Orientador: Ricardo da Fonseca / Coorientador: Aldrin Vieira Pires / Banca: Idalmo Garcia Pereira / Banca: Mauricio de Alvarenga Mudadu / Banca: Sandra Aidar de Queiroz / Banca: Roberto Carvalheiro / Resumo: No melhoramento genético animal a forma tradicional de realizar seleção é com base no fenótipo dos indivíduos e na informação do parentesco entre estes, porém é um processo lento, sendo assim, programas de melhoramento estão procurando identificar os genes responsáveis pela característica de interesse e assim realizar a seleção dos animais que carregam a informação desejada. Com as informações dos indivíduos genotipados, tornou-se possível a utilização da informação de genes idênticos em estado tornando viável a utilização de uma matriz de parentesco (G) permitindo aumentar a precisão das avaliações genéticas, porém, devido à dificuldade de se obter o genótipo de todos os animais de uma população, foi proposto um método que realiza a integração da matriz G com a matriz de parentesco (A) em uma matriz de parentesco-genômica (H). Embora tenham trabalhos que indiquem uma similaridade no progresso genético utilizando estas diferentes matrizes é importante a avaliação da contribuição da avaliação genômica nos processos de avaliação genética em populações com estruturas de parentesco diferentes, bem como avaliar a metodologia de seleção genômica em populações multirraciais, a fim de atender o sistema de criação de animais cruzados. Assim, o objetivo geral deste trabalho foi estudar os efeitos da informação genômica na avaliação genética animal por meio de diferentes matrizes genômicas, utilizando dados de bovinos de corte com diferentes estruturas populacionais e composições raciais. Primeiramente avaliou-se 3 diferentes metodologias de se obter a matriz H, com a frequência alélica observada (HGOF), menor frequência alélica (HGMF) e uma frequência de 0,5 para todos os SNPs (HG50). Foram feitas comparações entre estas matrizes genômicas e a matriz de parentesco tradicional (A) utilizando uma população de 1695 animais da raça Brahman (BB). De acordo com os ... / Abstract: In animal breeding methodologies, the traditional method of performing selection is based on the phenotype of individuals and information of relationship between them, but it is a slow process, so breeding programs are trying to identify the genes responsible for the trait of interest and thus achieve selection of animals that carry the interesting genes. With the information of genotyped individuals, it became possible to use the information of genes identical in state making it feasible to use a relationship matrix (G) which increase the accuracy of genetic evaluations, however, due to difficulty of obtaining the genotype of all animals in a population, we propose a method that performs the integration of the G matrix with the relationship matrix (A) in a pedigree-genomic relationship matrix (H). Although studies indicating a similarity in genetic progress using these matrices is important to evaluate the contribution of genomic evaluation in the process of genetic evaluation in populations with different structures of kinship, as well as evaluating the methodology of genomic selection in multiracial populations in order to cater to the creation of crossbred system. Thus the objective of this work was to study the effects of genomic information in genetic evaluation through different genomic arrays using data from beef cattle with different population structures and racial compositions. First we evaluated three different methods of obtaining the H matrix with the observed allele frequency (HGOF), lower allele frequency (HGMF) and a frequency of 0.5 for all SNPs (HG50). Comparisons between these genomic arrays and traditional kinship (A) using a population of 1695 animals breed Brahman (BB) matrix were made. According to the results , the HGOF was a matrix that showed the greatest similarity to the matrix A but the greatest differences were found in the classification of animals, when we evaluated the classification of animals ... / Doutor

