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Detecting signatures of selection on gene expression

Nature Ecology & Evolution volume 6, pages 1035–1045 (2022)Cite this article

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Matters Arising to this article was published on 07 November 2022

Abstract

A substantial amount of phenotypic diversity results from changes in gene expression levels and patterns. Understanding how the transcriptome evolves is therefore a key priority in identifying mechanisms of adaptive change. However, in contrast to powerful models of sequence evolution, we lack a consensus model of gene expression evolution. Furthermore, recent work has shown that many of the comparative approaches used to study gene expression are subject to biases that can lead to false signatures of selection. Here we first outline the main approaches for describing expression evolution and their inherent biases. Next, we bridge the gap between the fields of phylogenetic comparative methods and transcriptomics to reinforce the main pitfalls of inferring selection on expression patterns and use simulation studies to show that shifts in tissue composition can heavily bias inferences of selection. We close by highlighting the multi-dimensional nature of transcriptional variation and identifying major unanswered questions in disentangling how selection acts on the transcriptome.

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Fig. 1: Approaches to detect selection on gene expression.
Fig. 2: Variation in tissue composition can lead to the perception of differential expression.
Fig. 3: Inferring selection when expression level is measured from a heterogeneous tissue.
Fig. 4: The magnitude of allometric shift and covariance of expression level biases the inference of selection.

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Data availability

All data have been previously published71.

Code availability

All code is publicly available at https://github.com/Wright-lab-2021-Transcriptome-Evo/Inferring_expression_evolution_review.

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Acknowledgements

This work was funded by an NERC Independent Research Fellowship to C.R.C. (NE/T01105X/1); an NERC Independent Research Fellowship to A.E.W. (NE/N013948/1); a grant from the European Research Council (grant agreement 680951) and a Canada 150 Research Chair to J.E.M.; an NERC ACCE DTP to P.D.P.; and an NSF Postdoctoral Research Fellowship and an MSU Presidential Postdoctoral Fellowship to D.H.P.D. We thank M. Placzek, P. E. Pifarré, E. Josephs, A. Platts, M. Roberts, R. Panko, M. Wilson Brown and S. Buysse for helpful comments and suggestions on the manuscript.

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Author notes

  1. These authors contributed equally: Christopher R. Cooney, Alison E. Wright.

Authors and Affiliations

  1. Ecology and Evolutionary Biology, School of Biosciences, University of Sheffield, Sheffield, UK

    Peter D. Price, Daniela H. Palmer Droguett, Jessica A. Taylor, Thea F. Rogers, Christopher R. Cooney & Alison E. Wright

  2. Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, MI, USA

    Daniela H. Palmer Droguett

  3. Department of Biosciences, Durham University, Durham, UK

    Jessica A. Taylor

  4. Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Dong Won Kim

  5. Development, Regeneration and Neurophysiology, School of Biosciences, University of Sheffield, Sheffield, UK

    Elsie S. Place

  6. Department of Zoology, University of British Columbia, Vancouver, British Columbia, Canada

    Judith E. Mank

  7. Beaty Biodiversity Research Centre, University of British Columbia, Vancouver, British Columbia, Canada

    Judith E. Mank

  8. Centre for Ecology and Conservation, University of Exeter, Penryn, UK

    Judith E. Mank

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Contributions

A.E.W., C.R.C., D.H.P.D., P.D.P. and J.E.M. designed the study. D.W.K., E.S.P., A.E.W., C.R.C. and P.D.P. analysed the data. All authors wrote and edited the manuscript.

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Correspondence to Peter D. Price or Alison E. Wright.

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Price, P.D., Palmer Droguett, D.H., Taylor, J.A. et al. Detecting signatures of selection on gene expression. Nat Ecol Evol 6, 1035–1045 (2022). https://doi.org/10.1038/s41559-022-01761-8

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