Supervised learning-based tagSNP selection for genome-wide disease classifications

Date
2007-07-25
Authors
Liu, Qingzhong
Sung, Andrew H.
Chen, Zhongxue
Yang, Mary Qu
Huang, Xudong
Yang, Jack
Journal Title
Journal ISSN
Volume Title
Publisher
BIomed Central
Abstract

Comprehensive evaluation of common genetic variations through association of single nucleotide polymorphisms (SNPs) with complex human diseases on the genome-wide scale is an active area in human genome research. One of the fundamental questions in a SNP-disease association study is to find an optimal subset of SNPs with predicting power for disease status. To find that subset while reducing study burden in terms of time and costs, one can potentially reconcile information redundancy from associations between SNP markers

Description
The article was originally published by BMC Genomics. doi:10.1186/1471-2164-9-S1-S6
Keywords
association of single nucleotide polymorphisms (SNPs), predicting power, active area
Citation
Liu Q, Yang J, Chen Z, Yang M, Sung AH, Huang X (2008). Supervised-learning based TagSNPs for genome-wide disease classification, BMC Genomics 9 (Suppl 1): S6. doi:10.1186/1471-2164-9-S1-S6