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