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Nucleic Acids Research Advance Access originally published online on May 5, 2009
Nucleic Acids Research 2009 37(Web Server issue):W600-W605; doi:10.1093/nar/gkp290
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Nucleic Acids Research, 2009, Vol. 37, No. suppl_2 W600-W605
Published by Oxford University Press 2009
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.


Articles

SNPinfo: integrating GWAS and candidate gene information into functional SNP selection for genetic association studies

Zongli Xu1,* and Jack A. Taylor1,2,*

1Epidemiology Branch and 2Laboratory of Molecular Carcinogenesis, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, USA

*To whom correspondence should be addressed. Tel: +1 919 541 4631; Fax: +1 919 541 2511; Email: taylor{at}niehs.nih.gov

Correspondence may also be addressed to Zongli Xu. Tel: +1 919 541 1677; Fax: +1 919 541 2511; Email: xuz{at}niehs.nih.gov

Received February 13, 2009. Revised April 13, 2009. Accepted April 14, 2009.

We have developed a set of web-based SNP selection tools (freely available at http://www.niehs.nih.gov/snpinfo) where investigators can specify genes or linkage regions and select SNPs based on GWAS results, linkage disequilibrium (LD), and predicted functional characteristics of both coding and non-coding SNPs. The algorithm uses GWAS SNP P-value data and finds all SNPs in high LD with GWAS SNPs, so that selection is from a much larger set of SNPs than the GWAS itself. The program can also identify and choose tag SNPs for SNPs not in high LD with any GWAS SNP. We incorporate functional predictions of protein structure, gene regulation, splicing and miRNA binding, and consider whether the alternative alleles of a SNP are likely to have differential effects on function. Users can assign weights for different functional categories of SNPs to further tailor SNP selection. The program accounts for LD structure of different populations so that a GWAS study from one ethnic group can be used to choose SNPs for one or more other ethnic groups. Finally, we provide an example using prostate cancer and demonstrate that this algorithm can select a small panel of SNPs that include many of the recently validated prostate cancer SNPs.


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