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

基本信息

批准号：
8293144
负责人：
David Brian Dunson
金额：
$ 34.4万
依托单位：
DUKE UNIVERSITY
依托单位国家：
美国
项目类别：
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/8293144
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 Epidemiology 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 strategy Public Health Relative (related person)Role Sampling Sea Shapes Single Nucleotide Polymorphism Space Models Statistical Methods Tail Technology Time United States Work abstracting base design disorder risk epidemiology study gene environment interaction immortalized cell improved innovation lifestyle factors repaired response tool trait treatment strategy

项目摘要

Summary/Abstract 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.

摘要/摘要我们建议开发新的统计方法来研究基因 x 环境 (GxE) 使用分子流行病学研究数据进行相互作用。重点是有针对性研究使用单细胞凝胶电泳来测量 DNA 损伤。这技术对于 GxE 的研究具有巨大的潜力，因为人们可以评估个体细胞中 DNA 损伤的分布因实验而异状况。通过从具有已知基因型的个体的细胞系中提取，NIEHS Comet GxE 研究旨在识别与以下相关的单核苷酸多态性 (SNP) 基线 DNA 损伤、对基因毒性暴露的易感性和修复率。这在此类研究中，个体的表型是对应分布的集合不同条件下细胞特异性 DNA 损伤。需要新的方法有效地分析这种分布情况，同时允许之间的异质性受试者和 SNP 选择。检测 GxE 交互的能力受到广泛关注健康重要性，使医生能够更好地识别更敏感的患者药物治疗或环境暴露。有针对性的分子流行病学研究为传统流行病学设计提供有效的替代方案。我们的目标包括以下内容。 1. 开发允许分布概况的非参数贝叶斯统计方法灵活地根据个体和预测因素而变化，同时允许变量选择。 2. 将这些方法应用于 NIEHS Comet GxE 研究的数据来选择 SNP 与基线 DNA 损伤、易感性和修复率相关。 3. 制定将每个 SNP 的外部信息纳入其中的方法，包括是否在编码区、是同义、是非同义但处于氨基酸变化可能造成损害的位置，或者在内含子中或侧翼序列，但可能影响基因表达。 4. 另一个目标是开发近似贝叶斯方法迅速实施，同时鼓励分布概况的稀疏建模。