Abstract
The 450k Chip Analysis Methylation Pipeline (ChAMP) is a novel Illumina Infinium HumanMethylation450 BeadChip data processing algorithm that allows the analysis of copy number alterations (CNAs). With this pipeline we evaluated the prevalence of CNAs in peripheral blood leukocyte samples from healthy Argentinean Andean women with varying exposure to inorganic arsenic in drinking water. Arsenic exposure was assessed based on the sum concentrations of metabolites of inorganic arsenic in urine (U-As), which ranged from 10-663 μg L(-1) with a median of 185 μg L(-1). We used linear regression analysis to elucidate the association between U-As and the prevalence of CNAs. We found that increasing arsenic exposure was positively associated with the frequency of CNAs (p = 0.002), possibly in a dose-response relationship. Adjustment of the regression model for age and BMI of the subjects did not significantly change the effect estimate, although both covariates were significant predictors. Our results suggest that exposure to arsenic increases genomic instability in the form of CNAs.
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