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.
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.