17
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      genodive version 3.0: Easy‐to‐use software for the analysis of genetic data of diploids and polyploids

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          genodive version 3.0 is a user‐friendly program for the analysis of population genetic data. This version presents a major update from the previous version and now offers a wide spectrum of different types of analyses. 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. Furthermore, genodive seamlessly supports 15 different file formats for importing or exporting data from or to other programs. One major feature of genodive is that it supports both diploid and polyploid data, up to octaploidy (2 n = 8 x) for some analyses, but up to hexadecaploidy (2 n = 16 x) for other analyses. The different types of analyses offered by genodive include multiple statistics for estimating population differentiation ( φ ST, F ST, Fʹ ST, G ST, Gʹ ST, Gʹʹ 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. 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. 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. genodive is available for computers running Mac OS X 10.7 or higher and can be downloaded freely from: http://www.patrickmeirmans.com/software.

          Abstract

          see also the Perspective by Amanda S. Ackiss and Francisco Balao

          Related collections

          Most cited references19

          • Record: found
          • Abstract: found
          • Article: not found

          Genetic assignment methods for the direct, real-time estimation of migration rate: a simulation-based exploration of accuracy and power.

          Genetic assignment methods use genotype likelihoods to draw inference about where individuals were or were not born, potentially allowing direct, real-time estimates of dispersal. We used simulated data sets to test the power and accuracy of Monte Carlo resampling methods in generating statistical thresholds for identifying F0 immigrants in populations with ongoing gene flow, and hence for providing direct, real-time estimates of migration rates. The identification of accurate critical values required that resampling methods preserved the linkage disequilibrium deriving from recent generations of immigrants and reflected the sampling variance present in the data set being analysed. A novel Monte Carlo resampling method taking into account these aspects was proposed and its efficiency was evaluated. Power and error were relatively insensitive to the frequency assumed for missing alleles. Power to identify F0 immigrants was improved by using large sample size (up to about 50 individuals) and by sampling all populations from which migrants may have originated. A combination of plotting genotype likelihoods and calculating mean genotype likelihood ratios (DLR) appeared to be an effective way to predict whether F0 immigrants could be identified for a particular pair of populations using a given set of markers.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Spatial autocorrelation analysis of individual multiallele and multilocus genetic structure.

            Population genetic theory predicts that plant populations will exhibit internal spatial autocorrelation when propagule flow is restricted, but as an empirical reality, spatial structure is rarely consistent across loci or sites, and is generally weak. A lack of sensitivity in the statistical procedures may explain the discrepancy. Most work to date, based on allozymes, has involved pattern analysis for individual alleles, but new PCR-based genetic markers are coming into vogue, with vastly increased numbers of alleles. The field is badly in need of an explicitly multivariate approach to autocorrelation analysis, and our purpose here is to introduce a new approach that is applicable to multiallelic codominant, multilocus arrays. The procedure treats the genetic data set as a whole, strengthening the spatial signal and reducing the stochastic (allele-to-allele, and locus-to-locus) noise. We (i) develop a very general multivariate method, based on genetic distance methods, (ii) illustrate it for multiallelic codominant loci, and (iii) provide nonparametric permutational testing procedures for the full correlogram. We illustrate the new method with an example data set from the orchid Caladenia tentaculata, for which we show (iv) how the multivariate treatment compares with the single-allele treatment, (v) that intermediate frequency alleles from highly polymorphic loci perform well and rare alleles poorly, (vi) that a multilocus treatment provides clearer answers than separate single-locus treatments, and (vii) that weighting alleles differentially improves our resolution minimally. The results, though specific to Caladenia, offer encouragement for wider application.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              The trouble with isolation by distance.

              The genetic population structure of many species is characterised by a pattern of isolation by distance (IBD): due to limited dispersal, individuals that are geographically close tend to be genetically more similar than individuals that are far apart. Despite the ubiquity of IBD in nature, many commonly used statistical tests are based on a null model that is completely non-spatial, the Island model. Here, I argue that patterns of spatial autocorrelation deriving from IBD present a problem for such tests as it can severely bias their outcome. I use simulated data to illustrate this problem for two widely used types of tests: tests of hierarchical population structure and the detection of loci under selection. My results show that for both types of tests the presence of IBD can indeed lead to a large number of false positives. I therefore argue that all analyses in a study should take the spatial dependence in the data into account, unless it can be shown that there is no spatial autocorrelation in the allele frequency distribution that is under investigation. Thus, it is urgent to develop additional statistical approaches that are based on a spatially explicit null model instead of the non-spatial Island model. © 2012 Blackwell Publishing Ltd.
                Bookmark

                Author and article information

                Contributors
                p.g.meirmans@uva.nl
                Journal
                Mol Ecol Resour
                Mol Ecol Resour
                10.1111/(ISSN)1755-0998
                MEN
                Molecular Ecology Resources
                John Wiley and Sons Inc. (Hoboken )
                1755-098X
                1755-0998
                11 March 2020
                July 2020
                : 20
                : 4 ( doiID: 10.1111/men.v20.4 )
                : 1126-1131
                Affiliations
                [ 1 ] Institute for Biodiversity and Ecosystem Dynamics (IBED) University of Amsterdam Amsterdam The Netherlands
                Author notes
                [*] [* ] Correspondence

                Patrick G. Meirmans, Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, Amsterdam, The Netherlands.

                Email: p.g.meirmans@ 123456uva.nl

                Author information
                https://orcid.org/0000-0002-6395-8107
                Article
                MEN13145
                10.1111/1755-0998.13145
                7496249
                32061017
                0846ef96-3ad2-448a-a612-62e4add73787
                © 2020 The Authors. Molecular Ecology Resources published by John Wiley & Sons Ltd

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                History
                : 19 September 2019
                : 09 January 2020
                : 10 February 2020
                Page count
                Figures: 1, Tables: 0, Pages: 6, Words: 10054
                Categories
                Resource Article
                RESOURCE ARTICLES
                Computer Programs
                Custom metadata
                2.0
                July 2020
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.9.0 mode:remove_FC converted:11.09.2020

                Ecology
                amova,genetic distances,genetic diversity,k‐means,polyploidy,population differentiation
                Ecology
                amova, genetic distances, genetic diversity, k‐means, polyploidy, population differentiation

                Comments

                Comment on this article