- Title
- Critical evaluation of linear regression models for cell-subtype specific methylation signal from mixed blood cell DNA
- Creator
- Kennedy, Daniel W.; White, Nicole M.; Benton, Miles C.; Fox, Andrew; Scott, Rodney J.; Griffiths, Lyn R.; Mengersen, Kerrie; Lea, Rodney A.
- Relation
- PLoS One Vol. 13, Issue 12, no. e0208915
- Publisher Link
- http://dx.doi.org/10.1371/journal.pone.0208915
- Publisher
- Public Library of Science (PLoS)
- Resource Type
- journal article
- Date
- 2018
- Description
- Epigenome-wide association studies seek to identify DNA methylation sites associated with clinical outcomes. Difference in observed methylation between specific cell-subtypes is often of interest; however, available samples often comprise a mixture of cells. To date, cell-subtype estimates have been obtained from mixed-cell DNA data using linear regression models, but the accuracy of such estimates has not been critically assessed. We evaluated linear regression performance for cell-subtype specific methylation estimation using a 450K methylation array dataset of both mixed-cell and cell-subtype sorted samples from six healthy males. CpGs associated with each cell-subtype were first identified using t-tests between groups of cell-subtype sorted samples. Subsequent reduced panels of reliably accurate CpGs were identified from mixed-cell samples using an accuracy heuristic (D). Performance was assessed by comparing cell-subtype specific estimates from mixed-cells with corresponding cell-sorted mean using the mean absolute error (MAE) and the Coefficient of Determination (R²). At the cell-subtype level, methylation levels at 3272 CpGs could be estimated to within a MAE of 5% of the expected value. The cell-subtypes with the highest accuracy were CD56⁺ NK (R² = 0.56) and CD8⁺T (R² = 0.48), where 23% of sites were accurately estimated. Hierarchical clustering and pathways enrichment analysis confirmed the biological relevance of the panels. Our results suggest that linear regression for cell-subtype specific methylation estimation is accurate only for some cell-subtypes at a small fraction of cell-associated sites but may be applicable to EWASs of disease traits with a blood-based pathology. Although sample size was a limitation in this study, we suggest that alternative statistical methods will provide the greatest performance improvements.
- Subject
- linear regression; DNA; methylation; cell-subtypes
- Identifier
- http://hdl.handle.net/1959.13/1408881
- Identifier
- uon:35898
- Identifier
- ISSN:1932-6203
- Rights
- © 2018 Kennedy et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- Language
- eng
- Full Text
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