Busca por estruturas causais no contexto de modelos mistos em genética quantitativa

Abstract

Structural Equation Models (SEM) can be used to study recursive and simultaneous relationships in multivariate analyses. Nonetheless, the number of different recursive causal structures that can be usedfor fitting a SEM to multivariate data can be huge, even when only a few traits are considered. In recent applications of SEM in mixed model quantitative genetics settings, causal structures were preselectedbased on prior biological knowledge alone. Therefore, the wide range of possible causal structures has not been properly explored. Alternatively, causal structure spaces can be explored using algorithms which, using data driven evidence, can search for structures that are compatible with the joint distribution of the variables under study. However, the search cannot be performed directly on the joint distribution of the phenotypes as it is possibly confounded by genetic covariance among traits. In this thesis, we propose to search for recursive causal structures among phenotypes using the IC algorithm after adjusting the data for genetic effects. A standard multiple trait model is fitted to obtain a posterior covariance matrix of phenotypes conditional to unobservable additive genetic effects, which is then used as input for the IC algorithm.