Network-based feature selection reveals substructures of gene modules responding to salt stress in rice

Rice, an important food resource, is highly sensitive to salt stress, which is directly related to food security. Although many studies have identified physiological mechanisms that confer tolerance to the osmotic effects of salinity, the link between rice genotype and salt tolerance is not very clear yet. Association of gene co-expression network and rice phenotypic data under stress has penitential to identify stress-responsive genes, but there is no standard method to associate stress phenotype with gene co-expression network. A novel method for integration of gene co-expression network and stress phenotype data was developed to conduct a system analysis to link genotype to phenotype. We applied a LASSO-based method to the gene co-expression network of rice with salt stress to discover key genes and their interactions for salt tolerance-related phenotypes. Submodules in gene modules identified from the co-expression network were selected by the LASSO regression, which establishes a linear relationship between gene expression profiles and physiological responses, that is, sodium/potassium condenses under salt stress. Genes in these submodules have functions related to ion transport, osmotic adjustment, and oxidative tolerance. We argued that these genes in submodules are biologically meaningful and useful for studies on rice salt tolerance. This method can be applied to other studies to efficiently and reliably integrate co-expression network and phenotypic data.