Evidence map›Paper›PMID 29518222›Full record

ArticleInternational journal of epidemiology2018

Genome-wide average DNA methylation is determined in utero.

Shuai Li, Ee Ming Wong, Pierre-Antoine Dugué, Allan F McRae, Eunae Kim, Ji-Hoon Eric Joo, Tuong L Nguyen, Jennifer Stone, Gillian S Dite, Nicola J Armstrong and 15 more

Open access · hybridAbstract read
In one paragraph

Article in International journal of epidemiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
2.3field-weighted citation impact, top 11% 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

15 citing papers in PubMed, 42 citations in OpenAlex.

  1. Article
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  9. DNA methylation differences at birth after conception through ART.Human reproduction (Oxford, England) · 2021
    Article
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  11. Article
  12. Article
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  14. Article
  15. Developmental Tuning of Epigenetic Clock.Frontiers in genetics · 2018
    Article
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

25 authors at 11 institutions in 3 countries.

Shuai LiCentre for Epidemiology and Biostatistics.
Ee Ming WongGenetic Epidemiology Laboratory, University of Melbourne, Parkville, VIC, Australia.
Pierre-Antoine DuguéCentre for Epidemiology and Biostatistics.
Allan F McRaeQueensland Brain Institute, University of Queensland, Brisbane, QLD, Australia.
Eunae KimComplex Disease and Genome Epidemiology Branch, Department of Public Health Science, School of Public Health, Seoul National University, Seoul, Republic of Korea.
Ji-Hoon Eric JooGenetic Epidemiology Laboratory, University of Melbourne, Parkville, VIC, Australia.
Tuong L NguyenCentre for Epidemiology and Biostatistics.
Jennifer StoneCentre for Genetic Origins of Health and Disease, Curtin University and the University of Western Australia, Perth, WA, Australia.
Gillian S DiteCentre for Epidemiology and Biostatistics.
Nicola J ArmstrongMathematics and Statistics, Murdoch University, Perth, WA, Australia.
Karen A MatherCentre for Healthy Brain Ageing (CHeBA), University of New South Wales, Sydney, NSW, Australia.
Anbupalam ThalamuthuCentre for Healthy Brain Ageing (CHeBA), University of New South Wales, Sydney, NSW, Australia.
Margaret J WrightQueensland Brain Institute, University of Queensland, Brisbane, QLD, Australia.
David AmesNational Ageing Research Institute and University of Melbourne Academic Unit for Psychiatry of Old Age, Parkville, VIC, Australia.
Roger L MilneCentre for Epidemiology and Biostatistics.
Jeffrey M CraigMurdoch Childrens Research Institute, Royal Children's Hospital, Parkville, VIC, Australia.
Richard SafferyMurdoch Childrens Research Institute, Royal Children's Hospital, Parkville, VIC, Australia.
Grant W MontgomeryInstitute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia.
Yun-Mi SongSamsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
Joohon SungComplex Disease and Genome Epidemiology Branch, Department of Public Health Science, School of Public Health, Seoul National University, Seoul, Republic of Korea.
Timothy D SpectorDepartment of Twin Research and Genetic Epidemiology, Kings College London, London, UK.
Perminder S SachdevCentre for Healthy Brain Ageing (CHeBA), University of New South Wales, Sydney, NSW, Australia.
Graham G GilesCentre for Epidemiology and Biostatistics.
Melissa C SoutheyGenetic Epidemiology Laboratory, University of Melbourne, Parkville, VIC, Australia.
John L HopperCentre for Epidemiology and Biostatistics.
University of Melbourne · AUCancer Council Victoria · AUSeoul National University · KRUniversity of Queensland · AUUNSW Sydney · AUCurtin University · AUKing's College London · GBMonash University · AUMurdoch Children's Research Institute · AUMurdoch University · AUSamsung Medical Center · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInvestigating the genetic and environmental causes of variation in genome-wide average DNA methylation (GWAM), a global methylation measure from the HumanMethylation450 array, might give a better understanding of genetic and environmental influences on methylation.

methodsWe measured GWAM for 2299 individuals aged 0 to 90 years from seven twin and/or family studies. We estimated familial correlations, modelled correlations with cohabitation history and fitted variance components models for GWAM.

resultsThe correlation in GWAM for twin pairs was ∼0.8 at birth, decreased with age during adolescence and was constant at ∼0.4 throughout adulthood, with no evidence that twin pair correlations differed by zygosity. Non-twin first-degree relatives were correlated, from 0.17 [95% confidence interval (CI): 0.05-0.30] to 0.28 (95% CI: 0.08-0.48), except for middle-aged siblings (0.01, 95% CI: -0.10-0.12), and the correlation increased with time living together and decreased with time living apart. Spouse pairs were correlated in all studies, from 0.23 (95% CI: 0.3-0.43) to 0.31 (95% CI: 0.05-0.52), and the correlation increased with time living together. The variance explained by environmental factors shared by twins alone was 90% (95% CI: 74-95%) at birth, decreased in early life and plateaued at 28% (95% CI: 17-39%) in middle age and beyond. There was a cohabitation-related environmental component of variance.

conclusionsGWAM is determined in utero by prenatal environmental factors, the effects of which persist throughout life. The variation of GWAM is also influenced by environmental factors shared by family members, as well as by individual-specific environmental factors.

Indexed as

DNA methylationEpigenomicstwin study

Identifiers

PMID29518222
PMCPMC6005037
OpenAlexW2799312293

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

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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.