Applying data mining techniques to the mapping of complex disease genes

W. A. Czika, B. S. Weir, S. R. Edwards, R. W. Thompson, D. M. Nielsen, J. C. Brocklebank, C. Zinkus, E. R. Martin, K. E. Hobler

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

The simulated sequence data for the Genetic Analysis Workshop 12 were analyzed using data mining techniques provided by SAS ENTERPRISE MINER™ Release 4.0 in addition to traditional statistical tests for linkage and association of genetic markers with disease status. We examined two ways of combining these approaches to make use of the covariate data along with the genotypic data. The result of incorporating data mining techniques with more classical methods is an improvement in the analysis, both by correctly classifying the affection status of more individuals and by locating more single nucleotide polymorphisms related to the disease, relative to analyses that use classical methods alone.

Original languageEnglish (US)
Pages (from-to)S435-S440
JournalGenetic Epidemiology
Volume21
Issue numberSUPPL. 1
DOIs
StatePublished - 2001
Externally publishedYes

Keywords

  • Association tests
  • Data mining
  • Decision trees
  • Logistic regression
  • RC-TDT

ASJC Scopus subject areas

  • Genetics(clinical)
  • Epidemiology

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