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Structural equation models applied to quantitative genetics

Cerqueira, Pedro Henrique Ramos

Biblioteca Digital de Teses e Dissertações da USP; Universidade de São Paulo; Escola Superior de Agricultura Luiz de Queiroz 2015-09-03

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  • Título:
    Structural equation models applied to quantitative genetics
  • Autor: Cerqueira, Pedro Henrique Ramos
  • Orientador: Leandro, Roseli Aparecida; Rosa, Guilherme Jordão de Magalhães
  • Assuntos: Amostrador De Gibbs; Regressão Polinomial; Modelos Mistos Multi Característicos; Modelos Lineares Mistos; Modelos De Equações Estruturais; Inferência Bayesiana; Genética Quantitativa; Gado Leiteiro Da Raça Holandesa; Holstein Dairy Cattle; Bayesian Inference; Quantitative Genetics; Polynomial Regression; Multiple Trait Mixed Models; Gibbs Sampler; Linear Mixed Models; Structural Equation Models
  • Notas: Tese (Doutorado)
  • Descrição: Causal models have been used in different areas of knowledge in order to comprehend the causal associations between variables. Over the past decades, the amount of studies using these models have been growing a lot, especially those related to biological systems where studying and learning causal relationships among traits are essential for predicting the consequences of interventions in such system. Graph analysis (GA) and structural equation modeling (SEM) are tools used to explore such associations. While GA allows searching causal structures that express qualitatively how variables are causally connected, fitting SEM with a known causal structure allows to infer the magnitude of causal effects. Also SEM can be viewed as multiple regression models in which response variables can be explanatory variables for others. In quantitative genetics studies, SEM aimed to study the direct and indirect genetic effects associated to individuals through information related to them, beyond the observed characteristics, such as the kinship relations. In those studies typically the assumptions of linear relationships among traits are made. However, in some scenarios, nonlinear relationships can be observed, which make unsuitable the mentioned assumptions. To overcome this limitation, this paper proposes to use a mixed effects polynomial structural equation model, second or superior degree, to model those nonlinear relationships. Two studies were developed, a simulation and an application to real data. The first study involved simulation of 50 data sets, with a fully recursive causal structure involving three characteristics in which linear and nonlinear causal relations between them were allowed. The second study involved the analysis of traits related to dairy cows of the Holstein breed. Phenotypic relationships between traits were calving difficulty, gestation length and also the proportion of perionatal death. We compare the model of multiple traits and polynomials structural equations models, under different polynomials degrees in order to assess the benefits of the SEM polynomial of second or higher degree. For some situations the inappropriate assumption of linearity results in poor predictions of the direct, indirect and total of the genetic variances and covariance, either overestimating, underestimating, or even assign opposite signs to covariances. Therefore, we conclude that the inclusion of a polynomial degree increases the SEM expressive power.
  • DOI: 10.11606/T.11.2015.tde-05112015-145419
  • Editor: Biblioteca Digital de Teses e Dissertações da USP; Universidade de São Paulo; Escola Superior de Agricultura Luiz de Queiroz
  • Data de criação/publicação: 2015-09-03
  • Formato: Adobe PDF
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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