Evidence map›Paper›PMID 31318318›Full record

ReviewEpigenetics

Methodological challenges in constructing DNA methylation risk scores.

Anke Hüls, Darina Czamara

Abstract readReview
In one paragraph

Review in Epigenetics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
56citing 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

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

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Pooled it
  7. Pooled it
  8. Pooled it
  9. Article
  10. Article
  11. Article
  12. Epigenetics of Childhood Obesity.Hormone research in paediatrics · 2026
    Review
  13. Article
  14. Review
  15. Review
  16. Article
  17. Article
  18. Article
  19. Article
  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

2 authors.

Anke HülsDepartment of Human Genetics, Emory University, Atlanta, GA, USA.ORCID 0000-0002-6005-417X
Darina CzamaraDepartment of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany.ORCID 0000-0001-7381-904X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Polygenic approaches often access more variance of complex traits than is possible by single variant approaches. For genotype data, genetic risk scores (GRS) are widely used for risk prediction as well as in association and interaction studies. Recently, interest has been growing in transferring GRS approaches to DNA methylation data (methylation risk scores, MRS), which can be used 1) as biomarkers for environmental exposures, 2) in association analyses in which single CpG sites do not achieve significance, 3) as dimension reduction approach in interaction and mediation analyses, and 4) to predict individual risks of disease or treatment success. Most GRS approaches can directly be transferred to methylation data. However, since methylation data is more sensitive to confounding, e.g. by age and tissue, it is more complex to find appropriate external weights. In this review, we will outline the adaption of current GRS approaches to methylation data and highlight occurring challenges.

Indexed as

DNA MethylationGenetic Predisposition to DiseaseEpigenomicsGenetic TestingHumansMultifactorial Inheritanceepigenetic risk scoregenetic risk scoresPolygenic epidemiologypolygenic risk scoresprediction modelsweighting strategies

Identifiers

PMID31318318
PMCPMC6961658

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.