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GenAPoPop 1.0: A user‐friendly software to analyse genetic diversity and structure from partially clonal and selfed autopolyploid organisms
Autopolyploidy is quite common in most clades of eukaryotes. The emergence of sequence‐based genotyping methods with individual and marker tags now enables confident allele dosage, overcoming the main obstacle to the democratization of the population genetic approaches when studying ecology and evolution of autopolyploid populations and species. Reproductive modes, including clonality, selfing and allogamy, have deep consequences on the ecology and evolution of population and species. Analysing genetic diversity and its dynamics over generations is one efficient way to infer the relative importance of clonality, selfing and allogamy in populations. GenAPoPop is a user‐friendly solution to compute the specific corpus of population genetic indices, including indices about genotypic diversity, needed to analyse partially clonal, selfed and allogamous polysomic populations genotyped with confident allele dosage. It also easily provides the posterior probabilities of quantitative reproductive modes in autopolyploid populations genotyped at two‐time steps and a graphical representation of the minimum spanning trees of the genetic distances between polyploid individuals, facilitating the interpretation of the genetic coancestry between individuals in hierarchically structured populations. GenAPoPop complements the previously existing solutions, including SPAGEDI and POLYGENE, to use genotypings to study the ecology and evolution of autopolyploid populations. It was specially developed with a simple graphical interface and workflow, and comes with a simulator to facilitate practical courses and teaching of population genetics for autopolyploid populations.
Keyword(s)
Bayesian inference of reproductive modes, genetic differentiation, genotypic diversity, polyploidy, unrooted tree of genetic distances
Full Text
File | Pages | Size | Access | |
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Publisher's official version | 11 | 540 Ko | ||
Figure S1. | - | 7 Mo | ||
Preprint - 10.22541/au.166996062.20159219/v1 | 29 | 676 Ko |