Xu Z, Taylor JA. SNPinfo: integrating GWAS and candidate gene information into functional SNP selection for genetic association studies. Nucleic Acids Res 37: W600-W605

Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, USA.
Nucleic Acids Research (Impact Factor: 9.11). 06/2009; 37(Web Server issue):W600-5. DOI: 10.1093/nar/gkp290
Source: PubMed


We have developed a set of web-based SNP selection tools (freely available at 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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Available from: Zongli Xu, Jun 25, 2015
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    • "For instance , Michel et al. (2010) analyzed 566 SNPs from 14 candidate genes that are believed to be associated with asthma. Xu and Taylor (2009) developed tools to recommend SNPs based on information on gene expression studies , regulatory pathways and functional regions that appear to be linked to the disease. In their example, 1361 SNPs were recommended for a genetic association study on prostate cancer. "
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    • "SNPs examined in this study were selected on the basis of the literature data and in silico analysis performed using SNPinfo Web Server (Xu and Taylor, 2009) (please see Supplementary Table 2). This analysis encompassed the gene-transcribed sequences and 3000 bp upstream and 3000 bp downstream regions. "
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    • "-511 C Multivessel disease in coronary artery disease [22] -511 T and TT Myocardial infarction and ischemic stroke [17] a SNP ID in database dbSNP. promoter or intronic enhancer regions, and alternative splicing regulation by disrupting exonic splicing enhancers or silencers [28] [29]. "
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