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      Identification of loci conferring resistance to 4 foliar diseases of maize


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          Foliar diseases of maize are among the most important diseases of maize worldwide. This study focused on 4 major foliar diseases of maize: Goss's wilt, gray leaf spot, northern corn leaf blight, and southern corn leaf blight. QTL mapping for resistance to Goss’s wilt was conducted in 4 disease resistance introgression line populations with Oh7B as the common recurrent parent and Ki3, NC262, NC304, and NC344 as recurrent donor parents. Mapping results for Goss’s wilt resistance were combined with previous studies for gray leaf spot, northern corn leaf blight, and southern corn leaf blight resistance in the same 4 populations. We conducted (1) individual linkage mapping analysis to identify QTL specific to each disease and population; (2) Mahalanobis distance analysis to identify putative multiple disease resistance regions for each population; and 3) joint linkage mapping to identify QTL across the 4 populations for each disease. We identified 3 lines that were resistant to all 4 diseases. We mapped 13 Goss’s wilt QTLs in the individual populations and an additional 6 using joint linkage mapping. All Goss’s wilt QTL had small effects, confirming that resistance to Goss’s wilt is highly quantitative. We report several potentially important chromosomal bins associated with multiple disease resistance including 1.02, 1.03, 3.04, 4.06, 4.08, and 9.03. Together, these findings indicate that disease QTL distribution is not random and that there are locations in the genome that confer resistance to multiple diseases. Furthermore, resistance to bacterial and fungal diseases is not entirely distinct, and we identified lines resistant to both fungi and bacteria, as well as loci that confer resistance to both bacterial and fungal diseases.


          Multiple disease resistance is of interest to plant breeders as it can accelerate breeding for disease resistance. Qiu et al. analyze four maize introgression line populations to identify regions conferring resistance to multiple fungal diseases and the bacterial disease Goss’s wilt. They report several regions associated with resistance to multiple diseases, as well as several small effect QTL for Goss’s wilt resistance. Their data suggest that resistance to Goss’s wilt is highly quantitative.

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          TASSEL: software for association mapping of complex traits in diverse samples.

          Association analyses that exploit the natural diversity of a genome to map at very high resolutions are becoming increasingly important. In most studies, however, researchers must contend with the confounding effects of both population and family structure. TASSEL (Trait Analysis by aSSociation, Evolution and Linkage) implements general linear model and mixed linear model approaches for controlling population and family structure. For result interpretation, the program allows for linkage disequilibrium statistics to be calculated and visualized graphically. Database browsing and data importation is facilitated by integrated middleware. Other features include analyzing insertions/deletions, calculating diversity statistics, integration of phenotypic and genotypic data, imputing missing data and calculating principal components.
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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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              R/qtl: QTL mapping in experimental crosses


                Author and article information

                Role: Editor
                G3 (Bethesda)
                G3: Genes|Genomes|Genetics
                Oxford University Press (US )
                February 2024
                05 December 2023
                05 December 2023
                : 14
                : 2
                : jkad275
                Department of Crop Sciences, University of Illinois Urbana-Champaign , Urbana, IL 61801, USA
                Department of Crop Sciences, University of Illinois Urbana-Champaign , Urbana, IL 61801, USA
                Department of Entomology and Plant Pathology, North Carolina State University , Box 7616, Raleigh, NC 27695, USA
                Plant Science Research Unit, USDA-ARS , Raleigh, NC 27695, USA
                Department of Crop Sciences, University of Illinois Urbana-Champaign , Urbana, IL 61801, USA
                Author notes
                Corresponding author: Department of Crop Sciences, University of Illinois Urbana-Champaign, AE-104 Turner Hall, 1102 S. Goodwin Ave, Urbana, IL 61801. Email: tjamann@ 123456illinois.edu

                Conflicts of interest The author(s) declare no conflicts of interest.

                Author information
                © The Author(s) 2023. Published by Oxford University Press on behalf of The Genetics Society of America.

                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.

                : 04 April 2023
                : 10 November 2023
                : 20 December 2023
                Page count
                Pages: 12

                multiple disease resistance,maize,multivariate analysis,joint stepwise regression
                multiple disease resistance, maize, multivariate analysis, joint stepwise regression


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