A-S.B.26: Backtrack-free network propagation with in-degree normalization
Author
Keywords
network propagation, gene prioritization, guilt-by-association, interactome, hubs, backtrack-free walks
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Motivation: In network medicine, the protein-protein interaction network, or interactome, is an essential resource for identifying candidate proteins underlying diseases and other phenotypic traits. Indeed, it can be leveraged via the guilt-by-association (GBA) paradigm, which asserts that interacting proteins are likely to participate in the same molecular processes. Network propagation that combines the topology of the interactome with prior knowledge about disease genes is a promising strategy to identify new candidate genes contributing to diseases. However, existing network propagation algorithms are often biased toward highly connected proteins, called “hubs”.
Results: Here, we present Guilt-by-association centrality, a novel network propagation algorithm designed to avoid inflated scores for hubs. We tested GBA centrality across four human phenotypes: two male infertility phenotypes, a cardiomyopathy and dyschromatopsia. For each phenotype, we assessed whether GBA centrality could recover known phenotype-associated genes and whether new candidate genes were enriched in relevant tissues. Depending on the phenotype, GBA centrality outperformed or matched the state-of-the-art network propagation methods. Furthermore, the results confirmed that GBA centrality is free of bias toward high-degree nodes. Therefore, GBA centrality is a powerful method for identifying new genes across diverse phenotypes and constitutes a strong alternative to existing network propagation algorithms.
Availability: GBA centrality is implemented in Python and C. The code is available under the GNU GPL (v3.0) on: https://github.com/jedrzejkubica/GBA-centrality
Co-authors: Dariusz Plewczynski, Sébastien Déjean, Nicolas Thierry-Mieg
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