1Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, Netherlands.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. genodive can handle a wide range of data sets, from modest mi - crosatellite data sets with only a few loci to large RAD data sets with tens of thousands of SNPs.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).Īlso many statistical tools for the analysis of genetic data, such as AMOVA and genome scans, are available only for haploids and diploids. GenoDive enables some differentiation, clustering (i.e., the robust method for mixed-populations STRUCTURE Stift, Kol, & Meirmans, 2019), and ordination analysis (denoted by b, Table 1). We show how this can be done by adjusting the equations for calculating the Sums of Squares, degrees of freedom and covariance components. number of alleles of each sample and estimating their allele dos-age. raw data and extracted the matrix of 94 individuals x 42,587 SNPs. Variance is a measure of dispersion and can be defined as the spread of data from the mean of the given dataset. The method can be applied to a dataset containing a single ploidy level, but also to datasets with a mixture of ploidy levels. genodive sample data matrix full Finally, I randomly sampled 10,000 columns (SNPs) from the full data set, which is what we. the coordinates of populations), which can be combined in the statistical inferences. pairwise genetic distances between populations), and generic other data (e.g. SNPs from RAD sequencing), distance matrices (e.g. If the maximum ploidy needs to be reduced by random removal of alleles, it is possible to do this in the software GenoDive after importing the data. 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. GenoDive can handle three different types of data: markerdata (e.g. The number of individuals, number of populations, number of loci, and maximum ploidy of the sample are calculated automatically and entered in the second line of the file. 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.
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