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Biostatistics Advance Access originally published online on July 15, 2009
Biostatistics 2009 10(4):680-693; doi:10.1093/biostatistics/kxp023
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© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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

James Y. Dai* and Michael Leblanc

Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue N, M2-C200, Seattle, WA 98109, USA jdai{at}fhcrc.org

Nicholas L. Smith and Bruce Psaty

Department of Epidemiology, Cardiovascular Health Research Unit, University of Washington, Seattle, WA 98195, USA

Charles Kooperberg

Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue N, M2-C200, Seattle, WA 98109, USA

* To whom correspondence should be addressed.

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.

Keywords: Adaptive regression; Haplotype; Multilocus analysis; SNP

Received July 18, 2008; revised February 25, 2009; accepted for publication June 22, 2009.


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