Background: For men, prostate cancer (PC) is the most frequently diagnosed, solid tumor in the U.S. today. Although the variation of PC 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 single nucleotide polymorphisms [SNPs]) that are associated with increased risk of human diseases, including PC. These studies have been extremely productive, yielding a substantial number of well-validated SNPs (~25) that are associated with an increased risk of PC, and that number continues to grow. A significant problem with risk-SNPs identified by GWAS, however, is that many of these do not lie within or near a known gene, and they have no obvious functional properties. Thus, a key issue facing the PC research community is how to identify target genes for identified risk-SNPs. A promising strategy to address this problem involves the use of expression quantitative trait loci (eQTL) analysis.Objective/Hypothesis: We hypothesize that many of the PC disease-associated SNPs already identified to date will be located in regulatory domains involved in gene transcription. Furthermore, we hypothesize that candidate genes affected by these regulatory elements can be identified by taking advantage of eQTL datasets. Therefore, the objectives of this grant proposal are to: (1) construct a prostate tissue-specific eQTL dataset; (2) utilize this dataset to identify candidate genes for existing PC risk-SNPs that can then be followed up in future studies; and (3) make the eQTL dataset publically available.Specific Aims: 1) Create a tissue-specific eQTL dataset using genome-wide SNP analysis and genome-wide mRNA expression analysis on a common set of 500 samples of normal prostate tissue obtained from men with PC. 2) Systematically test currently available, validated PC risk-SNPs for their association with transcript level for all mRNAs utilizing this dataset.Study Design: To construct the tissue-specific eQTL dataset, we will perform a genome-wide SNP analysis, using the Illumina HumanOmni1-Quad SNP array, and a genome-wide mRNA expression analysis, using the Illumina humanht-12 BeadChip, on a common set of 500 samples of normal prostate tissue. We will then evaluate currently available validated risk-SNPs (~30) for their association with expression level for all mRNA transcripts using linear regression methods, regressing expression level on the number of minor alleles of each SNP genotype (i.e. assuming an additive effect of alleles on expression level).Innovation: A substantial number of SNPs associated with PC risk have been identified, and more will undoubtedly be identified in the coming years. Moreover, these same SNPs are also being evaluated for their association with the development of aggressive PC (indolent versus lethal tumor) and with PC survival. The function for most of these SNPs and their target genes, however, remains completely unknown. Therefore, the key innovative component of this application is our plan to develop a vital resource, which does not exist today, that allows for a systematic approach to identify candidate target genes for disease-causing SNPs. This strategy is not dependent on the distance between the regulatory-SNP and the target gene. Identifying genes whose expression levels are strongly associated with variations in DNA provides a different path to elucidate these genes in a more objective and unbiased fashion.Impact: The strategy outlined is amenable to helping identify candidate target genes for any SNP resulting from any of the GWAS performed to date. This dataset will be applied to all SNPs identified for PC, including markers of susceptibility (genetic risk of acquiring disease), prognosis (good versus poor outcome), and those for prediction (lethal from indolent disease). As additional SNPs are identified, the tissue-specific eQTL resource created today can be used for many years to come to address these as well as a number of other important research questions in future studies. Finally, this dataset will be made publically available so that it can be used by others in the research community.Overarching Challenges and Focus Areas: The proposed study is directly responsive to the Fiscal Year 2010 PCRP focus area of Genetics, and to the overarching challenge area of distinguishing lethal from indolent disease. The goal of this study is to create and utilize a resource (an eQTL dataset) that will help identify candidate target genes, a critical first step in truly understanding the function and role of SNPs (both predictive and prognostic) identified for PC. Further understanding the genetic mechanisms of PC will ultimately lead to better prognostic and predictive markers.
|Effective start/end date||4/1/11 → 4/30/14|
- Congressionally Directed Medical Research Programs: $709,650.00