3
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      High-throughput SNPs dataset reveal restricted population connectivity of marine gastropod within the narrow distribution range of peripheral oceanic islands

      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

          Molecular studies based on the high resolution genetic markers help us to grasp the factor shaping the genetic structure of marine organisms. Ecological factors linking to life history traits have often explained the process of genetic structuring in open and connectable oceanic environments. Besides, population genetic divergence can be affected by fragmented habitat, oceanic current, and past geographical events. In the present study, we demonstrated the genetic differentiation of marine gastropod Monodonta sp. within a narrow range of peripheral oceanic islands, the Ogasawara Islands. Genetic analyses were performed not only with a mitochondrial DNA marker but also with a high-throughput SNPs dataset obtained by ddRAD-seq. The results of the mtDNA analyses did not show genetic divergence among populations, while the SNPs dataset detected population genetic differentiation. Population demographic analyses and gene flow estimation suggested that the genetic structure was formed by sea level fluctuation associated with the past climatic change and regulated by temporal oceanographic conditions. These findings provide important insights into population genetic patterns in open and connectable environments.

          Related collections

          Most cited references92

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

          MUSCLE: multiple sequence alignment with high accuracy and high throughput.

          We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice

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

              Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows.

              We present here a new version of the Arlequin program available under three different forms: a Windows graphical version (Winarl35), a console version of Arlequin (arlecore), and a specific console version to compute summary statistics (arlsumstat). The command-line versions run under both Linux and Windows. The main innovations of the new version include enhanced outputs in XML format, the possibility to embed graphics displaying computation results directly into output files, and the implementation of a new method to detect loci under selection from genome scans. Command-line versions are designed to handle large series of files, and arlsumstat can be used to generate summary statistics from simulated data sets within an Approximate Bayesian Computation framework. © 2010 Blackwell Publishing Ltd.
                Bookmark

                Author and article information

                Contributors
                zaki.daishi@gmail.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                8 February 2022
                8 February 2022
                2022
                : 12
                : 2119
                Affiliations
                [1 ]GRID grid.69566.3a, ISNI 0000 0001 2248 6943, Center for Northeast Asian Studies, , Tohoku University, ; 41 Kawauchi, Aoba-ku, Sendai, Miyagi 980-8576 Japan
                [2 ]GRID grid.69566.3a, ISNI 0000 0001 2248 6943, Graduate School of Life Science, , Tohoku University, ; 2-1-1 Katahira, Aoba-ku, Sendai, Miyagi 980-8577 Japan
                [3 ]GRID grid.278276.e, ISNI 0000 0001 0659 9825, Faculty of Agriculture and Marine Science, , Kochi University, ; 200 Monobe, Nankoku, Kochi, 783-8502 Japan
                [4 ]Institute of Boninology, Chichijima-Aza-Nishimachi, Ogasawara, Tokyo 100-2101 Japan
                Article
                5026
                10.1038/s41598-022-05026-z
                8825847
                35136087
                df5327b8-20eb-46ff-a013-735b03de9182
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 16 July 2021
                : 29 December 2021
                Funding
                Funded by: Grant-in-Aid for Early-Career Scientists
                Award ID: 21K15161
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                population genetics,biogeography
                Uncategorized
                population genetics, biogeography

                Comments

                Comment on this article