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      Genetic Diversity and Population Structure Analysis of Tree Peony (Paeonia Section Moutan DC.) Germplasm Using Sixteen Functional SSR Markers

      , , , , , , , ,
      Forests
      MDPI AG

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          Abstract

          Tree peony (Paeonia section Moutan DC.) is a traditional ornamental flower of China, which has thousands of varieties with different flower colors and types after a long history of natural selection and artificial breeding. However, tree peony is a perennial woody plant with a long breeding, and there are still significant challenges to accelerate the process of genetic improvement of important ornamental traits. In this study, a total of sixteen primer pairs with high polymorphism and good universality were selected from the initial pool of 115 SSR markers. The SSR marker set was derived from published papers on the genetic linkage map and association analysis of tree peony. Furthermore, we conducted a genetic diversity and population structure analysis on 322 tree peony cultivars using molecular markers with functional. These SSRs amplified a total of 391 alleles, the average number of different alleles was 5.113 alleles across all loci. The average Shannon’s information index, gene diversity and polymorphism information content were 0.842, 0.532, and 0.503 over all loci, respectively. Population genetic diversity analysis indicated that the average expected heterozygosity of the total population was larger than the observed heterozygosity, showing the presence of a certain degree of heterozygous deletion phenomenon. The Japan varieties had the richest diversity with the highest H (0.508) and PIC (0.479) values. The Zhongyuan varieties showed the greatest variation may be related to its longstanding cultivation history. Moreover, the STRUCTURE and principal coordinate analyses indicated that 322 tree peony individuals from five populations were grouped into two clusters. An analysis of molecular variance demonstrated significant genetic diversity among different populations. This research may contribute to the sustainable management, conservation, and utilization of tree peony resources.

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          Detecting the number of clusters of individuals using the software structure: a simulation study

          The identification of genetically homogeneous groups of individuals is a long standing issue in population genetics. A recent Bayesian algorithm implemented in the software STRUCTURE allows the identification of such groups. However, the ability of this algorithm to detect the true number of clusters (K) in a sample of individuals when patterns of dispersal among populations are not homogeneous has not been tested. The goal of this study is to carry out such tests, using various dispersal scenarios from data generated with an individual-based model. We found that in most cases the estimated 'log probability of data' does not provide a correct estimation of the number of clusters, K. However, using an ad hoc statistic DeltaK based on the rate of change in the log probability of data between successive K values, we found that STRUCTURE accurately detects the uppermost hierarchical level of structure for the scenarios we tested. As might be expected, the results are sensitive to the type of genetic marker used (AFLP vs. microsatellite), the number of loci scored, the number of populations sampled, and the number of individuals typed in each sample.
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            STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method

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              GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update

              Summary: GenAlEx: Genetic Analysis in Excel is a cross-platform package for population genetic analyses that runs within Microsoft Excel. GenAlEx offers analysis of diploid codominant, haploid and binary genetic loci and DNA sequences. Both frequency-based (F-statistics, heterozygosity, HWE, population assignment, relatedness) and distance-based (AMOVA, PCoA, Mantel tests, multivariate spatial autocorrelation) analyses are provided. New features include calculation of new estimators of population structure: G′ST, G′′ST, Jost’s D est and F′ST through AMOVA, Shannon Information analysis, linkage disequilibrium analysis for biallelic data and novel heterogeneity tests for spatial autocorrelation analysis. Export to more than 30 other data formats is provided. Teaching tutorials and expanded step-by-step output options are included. The comprehensive guide has been fully revised. Availability and implementation: GenAlEx is written in VBA and provided as a Microsoft Excel Add-in (compatible with Excel 2003, 2007, 2010 on PC; Excel 2004, 2011 on Macintosh). GenAlEx, and supporting documentation and tutorials are freely available at: http://biology.anu.edu.au/GenAlEx. Contact: rod.peakall@anu.edu.au
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                Author and article information

                Contributors
                Journal
                FOREGK
                Forests
                Forests
                MDPI AG
                1999-4907
                October 2023
                September 25 2023
                : 14
                : 10
                : 1945
                Article
                10.3390/f14101945
                217e82f5-dfaa-41ed-9dd8-6f473dcc7b89
                © 2023

                https://creativecommons.org/licenses/by/4.0/

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