Identification of regulatory cancer risk SNPs for chemoprevention discovery

描述（由申请人提供）：虽然全基因组关联研究（GWAS）已经确定了许多癌症易感性等位基因，但其作用机制及其影响的基因都不清楚。只有了解这些SNP如何影响生物学功能，才能为我们提供癌症病因学知识，并导致旨在模拟非风险（保护性）等位基因的化学预防策略。这项建议的目的是开发一种计算方法来预测负责这些协会的功能变体。核心假设是这些功能变体将改变启动子和增强子元件中的转录因子结合位点，导致附近基因的误调节。其基本原理是，通过确定这些癌症易感性基因座的功能性调节SNP，将确定影响其错误表达影响癌症易感性的基因的上游因素，从而可能为癌症化学预防的新方法打开大门。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中调节性SNP的实验研究方面的丰富经验为开展拟议研究做好了充分准备。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SNP和它们改变其结合的转录因子。该算法将鉴定预测只有一个等位基因结合转录因子的SNP，并将其与转录调控的进化保守区域和表观遗传标记的信息联合收割机结合。 2.确定负责观察到的SNP与癌症易感性相关性的推定调节SNP。将鉴定与已知癌症风险基因座连锁不平衡的调节性SNP，并通过在Ractin全基因组SNP数据集中的统计插补来测试其与癌症的关联。 3.确定与卵巢癌风险相关的调节性SNP与附近基因表达水平之间的关系。将使用来自癌症基因组图谱的SNP和基因表达数据来测试卵巢癌风险基因座处的调节性SNP与肿瘤组织中邻近基因的表达水平的关联。 通过识别调节性癌症风险变体，以及它们调节的卵巢癌基因，将使未来的生物学研究和潜在的癌症化学预防剂的开发成为可能。 公共卫生相关性：已经确定了许多影响谁会患癌症的遗传基因变化。通过了解这些变化改变癌症风险的生物学，有可能设计出模拟那些使个体不太可能患癌症的遗传变异的疗法，从而帮助预防癌症的最初发生。

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. PUBLIC HEALTH RELEVANCE: Numerous hereditary genetic changes that influence who will get cancer have been identified. By understanding the biology by which these changes alter cancer risk, it may be possible to design therapies to mimic those genetic variations that make individuals less likely to develop cancer, thereby helping to prevent cancer from initially occurring.