• DocumentCode
    2089007
  • Title

    Web–Enabled Classification of SNPs for Genome–Wide Association Studies

  • Author

    Tendulkar, Abhijit R. ; Stojanovic, Nikola ; Barber, Robert

  • Author_Institution
    Dept. of Comput. Sci. & Eng., Univ. of Texas at Arlington, Arlington, TX, USA
  • fYear
    2008
  • fDate
    17-20 Dec. 2008
  • Firstpage
    112
  • Lastpage
    117
  • Abstract
    Whole genome association studies of the genetic underpinnings of complex phenotypes, and human diseases in particular, have been steadily gaining momentum over the past several years. Yet, the number of polymorphic sites in the human genome, including, but not limited to, single nucleotide polymorphisms (SNPs) is so large that identifying the combination of these few that have a significant effect on the condition of interest remains an overwhelming task. In this manuscript we present a new networked solution, and a program GeneNAB implementing it, to the computational identification and ranking of SNPs likely to be relevant for the phenotype of interest, genome-wide. We expect that the output of this program will be useful to guide further laboratory and clinical studies of these SNPs.
  • Keywords
    Internet; biology computing; classification; diseases; genetics; Web-enabled classification; complex phenotypes; genetic underpinnings; genome association studies; human diseases; human genome; polymorphic sites; program GeneNAB; single nucleotide polymorphism; Bioinformatics; Biology computing; Biomedical engineering; Cardiac disease; Computer networks; Computer science; Databases; Genetic engineering; Genomics; Humans; GWAS; Genetic variation; Internet; KEGG; SNP; XML; database; dbSNP; polymorphism;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Technology, 2008. ICIT '08. International Conference on
  • Conference_Location
    Bhubaneswar
  • Print_ISBN
    978-1-4244-3745-0
  • Type

    conf

  • DOI
    10.1109/ICIT.2008.65
  • Filename
    4731309