Muscadinia rotundifolia est une espèce de la famille des Vitaceae. C’est un sous-genre du genre Vitis, le deuxième sous-genre étant celui des Euvitis qui comprend l’espèce cultivée Vitis vinifera (2n=38). M. rotundifolia (2n=40) est une source de résistance aux maladies très importante pour l’amélioration de la vigne. Son génome commence seulement à être décrit avec deux cartes génétiques récemment publiées. Ma thèse a consisté à utiliser des ressources génomiques chez M. rotundifolia cv Regale (banque BAC, collection de séquence d’extrémités de BAC ou BES et séquences de BACs) pour caractériser le génome de cette espèce en comparaison avec celui de V. vinifera. Les résultats obtenus ne montrent pas de différence importante entre les génomes des deux espèces en termes de composition du génome en bases (GC%), en séquences codantes ou en éléments répétés. De même, à une échelle globale, la famille de gènes NBS-LRR semble être similaire en termes de nombre et de balance entre les sous-familles. A une échelle plus fine cependant (carte physique et séquences de BAC), des remaniements relativement importants sont observés dans des régions portant cette famille de gènes, aboutissant parfois à des contenus différents en gènes, de région normalement homologues : duplication différentielles de gènes, présence/absence de gènes. / Muscadinia Rotundifolia is a species of the Vitaceae family. It is a sub-genus of the Vitis genus along with the Euvitis sub-genus, which the cultivated species Vitis vinifera belongs to. M. rotundifolia (2n=40) is a very important source of resistance to diseases in grapevine breeding programs. Its genome is only starting to be described with the recent publication of two genetic maps. The present study aimed at using M. rotundifolia cv Regale genomic resources (BAC library, BAC end sequences or BES, BAC sequences) in order to characterize the genome of this species in comparison with the genome of V. vinifera. The results showed that there is no striking difference between the two species in term of base composition (GC %), repeats frequency and gene space. The NBS LRR gene family also seems to be globally quite similar between the two species in terms of numbers and balance between subfamilies. At a finer scale (physical map and BAC sequence), frequent rearrangements are observed in genomic regions carrying the NBS-LRR gene family sometimes clearly associated with a different gene content between the two species in homologous regions: differential gene duplication, presence/absence of genes.

This thesis has two overall themes, both of which involve the word functional, albeit in different contexts. The theme that motivates two of the chapters is the development of methods that enable a deeper understanding of the variability of functional data. The theme of the final chapter is the development of methods that enable a deeper understanding of the landscape of functionality across the human genome in different human tissues. The first chapter of this thesis provides a framework for quantifying the variability of functional data and for analyzing the factors that affect this variability. We extend functional principal components analysis by modeling the variance of principal component scores. We pose a Bayesian model, which we estimate using variational Bayes methods. We illustrate our model with an application to a kinematic dataset of two-dimensional planar reaching motions by healthy subjects, showing the effect of learning on motion variability. The second chapter of this thesis provides an alternative method for decomposing functional data that follows a Poisson distribution. Classical methods pose a latent Gaussian process that is then linked to the observed data via a logarithmic link function. We pose an alternative model that draws on ideas from non-negative matrix factorization, in which we constrain both scores and spline coefficient vectors for the functional prototypes to be non-negative. We impose smoothness on the functional prototypes. We estimate our model using the method of alternating minimization. We illustrate our model with an application to a dataset of accelerometer readings from elderly healthy Americans. The third chapter of this thesis focuses on functional genomics, rather than functional data analysis. Here we pose a method for unsupervised clustering of functional genomics data. Our method is non-parametric, allowing for flexible modeling of the functional genomics data without binarization. We estimate our model using variational Bayes methods, and illustrate it by calculating genome-wide functional scores (based on a partition of our clusters into functional and non-functional clusters) for 127 different human tissues. We show that these genome-wide and tissue-specific functional scores provide state-of-the-art functional prediction.

Computational genomics is at the intersection of computational applied physics, math, statistics, computer science and biology. With the advances in sequencing technology, large amounts of comprehensive genomic data are generated every year. However, the nature of genomic data is messy, complex and unstructured; it becomes extremely challenging to explore, analyze and understand the data based on traditional methods. The needs to develop new quantitative methods to analyze large-scale genomics datasets are urgent. By collecting, processing and organizing clean genomics datasets and using these datasets to extract insights and relevant information, we are able to develop novel methods and strategies to address specific genetics questions using the tools of applied mathematics, statistics, and human genetics. This thesis describes genetic and bioinformatics studies focused on utilizing and developing state-of-the-art computational methods and strategies in order to identify and interpret de novo mutations that are likely causing developmental disorders. We performed whole exome sequencing as well as whole genome sequencing on congenital diaphragmatic hernia parents-child trios and identified a new candidate risk gene MYRF. Additionally, we found male and female patients carry a different burden of likely-gene- disrupting mutations, and isolated and complex patients carry different gene expression levels in early development of diaphragm tissues for likely-gene-disrupting mutations. To increase the power to detect risk genes and risk variants, we developed a deep neural network classifier called MVP to accurately predict the pathogenicity of missense variants. MVP implemented an advanced structure of ResNet model and based on two independent data sets, MVP achieved clearly better results in prioritizing pathogenic variants than other methods. Additionally, we studied the genetic connection between developmental disorders and cancer. We found that in developmental disorder patients predicted deleterious de novo mutations are more enriched in cancer driver genes than non cancer driver genes. A Hidden Markov Model was implemented to discover cancer somatic missense mutation hotspots and we demonstrated many cancer driver genes shared a similar mode of action in developmental disorders and caner. By improving ability to interpret missense mutations and leveraging cancer genomics data, we can improve risk gene inference in developmental disorders.

