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Monitoring rapid evolution of plant populations at scale with Pool-Sequencing

View ORCID ProfileLucas Czech, Yunru Peng, View ORCID ProfileJeffrey P. Spence, View ORCID ProfilePatricia L.M. Lang, Tatiana Bellagio, Julia Hildebrandt, Katrin Fritschi, View ORCID ProfileRebecca Schwab, View ORCID ProfileBeth A. Rowan, GrENE-net consortium, View ORCID ProfileDetlef Weigel, View ORCID ProfileJ.F. Scheepens, View ORCID ProfileFrançois Vasseur, View ORCID ProfileMoises Exposito-Alonso
doi: https://doi.org/10.1101/2022.02.02.477408
Lucas Czech
1Department of Plant Biology, Carnegie Institution for Science, Stanford, CA 94305, USA
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Yunru Peng
1Department of Plant Biology, Carnegie Institution for Science, Stanford, CA 94305, USA
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Jeffrey P. Spence
2Department of Genetics, Stanford University, Stanford, CA 94305, USA
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Patricia L.M. Lang
3Department of Biology, Stanford University, Stanford, CA 94305, USA
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Tatiana Bellagio
1Department of Plant Biology, Carnegie Institution for Science, Stanford, CA 94305, USA
3Department of Biology, Stanford University, Stanford, CA 94305, USA
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Julia Hildebrandt
4Department of Molecular Biology, Max Planck Institute for Biology Tübingen, 72076 Tübingen, Germany
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Katrin Fritschi
4Department of Molecular Biology, Max Planck Institute for Biology Tübingen, 72076 Tübingen, Germany
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Rebecca Schwab
4Department of Molecular Biology, Max Planck Institute for Biology Tübingen, 72076 Tübingen, Germany
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Beth A. Rowan
4Department of Molecular Biology, Max Planck Institute for Biology Tübingen, 72076 Tübingen, Germany
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Detlef Weigel
4Department of Molecular Biology, Max Planck Institute for Biology Tübingen, 72076 Tübingen, Germany
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J.F. Scheepens
5Faculty of Biological Sciences, Goethe University, Frankfurt, Max-von-Laue-Str. 13, 60438 Frankfurt am Main, Germany
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François Vasseur
3Department of Biology, Stanford University, Stanford, CA 94305, USA
6Centre d’Écologie Fonctionnelle et Évolutive (CEFE), University of Montpellier, CNRS, EPHE, IRD, Univ Paul Valéry Montpellier 3, F-34090 Montpellier, France
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Moises Exposito-Alonso
1Department of Plant Biology, Carnegie Institution for Science, Stanford, CA 94305, USA
3Department of Biology, Stanford University, Stanford, CA 94305, USA
4Department of Molecular Biology, Max Planck Institute for Biology Tübingen, 72076 Tübingen, Germany
7Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305, USA
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  • For correspondence: moisesexpositoalonso@gmail.com
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Abstract

The change in allele frequencies within a population over time represents a fundamental process of evolution. By monitoring allele frequencies, we can analyze the effects of natural selection and genetic drift on populations. To efficiently track time-resolved genetic change, large experimental or wild populations can be sequenced as pools of individuals sampled over time using high-throughput genome sequencing (called the Evolve & Resequence approach, E&R). Here, we present a set of experiments using hundreds of natural genotypes of the model plant Arabidopsis thaliana to showcase the power of this approach to study rapid evolution at large scale. First, we validate that sequencing DNA directly extracted from pools of flowers from multiple plants -- organs that are relatively consistent in size and easy to sample -- produces comparable results to other, more expensive state-of-the-art approaches such as sampling and sequencing of individual leaves. Sequencing pools of flowers from 25-50 individuals at ∼40X coverage recovers genome-wide frequencies in diverse populations with accuracy r > 0.95. Secondly, to enable analyses of evolutionary adaptation using E&R approaches of plants in highly replicated environments, we provide open source tools that streamline sequencing data curation and calculate various population genetic statistics two orders of magnitude faster than current software. To directly demonstrate the usefulness of our method, we conducted a two-year outdoor evolution experiment with A. thaliana to show signals of rapid evolution in multiple genomic regions. We demonstrate how these laboratory and computational Pool-seq-based methods can be scaled to study hundreds of populations across many climates.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵% Shared co-first authors

  • ↵$ GrENE-net consortium authors and affiliations listed in the appendix

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted June 08, 2022.
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Monitoring rapid evolution of plant populations at scale with Pool-Sequencing
Lucas Czech, Yunru Peng, Jeffrey P. Spence, Patricia L.M. Lang, Tatiana Bellagio, Julia Hildebrandt, Katrin Fritschi, Rebecca Schwab, Beth A. Rowan, GrENE-net consortium, Detlef Weigel, J.F. Scheepens, François Vasseur, Moises Exposito-Alonso
bioRxiv 2022.02.02.477408; doi: https://doi.org/10.1101/2022.02.02.477408
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Monitoring rapid evolution of plant populations at scale with Pool-Sequencing
Lucas Czech, Yunru Peng, Jeffrey P. Spence, Patricia L.M. Lang, Tatiana Bellagio, Julia Hildebrandt, Katrin Fritschi, Rebecca Schwab, Beth A. Rowan, GrENE-net consortium, Detlef Weigel, J.F. Scheepens, François Vasseur, Moises Exposito-Alonso
bioRxiv 2022.02.02.477408; doi: https://doi.org/10.1101/2022.02.02.477408

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