creQTL provides an agnostic framework for predicting trans regulators of clusters of genes. It does not require any one particular biclustering method or statistical test and can be extended to any species for which genetic markers (including those derived from linkage) and gene expression data can be obtained; for example, creQTL mapping in mice would allow the study of gene regulation in many disease models and in normal biological processes such as development, which are highly relevant to human disease. Like eQTLs, creQTLs may be incorporated into additional analyses. As recent studies have used eQTL data to assemble genetic loci into directed, Bayesian networks to predict causal relationships (e.g. [16,42]), future studies could assemble creQTLs into directed or undirected gene networks. Because creQTL mapping groups co-regulated genes into a smaller number of modules, network construction using creQTL information in place of eQTL information should be computationally more efficient.
