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The discernible and hidden effects of clonality on the genotypic and genetic states of populations: improving our estimation of clonal rates
  • Solenn Stoeckel,
  • Barbara Porro,
  • Sophie Arnaud-Haond
Solenn Stoeckel
INRA
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Barbara Porro
Université de Nice Sophia Antipolis
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Sophie Arnaud-Haond
IFREMER Centre de Brest
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Abstract

Partial clonality is widespread across the tree of life, but most population genetics models are designed for exclusively clonal or sexual organisms. This gap hampers our understanding of the influence of clonality on evolutionary trajectories and the interpretation of population genetics data. We performed forward simulations of diploid populations at increasing rates of clonality (c), analysed their relationships with genotypic (clonal richness, R, and distribution of clonal sizes, Pareto β) and genetic (FIS and linkage disequilibrium) indices, and tested predictions of c from population genetics data through supervised machine learning. Two complementary behaviours emerged from the probability distributions of genotypic and genetic indices with increasing c. While the impact of c on R and Pareto β was easily described by simple mathematical equations, its effects on genetic indices were noticeable only at the highest levels (c>0.95). Consequently, genotypic indices allowed reliable estimates of c, while genetic descriptors led to poorer performances when c<0.95. These results provide clear baseline expectations for genotypic and genetic diversity and dynamics under partial clonality. Worryingly, however, the use of realistic sample sizes to acquire empirical data systematically led to gross underestimates (often of one to two orders of magnitude) of c, suggesting that many interpretations hitherto proposed in the literature, mostly based on genotypic richness, should be reappraised. We propose future avenues to derive realistic confidence intervals for c and show that, although still approximate, a supervised learning method would greatly improve the estimation of c from population genetics data.

Peer review status:IN REVISION

28 Apr 2020Submission Checks Completed
28 Apr 2020Assigned to Editor
06 May 2020Reviewer(s) Assigned
06 Jul 2020Review(s) Completed, Editorial Evaluation Pending
25 Aug 2020Editorial Decision: Revise Minor
22 Sep 2020Review(s) Completed, Editorial Evaluation Pending
22 Sep 20201st Revision Received