Bayesian Methods for Assessing Gene by Environment Interactions

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) 使用来自分子流行病学研究的数据进行相互作用。重点是有针对性的 研究，使用单细胞凝胶电泳法测量DNA损伤。这 技术对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损伤的分布在不同的实验中不同 条件。通过从具有已知基因的个体的细胞系中提取，NIEHS 彗星GxE研究试图确定与以下基因相关的单核苷酸多态(SNP) 基线DNA损伤，对遗传毒性暴露的敏感性，以及修复率。这个 在这种研究中，个体的表型是相应的分布的集合 不同条件下对细胞特异性DNA损伤的影响。需要新的方法来 高效地分析此类分布配置文件，同时允许 受试者和SNP选择。检测GxE交互的能力广为人知 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。靶向分子流行病学研究 为传统的流行病学设计提供了一个有效的替代方案。 我们的目标包括以下几个方面。 1.开发允许分布轮廓的非参数贝叶斯统计方法 在允许变量的同时，灵活地根据个人和预测值而变化 选择。 2.将这些方法应用于NIEHS彗星GxE研究的数据，以选择SNP 与基线DNA损伤、敏感性和修复率有关。 3.制定包括关于每个SNP的外部信息的方法，包括 无论它是在编码区，是同义的，都是非同义的，但在一个 氨基酸变化可能具有破坏性的位置，或在内含子或内含子中 侧翼序列，但可能影响基因表达。 4.另一个目标是开发近似贝叶斯方法 快速实施，同时鼓励对分布配置文件进行稀疏建模。

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.