Calculating Detection Limits and Uncertainty of Reference-Based Deconvolution of Whole-Blood DNA Methylation Data

Abstract

DNA methylation (DNAm)-based cell mixture deconvolution (CMD) has become a quintessential part of epigenome-wide association studies where DNAm is profiled in heterogeneous tissue types. Despite being introduced over a decade ago, detection limits, which represent the smallest fraction of a cell type in a mixed biospecimen that can be reliably detected, have yet to be determined in the context of DNAm-based CMD. Moreover, there has been little attention given to approaches for quantifying the uncertainty associated with DNAm-based CMD. Here, analytical frameworks for determining both cell-specific limits of detection and quantification of uncertainty associated with DNAm-based CMD are described. This work may contribute to improved rigor, reproducibility and replicability of epigenome-wide association studies involving CMD.

Keywords::

• cellular deconvolution
• DNA methylation
• EWAS
• in silico mixtures
• limit of blank
• limit of detection

Supplementary data

To view the supplementary data that accompany this paper please visit the journal website at: www.tandfonline.com/doi/suppl/10.2217/epi-2023-0006

Author contributions

S Bell-Glenn helped conceive the framework for calculating DNA methylation-based deconvolution detection limits and uncertainty, performed the statistical analyses to evaluate and assess the proposed framework and wrote the manuscript. LA Salas, AM Molinaro and BC Christensen helped direct the statistical analyses, contributed to the interpretation of study findings and assisted in manuscript writing and development. RA Butler participated in the processing of the samples used in this research, contributed to the interpretation of study findings and assisted in manuscript writing and development. KT Kelsey, JK Wiencke and DC Koestler provided guidance and direction in the evaluation and assessment of the method and assisted in manuscript writing and development.

Acknowledgments

The authors would like to extend their gratitude to: Samuel Boyd, Whitney Shae, Jonah Amponsah, Emily Nissen, Alexander Alsup, Jeffrey Thompson, Nanda Yellapu and Dong Pei for their constructive feedback on this manuscript.

Financial & competing interests disclosure

The research reported here was supported by: the National Cancer Institute Cancer Center Support Grant P30 CA168524; the Kansas IDeA Network of Biomedical Research Excellence Bioinformatics Core, supported by the National Institute of General Medical Science award P20 GM103418; and the Kansas Institute for Precision Medicine COBRE, supported by the National Institute of General Medical Science award P20 GM130423. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

Data sharing statement

The R scripts for the analyses presented here are available at https://github.com/ShelbyBellGlenn/LoD-Code.

Additional information

Funding

The research reported here was supported by: the National Cancer Institute Cancer Center Support Grant P30 CA168524; the Kansas IDeA Network of Biomedical Research Excellence Bioinformatics Core, supported by the National Institute of General Medical Science award P20 GM103418; and the Kansas Institute for Precision Medicine COBRE, supported by the National Institute of General Medical Science award P20 GM130423. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.