O uso de simulação de Monte Carlo via cadeias de Markov no melhoramento genético

Detalhes bibliográficos
Autor(a) principal: Nascimento, Moysés
Data de Publicação: 2009
Tipo de documento: Dissertação
Idioma: por
Título da fonte: LOCUS Repositório Institucional da UFV
Texto Completo: http://locus.ufv.br/handle/123456789/4022
Resumo: The objective of this work was to provide a theoretical and applied reference on the main Monte Carlo simulation methods via Markov chains (MCMC), seeking to focus on applications in genetic breeding. Thus, the algorithms of Metropolis-Hastings, simulated annealing and the Gibbs sampler were presented. The theoretical aspects of the methods were approached through a detailed discussion about their foundations based on the Markov chain theory. Besides the theoretical discussion, concrete applications were developed. The Metropolis-Hastings algorithm was used to achieve estimates from the frequencies of recombination between pairs of markers of a population F2, of co-dominant nature, with 200 individuals. The simulated annealing was applied to establish a better linking order in the construction of genetic maps of three simulated populations F2, with markers of co-dominant nature, containing 50, 100 and 200 individuals, respectively. For each population, it was established a genome with four linking groups, each with 100 cM of size. The linking groups present 51, 21, 11 and 6 markers, with a distance of 2, 5, 10 and 20 cM between the adjacent marks, respectively, providing different degrees of saturation. The Gibbs sampler, on the other hand, was used for the achievement of the estimates of the adaptability and stability parameters of the model proposed by Finlay and Wilkinson (1963), through the Bayesian inference. The data of the productivity averages of five genotypes evaluated in nine environments were used, come from essays in randomized blocks with four replications. In all the applications, the algorithms were computationally viable and achieved satisfactory results.
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spelling Nascimento, Moyséshttp://lattes.cnpq.br/6544887498494945Cecon, Paulo Robertohttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4788114T5Peternelli, Luiz Alexandrehttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4723301Z7Cruz, Cosme Damiãohttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4788274A6Ferreira, Adésiohttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4777896Y8Viana, José Marcelo Sorianohttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4786170D52015-03-26T13:32:07Z2009-08-062015-03-26T13:32:07Z2009-02-20NASCIMENTO, Moysés. The use of Monte Carlo simulation via Markov chains in genetic breeding. 2009. 111 f. Dissertação (Mestrado em Estatística Aplicada e Biometria) - Universidade Federal de Viçosa, Viçosa, 2009.http://locus.ufv.br/handle/123456789/4022The objective of this work was to provide a theoretical and applied reference on the main Monte Carlo simulation methods via Markov chains (MCMC), seeking to focus on applications in genetic breeding. Thus, the algorithms of Metropolis-Hastings, simulated annealing and the Gibbs sampler were presented. The theoretical aspects of the methods were approached through a detailed discussion about their foundations based on the Markov chain theory. Besides the theoretical discussion, concrete applications were developed. The Metropolis-Hastings algorithm was used to achieve estimates from the frequencies of recombination between pairs of markers of a population F2, of co-dominant nature, with 200 individuals. The simulated annealing was applied to establish a better linking order in the construction of genetic maps of three simulated populations F2, with markers of co-dominant nature, containing 50, 100 and 200 individuals, respectively. For each population, it was established a genome with four linking groups, each with 100 cM of size. The linking groups present 51, 21, 11 and 6 markers, with a distance of 2, 5, 10 and 20 cM between the adjacent marks, respectively, providing different degrees of saturation. The Gibbs sampler, on the other hand, was used for the achievement of the estimates of the adaptability and stability parameters of the model proposed by Finlay and Wilkinson (1963), through the Bayesian