Influence of Genetic Variance on Occupational Exposure Assessment for 1,6- Hexamethylene Diisocyanate

摘要

Significant differences in systemic response to xenobiotic exposure result from inter-individual genetic variation, but this variation is not included as a predictor of outcome in current exposure assessment models. We developed an approach to investigate and identify individual differences in genetic variation that influence biomarkers of exposure levels. Urine biomarker 1,6-hexamethylene diamine (HDA) was measured as a quantitative biological phenotype in a well-characterized population of automotive spray painters exposed to 1,6-hexamethylene diisocyanate (HDI). Exposure measurements were conducted over the course of an entire workday for up to three separate workdays that were spaced approximately one month apart. Inhalation exposure was measured using personal breathing-zone sampling while skin exposure was measured using tape-strip sampling. Urine samples were collected throughout the workday. Our innovative statistical modeling approach contains whole-genome markers along with other exposure predictors to determine the contribution of individual genetic variants to the observed urine biomarker levels among the exposed workers. The workers (n=33) were genotyped using genome-wide Affymetrix 6.0 microarrays, which feature -1.8 million genetic markers. PLINK was used for the candidate-gene and genome-wide association analysis, and the regression model that captured the most significant SNPs included population substructure, current smoking status, HDI exposure in the worker's breathing zone, and HDI skin exposure level as covariates. We identified 26 significant genome-wide variants that were associated with the urine HDA levels. Associations were adjusted for multiple comparisons at a false discovery rate (FDR) p<0.05. We also performed a candidate gene analysis using 19 genes encompassing 296 genetic markers associated with HDI exposure and occupational asthma selected from published literature. No candidate genes were significant at a FDR<0.05. The genetic contribution to variation observed in urine biomarker levels was determined using linear mixed-effects models (LAAM) that accounted for personal HDI exposure, individual exposure determinants, and whole-genome polymorphic markers. The biological relevance of significant variants was determined through predictive network analysis using GeneMania, Ingenuity Pathway Analysis, and Metacore. The most significant genetic marker, rs2697962 (p=9.0E-09), was also highly significant in the LAAM (p<0.0001). This marker is located in the 3'-UTR region of PRDAA2, which encodes a zinc finger protein that is a member of a nuclear histone/protein methyltransferase family. We found significant associations between urine HDA levels and genetic markers in this worker population. The results indicate that we can incorporate genetic markers with other exposure covariates in predictive exposure assessment models to better identify individual differences in biomarker levels.