Efficient Bayesian methods for mixture models with genetic applications

• Ano de defesa: 2016
• Autor(a) principal: Zuanetti, Daiane Aparecida
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Mixture models; data-driven Bayesian methods; nonparametric Bayesian methods; QTL mapping; clustering distributions; clustering big data; pedigree dat; Modelos de mistura; métodos Bayesianos direcionados pelos dados; metodos Bayesianos não-paramétricos; mapeamento de QTL; agrupamento de distribuições;
• Link de acesso: http://www.teses.usp.br/teses/disponiveis/104/104131/tde-20082019-083551/

We propose Bayesian methods for selecting and estimating different types of mixture models which are widely used inGenetics and MolecularBiology. We specifically propose data-driven selection and estimation methods for a generalized mixture model, which accommodates the usual (independent) and the first-order (dependent) models in one framework, and QTL (quantitativetrait locus) mapping models for independent and pedigree data. For clustering genes through a mixture model, we propose three nonparametric Bayesian methods: a marginal nested Dirichlet process (NDP), which is able to cluster distributions and, a predictive recursion clustering scheme (PRC) and a subset nonparametric Bayesian (SNOB) clustering algorithm for clustering bigdata. We analyze and compare the performance of the proposed methods and traditional procedures of selection, estimation and clustering in simulated and real datasets. The proposed methods are more flexible, improve the convergence of the algorithms and provide more accurate estimates in many situations. In addition, we propose methods for estimating non observable QTLs genotypes and missing parents and improve the Mendelian probability of inheritance of nonfounder genotype using conditional independence structures.We also suggest applying diagnostic measures to check the goodness of fit of QTLmappingmodels.