Decoding Complexity: Multi-Partite Network Analysis for Environmental and Genetic Associations

Oregon Convention Center 

Unraveling intricate relationships between diseases and genes poses challenges, demanding intuitive representation through smart visualization. Network analysis has gained prominence as a solution. While one-mode network analysis is common, it often falls short in identifying comprehensive information, such as gene-disease pairs or genes linked to the same environmental factors. In this study, we adopt multi-partite network analysis. A distinctive feature is the network's composition of mutually exclusive sets of nodes, with edges connecting nodes across different sets. Compressed relationships within sets are also explored through multi-level projections. We propose two types of projections for obtaining unipartite projections: sequential projection and concurrent projection. Applying this methodology to the Korean Association Resource (KARE) project, featuring 327,872 SNPs across 8,840 individuals, we considered three distinct datasets: genetic factors, environmental factors, and Metabolic Syndrome components. The resulting multi-partite network and projected lower mode network provided valuable insights into direct and indirect relationships.

Multi-Partite Network

projection

genomic data

environmental factor 

Korean International Statistical Society