Submitted by Geandra Rodrigues (geandrar@gmail.com) on 2018-09-12T19:05:00Z No. of bitstreams: 1 eduardoaugustovieiraferraz.pdf: 1074526 bytes, checksum: da431c9a5363c8e7fec1820ddeacb3b0 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2018-10-01T19:34:45Z (GMT) No. of bitstreams: 1 eduardoaugustovieiraferraz.pdf: 1074526 bytes, checksum: da431c9a5363c8e7fec1820ddeacb3b0 (MD5) / Made available in DSpace on 2018-10-01T19:34:45Z (GMT). No. of bitstreams: 1 eduardoaugustovieiraferraz.pdf: 1074526 bytes, checksum: da431c9a5363c8e7fec1820ddeacb3b0 (MD5) Previous issue date: 2018-07-09 / O presente trabalho propõe-se a estudar as acusações de feitiçaria registradas na região norte de Moçambique (distrito do Niassa), durante o período colonial. Pretendemos entender a relação entre essas acusações e os conflitos sociais, principalmente com as disputas pelo poder dentro da estrutura de parentesco local. O estudo nos possibilitará realizar uma história social dos Ajáuas. Através dos relatos transcritos em depoimentos podemos tentar compreender as lógicas simbólicas de sua linguagem, seu “entendimento de mundo” e também como o discurso de feitiçaria se enquadra nela. Veremos que a feitiçaria dialoga com o discurso de poder. Ela reproduz a hierarquia e as relações de dominação presentes na sociedade e na estrutura de parentesco. As frequentes acusações podem-nos revelar questões muito mais amplas. Estratégias políticas e rivalidades são, na maioria das fontes, as principais motivações das acusações de feitiçaria. Sob este ponto de vista, o discurso de feitiçaria aparece como um instrumento, uma ferramenta através da qual os indivíduos buscam legitimidade para justificar suas ações e crimes cometidos. Podemos verificar que o discurso da feitiçaria atua como um meio pelo qual rivalidades enraizadas em sentimentos de vingança, disputas pelo poder ou aquisição de bens econômicos emergem. / This work aims at studying the witchcraft accusations registered in northern Mozambique (Niassa province) during the colonial period. We intend to understand the correlation between such accusations and the social conflicts, especially the ones related to disputes over power inside the local relationship matrix. This study will make it possible to structure the social history of the Ajáua people. Based on the transcribed reports, we may comprehend the symbolic logic of their language, their “view of the world” and also how the witchcraft discourse is framed in that view. We will see that the witchcraft is related to the discourse of power. It reproduces the hierarchy and the dominance relations present in their society and relationship matrix. The frequent accusations may reveal much broader issues. According to most sources, political strategies and rivalries are the main motivations for witchcraft accusations. From this point of view, the witchcraft discourse is seen as an instrument, a tool whereby individuals seek legitimacy to justify their actions and the crimes they commit. We are able to verify that the witchcraft discourse is the means by which the rivalries emerge, motivated by revenge feelings, disputes over power or the acquisition of economic goods.

In this dissertation I use a Unified Relationship Matrix (URM) to represent a set of heterogeneous data objects and their inter-relationships. I argue that integrated and iterative computations over the Unified Relationship Matrix can help overcome the data sparseness problem (a common situation in various information application scenarios), and detect latent relationships (such as latent term associations discovered by LSI) among heterogeneous data objects. Thus, this kind of computation can be used to improve the quality of various information applications that require combining information from heterogeneous data sources. To support the argument, I further develop a unified link analysis algorithm, the Link Fusion algorithm, and a unified similarity-calculating algorithm, the SimFusion algorithm. Both algorithms attempt to better integrate information from heterogeneous sources by iteratively computing over the Unified Relationship Matrix in order to calculate some specific property of data object(s); such as the importance of a data object (as in the Link Fusion algorithm) and the similarity between a pair of data objects (as in the SimFusion algorithm). Then, I develop two set of experiments on real-world datasets to investigate whether the algorithms proposed in this dissertation can better integrate information from multiple sources. The performance of the algorithms is compared to that of traditional link analysis and similarity-calculating algorithms. Experimental results show that the algorithms developed can significantly outperform the traditional link analysis and similarity-calculating algorithms. I further investigate various pruning technologies aiming at improving efficiency and investigating the scalability of the algorithms designed. Experimental results showed that pruning technology can effectively be used to improve the efficiency of the algorithms. / Ph. D.

