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This statistical information will enable investigators to prioritize particular genes/SNPs for more in depth molecular study. We tested the performance of regSNPs in identifying the promoter mutations in 13 diseases, using experimentally validated SNPs from HGMD. The empirical P-value (Section 2) was calculated for each SNP-transcription factor pai


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It is especially valuable for prioritizing candidate SNPs identified by the GWAS, or novel or rare variants discovered from the direct human genome sequencing. Promoter regions were used as an example of regulatory regions to illustrate the procedures involved. In this study, we proposed a three-step bioinformatics approach to identify functional SNPs within the regulatory regions following GWAS. It has been reported that E2F1 can serve as both positive and negative regulator on their target genes (Crowe et al., 2001; Croxton et al., 2002; Stanelle et al., 2002). Consistent with this interpretation, the expression level of the TPM3 gene was found to be low in kidney cells and in all fetal tissues, as documented in the microarray gene expression database at the UCSC Genome Browser website (Fujita et al., 2011). A total of 869 genes were retrieved using IPA software by querying bone-related genes in the system (Supplementary Data 3


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RegSNPs offers several major improvements over other methods used for SNP prioritization. Finally, our model identified three putative functional promoter SNPs, with a FDR estimated to be 25%, in a region that influences BMD. This may be due to the low activity of the TPM3 promoter in HEK293 cells, the cell system used to test the promoter activity. For each SNP, the left allele and right allele with an arrow separated, are the reference and alternative alleles, respectively, based on Caucasian population in HapMap. This region was initially identified through linkage analysis using spine BMD as the phenotype of interest (Econs et al., 2004


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The regulatory component of the human genome is much less well defined but based upon conservation is 2 to 3 times larger than the coding region (Cooper et al., 2010). The large number of variants that will be identified generates an urgent need for bioinformatics and computational approaches capable of prioritizing the variants most likely to underlie the observed association, for further biological testing. One feasible solution is to catalog all DNA variants in the LD region of the association, both common and rare, by utilizing next-generation sequencing (NGS) technology. GWAS target common DNA variants, which could either directly contribute to the clinical phenotype or provide an indirect proxy for functional variants, which are in linkage disequilibrium (LD) with the SNP being tested. Recent studies of complex diseases and phenotypes have tended to focus on genome-wide association studies (GWAS) employing hundreds of thousands of single nucleotide polymorphisms (SNPs