![]() ![]() In addition, genodive makes it possible to run several external programs (lfmm, structure, instruct and vegan) directly from its own user interface, avoiding the need for data reformatting and use of the command line. A unique feature of genodive is that it can also open data sets with nongenetic variables, for example environmental data or geographical coordinates that can be included in the analysis. The different types of analyses offered by genodive include multiple statistics for estimating population differentiation (φ ST, F ST, F' ST, G ST, G' ST, G'' ST, D est, R ST, ρ), analysis of molecular variance-based K-means clustering, Hardy-Weinberg equilibrium, hybrid index, population assignment, clone assignment, Mantel test, Spatial Autocorrelation, 23 ways of calculating genetic distances, and both principal components and principal coordinates analyses. One major feature of genodive is that it supports both diploid and polyploid data, up to octaploidy (2n = 8x) for some analyses, but up to hexadecaploidy (2n = 16x) for other analyses. Furthermore, genodive seamlessly supports 15 different file formats for importing or exporting data from or to other programs. genodive has an intuitive graphical user interface that allows direct manipulation of the data through transformation, imputation of missing data, and exclusion and inclusion of individuals, population and/or loci. This version presents a major update from the previous version and now offers a wide spectrum of different types of analyses. Therefore, simulations such as we used throughout this review are an important tool to verify the results of analyses of polyploid genetic data.Genodive version 3.0 is a user-friendly program for the analysis of population genetic data. Furthermore, the availability of more data may aggravate the biases that can arise, and increase the risk of false inferences. ![]() Modern sequencing techniques will soon be able to overcome some of the current limitations to the analysis of polyploid data, though the techniques are lagging behind those available for diploids. From our overview, it is clear that the statistical toolbox that is available for the analysis of genetic data is flexible and still expanding. We also discuss for each type of inference what biases may arise from the polyploid-specific complications and how these biases can be overcome. For each, we point out how the statistical approach, expected result, and interpretation differ between different ploidy levels. We discuss several widely used types of inferences, including genetic diversity, Hardy-Weinberg equilibrium, population differentiation, genetic distance, and detecting population structure. Here, we review the theoretical and statistical aspects of the population genetics of polyploids. This is because of several polyploidy-specific complications in segregation and genotyping such as tetrasomy, double reduction, and missing dosage information. This is unfortunate since the analysis of polyploid genetic data-and the interpretation of the results-requires even more scrutiny than with diploid data. Though polyploidy is an important aspect of the evolutionary genetics of both plants and animals, the development of population genetic theory of polyploids has seriously lagged behind that of diploids. ![]()
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