ID
45886
Description
Principal Investigator: Stephen N. Thibodeau, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA MeSH: https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000985 Prostate Cancer (PrCa), the most frequently diagnosed solid tumor in men in the U.S., results in ~192,000 new cases and ~27,000 deaths per year. Although the variation of PrCa incidence is likely to be the result of several factors, there is a large body of literature that strongly implicates a genetic etiology. Genome-wide association studies (GWAS) have emerged as the most widely used contemporary approach to identify genetic variants (in particular SNPs) that are associated with increased risk of human disease. For PrCa, at least five GWAS have now been performed yielding a substantial number of well-validated SNPs that are associated with an increased risk of PrCa, and that number continues to grow. A significant problem for many of the PrCa risk-SNPs identified so far, however, is that they do not lie within or near a known gene and they have no obvious functional properties. These findings suggest that many (if not most) of these risk-SNPs will be located in regulatory regions that control gene expression rather than in coding regions that may directly affect protein function. Therefore, in order to define the functional role of currently known risk-SNPs, the target genes must first be identified. A promising strategy to address this problem involves the use of expression quantitative trait loci (eQTL) analysis. Unfortunately, most of the publically available SNP-Transcript eQTL datasets utilize lymphoblastoid cells with only a handful using tissue from target organs. Although useful, these datasets alone are unlikely to be sufficient. Recent studies have demonstrated that gene expression and gene regulation occur in both a tissue-specific and tissue independent fashion and suggest that a complete repertoire of regulatory SNPs can only be uncovered in the context of cell type specificity. To date, such a tissue-specific dataset for normal prostate tissue does not exist. In this study, we have constructed a normal prostate tissue specific eQTL data set.
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Versions (1)
- 18/11/2023 18/11/2023 - Simon Heim
Détendeur de droits
Stephen N. Thibodeau, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
Téléchargé le
18 novembre 2023
DOI
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Licence
Creative Commons BY 4.0
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dbGaP phs000985 Functional Significance of Prostate Cancer Risk-SNPs
Eligibility Criteria
- StudyEvent: SEV1
- Eligibility Criteria
- The subject consent file includes subject ID, consent information, and sex.
- This subject sample mapping data table contains a mapping of subject IDs to sample IDs. Samples are the final preps submitted for genotyping, sequencing, and/or expression data. For example, if one patient (subject ID) gave one sample, and that sample was processed differently to generate 2 sequencing runs, there would be two rows, both using the same subject ID, but having 2 unique sample IDs.
- This subject phenotype table contains subject ID, epithelium percentage and tumor infiltrating lymphocytes in sample tissues, gene expression principal components 1-14, case group, and affection status of the the subject in prostate cancer (PrCa). All subjects are low-grade PrCa patients but the examined samples are from normal prostate tissues.
- This sample attributes table contains sample ID, body site where sample was collected, analyte type, histological type, and tumor status.
Similar models
Eligibility Criteria
- StudyEvent: SEV1
- Eligibility Criteria
- The subject consent file includes subject ID, consent information, and sex.
- This subject sample mapping data table contains a mapping of subject IDs to sample IDs. Samples are the final preps submitted for genotyping, sequencing, and/or expression data. For example, if one patient (subject ID) gave one sample, and that sample was processed differently to generate 2 sequencing runs, there would be two rows, both using the same subject ID, but having 2 unique sample IDs.
- This subject phenotype table contains subject ID, epithelium percentage and tumor infiltrating lymphocytes in sample tissues, gene expression principal components 1-14, case group, and affection status of the the subject in prostate cancer (PrCa). All subjects are low-grade PrCa patients but the examined samples are from normal prostate tissues.
- This sample attributes table contains sample ID, body site where sample was collected, analyte type, histological type, and tumor status.
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