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Comprehensive analysis of single nucleotide polymorphisms in human microRNAs.

Han M, Zheng Y - PLoS ONE (2013)

Bottom Line: Our results suggest that conservation, genomic context, secondary structure, and functional importance of human miRNAs affect the accumulations of SNPs in these genes.Our results also show that the number of SNPs with significantly different frequencies among various populations in the HapMap and 1000 Genome Project data are consistent with the geographical distributions of these populations.These analyses provide a better insight of SNPs in human miRNAs and the spreading of the SNPs in miRNAs in different populations.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Genetic Engineering and Institute of Developmental Biology and Molecular Medicine, School of Life Sciences, Fudan University, Shanghai, China.

ABSTRACT
MicroRNAs (miRNAs) are endogenous small non-coding RNAs that repress their targets at post transcriptional level. Single Nucleotide Polymorphisms (SNPs) in miRNAs can lead to severe defects to the functions of miRNAs and might result in diseases. Although several studies have tried to identify the SNPs in human miRNA genes or only in the mature miRNAs, there are only limited endeavors to explain the distribution of SNPs in these important genes. After a genome-wide scan for SNPs in human miRNAs, we totally identified 1899 SNPs in 961 out of the 1527 reported miRNA precursors of human, which is the most complete list of SNPs in human miRNAs to date. More importantly, to explain the distributions of SNPs existed in human miRNAs, we comprehensively and systematically analyzed the identified SNPs in miRNAs from several aspects. Our results suggest that conservation, genomic context, secondary structure, and functional importance of human miRNAs affect the accumulations of SNPs in these genes. Our results also show that the number of SNPs with significantly different frequencies among various populations in the HapMap and 1000 Genome Project data are consistent with the geographical distributions of these populations. These analyses provide a better insight of SNPs in human miRNAs and the spreading of the SNPs in miRNAs in different populations.

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Related in: MedlinePlus

Analysis of SNPs in miRNAs associated with diseases and QTLs.Part A shows the proportion of disease miRNAs in all the miRNAs. Part B shows the comparisons of SNP densities in disease miRNAs and no-disease miRNAs with two sample one tailed  test. In part B, *, ** and *** means -values smaller than 0.05, 0.01 and 0.001, respectively. Error bar indicate the SEM. Part C shows the distribution of the numbers of associated diseases for miRNAs in HMDD. Part D shows the number of SNPs and the number of associated diseases of the miRNAs. MiRNAs are grouped into different groups according to the number of SNPs in them and the average numbers of associated disease for all groups were calculated, shown as green triangles. The green triangles are connected with yellow lines. Part E shows the number of SNPs in the miRNAs and the number of QTLs which the miRNAs are overlapped with. MiRNAs are grouped into different groups according to the number of SNPs in them and the average number of QTLs for each group was calculated, shown as green triangles. The green triangles are connected with yellow line.
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pone-0078028-g005: Analysis of SNPs in miRNAs associated with diseases and QTLs.Part A shows the proportion of disease miRNAs in all the miRNAs. Part B shows the comparisons of SNP densities in disease miRNAs and no-disease miRNAs with two sample one tailed test. In part B, *, ** and *** means -values smaller than 0.05, 0.01 and 0.001, respectively. Error bar indicate the SEM. Part C shows the distribution of the numbers of associated diseases for miRNAs in HMDD. Part D shows the number of SNPs and the number of associated diseases of the miRNAs. MiRNAs are grouped into different groups according to the number of SNPs in them and the average numbers of associated disease for all groups were calculated, shown as green triangles. The green triangles are connected with yellow lines. Part E shows the number of SNPs in the miRNAs and the number of QTLs which the miRNAs are overlapped with. MiRNAs are grouped into different groups according to the number of SNPs in them and the average number of QTLs for each group was calculated, shown as green triangles. The green triangles are connected with yellow line.

Mentions: Gong et al., [22] recently analyzed the minimal free energies of 785 miRNAs with SNPs. In comparison, we introduced in Equation 2 to clarify that different SNPs may have different effects on the minimal free energies of miRNAs, as shown in Figure 5A. In addition, we also categorized different SNPs based on their nucleotide changes, as shown in Table 1. Finally, the number of miRNAs with SNPs analyzed here are much larger than existing studies [22].


