Relationships among traits were investigated on the genomic and residual levels using novel methodology. This included inference on these relationships via Bayesian networks and an assessment of the networks with structural equation models. The methodology employed three steps. First, a Bayesian multiple-trait Gaussian model was fitted to the data to decompose phenotypic values into their genomic and residual components. Second, genomic and residual network structures among traits were learned from estimates of these two components. Network learning was performed using six different algorithmic settings for comparison, of which two were score-based and four were constraint-based approaches. Third, structural equation model analyses ranked the networks in terms of goodness of fit and predictive ability, and compared them with the standard multiple-trait fully recursive network. The methodology was applied to experimental data representing the European heterotic maize pools Dent and Flint (Zea mays L.). Inferences on genomic and residual trait connections were depicted separately as directed acyclic graphs. These graphs provide information beyond mere pairwise genetic or residual associations between traits, illustrating for example conditional independencies and hinting at potential causal links among traits. Network analysis suggested some genetic correlations as potentially spurious. Genomic and residual networks were compared between Dent and Flint.

译文

使用新颖的方法在基因组和残留水平上研究了性状之间的关系。这包括通过贝叶斯网络对这些关系的推断以及使用结构方程模型对网络的评估。该方法采用了三个步骤。首先,将贝叶斯多性状高斯模型拟合到数据,以将表型值分解为其基因组和残差成分。其次,从这两个组成部分的估计中学习了性状之间的基因组和残余网络结构。网络学习是使用六种不同的算法设置进行比较的,其中两种是基于分数的,四种是基于约束的方法。第三,结构方程模型分析根据拟合优度和预测能力对网络进行了排名,并将它们与标准的多性状全递归网络进行了比较。该方法应用于代表欧洲杂种玉米库Dent和Flint (Zea mays L.) 的实验数据。关于基因组和残余性状连接的推论被分别描述为有向无环图。这些图提供的信息不仅仅是性状之间的成对遗传或残留关联,例如说明条件独立性并暗示性状之间的潜在因果关系。网络分析表明,某些遗传相关性可能是虚假的。比较了凹痕和火石之间的基因组和残余网络。

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