利用eQTL构建基因-基因网络挖掘类风湿性关节炎风险基因
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摘要
目的:类风湿性关节炎是一种全身的慢性炎症型疾病,可能影响许多组织和器官,主要发作于灵活的关节。全世界人群中大约有1%会患有类风湿性关节炎。目前已经证实了一些基因与类风湿性关节炎相关,但是这些基因只能解释一小部分遗传风险,因此我们需要新的策略和方法来解决这个问题。方法:表达数量性状位点(eQTL)是指能够调控基因或蛋白质表达的基因组位点,本文采用了eQTL数据构建基因-基因网络并挖掘候选类风湿性关节炎风险基因。结果:首先,利用eQTL数据,基于基因之间的共调控系数,建立基因-基因网络,我们建立了5个不同阈值(0、0.2、0.4、0.6和0.8)的基因-基因网络;然后,在OMIM和GAD数据库中搜索已经证实的与类风湿性关节炎相关的186个基因;最后我们将已证实与类风湿性关节炎相关的186个基因分别投入到这5个网络中,利用基因与基因之间的相关性来挖掘到一些可能与类风湿性关节炎相关的候选风险基因。结论:本文基于eQTL构建了基因-基因网络,结合已知类风湿性关节炎风险基因,挖掘未知风险基因,得到了较好的结果,证明了本方法的有效性,且对于类风湿性关节炎的发病机制研究具有重要价值。除了类风湿性关节炎外,本方法还可推广到其它复杂疾病中,因此本方法对人类复杂疾病的研究具有很强的学术理论价值和应用价值。
Objective: Rheumatoid arthritis(RA) is a chronic and systemic inflammatory disease that may affect many tissues and organs, and the main attack is the flexible joints. About 1% of the world's population suffers from rheumatoid arthritis, the incidence of the most frequent ages is between 40 and 50 years old, but people may be sick at any age. At present, we have confirmed that several genes associate with rheumatoid arthritis, but this can only explain a small fraction of the genetic risks associated with RA, so new strategies and statistical approaches are needed to address this lack of explanation. Methods: Expression quantitative trait loci(eQTLs) are genomic loci that regulate expression levels of mRNAs or proteins. Expression traits differ from most other classical complex traits in one important respect—the measured mRNA or protein trait almost always is the product of a single gene with a specific chromosomal location. In this paper, we build gene-gene networks using eQTL data, and mine the risk genes that maybe associated with RA. Results: First, we used the eQTLs data to build gene-gene networks consist of genes based on the gene-gene co-regulation coefficient. In order to illustrate better, we qualified with five different thresholds(0, 0.2, 0.4, 0.6 and 0.8) to build genes-gene networks. Next, we searched the known 186 rheumatoid arthritis risk genes from OMIM and GAD database. Then we input these genes that have been confirmed to be associated with rheumatoid arthritis into the observed five networks, respectively. Using the correlation between genes and known RA risk genes, we discover some potential risk genes associated with rheumatoid arthritis. Conclusion: We built gene-gene networks based on eQTL data, and mined unknown risk gene by combining with the known Rheumatoid arthritis risk genes. We got satisfying results, and it demonstrated the effectiveness of this method, so it had important values for the pathogenesis of rheumatoid arthritis research. In addition to rheumatoid arthritis, this method can be applied to other complex diseases as a way to understand the complex diseases in a different view. Therefore, the method has a strong academic and practical value to complex human disease research.
Objective: Rheumatoid arthritis(RA) is a chronic and systemic inflammatory disease that may affect many tissues and organs, and the main attack is the flexible joints. About 1% of the world's population suffers from rheumatoid arthritis, the incidence of the most frequent ages is between 40 and 50 years old, but people may be sick at any age. At present, we have confirmed that several genes associate with rheumatoid arthritis, but this can only explain a small fraction of the genetic risks associated with RA, so new strategies and statistical approaches are needed to address this lack of explanation. Methods: Expression quantitative trait loci(eQTLs) are genomic loci that regulate expression levels of mRNAs or proteins. Expression traits differ from most other classical complex traits in one important respect—the measured mRNA or protein trait almost always is the product of a single gene with a specific chromosomal location. In this paper, we build gene-gene networks using eQTL data, and mine the risk genes that maybe associated with RA. Results: First, we used the eQTLs data to build gene-gene networks consist of genes based on the gene-gene co-regulation coefficient. In order to illustrate better, we qualified with five different thresholds(0, 0.2, 0.4, 0.6 and 0.8) to build genes-gene networks. Next, we searched the known 186 rheumatoid arthritis risk genes from OMIM and GAD database. Then we input these genes that have been confirmed to be associated with rheumatoid arthritis into the observed five networks, respectively. Using the correlation between genes and known RA risk genes, we discover some potential risk genes associated with rheumatoid arthritis. Conclusion: We built gene-gene networks based on eQTL data, and mined unknown risk gene by combining with the known Rheumatoid arthritis risk genes. We got satisfying results, and it demonstrated the effectiveness of this method, so it had important values for the pathogenesis of rheumatoid arthritis research. In addition to rheumatoid arthritis, this method can be applied to other complex diseases as a way to understand the complex diseases in a different view. Therefore, the method has a strong academic and practical value to complex human disease research.
