Phenotypic Bayesian phylodynamics : hierarchical graph models, antigenic clustering and latent liabilities
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Tipo de documento: | Tese |
Idioma: | eng |
Título da fonte: | Biblioteca Digital de Teses e Dissertações da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/132858 |
Resumo: | Combining models for phenotypic and molecular evolution can lead to powerful inference tools. Under the flexible framework of Bayesian phylogenetics, I develop statistical methods to address phylodynamic problems in this intersection. First, I present a hierarchical phylogeographic method that combines information across multiple datasets to draw inference on a common geographical spread process. Each dataset represents a parallel realization of this geographic process on a different group of taxa, and the method shares information between these realizations through a hierarchical graph structure. Additionally, I develop a multivariate latent liability model for assessing phenotypic correlation among sets of traits, while controlling for shared evolutionary history. This method can efficiently estimate correlations between multiple continuous traits, binary traits and discrete traits with many ordered or unordered outcomes. Finally, I present a method that uses phylogenetic information to study the evolution of antigenic clusters in influenza. The method builds an antigenic cartography map informed by the assignment of each influenza strain to one of the antigenic clusters. |
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Cybis, Gabriela BettellaSuchard, Marc A.2016-02-24T02:05:01Z2014http://hdl.handle.net/10183/132858000946621Combining models for phenotypic and molecular evolution can lead to powerful inference tools. Under the flexible framework of Bayesian phylogenetics, I develop statistical methods to address phylodynamic problems in this intersection. First, I present a hierarchical phylogeographic method that combines information across multiple datasets to draw inference on a common geographical spread process. Each dataset represents a parallel realization of this geographic process on a different group of taxa, and the method shares information between these realizations through a hierarchical graph structure. Additionally, I develop a multivariate latent liability model for assessing phenotypic correlation among sets of traits, while controlling for shared evolutionary history. This method can efficiently estimate correlations between multiple continuous traits, binary traits and discrete traits with many ordered or unordered outcomes. Finally, I present a method that uses phylogenetic information to study the evolution of antigenic clusters in influenza. The method builds an antigenic cartography map informed by the assignment of each influenza strain to one of the antigenic clusters.application/pdfengBioestatísticaPhenotypic Bayesian phylodynamics : hierarchical graph models, antigenic clustering and latent liabilitiesinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisUniversity of CaliforniaDepartments of Biomathematics, Biostatistics and Human GeneticsLos Angeles, Cal-USA2014doutoradoinfo:eu-repo/semantics/openAccessreponame:Biblioteca Digital de Teses e Dissertações da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSTEXT000946621.pdf.txt000946621.pdf.txtExtracted Texttext/plain216274http://www.lume.ufrgs.br/bitstream/10183/132858/2/000946621.pdf.txtfab6b8325e39c912a80346e9c4590d7fMD52ORIGINAL000946621.pdf000946621.pdfTexto completo (inglês)application/pdf3802377http://www.lume.ufrgs.br/bitstream/10183/132858/1/000946621.pdf7969ab655a1ab0db5ee90f3f603e1d34MD5110183/1328582019-01-13 04:08:28.569259oai:www.lume.ufrgs.br:10183/132858Biblioteca Digital de Teses e Dissertaçõeshttps://lume.ufrgs.br/handle/10183/2PUBhttps://lume.ufrgs.br/oai/requestlume@ufrgs.br||lume@ufrgs.bropendoar:18532019-01-13T06:08:28Biblioteca Digital de Teses e Dissertações da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
Phenotypic Bayesian phylodynamics : hierarchical graph models, antigenic clustering and latent liabilities |
title |
Phenotypic Bayesian phylodynamics : hierarchical graph models, antigenic clustering and latent liabilities |
spellingShingle |
Phenotypic Bayesian phylodynamics : hierarchical graph models, antigenic clustering and latent liabilities Cybis, Gabriela Bettella Bioestatística |
title_short |
Phenotypic Bayesian phylodynamics : hierarchical graph models, antigenic clustering and latent liabilities |
title_full |
Phenotypic Bayesian phylodynamics : hierarchical graph models, antigenic clustering and latent liabilities |
title_fullStr |
Phenotypic Bayesian phylodynamics : hierarchical graph models, antigenic clustering and latent liabilities |
title_full_unstemmed |
Phenotypic Bayesian phylodynamics : hierarchical graph models, antigenic clustering and latent liabilities |
title_sort |
Phenotypic Bayesian phylodynamics : hierarchical graph models, antigenic clustering and latent liabilities |
author |
Cybis, Gabriela Bettella |
author_facet |
Cybis, Gabriela Bettella |
author_role |
author |
dc.contributor.author.fl_str_mv |
Cybis, Gabriela Bettella |
dc.contributor.advisor1.fl_str_mv |
Suchard, Marc A. |
contributor_str_mv |
Suchard, Marc A. |
dc.subject.por.fl_str_mv |
Bioestatística |
topic |
Bioestatística |
description |
Combining models for phenotypic and molecular evolution can lead to powerful inference tools. Under the flexible framework of Bayesian phylogenetics, I develop statistical methods to address phylodynamic problems in this intersection. First, I present a hierarchical phylogeographic method that combines information across multiple datasets to draw inference on a common geographical spread process. Each dataset represents a parallel realization of this geographic process on a different group of taxa, and the method shares information between these realizations through a hierarchical graph structure. Additionally, I develop a multivariate latent liability model for assessing phenotypic correlation among sets of traits, while controlling for shared evolutionary history. This method can efficiently estimate correlations between multiple continuous traits, binary traits and discrete traits with many ordered or unordered outcomes. Finally, I present a method that uses phylogenetic information to study the evolution of antigenic clusters in influenza. The method builds an antigenic cartography map informed by the assignment of each influenza strain to one of the antigenic clusters. |
publishDate |
2014 |
dc.date.issued.fl_str_mv |
2014 |
dc.date.accessioned.fl_str_mv |
2016-02-24T02:05:01Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
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publishedVersion |
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http://hdl.handle.net/10183/132858 |
dc.identifier.nrb.pt_BR.fl_str_mv |
000946621 |
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eng |
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openAccess |
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application/pdf |
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Biblioteca Digital de Teses e Dissertações da UFRGS |
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