Network analysis reveals rare disease signatures across multiple levels of biological organization

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.

Automated summary

Researchers introduced a network approach to evaluate the impact of rare gene defects across biological scales. They constructed a multiplex network and analyzed rare diseases, discovering distinct phenotypic modules that can predict rare disease gene candidates. These findings open up new avenues for applying network-based tools in cross-scale data integration.