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

TL;DR

This study uses simulations and machine learning to improve estimation of clonality rates in populations, revealing limitations of current methods and providing new baseline expectations for genetic and genotypic diversity.

Contribution

It introduces a combined simulation and supervised learning approach to better estimate clonality rates from population genetics data, addressing previous methodological gaps.

Findings

Genotypic indices reliably estimate clonality c, especially when c>0.95.

Genetic indices are less effective at lower clonality levels.

Empirical data often significantly underestimate true clonality 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 \beta) 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 \beta 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.