RNetDys: identification of disease-related impaired regulatory interactions due to SNPs

Abstract

The dysregulation of regulatory mechanisms due to Single Nucleotide Polymorphisms (SNPs) can lead to diseases and does not affect all cell (sub)types equally. Current approaches to study the impact of SNPs in diseases lack mechanistic insights. Indeed, they do not account for the regulatory landscape to decipher cell (sub)type specific regulatory interactions impaired due to disease-related SNPs. Therefore, characterizing the impact of disease-related SNPs in cell (sub)type specific regulatory mechanisms would provide novel therapeutical targets, such as promoter and enhancer regions, for the development of gene-based therapies directed at preventing or treating diseases. We present RNetDys, a pipeline to decipher cell (sub)type specific regulatory interactions impaired by disease-related SNPs based on multi-OMICS data. RNetDys leverages the information obtained from the generated cell (sub)type specific GRNs to provide detailed information on impaired regulatory elements and their regulated genes due to the presence of SNPs. We applied RNetDys in five disease cases to study the cell (sub)type differential impairment due to SNPs and leveraged the GRN information to guide the characterization of dysregulated mechanisms. We were able to validate the relevance of the identified impaired regulatory interactions by verifying their connection to disease-related genes. In addition, we showed that RNetDys identifies more precisely dysregulated interactions linked to disease-related genes than expression Quantitative Trait Loci (eQTL) and provides additional mechanistic insights. RNetDys is a pipeline available at https://github.com/BarlierC/RNetDys.git