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Correlational selection in the age of genomics

Nature Ecology & Evolution volume 5, pages 562–573 (2021)Cite this article

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Abstract

Ecologists and evolutionary biologists are well aware that natural and sexual selection do not operate on traits in isolation, but instead act on combinations of traits. This long-recognized and pervasive phenomenon is known as multivariate selection, or—in the particular case where it favours correlations between interacting traits—correlational selection. Despite broad acknowledgement of correlational selection, the relevant theory has often been overlooked in genomic research. Here, we discuss theory and empirical findings from ecological, quantitative genetic and genomic research, linking key insights from different fields. Correlational selection can operate on both discrete trait combinations and quantitative characters, with profound implications for genomic architecture, linkage, pleiotropy, evolvability, modularity, phenotypic integration and phenotypic plasticity. We synthesize current knowledge and discuss promising research approaches that will enable us to understand how correlational selection shapes genomic architecture, thereby linking quantitative genetic approaches with emerging genomic methods. We suggest that research on correlational selection has great potential to integrate multiple fields in evolutionary biology, including developmental and functional biology, ecology, quantitative genetics, phenotypic polymorphisms, hybrid zones and speciation processes.

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Fig. 1: The scope of correlational selection and its links to different fields in evolutionary biology.
Fig. 2: Phenotypic and quantitative genetics studies on organisms and traits in which correlational selection has experimentally been demonstrated or inferred, in the field or in laboratory studies.
Fig. 3: Illustration of correlational selection, with important parameters used to quantify it and determine how its effects are carried across generations.
Fig. 4: Examples of genomic trait architectures that might reflect past or ongoing correlational selection.

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Acknowledgements

We are grateful to D. Goedert for comments on the first draft of this manuscript. E.I.S. and A.R. were funded by grants from the Swedish Research Council (VR; grant numbers 2016-03356 and 2018-04537, respectively). D.A.M. was supported by the Swiss National Science Foundation (grant no. 31003A_163338 to O. Seehausen, L. Excoffier and R. Bruggmann). J.D. acknowledges support by NSF 13-510 Systems & Synthetic Biology, award no. 1714550. J.M.H. is supported by the German Federal Ministry of Education and Research (BMBF). K.C. was supported by a Swiss National Science Foundation grant (CRSK-3_190288). K.M. was funded by the Australian Research Council (DP190101661). M.N.S. was supported by Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), projects 2015/19556-4 and 2016/22159-0. We also wish to thank B. Brodie for kindly providing us with the photograph of the garter snakes in Fig. 2a and the original figure of his classic fitness surface in Box 1.

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Authors and Affiliations

  1. Department of Biology, Lund University, Lund, Sweden

    Erik I. Svensson, Stephen De Lisle & Anna Runemark

  2. Department of Integrative Biology, Oregon State University, Corvallis, OR, USA

    Stevan J. Arnold & Monique N. Simon

  3. Faculty of Mathematics, University of Vienna, Vienna, Austria

    Reinhard Bürger

  4. Land Change Science, Swiss Federal Research Institute WSL, Birmensdorf, Switzerland

    Katalin Csilléry

  5. Department of Biological Sciences, Virginia Tech, Blacksburg, VA, USA

    Jeremy Draghi

  6. Department of Biological Sciences, University of Idaho, Moscow, ID, USA

    Jonathan M. Henshaw & Adam G. Jones

  7. Institute of Biology I (Zoology), University of Freiburg, Freiburg, Germany

    Jonathan M. Henshaw

  8. Department of Ecology & Evolutionary Biology, University of Connecticut, Storrs, CT, USA

    Stephen De Lisle

  9. Department of Fish Ecology and Evolution, Eawag: Swiss Federal Institute of Aquatic Science and Technology, Kastanienbaum, Switzerland

    David A. Marques

  10. Aquatic Ecology & Evolution, Institute of Ecology and Evolution, University of Bern, Bern, Switzerland

    David A. Marques

  11. School of Biological Sciences, The University of Queensland, Brisbane, Queensland, Australia

    Katrina McGuigan

  12. Department of Genetics and Evolutionary Biology, University of Sao Paulo, Sao Paulo, Brazil

    Monique N. Simon

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Contributions

E.I.S. and A.R. conceived the paper, organized the writing and put together the first draft, based on input and written material from the other authors. All authors contributed to written sections, figures, Supplementary information, and improving and finalizing the manuscript. All authors approved the final manuscript version prior to submission and after acceptance.

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Correspondence to Erik I. Svensson.

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Peer review information Nature Ecology & Evolution thanks Alison Bell, Frederic Guillaume and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary information

1. Evolution of the G matrix by correlational selection. 2. The role of gmax in modularity and plasticity. 3. Supplemental material for Fig. 3.

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Svensson, E.I., Arnold, S.J., Bürger, R. et al. Correlational selection in the age of genomics. Nat Ecol Evol 5, 562–573 (2021). https://doi.org/10.1038/s41559-021-01413-3

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