Evidence map›Paper›PMID 26580635›Full record

ArticleInternational journal of environmental research and public health2015

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, Kate Tilling

Open access · goldAbstract read
In one paragraph

Article in International journal of environmental research and public health, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.6field-weighted citation impact, top 31% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
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  7. Review
  8. Early Life Exposures and Adult Cancer Risk.Epidemiologic reviews · 2017
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Frank de VochtSchool of Social and Community Medicine, University of Bristol, Bristol BS8 2PS, UK. frank.devocht@bristol.ac.uk.
Andrew J SimpkinSchool of Social and Community Medicine, University of Bristol, Bristol BS8 2PS, UK. Andrew.Simpkin@bristol.ac.uk.
Rebecca C RichmondSchool of Social and Community Medicine, University of Bristol, Bristol BS8 2PS, UK. Rebecca.Richmond@bristol.ac.uk.
Caroline ReltonSchool of Social and Community Medicine, University of Bristol, Bristol BS8 2PS, UK. Caroline.Relton@bristol.ac.uk.
Kate TillingSchool of Social and Community Medicine, University of Bristol, Bristol BS8 2PS, UK. Kate.Tilling@bristol.ac.uk.
University of Bristol · GB

Funding

Biotechnology and Biological Sciences Research Council BB/I025263/1Biotechnology and Biological Sciences Research Council BBI025751/1Cancer Research UK A19169Medical Research Council MC_PC_15018Medical Research Council MC_UU_12013/2Medical Research Council MC_UU_12013/8Medical Research Council MR/K023233/1Wellcome Trust 102215Wellcome Trust 102215/2/13/1
6 · The paper itself

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.

Indexed as

CpG IslandsDNA MethylationEpigenesis, GeneticMaternal BehaviorAdolescentBayes TheoremChildFemaleHumansInfant, NewbornLongitudinal StudiesMaleModels, StatisticalPregnancyPrenatal Exposure Delayed EffectsSmokingALSPACBayesianDNA methylationepigeneticslongitudinal datamixture modellingpregnancysmoking

Identifiers

PMID26580635
PMCPMC4661660
OpenAlexW2098481057

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.