Training a Model for Estimating Leukocyte Composition using Whole-Blood DNA Methylation and Cell Counts as Reference | Zendy

Jonathan Heiss | Zendy; Lutz Philipp Breitling | Zendy; Benjamin Lehne | Zendy; Jaspal S. Kooner | Zendy; John C. Chambers | Zendy; Hermann Brenner | Zendy

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Training a Model for Estimating Leukocyte Composition using Whole-Blood DNA Methylation and Cell Counts as Reference

Author(s) -

Jonathan Heiss,

Lutz Philipp Breitling,

Benjamin Lehne,

Jaspal S. Kooner,

John C. Chambers,

Hermann Brenner

Publication year - 2016

Publication title -

epigenomics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.265

H-Index - 60

eISSN - 1750-1911

pISSN - 1750-192X

DOI - 10.2217/epi-2016-0091

Subject(s) - dna methylation , biology , whole blood , confounding , methylation , blood cell , dna , immunology , population , cell , epigenome , genetics , medicine , gene , gene expression , environmental health

Whole-blood DNA methylation depends on the underlying leukocyte composition and confounding hereby is a major concern in epigenome-wide association studies. Cell counts are often missing or may not be feasible. Computational approaches estimate leukocyte composition from DNA methylation based on reference datasets of purified leukocytes. We explored the possibility to train such a model on whole-blood DNA methylation and cell counts without the need for purification.

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