Evidence map›Paper›PMID 33402197›Full record

ArticleGenome biology2021

Equivalent DNA methylation variation between monozygotic co-twins and unrelated individuals reveals universal epigenetic inter-individual dissimilarity.

Benjamin Planterose Jiménez, Fan Liu, Amke Caliebe, Diego Montiel González, Jordana T Bell, Manfred Kayser, Athina Vidaki

Open access · goldAbstract read
In one paragraph

Article in Genome biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
2.9field-weighted citation impact, top 8% 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

24 citing papers in PubMed, 43 citations in OpenAlex.

  1. Article
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  4. Forensic genetics in the omics era.Nature reviews. Genetics · 2026
    Review
  5. Emerging opportunities for DNA methylation biomarkers in cattle improvement.The Journal of reproduction and development · 2026
    Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Epigenetic analyses in forensic medicine: future and challenges.International journal of legal medicine · 2024
    Review
  11. Review
  12. Article
  13. Article
  14. Article
  15. DNA and protein analyses of hair in forensic genetics.International journal of legal medicine · 2023
    Review
  16. Article
  17. Article
  18. Article
  19. Making sense of the ageing methylome.Nature reviews. Genetics · 2022
    Review
  20. 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

7 authors at 4 institutions in 4 countries.

Benjamin Planterose JiménezDepartment of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands.
Fan LiuDepartment of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands.
Amke CaliebeInstitute of Medical Informatics and Statistics, Kiel University, Kiel, Germany.
Diego Montiel GonzálezDepartment of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands.
Jordana T BellDepartment of Twin Research and Genetic Epidemiology, King's College London, London, UK.
Manfred KayserDepartment of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands.
Athina VidakiDepartment of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands. a.vidaki@erasmusmc.nl.ORCID 0000-0002-5470-245X
Erasmus MC · NLChinese Academy of Sciences · CNChristian-Albrechts-Universität zu Kiel · DEKing's College London · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlthough the genomes of monozygotic twins are practically identical, their methylomes may evolve divergently throughout their lifetime as a consequence of factors such as the environment or aging. Particularly for young and healthy monozygotic twins, DNA methylation divergence, if any, may be restricted to stochastic processes occurring post-twinning during embryonic development and early life. However, to what extent such stochastic mechanisms can systematically provide a stable source of inter-individual epigenetic variation remains uncertain until now.

resultsWe enriched for inter-individual stochastic variation by using an equivalence testing-based statistical approach on whole blood methylation microarray data from healthy adolescent monozygotic twins. As a result, we identified 333 CpGs displaying similarly large methylation variation between monozygotic co-twins and unrelated individuals. Although their methylation variation surpasses measurement error and is stable in a short timescale, susceptibility to aging is apparent in the long term. Additionally, 46% of these CpGs were replicated in adipose tissue. The identified sites are significantly enriched at the clustered protocadherin loci, known for stochastic methylation in developing neurons. We also confirmed an enrichment in monozygotic twin DNA methylation discordance at these loci in whole genome bisulfite sequencing data from blood and adipose tissue.

conclusionsWe have isolated a component of stochastic methylation variation, distinct from genetic influence, measurement error, and epigenetic drift. Biomarkers enriched in this component may serve in the future as the basis for universal epigenetic fingerprinting, relevant for instance in the discrimination of monozygotic twin individuals in forensic applications, currently impossible with standard DNA profiling.

Indexed as

DNA MethylationEpigenesis, GeneticAdolescentAdultAgedAged, 80 and overChildCpG IslandsFemaleGenome, HumanHumansMaleMiddle AgedTwins, MonozygoticYoung AdultClustered protocadherinsDNA methylationEpigenetic driftEpigeneticsInter-individual variationMetastable epiallelesMonozygotic twin discordanceMonozygotic twins

Identifiers

PMID33402197
PMCPMC7786996
OpenAlexW3120885871

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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.