• DocumentCode
    1987954
  • Title

    Haplotype pattern mining & classification for detecting disease associated site

  • Author

    Kido, Takashi ; Baba, Masanori ; Matsumine, Hirohito ; Higashi, Yoko ; Higuchi, Hirotaka ; Muramatsu, Masaaki

  • Author_Institution
    HuBit Genomix Inc., Tokyo, Japan
  • fYear
    2003
  • fDate
    11-14 Aug. 2003
  • Firstpage
    452
  • Lastpage
    453
  • Abstract
    Finding the causative genes for common diseases using SNP (single nucleotide polymorphism) markers is now becoming a real challenge. Although traditional statistical SNP association tests exist, these tests could not explain the effects of SNP combinations or probable recombination histories from ancestral chromosomes. Haplotype analysis of disease associated site provides more powerful markers than individual SNP analysis, and can help identify probable causative mutations. In this paper, we introduce a new method for effective haplotype pattern mining to detect disease associated mutations. Using this procedure, we can discover some of the new disease associated SNPs, which can not be detected by traditional methods. We will introduce a powerful tool for implementing this procedure with some worked examples.
  • Keywords
    biology computing; data mining; diseases; genetics; organic compounds; pattern classification; polymorphism; SNP combinations; ancestral chromosomes; causative genes; causative mutations; disease associated site detection; haplotype pattern mining; pattern classification; recombination history; single nucleotide polymorphism markers; statistical SNP association test; Bioinformatics; Dentistry; Diseases; Frequency estimation; Genetic mutations; Genomics; Phylogeny; Poles and towers; Testing; Tree graphs;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Bioinformatics Conference, 2003. CSB 2003. Proceedings of the 2003 IEEE
  • Print_ISBN
    0-7695-2000-6
  • Type

    conf

  • DOI
    10.1109/CSB.2003.1227369
  • Filename
    1227369