The discernible and hidden effects of clonality on the genotypic and genetic states of populations: improving our estimation of clonal rates

Type Article
Date 2021-05
Language English
Author(s) Stoeckel SolennORCID1, Porro BarbaraORCID2, 3, Arnaud‐haond SophieORCID3
Affiliation(s) 1 : Institute for Genetics, Environment and Plant Protection INRA, UMR1349 Le Rheu ,France
2 : Institute for Research on Cancer and Aging (IRCAN) CNRS UMR 7284, INSERM U1081, Université de Nice‐Sophia‐Antipolis Nice 06107 ,France
3 : MARBEC – Marine biodiversity exploitation and conservation Univ Montpellier, CNRS, Ifremer, IRD, MARBEC F‐34200 Sète, France
Source Molecular Ecology Resources (1755-098X) (Wiley), 2021-05 , Vol. 21 , N. 4 , P. 1068-1084
DOI 10.1111/1755-0998.13316
WOS© Times Cited 4
Keyword(s) F-statistics, genotypic diversity, population genetics, rates of clonality, sampling
Abstract

Partial clonality is widespread across the tree of life, but most population genetic 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 genetic 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 genetic 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 genetic data.

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