Bayesian Methods for Assessing Gene by Environment Interactions

描述（由申请人提供）：我们建议开发新的统计方法，用于研究基因x环境（GxE）的相互作用，使用分子流行病学研究的数据。重点是有针对性的研究，使用单细胞凝胶电泳来测量DNA损伤。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如何在实验条件之间变化。通过从已知基因型个体的细胞系中提取，NIEHS Comet GxE研究旨在确定与基线DNA损伤、遗传毒性暴露易感性和修复率相关的单核苷酸多态性（SNP）。在这些研究中，个体的表型是在不同条件下对应于细胞特异性DNA损伤的分布的集合。需要新的方法来有效地分析这种分布概况，同时允许受试者之间的异质性和SNP选择。检测GxE相互作用的能力具有重要的公共卫生意义，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。靶向分子流行病学研究为传统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和预测因子之间灵活变化，同时允许变量选择。2.将这些方法应用于NIEHS彗星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.开发包括每个SNP的外部信息的方法，包括它是否在编码区，是否是同义的，是否是非同义的，但在氨基酸变化可能是破坏性的位置，或是否在内含子或侧翼序列，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，同时鼓励稀疏建模的分布配置文件。 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易感性与各种生活方式因素（包括饮食和环境暴露）之间的相互作用。识别基因-环境相互作用是更好地了解疾病病因的关键步骤，同时也是制定更有效的个性化预防和治疗策略的关键步骤。我们提供了必要的统计工具，有效地检测基因与环境的相互作用，利用创新的新分子流行病学设计的数据。

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.