![]() The analysis of population genetic structure of autopolyploids may therefore reveal a lot about these processes. In a species with different ploidy levels, the different cytotypes often show intricate geographical patterns in their distribution, which may be the result of historical, demographic, ecological, or genetic processes ( Glennon et al., 2014 Kolár et al., 2017). Autopolyploidy has many effects on the mechanisms of evolution, not only because of the increase in genomic content and the flexibility for developing new traits ( Larkin et al., 2016), but also because, compared to diploidy, it generates different dynamics of allele frequencies that interact with various demographic processes, influencing adaptation and speciation ( Parisod et al., 2010). There are many species in which multiple ploidy levels (cytotypes) exist and often each cytotype itself conforms to the requirements of several widely used species concepts ( Soltis et al., 2007). We also hope that this research will increase the adoption of the ploidy-independent ρ-statistic, which has many qualities that makes it better suited for comparisons among species than the standard F ST, both for diploids and for polyploids.Īutopolyploidy is an important, but often overlooked, aspect of the evolution of all major groups of Eukaryotes-plants, animals, and fungi- and may constitute an underappreciated source of biodiversity ( Hardy, 2015). We hope that the development of AMOVA for autopolyploids will help to narrow the gap in availability of statistical tools for diploids and polyploids. The problem of missing dosage information cannot be taken into account directly into the analysis, but can be remedied effectively by imputation of the allele frequencies. ![]() We tested the method using data simulated under a hierarchical island model: the results of the analyses of the simulated data closely matched the values derived from theoretical expectations. The ρ-statistic is well suited for polyploid data since its expected value is independent of the ploidy level, the rate of double reduction, the frequency of polysomic inheritance, and the mating system. The ρ-statistic can be calculated in an AMOVA by first calculating a matrix of squared Euclidean distances for all pairs of individuals, based on the within-individual allele frequencies. In addition, we show how AMOVA can be used to estimate the summary statistic ρ, which was developed especially for polyploid data, but unfortunately has seen very little use. The method can be applied to a dataset containing a single ploidy level, but also to datasets with a mixture of ploidy levels. We show how this can be done by adjusting the equations for calculating the Sums of Squares, degrees of freedom and covariance components. In this paper, we show how the Analysis of Molecular Variance (AMOVA) framework can be extended to include autopolyploid data, which will allow calculating several genetic summary statistics for estimating the strength of genetic differentiation among autopolyploid populations ( F ST, φ ST, or R ST). Also many statistical tools for the analysis of genetic data, such as AMOVA and genome scans, are available only for haploids and diploids. 2Department of Bioscience, Aarhus University, Aarhus, DenmarkĪutopolyploids present several challenges to researchers studying population genetics, since almost all population genetics theory, and the expectations derived from this theory, has been developed for haploids and diploids.1Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, Netherlands.
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