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

References in zbMATH (referenced in 3 articles )

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  1. Li, Jiahan; Wang, Zhong; Li, Runze; Wu, Rongling: Bayesian group Lasso for nonparametric varying-coefficient models with application to functional genome-wide association studies (2015)
  2. Üstünkar, Gürkan; Özöğür-Akyüz, Süreyya; Weber, Gerhard W.: Selection of representative SNP sets for genome-wide association studies: a metaheuristic approach (2012)
  3. Xu, Zongli; Taylor, Jack A.: Snpinfo: integrating GWAS and candidate gene information into functional SNP selection for genetic association studies (2009) ioport