Open AccessMining disease genes using integrated protein–protein interaction and gene–gene co‐regulation information
Author(s) -
Li Jin,
Wang Limei,
Guo Maozu,
Zhang Ruijie,
Dai Qiguo,
Liu Xiaoyan,
Wang Chunyu,
Teng Zhixia,
Xuan Ping,
Zhang Mingming
Publication year - 2015
Publication title -
febs open bio
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.718
H-Index - 31
ISSN - 2211-5463
DOI - 10.1016/j.fob.2015.03.011
Subject(s) - gene , expression quantitative trait loci , disease , computational biology , biology , gene regulatory network , kegg , genetics , omim : online mendelian inheritance in man , gene expression , phenotype , medicine , gene ontology , genotype , single nucleotide polymorphism , pathology
In humans, despite the rapid increase in disease‐associated gene discovery, a large proportion of disease‐associated genes are still unknown. Many network‐based approaches have been used to prioritize disease genes. Many networks, such as the protein–protein interaction (PPI), KEGG, and gene co‐expression networks, have been used. Expression quantitative trait loci (eQTLs) have been successfully applied for the determination of genes associated with several diseases. In this study, we constructed an eQTL‐based gene–gene co‐regulation network (GGCRN) and used it to mine for disease genes. We adopted the random walk with restart (RWR) algorithm to mine for genes associated with Alzheimer disease. Compared to the Human Protein Reference Database (HPRD) PPI network alone, the integrated HPRD PPI and GGCRN networks provided faster convergence and revealed new disease‐related genes. Therefore, using the RWR algorithm for integrated PPI and GGCRN is an effective method for disease‐associated gene mining.