Evidence map›Paper›PMID 30326963›Full record

ArticleClinical epigenetics2018

Systematic evaluation of DNA methylation age estimation with common preprocessing methods and the Infinium MethylationEPIC BeadChip array.

Lisa M McEwen, Meaghan J Jones, David Tse Shen Lin, Rachel D Edgar, Lucas T Husquin, Julia L MacIsaac, Katia E Ramadori, Alexander M Morin, Christopher F Rider, Chris Carlsten and 3 more

Open access · goldAbstract read
In one paragraph

Article in Clinical epigenetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 96 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
96citing papers in PubMed, 3 pooled it
5.7field-weighted citation impact, top 3% 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

96 citing papers in PubMed, 3 syntheses or guidelines pooled it, 167 citations in OpenAlex.

  1. Pooled it
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  4. Functional, molecular, and digital measurements of biological age.The Journal of clinical investigation · 2026
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36 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

13 authors at 3 institutions in 3 countries.

Lisa M McEwenBC Children's Hospital Research Institute, Department of Medical Genetics, University of British Columbia, 950 West 28th Avenue, TRB A5-151, Vancouver, BC, V5Z 4H4, Canada. lmcewen@bcchr.ca.ORCID 0000-0002-0500-9173
Meaghan J JonesBC Children's Hospital Research Institute, Department of Medical Genetics, University of British Columbia, 950 West 28th Avenue, TRB A5-151, Vancouver, BC, V5Z 4H4, Canada.
David Tse Shen LinBC Children's Hospital Research Institute, Department of Medical Genetics, University of British Columbia, 950 West 28th Avenue, TRB A5-151, Vancouver, BC, V5Z 4H4, Canada.
Rachel D EdgarBC Children's Hospital Research Institute, Department of Medical Genetics, University of British Columbia, 950 West 28th Avenue, TRB A5-151, Vancouver, BC, V5Z 4H4, Canada.
Lucas T HusquinUnit of Human Evolutionary Genetics, Institut Pasteur, 75015, Paris, France.
Julia L MacIsaacBC Children's Hospital Research Institute, Department of Medical Genetics, University of British Columbia, 950 West 28th Avenue, TRB A5-151, Vancouver, BC, V5Z 4H4, Canada.
Katia E RamadoriBC Children's Hospital Research Institute, Department of Medical Genetics, University of British Columbia, 950 West 28th Avenue, TRB A5-151, Vancouver, BC, V5Z 4H4, Canada.
Alexander M MorinBC Children's Hospital Research Institute, Department of Medical Genetics, University of British Columbia, 950 West 28th Avenue, TRB A5-151, Vancouver, BC, V5Z 4H4, Canada.
Christopher F RiderDepartment of Medicine, Division of Respiratory Medicine, University of British Columbia, Vancouver, BC, Canada.
Chris CarlstenDepartment of Medicine, Division of Respiratory Medicine, University of British Columbia, Vancouver, BC, Canada.
Lluís Quintana-MurciUnit of Human Evolutionary Genetics, Institut Pasteur, 75015, Paris, France.
Steve HorvathDepartment of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
Michael S KoborBC Children's Hospital Research Institute, Department of Medical Genetics, University of British Columbia, 950 West 28th Avenue, TRB A5-151, Vancouver, BC, V5Z 4H4, Canada.
University of British Columbia · CACentre National de la Recherche Scientifique · FRUniversity of California, Los Angeles · US

Funding

Validation and optimization of epigenetic clocksU01AG060908 · NIA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI RITZ, BEATE R. · 2018 to 2022
$3.4M
The epiGenetIcs Leads to aGe-relAted diseases (GILGA-mesh) NetworkU34AG051425 · NIA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI HORVATH, STEVE · 2015 to 2017
$954k
CIHR F15-04283NIA NIH HHS U01 AG060908NIA NIH HHS U34 AG051425
6 · The paper itself

Abstract

backgroundThe capacity of technologies measuring DNA methylation (DNAm) is rapidly evolving, as are the options for applicable bioinformatics methods. The most commonly used DNAm microarray, the Illumina Infinium HumanMethylation450 (450K array), has recently been replaced by the Illumina Infinium HumanMethylationEPIC (EPIC array), nearly doubling the number of targeted CpG sites. Given that a subset of 450K CpG sites is absent on the EPIC array and that several tools for both data normalization and analyses were developed on the 450K array, it is important to assess their utility when applied to EPIC array data. One of the most commonly used 450K tools is the pan-tissue epigenetic clock, a multivariate predictor of biological age based on DNAm at 353 CpG sites. Of these CpGs, 19 are missing from the EPIC array, thus raising the question of whether EPIC data can be used to accurately estimate DNAm age. We also investigated a 71-CpG epigenetic age predictor, referred to as the Hannum method, which lacks 6 probes on the EPIC array. To evaluate these epigenetic clocks in EPIC data properly, a prior assessment of the effects of data preprocessing methods on DNAm age is also required.

methodsDNAm was quantified, on both the 450K and EPIC platforms, from human primary monocytes derived from 172 individuals. We calculated DNAm age from raw, and three different preprocessed data forms to assess the effects of different processing methods on the DNAm age estimate. Using an additional cohort, we also investigated DNAm age of peripheral blood mononuclear cells, bronchoalveolar lavage, and bronchial brushing samples using the EPIC array.

resultsUsing monocyte-derived data from subjects on both the 450K and EPIC, we found that DNAm age was highly correlated across both raw and preprocessing methods (r > 0.91). Thus, the correlation between chronological age and the DNAm age estimate is largely unaffected by platform differences and normalization methods. However, we found that the choice of normalization method and measurement platform can lead to a systematic offset in the age estimate which in turn leads to an increase in the median error. Comparing the 450K and EPIC DNAm age estimates, we observed that the median absolute difference was 1.44-3.10 years across preprocessing methods.

conclusionsHere, we have provided evidence that the epigenetic clock is resistant to the lack of 19 CpG sites missing from the EPIC array as well as highlighted the importance of considering the technical variance of the epigenetic when interpreting group differences below the reported error. Furthermore, our study highlights the utility of epigenetic age acceleration measure, the residuals from a linear regression of DNAm age on chronological age, as the resulting values are robust with respect to normalization methods and measurement platforms.

Indexed as

DNA MethylationAdultAgingBronchoalveolar Lavage FluidCpG IslandsEpigenesis, GeneticEpigenomicsFemaleHumansLeukocytes, MononuclearMaleMiddle AgedOligonucleotide Array Sequence AnalysisYoung Adult450KDNA methylationDNA methylation ageEPICEpigenetic ageEpigenetic clockHumanMicroarrayPreprocessing

Identifiers

PMID30326963
PMCPMC6192219
OpenAlexW2897267849

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.