%0 Journal Article
%T Searching for phenotypic causal networks involving complex traits: an application to European quail
%A Bruno D Valente
%A Guilherme JM Rosa
%A Martinho A Silva
%A Rafael B Teixeira
%A Robledo A Torres
%J Genetics Selection Evolution
%D 2011
%I BioMed Central
%R 10.1186/1297-9686-43-37
%X Here, we applied this approach to five traits in European quail: birth weight (BW), weight at 35 days of age (W35), age at first egg (AFE), average egg weight from 77 to 110 days of age (AEW), and number of eggs laid in the same period (NE). We have focused the discussion on the challenges and difficulties resulting from applying this method to field data. Statistical decisions regarding partial correlations were based on different Highest Posterior Density (HPD) interval contents and models based on the selected causal structures were compared using the Deviance Information Criterion (DIC). In addition, we used temporal information to perform additional edge orienting, overriding the algorithm output when necessary.As a result, the final causal structure consisted of two separated substructures: BW¡úAEW and W35¡úAFE¡úNE, where an arrow represents a direct effect. Comparison between a SEM with the selected structure and a Multiple Trait Animal Model using DIC indicated that the SEM is more plausible.Coupling prior knowledge with the output provided by the IC algorithm allowed further learning regarding phenotypic causal structures when compared to standard mixed effects SEM applications.Structural equation models or SEM [1,2] are used to model multiple traits and functional links among them, which may be interpreted as causal relationships. These models were adapted for the context of quantitative genetics mixed models by [3], and henceforth applied and extended by a number of authors [4-11].Fitting SEM requires choosing a causal structure a priori. This structure describes qualitatively the causal relationships among traits by determining the subset of traits that imposes causal influence on each phenotype studied. By fitting a SEM, it is possible then to infer the magnitude of each causal relationship pertaining to the causal structure, which is quantified by model parameters called structural coefficients. However, choosing the causal structure may be cumbersome, gi
%U http://www.gsejournal.org/content/43/1/37