![]() ![]() Several genes we identified that are implicated in sister arenosa, indicating selection may have acted on standing variation already present in the diploid. For a critical meiosis gene, ASYNAPSIS1, we identified a non-synonymous mutation that is highly differentiated by cytotype, but present as a rare variant in diploid A. ![]() Numerous encoded proteins are predicted to interact with one another. Many of these are at genes implicated in genome maintenance functions, including chromosome cohesion and segregation, DNA repair, homologous recombination, transcriptional regulation, and chromatin structure. ![]() arenosa individuals and found signatures suggestive of recent and ongoing selective sweeps throughout the genome. It thus provides a rare opportunity to leverage genomic tools to investigate the genetic basis of polyploid stabilization. Arabidopsis arenosa includes stable tetraploid populations and is related to well-characterized diploids A. Understanding how natural selection enabled these species to overcome early challenges can provide important insights into the mechanisms by which core cellular functions can adapt to perturbations of the genomic environment. Nevertheless, stable polyploid species occur in both plants and animals. In mammals, genome duplication is associated with cancer and spontaneous abortion of embryos. Genome duplications occur spontaneously in a range of taxa and problems such as sterility, aneuploidy, and gene expression aberrations are common in newly formed polyploids. Therefore, simulations such as we used throughout this review are an important tool to verify the results of analyses of polyploid genetic data.Genome duplication, which results in polyploidy, is disruptive to fundamental biological processes. 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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