Assessment of Offspring DNA Methylation across the Lifecourse Associated with Prenatal Maternal Smoking Using Bayesian Mixture Modelling.

Frank De Vocht,Caroline Relton,Andrew Simpkin,Rebecca Richmond,Kate Tilling

doi:10.3390/ijerph121114461

Abstract

A growing body of research has implicated DNA methylation as a potential mediator of the effects of maternal smoking in pregnancy on offspring ill-health. Data were available from a UK birth cohort of children with DNA methylation measured at birth, age 7 and 17. One issue when analysing genome-wide DNA methylation data is the correlation of methylation levels between CpG sites, though this can be crudely bypassed using a data reduction method. In this manuscript we investigate the effect of sustained maternal smoking in pregnancy on longitudinal DNA methylation in their offspring using a Bayesian hierarchical mixture model. This model avoids the data reduction used in previous analyses. Four of the 28 previously identified, smoking related CpG sites were shown to have offspring methylation related to maternal smoking using this method, replicating findings in well-known smoking related genes MYO1G and GFI1. Further weak associations were found at the AHRR and CYP1A1 loci. In conclusion, we have demonstrated the utility of the Bayesian mixture model method for investigation of longitudinal DNA methylation data and this method should be considered for use in whole genome applications.

Highlights

Epigenetics, the study of genome modifications not involving changes in the nucleotide sequence, offers the potential to identify molecular mechanisms by which environmental and lifestyle exposures may affect health [1,2]
Cigarette smoke is known to be associated with DNA methylation [13,14,15], and since higher DNA methylation levels in the foetus have been demonstrated in the genes involved in developmental processes [16,17] associated with maternal smoking during pregnancy, this seems to indicate a mediating role of epigenetic processes
We explore whether in the context of such highly correlated, cross-sectional, data that required two dimensionality reduction steps, a Bayesian mixture model (BMM) previously developed for the analyses of gene-environment interactions [32], and subsequently evaluated in the context of highly correlated environmental exposure mixtures [33], may be beneficial

Summary

Introduction

Epigenetics, the study of genome modifications not involving changes in the nucleotide sequence, offers the potential to identify molecular mechanisms by which environmental and lifestyle exposures may affect health [1,2]. Suboptimal growth has in turn been linked to increased risk of cardiovascular disease, diabetes mellitus type 2, dyslipidemia and end-stage renal disease in adulthood [7], which may adversely affect reproductive health of offspring [8], and may affect intelligence and cognitive development [9]. Despite these risks to newborns (as well as known risks of tobacco smoking for the mothers), in England about 12% of pregnant women are still smoking at the time of delivery [10], with similar prevalence reported in other western, high-income countries [11,12]. Cigarette smoke is known to be associated with DNA methylation [13,14,15], and since higher DNA methylation levels in the foetus have been demonstrated in the genes involved in developmental processes [16,17] associated with maternal smoking during pregnancy, this seems to indicate a mediating role of epigenetic processes

Methods

Results

Conclusion