Tesis por compendio / [ES] El análisis del control genético del hospedador sobre su microbiota ha sido señalado recientemente como un tema prometedor en diferentes campos de estudio. La relación entre el holobionte hospedador-microbioma y los fenotipos en el ganado lechero podría conducir a nuevos conocimientos en los programas de selección genética. Dentro de esta tesis doctoral, se realizó la estimación y análisis a través de diferentes enfoques estadísticos con el objetivo de desentrañar el control genético del hospedador sobre la microbiota en ganado lechero. Además, se analizó el rasgo de concentración de metano como un fenotipo potencial para ser incluido en el programa de mejora de ganado lechero español. Mayor abundancia relativa de la mayoría de los eucariotas (principalmente protozoos ciliados y hongos) y algunas arqueas (Methanobrevibacter spp. Methanothermus spp. y Methanosphaera spp.) fueron factores de riesgo para ser clasificadas en la categoría alta. Se propuso un conjunto de modelos de ecuaciones estructurales (SEM) de tipo recursivo dentro de un marco de Cadenas de Markov Monte Carlo (MCMC) para analizar conjuntamente la relación hospedador-metagenoma-fenotipo. Se estableció un modelo bivariado no-recursivo como punto de referencia. La heredabilidad de CH4 se estimó en 0,12 ± 0,01 en ambos modelos, recursivo y no recursivo. Asimismo, las estimaciones de heredabilidad para la abundancia relativa de los taxones se superpusieron entre los modelos y variaron entre 0.08 y 0.48. Las correlaciones genéticas entre la composición microbiana y el CH4 variaron de -0,76 a 0,65 en el modelo bivariado no recursivo y de -0,68 a 0,69 en el modelo recursivo. Doce matrices de relación de microbiota (K) fueron construidas a partir de diferentes métricas de distancia del microbioma, con el objetivo de comparar su desempeño dentro de un marco de estimación de componentes de varianza para CH4 y toda la microbiota. Análisis de simulación (n = 1000) y datos reales fueron desarrollados considerando cuatro modelos posibles: un modelo genómico aditivo (GBLUP), un modelo de microbioma (MBLUP), un modelo de efectos genéticos y microbioma (HBLUP) y un modelo de efectos de interacción genético, microbioma y genético × microbioma (HiBLUP). Un nuevo término "Holobiabilidad" fue definido para referirse a la proporción de la varianza atribuible a los efectos del holobionte hospedador-microbioma. Las estimaciones a partir de datos reales usando HiBLUP variaron dependiendo de la K utilizada y estuvieron entre 0.15-0.17, 0.15-0.21 y 0.42-0.59 para heredabilidad, microbiabilidad y holobiabilidad, respectivamente. El conjunto de datos de microbioma fue agregado a través de análisis de componentes principales (PCA), en pocos componentes principales (PCs) que fueron utilizados como aproximaciones del metagenoma central. Parte de la variabilidad condensada en estos PC está controlada por el genoma de la vaca, con estimaciones de heredabilidad para el primer PC (PC1) de ~ 0,30 en todos los niveles taxonómicos, con una gran probabilidad (> 83%) de que la distribución posterior sea > 0,20 y con un intervalo de mayor densidad posterior al 95% (95% HPD) no conteniendo cero. La mayoría de las estimaciones de correlación genética entre PC1 y metano fueron grandes (>0,70) en todos los niveles taxonómicos, con la mayor parte de la distribución posterior (> 82%) siendo > 0,50 y con su 95% HPD no conteniendo cero. Estos resultados sugieren que todo el metagenoma del rumen regula recursivamente las emisiones de metano en las vacas lecheras, y que tanto el CH4 como las composiciones de la microbiota están parcialmente controladas por el genotipo del hospedador. Las variables agregadas (PC) propuestas podrían ser usadas en programas de mejora de animales para reducir las emisiones de metano en las generaciones futuras. / [CA] L'anàlisi del control genètic de l'hoste sobre la seva microbiota s'ha assenyalat recentment com un tema prometedor en diferents camps d'estudi. La relació entre el holobiont hoste-microbioma i els fenotips en bovins de llet podria conduir a nous coneixements en els programes de cria. Dins d'aquest doctorat es van realitzar tesis, estimacions i anàlisis mitjançant diferents enfocaments estadístics amb l'objectiu de desentranyar el control genètic de l'hoste sobre la microbiota en bestiar lleter. A més, es va analitzar el tret de concentració de metà com a fenotip potencial a incloure en el programa espanyol de cria de bestiar lleter. La major abundància relativa de la majoria dels eucariotes (principalment protozous i fongs ciliats) i algunes arquees (Methanobrevibacter spp. Methanothermus spp i Methanosphera spp.) Van ser factors de risc per classificar-se en les categories altes. Es va proposar un conjunt de models