SHARE: an adaptive algorithm to select the most informative set of SNPs for candidate genetic association.

ABSTRACT: Association studies have been widely used to identify genetic liability variants for complex diseases. While scanning the chromosomal region 1 single nucleotide polymorphism (SNP) at a time may not fully explore linkage disequilibrium, haplotype analyses tend to require a fairly large number of parameters, thus potentially losing power. Clustering algorithms, such as the cladistic approach, have been proposed to reduce the dimensionality, yet they have important limitations. We propose a SNP-Haplotype Adaptive REgression (SHARE) algorithm that seeks the most informative set of SNPs for genetic association in a targeted candidate region by growing and shrinking haplotypes with 1 more or less SNP in a stepwise fashion, and comparing prediction errors of different models via cross-validation.

SUBMITTER: Dai JY 

PROVIDER: S-EPMC2742496 | biostudies-literature | 2009 Oct

REPOSITORIES: biostudies-literature