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（由申请方提供）：在当前暴露评估模型中，导致对外源性生物质暴露的全身反应存在潜在显著差异的个体遗传变异未被视为结局的预测因素。我们建议开发一种方法和统计工具来调查和识别个体遗传变异影响 生物标志物水平，作为定量的中间生物表型，在两个小的良好表征的工人群体的汽车喷漆暴露于1，6-二异氰酸酯（HDI）。为了实现这一目标，我们将利用一种创新的统计暴露评估建模方法，该方法包含遗传成分，以确定个体遗传变异沿着其他个人和工作场所预测因子对暴露工人尿液和血液中观察到的生物标志物水平的相应相对贡献。首先，我们将使用单核苷酸多态性（SNP）和拷贝数变异（CNV）的密集阵列对所有工人全基因组进行基因分型。数据处理后，我们将识别与暴露生物标志物和内部剂量水平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显著遗传变异标记对观察到的生物标志物水平的生物相关性和影响。在职业和环境暴露评估研究中，对环境（外在）、个体遗传变异（内在）和生物学结果（表型）之间的潜在预测相互作用的调查可能提供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接触生物标志物和/或早期生物效应分类的不确定性，从而改进职业流行病学研究中的接触分类，并大大有助于加深对接触剂量-效应关系的理解。该研究项目将有利于NIOSH诺拉和研究实践（r2 p）的倡议，提供关键的新信息的因素影响人类发展指数生物标志物水平的个体差异，并评估战略，以实施未来的干预措施。 公共卫生相关性：个体遗传差异可能在毒物代谢中起重要作用，并解释了观察到的外源性物质暴露的尿液和血液生物标志物水平的变异性。我们将开发暴露评估方法，以研究个体遗传多态性作为生物标志物水平变化的标志物和潜在修饰剂的作用。在这个项目中获得的结果将增加我们的知识和数据，可用于职业和流行病学研究中的基因-环境相互作用的调查，并导致更多的预测性暴露评估模型，反过来，这将使我们能够制定更好的工人保护策略。