TY - JOUR
T1 - Recountmethylation enables flexible analysis of public blood DNA methylation array data
AU - Maden, Sean K.
AU - Walsh, Brian
AU - Ellrott, Kyle
AU - Hansen, Kasper D.
AU - Thompson, Reid F.
AU - Nellore, Abhinav
N1 - Publisher Copyright:
© 2023 The Author(s). Published by Oxford University Press.
PY - 2023
Y1 - 2023
N2 - Summary: Thousands of DNA methylation (DNAm) array samples from human blood are publicly available on the Gene Expression Omnibus (GEO), but they remain underutilized for experiment planning, replication and cross-study and cross-platform analyses. To facilitate these tasks, we augmented our recountmethylation R/Bioconductor package with 12 537 uniformly processed EPIC and HM450K blood samples on GEO as well as several new features. We subsequently used our updated package in several illustrative analyses, finding (i) study ID bias adjustment increased variation explained by biological and demographic variables, (ii) most variation in autosomal DNAm was explained by genetic ancestry and CD4+ T-cell fractions and (iii) the dependence of power to detect differential methylation on sample size was similar for each of peripheral blood mononuclear cells (PBMC), whole blood and umbilical cord blood. Finally, we used PBMC and whole blood to perform independent validations, and we recovered 38-46% of differentially methylated probes between sexes from two previously published epigenome-wide association studies.
AB - Summary: Thousands of DNA methylation (DNAm) array samples from human blood are publicly available on the Gene Expression Omnibus (GEO), but they remain underutilized for experiment planning, replication and cross-study and cross-platform analyses. To facilitate these tasks, we augmented our recountmethylation R/Bioconductor package with 12 537 uniformly processed EPIC and HM450K blood samples on GEO as well as several new features. We subsequently used our updated package in several illustrative analyses, finding (i) study ID bias adjustment increased variation explained by biological and demographic variables, (ii) most variation in autosomal DNAm was explained by genetic ancestry and CD4+ T-cell fractions and (iii) the dependence of power to detect differential methylation on sample size was similar for each of peripheral blood mononuclear cells (PBMC), whole blood and umbilical cord blood. Finally, we used PBMC and whole blood to perform independent validations, and we recovered 38-46% of differentially methylated probes between sexes from two previously published epigenome-wide association studies.
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U2 - 10.1093/bioadv/vbad020
DO - 10.1093/bioadv/vbad020
M3 - Article
C2 - 36874953
AN - SCOPUS:85149928758
SN - 2635-0041
VL - 3
JO - Bioinformatics Advances
JF - Bioinformatics Advances
IS - 1
M1 - vbad020
ER -