inference. The data of the productivity averages of five genotypes evaluated in nine environments were used, come from essays in randomized blocks with four replications. In all the applications, the algorithms were computationally viable and achieved satisfactory results.Este trabalho teve por objetivo fornecer um referencial teórico e aplicado sobre os principais métodos de simulação de Monte Carlo via cadeias de Markov (MCMC), buscando dar ênfase em aplicações no melhoramento genético. Assim, apresentaram-se os algoritmos de Metropolis-Hastings, simulated annealing e amostrador de Gibbs. Os aspectos teóricos dos métodos foram abordados através de uma discussão detalhada de seus fundamentos com base na teoria de cadeias de Markov. Além da discussão teórica, aplicações concretas foram desenvolvidas. O algoritmo de Metropolis- Hastings foi utilizado para obter estimativas das freqüências de recombinação entre pares de marcadores de uma população F2, de natureza codominante, constituída de 200 indivíduos. O simulated annealing foi aplicado no estabelecimento da melhor ordem de ligação na construção de mapas genéticos de três populações F2 simuladas, com marcadores de natureza codominantes, de tamanhos 50, 100 e 200 indivíduos respectivamente. Para cada população foi estabelecido um genoma com quatro grupos de ligação, com 100 cM de tamanho cada. Os grupos de ligação possuem 51, 21, 11 e 6 marcadores, com uma distância de 2, 5, 10 e 20 cM entre marcas adjacentes respectivamente, ocasionando diferentes graus de saturação. Já o amostrador de Gibbs foi utilizado na obtenção das estimativas dos parâmetros de adaptabilidade e estabilidade, do modelo proposto por Finlay e Wilkinson (1963), através da inferência bayesiana. Foram utilizados os dados de médias de rendimento de cinco genótipos avaliados em nove ambientes, provenientes de ensaios em blocos ao acaso com quatro repetições. Em todas as aplicações os algoritmos se mostraram computacionalmente viáveis e obtiveram resultados satisfatórios.Universidade Federal de Viçosaapplication/pdfporUniversidade Federal de ViçosaMestrado em Estatística Aplicada e BiometriaUFVBREstatística Aplicada e BiometriaSimulação estocásticaMCMCEstatística genômicaInferência bayesianaStochastic simulationMCMCGenomic statisticsBayesian inferenceCNPQ::CIENCIAS AGRARIASO uso de simulação de Monte Carlo via cadeias de Markov no melhoramento genéticoThe use of Monte Carlo simulation via Markov chains in genetic breedinginfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/openAccessreponame:LOCUS Repositório Institucional da UFVinstname:Universidade Federal de Viçosa (UFV)instacron:UFVORIGINALtexto completo.pdfapplication/pdf860520https://locus.ufv.br//bitstream/123456789/4022/1/texto%20completo.pdf10a14565e22ba73ede77de9091dd7ed9MD51TEXTtexto completo.pdf.txttexto completo.pdf.txtExtracted texttext/plain152674https://locus.ufv.br//bitstream/123456789/4022/2/texto%20completo.pdf.txt19ee131447ab2d2c39a0c4ef737d256dMD52THUMBNAILtexto completo.pdf.jpgtexto completo.pdf.jpgIM Thumbnailimage/jpeg3616https://locus.ufv.br//bitstream/123456789/4022/3/texto%20completo.pdf.jpgc54b30136bceab7f87d2837c09c2f385MD53123456789/40222016-04-09 23:17:07.728oai:locus.ufv.br:123456789/4022Repositório InstitucionalPUBhttps://www.locus.ufv.br/oai/requestfabiojreis@ufv.bropendoar:21452016-04-10T02:17:07LOCUS Repositório Institucional da UFV - Universidade Federal de Viçosa (UFV)false
dc.title.por.fl_str_mv O uso de simulação de Monte Carlo via cadeias de Markov no melhoramento genético
dc.title.alternative.eng.fl_str_mv The use of Monte Carlo simulation via Markov chains in genetic breeding
title O uso de simulação de Monte Carlo via cadeias de Markov no melhoramento genético
spellingShingle O uso de simulação de Monte Carlo via cadeias de Markov no melhoramento genético
Nascimento, Moysés
Simulação estocástica
MCMC
Estatística genômica
Inferência bayesiana
Stochastic simulation
MCMC
Genomic statistics
Bayesian inference
CNPQ::CIENCIAS AGRARIAS
title_short O uso de simulação de Monte Carlo via cadeias de Markov no melhoramento genético
title_full O uso de simulação de Monte Carlo via cadeias de Markov no melhoramento genético
title_fullStr O uso de simulação de Monte Carlo via cadeias de Markov no melhoramento genético
