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1. Identification of loci conferring resistance to 4 foliar diseases of maize

   https://doi.org/10.1093/g3journal/jkad275  

   Qiu, Yuting; Adhikari, Pragya; Balint-Kurti, Peter; Jamann, Tiffany (December 2023, G3: Genes, Genomes, Genetics)

   McIntyre, L (Ed.)

   Abstract 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. 

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2. Inoculation of Maize Leaves with the Bacterial Foliar Pathogen Clavibacter nebraskensis

   https://doi.org/10.1101/pdb.prot108642  

   Mullens, Alexander; Sorensen, Peyton; Cooper, Julian; Jamann, Tiffany (June 2025, Cold Spring Harbor Protocols)

   Maize significantly contributes to food and fuel production. Yields can be reduced due to foliar diseases, which reduce photosynthetic leaf area. The bacterial foliar disease Goss's wilt (caused byClavibacter nebraskensis) can cause significant yield losses in susceptible maize varieties.C. nebraskensiscan infect leaves through wounds and colonize the vascular tissue of the leaf. We present a protocol that replicates this process with the use of a “clapper” with pins on one end to create wounds and a sponge soaked in inoculum on the other end, which allows for efficient field inoculations of maize leaves. Disease severity is then rated on a percentage scale multiple times over the season to generate an area under disease progress curve (AUDPC). Genetic host resistance is one of the most effective forms of foliar disease control in maize, as there are few effective forms of chemical control for bacterial diseases that affect maize. Screening for resistance in diverse germplasm, or for fine mapping a specific resistance gene, requires inoculating large populations in the field for obtaining phenotypic data. Our high-throughput protocol allows for large-scale disease evaluations and is useful for finding forms of genetic resistance or to understand plant–pathogen interactions of bacterial foliar pathogens. 

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3. Molecular Mapping of Quantitative Trait Loci for Fusarium Head Blight Resistance in the Brazilian Spring Wheat Cultivar “Surpresa”

   https://doi.org/10.3389/fpls.2021.778472  

   Poudel, Bikash; Mullins, Joseph; Puri, Krishna D.; Leng, Yueqiang; Karmacharya, Anil; Liu, Yuan; Hegstad, Justin; Li, Xuehui; Zhong, Shaobin (January 2022, Frontiers in Plant Science)

   Fusarium head blight (FHB) is a devastating disease in wheat. The use of resistant germplasm from diverse sources can significantly improve resistance to the disease. “Surpresa” is a Brazilian spring wheat cultivar with moderate FHB resistance, different from currently used sources. In this study, we aimed to identify and map the genetic loci for FHB resistance in Surpresa. A mapping population consisting of 187 recombinant inbred lines (RILs) was developed from a cross between Surpresa and a susceptible spring wheat cultivar, “Wheaton.” The population was evaluated for FHB by the point-inoculation method in three greenhouse experiments and four field trials between 2016 and 2018. Mean disease severity for Surpresa and Wheaton was 41.2 and 84.9% across the 3 years of experiments, ranging from 30.3 to 59.1% and 74.3 to 91.4%, respectively. The mean FHB severity of the NILs was 57%, with an overall range from 7 to 100%, suggesting transgressive segregation in the population. The population was genotyped using a two-enzyme genotyping-by-sequencing approach, and a genetic map was constructed with 5,431 single nucleotide polymorphism (SNP) markers. Four QTL for type II resistance were detected on chromosomes 3A, 5A, 6A, and 7A, explaining 10.4–14.4% of the total phenotypic variation. The largest effect QTL was mapped on chromosome 7A and explained 14.4% of the phenotypic variation; however, it co-localized with a QTL governing the days to anthesis trait. A QTL for mycotoxin accumulation was also detected on chromosome 1B, explaining 18.8% of the total phenotypic variation. The QTL for FHB resistance identified in the study may diversify the FHB resistance gene pool and increase overall resistance to the disease in wheat. 

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4. A maize near‐isogenic line population designed for gene discovery and characterization of allelic effects

   https://doi.org/10.1111/tpj.70228  

   Zhong, Tao; Mullens, Alex; Morales, Laura; Swarts, Kelly_L; Stafstrom, William_C; He, Yijian; Sermons, Shannon_M; Yang, Qin; Lopez‐Zuniga, Luis_O; Rucker, Elizabeth; et al (June 2025, The Plant Journal)

   SUMMARY In this study, we characterized a panel of 1264 maize near‐isogenic lines (NILs), developed from crosses between 18 diverse inbred lines and the recurrent parent B73, referred to as nested NILs (nNILs). In this study, 888 of the nNILs were genotyped using genotyping‐by‐sequencing (GBS). Subsequently, 24 of these nNILs, and all the parental lines, were re‐genotyped using a high‐density single nucleotide polymorphism (SNP) chip. A novel pipeline for calling introgressions, which does not rely on knowing the donor parent of each nNIL, was developed based on a hidden Markov model (HMM) algorithm. By comparing the introgressions detected using GBS data with those identified using chip data, we optimized the HMM parameters for analyzing the entire nNIL population. A total of 2969 introgressions were identified across the 888 nNILs. Individual introgression blocks ranged from 21 bp to 204 Mbp, with an average size of 17 Mbp. By comparing SNP genotypes within introgressed segments to the known genotypes of the donor lines, we determined that in about one third of the lines, the identity of the donors did not match expectation based on their pedigrees. We characterized the entire nNIL population for three foliar diseases. Using these data, we mapped a number of quantitative trait loci (QTL) for disease resistance in the nNIL population and observed extensive variation in effects among the alleles from different donor parents at most QTL identified. This population will be of significant utility for dissecting complex agronomic traits and allelic series in maize. 

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5. Image‐based assessment of plant disease progression identifies new genetic loci for resistance to Ralstonia solanacearum in tomato

   https://doi.org/10.1111/tpj.16101  

   Méline, Valérian; Caldwell, Denise L; Kim, Bong‐Suk; Khangura, Rajdeep S; Baireddy, Sriram; Yang, Changye; Sparks, Erin E; Dilkes, Brian; Delp, Edward J; Iyer‐Pascuzzi, Anjali S (March 2023, The Plant Journal)

   SUMMARY A major challenge in global crop production is mitigating yield loss due to plant diseases. One of the best strategies to control these losses is through breeding for disease resistance. One barrier to the identification of resistance genes is the quantification of disease severity, which is typically based on the determination of a subjective score by a human observer. We hypothesized that image‐based, non‐destructive measurements of plant morphology over an extended period after pathogen infection would capture subtle quantitative differences between genotypes, and thus enable identification of new disease resistance loci. To test this, we inoculated a genetically diverse biparental mapping population of tomato (Solanum lycopersicum) withRalstonia solanacearum, a soilborne pathogen that causes bacterial wilt disease. We acquired over 40 000 time‐series images of disease progression in this population, and developed an image analysis pipeline providing a suite of 10 traits to quantify bacterial wilt disease based on plant shape and size. Quantitative trait locus (QTL) analyses using image‐based phenotyping for single and multi‐traits identified QTLs that were both unique and shared compared with those identified by human assessment of wilting, and could detect QTLs earlier than human assessment. Expanding the phenotypic space of disease with image‐based, non‐destructive phenotyping both allowed earlier detection and identified new genetic components of resistance. 

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