Exposure to polybrominated biphenyl and stochastic epigenetic mutations: application of a novel epigenetic approach to environmental exposure in the Michigan polybrominated biphenyl registry

Abstract

Endocrine-disrupting compounds are associated with altered epigenetic regulation and adverse health outcomes, although inconsistent results suggest that people have varied responses to the same exposure. Interpersonal variation in response to environmental exposures is not identified using standard, population-based methods. However, methods that capture an individual's response, such as analyzing stochastic epigenetic mutations (SEMs), may capture currently missed effects of environmental exposure. To test whether polybrominated biphenyl (PBB) was associated with SEMs, DNA methylation was measured using Illumina's MethylationEPIC array in PBB-exposed individuals, and SEMs were identified. Association was tested using a linear regression with robust sandwich variance estimators, controlling for age, sex, lipids, and cell types. The number of SEMs was variable (range: 119-18,309), and positively associated with age (p = 1.23e-17), but not with sex (p = 0.97). PBBs and SEMs were only positively associated in people who were older when they were exposed (p = 0.02 vs. p = 0.91). Many subjects had SEMs enriched in biological pathways, particularly in pathways involved with xenobiotic metabolism and endocrine function. Higher number of SEMs was also associated with higher age acceleration (intrinsic: p = 1.70e-3; extrinsic: p = 3.59e-11), indicating that SEMs may be associated with age-related health problems. Finding an association between environmental contaminants and higher SEMs may provide insight into individual differences in response to environmental contaminants, as well as into the biological mechanism behind SEM formation. Furthermore, these results suggest that people may be particularly vulnerable to epigenetic dysregulation from environmental exposures as they age.

DNA methylation outlier burden, health, and ageing in Generation Scotland and the Lothian Birth Cohorts of 1921 and 1936

“…DNA methylation outlier burden measures were strongly associated with cell proportions and other technical factors, again, confirming previous findings [13,14]. Moreover, in this study, batch effects were confounded by wave of data collection in the LBC1921 and LBC1936.…”

“…DNA methylation outlier burden was highly positively skewed and consequently a log 10 -transformed version of outlier burden used in all analyses. This is consistent with previous findings [13,14]. When looking at outlier burden per CpG site rather than per individual, Wang and colleagues [13] reported that at 64% of CpG sites, at least one out of 385 individuals in SATSA had an outlier.…”

“…The authors defined outliers as methylation levels of greater than 3 times the inter-quartile range (IQR) from the 25th and 75th percentiles compared to the rest of the population. More recently, two further studies replicated this finding using data from 658 individuals of a wide age range (mean = 54.3 years, SD = 12.7 years) and longitudinal data from 375 older individuals (48 to 98 years), respectively [13,14]. Both studies reported individual outlier burden to be highly variable (range 119-18,308 out of 769,042 CpGs [14]; range 58-26,291 out of 370, 234 CpGs [13]) and non-normally distributed.…”

“…More recently, two further studies replicated this finding using data from 658 individuals of a wide age range (mean = 54.3 years, SD = 12.7 years) and longitudinal data from 375 older individuals (48 to 98 years), respectively [13,14]. Both studies reported individual outlier burden to be highly variable (range 119-18,308 out of 769,042 CpGs [14]; range 58-26,291 out of 370, 234 CpGs [13]) and non-normally distributed. Following log-transformation, these studies found outlier burden to be higher in older individuals (z = 8.54; p = 1.2 × 10 −17 [14]; β = 8.3 × 10 −3 , p = 1.2 × 10 −13 [13]).…”

“…Both studies reported individual outlier burden to be highly variable (range 119-18,308 out of 769,042 CpGs [14]; range 58-26,291 out of 370, 234 CpGs [13]) and non-normally distributed. Following log-transformation, these studies found outlier burden to be higher in older individuals (z = 8.54; p = 1.2 × 10 −17 [14]; β = 8.3 × 10 −3 , p = 1.2 × 10 −13 [13]). In addition, white blood cell proportions were found to be significantly associated with outlier burden in both studies.…”