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      Genome‐wide association studies in a diverse strawberry collection unveil loci controlling agronomic and fruit quality traits

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          Abstract

          Strawberries ( Fragaria sp.) are cherished for their organoleptic properties and nutritional value. However, breeding new cultivars involves the simultaneous selection of many agronomic and fruit quality traits, including fruit firmness and extended postharvest life. The strawberry germplasm collection here studied exhibited extensive phenotypic variation in 26 agronomic and fruit quality traits across three consecutive seasons. Phenotypic correlations and principal component analysis revealed relationships among traits and accessions, emphasizing the impact of plant breeding on fruit weight and firmness to the detriment of sugar or vitamin C content. Genetic diversity analysis on 124 accessions using 44,408 markers denoted a population structure divided into six subpopulations still retaining considerable diversity. Genome‐wide association studies for the 26 traits unveiled 121 significant marker‐trait associations distributed across 95 quantitative trait loci (QTLs). Multiple associations were detected for fruit firmness, a key breeding target, including a prominent locus on chromosome 6A. The candidate gene FaPG1, controlling fruit softening and postharvest shelf life, was identified within this QTL region. Differential expression of FaPG1 confirmed its role as the primary contributor to natural variation in fruit firmness. A kompetitive allele‐specific PCR assay based on the single nucleotide polymorphism (SNP) AX‐184242253, associated with the 6A QTL, predicts a substantial increase in fruit firmness, validating its utility for marker‐assisted selection. In essence, this comprehensive study provides insights into the phenotypic and genetic landscape of the strawberry collection and lays a robust foundation for propelling the development of superior strawberry cultivars through precision breeding.

          Core Ideas

          • A collection of 124 diverse strawberry accessions was phenotyped for 26 agronomic and fruit quality traits.

          • Several quantitative trait locus (QTL) controlling agronomic and fruit quality traits were detected by genome‐wide association studies.

          • Natural variation in FaPG1 expression is associated with a major and stable QTL for fruit firmness.

          • A marker assay was developed and validated for marker‐assisted improvement of fruit firmness in strawberry.

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          Most cited references71

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          Second-generation PLINK: rising to the challenge of larger and richer datasets

          PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for even faster and more scalable implementations of key functions. In addition, GWAS and population-genetic data now frequently contain probabilistic calls, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1's primary data format. To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, O(sqrt(n))-time/constant-space Hardy-Weinberg equilibrium and Fisher's exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. This will be followed by PLINK 2.0, which will introduce (a) a new data format capable of efficiently representing probabilities, phase, and multiallelic variants, and (b) extensions of many functions to account for the new types of information. The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.
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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.
              • Record: found
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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
                iraida.amaya@juntadeandalucia.es
                Journal
                Plant Genome
                Plant Genome
                10.1002/(ISSN)1940-3372
                TPG2
                The Plant Genome
                John Wiley and Sons Inc. (Hoboken )
                1940-3372
                15 October 2024
                December 2024
                : 17
                : 4 ( doiID: 10.1002/tpg2.v17.4 )
                : e20509
                Affiliations
                [ 1 ] Centro IFAPA de Málaga, Instituto Andaluz de Investigación y Formación Agraria y Pesquera (IFAPA) Málaga Spain
                [ 2 ] Department of Horticultural Sciences, IFAS Gulf Coast Research and Education Center University of Florida Wimauma Florida USA
                [ 3 ] Unidad Asociada de I+D+i IFAPA‐CSIC Biotecnología y Mejora en Fresa Málaga Spain
                Author notes
                [*] [* ] Correspondence

                Iraida Amaya, Centro IFAPA de Málaga, Instituto Andaluz de Investigación y Formación Agraria y Pesquera (IFAPA), Málaga, 29140, Spain. Email: iraida.amaya@ 123456juntadeandalucia.es

                Author information
                https://orcid.org/0000-0003-1673-3009
                https://orcid.org/0000-0001-5775-3570
                https://orcid.org/0000-0002-4083-8022
                https://orcid.org/0009-0007-8333-5410
                https://orcid.org/0000-0003-4929-4057
                https://orcid.org/0000-0003-2596-8790
                https://orcid.org/0000-0002-6690-7196
                https://orcid.org/0000-0002-4612-8902
                Article
                TPG220509
                10.1002/tpg2.20509
                11628880
                39406253
                a58e8003-7f4f-40c5-a3ac-1aa7183d784a
                © 2024 The Author(s). The Plant Genome published by Wiley Periodicals LLC on behalf of Crop Science Society of America.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 13 August 2024
                : 29 May 2024
                : 16 August 2024
                Page count
                Figures: 8, Tables: 3, Pages: 24, Words: 13398
                Funding
                Funded by: Consejería de Economía, Innovación, Ciencia y Empleo, Junta de Andalucía , doi 10.13039/501100002878;
                Award ID: P18‐RT‐4856
                Funded by: Horizon 2020 Framework Programme , doi 10.13039/100010661;
                Award ID: BreedingValue 101000747
                Funded by: Ministerio de Ciencia e Innovación y Agencia Estatal de Investigación
                Award ID: PID2022‐138290OR‐I00/AEI/10.13039/501100011033
                Funded by: FEDER, UE Ministerio de Ciencia e Innovación y Agencia Estatal de Investigación
                Award ID: PID2019‐111496RR‐I00/AEI/10.13039/501100011033
                Categories
                Original Article
                Original Article
                Custom metadata
                2.0
                December 2024
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.5.1 mode:remove_FC converted:10.12.2024

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