d'equacions estructurals (SEM) de tipus recursiu dins d'un marc de cadena Markov Monte Carlo (MCMC) per analitzar conjuntament la relació hoste-metagenoma-fenotip. Es van establir models no recursius com a referència. L'heretabilitat del CH4 es va estimar en 0,12 ± 0,01 en ambdós models, recursius i no recursius. De la mateixa manera, les estimacions d'heretabilitat de l'abundància relativa dels tàxons es van superposar entre models i van oscil·lar entre 0,08 i 0,48. Les correlacions genètiques entre la composició microbiana i el CH4 van oscil·lar entre -0,76 i 0,65 en els models bivariables no recursius i de -0,68 a 0,69 en els models recursius. Dotze matrius de relació de microbiota (K) de diferents mètriques de distància de microbiomes, amb l'objectiu de comparar el seu rendiment dins d'un marc d'estimació de components de variància per CH4 i anàlisi de microbiomes sencers en simulació (n = 1000, 25 rèpliques) i es van realitzar dades reals , considerant quatre possibles models: un model genòmic additiu (GBLUP), un model de microbioma (MBLUP), un model d'efectes genètics i microbiomes (HBLUP) i un model d'efectes d'interacció genètics, microbiomes i genètics × microbiomes (HiBLUP). Es va definir un nou terme "Holobiabilitat" per referir-se a la proporció de la variància fenotípica atribuïble als efectes holobiont del microbioma host. Les estimacions de dades reals mitjançant HiBLUP van variar en funció de la K utilitzada i van oscil·lar entre 0,15-0,17, 0,15-0,21 i 0,42-0,59 per heretabilitat, microbiabilitat i holobiabilitat, respectivament. El conjunt de dades de microbiomes es va agregar mitjançant l'anàlisi de components principals (PCA) en pocs components principals (PC) que es van utilitzar com a proxies del metagenoma principal. Part de la variabilitat condensada en aquestes PC està controlada pel genoma de la vaca, amb estimacions d'heretabilitat per a la primera PC (PC1) de ~ 0,30 a tots els nivells taxonòmics, amb una gran probabilitat (> 83%) de la distribució posterior> 0,20 i amb un 95% més alt interval de densitat posterior (95% HPD) que no conté zero. La majoria de les estimacions de correlació genètica entre PC1 i metà eren grans (>0,70) en tots els nivells taxonòmics, amb una gran part de la distribució posterior (> 82%)> 0,50 i amb un 95% de HPD que no contenia zero. Aquests resultats suggereixen que tot el metagenoma del rumen regula recursivament les emissions de metà en vaques lleteres i que tant el CH4 com les composicions de microbiota estan parcialment controlades pel genotip de l'hoste. Les variables agregades proposades (PC) es podrien utilitzar en programes de cria d'animals per reduir les emissions de metà en les generacions futures. / [EN] The analysis of the host genetic control over its microbiota has recently been pointed out as a promising theme in different fields of study. The relationship between the host-microbiome holobiont and phenotypes in dairy cattle could lead to new insights in breeding programs. Within this Ph.D. thesis, estimation and analysis through different statistical approaches were performed aiming to unravel the host genetic control over the microbiota in dairy cattle. Besides, methane concentration trait was analyzed as a potential phenotype to be included in the Spanish dairy cattle breeding program. Higher relative abundance of most eukaryotes (mainly ciliate protozoa and fungi) and some archaea (Methanobrevibacter spp. Methanothermus spp and Methanosphera spp.) were risk factors for being classified in the high categories. a set of structural equation models (SEMs) of a recursive type within a Markov chain Monte Carlo (MCMC) framework was proposed to jointly analyze the host-metagenome-phenotype relationship. Non-recursive models were set as benchmark. Heritability of CH4 was estimated at 0.12 ± 0.01 in both, the recursive and non-recursive, models. Likewise, heritability estimates for the relative abundance of the taxa overlapped between models and ranged between 0.08 and 0.48. Genetic correlations between the microbial composition and CH4 ranged from -0.76 to 0.65 in the non-recursive bivariate models and from -0.68 to 0.69 in the recursive models. Regardless of the statistical model used, positive genetic correlations with methane were estimated consistently for the 7 genera pertaining to the Ciliophora phylum, as well as for those genera belonging to the Euryarchaeota (Methanobrevibacter sp.), Chytridiomycota (Neocallimastix sp.) and Fibrobacteres (Fibrobacter sp.) phyla. Twelve microbiota relationship matrices (K) from different microbiome distance metrics were built, aiming to compare its performance within a variance component estimation framework for CH4 and whole microbiome analysis on simulation (n = 1000, 25 replicates) and real