Using Stochastic Causal Trees to Augment Bayesian Networks for Modeling eQTL Datasets

Background: The combination of genotypic and genome-wide expression data arising from segregating populations offers an unprecedented opportunity to model and dissect complex phenotypes. The immense potential offered by these data derives from the fact that genotypic variation is the sole source of perturbation and can therefore be used to reconcile changes in gene expression programs with the parental genotypes. To date, several methodologies have been developed for modeling eQTL data. These methods generally leverage genotypic data to resolve causal relationships among gene pairs implicated as associates in the expression data. In particular, leading studies have augmented Bayesian networks with genotypic data, providing a powerful framework for learning and modeling causal relationships. While these initial efforts have provided promising results, one major drawback associated with these methods is that they are generally limited to resolving causal orderings for transcripts most p...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Kyle C. Chipman, Ambuj K. Singh
• Keywords: Artificial Intelligence, Bayesian Network, BMCBI 2011, Gene Network Reconstruction, Genotypic