INTRODUCTION: MicroRNAs (miRNAs) are small RNAs that play an important role in the regulation of gene expression. miRNA dysregulation has been associated with phenotypic changes, including cardiovascular diseases (CVDs). OBJECTIVES: The aim of the study was to obtain a list of single nucleotide polymorphisms (SNPs) related to CVDs, with computationally predicted effect on miRNA binding sites, which would verify the hypothesis that miRNA dysregulation can lead to the development of CVDs. MATERIALS AND METHODS: SNPs, CVDs, and miRNAs were the 3 factors subjected to analysis. Based on the publicly available databases, we created a set of SNPs associated with the phenotype of interest and of SNPs located in known miRNA binding sites. We then merged the records assigned by the same SNP, which allowed us to indicate miRNA target sites, whose variants may be associated with CVDs. The results were supplemented with the additional data such as miRNA and mRNA coexpression, differences in the expression between various tissues, and Expression Quantitative Trait Locus analysis. Only in-silico methods, on the basis of publically available information tools and databases, were used. RESULTS: We obtained a list of 47 entries, constituting unique miRNA-SNP allele-phenotype linkages. CONCLUSIONS: Computational approach supports the hypothesis of the linkage between alterations in miRNA function and numerous CVDs. Given the high frequency of SNP incidence, this pathomechanism may be common in the population. Although the obtained results need to be further experimentally validated, limiting the number of interactions to the most probable ones will facilitate the identification of clinically significant associations.
INTRODUCTION: MicroRNAs (miRNAs) are small RNAs that play an important role in the regulation of gene expression. miRNA dysregulation has been associated with phenotypic changes, including cardiovascular diseases (CVDs). OBJECTIVES: The aim of the study was to obtain a list of single nucleotide polymorphisms (SNPs) related to CVDs, with computationally predicted effect on miRNA binding sites, which would verify the hypothesis that miRNA dysregulation can lead to the development of CVDs. MATERIALS AND METHODS: SNPs, CVDs, and miRNAs were the 3 factors subjected to analysis. Based on the publicly available databases, we created a set of SNPs associated with the phenotype of interest and of SNPs located in known miRNA binding sites. We then merged the records assigned by the same SNP, which allowed us to indicate miRNA target sites, whose variants may be associated with CVDs. The results were supplemented with the additional data such as miRNA and mRNA coexpression, differences in the expression between various tissues, and Expression Quantitative Trait Locus analysis. Only in-silico methods, on the basis of publically available information tools and databases, were used. RESULTS: We obtained a list of 47 entries, constituting unique miRNA-SNP allele-phenotype linkages. CONCLUSIONS: Computational approach supports the hypothesis of the linkage between alterations in miRNA function and numerous CVDs. Given the high frequency of SNP incidence, this pathomechanism may be common in the population. Although the obtained results need to be further experimentally validated, limiting the number of interactions to the most probable ones will facilitate the identification of clinically significant associations.