data were performed, considering four possible models: an additive genomic model (GBLUP), a microbiome model (MBLUP), a genetic and microbiome effects model (HBLUP) and a genetic, microbiome and genetic × microbiome interaction effects model (HiBLUP). A new term "Holobiability" was defined to refer to the proportion of the phenotypic variance attributable to the host-microbiome holobiont effects. Estimates from real data using HiBLUP varied depending on the K used and ranged between 0.15-0.17, 0.15-0.21 and 0.42-0.59 for heritability, microbiability and holobiability, respectively. The microbiome dataset was aggregated through Principal Component Analysis (PCA) into few principal components (PCs) that were used as proxies of the core metagenome. Part of the variability condensed in these PCs is controlled by the cow genome, with heritability estimates for the first PC (PC1) of ~0.30 at all taxonomic levels, with a large probability (>83%) of the posterior distribution being > 0.20 and with the 95% highest posterior density interval (95%HPD) not containing zero. Most genetic correlation estimates between PC1 and methane were large (>0.70) at all taxonomic levels, with most of the posterior distribution (>82%) being >0.50 and with its 95%HPD not containing zero. These results suggest that rumen's whole metagenome recursively regulate methane emissions in dairy cows, and that both CH4 and the microbiota compositions are partially controlled by the host genotype. The purposed aggregated variables (PCs) could be used in animal breeding programs to reduce methane emissions in future generations. / This research was financed by RTA2015-00022-C03-02 (METALGEN) project from the national plan of research, development and innovation 2013-2020 and the Department of Economic Development and Competitiveness (Madrid, Spain). We thank the regional Holstein Associations and farmers collaborating in the project. Computational support from the High-Performance Computing Centre in Galicia (Spain) is acknowledged. Alejandro Saborío-Montero acknowledges the scholarship from Universidad de Costa Rica for his doctorate studies which partially conducted to the progress of this study. / Saborío Montero, A. (2021). Study of the Host Genetic Control over the Ruminal Microbiota and their Relationships with Methane Emissions in Dairy Cattle [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/172633 / TESIS / Compendio

Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2015-01-16T13:14:38Z No. of bitstreams: 2 license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Tese - Cláudio Roberto Cardoso de Godoi - 2014.pdf: 1446327 bytes, checksum: 78154341b9ccb5964b8508984eea19e1 (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2015-01-16T13:48:33Z (GMT) No. of bitstreams: 2 license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Tese - Cláudio Roberto Cardoso de Godoi - 2014.pdf: 1446327 bytes, checksum: 78154341b9ccb5964b8508984eea19e1 (MD5) / Made available in DSpace on 2015-01-16T13:48:33Z (GMT). No. of bitstreams: 2 license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Tese - Cláudio Roberto Cardoso de Godoi - 2014.pdf: 1446327 bytes, checksum: 78154341b9ccb5964b8508984eea19e1 (MD5) Previous issue date: 2014-05-07 / Soybean breeding programs practice selection of high genetic value genotypes with two main objectives: a) to use them as parents in the hybridization process (first stage of the program), and b) to indicate them as new cultivars (final stage of the program). In this context, a first study used microsatellite markers (SSR) to assess the genetic diversity of soybean germplasm adapted to the Brazilian conditions. The experimental material consisted of 192 accessions, which included both introductions and Brazilian germplasm. The genetic divergence was assessed by descriptive analysis and the Rogers-W genetic distance. A total of 222 alleles were identified in the 37 genotyped loci, with an average of six alleles and a range of 2 to 14 alleles per locus. The genotypes were clustered according to the origin of the germplasm, and resulted in two groups: one group formed by introductions and other by Brazilian genotypes. Eighty five percent of the genetic distances estimates were above 0.70, suggesting that the assessed germplasm has good potential for hybridization in soybean breeding programs. It was concluded that the SSR markers are useful to identify divergent genotypic groups, as well as genotypic combinations with high genetic variability. It also became clear that the use of introduced germplasm ensures the incorporation of alleles necessary to increase the genetic base of soybeans and, consequently, the variability needed for the selective process. In a second study, the mixed model approach was