Bayesian Methods for Assessing Gene by Environment Interactions

总结/摘要 我们提出了一种新的研究基因x环境（GxE）的统计方法 利用分子流行病学研究的数据。重点是有针对性 使用单细胞凝胶电泳来测量DNA损伤的研究。这 这项技术在研究GxE方面具有很大的潜力，因为人们可以评估 DNA损伤在个体细胞中的分布在不同的实验条件下是不同的。 条件通过从已知基因型个体的细胞系中提取，NIEHS 彗星GxE研究旨在确定与以下相关的单核苷酸多态性（SNP）： 基线DNA损伤、遗传毒性暴露敏感性和修复率。的 在这样的研究中，个体的表型是对应于 在不同条件下对细胞特异性DNA的损伤。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。检测GxE相互作用的能力是非常重要的 健康的重要性，使医生能够更好地识别更敏感的患者 与药物治疗或环境暴露有关靶向分子流行病学研究 为传统的流行病学设计提供了一种有效的替代方案。 我们的目标包括以下内容。 1.开发非参数贝叶斯统计方法，允许分布特征 在个体之间和预测因素之间灵活变化，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SNP 与基线DNA损伤、易感性和修复率相关。 3.制定包括每个SNP外部信息的方法，包括 无论它是在编码区，是同义的，是非同义的，但在一个 氨基酸变化可能具有破坏性的位置，或者位于内含子中， 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，同时鼓励分布轮廓的稀疏建模。

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.