Evidence map›Paper›PMID 31088362›Full record

ArticleBMC genomics2019

Guidance for DNA methylation studies: statistical insights from the Illumina EPIC array.

Georgina Mansell, Tyler J Gorrie-Stone, Yanchun Bao, Meena Kumari, Leonard S Schalkwyk, Jonathan Mill, Eilis Hannon

Registry-linked trialOpen access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05779904 (DNA Methylation, Microbiome Profile, Nutritional Status, and Developmental Outcomes of Stunted and Non-stunted Children Growing up During Covid-19 Pandemic), which is not on this map. Cited by 221 papers, 18 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
221citing papers in PubMed, 18 pooled it
15.5field-weighted citation impact, top 1% 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.

NCT05779904 unknown statusnot on this mapstarted 2023, after this paper: background citation

DNA Methylation, Microbiome Profile, Nutritional Status, and Developmental Outcomes of Stunted and Non-stunted Children Growing up During Covid-19 Pandemic: a Case-control Study

TypeobservationalSponsorSEAMEO Regional Centre for Food and NutritionRan2023 to 2024Enrolled300ConditionsStunting, Development, Child, DNA Methylation, Epigenetics
3 · Its place in the literature

Who cites it

221 citing papers in PubMed, 18 syntheses or guidelines pooled it, 383 citations in OpenAlex.

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161 more citing papers are in PubMed but not listed here.

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

7 authors at 2 institutions in 1 country.

Georgina MansellUniversity of Exeter Medical School, University of Exeter, RD&E Hospital, Barrack Road, Exeter, Devon, EX2 5DW, UK.
Tyler J Gorrie-StoneSchool of Biological Sciences, University of Essex, Colchester, Essex, CO4 3SQ, UK.
Yanchun BaoInstitute for Social and Economic Research, University of Essex, Colchester, Essex, CO3 3LG, UK.
Meena KumariInstitute for Social and Economic Research, University of Essex, Colchester, Essex, CO3 3LG, UK.
Leonard S SchalkwykSchool of Biological Sciences, University of Essex, Colchester, Essex, CO4 3SQ, UK.
Jonathan MillUniversity of Exeter Medical School, University of Exeter, RD&E Hospital, Barrack Road, Exeter, Devon, EX2 5DW, UK.
Eilis HannonUniversity of Exeter Medical School, University of Exeter, RD&E Hospital, Barrack Road, Exeter, Devon, EX2 5DW, UK. e.j.hannon@exeter.ac.uk.ORCID http://orcid.org/0000-0001-6840-072X
University of Essex · GBUniversity of Exeter · GB

Funding

Economic and Social Research Council ES/K005146/1Economic and Social Research Council ES/N00812X/1Medical Research Council G1001799Medical Research Council K013807Medical Research Council MR/N01104X/1Medical Research Council MR/N01104X/2
6 · The paper itself

Abstract

backgroundThere has been a steady increase in the number of studies aiming to identify DNA methylation differences associated with complex phenotypes. Many of the challenges of epigenetic epidemiology regarding study design and interpretation have been discussed in detail, however there are analytical concerns that are outstanding and require further exploration. In this study we seek to address three analytical issues. First, we quantify the multiple testing burden and propose a standard statistical significance threshold for identifying DNA methylation sites that are associated with an outcome. Second, we establish whether linear regression, the chosen statistical tool for the majority of studies, is appropriate and whether it is biased by the underlying distribution of DNA methylation data. Finally, we assess the sample size required for adequately powered DNA methylation association studies.

resultsWe quantified DNA methylation in the Understanding Society cohort (n = 1175), a large population based study, using the Illumina EPIC array to assess the statistical properties of DNA methylation association analyses. By simulating null DNA methylation studies, we generated the distribution of p-values expected by chance and calculated the 5% family-wise error for EPIC array studies to be 9 × 10

conclusionWe propose that a significance threshold of P < 9 × 10

Indexed as

DNA MethylationCpG IslandsEpigenesis, GeneticEpigenomicsGenome-Wide Association StudyHumansLinear ModelsOligonucleotide Array Sequence AnalysisDNA methylationEpigenome-wide association study (EWAS)Illumina EPIC arrayMultiple testingPower

Identifiers

PMID31088362
PMCPMC6518823
OpenAlexW2944944066

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.