Bayesian Methods for Assessing Gene by Environment Interactions

通过环境相互作用评估基因的贝叶斯方法

• 批准号: 7697425
• 负责人: David Brian Dunson
• 金额: $ 32.58万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-25 至 2014-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7697425
• 关键词: Address; Amino Acids; Back; Bayesian Method; Cell Line; Cell physiology; Cells; Code; Collection; Comet Assay; Complex; DNA Damage; DNA Repair; DNA Repair Gene; DNA strand break; Data; Development; Diabetes Mellitus; Diet; Disease; Environment; Environmental Exposure; Environmental Risk Factor; Etiology; Exposure to; Frequencies; Gene Expression; Genes; Genetic; Genetic Polymorphism; Genetic Predisposition to Disease; Genome; Genotype; Goals; Haplotypes; Heterogeneity; Hydrogen Peroxide; Individual; Introns; Ionizing radiation; Linear Regressions; Location; Malignant Neoplasms; Measures; Methods; Modeling; Molecular Epidemiology; Monte Carlo Method; Motivation; Mutagens; Mutation; National Institute of Environmental Health Sciences; Olives - dietary; Pathway interactions; Patients; Pharmacotherapy; Phenotype; Physicians; Play; Population; Predisposition; Prevention; Public Health; Relative (related person); Role; Sampling; Sea; Shapes; Single Nucleotide Polymorphism; Space Models; Statistical Methods; Tail; Technology; Time; United States; Work; base; design; disorder risk; epidemiology study; gene environment interaction; immortalized cell; improved; innovation; lifestyle factors; public health relevance; repaired; response; tool; trait; treatment strategy

DESCRIPTION (provided by applicant): We propose to develop new statistical methods for studying gene x environment (GxE) interactions using data from molecular epidemiology studies. The focus is on targeted studies, which use single cell gel electrophoresis to measure DNA damage. This technology has great potential for study of GxE, since one can assess how the distribution of DNA damage across cells from an individual varies between experimental conditions. By drawing from cell lines for individuals with known genotype, the NIEHS Comet GxE study seeks to identify single nucleotide polymorphisms (SNPs) related to baseline DNA damage, susceptibility to genotoxic exposures, and repair rate. The phenotype for an individual in such studies is a collection of distributions corresponding to cell-specific DNA damage under different conditions. New methods are needed to efficiently analyze such distributional profiles, while allowing heterogeneity among subjects and SNP selection. The ability to detect GxE interactions is of great public health importance, allowing physicians to better identify patients that are more sensitive to a drug therapy or environmental exposure. Targeted molecular epidemiology studies provide an efficient alternative to traditional epidemiologic designs. Our goals include the following. 1. Develop nonparametric Bayesian statistical methods that allow a distributional profile to vary flexibly across individuals and with predictors, while allowing variable selection. 2. Apply these methods to data from the NIEHS Comet GxE Study to select SNPs associated with baseline DNA damage, susceptibility and repair rates. 3. Develop approaches for including outside information on each SNP, including whether it is in the coding region, is synonymous, is non-synonymous but at a location at which an amino acid change is likely to be damaging, or is in an intron or flanking sequence but is likely to impact gene expression. 4. An additional goal is to develop approximate Bayes methods that can be implemented rapidly, while encouraging sparse modeling of distributional profiles. PUBLIC HEALTH RELEVANCE: The development of complex diseases, such as cancer and diabetes, depends on the interaction between genetic predisposition and a variety of lifestyle factors, including diet and environmental exposures. Identifying gene-environment interactions is a critical step in obtaining a better understanding of disease etiology, while also developing more effective personalized prevention and treatment strategies. We provide the statistical tools necessary to efficiently detect gene-environment interactions utilizing data from innovative new molecular epidemiology designs.

描述（由申请人提供）：我们建议开发新的统计方法，利用分子流行病学研究的数据来研究基因 x 环境 (GxE) 相互作用。重点是针对性研究，使用单细胞凝胶电泳来测量 DNA 损伤。这项技术对于 GxE 研究具有巨大的潜力，因为人们可以评估个体细胞中 DNA 损伤的分布在不同实验条件下的变化。通过从具有已知基因型的个体的细胞系中提取数据，NIEHS Comet GxE 研究旨在识别与基线 DNA 损伤、基因毒性暴露易感性和修复率相关的单核苷酸多态性 (SNP)。此类研究中个体的表型是与不同条件下细胞特异性 DNA 损伤相对应的分布的集合。需要新的方法来有效地分析这种分布概况，同时允许受试者和 SNP 选择之间的异质性。检测 GxE 相互作用的能力对于公共卫生具有重要意义，使医生能够更好地识别对药物治疗或环境暴露更敏感的患者。有针对性的分子流行病学研究为传统流行病学设计提供了有效的替代方案。我们的目标包括以下内容。 1. 开发非参数贝叶斯统计方法，允许分布图在个体和预测变量之间灵活变化，同时允许变量选择。 2. 将这些方法应用于 NIEHS Comet GxE 研究的数据，以选择与基线 DNA 损伤、易感性和修复率相关的 SNP。 3. 开发包含每个 SNP 外部信息的方法，包括它是否位于编码区、同义、非同义但位于氨基酸变化可能造成损害的位置，或者位于内含子或内含子中。侧翼序列，但可能影响基因表达。 4. 另一个目标是开发可以快速实施的近似贝叶斯方法，同时鼓励分布图的稀疏建模。 公共卫生相关性：癌症和糖尿病等复杂疾病的发展取决于遗传倾向和各种生活方式因素（包括饮食和环境暴露）之间的相互作用。识别基因与环境的相互作用是更好地了解疾病病因、同时制定更有效的个性化预防和治疗策略的关键一步。我们提供必要的统计工具，利用创新的新分子流行病学设计的数据有效检测基因与环境的相互作用。