Bayesian Networks Illustrate Genomic and Residual Trait Connections in Maize (Zea mays L.).

ABSTRACT: 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 predic

SUBMITTER: Topner K 

PROVIDER: S-EPMC5555481 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature