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Applying Data Mining Techniques to the Mapping of Complex Disease Genes
Author(s) -
Czika W.A.,
Weir B.S.,
Edwards S.R.,
Thompson R.W.,
Nielsen D.M.,
Brocklebank J.C.,
Zinkus C.,
Martin E.R.,
Nobler K.E.
Publication year - 2001
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.2001.21.s1.s435
Subject(s) - covariate , linkage (software) , genetic data , genetic association , data mining , computer science , single nucleotide polymorphism , computational biology , genotype , biology , genetics , gene , machine learning , medicine , population , environmental health
The simulated sequence data for the Genetic Analysis Workshop 12 were analyzed using data mining techniques provided by SAS ENTERPRISE MINER TM 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. © 2001 Wiley‐Liss, Inc.