Abstract
Statistical methods that allow for cell type specific DNA methylation (DNAm) analyses based on bulk-tissue methylation data have great potential to improve our understanding of human disease and have created unprecedented opportunities for new insights using the wealth of publicly available bulk-tissue methylation data. These methodologies involve incorporating interaction terms formed between the phenotypes/exposures of interest and proportions of the cell types underlying the bulk-tissue sample used for DNAm profiling. Despite growing interest in such “interaction-based” methods, there has been no comprehensive assessment how variability in the cellular landscape across study samples affects their performance. To answer this question, we used numerous publicly available whole-blood DNAm data sets along with extensive simulation studies and evaluated the performance of interaction-based approaches in detecting cell-specific methylation effects. Our results show that low cell proportion variability results in large estimation error and low statistical power for detecting cell-specific effects of DNAm. Further, we identified that many studies targeting methylation profiling in whole-blood may be at risk to be underpowered due to low variability in the cellular landscape across study samples. Finally, we discuss guidelines for researchers seeking to conduct studies utilizing interaction-based approaches to help ensure that their studies are adequately powered.
Funding source: National Institute of General Medical Sciences
Award Identifier / Grant number: P20GM103428
Funding source: National Cancer Institute
Award Identifier / Grant number: P30 CA168524, R01 CA166150
Acknowledgement
We would like to extend our gratitude to Jeffrey A. Thompson, Lynn C. Hinton, Prabhakar Chalise, Jinxiang Hu, Nanda Kumar, Dong Pei, Qing Xia, Lisa Neums, Shachi Patel, Shelby Bell-Glenn, Bo Zhang, Samuel Boyd, Jonah Amponsah, and Whitney Shae of the Department of Biostatistics & Data Science at the University of Kansas Medical Center for their constructive feedback on the manuscript.
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Author contribution: RM wrote the manuscript and drafted the R script conducting the simulation study. EN conducted the data acquisition. RM and EN performed preliminary analyses of data sets. DCK helped with conception of the simulation study and supervised the implementation. EN and DCK edited the manuscript. All authors read and approved the final version of the manuscript.
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Research funding: Research reported in this publication was supported by NIH/National Cancer Institute grants R01 CA166150 and P30 CA168524 as well as the Kansas IDeA Network of Biomedical Research Excellence Bioinformatics Core, supported in part by the National Institute of General Medical Science award P20GM103428.
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Competing interests: We have no competing interests to declare.
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Supplementary Material
The online version of this article offers supplementary material (https://doi.org/10.1515/sagmb-2021-0004).
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