Network analysis reveals rare disease signatures across multiple levels of biological organization
| Autor: | Jörg Menche, Pisanu Buphamalai, Tomislav Kokotović, Vanja Nagy |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Genotype
Science General Physics and Astronomy Disease Computational biology Biology computer.software_genre Models Biological Article General Biochemistry Genetics and Molecular Biology Rare Diseases Genotype-phenotype distinction Protein Interaction Mapping Genetics research Humans Computational models Gene Regulatory Networks Gene Multidisciplinary Genetic interaction Computational Biology General Chemistry Phenotype Regulatory networks Data integration computer Algorithms Network approach Network analysis Rare disease |
| Zdroj: | Nature Communications, Vol 12, Iss 1, Pp 1-15 (2021) Nature Communications |
| Popis: | Rare genetic diseases are typically caused by a single gene defect. Despite this clear causal relationship between genotype and phenotype, identifying the pathobiological mechanisms at various levels of biological organization remains a practical and conceptual challenge. Here, we introduce a network approach for evaluating the impact of rare gene defects across biological scales. We construct a multiplex network consisting of over 20 million gene relationships that are organized into 46 network layers spanning six major biological scales between genotype and phenotype. A comprehensive analysis of 3,771 rare diseases reveals distinct phenotypic modules within individual layers. These modules can be exploited to mechanistically dissect the impact of gene defects and accurately predict rare disease gene candidates. Our results show that the disease module formalism can be applied to rare diseases and generalized beyond physical interaction networks. These findings open up new venues to apply network-based tools for cross-scale data integration. Despite the clear causal relationship between genotype and phenotype in rare diseases, identifying the pathobiological mechanisms at various levels of biological organization remains a practical and conceptual challenge. Here, the authors introduce a network approach for evaluating the impact of rare gene defects across biological scales. |
| Databáze: | OpenAIRE |
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