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      Unlocking the genetic diversity and population structure of the newly introduced two-row spring European HerItage Barley collecTion (ExHIBiT)

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          Abstract

          In the last century, breeding programs have traditionally favoured yield-related traits, grown under high-input conditions, resulting in a loss of genetic diversity and an increased susceptibility to stresses in crops. Thus, exploiting understudied genetic resources, that potentially harbour tolerance genes, is vital for sustainable agriculture. Northern European barley germplasm has been relatively understudied despite its key role within the malting industry. The European Heritage Barley collection (ExHIBiT) was assembled to explore the genetic diversity in European barley focusing on Northern European accessions and further address environmental pressures. ExHIBiT consists of 363 spring-barley accessions, focusing on two-row type. The collection consists of landraces (~14%), old cultivars (~18%), elite cultivars (~67%) and accessions with unknown breeding history (~1%), with 70% of the collection from Northern Europe. The population structure of the ExHIBiT collection was subdivided into three main clusters primarily based on the accession’s year of release using 26,585 informative SNPs based on 50k iSelect single nucleotide polymorphism (SNP) array data. Power analysis established a representative core collection of 230 genotypically and phenotypically diverse accessions. The effectiveness of this core collection for conducting statistical and association analysis was explored by undertaking genome-wide association studies (GWAS) using 24,876 SNPs for nine phenotypic traits, four of which were associated with SNPs. Genomic regions overlapping with previously characterised flowering genes (HvZTLb) were identified, demonstrating the utility of the ExHIBiT core collection for locating genetic regions that determine important traits. Overall, the ExHIBiT core collection represents the high level of untapped diversity within Northern European barley, providing a powerful resource for researchers and breeders to address future climate scenarios.

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          The neighbor-joining method: a new method for reconstructing phylogenetic trees.

          N Saitou, M Nei (1987)
          A new method called the neighbor-joining method is proposed for reconstructing phylogenetic trees from evolutionary distance data. The principle of this method is to find pairs of operational taxonomic units (OTUs [= neighbors]) that minimize the total branch length at each stage of clustering of OTUs starting with a starlike tree. The branch lengths as well as the topology of a parsimonious tree can quickly be obtained by using this method. Using computer simulation, we studied the efficiency of this method in obtaining the correct unrooted tree in comparison with that of five other tree-making methods: the unweighted pair group method of analysis, Farris's method, Sattath and Tversky's method, Li's method, and Tateno et al.'s modified Farris method. The new, neighbor-joining method and Sattath and Tversky's method are shown to be generally better than the other methods.
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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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                Author and article information

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                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                1664-462X
                20 March 2024
                2024
                : 15
                : 1268847
                Affiliations
                [1] 1 School of Biology and Environmental Science, University College Dublin , Dublin, Ireland
                [2] 2 School of Agriculture and Food Science, University College Dublin , Dublin, Ireland
                [3] 3 Cell and Molecular Sciences, The James Hutton Institute , Dundee, United Kingdom
                [4] 4 Department of Information and Computational Sciences, The James Hutton Institute , Dundee, United Kingdom
                [5] 5 School of Biosystems Engineering, University College Dublin , Dublin, Ireland
                [6] 6 Division of Plant Sciences, University of Dundee at The James Hutton Institute , Dundee, United Kingdom
                Author notes

                Edited by: Maarten Van Zonneveld, World Vegetable Center, Taiwan

                Reviewed by: Photini V. Mylona, Hellenic Agricultural Organisation (HAO), Greece

                Katherine Steele, Bangor University, United Kingdom

                *Correspondence: Sónia Negrão, sonia.negrao@ 123456ucd.ie
                Article
                10.3389/fpls.2024.1268847
                10987740
                38571708
                f7f425ea-6afa-41a0-a7a9-b0e10810a9d9
                Copyright © 2024 Bernád, Al-Tamimi, Langan, Gillespie, Dempsey, Henchy, Harty, Ramsay, Houston, Macaulay, Shaw, Raubach, Mcdonnel, Russell, Waugh, Khodaeiaminjan and Negrão

                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
                : 28 July 2023
                : 28 February 2024
                Page count
                Figures: 8, Tables: 3, Equations: 1, References: 111, Pages: 17, Words: 9004
                Funding
                Funded by: Science Foundation Ireland , doi 10.13039/501100001602;
                The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. The research reported in this publication was supported by Science Foundation Ireland centre by the SFI President of Ireland Future Research Leaders to SN under Grant No. 18/FRL/6197.
                Categories
                Plant Science
                Original Research
                Custom metadata
                Plant Breeding

                Plant science & Botany
                barley,genetic resources,agronomic characterization,germplasm collection,genome-wide association studies,plant phenotyping

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