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. 2012 Nov 23:13:661.
doi: 10.1186/1471-2164-13-661.

MirSNP, a database of polymorphisms altering miRNA target sites, identifies miRNA-related SNPs in GWAS SNPs and eQTLs

Chenxing Liu  1 , Fuquan Zhang, Tingting Li, Ming Lu, Lifang Wang, Weihua Yue, Dai Zhang
Affiliations

MirSNP, a database of polymorphisms altering miRNA target sites, identifies miRNA-related SNPs in GWAS SNPs and eQTLs

Chenxing Liu et al. BMC Genomics. .

Abstract

Background: Numerous single nucleotide polymorphisms (SNPs) associated with complex diseases have been identified by genome-wide association studies (GWAS) and expression quantitative trait loci (eQTLs) studies. However, few of these SNPs have explicit biological functions. Recent studies indicated that the SNPs within the 3'UTR regions of susceptibility genes could affect complex traits/diseases by affecting the function of miRNAs. These 3'UTR SNPs are functional candidates and therefore of interest to GWAS and eQTL researchers.

Description: We developed a publicly available online database, MirSNP (http://cmbi.bjmu.edu.cn/mirsnp), which is a collection of human SNPs in predicted miRNA-mRNA binding sites. We identified 414,510 SNPs that might affect miRNA-mRNA binding. Annotations were added to these SNPs to predict whether a SNP within the target site would decrease/break or enhance/create an miRNA-mRNA binding site. By applying MirSNP database to three brain eQTL data sets, we identified four unreported SNPs (rs3087822, rs13042, rs1058381, and rs1058398), which might affect miRNA binding and thus affect the expression of their host genes in the brain. We also applied the MirSNP database to our GWAS for schizophrenia: seven predicted miRNA-related SNPs (p < 0.0001) were found in the schizophrenia GWAS. Our findings identified the possible functions of these SNP loci, and provide the basis for subsequent functional research.

Conclusion: MirSNP could identify the putative miRNA-related SNPs from GWAS and eQTLs researches and provide the direction for subsequent functional researches.

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Figures

Figure 1
Figure 1
Workflow depicting analysis of variations in/near miRNA genes.
Figure 2
Figure 2
Density of SNPs in human miRNA genes. (A) SNP density in human pre-miRNAs and flanking regions. Each bar represents the average level of all human miRNAs, error bars represent the standard deviation of the mean value. MiRNA and SNP information came from mirBASE18 and dbSNP135, respectively. Figure A shows the result of all SNPs in dbSNP135. (B) Figure B only shows SNPs with a calculated MAF > 0.01 in at least one population from four (CEU, HCB, JPT, YRI).
Figure 3
Figure 3
(A) Workflow depicting the analysis of variations in miRNA-mRNA binding sites.(B) An example of how we determinate a SNP in predicted mRNA target site.
Figure 4
Figure 4
(A) The “Single search” frame of the MirSNP web site. (B) An example of miRNA-related SNP search results.
Figure 5
Figure 5
The “Query disease & trait associated SNPs” frame of the MirSNP web site.
Figure 6
Figure 6
Comparison between MirSNP and three similar databases.
See this image and copyright information in PMC

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