Align human interactome with phenome to identify causative genes and networks underlying disease families

• Published in: Bioinformatics Vol. 25; no. 1; pp. 98 - 104
• Main Authors: Wu, Xuebing, Liu, Qifang, Jiang, Rui
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 01.01.2009; Oxford Publishing Limited (England)
• Subjects: Biological and medical sciences; Computational Biology; Crohn Disease - genetics; Disease - genetics; Fundamental and applied biological sciences. Psychology; Gene Regulatory Networks; General aspects; Genome, Human; Humans; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Phenotype; Human; Multigene family; Identification; Gene; Disease; Network
• ISSN: 1367-4803; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btn593

Motivation: Understanding the complexity in gene–phenotype relationship is vital for revealing the genetic basis of common diseases. Recent studies on the basis of human interactome and phenome not only uncovers prevalent phenotypic overlap and genetic overlap between diseases, but also reveals a modular organization of the genetic landscape of human diseases, providing new opportunities to reduce the complexity in dissecting the gene–phenotype association. Results: We provide systematic and quantitative evidence that phenotypic overlap implies genetic overlap. With these results, we perform the first heterogeneous alignment of human interactome and phenome via a network alignment technique and identify 39 disease families with corresponding causative gene networks. Finally, we propose AlignPI, an alignment-based framework to predict disease genes, and identify plausible candidates for 70 diseases. Our method scales well to the whole genome, as demonstrated by prioritizing 6154 genes across 37 chromosome regions for Crohn's disease (CD). Results are consistent with a recent meta-analysis of genome-wide association studies for CD. Availability: Bi-modules and disease gene predictions are freely available at the URL http://bioinfo.au.tsinghua.edu.cn/alignpi/ Contact: ruijiang@tsinghua.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.