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Maximizing ecological and evolutionary insight in bisulfite sequencing data sets

Nature Ecology & Evolution volume 1, pages 1074–1083 (2017)Cite this article

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Abstract

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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Figure 1: Overview of reduced representation bisulfite sequencing and whole-genome bisulfite sequencing.
Figure 2: Estimates of effect sizes and their impact on the power of differential methylation analysis.
Figure 3: Properties of CpG methylation levels vary across data sets and influence power.

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Acknowledgements

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.

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  1. Amanda J. Lea

    Present address: Lewis-Sigler Institute for Integrative Genomics, Carl Icahn Laboratory, Washington Road, Princeton University, Princeton, NJ, 08540, USA

Authors and Affiliations

  1. Department of Biology, Duke University, Durham, NC, 27708, USA

    Amanda J. Lea & Jenny Tung

  2. Department of Evolutionary Anthropology, Duke University, Durham, NC, 27708, USA

    Tauras P. Vilgalys & Jenny Tung

  3. Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA

    Paul A. P. Durst

  4. Institute of Primate Research, National Museums of Kenya, Nairobi, 00502, Kenya

    Jenny Tung

  5. Duke University Population Research Institute, Duke University, Durham, NC, 27708, USA

    Jenny Tung

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Contributions

A.J.L. and J.T. conceived the study; A.J.L., T.P.V. and P.A.P.D. analysed previously published and simulated data; T.P.V. wrote the R Shiny app; and A.J.L. and J.T. wrote the manuscript, with input from all co-authors. All authors gave final approval for publication.

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Correspondence to Amanda J. Lea or Jenny Tung.

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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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