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      Genome-wide association analysis of Septoria tritici blotch for adult plant resistance in elite bread wheat ( Triticum aestivum L) genotypes

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          Abstract

          Septoria tritici blotch (STB) is a predominant foliar disease of wheat, caused by the pathogen Zymoseptoria tritici. This disease can lead to substantial yield losses warranting control by using expensive fungicides. One effective method of STB control is the utilization of resistant wheat varieties. In this particular study, a panel comprising of 186 bread wheat genotypes was assessed for their adult plant resistance (APR) to STB. Field trials were conducted across five environments in Ethiopia during the 2022 and 2023 growing seasons under natural infestation conditions. The association panel was genotyped using 20K single nucleotide polymorphism (SNP) markers. To determine the relationship between genetic markers and STB resistance, a mixed linear model (MLM) analysis was performed using the statgen GWAS R software package. Heritability estimates for STB resistance ranged from 0.39 to 0.95, underscoring the genetic variability and the potential for selection. The study identified 52 marker-trait associations (MTAs) for STB resistance at maturity (SDSM) and 62 MTAs at heading (SDSH). Chromosome 5A contains a high concentration of MTAs that confer resistance to STB, hosting multiple significant MTAs, including four consistently associated markers (‘Kukri_c10033_724’, ‘RAC875_rep_c116420_103’, ‘TG0019’, and ‘RAC875_c30566_230’). Additionally, chromosomes 1B, 2B, 5B, and 7A were found to harbor important MTAs, contributing to resistance across various environments. Notably, two QTLs, qtSTB23 (5A) and qtSTB38 (7B), exhibited stability across multiple environments, making them robust candidates for breeding programs. Furthermore, novel resistance loci on chromosome 2A were discovered, offering new opportunities for enhancing resistance. Therefore, these findings provide an opportunity for improving STB resistance through gene stacking using marker-assisted selection (MAS).

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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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            Efficient methods to compute genomic predictions.

            Efficient methods for processing genomic data were developed to increase reliability of estimated breeding values and to estimate thousands of marker effects simultaneously. Algorithms were derived and computer programs tested with simulated data for 2,967 bulls and 50,000 markers distributed randomly across 30 chromosomes. Estimation of genomic inbreeding coefficients required accurate estimates of allele frequencies in the base population. Linear model predictions of breeding values were computed by 3 equivalent methods: 1) iteration for individual allele effects followed by summation across loci to obtain estimated breeding values, 2) selection index including a genomic relationship matrix, and 3) mixed model equations including the inverse of genomic relationships. A blend of first- and second-order Jacobi iteration using 2 separate relaxation factors converged well for allele frequencies and effects. Reliability of predicted net merit for young bulls was 63% compared with 32% using the traditional relationship matrix. Nonlinear predictions were also computed using iteration on data and nonlinear regression on marker deviations; an additional (about 3%) gain in reliability for young bulls increased average reliability to 66%. Computing times increased linearly with number of genotypes. Estimation of allele frequencies required 2 processor days, and genomic predictions required <1 d per trait, and traits were processed in parallel. Information from genotyping was equivalent to about 20 daughters with phenotypic records. Actual gains may differ because the simulation did not account for linkage disequilibrium in the base population or selection in subsequent generations.
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              The global burden of pathogens and pests on major food crops

              Crop pathogens and pests reduce the yield and quality of agricultural production. They cause substantial economic losses and reduce food security at household, national and global levels. Quantitative, standardized information on crop losses is difficult to compile and compare across crops, agroecosystems and regions. Here, we report on an expert-based assessment of crop health, and provide numerical estimates of yield losses on an individual pathogen and pest basis for five major crops globally and in food security hotspots. Our results document losses associated with 137 pathogens and pests associated with wheat, rice, maize, potato and soybean worldwide. Our yield loss (range) estimates at a global level and per hotspot for wheat (21.5% (10.1-28.1%)), rice (30.0% (24.6-40.9%)), maize (22.5% (19.5-41.1%)), potato (17.2% (8.1-21.0%)) and soybean (21.4% (11.0-32.4%)) suggest that the highest losses are associated with food-deficit regions with fast-growing populations, and frequently with emerging or re-emerging pests and diseases. Our assessment highlights differences in impacts among crop pathogens and pests and among food security hotspots. This analysis contributes critical information to prioritize crop health management to improve the sustainability of agroecosystems in delivering services to societies.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: MethodologyRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: MethodologyRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS One
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                10 March 2025
                2025
                : 20
                : 3
                : e0317603
                Affiliations
                [1 ] Bahir Dar University, College of Agriculture and Environmental Sciences, Bahir Dar, Ethiopia
                [2 ] Amhara Regional Agricultural Research Institute, Bahir Dar, Ethiopia
                [3 ] Plant Breeding Department, University of Bonn, Bonn, Germany
                [4 ] Sasakawa Africa Association (SAA), Addis Ababa, Ethiopia
                [5 ] International Center for Agricultural Research in the Dry Areas (ICARDA), Rabat, Morocco
                Texas A&M University and Texas A&M Agrilife Research, UNITED STATES OF AMERICA
                Author notes

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

                Author information
                https://orcid.org/0000-0002-5921-6338
                Article
                PONE-D-24-31917
                10.1371/journal.pone.0317603
                11892845
                40063614
                6e15ba0b-32e5-4329-af73-daf364eacc1d
                © 2025 Kassie 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
                : 30 July 2024
                : 31 December 2024
                Page count
                Figures: 8, Tables: 4, Pages: 20
                Funding
                The author(s) received no specific funding for this work.
                Categories
                Research Article
                Biology and Life Sciences
                Computational Biology
                Genome Analysis
                Genome-Wide Association Studies
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Analysis
                Genome-Wide Association Studies
                Biology and Life Sciences
                Genetics
                Human Genetics
                Genome-Wide Association Studies
                Biology and Life Sciences
                Organisms
                Eukaryota
                Plants
                Grasses
                Wheat
                Biology and Life Sciences
                Genetics
                Genetic Loci
                Quantitative Trait Loci
                Biology and Life Sciences
                Genetics
                Single Nucleotide Polymorphisms
                Biology and Life Sciences
                Nutrition
                Diet
                Food
                Bread
                Medicine and Health Sciences
                Nutrition
                Diet
                Food
                Bread
                Biology and Life Sciences
                Genetics
                Genomics
                Biology and Life Sciences
                Plant Science
                Plant Pathology
                Biology and Life Sciences
                Genetics
                Heredity
                Custom metadata
                All relevant data are within the article and its Supporting Information files.

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