Applying the Complexity of Networks to Mine Disease Risk Genes

Abstract:

Rheumatoid arthritis (RA) is a complex disease determined by multilocus genetic factors. Although genome-wide association studies have been proven to be a powerful approach to identify risk loci, the molecular regulatory mechanisms of RA are still not clearly understood. It is therefore important to consider the interplay between genetic factors and biological networks in elucidating the mechanisms of RA pathogenesis. Here, we applied the complexity of Protein-Protein Interaction (PPI) network to identify disease risk genes. First, we assigned risk SNPs to genes from UCSC genome database and mapped these genes to PPI networks. With the aid in PPI networks, gene modules were extracted and risk feature genes were identified. As a result, risk feature genes, such as CD40, PKCA, were identified as significant risk gene sets associated with RA.

• Periodical: Applied Mechanics and Materials (Volumes 556-562)
• Pages: 5958-5963
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.556-562.5958
• Online since: May 2014
• Authors: Hua Lin*, Xia Hong, Zhou Ping
• Keywords: Gene Modules, Protein-Protein Interaction Network, Rheumatoid Arthritis, SNP

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