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Progress in Modern Biomedicine. 2014, (08)

Mining RA risk gene via gene-gene network constructed using eQTLChinese Full Text

ZHANG Zhuo-ran;BI Xiao-man;LI Jin;WANG Li-mei;Department of Pharmacy, The Fourth Affiliated Hospital of Harbin Medical University;Basic Medical Science College, Harbin Medical University;School of Life Science and Technology,Harbin Institute of Technology;

Abstract: 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.
Keywords：

Expression quantitative trait loci（eQTLs）; Rheumatoid arthritis; Gene-gene network; Functional annotation;

  • DOI：

    10.13241/j.cnki.pmb.2014.08.008

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  • Classification Code：

    R593.22

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