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

Author:

Maden Sean K12ORCID,Thompson Reid F12345ORCID,Hansen Kasper D67ORCID,Nellore Abhinav128ORCID

Affiliation:

1. Computational Biology Program, Oregon Health & Science University, Portland, OR 97239, USA

2. Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA

3. VA Portland Healthcare System, Portland, OR 97239, USA

4. Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA

5. Department of Radiation Medicine, Oregon Health & Science University, Portland, OR 97239, USA

6. Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA

7. Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA

8. Department of Surgery, Oregon Health & Science University, Portland, OR 97239, USA

Abstract

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. 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. About 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.

Funder

NIH

Publisher

Oxford University Press (OUP)

Subject

General Medicine

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