Adjustment of Cell-Type Composition Minimizes Systematic Bias in Blood DNA Methylation Profiles Derived by DNA Collection Protocols

PLoS One. 2016 Jan 22;11(1):e0147519. doi: 10.1371/journal.pone.0147519. eCollection 2016.

Abstract

Differences in DNA collection protocols may be a potential confounder in epigenome-wide association studies (EWAS) using a large number of blood specimens from multiple biobanks and/or cohorts. Here we show that pre-analytical procedures involved in DNA collection can induce systematic bias in the DNA methylation profiles of blood cells that can be adjusted by cell-type composition variables. In Experiment 1, whole blood from 16 volunteers was collected to examine the effect of a 24 h storage period at 4°C on DNA methylation profiles as measured using the Infinium HumanMethylation450 BeadChip array. Our statistical analysis showed that the P-value distribution of more than 450,000 CpG sites was similar to the theoretical distribution (in quantile-quantile plot, λ = 1.03) when comparing two control replicates, which was remarkably deviated from the theoretical distribution (λ = 1.50) when comparing control and storage conditions. We then considered cell-type composition as a possible cause of the observed bias in DNA methylation profiles and found that the bias associated with the cold storage condition was largely decreased (λ adjusted = 1.14) by taking into account a cell-type composition variable. As such, we compared four respective sample collection protocols used in large-scale Japanese biobanks or cohorts as well as two control replicates. Systematic biases in DNA methylation profiles were observed between control and three of four protocols without adjustment of cell-type composition (λ = 1.12-1.45) and no remarkable biases were seen after adjusting for cell-type composition in all four protocols (λ adjusted = 1.00-1.17). These results revealed important implications for comparing DNA methylation profiles between blood specimens from different sources and may lead to discovery of disease-associated DNA methylation markers and the development of DNA methylation profile-based predictive risk models.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bias
  • Blood Cells / metabolism*
  • Blood Specimen Collection / methods*
  • Blood Specimen Collection / standards
  • CpG Islands / genetics
  • DNA / genetics
  • DNA / isolation & purification*
  • DNA Methylation*
  • Female
  • Flow Cytometry
  • Genome-Wide Association Study / methods
  • Genome-Wide Association Study / standards
  • Humans
  • Male
  • Oligonucleotide Array Sequence Analysis / methods
  • Oligonucleotide Array Sequence Analysis / standards

Substances

  • DNA

Grant support

This work was supported by the Tohoku Medical Megabank Project (Special Account for reconstruction from the Great East Japan Earthquake) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) and Japan Agency for Medical Research and Development (AMED). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.