ClonEstiMate, a Bayesian method for quantifying rates of clonality of populations genotyped at two-time steps.

Partial clonality is commonly used in eukaryotes and has large consequences for their evolution and ecology. Assessing accurately the relative importance of clonal vs. sexual reproduction matters for studying and managing such species. Here, we proposed a Bayesian approach, ClonEstiMate, to infer rates of clonality c from populations sampled twice over a short time interval, ideally one generation time. The method relies on the likelihood of the transitions between genotype frequencies of ancestral and descendent populations, using an extended Wright-Fisher model explicitly integrating reproductive modes. Our model provides posterior probability distribution of inferred c, given the assumed rates of mutation, as well as inbreeding and selfing when occurring. Tested under various conditions, this model provided accurate inferences of c, especially when the amount of information was modest, that is low sample sizes, few loci, low polymorphism and strong linkage disequilibrium. Inferences remained robust when mutation models and rates were misinformed. However, the method was sensitive to moderate frequencies of null alleles and when the time interval between required samplings exceeding two generations. Misinformed rates on mating modes (inbreeding and selfing) also resulted in biased inferences. Our method was tested on eleven data sets covering five partially clonal species, for which the extent of clonality was formerly deciphered. It delivered highly consistent results with previous information on the biology of those species. ClonEstiMate represents a powerful tool for detecting and inferring clonality in finite populations, genotyped with SNPs or microsatellites. It is freely available at https://www6.rennes.inra.fr/igepp_eng/Productions/Software.