chapter  10
Elucidating Gene Regulatory Networks Underlying Complex Phenotypes: Genetical Genomics and Bayesian Network
Pages 13

Microarray-based technologies have enabled comprehensive transcriptome profiling.It is becoming feasible to reconstruct gene transcriptional regulatory networks from microarray data. In this chapter, I oudine a new strategy for reconstructing gene regulatory networks as part of the causal network through which genetic variations influence phenotypes. A central step of phenotype manifestation is gene transcription. The gene expres­ sion programs encoded in DNA sequences are executed via the network of transcriptional regulation. Thus, the gene regulatory networks can be studied as the causal pathways connect­ ing genetic loci and phenotypes. Bayesian network modeling combined with genetical genomics methods provides a promising method for inferring the causal network from multiple types of data. The complete causal network should include nodes (variables) representing genetic varia­ tions (e.g., single nucleotide polymorphisms); environmental factors (experimental conditions); phenotypes; and abundances of RNAs, proteins and other bio-molecules. Currently, the recon­ struction of the causal network focuses on the interactions between genetic variations, abun­ dance of mRNAs, and phenotypes because large-scale data are available for these components. Advances in molecular profiling will eventually provide sufficient data for all the bio-molecules and thus enable the reconstruction of the complete causal network underlying complex pheno­ types. This causal network is important not only for understanding the structure of gene regu­ latory network, but also because it provides deep insights into the molecular underpinnings of genotype-environment-phenotype relations, which are invaluable for uncovering the genetic basis of diseases and predicting the outcomes of therapeutic interventions.