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Methods for phylogenetic analysis of microbiome data

Nature Microbiology volume 3, pages 652–661 (2018)Cite this article

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Abstract

How does knowing the evolutionary history of microorganisms affect our analysis of microbiological datasets? Depending on the research question, the common ancestry of microorganisms can be a source of confounding variation, or a scaffolding used for inference. For example, when performing regression on traits, common ancestry is a source of dependence among observations, whereas when searching for clades with correlated abundances, common ancestry is the scaffolding for inference. The common ancestry of microorganisms and their genes are organized in trees—phylogenies—which can and should be incorporated into analyses of microbial datasets. While there has been a recent expansion of phylogenetically informed analytical tools, little guidance exists for which method best answers which biological questions. Here, we review methods for phylogeny-aware analyses of microbiome datasets, considerations for choosing the appropriate method and challenges inherent in these methods. We introduce a conceptual organization of these tools, breaking them down into phylogenetic comparative methods, ancestral state reconstruction and analysis of phylogenetic variables and distances, and provide examples in Supplementary Online Tutorials. Careful consideration of the research question and ecological and evolutionary assumptions will help researchers choose a phylogeny and appropriate methods to produce accurate, biologically informative and previously unreported insights.

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Fig. 1: PCMs control for the statistical dependence among traits resulting from evolution of traits along the phylogenetic tree.
Fig. 2: Phylogenies define the geometry of community ecological data, much like a sphere defines the geometry of GPS data.
Fig. 3: Phylogeny-aware distances.

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Acknowledgements

A.D.W. received support from Duke University Biology Department’s provision of start-up funds for D. Nemergut (deceased) and the Defense Advanced Research Projects Agency (DARPA) grant D16AP0013. This paper is published in the spirit of D. Nemergut’s contagious love of science.

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  1. These authors contributed equally: Alex D. Washburne and James T. Morton.

Authors and Affiliations

  1. Department of Microbiology and Immunology, Montana State University, Bozeman, MT, USA

    Alex D. Washburne

  2. Department of Computer Science, University of California San Diego, La Jolla, CA, USA

    James T. Morton & Rob Knight

  3. Department of Pediatrics, University of California San Diego, La Jolla, CA, USA

    James T. Morton, Jon Sanders, Daniel McDonald, Qiyun Zhu & Rob Knight

  4. Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, CO, USA

    Angela M. Oliverio

  5. Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA

    Angela M. Oliverio

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Washburne, A.D., Morton, J.T., Sanders, J. et al. Methods for phylogenetic analysis of microbiome data. Nat Microbiol 3, 652–661 (2018). https://doi.org/10.1038/s41564-018-0156-0

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