used to assess some strategies of estimation and prediction of genotypic values for grain yield in the soybean regional yield trials. A total of 111 genotypes classified into three maturity groups were sown in up to 23 experiments in Central Brazil. The experiments were carried out in randomized complete block designs, with three replications. The biometrical analyses followed the fixed model and mixed model approaches, in the latter case assuming the genotypic effects as random. In the mixed model approach, analyses were made with or without information from the relationship estimates obtained either by genealogy or SSR markers, arranged in a genotypic covariance matrix (G). Also, in a context of spatial analysis, different structures were used in the residual covariance matrix (R) for each mixed model adjusted. The following conclusions were obtained: i) the fixed model analysis is adequate to estimate genotypic values in soybean trials with balanced data and orthogonal design; ii) under such conditions and intermediate to low heritability, the inclusion of relationship information associated to G matrix, although does not ensure the best fit models, improves the precision in predicting genotypic values; iii) the use of spatial structures associated to R matrix, in presence of the residual autocorrelation, improves the goodness of model fit to the data; and, iv) the choice of model for the analysis does not change the ranking of the genotypes in high heritability situations and, therefore, does not impact significantly on the selection of superior genotypes. / Os programas de melhoramento de soja visam à seleção de genótipos de alto valor genético, com a finalidade de uso principalmente em duas de suas etapas: a) como genitores no processo de hibridação (fase inicial); e, b) para indicação como nova cultivar (fase final). Nesse contexto, num primeiro estudo avaliou-se, por meio de marcadores microssatélites (SSR), a diversidade genética em germoplasma de soja adaptado às condições brasileiras. O material experimental constituiu-se de 192 acessos, entre introduções e germoplasma de origem nacional. Na avaliação da divergência genética, considerou-se a análise descritiva e a distância genética de Rogers-W. Nos 37 locos genotipados, identificaram-se 222 alelos, com média de seis alelos por loco e variação de 2 a 14 alelos. O agrupamento dos genótipos mostrou-se associado à origem do germoplasma, resultando em dois grupos: um introduzido e outro brasileiro. Das estimativas de distâncias genéticas obtidas, 85% foram superiores a 0,70, indicando bom potencial do germoplasma para hibridações em programas de melhoramento da soja. Concluiu-se que os marcadores SSR são úteis na identificação de grupos genotípicos divergentes, bem como de combinações de alta variabilidade genética. Ademais, o uso de germoplasma introduzido garante a incorporação de alelos necessários à ampliação da base genética da espécie e, consequentemente, da variabilidade necessária para uso no processo seletivo. Num segundo estudo, no contexto da análise de modelos mistos, avaliaram-se estratégias de estimação e predição de valores genotípicos para produtividade de grãos, a partir de ensaios de competição final de linhagens de soja. Os genótipos, em número de 111 e classificados em três grupos de maturação, foram semeados em até 23 experimentos conduzidos na região central do Brasil. Os experimentos foram conduzidos no delineamento de blocos completos casualizados, com três repetições. Nas análises biométricas adotaram-se as abordagens de modelo fixo e de modelo misto, neste caso, assumindo-se efeitos genotípicos como aleatórios. Na última abordagem, consideraram-se ainda análises com ou sem uso da informação de parentesco genético, obtida a partir de genealogias ou por marcadores SSR, e associada à matriz de covariâncias dos efeitos aleatórios (G). Para cada modelo, num contexto de análise espacial, adotaram-se também distintas estruturas para a matriz de covariâncias residuais (R). Concluiu-se, então, que: i) a análise com modelo fixo é adequada para estimar efeitos genotípicos em soja, sob condições de balanceamento dos dados e ortogonalidade do delineamento; ii) sob tais condições, a inclusão da informação de parentesco associada à matriz G, embora não garanta melhor ajuste aos modelos, sob herdabilidade moderada ou baixa, melhora a precisão das predições de valores genotípicos; iii) o uso de estruturas espaciais associadas à matriz R, na presença de autocorrelação residual, melhora o ajuste estatístico dos modelos; e, iv) corrobora-se a tese de que, sob alta herdabilidade, a escolha do modelo de análise não altera o posicionamento relativo dos genótipos, e, portanto, não impacta significativamente na seleção de genótipos superiores.

