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      Harnessing genome-wide genetic diversity, population structure and linkage disequilibrium in Ethiopian durum wheat gene pool

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          Abstract

          Yanyang Liu, Henan Academy of Agricultural Sciences (HNAAS), China; Landraces are an important genetic source for transferring valuable novel genes and alleles required to enhance genetic variation. Therefore, information on the gene pool’s genetic diversity and population structure is essential for the conservation and sustainable use of durum wheat genetic resources. Hence, the aim of this study was to assess genetic diversity, population structure, and linkage disequilibrium, as well as to identify regions with selection signature. Five hundred (500) individuals representing 46 landraces, along with 28 cultivars were evaluated using the Illumina Infinium 25K wheat SNP array, resulting in 8,178 SNPs for further analysis. Gene diversity (GD) and the polymorphic information content (PIC) ranged from 0.13–0.50 and 0.12–0.38, with mean GD and PIC values of 0.34 and 0.27, respectively. Linkage disequilibrium (LD) revealed 353,600 pairs of significant SNPs at a cut-off ( r 2 > 0.20, P < 0.01), with an average r 2 of 0.21 for marker pairs. The nucleotide diversity (π) and Tajima’s D (TD) per chromosome for the populations ranged from 0.29–0.36 and 3.46–5.06, respectively, with genome level, mean π values of 0.33 and TD values of 4.43. Genomic scan using the F st outlier test revealed 85 loci under selection signatures, with 65 loci under balancing selection and 17 under directional selection. Putative candidate genes co-localized with regions exhibiting strong selection signatures were associated with grain yield, plant height, host plant resistance to pathogens, heading date, grain quality, and phenolic content. The Bayesian Model (STRUCTURE) and distance-based (principal coordinate analysis, PCoA, and unweighted pair group method with arithmetic mean, UPGMA) methods grouped the genotypes into five subpopulations, where landraces from geographically non-adjoining environments were clustered in the same cluster. This research provides further insights into population structure and genetic relationships in a diverse set of durum wheat germplasm, which could be further used in wheat breeding programs to address production challenges sustainably.

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          Most cited references106

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          MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms.

          The Molecular Evolutionary Genetics Analysis (Mega) software implements many analytical methods and tools for phylogenomics and phylomedicine. Here, we report a transformation of Mega to enable cross-platform use on Microsoft Windows and Linux operating systems. Mega X does not require virtualization or emulation software and provides a uniform user experience across platforms. Mega X has additionally been upgraded to use multiple computing cores for many molecular evolutionary analyses. Mega X is available in two interfaces (graphical and command line) and can be downloaded from www.megasoftware.net free of charge.
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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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              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.

                Author and article information

                Contributors
                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                1664-462X
                20 July 2023
                2023
                : 14
                : 1192356
                Affiliations
                [1] 1 Institute of Biotechnology, Addis Ababa University , Addis Ababa, Ethiopia
                [2] 2 Department of Plant Breeding, Swedish University of Agricultural Sciences , Alnarp, Sweden
                [3] 3 Sinana Agricultural Research Center, Oromia Agricultural Research Institute , Bale-Robe, Ethiopia
                [4] 4 Bio and Emerging Technology Institute , Addis Ababa, Ethiopia
                [5] 5 Department of Biology and Biotechnology, Wollo University , Dessie, Ethiopia
                Author notes

                Edited by: Carolina Ballen-Taborda, Clemson University, United States

                Reviewed by: Umesh K. Reddy, West Virginia State University, United States; Yogendra Khedikar, Agriculture and Agri-Food Canada (AAFC), Canada

                Article
                10.3389/fpls.2023.1192356
                10400094
                37546270
                fb21a8e3-6352-4488-a1ae-8f8743c4098c
                Copyright © 2023 Mulugeta, Ortiz, Geleta, Hailesilassie, Hammenhag, Hailu and Tesfaye

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 23 March 2023
                : 05 July 2023
                Page count
                Figures: 6, Tables: 3, Equations: 0, References: 106, Pages: 18, Words: 10225
                Funding
                The study was funded by the Swedish International Development Cooperation Agency (Sida) grant awarded to Addis Ababa University and the Swedish University of Agricultural Sciences for a bilateral capacity-building program in biotechnology. The funding information is available on “ https://sida.aau.edu.et/index.php/biotechnology-phdprogram/; accessed on May 21, 2022”. The funders played no role in the design of the study, data collection, analysis, decision to publish, or preparation of the manuscript.
                Categories
                Plant Science
                Original Research
                Custom metadata
                Functional and Applied Plant Genomics

                Plant science & Botany
                domestication,durum wheat,landraces,nucleotide diversity,polymorphic information content,selection signature,single nucleotide polymorphisms

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