Mining Functional Gene Modules Linked with Rheumatoid Arthritis Using a SNP-SNP Network  

作　　者：Lin Hua Hui Lin Dongguo Li Lin Li Zhicheng Liu 

机构地区：[1]Biomedical Engineering Institute,Capital Medical University,Beijing 100069,China

出　　处：《Genomics, Proteomics & Bioinformatics》2012年第1期23-34,共12页基因组蛋白质组与生物信息学报（英文版）

基　　金：supported in part by the National Natural Science Foundation of China (Grant No. 30871394, 30370798 and 30571034);the Science Technology Development Projects of Beijing Municipal Education Commission (KM200910025006 and KM201210025008)

摘　　要：The identification of functional gene modules that are derived from integration of information from different types of networks is a powerful strategy for interpreting the etiology of complex diseases such as rheumatoid arthritis （RA）. Genetic variants are known to increase the risk of developing RA. Here, a novel method, the construction of a genetic network, was used to mine functional gene modules linked with RA. A polymorphism interaction analy- sis （PIA） algorithm was used to obtain cooperating single nucleotide polymorphisms （SNPs） that contribute to RA disease. The acquired SNP pairs were used to construct a SNP-SNP network. Sub-networks defined by hub SNPs were then extracted and turned into gene modules by mapping SNPs to genes using dbSNP database. We per- formed Gene Ontology （GO） analysis on each gene module, and some GO terms enriched in the gene modules can be used to investigate clustered gene function for better understanding RA pathogenesis. This method was applied to the Genetic Analysis Workshop 15 （GAW 15） RA dataset. The results show that genes involved in func- tional gene modules, such as CD160 （rs744877） and RUNX1 （rs2051179）, are especially relevant to RA, which is supported by previous reports. Furthermore, the 43 SNPs involved in the identified gene modules were found to be the best classifiers when used as variables for sample classification.The identification of functional gene modules that are derived from integration of information from different types of networks is a powerful strategy for interpreting the etiology of complex diseases such as rheumatoid arthritis （RA）. Genetic variants are known to increase the risk of developing RA. Here, a novel method, the construction of a genetic network, was used to mine functional gene modules linked with RA. A polymorphism interaction analy- sis （PIA） algorithm was used to obtain cooperating single nucleotide polymorphisms （SNPs） that contribute to RA disease. The acquired SNP pairs were used to construct a SNP-SNP network. Sub-networks defined by hub SNPs were then extracted and turned into gene modules by mapping SNPs to genes using dbSNP database. We per- formed Gene Ontology （GO） analysis on each gene module, and some GO terms enriched in the gene modules can be used to investigate clustered gene function for better understanding RA pathogenesis. This method was applied to the Genetic Analysis Workshop 15 （GAW 15） RA dataset. The results show that genes involved in func- tional gene modules, such as CD160 （rs744877） and RUNX1 （rs2051179）, are especially relevant to RA, which is supported by previous reports. Furthermore, the 43 SNPs involved in the identified gene modules were found to be the best classifiers when used as variables for sample classification.

关 键 词：polymorphism interaction analysis hub SNP sub-networks GO enrichment analysis 

分 类 号：S225.72[农业科学—农业机械化工程] TQ460.72[农业科学—农业工程]