Dataset Information

In silico whole-genome screening for cancer-related single-nucleotide polymorphisms located in human mRNA untranslated regions.

ABSTRACT: BACKGROUND: A promising application of the huge amounts of genetic data currently available lies in developing a better understanding of complex diseases, such as cancer. Analysis of publicly available databases can help identify potential candidates for genes or mutations specifically related to the cancer phenotype. In spite of their huge potential to affect gene function, no systematic attention has been paid so far to the changes that occur in untranslated regions of mRNA. RESULTS: In this study, we used Expressed Sequence Tag (EST) databases as a source for cancer-related sequence polymorphism discovery at the whole-genome level. Using a novel computational procedure, we focused on the identification of untranslated region (UTR)-localized non-coding Single Nucleotide Polymorphisms (UTR-SNPs) significantly associated with the tumoral state. To explore possible relationships between genetic mutation and phenotypic variation, bioinformatic tools were used to predict the potential impact of cancer-associated UTR-SNPs on mRNA secondary structure and UTR regulatory elements. We provide a comprehensive and unbiased description of cancer-associated UTR-SNPs that may be useful to define genotypic markers or to propose polymorphisms that can act to alter gene expression levels. Our results suggest that a fraction of cancer-associated UTR-SNPs may have functional consequences on mRNA stability and/or expression. CONCLUSION: We have undertaken a comprehensive effort to identify cancer-associated polymorphisms in untranslated regions of mRNA and to characterize putative functional UTR-SNPs. Alteration of translational control can change the expression of genes in tumor cells, causing an increase or decrease in the concentration of specific proteins. Through the description of testable candidates and the experimental validation of a number of UTR-SNPs discovered on the secreted protein acidic and rich in cysteine (SPARC) gene, this report illustrates the utility of a cross-talk between in silico transcriptomics and cancer genetics.

SUBMITTER: Aouacheria A

PROVIDER: S-EPMC1774567 | biostudies-literature | 2007

REPOSITORIES: biostudies-literature

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In silico whole-genome screening for cancer-related single-nucleotide polymorphisms located in human mRNA untranslated regions.

Aouacheria Abdel A Navratil Vincent V López-Pérez Ricardo R Gutiérrez Norma C NC Churkin Alexander A Barash Danny D Mouchiroud Dominique D Gautier Christian C

BMC genomics 20070103

<h4>Background</h4>A promising application of the huge amounts of genetic data currently available lies in developing a better understanding of complex diseases, such as cancer. Analysis of publicly available databases can help identify potential candidates for genes or mutations specifically related to the cancer phenotype. In spite of their huge potential to affect gene function, no systematic attention has been paid so far to the changes that occur in untranslated regions of mRNA.<h4>Results< ...[more]

PMID: 17201911

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Project description:The advent of high throughput sequencing technology has enabled the 1000 Genomes Project Pilot 3 to generate complete sequence data for more than 906 genes and 8,140 exons representing 697 subjects. The 1000 Genomes database provides a critical opportunity for further interpreting disease associations with single nucleotide polymorphisms (SNPs) discovered from genetic association studies. Currently, direct sequencing of candidate genes or regions on a large number of subjects remains both cost- and time-prohibitive.To accelerate the translation from discovery to functional studies, we propose an in silico gene sequencing method (ISS), which predicts phased sequences of intragenic regions, using SNPs. The key underlying idea of our method is to infer diploid sequences (a pair of phased sequences/alleles) at every functional locus utilizing the deep sequencing data from the 1000 Genomes Project and SNP data from the HapMap Project, and to build prediction models using flanking SNPs. Using this method, we have developed a database of prediction models for 611 known genes. Sequence prediction accuracy for these genes is 96.26% on average (ranges 79%-100%). This database of prediction models can be enhanced and scaled up to include new genes as the 1000 Genomes Project sequences additional genes on additional individuals. Applying our predictive model for the KCNJ11 gene to the Wellcome Trust Case Control Consortium (WTCCC) Type 2 diabetes cohort, we demonstrate how the prediction of phased sequences inferred from GWAS SNP genotype data can be used to facilitate interpretation and identify a probable functional mechanism such as protein changes.Prior to the general availability of routine sequencing of all subjects, the ISS method proposed here provides a time- and cost-effective approach to broadening the characterization of disease associated SNPs and regions, and facilitating the prioritization of candidate genes for more detailed functional and mechanistic studies.

Project description:OBJECTIVES:Excessive acetabular coverage is the most common cause of pincer-type femoroacetabular impingement. To date, an association between acetabular over-coverage and genetic variations has not been studied. In this study we investigated the association between single nucleotide polymorphisms (SNPs) of paralogous Homeobox (HOX)9 genes and acetabular coverage in Japanese individuals to identify a possible genetic variation associated with acetabular over-coverage. METHODS:We investigated 19 total SNPs in the four HOX9 paralogs, then focused in detail on seven of those located in the 3' untranslated region of HOXB9 (rs8844, rs3826541, rs3826540, rs7405887, rs2303485, rs2303486, rs79931349) using a case-control association study. The seven HOXB9 SNPs were genotyped in 316 subjects who had all undergone radiological examination. The association study was performed by both single-locus and haplotype-based analyses. RESULTS:The genotype and allele frequencies of the five HOXB9 SNPs showed significant association with acetabular over-coverage compared with controls (rs7405887 OR = 3.16, p = 5.29E-6, 95% CI 1.91 to 5.25). A significant difference was also detected when haplotypes were evaluated (OR = 2.59, p = 2.61E-5, 95% CI 1.65 to 4.08). The two HOXB9 SNPs (rs2303485, rs2303486) were associated with decreased acetabular coverage (rs2303485 OR = 0.524, p = 0.0091, 95% CI 0.322 to 0.855; rs2303486 OR = 0.519, p = 0.011, 95% CI 0.312 to 0.865). CONCLUSIONS:The five HOXB9 SNPs (rs8844, rs3826541, rs3826540, rs7405887, rs79931349) were associated with acetabular over-coverage. On the other hand, the two SNPs (rs2303485 and rs2303486) were associated with the lower acetabular coverage. The association of rs2303486 would be consistent with the previous study. Therefore, the HOXB9 SNPs might be involved in the morphogenesis of acetabular coverage, and could be an independent risk factor for developing pincer-type femoroacetabular impingement. Cite this article: Bone Joint Res 2015;4:50-5.

Dataset Information

In silico whole-genome screening for cancer-related single-nucleotide polymorphisms located in human mRNA untranslated regions.

Publications

In silico whole-genome screening for cancer-related single-nucleotide polymorphisms located in human mRNA untranslated regions.

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