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Published online: 28 May 2008 | doi:10.1038/nchina.2008.124
Disease genes: Finding the culprits
Tim Reid
Abstract
A new computer algorithm could help identify human disease genes by analysing known protein interactions
Original article citation
Network-based global inference of human disease genes. Mol. Sys. Biol. 4, 189 (2008).Introduction
© (2008) Molecular Systems Biology
The foremost goal of biomedical research is identifying the genes responsible for specific diseases. The traditional approach is the study of links between diseases and 'genomic regions' containing tens or even hundreds of genes. However, these regions are often so large that they become very difficult, if not impossible, to analyse. Shao Li at Tsinghua University in Beijing and co-workers1 have developed a computer algorithm for prioritizing candidate genes that are most likely to be disease-causing genes.
Studies have shown that diseases sharing similar symptoms (phenotypes) are often caused by abnormalities in functionally related genes (genotypes). Previous algorithms rank genes based on how strongly they are linked to a particular phenotype (gene–phenotype association). The new algorithm, called CIPHER, integrates protein–protein interactions, phenotype similarities, and known gene–phenotype associations to capture the complex relationships between phenotypes and genotypes.
CIPHER can reproduce many of the known gene–disease associations from genetic databases. In a case study of breast cancer, 10 out of 16 well-established breast-cancer genes were given high association scores. Furthermore, the program assigned high rankings to 15 genes that have more recently been proposed as linked to breast cancer. This highlights the effectiveness of CIPHER as a tool for facilitating the discovery of disease genes.
The authors of this work are from:
MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing, China; Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA.
Reference
- Wu, X., Jiang, R., Zhang, M. Q. & Li, S. Network-based global inference of human disease genes. Mol. Sys. Biol. 4, 189 (2008).
