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Human methylome variation across Infinium 450K data on the Gene Expression Omnibus

View ORCID ProfileSean K. Maden, View ORCID ProfileReid F. Thompson, View ORCID ProfileKasper D. Hansen, View ORCID ProfileAbhinav Nellore
doi: https://doi.org/10.1101/2020.11.17.387548
Sean K. Maden
1Computational Biology Program, Oregon Health & Science University, Portland, OR, USA
2Dept. of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
4Dept. of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA
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Reid F. Thompson
1Computational Biology Program, Oregon Health & Science University, Portland, OR, USA
2Dept. of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
3Portland VA Research Foundation, Portland, OR, USA
4Dept. of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA
5Dept. of Radiation Medicine, Oregon Health & Science University, Portland, OR, USA
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Kasper D. Hansen
6Dept. of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
7Dept. of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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  • For correspondence: khansen@jhsph.edu anellore@gmail.com
Abhinav Nellore
1Computational Biology Program, Oregon Health & Science University, Portland, OR, USA
2Dept. of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
8Dept. of Surgery, Oregon Health & Science University, Portland, OR, USA
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  • For correspondence: khansen@jhsph.edu anellore@gmail.com
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Abstract

While DNA methylation (DNAm) is the most-studied epigenetic mark, few recent studies probe the breadth of publicly available DNAm array samples. We collectively analyzed 35,360 Illumina Infinium HumanMethylation450K DNAm array samples published on the Gene Expression Omnibus (GEO). We learned a controlled vocabulary of sample labels by applying regular expressions to metadata and used existing models to predict various sample properties including epigenetic age. We found approximately two-thirds of samples were from blood, one-quarter were from brain, and one-third were from cancer patients. 19% of samples failed at least one of Illumina’s 17 prescribed quality assessments; signal distributions across samples suggest modifying manufacturer-recommended thresholds for failure would make these assessments more informative. We further analyzed DNAm variances in seven tissues (adipose, nasal, blood, brain, buccal, sperm, and liver) and characterized specific probes distinguishing them. Finally, we compiled DNAm array data and metadata, including our learned and predicted sample labels, into database files accessible via the recountmethylation R/Bioconductor companion package. Its vignettes walk the user through some analyses contained in this paper.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Manuscript Subject Area was changed from "Bioinformatics" to "Genomics". Table S4 legend was updated to include all columns and more details. Table S7 legend was added.

  • https://doi.org/doi/10.18129/B9.bioc.recountmethylation

  • https://github.com/metamaden/recountmethylationManuscriptSupplement

  • https://figshare.com/account/home#/projects/90758

  • https://recount.bio/data/recountmethylation_manuscript_supplement/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted November 26, 2020.
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Human methylome variation across Infinium 450K data on the Gene Expression Omnibus
Sean K. Maden, Reid F. Thompson, Kasper D. Hansen, Abhinav Nellore
bioRxiv 2020.11.17.387548; doi: https://doi.org/10.1101/2020.11.17.387548
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Human methylome variation across Infinium 450K data on the Gene Expression Omnibus
Sean K. Maden, Reid F. Thompson, Kasper D. Hansen, Abhinav Nellore
bioRxiv 2020.11.17.387548; doi: https://doi.org/10.1101/2020.11.17.387548

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