Comprehensive analysis of single nucleotide polymorphisms in human microRNAs.

Han M, Zheng Y - PLoS ONE (2013)

Analysis of SNPs in miRNAs associated with diseases and QTLs.Part A shows the proportion of disease miRNAs in all the miRNAs. Part B shows the comparisons of SNP densities in disease miRNAs and no-disease miRNAs with two sample one tailed  test. In part B, *, ** and *** means -values smaller than 0.05, 0.01 and 0.001, respectively. Error bar indicate the SEM. Part C shows the distribution of the numbers of associated diseases for miRNAs in HMDD. Part D shows the number of SNPs and the number of associated diseases of the miRNAs. MiRNAs are grouped into different groups according to the number of SNPs in them and the average numbers of associated disease for all groups were calculated, shown as green triangles. The green triangles are connected with yellow lines. Part E shows the number of SNPs in the miRNAs and the number of QTLs which the miRNAs are overlapped with. MiRNAs are grouped into different groups according to the number of SNPs in them and the average number of QTLs for each group was calculated, shown as green triangles. The green triangles are connected with yellow line.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3818353&req=5

pone-0078028-g005: Analysis of SNPs in miRNAs associated with diseases and QTLs.Part A shows the proportion of disease miRNAs in all the miRNAs. Part B shows the comparisons of SNP densities in disease miRNAs and no-disease miRNAs with two sample one tailed test. In part B, *, ** and *** means -values smaller than 0.05, 0.01 and 0.001, respectively. Error bar indicate the SEM. Part C shows the distribution of the numbers of associated diseases for miRNAs in HMDD. Part D shows the number of SNPs and the number of associated diseases of the miRNAs. MiRNAs are grouped into different groups according to the number of SNPs in them and the average numbers of associated disease for all groups were calculated, shown as green triangles. The green triangles are connected with yellow lines. Part E shows the number of SNPs in the miRNAs and the number of QTLs which the miRNAs are overlapped with. MiRNAs are grouped into different groups according to the number of SNPs in them and the average number of QTLs for each group was calculated, shown as green triangles. The green triangles are connected with yellow line.
Mentions: Gong et al., [22] recently analyzed the minimal free energies of 785 miRNAs with SNPs. In comparison, we introduced in Equation 2 to clarify that different SNPs may have different effects on the minimal free energies of miRNAs, as shown in Figure 5A. In addition, we also categorized different SNPs based on their nucleotide changes, as shown in Table 1. Finally, the number of miRNAs with SNPs analyzed here are much larger than existing studies [22].

Bottom Line: Our results suggest that conservation, genomic context, secondary structure, and functional importance of human miRNAs affect the accumulations of SNPs in these genes.Our results also show that the number of SNPs with significantly different frequencies among various populations in the HapMap and 1000 Genome Project data are consistent with the geographical distributions of these populations.These analyses provide a better insight of SNPs in human miRNAs and the spreading of the SNPs in miRNAs in different populations.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Genetic Engineering and Institute of Developmental Biology and Molecular Medicine, School of Life Sciences, Fudan University, Shanghai, China.

ABSTRACT
MicroRNAs (miRNAs) are endogenous small non-coding RNAs that repress their targets at post transcriptional level. Single Nucleotide Polymorphisms (SNPs) in miRNAs can lead to severe defects to the functions of miRNAs and might result in diseases. Although several studies have tried to identify the SNPs in human miRNA genes or only in the mature miRNAs, there are only limited endeavors to explain the distribution of SNPs in these important genes. After a genome-wide scan for SNPs in human miRNAs, we totally identified 1899 SNPs in 961 out of the 1527 reported miRNA precursors of human, which is the most complete list of SNPs in human miRNAs to date. More importantly, to explain the distributions of SNPs existed in human miRNAs, we comprehensively and systematically analyzed the identified SNPs in miRNAs from several aspects. Our results suggest that conservation, genomic context, secondary structure, and functional importance of human miRNAs affect the accumulations of SNPs in these genes. Our results also show that the number of SNPs with significantly different frequencies among various populations in the HapMap and 1000 Genome Project data are consistent with the geographical distributions of these populations. These analyses provide a better insight of SNPs in human miRNAs and the spreading of the SNPs in miRNAs in different populations.

Show MeSH
Related in: MedlinePlus