Database of Functional SNPs in Cancer-Related Environmentally Responsive Genes

描述（由申请人提供）：随着数百万个单核苷酸多态性（SNP）的鉴定，现在非常需要优先考虑最有可能影响表型功能并最终导致疾病发展的潜在功能性SNP。该提案将探索生物信息学方法在功能SNP选择中的应用，重点关注负责环境反应的基因。这些方法的有效性将通过文献检索和经验验证进行评估。具体而言，本提案的目的是：1）使用从公共SNP数据库收集并通过可用的生物信息学工具筛选的SNP，在大约700个环境响应基因中鉴定具有潜在功能重要性的SNP。将构建功能性SNP数据库。2）.调查以前的研究SNP功能及其在癌症病例对照研究中的作用的出版物。结果将与SNP数据库集成。3)确定SNP功能性的预测价值在多大程度上反映在分子流行病学研究结果中。由于大多数这些环境响应基因参与癌症的发展，我们的假设是，预测对蛋白质功能有显着影响的SNPs更可能与癌症风险相关的优势比在以前的病例对照研究中检测到。从这种跨学科的方法产生的功能SNP将成为公开可用的。这项研究的结果将加强使用SNP在未来的表型研究和分子流行病学的方法，以解决与疾病风险估计的基因-环境相互作用的问题。

DESCRIPTION (provided by applicant): With millions of single nucleotide polymorphisms (SNPs) identified, there is now a great need for prioritizing potential functional SNPs that are most likely to affect phenotypic functions and ultimately contribute to disease development. This proposal will explore the application of available bioinformatic methods in functional SNP selection by focusing on genes responsible for environmental responses. Validity of these approaches will be assessed by literature search and empirical confirmations. Specifically, the aims of this proposal are: 1) To identify SNPs with potential functional importance in approximately 700 environmentally responsive genes using SNPs collected from public SNP databases and screened by available bioinformatic tools. A functional SNP database will be constructed. 2).To survey previous publications examining SNP functionality and their roles in cancer case-control studies. Results will be integrated with the SNP database. 3) To determine to what extent the predictive value of SNP functionality is mirrored in findings from molecular epidemiologic studies. Since most of these environmentally responsive genes are involved in cancer development, our hypothesis is that SNPs predicted to have significant impact on protein function are more likely to be associated with cancer risk in terms of odds ratios detected in previous case-control studies. Functional SNPs generated from this interdisciplinary approach will become publicly available. Findings from this study will enhance the use of SNPs in future phenotypic studies and molecular epidemiologic approaches to address questions related to gene-environment interaction in disease risk estimate.