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Related Experiment Videos

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

Zhaohui S Qin1, Shyam Gopalakrishnan, Gonçalo R Abecasis

  • 1Center for Statistical Genetics, Department of Biostatistics, School of Public Health, University of Michigan 1420 Washington Heights, Ann Arbor, MI 48109-2029, USA. qin@umich.edu

Bioinformatics (Oxford, England)
|November 5, 2005
PubMed
Summary
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This study presents an improved algorithm for selecting single nucleotide polymorphism (SNP) markers for genome-wide association studies. The new method reduces the number of markers needed, lowering genotyping costs while maintaining high information content.

Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Selecting single nucleotide polymorphism (SNP) markers for genome-wide association studies (GWAS) is crucial for reducing genotyping costs.
  • Maximizing information content from selected markers is essential for study efficiency.

Purpose of the Study:

  • To develop an improved algorithm for tagSNP selection.
  • To minimize the number of markers genotyped while retaining maximal information content.

Main Methods:

  • An improved algorithm for tagSNP selection using the pairwise r(2) criterion was devised.
  • Large marker sets were broken down into disjoint pieces for exhaustive searches, replacing the greedy algorithm.
  • Multiple solutions were evaluated to accommodate additional constraints based on linkage disequilibrium criteria.

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Main Results:

  • The improved algorithm generated smaller tagSNP sets compared to previous methods.
  • Performance was assessed using HapMap data, demonstrating the algorithm's effectiveness.
  • A freely available computer program, FESTA, was developed based on this algorithm.

Conclusions:

  • The developed algorithm offers an efficient approach to tagSNP selection for GWAS.
  • FESTA provides a valuable tool for researchers aiming to optimize marker selection and reduce genotyping costs.