A roadmap to fill the gaps
Given the current state of knowledge, the characterisation of the
genotypic (based on groups of clones) and genetic (based on allele and
genotype frequencies at loci) compositions of populations is both the
target and the proxy of population genetics studies aiming to
understand the influence of clonal reproduction on the dynamics and
evolution of natural populations. Reconciling the effects of PC on both
the genotypic and genetic compositions of populations in a robust
theoretical framework is thus necessary to illuminate the concomitant
changes in their respective estimators depending on the rate of
clonality.
Here, we propose a simulation-based exploratory approach both to enhance
our understanding of the consequences of clonality and to improve our
ability to reliably assess its rate within natural populations. We aim
to provide the first exploration of the effect of increasing c on
the genotypic and genetic compositions of populations to provide
baseline expectations for the composition of natural populations
depending on the extent of clonality. We used comprehensive forward
individual-based simulations to obtain the theoretical distribution of
genotypic (genotypic richness and size distribution of lineages) and
genetic (departure from HWE and LD) parameters describing the population
composition at increasing rates of clonality from 0 to 1, including all
populations with some clonal reproduction, hereafter denoted PC for
brevity, ranging from partial (0<c<1) to strict
clonality (c=1), and populations with solely sexual reproduction (c=0),
hereafter denoted sexual populations. We explored the temporal evolution
of these populations along trajectories towards equilibrium and under
various levels of genetic drift (population sizes spanning three orders
of magnitude). To move from insights about the expected effects of PC on
natural populations towards more reproducible and formalised arguments,
we assessed the signature of PC in the genotypic and genetic index
distributions using a classical and robust Bayesian supervised learning
method. This method allowed the selection of descriptors that were more
clearly affected to in turn develop sound estimates of the extent of
clonality. Finally, we tested the robustness of the method in examining
the influence of sample size on the accuracy of estimates and proposed
further improvements based on realistic sample sizes.