title_full_unstemmed O uso de simulação de Monte Carlo via cadeias de Markov no melhoramento genético
title_sort O uso de simulação de Monte Carlo via cadeias de Markov no melhoramento genético
author Nascimento, Moysés
author_facet Nascimento, Moysés
author_role author
dc.contributor.authorLattes.por.fl_str_mv http://lattes.cnpq.br/6544887498494945
dc.contributor.author.fl_str_mv Nascimento, Moysés
dc.contributor.advisor-co1.fl_str_mv Cecon, Paulo Roberto
dc.contributor.advisor-co1Lattes.fl_str_mv http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4788114T5
dc.contributor.advisor-co2.fl_str_mv Peternelli, Luiz Alexandre
dc.contributor.advisor-co2Lattes.fl_str_mv http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4723301Z7
dc.contributor.advisor1.fl_str_mv Cruz, Cosme Damião
dc.contributor.advisor1Lattes.fl_str_mv http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4788274A6
dc.contributor.referee1.fl_str_mv Ferreira, Adésio
dc.contributor.referee1Lattes.fl_str_mv http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4777896Y8
dc.contributor.referee2.fl_str_mv Viana, José Marcelo Soriano
dc.contributor.referee2Lattes.fl_str_mv http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4786170D5
contributor_str_mv Cecon, Paulo Roberto
Peternelli, Luiz Alexandre
Cruz, Cosme Damião
Ferreira, Adésio
Viana, José Marcelo Soriano
dc.subject.por.fl_str_mv Simulação estocástica
MCMC
Estatística genômica
Inferência bayesiana
topic Simulação estocástica
MCMC
Estatística genômica
Inferência bayesiana
Stochastic simulation
MCMC
Genomic statistics
Bayesian inference
CNPQ::CIENCIAS AGRARIAS
dc.subject.eng.fl_str_mv Stochastic simulation
MCMC
Genomic statistics
Bayesian inference
dc.subject.cnpq.fl_str_mv CNPQ::CIENCIAS AGRARIAS
description The objective of this work was to provide a theoretical and applied reference on the main Monte Carlo simulation methods via Markov chains (MCMC), seeking to focus on applications in genetic breeding. Thus, the algorithms of Metropolis-Hastings, simulated annealing and the Gibbs sampler were presented. The theoretical aspects of the methods were approached through a detailed discussion about their foundations based on the Markov chain theory. Besides the theoretical discussion, concrete applications were developed. The Metropolis-Hastings algorithm was used to achieve estimates from the frequencies of recombination between pairs of markers of a population F2, of co-dominant nature, with 200 individuals. The simulated annealing was applied to establish a better linking order in the construction of genetic maps of three simulated populations F2, with markers of co-dominant nature, containing 50, 100 and 200 individuals, respectively. For each population, it was established a genome with four linking groups, each with 100 cM of size. The linking groups present 51, 21, 11 and 6 markers, with a distance of 2, 5, 10 and 20 cM between the adjacent marks, respectively, providing different degrees of saturation. The Gibbs sampler, on the other hand, was used for the achievement of the estimates of the adaptability and stability parameters of the model proposed by Finlay and Wilkinson (1963), through the Bayesian inference. The data of the productivity averages of five genotypes evaluated in nine environments were used, come from essays in randomized blocks with four replications. In all the applications, the algorithms were computationally viable and achieved satisfactory results.
publishDate 2009
dc.date.available.fl_str_mv 2009-08-06
2015-03-26T13:32:07Z
dc.date.issued.fl_str_mv 2009-02-20
dc.date.accessioned.fl_str_mv 2015-03-26T13:32:07Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
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dc.identifier.citation.fl_str_mv NASCIMENTO, Moysés. The use of Monte Carlo simulation via Markov chains in genetic breeding. 2009. 111 f. Dissertação (Mestrado em Estatística Aplicada e Biometria) - Universidade Federal de Viçosa, Viçosa, 2009.
dc.identifier.uri.fl_str_mv http://locus.ufv.br/handle/123456789/4022
identifier_str_mv NASCIMENTO, Moysés. The use of Monte Carlo simulation via Markov chains in genetic breeding. 2009. 111 f. Dissertação (Mestrado em Estatística Aplicada e Biometria) - Universidade Federal de Viçosa, Viçosa, 2009.
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