No description available.

Quantitative Trait Loci (QTL) mapping has been widely used to identify genetic loci attributable to the variation observed in complex traits. In recent years, gene expression phenotypes have emerged as a new type of quantitative trait for which QTL can be mapped. Locating sequence variation that has an effect on gene expression (eQTL) is thought to be a promising way to elucidate the genetic architecture of quantitative traits. This thesis explores a number of methodological aspects of eQTL mapping (also known as “genetical genomics”) and considers some practical strategies for applying this approach to livestock populations. One of the exciting prospects of genetical genomics is that the combination of expression studies with fine mapping of functional trait loci can guide the reconstruction of gene networks. The thesis begins with an analysis in which correlations between gene expression and meat quality traits in pigs are investigated in relation to a pork meat quality QTL previously identified. The influence on power due to factors including sample size and records of matched subjects is discussed. An efficient experimental design for two-colour microarrays is then put forward, and it is shown to be an effective use of microarrays for mapping additive eQTL in outbred crosses under simulation. However, designs optimised for detecting both additive and dominance eQTL are found to be less effective. Data collected from livestock populations usually have a pedigreed structure. Many family-based association mapping methods are rather computationally intensive, hence are time-consuming when analysing very large numbers of traits. The application of a novel family-based association method is demonstrated; it is shown to be fast, accurate and flexible for genetical genomics. Furthermore, the results show that multiple testing correction alone is not sufficient to control type I errors in genetical genomics and that careful data filtering is essential. While it is important to limit false positives, it is desirable not to miss many true signals. A multi-trait analysis based on grouping of functionally related genes is devised to detect some of the signals overlooked by a univariate analysis. Using an inbred rat dataset, 13 loci are identified with significant linkage to gene sets of various functions defined by Gene Ontology. Applying this method to livestock species is possible, but the current level of annotations is a limiting factor. Finally, the thesis concludes with some current opinions on the development of genetical genomics and its impact on livestock genetics research.

Horizontal gene transfer (HGT) and gene loss are important processes in the evolution of prokaryotic lineages. HGT involves the movement of genetic material between distantly related species, and can facilitate adaptation when gained genes confer advantageous phenotypes to recipient lineages. However, high levels of gene gain and loss are predicted to obfuscate patterns of vertical descent and homogenise nucleotide diversity across ecological and phylogenetic boundaries. Thus, a holistic understanding of the role of genome fluctuation in the emergence and maintenance of genetically and ecologically cohesive bacterial groups remains to be fully elucidated. In this thesis, I use the plant-associated bacterium Pseudomonas syringae as a model system to investigate the impact of HGT and gene loss on evolutionary processes such as adaptation, diversification and speciation. The Gram-negative Gammaproteobacterium P. syringae is an opportunistic plant pathogen, and has been used for decades as a model system with which to study the interaction between plants and their microbial pathogens. Recently, the diversification of lineages within this species has involved a number of host jumps onto a range of woody host plant species, resulting in the emergence of diseases such as bacterial canker of kiwi and bleeding canker of the European horse chestnut. Using whole-genome sequence data and a range of comparative genomics and phylogenetics methods, I quantitatively reconstruct the history of gene gain and loss in P. syringae and show HGT to be the predominant evolutionary force in this species. Genomes of this species are under constant permutation, are subject to a highly diverse HGT genepool and show marked differences in patterns of codon usage between imported and core genes. I then generate additional genome data for 26 strains of P. syringae that are pathogenic on a range of different woody plants, and investigate the contribution from HGT to the adaptation of these strains into the woody niche. Using a method that accounts for the underlying phylogenetic relationships among P. syringae strains, I look for the correlated evolution between gained genes and the woody niche, and find that a substantial proportion of the genome is associated with this ecological niche. I then investigate the recent adapitation of P. syringae pv. aesculi onto the European horse chestnut, and show that a number of genomic events that include both homologous and non-homologous recombination are likely to have led to the evolution of this bacterium onto its host, where it has become the causal agent of the bleeding canker disease that is currently epidemic across much of northern and central Europe. Overall, this thesis is an investigation into how HGT contributes to niche adaptation in P. syringae, and aims to further our understanding of the mechanisms that underlie bacterial evolution.

