Enhancing the Prioritization of Disease-Causing Genes through Tissue Specific Protein Interaction Networks
By Oded Magger, Yedael Y. Waldman, Eytan Ruppin, Roded Sharan. 2012
164 citations recorded by the source corpus.
The prioritization of candidate disease-causing genes is a fundamental challenge in the post-genomic era. Current state of the art methods exploit a protein-protein interaction (PPI) network for this task. They are based on the observation that genes causing phenotypically-similar diseases tend to lie close to one another in a PPI network. However, to date, these methods have used a static picture of human PPIs, while diseases impact specific tissues in which the PPI networks may be dramatically different. Here, for the first time, we perform a large-scale assessment of the contribution of tissue-specific information to gene prioritization. By integrating tissue-specific gene expression data with PPI information, we construct tissue-specific PPI networks for 60 tissues and investigate their prioritization power. We find that tissue-specific PPI networks considerably improve the prioritization results compared to those obtained using a generic PPI network. Furthermore, they allow predicting novel disease-tissue associations, pointing to sub-clinical tissue effects that may escape early detection.
Subfields: Molecular Biology, Computational Biology & Bioinformatics
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Molecular Biology
- Marinka Zitnik
- Jure Leskovec
- Roded Sharan
- Trey Ideker
- Lenore J. Cowen
- Benjamin J. Raphael
- Petar Velickovic
- William L. Hamilton
In Computational Biology & Bioinformatics
- Jure Leskovec
- Marinka Zitnik
- Roded Sharan
- Lenore J. Cowen
- Trey Ideker
- Petar Velickovic
- Yiding Yang
- Xinchao Wang
Citations in the corpus
- Predicting multicellular function through multi-layer tissue networks (2017, background): “• Tissue-specific network propagation (Magger et al., 2012): This approach assigns a prior score to proteins associated with known functions that are phenotypically similar to the query function.”
- Network propagation: a universal amplifier of genetic associations (2017, background): “There has been promising research into developing such networks 84,95 , with important developments underway from the Genotype-Tissue Expression (GTEx) project, which enables the construction of a specific network for each major human tissue 96 .”
- Prediction of disease genes using tissue-specified gene-gene network (2014, unknown): “by Magger et al [38], and BlockRank method by Jiang et al [39].”
- Human protein interaction networks across tissues and diseases (2015, background): “Magger et al. (2012) showed that the usage of tissue interactomes, created from a generic interactome by removing or penalizing interactions involving non-expressed proteins, considerably improved the prioritization of disease genes.”