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      Genome size, genetic diversity, and phenotypic variability imply the effect of genetic variation instead of ploidy on trait plasticity in the cross-pollinated tree species of mulberry

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          Abstract

          Elucidation of genome size (GS), genetic and phenotypic variation is the fundamental aspect of crop improvement programs. Mulberry is a cross-pollinated, highly heterozygous tree eudicot, and comprised of huge ploidy variation with great adaptability across the world. However, because of inadequate information on GS, ploidy-associated traits, as well as the correlation between genetic and phenotypic variation hinder the further improvement of mulberry. In this present research, a core set of 157 germplasm accessions belonging to eight accepted species of Morus including promising functional varieties were chosen to represent the genetic spectrum from the whole germplasm collection. To estimate the GS, accessions were subjected to flow cytometry (FCM) analysis and the result suggested that four different ploidies (2n = 2x, 3x, 4x, and 6x) with GS ranging from 0.72±0.005pg (S-30) to 2.89±0.015pg ( M. serrata), accounting~4.01 fold difference. The predicted polyploidy was further confirmed with metaphase chromosome count. In addition, the genetic variation was estimated by selecting a representative morphologically, diverse population of 82 accessions comprised of all ploidy variations using simple sequence repeats (SSR). The estimated average Polymorphism Information Content (PIC) and expected heterozygosity showed high levels of genetic diversity. Additionally, three populations were identified by the model-based population structure (k = 3) with a moderate level of correlation between the populations and different species of mulberry, which imply the effect of genetic variation instead of ploidy on trait plasticity that could be a consequence of the high level of heterozygosity imposed by natural cross-pollination. Further, the correlation between ploidies, especially diploid and triploid with selected phenotypic traits was identified, however, consistency could not be defined with higher ploidy levels (>3x). Moreover, incite gained here can serve as a platform for future omics approaches to the improvement of mulberry traits.

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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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            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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              STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: Formal analysisRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Methodology
                Role: Data curationRole: Formal analysisRole: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: Data curation
                Role: Data curation
                Role: Data curationRole: Methodology
                Role: Methodology
                Role: MethodologyRole: ResourcesRole: Supervision
                Role: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                11 August 2023
                2023
                : 18
                : 8
                : e0289766
                Affiliations
                [1 ] Molecular Biology Laboratory-1, Central Sericultural Research and Training Institute, Mysuru, Karnataka, India
                [2 ] Mulberry Tissue Culture Lab, Central Sericultural Germplasm Resources Centre, Hosur, Tamil Nadu, India
                [3 ] Agri-Biotechnology Division, National Agri-Food Biotechnology Institute, Mohali, Punjab, India
                [4 ] Auxochromofours Solutions Pvt. Ltd., Bangalore‎, Karnataka, India
                Ben-Gurion University, ISRAEL
                Author notes

                Competing Interests: NO authors have competing interests

                Author information
                https://orcid.org/0000-0003-0253-5394
                https://orcid.org/0000-0001-7526-1940
                https://orcid.org/0000-0003-3614-2708
                Article
                PONE-D-23-08113
                10.1371/journal.pone.0289766
                10420377
                37566619
                c88fc495-5802-4aff-908e-c9a1f61eb831
                © 2023 Gnanesh et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 18 March 2023
                : 25 July 2023
                Page count
                Figures: 6, Tables: 1, Pages: 23
                Funding
                Funded by: DST-SERB
                Award ID: SERB SB/S2/RJN-049/2015
                Award Recipient :
                This research was funded by Science and Engineering Research Board (SERB), New Delhi, grant number SERB SB/S2/RJN-049/2015.
                Categories
                Research Article
                Biology and Life Sciences
                Evolutionary Biology
                Population Genetics
                Ploidy
                Biology and Life Sciences
                Genetics
                Population Genetics
                Ploidy
                Biology and Life Sciences
                Population Biology
                Population Genetics
                Ploidy
                Biology and Life Sciences
                Evolutionary Biology
                Population Genetics
                Biology and Life Sciences
                Genetics
                Population Genetics
                Biology and Life Sciences
                Population Biology
                Population Genetics
                Biology and Life Sciences
                Genetics
                Departures from Diploidy
                Polyploidy
                Triploidy
                Biology and Life Sciences
                Evolutionary Biology
                Population Genetics
                Genetic Polymorphism
                Biology and Life Sciences
                Genetics
                Population Genetics
                Genetic Polymorphism
                Biology and Life Sciences
                Population Biology
                Population Genetics
                Genetic Polymorphism
                Biology and Life Sciences
                Genetics
                Genomics
                Biology and Life Sciences
                Ecology
                Ecological Metrics
                Species Diversity
                Ecology and Environmental Sciences
                Ecology
                Ecological Metrics
                Species Diversity
                Biology and Life Sciences
                Genetics
                Departures from Diploidy
                Polyploidy
                Tetraploidy
                Biology and Life Sciences
                Genetics
                Departures from Diploidy
                Polyploidy
                Custom metadata
                All relevant data are within the paper and its Supporting Information files.

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