Inference of gene regulatory networks by integrating gene expressions and genetic perturbations

To elucidate the overall relationships between gene expressions and genetic perturbations, many approaches for SNPs to be involved in gene regulatory network (GRNs) inference have been suggested. In the most of the inferences of networks named as SNP-Gene Regulatory Networks (SGRNs) inference, pairs of SNP-gene are still separately given by performing eQTL mappings but eQTLs are not identified during network constructions. In order to build a SGRN for a given set of genes and SNPs without pre-defined eQTL information, we propose a method that is based on a structural equation model. The method consists of three steps: (i) ridge regression, (ii) elastic net regression, and (iii) iterative adaptive lasso regression. In the inference, it is assumed that each gene has a single unknown eQTL. The first two steps are to remove false positive edges keeping as many true positive edges as possible, and then lastly, final edges are selected by iterative adaptive lasso regression that iteratively gives more weight to the edge whose coefficient value is relatively high. To evaluate the performance, the method is applied to data that is randomly generated from the simulated networks and parameters. The method is also applied to psychiatric disorder data. There are three main contributions in this work. First, the proposed method provides both the gene regulatory inference and the identification of eQTL that affect genes in the network. Second, the simulation result proves that an integrative approach of multiple regression methods can effectively detect true edges as well as filter false positive edges. Lastly, it is demonstrated by applying it to psychiatric disorder data that our inference without eQTL information can discover the SGRNs that partially confirms eQTLs identified by our previous work.