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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    ABSTRACT: Although genome-wide association studies (GWAS) have proven powerful for comprehending the genetic architecture of complex traits, they are challenged by a high dimension of single-nucleotide polymorphisms (SNPs) as predictors, the presence of complex environmental factors, and longitudinal or functional natures of many complex traits or diseases. To address these challenges, we propose a high-dimensional varying-coefficient model for incorporating functional aspects of phenotypic traits into GWAS to formulate a so-called functional GWAS or fGWAS. The Bayesian group lasso and the associated MCMC algorithms are developed to identify significant SNPs and estimate how they affect longitudinal traits through time-varying genetic actions. The model is generalized to analyze the genetic control of complex traits using subject-specific sparse longitudinal data. The statistical properties of the new model are investigated through simulation studies. We use the new model to analyze a real GWAS data set from the Framingham Heart Study, leading to the identification of several significant SNPs associated with age-specific changes of body mass index. The fGWAS model, equipped with the Bayesian group lasso, will provide a useful tool for genetic and developmental analysis of complex traits or diseases.
    The Annals of Applied Statistics 09/2015; 9(2). DOI:10.1214/15-AOAS808 · 1.46 Impact Factor
    • "-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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    Immunology letters 08/2015; 168(1). DOI:10.1016/j.imlet.2015.08.005 · 2.51 Impact Factor
    • "QIAamp DNA Mini Kit (Qiagen GmbH, Hilden, Germany). Selection of tag SNPs: tag SNPs were selected for TPH2 using National Institute of Environmental Health Sciences (NIEHS) Tag SNP selection tool using r 2 threshold of 0.8 [31] "
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