High throughput multiple combination extraction from large scale polymorphism data by exact tree method

Koichi Miyaki, Kazuyuki Omae, Mitsuru Murata, Norio Tanahashi, Ikuo Saito, Kiyoaki Watanabe

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Single nucleotide polymorphisms (SNPs) are increasingly becoming important in clinical settings as useful genetic markers. For the evaluation of genetic risk factors of multifactorial diseases, it is not sufficient to focus on individual SNPs. It is preferable to evaluate combinations of multiple markers, because it allows us to examine the interactions between multiple factors. If all the combinations possible were evaluated round-robin, the number of calculations would rapidly explode as the number of markers analyzed increased. To overcome this limitation, we devised the exact tree method based on decision tree analysis and applied it to 14 SNP data from 68 Japanese stroke patients and 189 healthy controls. From the obtained tree models, we succeeded in extracting multiple statistically significant combinations that elevate the risk of stroke. From this result, we inferred that this method would work more efficiently in the whole genome study, which handles thousands of genetic markers. This exploratory data mining method will facilitate the extraction of combinations from large-scale genetic data and provide a good foothold for further verificatory research.

Original languageEnglish
Pages (from-to)455-462
Number of pages8
JournalJournal of Human Genetics
Issue number9
Publication statusPublished - 2004


  • Combination
  • Data mining
  • Exact tree
  • Genetic polymorphism
  • Interaction
  • Multifactorial disease
  • Multiple factor
  • SNP

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)


Dive into the research topics of 'High throughput multiple combination extraction from large scale polymorphism data by exact tree method'. Together they form a unique fingerprint.

Cite this