Gene-Disease Network Analysis Reveals Functional Modules in Mendelian, Complex and Environmental Diseases

Abstract

BackgroundScientists have been trying to understand the molecular mechanisms of diseases to design preventive and therapeutic strategies for a long time. For some diseases, it has become evident that it is not enough to obtain a catalogue of the disease-related genes but to uncover how disruptions of molecular networks in the cell give rise to disease phenotypes. Moreover, with the unprecedented wealth of information available, even obtaining such catalogue is extremely difficult.Principal FindingsWe developed a comprehensive gene-disease association database by integrating associations from several sources that cover different biomedical aspects of diseases. In particular, we focus on the current knowledge of human genetic diseases including mendelian, complex and environmental diseases. To assess the concept of modularity of human diseases, we performed a systematic study of the emergent properties of human gene-disease networks by means of network topology and functional annotation analysis. The results indicate a highly shared genetic origin of human diseases and show that for most diseases, including mendelian, complex and environmental diseases, functional modules exist. Moreover, a core set of biological pathways is found to be associated with most human diseases. We obtained similar results when studying clusters of diseases, suggesting that related diseases might arise due to dysfunction of common biological processes in the cell.ConclusionsFor the first time, we include mendelian, complex and environmental diseases in an integrated gene-disease association database and show that the concept of modularity applies for all of them. We furthermore provide a functional analysis of disease-related modules providing important new biological insights, which might not be discovered when considering each of the gene-disease association repositories independently. Hence, we present a suitable framework for the study of how genetic and environmental factors, such as drugs, contribute to diseases.AvailabilityThe gene-disease networks used in this study and part of the analysis are available at http://ibi.imim.es/DisGeNET/DisGeNETweb.html#Download.

Semantics based approach for analyzing disease-target associations

“…There are numerous studies that report that analyzing various functional interactions of genes (individual and integrated approach) involved in a disease is important in deciphering the disease cause [1], [14][15][16][17]. For example, gene interactions have been studied in complex diseases like cancer using network analyses approach.…”

“…Anna Bauer-Mehren et al presented how integrating gene-disease associations from various database sources can fill gaps in gene-disease networks for Mendelian and complex diseases [14]. Recently in 2015, a study was published which focused on identifying crucial genes involved in cancer by using a statistical based method [18].…”

Gene Set to Diseases (GS2D): disease enrichment analysis on human gene sets with literature data

“…We evaluated CTD-based analyses as second best performing method. CTD contains considerably more gene-disease associations than the GWAS Catalog, OMIM and other curated datasets ( [27] and DisGeNet database statistics 2 ). Therefore, we compared the CTD data to automatically generated data used by GS2D.…”

“…More specifically, ToppGene-OMIM and ToppGene-GWAS performed worse than ToppGene-CTD. This may be explained by the bigger size of the CTD data ( [27] and DisGeNet database statistics) or by its higher quality compared to GWAS data which is not always reproducible [28].…”

Personalized Respiratory Medicine: Exploring the Horizon, Addressing the Issues. Summary of a BRN-AJRCCM Workshop Held in Barcelona on June 12, 2014

“…There are increasingly large amounts of biological and clinically relevant information stored electronically that can be useful to progress to PM (29). For instance, there are currently 23 million scientific papers referenced in PubMed, and more than 700,000 are added each year, not to mention additional medical knowledge beyond PubMed, including electronic medical records (30,31).…”