Data processing choices can affect findings in differential methylation analyses: an investigation using data from the LIMIT RCT

Jennie Louise, Andrea R. Deussen, Jodie M. Dodd

Research output: Contribution to journalArticlepeer-review

Abstract

Objective. A wide array of methods exist for processing and analysing DNA methylation data. We aimed to perform a systematic comparison of the behaviour of these methods, using cord blood DNAm from the LIMIT RCT, in relation to detecting hypothesised effects of interest (intervention and pre-pregnancy maternal BMI) as well as effects known to be spurious, and known to be present. Methods. DNAm data, from 645 cord blood samples analysed using Illumina 450K BeadChip arrays, were normalised using three different methods (with probe filtering undertaken pre- or post- normalisation). Batch effects were handled with a supervised algorithm, an unsupervised algorithm, or adjustment in the analysis model. Analysis was undertaken with and without adjustment for estimated cell type proportions. The effects estimated included intervention and BMI (effects of interest in the original study), infant sex and randomly assigned groups. Data processing and analysis methods were compared in relation to number and identity of differentially methylated probes, rankings of probes by p value and log-fold-change, and distributions of p values and log-fold-change estimates. Results. There were differences corresponding to each of the processing and analysis choices. Importantly, some combinations of data processing choices resulted in a substantial number of spurious ‘significant’ findings. We recommend greater emphasis on replication and greater use of sensitivity analyses.

Original languageEnglish
Article numbere14786
JournalPeerJ
Volume11
DOIs
Publication statusPublished or Issued - Feb 2023
Externally publishedYes

Keywords

  • Bioinformatics
  • DNA methylation
  • Differential methylation
  • Reproducibility

ASJC Scopus subject areas

  • Neuroscience(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

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