Evidence map›Paper›PMID 34689793›Full record

ArticleBMC research notes2021

Repeatability of methylation measures using a QIAseq targeted methyl panel and comparison with the Illumina HumanMethylation450 assay.

Chenglong Yu, Pierre-Antoine Dugué, James G Dowty, Fleur Hammet, JiHoon E Joo, Ee Ming Wong, Mahnaz Hosseinpour, Graham G Giles, John L Hopper, Tu Nguyen-Dumont and 2 more

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In one paragraph

Article in BMC research notes, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. The Role of theBiomedicines · 2023
    Article
  4. 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

12 authors.

Chenglong YuPrecision Medicine, School of Clinical Sciences at Monash Health, Monash University, Victoria, Australia.
Pierre-Antoine DuguéPrecision Medicine, School of Clinical Sciences at Monash Health, Monash University, Victoria, Australia.
James G DowtyCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia.
Fleur HammetPrecision Medicine, School of Clinical Sciences at Monash Health, Monash University, Victoria, Australia.
JiHoon E JooDepartment of Clinical Pathology, The Melbourne Medical School, The University of Melbourne, Melbourne, VIC, Australia.
Ee Ming WongPrecision Medicine, School of Clinical Sciences at Monash Health, Monash University, Victoria, Australia.
Mahnaz HosseinpourPrecision Medicine, School of Clinical Sciences at Monash Health, Monash University, Victoria, Australia.
Graham G GilesPrecision Medicine, School of Clinical Sciences at Monash Health, Monash University, Victoria, Australia.
John L HopperCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia.
Tu Nguyen-DumontPrecision Medicine, School of Clinical Sciences at Monash Health, Monash University, Victoria, Australia.
Robert J MacInnisCancer Council Victoria, Cancer Epidemiology Division, Melbourne, Australia.
Melissa C SoutheyPrecision Medicine, School of Clinical Sciences at Monash Health, Monash University, Victoria, Australia. melissa.southey@monash.edu.ORCID http://orcid.org/0000-0002-6313-9005

Funding

Breast Cancer Family Registry CohortUM1CA164920 · NCI · CANCER PREVENTION INSTIT OF CALIFORNIA · PI ANDRULIS, IRENE, BUYS, SAUNDRA S · 2012 to 2017
$12.7M
NCI NIH HHS UM1 CA164920
6 · The paper itself

Abstract

objectiveIn previous studies using Illumina Infinium methylation arrays, we have identified DNA methylation marks associated with cancer predisposition and progression. In the present study, we have sought to find appropriate technology to both technically validate our data and expand our understanding of DNA methylation in these genomic regions. Here, we aimed to assess the repeatability of methylation measures made using QIAseq targeted methyl panel and to compare them with those obtained from the Illumina HumanMethylation450 (HM450K) assay. We included in the analysis high molecular weight DNA extracted from whole blood (WB) and DNA extracted from formalin-fixed paraffin-embedded tissues (FFPE).

resultsThe repeatability of QIAseq-methylation measures was assessed at 40 CpGs, using the Intraclass Correlation Coefficient (ICC). The mean ICCs and 95% confidence intervals (CI) were 0.72 (0.62-0.81), 0.59 (0.47-0.71) and 0.80 (0.73-0.88) for WB, FFPE and both sample types combined, respectively. For technical replicates measured using QIAseq and HM450K, the mean ICCs (95% CI) were 0.53 (0.39-0.68), 0.43 (0.31-0.56) and 0.70 (0.59-0.80), respectively. Bland-Altman plots indicated good agreement between QIAseq and HM450K measurements. These results demonstrate that the QIAseq targeted methyl panel produces reliable and reproducible methylation measurements across the 40 CpGs that were examined.

Indexed as

DNA MethylationHigh-Throughput Nucleotide SequencingGenomicsOligonucleotide Array Sequence AnalysisParaffin EmbeddingBland–Altman plotDNA methylationFormalin-fixed paraffin-embeddedIllumina Infinium methylation arraysIntraclass correlation coefficientTargeted bisulphite sequencingWhole blood

Identifiers

PMID34689793
PMCPMC8543877

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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.