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PMID: 16269414 Published · ppublish English Evaluation Study Journal Article Research Support, N.I.H., Extramural

An efficient comprehensive search algorithm for tagSNP selection using linkage disequilibrium criteria.

Bioinformatics (Oxford, England) ·Vol. 22 ·No. 2 ·2006-01-15 ·Pages 220-5

Qin ZS, Gopalakrishnan S, Abecasis GR

Abstract

Selecting SNP markers for genome-wide association studies is an important and challenging task. The goal is to minimize the number of markers selected for genotyping in a particular platform and therefore reduce genotyping cost while simultaneously maximizing the information content provided by selected markers. We devised an improved algorithm for tagSNP selection using the pairwise r(2) criterion. We first break down large marker sets into disjoint pieces, where more exhaustive searches can replace the greedy algorithm for tagSNP selection. These exhaustive searches lead to smaller tagSNP sets being generated. In addition, our method evaluates multiple solutions that are equivalent according to the linkage disequilibrium criteria to accommodate additional constraints. Its performance was assessed using HapMap data. A computer program named FESTA has been developed based on this algorithm. The program is freely available and can be downloaded at http://www.sph.umich.edu/csg/qin/FESTA/

MeSH Terms
Algorithms Artificial Intelligence Base Sequence Chromosome Mapping/methods Expressed Sequence Tags Genetic Markers/genetics Linkage Disequilibrium/genetics Molecular Sequence Data Pattern Recognition, Automated/methods Polymorphism, Single Nucleotide/genetics Sequence Alignment/methods Sequence Analysis, DNA/methods Software
Chemicals
Genetic Markers
Authors & Affiliations
3 authors, click to expand affiliations / ORCID
Qin Zhaohui S
Center for Statistical Genetics, Department of Biostatistics, School of Public Health, University of Michigan 1420 Washington Heights, Ann Arbor, MI 48109-2029, USA. [email protected]
Gopalakrishnan Shyam
Abecasis Gonçalo R
Article Info
Journal
Bioinformatics (Oxford, England)
Abbr.
Bioinformatics
ISSN
1367-4803
Published
2006-01-15
Epub
2005-00-03
Pages
220-5
Language
English
Region
England
NLM ID
9808944
Subset
IM
Grants
NHGRI NIH HHS · R01-HG002651-01 · United States
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