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      Stronger genetic differentiation among within-population genetic groups than among populations in Scots pine provides new insights into within-population genetic structuring

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          Abstract

          We investigated the presence of spatial genetic groups within forest tree populations and determined if the genetic divergence among these groups is greater than that between populations using Scots pine ( Pinus sylvestris) as a model species. We genotyped 890 adult trees of Scots pine in six natural populations in Lithuania at 11 nuclear microsatellite loci. We used a Bayesian clustering approach to identify the within-population genetic groups within each of the six populations. We calculated the differentiation indexes among the genetic groups within each population and among the six populations by ignoring the genetic groups. The Bayesian clustering revealed 2 to 6 distinct genetic groups of varying size as the most likely genetic structures within populations. The genetic differentiation indexes among the genetic groups within populations were nearly tenfold greater ( F ST = 0.012–0.070) than those between the populations ( F ST = 0.003). We conclude on the existence of markedly stronger structuring of genetic variation within populations than between populations of Scots pine in large forest tracts of northern Europe. Such genetic structures serve as a contributing factor to large within population genetic diversity in northern conifers. We assume that within population mating in Scots pine is not completely random but rather is stratified into genetic clusters. Our study provides pioneering novel key insights into structuring of genetic variation within populations. Our findings have implications for examining within-population genetic diversity and genetic structure, conservation, and management of genetic resources.

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          Inference of Population Structure Using Multilocus Genotype Data

          We describe a model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations. We assume a model in which there are K populations (where K may be unknown), each of which is characterized by a set of allele frequencies at each locus. Individuals in the sample are assigned (probabilistically) to populations, or jointly to two or more populations if their genotypes indicate that they are admixed. Our model does not assume a particular mutation process, and it can be applied to most of the commonly used genetic markers, provided that they are not closely linked. Applications of our method include demonstrating the presence of population structure, assigning individuals to populations, studying hybrid zones, and identifying migrants and admixed individuals. We show that the method can produce highly accurate assignments using modest numbers of loci—e.g., seven microsatellite loci in an example using genotype data from an endangered bird species. The software used for this article is available from http://www.stats.ox.ac.uk/~pritch/home.html.
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            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.
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              genalex 6: genetic analysis in Excel. Population genetic software for teaching and research

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                Author and article information

                Contributors
                darius.danusevicius@vdu.lt
                Om.Rajora@unb.ca
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                1 February 2024
                1 February 2024
                2024
                : 14
                : 2713
                Affiliations
                [1 ]Vytautas Magnus University, ( https://ror.org/04y7eh037) K. Donelaičio Str. 58, 44248 Kaunas, Lithuania
                [2 ]Faculty of Forestry and Environmental Management, University of New Brunswick, ( https://ror.org/05nkf0n29) PO Box 4400, 28 Dineen Drive, Fredericton, NB E3B 5A3 Canada
                [3 ]Lithuanian Research Centre for Agriculture and Forestry, Forestry Institute, ( https://ror.org/0480smc83) Liepu Str. 1, 53101 Kaunas Reg., Lithuania
                Author information
                http://orcid.org/0000-0002-1196-9293
                http://orcid.org/0000-0003-0148-6009
                Article
                52769
                10.1038/s41598-024-52769-y
                10834436
                38302512
                67d1997d-700f-4ccc-a1ad-f9c50a716253
                © The Author(s) 2024

                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
                : 22 September 2023
                : 23 January 2024
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                © Springer Nature Limited 2024

                Uncategorized
                ecological genetics,forestry
                Uncategorized
                ecological genetics, forestry

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