eLifeGenetic & Genomic MedicinePeer-reviewed journal article
Maternal smoking DNA methylation risk score associated with health outcomes in offspring of European and South Asian ancestry
Wei Q. DengiD, Nathan Cawte, Natalie CampbelliD, Sandi M. AzabiD, Russell J. de SouzaiD, Amel LamriiD, Katherine Mary MorrisoniD, Stephanie A. AtkinsoniD, Padmaja SubbaraoiD, Stuart E. TurveyiD, Theo J. MoraesiD, Koon Kang TeoiD, Piush J. MandhaneiD, Meghan B. AzadiD, Elinor SimonsiD, Guillaume ParéiD, Sonia Savitri AnandiD
- Citations
- 5
- FWCI
- 0.68
- Field percentile
- top 36%
Abstract
Maternal smoking has been linked to adverse health outcomes in newborns but the extent to which it impacts newborn health has not been quantified through an aggregated cord blood DNA methylation (DNAm) score. Here we examine the feasibility of using cord blood DNAm scores leveraging large external studies as discovery samples to capture the epigenetic signature of maternal smoking and its influence on newborns in White European and South Asian populations. We first examined association between individual CpGs and cigarette smoking during pregnancy, smoking exposure in two White European birth cohorts (n = 744). Several previously reported genes for maternal smoking were supported, with the strongest and most consistent signal from the GFI1 gene (6 CpGs with p 0.05) in South Asians. The same score was consistently associated with a smaller birth size (-0.37{+/-}0.12 cm, p=0.0023) in the South Asian cohort and a lower birth weight (-0.043{+/-}0.013kg, p=0.0011) in the combined cohorts. This cord blood epigenetic score can help identify babies exposed to maternal smoking and assess its long-term impact on growth. Notably, these results indicate a consistent association between the DNAm signature of maternal smoking and a small body size and low birthweight in newborns, in both white European mothers who exhibited some amount of smoking and in South Asian mothers who themselves were not active smokers.
- organism: human
- assay: DNA methylation
- research focus: differential expression
- approach: statistical model
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