Network-based global inference of human disease genes

被引:530
作者
Wu, Xuebing [1 ,2 ]
Jiang, Rui [1 ,2 ]
Zhang, Michael Q. [1 ,2 ,3 ]
Li, Shao [1 ,2 ]
机构
[1] Tsinghua Univ, MOE Key Lab Bioinformat, TNLIST, Dept Automat, Beijing 100084, Peoples R China
[2] Tsinghua Univ, Bioinformat Div, TNLIST, Dept Automat, Beijing 100084, Peoples R China
[3] Cold Spring Harbor Lab, Cold Spring Harbor, NY 11724 USA
关键词
disease gene; human disease; modularity; network; prioritization;
D O I
10.1038/msb.2008.27
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Deciphering the genetic basis of human diseases is an important goal of biomedical research. On the basis of the assumption that phenotypically similar diseases are caused by functionally related genes, we propose a computational framework that integrates human protein-protein interactions, disease phenotype similarities, and known gene-phenotype associations to capture the complex relationships between phenotypes and genotypes. We develop a tool named CIPHER to predict and prioritize disease genes, and we show that the global concordance between the human protein network and the phenotype network reliably predicts disease genes. Our method is applicable to genetically uncharacterized phenotypes, effective in the genome-wide scan of disease genes, and also extendable to explore gene cooperativity in complex diseases. The predicted genetic landscape of over 1000 human phenotypes, which reveals the global modular organization of phenotype genotype relationships. The genome-wide prioritization of candidate genes for over 5000 human phenotypes, including those with under-characterized disease loci or even those lacking known association, is publicly released to facilitate future discovery of disease genes.
引用
收藏
页数:11
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