Ranking Candidate Disease Genes from Gene Expression and Protein Interaction: A Katz-Centrality Based Approach

• Published in: PloS one Vol. 6; no. 9; p. e24306
• Main Authors: Zhao, Jing, Yang, Ting-Hong, Huang, Yongxu, Holme, Petter
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 02.09.2011; Public Library of Science (PLoS)
• Subjects: Acids; Algorithms; Alzheimer's disease; Animals; Anopheles; Apoptosis; Bioinformatics; Biological evolution; Biology; Biotechnology; Computational Biology - methods; Computer programs; Computer Science; Databases, Genetic; Datasets; Disease - genetics; Disease control; Diseases; Drug discovery; Encyclopedias; Evolution; Gene expression; Genes; Genetic disorders; Genome, Human - genetics; Genomes; genomics; Humans; Laboratories; Medical research; Medical treatment; Metabolism; Methods; Mice; Pathogenesis; Pattern recognition; Physics; Physiological aspects; Pleiotropy; Protein interaction; Protein Interaction Maps - genetics; Protein-protein interactions; Proteins; Proteome - genetics; Proteome - metabolism; Theoretical physics; Transcriptome - genetics; Trends; Type 2 diabetes; Beijing China; China; onset alzheimer-disease; identification; pathology; microarray data; polymorphism; model; risk; genome-wide association; interaction networks; prioritization
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0024306

Many diseases have complex genetic causes, where a set of alleles can affect the propensity of getting the disease. The identification of such disease genes is important to understand the mechanistic and evolutionary aspects of pathogenesis, improve diagnosis and treatment of the disease, and aid in drug discovery. Current genetic studies typically identify chromosomal regions associated specific diseases. But picking out an unknown disease gene from hundreds of candidates located on the same genomic interval is still challenging. In this study, we propose an approach to prioritize candidate genes by integrating data of gene expression level, protein-protein interaction strength and known disease genes. Our method is based only on two, simple, biologically motivated assumptions--that a gene is a good disease-gene candidate if it is differentially expressed in cases and controls, or that it is close to other disease-gene candidates in its protein interaction network. We tested our method on 40 diseases in 58 gene expression datasets of the NCBI Gene Expression Omnibus database. On these datasets our method is able to predict unknown disease genes as well as identifying pleiotropic genes involved in the physiological cellular processes of many diseases. Our study not only provides an effective algorithm for prioritizing candidate disease genes but is also a way to discover phenotypic interdependency, cooccurrence and shared pathophysiology between different disorders.