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      Unravelling the novel genetic diversity and marker-trait associations of corn leaf aphid resistance in wheat using microsatellite markers

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          Abstract

          The study was conducted to identify novel simple sequence repeat (SSR) markers associated with resistance to corn aphid (CLA), Rhopalosiphum maidis L. in 48 selected bread wheat ( Triticum aestivum L.) and wild wheat ( Aegilops spp. & T. dicoccoides) genotypes during two consecutive cropping seasons (2018–19 and 2019–20). A total of 51 polymorphic markers containing 143 alleles were used for the analysis. The frequency of the major allele ranged from 0.552 ( Xgwm113) to 0.938 ( Xcfd45, Xgwm194 and Xgwm526), with a mean of 0.731. Gene diversity ranged from 0.116 ( Xgwm526) to 0.489 ( Xgwm113), with a mean of 0.354. The polymorphic information content (PIC) value for the SSR markers ranged from 0.107 ( Xgwm526) to 0.370 ( Xgwm113) with a mean of 0.282. The results of the STRUCTURE analysis revealed the presence of four main subgroups in the populations. Analysis of molecular variance (AMOVA) showed that the between-group difference was around 37 per cent of the total variation contributed to the diversity by the whole germplasm, while 63 per cent of the variation was attributed between individuals within the group. A general linear model (GLM) was used to identify marker-trait associations, which detected a total of 23 and 27 significant new marker-trait associations (MTAs) at the p < 0.01 significance level during the 2018–19 and 2019–20 crop seasons, respectively. The findings of this study have important implications for the identification of molecular markers associated with CLA resistance. These markers can increase the accuracy and efficiency of aphid-resistant germplasm selection, ultimately facilitating the transfer of resistance traits to desirable wheat genotypes.

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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: Data curationRole: Formal analysisRole: Writing – original draft
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: Project administrationRole: Supervision
                Role: ConceptualizationRole: MethodologyRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Formal analysisRole: InvestigationRole: MethodologyRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Formal analysisRole: Project administrationRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: Project administrationRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: Project administrationRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                22 February 2024
                2024
                : 19
                : 2
                : e0289527
                Affiliations
                [1 ] ICAR- Indian Institute of Wheat and Barley Research, Karnal, Haryana, India
                [2 ] CCS Haryana Agricultural University, Hisar, Haryana, India
                [3 ] ICAR- National Bureau of Plant Genetic Resources, New Delhi, India
                KGUT: Graduate University of Advanced Technology, ISLAMIC REPUBLIC OF IRAN
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0002-6229-1873
                https://orcid.org/0000-0001-5615-8956
                https://orcid.org/0000-0002-1405-4814
                Article
                PONE-D-23-22687
                10.1371/journal.pone.0289527
                10883527
                38386640
                5d6c3b56-c9b7-45a8-9ccc-2ab5cbbd7b23
                © 2024 Yadav 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
                : 19 July 2023
                : 17 October 2023
                Page count
                Figures: 6, Tables: 7, Pages: 22
                Funding
                Funded by: ICAR-ICAR- Indian Institute of Wheat and Barley Research
                Award ID: Karnal 132001
                Yes. The funding for conducting the experiment was provided under the Institute project “Management of wheat insect-pests through climate-smart pest management strategies” of ICAR-ICAR- Indian Institute of Wheat and Barley Research, Karnal 132001, Haryana, India.
                Categories
                Research Article
                Biology and Life Sciences
                Organisms
                Eukaryota
                Plants
                Grasses
                Wheat
                Biology and Life Sciences
                Genetics
                Genetic Loci
                Biology and Life Sciences
                Zoology
                Entomology
                Insects
                Aphids
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Invertebrates
                Arthropoda
                Insects
                Aphids
                Biology and Life Sciences
                Zoology
                Animals
                Invertebrates
                Arthropoda
                Insects
                Aphids
                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
                Agriculture
                Agronomy
                Plant Breeding
                Biology and Life Sciences
                Nutrition
                Diet
                Food
                Bread
                Medicine and Health Sciences
                Nutrition
                Diet
                Food
                Bread
                Biology and Life Sciences
                Genetics
                Heredity
                Genetic Mapping
                Variant Genotypes
                Biology and Life Sciences
                Genetics
                Genetic Loci
                Alleles
                Custom metadata
                All relevant data are within the paper.

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                Uncategorized

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