Heat Diffusion Kernel Algorithm-based Pathogenesis Interpretation Of Genetic Disease And Its Application In Drug Discovery

The rapid development in omics in the past decade has allowed biological study at genome-wide level.Genome-wide association study(GWAS)have identified thousands of phenotype-associated loci.However,because the limitations of traditional GWAS,in the field of genetics,efficiently utilizing these data to infer pathogenesis underlying genetic diseases and further find novel drugs to protect human health is a great challenge.It is well known that protein-protein interaction(PPI)networks play an important role in the pathogenesis of complex diseases.Therefore,based on existing GWAS data,constructing the PPI networks that are significantly associated with complex diseases will be one of the effective methods to solve the GWAS dilemma.Hot Net diffusion-oriented subnetworks(Hot Net2)algorithm is based on an insulated heat diffusion kernel algorithm that considers the heats(reflecting genetic importance)of individual genes as well as the topology of gene-gene interactions.This method can reveal functionally interacted genes within subnetworks,thus overcoming the limitations of traditional GWAS methods for elucidating disease pathogenesis.In this study,based on Hot Net2,we combined the genetic disease-associated strength of genes(collected from database STOPGAP and have been standardized)with existing human PPI data,and successfully identified significant subnetworks for 202 types of genetic diseases.Further,the reliability of PPI networks has been validated by disease similarity test,other disease gene database validation and tissue-specific expression patterns.Next,we show that Hot Net2 is of great value in establishing reliable diseaseassociated PPI networks,which can help the elucidation of pathogenesis from two aspects of transcriptional regulation and protein 3D structure.And the comparison of genetic disease-associated with non-disease trait-related PPI networks can further facilitate the exploration of pathogenic mechanisms and drug repositioning.In addition to inferring pathogenic mechanisms of genetic diseases,this study shows that disease-associated PPI networks enable drug/combinatorial drug discovery at genome-wide level as well.In summary,in this omics era,we are facing a flood of biomedical data.Integrating high-throughput sequencing technology and genetic approaches reveals more and more disease-associated variants/genes.In this study,we demonstrate that the synthesis of GWAS data and Hot Net2 method can efficiently elucidate pathogenesis and promise drug/drug combination discovery.Therefore,this methodology provides a feasible paradigm for how to effectively use these accumulated data to infer the pathogenesis of genetic diseases and to promote drug discovery in “post-omics era”,hence has great biological and medical significances.