Functional Significance of Prostate Cancer Risk-SNPs

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.

• Study Design:
  • Control Set
• Study Type:
  • Cohort
  • Control Set
• dbGaP estimated ancestry using GRAF-pop
• Total number of consented subjects: 471
• Subject Sample Telemetry Report (SSTR)

• BioProject
• PubMed
• PMC

Normal prostate tissue samples were examined to select samples with the following characteristics: 1) absence of PrCa; 2) absence of high-grade prostatic intraepithelial neoplasia (PIN) and benign prostatic hyperplasia (BPH); 3) normal prostatic epithelial glands representing > 40% of all cells; 4) lymphocytic population representing < 2% of all cells; and 5) the normal epithelium was from the posterior region of the prostate (region most consistent with PrCa).

Among the 471 normal tissue samples with available genome-wide genotyping and RNA-Seq data, we identified 444 total samples with sufficient residual material for small RNA extraction. We performed an miRNA transcriptome-wide association study (TWAS) of PrCa risk using small RNA sequencing and genome-wide genotyping data. Of the N = 444 normal tissue samples processed for small RNA sequencing, 441 (99.3%) passed sample-level quality control thresholds for analysis.

RNA was extracted using the Qiagen miRNAeasy Mini Kit and the QIAcube instrument. Small RNA sequencing was performed on an Illumina HiSeq 4000 instrument using NEBNext® Multiplex Small RNA Library Prep Kit, multiplexing up to 48 samples per lane. 

• Primary Phenotype: Prostatic Neoplasms

• Principal Investigator
  • Stephen N. Thibodeau. National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
• Funding Source
  • R01CA151254-05. National Institutes of Health, Bethesda, MD, USA.