Influence of Genetic Markers on Exposure Assessment Models

遗传标记对暴露评估模型的影响

• 批准号: 8301325
• 负责人: LEENA A NYLANDER-FRENCH
• 金额: $ 20.59万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2014-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8301325
• 关键词: Influence; Genetic; Markers; Exposure; Assessment

DESCRIPTION (provided by applicant): Individual genetic variation that results in potential significant differences in systemic response to xenobiotic exposure is not accounted for as a predictor of outcome in current exposure assessment models. We propose to develop an approach and statistical tools to investigate and identify individual genetic variation influencing biomarker levels, as quantitative intermediate biological phenotypes, in two small well-characterized worker populations of automotive spray painters exposed to 1,6-hexamethylene diisocyanate (HDI). To accomplish this goal, we will utilize an innovative statistical exposure assessment modeling approach that contains a genetic component to determine the corresponding relative contribution of individual genetic variants along with other personal and workplace predictors to observed biomarker levels in urine and blood among exposed workers. First, we will genotype all workers genome-wide using dense arrays for single nucleotide polymorphisms (SNP) and copy number variants (CNV). Following data processing, we will identify significant genetic variants that are associated with biomarkers of exposure and internal dose levels. Finally, we will determine the biological relevance and impact of significant genetic variant markers on the observed biomarker levels through bioinformatic and network analysis. The investigation of the potential predicted interactions between environment (extrinsic), individual genetic variation (intrinsic), and the biological outcome (phenotype) in occupational and environmental exposure assessment studies may provide an effective approach to identify human gene-environment interactions. These tools have the potential to reduce uncertainty in biomarker of exposure and/or early biological effect classification and, thus, improve exposure classification in occupational epidemiology studies and significantly contribute to increased understanding of exposure dose-effects relationships. This research project will benefit NIOSH NORA and Research to Practice (r2p) initiatives by providing critical new information on factors affecting individual differences in HDI biomarker levels and to assess strategies to implement future interventions. PUBLIC HEALTH RELEVANCE: Individual genetic differences may play an important role in toxicokinetics and explain the variability in observed urine and blood biomarker levels of xenobiotics exposure. We will develop exposure assessment methodologies to investigate the role of individual genetic polymorphisms as markers and potential modifiers to variation observed in biomarker levels. The results obtained in this project will increase our knowledge and data available for investigation of gene-environment interactions in occupational and epidemiology studies and lead to more predictive exposure assessment models that, in turn, will allow us to develop better worker protection strategies.

描述（由申请人提供）：在当前暴露评估模型中，未作为预测结果的预测因素，导致对异生元暴露的全身反应有显着差异的个体遗传变异。我们建议开发一种方法和统计工具，以调查和识别影响的个体遗传变异 生物标志物水平作为定量中间生物学表型，在暴露于1,6-己二甲基二异氰酸酯（HDI）的两个小型的汽车喷雾剂绘画家中。为了实现这一目标，我们将利用一种创新的统计暴露评估模型方法，其中包含遗传成分，以确定个体遗传变异的相应相对贡献以及其他个人和工作场所预测因子对暴露工人中尿液和血液中观察到的生物标志物水平的其他个人和工作场所预测指标。首先，我们将使用密集的阵列用于单核苷酸多态性（SNP）和拷贝数变体（CNV）的所有工人基因组。在数据处理之后，我们将确定与暴露和内部剂量水平的生物标志物相关的重要遗传变异。最后，我们将通过生物信息学和网络分析确定重要的遗传变异标记物对观察到的生物标志物水平的生物学相关性和影响。在职业和环境暴露评估研究中，对环境（外部），个体遗传变异（内在）和生物学结果（表型）之间的潜在相互作用的研究可能提供了一种有效的方法来识别人类基因环境相互作用。这些工具有可能减少暴露和/或早期生物学效应分类的生物标志物的不确定性，从而改善职业流行病学研究中的暴露分类，并显着有助于增加对暴露剂量效应关系的了解。该研究项目将通过提供有关影响HDI生物标志物水平个体差异的因素并评估实施未来干预措施的策略，从而使NIOSH NORA和研究计划（R2P）倡议受益。 公共卫生相关性：个体遗传差异可能在有毒动物学中起重要作用，并解释观察到的尿液和血液生物标志物的变异性。我们将开发暴露评估方法，以研究单个遗传多态性作为标志物和潜在的修饰符的作用，以在生物标志物水平上观察到的变异。该项目中获得的结果将增加我们的知识和数据，可用于研究职业和流行病学研究中基因环境相互作用，并导致更具预测性的暴露评估模型，而这些模型又将使我们能够制定更好的工人保护策略。