Although the causes of human cancers are attributable to many factors, there is substantial evidence that genetics likely plays a key role. Previous studies have used population-based approaches, such as genome- wide association studies (GWASs), to identify cancer-associated genetic susceptibility variants (single nucleotide polymorphisms or SNPs) in the human genome. Although GWASs have reported thousands of SNP loci associated with an increased cancer risk, functional effects of these risk-SNPs remain largely unknown. Because many of the risk-SNPs are located in genomic regions without known protein-coding genes and some reside several hundred kilobases from any nearby gene, it is believed that many, if not most, of these SNPs have regulatory effects on the genes that cause these cancers. To identify regulatory SNPs responsible for the disease risk, we propose to apply two novel high-throughput sequencing technologies to screen thousands of candidate SNPs at prostate cancer risk loci.
Aim 1 is to determine SNP-dependent transcription factor (TF) binding differences at prostate cancer risk loci through IP-SNPs-seq.
Aim 2 is to determine biological significance of SNP-dependent sequence variants at prostate cancer risk loci through CRISPRi-SNPs-seq.
Aim 3 is to functionally characterize a set of selected SNPs and their target genes. Successful completion of the proposed study will gain further understanding of the functional role of GWAS-implicated SNPs. Characterization of the functional effects of cancer risk loci will facilitate the translation of population-based discovery into biological mechanisms and will eventually benefit clinical practice.
Although genome-wide association studies have reported thousands of SNP loci that are associated with an increased risk for human cancers, functional effects of these risk-SNPs remain largely unknown. The proposed study will apply two innovative technologies (IP-SNPs-seq and CRISPRi-SNPs-seq) to screen and characterize functional risk SNPs implicated in prostate cancer. Successful completion of the study will facilitate the translation of population-based discovery into biological mechanisms and will eventually benefit clinical practice.