Using graphical models to investigate phenotypic networks involving polygenic traits

• Ano de defesa: 2018
• Autor(a) principal: Pinto, Renan Mercuri
• 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: Bayesian networks; Causal networks; Modelos de equações estruturais; Redes Bayesianas; Redes causais; Structural equation models
• Link de acesso: http://www.teses.usp.br/teses/disponiveis/11/11134/tde-25072018-180027/

Understanding the causal architecture underlying complex systems biology has a great value in agriculture production for the development of optimal management strategies and selective breeding. So far, most studies in this area use only prior knowledge to propose causal networks and/or do not consider the possible genetic confounding factors on the structure search, which may hide important relationships among phenotypes and also bias the resulting inferred causal network. In this dissertation, we explore many structural learning algorithms and present a new one, called PolyMaGNet (Polygenic traits with Major Genes Network analysis), to search for recursive causal structures involving complex phenotypic traits with polygenic inheritance and also allowing the possibility of major genes affecting the traits. Briefly, a multiple-trait animal mixed model is fitted using a Bayesian approach considering major genes as covariates. Next, posterior samples of the residual covariance matrix are used as input for the Inductive Causation algorithm to search for putative causal structures, which are compared to each other using the Akaike information criterion. The performance of PolyMaGNet was evaluated and compared with another widely used approach in a simulated study considering a QTL mapping population. Results showed that, in the presence of major genes, our method recovered the true skeleton structure as well as the causal directions with a higher rate of true positives. The PolyMaGNet approach was also applied to a real dataset of an F2 Duroc × Pietrain pig resource population to recover the causal structure underlying on carcass, meat quality and chemical composition traits. Results corroborated with the literature regarding the cause-effect relationships between these traits and also provided new insights about phenotypic causal networks and its genetic architectures in complex systems biology.