Evidence map›Paper›PMID 27052998›Full record

ReviewEpigenomics2016

How to make DNA methylome wide association studies more powerful.

Xinyi Lin, Sheila Barton, Joanna D Holbrook

Open access · hybridAbstract readReview
In one paragraph

Review in Epigenomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
3.0field-weighted citation impact, top 9% 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

16 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Emerging opportunities for DNA methylation biomarkers in cattle improvement.The Journal of reproduction and development · 2026
    Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. RNA biomarkers for alcohol use disorder.Frontiers in molecular neuroscience · 2022
    Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. 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

3 authors at 3 institutions in 2 countries.

Xinyi LinSingapore Institute for Clinical Sciences (SICS), Agency for Science & Technology Research (A*STAR), Brenner Centre for Molecular Medicine, 30 Medical Drive, 117609, Singapore.
Sheila BartonMRC Lifecourse Epidemiology Unit, Faculty of Medicine, University of Southampton, Southampton, SO16 6YD, UK.
Joanna D HolbrookSingapore Institute for Clinical Sciences (SICS), Agency for Science & Technology Research (A*STAR), Brenner Centre for Molecular Medicine, 30 Medical Drive, 117609, Singapore.
Agency for Science, Technology and Research · SGMRC Lifecourse Epidemiology Unit · GBSingapore Institute for Clinical Sciences · SG

Funding

STUDIES OF EPSTEIN-BARR VIRUSR37CA012055 · NCI · YALE UNIVERSITY · PI MILLER, I. GEORGE · 1989 to 2012
$4.4M
STUDIES OF EPSTEIN-BARR VIRUSR01CA012055 · NCI · YALE UNIVERSITY · PI MILLER, I. GEORGE · 1985 to 2002
$737k
Arthritis Research UKBritish Heart Foundation RG/07/009Medical Research Council G0400491Medical Research Council MC_ST_U12055Medical Research Council MC_U147585819Medical Research Council MC_U147585824Medical Research Council MC_U147585827Medical Research Council MC_UP_A620_1014Medical Research Council MC_UU_12011/1
6 · The paper itself

Abstract

Genome-wide association studies had a troublesome adolescence, while researchers increased statistical power, in part by increasing subject numbers. Interrogating the interaction of genetic and environmental influences raised new challenges of statistical power, which were not easily bested by the addition of subjects. Screening the DNA methylome offers an attractive alternative as methylation can be thought of as a proxy for the combined influences of genetics and environment. There are statistical challenges unique to DNA methylome data and also multiple features, which can be exploited to increase power. We anticipate the development of DNA methylome association study designs and new analytical methods, together with integration of data from other molecular species and other studies, which will boost statistical power and tackle causality. In this way, the molecular trajectories that underlie disease development will be uncovered.

Indexed as

DNA MethylationAnimalsEpigenomicsGene-Environment InteractionGenome-Wide Association StudyHumansdevelopmental trajectoriesDNA methylationDNA methylation association studies (methWAS)epigenetic epidemiologyepigenome-wide association study (EWAS)gene × environment (GxE)

Identifiers

PMID27052998
PMCPMC5066141
OpenAlexW2326558177

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.