Applying data mining techniques to the mapping of complex disease genes

被引:7
作者
Czika, WA
Weir, BS
Edwards, SR
Thompson, RW
Nielsen, DM
Brocklebank, JC
Zinkus, C
Martin, ER
Hobler, KE
机构
[1] SAS Inst, Carey, NC 27513 USA
[2] N Carolina State Univ, Raleigh, NC 27695 USA
[3] Duke Univ, Ctr Human Genet, Durham, NC 27706 USA
关键词
association tests; data mining; decision trees; logistic regression; RC-TDT;
D O I
10.1002/gepi.2001.21.s1.s435
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
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. ((C)) 2001 Wiley-Liss, Inc.
引用
收藏
页码:S435 / S440
页数:6
相关论文
共 3 条
[1]  
[Anonymous], DATA MINING SOLUTION
[2]   The transmission/disequilibrium test and parental-genotype reconstruction: The reconstruction-combined transmission/disequilibrium test [J].
Knapp, M .
AMERICAN JOURNAL OF HUMAN GENETICS, 1999, 64 (03) :861-870
[3]  
Weir BS., 1996, GENETIC DATA ANAL