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

Frank De Vocht, Andrew J Simpkin, Rebecca C. Richmond, Caroline Relton and Kate Tilling
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Frank De Vocht: School of Social and Community Medicine, University of Bristol, Bristol BS8 2PS, UK
Andrew J Simpkin: School of Social and Community Medicine, University of Bristol, Bristol BS8 2PS, UK
Rebecca C. Richmond: School of Social and Community Medicine, University of Bristol, Bristol BS8 2PS, UK
Caroline Relton: School of Social and Community Medicine, University of Bristol, Bristol BS8 2PS, UK
Kate Tilling: School of Social and Community Medicine, University of Bristol, Bristol BS8 2PS, UK

IJERPH, 2015, vol. 12, issue 11, 1-16

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.

Keywords: ALSPAC; Bayesian; DNA methylation; epigenetics; longitudinal data; mixture modelling; pregnancy; smoking (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2015
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