Identification of regulatory cancer risk SNPs for chemoprevention discovery

描述(由申请人提供)：尽管全基因组关联研究(GWAS)已经确定了许多癌症易感等位基因，但它们的作用机制和它们影响的基因都不清楚。只有了解这些SNP如何影响生物功能，才能让我们了解癌症病因学的知识，并导致旨在模仿非危险(保护性)等位基因的化学预防策略。这项提议的目标是开发一种计算方法来预测导致这些关联的功能变体。中心假设是，这些功能变异会改变启动子和增强子元件中的转录因子结合部位，导致附近基因的错误调控(S)。其基本原理是，通过识别这些癌症易感基因上的功能调节SNPs，将识别影响其错误表达影响癌症易感性的基因的上游因素，从而潜在地打开癌症化学预防新方法的大门。实验团队在整合基因组数据以分析全基因组SNP关联研究和正在进行的癌症调节SNPs实验研究方面的丰富经验，为开展拟议的研究做了强有力的准备。将通过完成以下具体目标来检验中心假设： 1.开发一种算法来识别调节性SNPs和它们改变其结合的转录因子。该算法将识别只有一个等位基因与转录因子结合的SNPs，并将其与进化保守区域和转录调控的表观遗传标记的信息相结合。 2.确定可能的调节性SNP负责观察到的SNP与癌症易感性的关联。与已知癌症风险基因连锁不平衡的调节性SNP将通过现有的全基因组SNP数据集中的统计归因来识别和测试与癌症的关联。 3.确定与卵巢癌风险相关的调控SNPs与邻近基因表达水平之间的关系。将使用SNP和来自癌症基因组图谱的基因表达数据来测试卵巢癌风险基因上的调节性SNPs与肿瘤组织中邻近基因的表达水平的相关性。 通过识别癌症风险的调节性变异，以及卵巢癌，它们调控的基因将使未来的生物学研究成为可能，并有可能开发出癌症的化学预防药物。

DESCRIPTION (provided by applicant): While genome-wide association studies (GWAS) have identified numerous cancer susceptibility alleles, neither the mechanism underlying their action nor the genes they influence are understood. Only by understanding how these SNPs influence biological function can they inform our knowledge of cancer etiology and lead to chemoprevention strategies aimed at mimicking the non-risk (protective) alleles. The objective of this proposal is to develop a computational approach to predict the functional variants responsible for these associations. The central hypothesis is that these functional variants will alter transcription factor binding sites in promoter and enhancer elements, resulting in misregulation of nearby gene(s). The rationale is that by identifying the functional regulatory SNPs at these cancer susceptibility loci, the upstream factors that affect genes whose misexpression influences cancer susceptibility will be identified, potentially opening the door to new methods for cancer chemoprevention. The experimental team's extensive experience with integrating genomic data to analyze genome-wide SNP association studies and ongoing experimental research on regulatory SNPs in cancer is strong preparation for undertaking the proposed studies. The central hypothesis will be tested through completion of the following specific aims: 1. Develop an algorithm to identify regulatory SNPs and the transcription factor whose binding they alter. This algorithm will identify SNPs for which only one allele is predicted to bind a transcription factor and combine this with information on evolutionarily conserved regions and epigenetic marks of transcriptional regulation. 2. Identify putative regulatory SNPs responsible for observed SNP associations with cancer susceptibility. Regulatory SNPs in linkage disequilibrium with known cancer risk loci will be identified and tested for association with cancer through statistical imputation in existin genome-wide SNP datasets. 3. Determine the relationship between regulatory SNPs associated with ovarian cancer risk and expression levels of nearby genes. Regulatory SNPs at ovarian cancer risk loci will be tested for association with expression levels of nearby genes in tumor tissue using SNP and gene expression data from The Cancer Genome Atlas. By identifying regulatory cancer risk variants and, for ovarian cancer, the genes they regulate will enable future biological inquiry and potentially the development of chemopreventative agents for cancer.