Skip Navigation



Bioinformatics Advance Access published online on November 15, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm496
This Article
Right arrow Advance Access manuscript (PDF)
Right arrow Supplementary Data
Right arrow All Versions of this Article:
23/23/3178    most recent
btm496v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Hao, K.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Hao, K.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Genome-Wide Selection of Tag SNPs Using Multiple-Marker Correlation

K. Hao *

Algorithm and Data Analysis, Affymetrix, Inc., 3420 Central Expressway, Santa Clara, California

To whom correspondence should be addressed. Dr. Ke Hao, E-mail: ke_hao3{at}yahoo.com


  Abstract

Motivations: The tag SNP approach is a valuable tool in whole genome association studies (WGAS), and a variety of algorithms have been proposed to identify the optimal tag SNP set. Currently, most tag SNP selection is based on two-marker (pair-wise) LD. Recent literature has shown that multiple-marker LD also contains useful information that can further increase the genetic coverage of the tag SNP set. Thus, tag SNP selection methods that incorporate multiple-marker LD are expected to have advantages in terms of genetic coverage and statistical power.

Results: We propose a novel algorithm to select tag SNPs in an iterative procedure. In each iteration loop, the SNP that captures the most neighboring SNPs (through pair-wise and multiple-marker LD) is selected as a tag SNP. We optimize the algorithm and computer program to make our approach feasible on today's typical workstations. Benchmarked using HapMap release 21, our algorithm outperforms standard pair-wise LD approach in several aspects. (i) It improves genetic coverage (e.g., by 7.2% for 200K tag SNPs in HapMap CEU) compared to its conventional pairwise counterpart, when conditioning on a fixed tag SNP number. (ii) It saves genotyping costs substantially when conditioning on fixed genetic coverage (e.g., 34.1% saving in HapMap CEU at 90% coverage). (iii) Tag SNPs identified using multiple marker LD have good portability across closely related ethnic groups, (iv) and show higher statistical power in association tests than those selected using conventional methods.

Availability: A computer software suite, multiTag, has been developed based on this novel algorithm. The program is freely available by written request to the author at ke_hao@merck.com

Keywords:Tag SNP, Genetic Coverage, Linkage Disequilibrium (LD), Multiple-Marker Correlation

Running title: Selection of tag SNPs using multiple-marker correlation

Associate Editor: Prof. Martin Bishop

*Current affiliation: Rosetta Inpharmatics, a wholly owned subsidiary of Merck and Co. Inc., 401 Terry Ave. N., Seattle, WA

Received on May 21, 2007; revised on September 8, 2007; accepted on September 28, 2007

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.