GenPopPoly tab
This tab allows users to compute a list of population genetic indices suitable to analyse genetic diversity and population structure of polyploid populations with a special focus on reproductive modes. These indices are useful and efficient to estimate rates of clonality, autogamy (selfing) and allogamy on genotypes of sampled populations sampled at one time (Castric et al. 2002, David et al. 2007, Hardy 2016, Stoeckel et al. 2021). Users select the population(s) to be analysed, select the analyses to be computed and reported, launch the computation and can directly browse the results for a first sight in the integrated calc viewer. The results are also saved in a text-file (separator tabulation) in the folder containing GenAPoPop executable. Result files can readily be opened by all spreadsheet applications to be explored and manipulated to do tables and figures. The output file presents first all intra-population indices computed per population, then computed overall populations. It includes genotypic and genetic diversity indices as recommended in Stoeckel et al. (2021), probabilities of identity for diploids and autopolyploids (Jacquard 1970, Evett & Weir 1998, Waits et al. 2001, Huang et al. 2015), the four first moments (i.e., mean, variance, skewness and kurtosis) of inbreeding coefficient FIS in populations (Stoeckel & Masson 2014). It also provides a list of multi-locus genotypes (commonly named MLG in literature or genet) with their shared genotype, and in the last column, the number of repeated genotypes (ramet) found in the considered population. In each and overall populations, it reports genotypic diversity indices including the index of clonal diversity (R , Dorken & Eckert, 2001) and the size distribution of lineages (D* of Simpson and Pareto 𝛽 , Arnaud-Haond et al., 2007) computed properly for autopolyploids. We deliberately discarded many other indices to help users robustly interpreting genotypic diversity in their populations. Despite Pareto 𝛽 is far more robust than the R to assess genotypic diversity in sampled populations (Stoeckel et al. 2021, Arnaud-Haond et al. 2020), we still compute R for reference, as this one was historically massively reported in past literature. The output also provides the mean correlation coefficient of genetic distances between unordered alleles at all loci, usually named \({\overset{\overline{}}{r}}_{d}\) as an overall measure of linkage disequilibrium per population and overall populations (Agapow & Burt, 2001). This index, ranging from slightly negative or 0 (no correlation) to 1 (maximum association of alleles over all loci), presents the advantage of limiting the dependency of the correlation coefficient on the number of alleles and loci. GenAPoPop also provides per population and overall populations a table of classical intra-populational genetic indices per locus: observed heterozygosity, raw and unbiased expected heterozygosity (also name gene diversity), resulting raw and unbiased inbreeding coefficient (Fis ) accounting for intra-individual genetic variation as a departure from Hardy-Weinberg assumptions of the genotyped populations and the raw and effective number of alleles (Ae , Weir 1996). On a side and more experimental part, GenAPoPop allows computing analysis of molecular variance (AMOVA) computed following Meirmans & Liu (2018) and Weir (1996) equations and recommendations, including the Fis, Fst and Fit per population, over all populations, per marker and over all markers. These results can already be obtained using Polygene and Genodive. GenAPoPop also provides in this section the overall and pairwise-population rhost . rhost measures the genetic differentiation between populations as the Fst value that would have the same haploid population sizes connected with the same migration rate, and present the advantage to be comparable between species and populations of different ploidy levels (Ronfort et al. 1998, Meirmans & Van Tienderen 2013). These indices of genetic differentiation/structuration are a good complement to the minimum spanning tree of the genetic distances between individuals when coloured or tagged by population to get a picture of the genetic structure of genotyped populations (see below). As these indices are also computed in Spagedi and Polygene, we invite users to also compare their results with these softwares.
GenAPoPop was thinked and designed to complement Genodive, Polygene that performs hierarchical, Bayesian clustering and parentage analysis, and Spagedi that already performs multiple spatial analyses and that can be used to estimate selfing rate. In this tab, GenAPoPop users can export automatically their datasets in a Spagedi-format file that will be recorded in the same folder under the same imported data name extend with “_spagedi_ready.txt ”. This file that can be easily imported to extend and access complementary analyses in the previously cited software, including Spagedi and Polygene, and we greatly encourage future GenAPoPop users to analyse their data with multiple softwares to get the most complete view of their dataset.