Genetical and Computational Genomics of Gene Networks
Sequential and contingent changes in gene expression strongly influence the development of organisms and their responses to the environment. These
dynamic biological programs are executed via complex and still poorly defined networks of interactions among genes, transcripts, proteins, and numerous
small molecules and cofactors. An adequate definition of these flexible and complex molecular circuits is an essential goal of functional genomics.
High-throughput methods including transcriptome analysis and genome sequencing have generated huge amounts of data that can be exploited to
systematically identify gene modulatory networks. A recent step forward in this direction involves merging complex trait analysis with transcriptome
analysis. This genetical genomics approach treats normal variation in the expression of each gene as a quantitative trait. Quantitative trait locus
(QTL) mapping methods are then used to identify the chromosomal intervals that harbor sequence variants (polymorphisms) that produce downstream
variations in expression. We developed an integrated computational framework based on transcriptome QTL mapping, single nucleotide polymorphism (SNP)
analysis and Bayesian network. Our method extends beyond mapping of regulatory loci to a systematic evaluation of possible gene modulatory relations
using genome-wide genotype, SNP and gene expression data. (Reference)
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