Integrating human omics data to prioritize candidate genes
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1755-8794-6-57.pdf
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Author(s)
Wu, Xuebing
Jiang, Rui
Chen, Yong, 1979-
Date Issued
December 2013
Journal
BMC Medical Genomics
Publisher
BioMed Central Ltd
Citation
Chen, Yong, Xuebing Wu, and Rui Jiang. “Integrating Human Omics Data to Prioritize Candidate Genes.” BMC Medical Genomics 6.1 (2013): 57.
Version
Final published version
Abstract
Background:
The identification of genes involved in human complex diseases remains a great challenge in computational systems biology. Although methods have been developed to use disease phenotypic similarities with a protein-protein interaction network for the prioritization of candidate genes, other valuable omics data sources have been largely overlooked in these methods.
Methods:
With this understanding, we proposed a method called BRIDGE to prioritize candidate genes by integrating disease phenotypic similarities with such omics data as protein-protein interactions, gene sequence similarities, gene expression patterns, gene ontology annotations, and gene pathway memberships. BRIDGE utilizes a multiple regression model with lasso penalty to automatically weight different data sources and is capable of discovering genes associated with diseases whose genetic bases are completely unknown.
Results:
We conducted large-scale cross-validation experiments and demonstrated that more than 60% known disease genes can be ranked top one by BRIDGE in simulated linkage intervals, suggesting the superior performance of this method. We further performed two comprehensive case studies by applying BRIDGE to predict novel genes and transcriptional networks involved in obesity and type II diabetes.
Conclusion:
The proposed method provides an effective and scalable way for integrating multi omics data to infer disease genes. Further applications of BRIDGE will be benefit to providing novel disease genes and underlying mechanisms of human diseases.
MIT Department
Massachusetts Institute of Technology. Computational and Systems Biology Program
Koch Institute for Integrative Cancer Research at MIT
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1186/1755-8794-6-57
