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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
