Using Functional Data to Reveal Gene-Environment Interaction in Colorectal Cancer

使用功能数据揭示结直肠癌中基因与环境的相互作用

• 批准号: 8986781
• 负责人: Li Hsu
• 金额: $ 19.14万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-12-15 至 2017-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8986781
• 关键词: Alcohols; Anti-Inflammatory Agents; Anti-inflammatory; Bioinformatics; Calcium; Cancer Etiology; Case-Control Studies; Cataloging; Catalogs; Cell Line; Cessation of life; Colorectal Cancer; Complex; Computer Simulation; DNA; Data; Data Aggregation; Data Set; Databases; Development; Diet; Dietary Factors; Disease; Dose; Drug usage; Elements; Environmental Risk Factor; Etiology; Fiber; Folic Acid; Formulation; Future; Gene Frequency; Genetic; Genetic Risk; Genetic Variation; Genome; Genotype; Genotype-Tissue Expression Project; Health; Individual; Investigation; Lead; Life Style; Meat; Minor; Non-Insulin-Dependent Diabetes Mellitus; Obesity; Pharmacologic Substance; Physical activity; Play; Postmenopause; Prevention strategy; Processed Meats; Research; Resources; Risk; Role; Sample Size; Sampling; Second Primary Cancers; Signal Transduction; Smoking; Statistical Methods; Susceptibility Gene; Testing; The Cancer Genome Atlas; Tissues; United States; United States National Institutes of Health; Variant; Weight; base; cancer diagnosis; cancer risk; case control; cohort; colon cancer family registry; experience; fruits and vegetables; functional genomics; gene environment interaction; genetic epidemiology; genetic variant; genome browser; genome wide association study; genome-wide; hormone therapy; improved; insight; novel; population based; rare variant; study population; success; therapy duration; treatment strategy; tumor; whole genome

DESCRIPTION (provided by applicant): Colorectal cancer (CRC) is the third most common cancer and the second-leading cause of cancer death in the United States. Both genetic (G) and environmental (E) factors play important roles in CRC. It is thus important to study the interplay between G and E to better understand the etiology of this complex disease. The recent availability of genome-wide genotype data and advances in statistical methods has enabled agnostic genome-wide searches for gene-environment interaction (GxE), which have identified several novel interactions. Despite these successes, limited statistical power remains a primary concern in GxE analysis as the sample size required to detect interactions is at least 4x that required to detect main effects of similar magnitude. This limitation is particularly relevant following stringent correction for multiple tests in genome- wide GxE analysis. Further, despite the potential importance of rare variants in CRC, existing GxE studies focus on common variants. We thus propose to use functional data to inform GxE testing for both common and rare variants across the genome. We will apply novel statistical methods to aggregate interaction signals among a set of G's or a set of E's to increase power. We will leverage the existing resources in the Genetics and Epidemiology of Colorectal Cancer Consortium and the Colon Cancer Family Registry, in which genetic and well harmonized environmental data are available for close to 40,000 CRC cases and controls. In addition, we are currently building a CRC-specific functional annotation database based on >80 annotation datasets across public databases such as UCSC Genome Browser, ENCODE, NIH Roadmap, GTEx, and TCGA. In Aim 1, we will examine whether common variants (MAF>1%) predicted in silico to have functional importance modify the effect of environmental risk factors on CRC risk. By prioritizing functional candidates for GxE testing, we will greatly reduce the multiple testing burden that arises from testing millions of SNPs across the genome. In Aim 2, we will perform aggregate association testing to examine interactions between rare variants (MAF<1%) and environmental risk factors for CRC. Functional information will be used to give greater weights to biologically important variants when aggregating interaction signals. In Aim 3, we will test GxE for an aggregated set of environmental variables that capture different components of a single environmental risk factor (e.g., for smoking the components include current/ever/never use, dose, and duration). As these may influence CRC in distinct ways, we will aggregate the interaction signals across components, which will increase power to detect GxE. Overall, the proposed study provides a unique opportunity to detect novel GxE findings for CRC risk-particularly for functionally important or rare variants across the genome. We expect that our findings will help provide a better understanding of the interplay between genetic and environmental factors in CRC development. By identifying carcinogenic mechanisms and, in turn, potential targets for future therapies, these insights can help improve current prevention and treatment strategies for CRC.

描述（由申请人提供）：结直肠癌（CRC）是美国第三大常见癌症和第二大癌症死亡原因。遗传（G）和环境（E）因素在CRC中起重要作用。因此，重要的是要研究G和E之间的相互作用，以更好地了解这种复杂疾病的病因。最近可用的全基因组基因型数据和统计方法的进步，使不可知的全基因组搜索基因-环境相互作用（GXE），这已经确定了几个新的相互作用。尽管取得了这些成功，但有限的统计功效仍然是GxE分析中的主要问题，因为检测相互作用所需的样本量至少是检测相似量级主效应所需样本量的4倍。在全基因组GxE分析中对多个测试进行严格校正后，该限制尤其相关。此外，尽管罕见变异在CRC中具有潜在的重要性，但现有的GxE研究集中在常见变异上。因此，我们建议使用功能数据来告知GxE测试整个基因组中的常见和罕见变异。我们将应用新的统计方法来聚合一组G或一组E之间的相互作用信号，以增加功率。我们将利用结直肠癌遗传学和流行病学联盟和结肠癌家族登记处的现有资源，其中提供了近40，000例CRC病例和对照的遗传和良好协调的环境数据。此外，我们目前正在构建一个CRC特定功能注释数据库，该数据库基于UCSC Genome Browser、ENCODE、NIH Roadmap、GTEx和TCGA等公共数据库中的>80个注释数据集。在目标1中，我们将研究计算机模拟预测具有功能重要性的常见变异（MAF>1%）是否会改变环境风险因素对CRC风险的影响。通过优先考虑GxE测试的功能候选人，我们将大大减少测试整个基因组中数百万个SNP所带来的多重测试负担。在目标2中，我们将进行聚集关联检验，以检查罕见变异（MAF<1%）与CRC环境风险因素之间的相互作用。当聚集相互作用信号时，功能信息将用于为生物学上重要的变体赋予更大的权重。在目标3中，我们将测试GxE的一组聚集的环境变量，这些变量捕获单个环境风险因素的不同组成部分（例如，对于吸烟，组分包括当前/曾经/从未使用、剂量和持续时间）。由于这些可能以不同的方式影响CRC，我们将聚合组件之间的相互作用信号，这将增加检测GxE的能力。总的来说，这项研究提供了一个独特的机会来检测新的GxE发现的CRC风险，特别是在整个基因组的功能重要或罕见的变异。我们希望我们的研究结果将有助于更好地了解CRC发展中遗传和环境因素之间的相互作用。通过确定致癌机制，进而确定未来治疗的潜在靶点，这些见解可以帮助改善目前CRC的预防和治疗策略。