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- Title
SHARE: an adaptive algorithm to select the most informative set of SNPs for candidate genetic association.
- Authors
Dai, James Y; Leblanc, Michael; Smith, Nicholas L; Psaty, Bruce; Kooperberg, Charles
- 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. Depending on the evolutionary history of the disease mutations and the markers, this set may contain a single SNP or several SNPs that lay a foundation for haplotype analyses. Haplotype phase ambiguity is effectively accounted for by treating haplotype reconstruction as a part of the learning procedure. Simulations and a data application show that our method has improved power over existing methodologies and that the results are informative in the search for disease-causal loci.
- Publication
Biostatistics (Oxford, England), 2009, Vol 10, Issue 4, p680
- ISSN
1468-4357
- Publication type
Journal Article
- DOI
10.1093/biostatistics/kxp023