引文
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[2]Firestein G S.Evolving concepts of rheumatoid arthritis[J].Nature,2003,423(6937):356-361
[3]Matsumoto T,Okumura N,Uenishi H,et al.Porcine Single Nucleotide Polymorphism(SNP)Development and Population Structure of Pigs Assessed by Validated SNPs[J].Biochem Genet,2012,50(5-6):428-439
[4]Wang P,Dawson J A,Keller M P,et al.A model selection approach for expression quantitative trait loci(eQTL)mapping[J].Genetics,2011,187(2):611-621
[5]Nica A C,Dermitzakis E T.Expression quantitative trait loci:present and future[J].Philos Trans R Soc Lond B Biol Sci,2013,368(1620):20120362
[6]Lee S I,Dudley A M,Drubin D,et al.Learning a prior on regulatory potential from eQTL data[J].PLoS Genet,2009,5(1):e1000358
[7]Haibe-Kains B,Olsen C,Djebbari A,et al.Predictive networks:a flexible,open source,web application for integration and analysis of human gene networks[J].Nucleic Acids Res,2012,40(Database issue):D866-875
[8]Huang CH,Cong L,Xie J,et al.Rheumatoid arthritis-associated gene-gene interaction network for rheumatoid arthritis candidate genes[J].BMC Proc,2009,3 Suppl 7:S75
[9]Xia K,Shabalin A A,Huang S,et al.seeQTL:a searchable database for human eQTLs[J].Bioinformatics,2011,28(3):451-452
[10]Choy E,Yelensky R,Bonakdar S,et al.Genetic analysis of human traits in vitro:drug response and gene expression in lymphoblastoid cell lines[J].PLoS Genet,2008,4(11):e1000287
[11]Dimas A S,Deutsch S,Stranger B E,et al.Common regulatory variation impacts gene expression in a cell type-dependent manner[J].Science,2009,325(5945):1246-1250
[12]Grundberg E,Kwan T,Ge B,et al.Population genomics in a disease targeted primary cell model[J].Genome Res,2009,19(11):1942-1952
[13]Schadt E E,Molony C,Chudin E,et al.Mapping the genetic architecture of gene expression in human liver[J].PLoS Biol,2008,6(5):e107
[14]Stranger B E,Forrest M S,Dunning M,et al.Relative impact of nucleotide and copy number variation on gene expression phenotypes[J].Science,2007,315(5813):848-853
[15]Zeller T,Wild P,Szymczak S,et al.Genetics and beyond--the transcriptome of human monocytes and disease susceptibility[J].PLoS One,2010,5(5):e10693
[16]Hamosh A,Scott A F,Amberger J,et al.Online Mendelian Inheritance in Man(OMIM)[J].Hum Mutat,2000,15(1):57-61
[17]Zhang Y,De S,Garner J R,et al.Systematic analysis,comparison,and integration of disease based human genetic association data and mouse genetic phenotypic information[J].BMC Med Genomics,2010,3:1
[18]Shannon P,Markiel A,Ozier O,et al.Cytoscape:a software environment for integrated models of biomolecular interaction networks[J].Genome Res,2003,13(11):2498-2504
[19]Cline M S,Smoot M,Cerami E,et al.Integration of biological networks and gene expression data using Cytoscape[J].Nat Protoc,2007,2(10):2366-2382
[20]Smoot M E,Ono K,Ruscheinski J,et al.Cytoscape 2.8:new features for data integration and network visualization[J].Bioinformatics,2011,27(3):431-432
[21]Saito R,Smoot M E,Ono K,et al.A travel guide to Cytoscape plugins[J].Nat Methods,2012,9(11):1069-1076