Genome-scale bisulfite sequencing approaches have opened the door to ecological and evolutionary studies of DNA methylation in many organisms. These approaches can be powerful. However, they introduce new methodological and statistical considerations, some of which are particularly relevant to non-model systems. Here, we highlight how these considerations influence a study’s power to link methylation variation with a predictor variable of interest. Relative to current practice, we argue that sample sizes will need to increase to provide robust insights. We also provide recommendations for overcoming common challenges and an R Shiny app to aid in study design.
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We thank K. Hansen and I. Hernando-Herraez for providing processed file formats from their previously published work. We also thank N. Snyder-Mackler, L. Barreiro and X. Zhou for helpful comments and suggestions, M. Cetinkaya-Rundel for coding suggestions on the R Shiny app, M. Gavery for beta-testing it, the Baylor College of Medicine Human Genome Sequencing Center for access to the current version of the baboon genome assembly (Panu 2.0). This work was supported by NIH R21-AG049936 and 1R01GM102562 to J.T., NSF BCS-1455808 to J.T. and A.J.L.; P.A.P.D. is supported by NIH K12GM000678 from the Training, Workforce Development and Diversity division of the National Institute of General Medical Sciences.
The authors declare no competing financial interests.
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Lea, A.J., Vilgalys, T.P., Durst, P.A.P. et al. Maximizing ecological and evolutionary insight in bisulfite sequencing data sets. Nat Ecol Evol 1, 1074–1083 (2017). https://doi.org/10.1038/s41559-017-0229-0
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