démontrant que plus la part de variance expliquée par les réponses au stress considéré est importante, plus le modèle FR-gBLUP apporte un gain de précision. Pour prédire les réponses variétales à un stress particulier, les environnements doivent être finement caractérisés pour les stress limitant le développement des plantes. En nous intéressant plus particulièrement au stress azoté en France, nous avons établi des indicateurs de stress à partir d’un modèle de culture, et les avons comparés à des indicateurs classiques, tels que le type de conduite azotée ou l’azote disponible. Nous avons ainsi mis en évidence l’intérêt des modèles de culture pour caractériser les interactions GxE et pour prédire la réponse génomique au stress azoté, à condition que le signal d’interaction soit assez fort. Au-delà de l’application potentielle de ces méthodes pour la sélection ou la recommandation de variétés de blés plus adaptées ou plus résistantes au changement climatique, les résultats de ce travail démontrent aussi l’intérêt de la complémentarité des approches éco-physiologiques et génétiques. / In a climate change context, assuring high and stable yield in more sustainable agricultural systems is a major challenge for plant breeding. We are aiming for future wheat varieties which will be heat and drought tolerant, and also productive in limited fertilization input environments. New prediction methods of the response to these stresses are needed to move forward. In this study, we first identified stresses that generated interactions between genotypes and environments (GxE) in our experimental trials and then developed a genomic model for adaptation to a particular environmental stress (Factorial Regression genomic Best Linear Unbiased Prediction ou FR-gBLUP), in our case drought. This model hypothesizes that the more individuals are genetically close, the more their response to a stress will resemble. We used cross-validations to measure prediction accuracy gains compared to an additive model and observed gains between 3.5% and 15.4%. Besides, simulation studies showed that the more the variance explained by the responses to the stress is important, the more the FR-gBLUP model will improve the additive model. Furthermore, fine characterization of the stresses limiting the plants’ growth is required to predict varietal responses to a particular stress. We focused on the particular case of nitrogen stress in France. By establishing crop model based stress indicators and comparing them to classical indicators, such as the management system or the available nitrogen, we pointed out the interest of crop model to characterize GxE interactions and to predict the genomic response to nitrogen stress, as long as the GxE interaction signal is strong enough. Beyond the potential applications of these methods for breeding or recommendation for varieties more adapted or tolerant to environmental stresses, this study also raises the interest of coupling eco-physiological and genetics approaches.

<p>Breeding for improved animal welfare is paramount for increasing the long-term sustainability of the animal food industry. In this context, the main objectives of this dissertation were to understand the genetic and genomic background of welfare indicators in livestock and evaluate the feasibility of single-step Genomic Best Linear Unbiased Prediction (ssGBLUP) for performing genomic selection in beef cattle. This dissertation includes five studies. First, we aimed to test and identify an optimal ssGBLUP scenario for crossbreeding schemes. We simulated multiple populations differing based on the genetic background of the trait, and then we tested alternative models, such as multiple-trait weighted ssGBLUP. Even though more elaborated scenarios were evaluated, a single-trait ssGBLUP approach was recommended when genetic correlation across populations were higher than 0.70. The goal of the second study was to identify genomic regions controlling behavior traits that are conserved across livestock species. We systematically reviewed genomic regions associated with behavioral indicators in beef and dairy cattle, pigs, and sheep. The genomic regions identified in this study were located in genes previously reported controlling human behavioral, neural, and mental disorders. In the third study we used a large dataset (675,678 records) from North American Angus cattle to investigate the genetic background of temperament, a behavioral indicator, recorded on one-year-old calves, and provide the models and protocols for implementing genomic selection. We reported a heritability estimate equal to 0.38 for yearling temperament, and it was, in general, genetically favorably correlated with other productivity and fertility traits. Candidate genomic regions controlling yearling temperament were also identified. The fourth study was based on temperament recorded on North American Angus cows from 2 to 15 years of age (797,187 records). The goal was to understand the genetic and genomic background of temperament across the animal’s lifetime. By fitting a random regression model, we observed that temperament is highly genetically correlated across time. However, animals have differential learning and behavioral plasticity (LBP; changes of the phenotype overtime), although the LBP heritability is low. In our last study we evaluated foot scores (foot angle, FA; and claw set, CS) in American (US) and Australian (AU) Angus cattle aiming to assess the genetic and genomic background of foot scores and investigate the feasibility of performing an across-country genomic evaluation (~1.15 million animals genotyped). Foot scores are heritable (heritability from 0.22 to 0.27), and genotype-by-environment interaction was observed between US and AU Angus populations (genetic correlation equal to 0.61 for FA and 0.76 for CS). An across-country genomic prediction outperformed within-country evaluations in terms of predictivity ability (bias, dispersion, and validation accuracy) and theoretical accuracies. We have also identified genes associated with FA and CS previously reported in human’s bone structure and repair mechanism. In conclusion, this dissertation presents a comprehensive genetic and genomic characterization of welfare indicators (temperament and foot scores) in (inter)national livestock populations. </p>

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