Evidence map›Paper›PMID 29129922›Full record

ReviewNature reviews. Genetics2018

Statistical and integrative system-level analysis of DNA methylation data.

Andrew E Teschendorff, Caroline L Relton

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 174 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
174citing papers in PubMed, 8 pooled it
–field-weighted citation impact
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

174 citing papers in PubMed, 8 syntheses or guidelines pooled it.

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  8. Epigenome-wide meta-analysis of DNA methylation and childhood asthma.The Journal of allergy and clinical immunology · 2019
    Pooled it
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114 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

2 authors.

Andrew E TeschendorffDepartment of Women's Cancer, University College London, 74 Huntley Street, London WC1E 6AU, UK.
Caroline L ReltonMedical Research Council Integrative Epidemiology Unit (MRC IEU), School of Social & Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK.

Funding

Medical Research Council MC_UU_12013/2
6 · The paper itself

Abstract

Epigenetics plays a key role in cellular development and function. Alterations to the epigenome are thought to capture and mediate the effects of genetic and environmental risk factors on complex disease. Currently, DNA methylation is the only epigenetic mark that can be measured reliably and genome-wide in large numbers of samples. This Review discusses some of the key statistical challenges and algorithms associated with drawing inferences from DNA methylation data, including cell-type heterogeneity, feature selection, reverse causation and system-level analyses that require integration with other data types such as gene expression, genotype, transcription factor binding and other epigenetic information.

Indexed as

Databases, GeneticDNA MethylationEpigenesis, GeneticAnimalsEpigenomicsHumans

Identifiers

PMID29129922

What OpenQuestion holds

Textmetadata
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.