ClonEstiMatePoly tab
This tab allows users to compute the posterior probabilities of joint rates of clonality and selfing in polyploid populations genotyped at, at least, two-time steps. This method was demonstrated to be the most accurate way to quantitatively assess reproductive modes in diploid populations over multiple Eukaryotes species, especially for detecting low rates of clonality (Becheler et al. 2017). It should facilitate the detection of clonal reproduction, the estimation of the rates of clonality in polyploid populations, and promote the study of reproductive modes and their genetic consequences in such species. It should be a nice addition to the method of estimation of selfing rates using multilocus standardized identity disequilibrium coefficient found in spagedi (Hardy 2016).
Here, we extended to autopolyploids the Bayesian formula and method ClonEstiMate from Becheler et al. (2017). It exploits the likelihood of transitions of genotype frequencies from one generation to another to accurately estimate rates of mutation, clonality and selfing, and thus works well even in the absence of equilibrium between evolutionary forces (genetic drift, mutation and rates of clonality) which is quite common in partially clonal populations (Reichel et al. 2016). This method remains accurate using from about ten polymorphic markers, even physically linked and mutating with other mutation model, and from 30 sampled individuals. It is however sensitive to erroneous assumed or restricted prior values of clonal and selfing rates, null alleles and sampling time interval greater than two generations. Extended equations for autopolyploids can be found in the documentation in supplementary material. This discretized Bayesian method needs an analysis plan listing discretized priors on rates of mutation, clonality and selfing for each population (Fig. S1). Restricted ranges of prior on each of these parameters allows better inferences on other targeted parameters. Analysis plan can be uploaded or prepared (and saved for future use) using the graphical interface. Analysis plan can be browsed and checked using the integrated browser before launching the computations. To speed-up the calculations, computations per locus and population of the analysis plan were parallelized using the maximum number of threads available by the operating system. Results are stored in the folder containing GenAPoPop in a text-file separator tabulation file that can be readily handled using any spreadsheet application. Results are presented per population between two time-steps as a list of discrete joined values of mutation rate, rates of clonality and selfing with the corresponding posterior probabilities of such joined combination of priors. This presentation of the results makes it easy to combine the posterior probability mass functions per population and generations into table and/or into plots of their distributions. If found in the dataset, it also returns the list of monomorphic loci at, at least, one sampling time. Monomorphic loci decrease the inference power of the dataset to assess rates of mutation, clonality and selfing between the two sampled generations.