Pervasive genetic hitchhiking and clonal interference in forty evolving yeast populations

Journal name:
Nature
Volume:
500,
Pages:
571–574
Date published:
DOI:
doi:10.1038/nature12344
Received
Accepted
Published online

The dynamics of adaptation determine which mutations fix in a population, and hence how reproducible evolution will be. This is central to understanding the spectra of mutations recovered in the evolution of antibiotic resistance1, the response of pathogens to immune selection2, 3, and the dynamics of cancer progression4, 5. In laboratory evolution experiments, demonstrably beneficial mutations are found repeatedly6, 7, 8, but are often accompanied by other mutations with no obvious benefit. Here we use whole-genome whole-population sequencing to examine the dynamics of genome sequence evolution at high temporal resolution in 40replicate Saccharomyces cerevisiae populations growing in rich medium for 1,000generations. We find pervasive genetic hitchhiking: multiple mutations arise and move synchronously through the population as mutational ‘cohorts’. Multiple clonal cohorts are often present simultaneously, competing with each other in the same population. Our results show that patterns of sequence evolution are driven by a balance between these chance effects of hitchhiking and interference, which increase stochastic variation in evolutionary outcomes, and the deterministic action of selection on individual mutations, which favours parallel evolutionary solutions in replicate populations.

At a glance

Figures

  1. The fates of individual spontaneously arising mutations.
    Figure 1: The fates of individual spontaneously arising mutations.

    We show the frequency of all identified mutations through 1,000generations in 6of the 40sequenced populations. Non-synonymous mutations are solid lines with solid circles, and synonymous and intergenic mutations are dotted lines with open circles and squares, respectively. Populations in the left and right columns were evolved at small (105) and large (106) population sizes, respectively. We observe qualitatively similar patterns in the other populations (Supplementary Fig. 1).

  2. Statistical analysis across 40[thinsp]replicate populations.
    Figure 2: Statistical analysis across 40replicate populations.

    a, The per-population number of total mutations, fixed mutations, extinct mutations and mutations that are currently polymorphic over the course of the 1,000generations. b, The distribution of the number of new mutations detected at each time point (solid blue line; see Methods for details) and a Poisson distribution with the same mean (dashed red line). c, d, Mutation fixation probability as a function of initial relative fitness. Data are mean±s.e.m.

  3. The dynamics of sequence evolution in BYB1-G07.
    Figure 3: The dynamics of sequence evolution in BYB1-G07.

    a, The trajectories of the 15 mutations that attain a frequency of at least 30%, hierarchically clustered into several distinct mutation ‘cohorts’, each of which is represented by a different colour (Methods). b, Muller diagram showing the dynamics of the six main cohorts in the population. The number of times a mutation was observed in a given gene across all 40populations is indicated in parentheses. Mutations in genes observed in more than three replicate populations (Table 1) are indicated in bold.

  4. Genetic dissection of BYS1-A08.
    Figure 4: Genetic dissection of BYS1-A08.

    a, The trajectories of observed mutations. b, We crossed evolved clones from generation 545(dotted grey line in a) to the ancestor; shown here are the fitnesses and genotypes of parental clones and 80haploid progeny.

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References

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Author information

  1. These authors contributed equally to this work.

    • Gregory I. Lang &
    • Daniel P. Rice

Affiliations

  1. Lewis-Sigler Institute for Integrative Genomics and Department of Molecular Biology, Princeton University, Princeton, New Jersey 08544, USA

    • Gregory I. Lang &
    • David Botstein
  2. Departments of Organismic and Evolutionary Biology and of Physics, and FAS Center for Systems Biology, Harvard University, Cambridge, Massachusetts 02138, USA

    • Daniel P. Rice &
    • Michael M. Desai
  3. Departments of Biological Sciences and Chemistry & Biochemistry, Rowan University, Glassboro, New Jersey 08028, USA

    • Mark J. Hickman
  4. The Genome Institute, Washington University School of Medicine, St. Louis, Missouri 63108, USA

    • Erica Sodergren &
    • George M. Weinstock
  5. Present address: Department of Biological Sciences, Lehigh University, Bethlehem, Pennsylvania 18015, USA.

    • Gregory I. Lang

Contributions

G.I.L., D.B. and M.M.D. designed the project; E.S. and G.M.W. generated the sequencing data; G.I.L., D.P.R., M.J.H. and M.M.D. analysed the sequencing data; G.I.L. performed the experiments; G.I.L., D.P.R., D.B. and M.M.D. wrote the paper. Co-senior authors, D.B. and M.M.D.

Competing financial interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to:

Genome sequence data have been deposited to GenBank under the BioProject identifier PRJNA205542.

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Supplementary information

PDF files

  1. Supplementary Information (5.9 MB)

    This file contains Supplementary Tables 2-3 (see separate file for Supplementary Table 1) and Supplementary Figures 1-3.

Excel files

  1. Supplementary Table 1 (412 KB)

    This file contains details of the 1,020 mutations identified in the 40 sequenced populations. It also contains complete descriptions of all mutations we observed in all 40 populations, and their frequency trajectories over the 1,000 generations of the experiment, as estimated by both independent pipelines (Methods).

Additional data