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      Exploration of a European-centered strawberry diversity panel provides markers and candidate genes for the control of fruit quality traits

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          Abstract

          Fruit quality traits are major breeding targets in cultivated strawberry ( Fragaria × ananassa). Taking into account the requirements of both growers and consumers when selecting high-quality cultivars is a real challenge. Here, we used a diversity panel enriched with unique European accessions and the 50 K FanaSNP array to highlight the evolution of strawberry diversity over the past 160 years, investigate the molecular basis of 12 major fruit quality traits by genome-wide association studies (GWAS), and provide genetic markers for breeding. Results show that considerable improvements of key breeding targets including fruit weight, firmness, composition, and appearance occurred simultaneously in European and American cultivars. Despite the high genetic diversity of our panel, we observed a drop in nucleotide diversity in certain chromosomal regions, revealing the impact of selection. GWAS identified 71 associations with 11 quality traits and, while validating known associations (firmness, sugar), highlighted the predominance of new quantitative trait locus (QTL), demonstrating the value of using untapped genetic resources. Three of the six selective sweeps detected are related to glossiness or skin resistance, two little-studied traits important for fruit attractiveness and, potentially, postharvest shelf life. Moreover, major QTL for firmness, glossiness, skin resistance, and susceptibility to bruising are found within a low diversity region of chromosome 3D. Stringent search for candidate genes underlying QTL uncovered strong candidates for fruit color, firmness, sugar and acid composition, glossiness, and skin resistance. Overall, our study provides a potential avenue for extending shelf life without compromising flavor and color as well as the genetic markers needed to achieve this goal.

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          IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era

          Abstract IQ-TREE (http://www.iqtree.org, last accessed February 6, 2020) is a user-friendly and widely used software package for phylogenetic inference using maximum likelihood. Since the release of version 1 in 2014, we have continuously expanded IQ-TREE to integrate a plethora of new models of sequence evolution and efficient computational approaches of phylogenetic inference to deal with genomic data. Here, we describe notable features of IQ-TREE version 2 and highlight the key advantages over other software.
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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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              FactoMineR: AnRPackage for Multivariate Analysis

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                Author and article information

                Contributors
                Journal
                Hortic Res
                Hortic Res
                hr
                Horticulture Research
                Oxford University Press
                2662-6810
                2052-7276
                July 2024
                14 May 2024
                14 May 2024
                : 11
                : 7
                : uhae137
                Affiliations
                Univ. Bordeaux, INRAE , UMR BFP, F-33140 Villenave d’Ornon, France
                Invenio, MIN de Brienne , 110 Quai de Paludate, 33000 Bordeaux, France
                Univ. Bordeaux, INRAE , UMR BFP, F-33140 Villenave d’Ornon, France
                Univ. Bordeaux, INRAE , UMR BFP, F-33140 Villenave d’Ornon, France
                Invenio, MIN de Brienne , 110 Quai de Paludate, 33000 Bordeaux, France
                Invenio, MIN de Brienne , 110 Quai de Paludate, 33000 Bordeaux, France
                Univ. Bordeaux, INRAE , UMR BFP, F-33140 Villenave d’Ornon, France
                Univ. Bordeaux, INRAE , UMR BFP, F-33140 Villenave d’Ornon, France
                Author notes
                Author information
                https://orcid.org/(0009-0004-0395-7470)
                https://orcid.org/(0000-0002-3685-2470)
                https://orcid.org/(0000-0002-6746-1755)
                https://orcid.org/(0000-0003-1577-9072)
                https://orcid.org/(0000-0002-6831-2823)
                https://orcid.org/(0000-0002-0369-9609)
                Article
                uhae137
                10.1093/hr/uhae137
                11233882
                38988619
                aea27cb0-1b1f-4065-9eef-00f62f2d4e3f
                © The Author(s) 2024. Published by Oxford University Press on behalf of Nanjing Agricultural University.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 04 March 2024
                : 05 May 2024
                : 01 July 2024
                Page count
                Pages: 18
                Funding
                Funded by: funder-nameCIFRE (Convention Industrielle de Formation par la Recherche);
                Funded by: funder-nameEuropean Union Horizon 2020 research and innovation program;
                Award ID: 101000747
                Funded by: funder-nameNouvelle-Aquitaine Region and the European Regional Development Fund (ERDF);
                Award ID: 2018-1R20202
                Award ID: 67822110
                Categories
                AcademicSubjects/SCI01210
                AcademicSubjects/SCI01140
                Article

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