Comprehensive Analysis To Identify Functional Basis Of Prostate Cancer Risk Snps

Introduction: Genome-wide association studies (GWAS) have identified numerous common single nucleotide polymorphisms (SNPs) associated with the risk of developing prostate cancer. Many of these prostate cancer GWAS hits occur in intergenic regions, distant from any annotated gene. Little is yet known about how these SNPs function to alter an individual9s risk of prostate cancer. Here we test the hypothesis that many of these SNPs, or SNPs with which they are correlated, alter regulatory regions and thus gene expression in the prostate. Materials and Methods: To test if prostate cancer risk SNPs are correlated with gene expression changes, we conducted expression quantitative trait locus (eQTL) association analysis. Using genome-wide high-density genotypes and gene expression data from 56 tumor and 58 adjacent normal prostate tissues we asked if any of the prostate cancer risk SNPs correlate with expression changes in nearby genes using linear regression, adjusting gene expression levels for principal components of ancestry and different batches. We also asked if individuals who have a higher genetic risk of prostate cancer based on the genotype of all of their risk SNPs show different gene expression patterns in the prostate than those at lower risk of prostate cancer. Results: We have identified several novel cis-eQTLs in prostate tissue for prostate cancer risk SNPs including rs1933488 with RGS17. We have also replicated several previously known cis-eQTLs, including those for IRX4, PPP1R14A, and FOXP4. Individuals at higher genetic risk of developing prostate cancer have altered expression of 37 genes (p Conclusions: These data demonstrate that several prostate cancer risk SNPs are associated with expression changes in nearby genes in both pathologically malignant and pathologically benign prostate tissue from patients who underwent radical prostatectomy. Additional genes, including HMOX1, CSGALNACT1, and WDR36 show altered expression in patients with different levels of genetic risk of prostate cancer. Citation Format: Mridu Middha, Xing Xu, Riina-Minna Vaananen, James Hayes, Pekka Taimen, Xiaoni Gao, Hans G. Lilja, Kim Pettersson, Robert J. Klein. Comprehensive analysis to identify functional basis of prostate cancer risk SNPs. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2566.