Gibbon classification at the species and subspecies levels has been hotly debated for the last 200 years. This thesis explores the reasons for this debate. Authorities agree that siamang, concolor, kloss and hoolock are species, while there is complete lack of agreement on lar, agile, moloch, Mueller's and pileated. The disagreement results from the use and emphasis of different character traits, and from debate on the occurrence and importance of gene flow.

Viruses infecting the Archaea exhibit a tremendous amount of morphological and genetic diversity. This is especially true for crenarchaeal viruses from the family Fuselloviridae, which possess spindle-shaped capsids and genomes that harbor a great number of uncharacterized genes. The functions of these unidentified gene products are of interest as they have the potential to provide valuable insights into the fusellovirus infection cycle and archaeal viruses in general. In an effort to better characterize the genetic requirements of the Fuselloviridae, we have performed genetic and biochemical experiments using the best studied fusellovirus, Sulfolobus spindle-shaped virus 1 (SSV1). A comprehensive genetic analysis of SSV1 was conducted using long inverse PCR and transposon mutagenesis. The results of this work illustrate that SSV1 is highly tolerant of mutagenesis. A robust protocol for the purification of recombinant VP2 protein from E. coli was developed and should be useful for future studies aimed at characterizing the biochemical and structural characteristics of this SSV1 structural protein. Finally, the first insights into a fusellovirus infection are presented and provide the framework for a detailed characterization of the fusellovirus infection cycle. The results and significance of this work are presented in the chapters that follow.

Culture independent studies have revealed a greater diversity of Archaea than the two kingdoms initially defined by Carl Woese. Culturing species from the newly discovered archaeal lineages, as with the majority of microbes, has been difficult. To overcome the culturing dilemma, metagenomics is being used to reconstruct environmental genomes. Two metagenomic studies are presented in this thesis, with the aim of recovering newly proposed archaeal genomes from the Nanoarchaeota and Korarchaeota. In the first study, a sediment sample, NZ13, was collected from a terrestrial New Zealand hot spring. Along with the sediment sample, two complex enrichments were sequenced on an Illumina MiSeq platform. Assembly and differential binning recovered two nearly complete genomes of a nanoarchaeote and a korarchaeote. The NZ13 nanoarchaeote is similar to other terrestrial nanoarchaeotes, which lack an ATP synthase and encode genes for glycolysis/gluconeogenesis and archaella. One notable difference is the NZ13 nanoarchaeote contains CRISPR genes, which are absent in other terrestrial nanoarchaeotes, although present in a marine nanoarchaeote, Nanoarchaeum equitans. The NZ13 korarchaeote mirrors Candidatus Korarchaeum cryptofilum, lacking genes for de novo synthesis of purines and several cofactors, while containing an abundance of peptide transporters and amino acid fermentation pathways. The second study focused on sulfide samples collected from deep-sea hydrothermal vent fields in southwestern Pacific Ocean along the Eastern Lau Spreading Center. Ten sulfide samples were sequenced on an Illumina HiSeq platform. Small subunit ribosomal RNA genes were extracted from the metagenome reads and aligned against the SILVA Ref NR 99 123 database. The preliminary results identified which samples could be prioritized for genome reconstruction of uncultured bacterial and archaeal lineages. Three uncultured bacteria, candidate division SR1, Gracilibacteria (GN02), and Parcubacteria (OD1) were identified in several samples. Many uncultured deep-sea hydrothermal archaeal lineages were identified in all samples. In particular, korarchaeotal sequences were in high relative archaeal abundances in two samples, ABE 1 and Vai Lili-2, while few nanoarchaeotal reads were classified.