Genomische Selektion ist ein Routine-Verfahren bei verschiedenen Nutztierarten, aber noch nicht bei der Honigbiene wegen der Besonderheiten dieser Spezies. Für die Zuchtwertschätzung bei der Honigbiene ist eine spezielle genetische Verwandtschaftsmatrix erforderlich, da die Paarungsbiologie dieser Spezies ungesicherte Vaterschaft, diploide Königinnen und haploide Drohnen umfasst. Die Arbeit präsentiert einen neu-entwickelten Algorithmus zur effizienten Berechnung der Inversen der genetischen Verwandtschaftsmatrix und der Inzuchtkoeffizienten auf großen Datensätzen. Die Methode wurde zur Voraussage von genomischen und Stammbaum-basierten Zuchtwerten in einer Simulationsstudie genutzt. Die Genauigkeit und die Verzerrung der geschätzten Zuchtwerte wurden ausgewertet unter Berücksichtigung verschiedener Größen der Referenzpopulation. Außerdem wurde der Zuchtfortschritt im ersten Durchlauf von Zuchtprogrammen ausgewertet, die Zuchtschemata mit genomischer oder Stammbaum-basierter Selektion nutzten. Ein erheblich größerer Zuchtfortschritt als bei Stammbaum-basierter Selektion wurde mit genomischer Vorselektion erzielt, für die junge Königinnen genotypisiert wurden, und nur die Kandidaten mit den höchsten genomischen Zuchtwerten zur Anpaarung oder Leistungsprüfung zugelassen wurden. Für einen realen Datensatz von ungefähr 3000 genotypisierten Königinnen wurden Stammbaum-basierte und genomische Zuchtwerte für sechs wirtschaftlich bedeutende Merkmale vorhergesagt. Drei Merkmale zeigten eine signifikant höhere Vorhersagegenauigkeit bei genomischer Zuchtwertschätzung gegenüber Stammbaum-basierten Verfahren und die Unterschiede zwischen allen sechs Merkmalen konnten im Wesentlichen aus den genetischen Parametern der Merkmale und der begrenzten Größe der Referenzpopulation erklärt werden. Damit zeigt die Arbeit, dass die genomische Selektion bei der Honigbiene genutzt werden kann, den Zuchtfortschritt zu erhöhen. / Genomic selection is a routine practice for several important livestock species but not yet in honey bees, due to the peculiarities of this species. For honey bees, a specialized genetic relationship matrix is required for the prediction of breeding values, since their mating biology involves uncertain paternity, diploid queens, and haploid drones. The thesis presents a novel algorithm for the efficient computation of the inverse of the numerator relationship matrix and the coefficients of inbreeding on large data sets. The method was used to estimate genomic and pedigree-based breeding values in a simulation study. The accuracy and bias of the estimated breeding values were evaluated and various sizes of the reference population were considered. Subsequently, the genetic gain in the initial cycle of breeding programs was evaluated for several breeding schemes employing genomic or pedigree-based selection. A considerably higher genetic gain than with pedigree-based selection was achieved with genomic preselection, for which queens were genotyped early in life, and only the candidates of high genomic breeding value were admitted for mating or phenotyping. On a real data set of about 3000 genotyped queens, pedigree-based and genomic breeding values were predicted for six economically relevant traits. Three traits showed significantly higher prediction accuracy with genomic compared to pedigree-based methods, and the differences between all the six traits could be explained mainly from their genetic parameters and the limited size of the reference population. The results show that genomic selection can be applied in honey bees, and the thesis provides appropriate breeding schemes and mathematical methods for its implementation.