The multiplex network of human diseases

• Published in: NPJ systems biology and applications Vol. 5; no. 1; p. 15
• Main Authors: Halu, Arda, De Domenico, Manlio, Arenas, Alex, Sharma, Amitabh
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 23.04.2019; Nature Publishing Group
• Subjects: 631/114; 631/553; Bioinformatics; Biomedical and Life Sciences; Classification; Classification systems; Computational Biology - methods; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Disease - classification; Disease - genetics; Gene Regulatory Networks - genetics; Gene Regulatory Networks - physiology; Genotype; Genotype & phenotype; Genotypes; Humans; Life Sciences; Phenotype; Phenotypes; Precision medicine; Risk factors; Systems Biology; Systems biology; Computational biology and bioinformatics
• ISSN: 2056-7189; 2056-7189
• DOI: 10.1038/s41540-019-0092-5

Untangling the complex interplay between phenotype and genotype is crucial to the effective characterization and subtyping of diseases. Here we build and analyze the multiplex network of 779 human diseases, which consists of a genotype-based layer and a phenotype-based layer. We show that diseases with common genetic constituents tend to share symptoms, and uncover how phenotype information helps boost genotype information. Moreover, we offer a flexible classification of diseases that considers their molecular underpinnings alongside their clinical manifestations. We detect cohesive groups of diseases that have high intra-group similarity at both the molecular and the phenotypic level. Inspecting these disease communities, we demonstrate the underlying pathways that connect diseases mechanistically. We observe monogenic disorders grouped together with complex diseases for which they increase the risk factor. We propose potentially new disease associations that arise as a unique feature of the information flow within and across the two layers. Precision medicine: multi-layered map of diseases enables new disease classification The joint analysis of shared genes and symptoms on a multi-layered disease network uncovers an alternative grouping of diseases. An international team of researchers from Brigham and Women’s Hospital and Universitat Rovira i Virgili built and analyzed a large-scale network of diseases that consisted of two layers representing gene and symptom relationships. On a global scale, diseases that shared genes also tended to share symptoms. An algorithm specifically designed to identify patterns in multi-layered networks determined groups of diseases that were highly similar to each other both genetically and symptomatically. This approach holds the potential to transcend today’s clinical observation-based disease classification systems. It may pave the way for a molecular-based disease classification, the discovery of novel disease relationships, and ultimately personalized diagnosis and treatment.