Transcriptome-wide gene regulatory network construction

Music City Center 

Constructing gene regulatory networks is crucial to understand the genetic architecture of complex traits. However, constructing directed networks with genome-wide genes remains a challenge due to the high dimensionality. Taking advantage of both transcriptomic and single nucleotide polymorphism data, we proposed a two-stage penalized least squares method to build large systems of structural equations for directional network construction. A large system of structural equations can be constructed via consistent estimation of a set of conditional expectations at the first stage, and a consistent selection of regulatory effects was obtained at the second stage. The proposed method can simultaneously investigate all the genes across the entire genome, and the computation is fast due to the parallel implementation. Such unbiased network construction will enable the determination of the causal relationship between genes and facilitate our understanding of disease mechanisms. We demonstrate the superior performance and effectiveness of the method using simulation studies, and the method has been successfully applied to real data.