Gene-disease network analysis reveals functional modules in mendelian, complex and environmental diseases

Scientists 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. We 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. For 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. The gene-disease
networks used in this study and part of the analysis are available
at http://ibi.imim.es/DisGeNET/DisGeNETweb.html#Download.