Exploration of QTLs for Resistance to Fusarium Ear Rot in Maize

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Abstract

Maize ear rot is a fungal disease that poses a serious threat to maize production. Many different pathogens can cause this disease, and one of the most frequently found is Fusarium verticillioides, which causes Fusarium ear rot (FER). To explore quantitative trait loci (QTLs) controlling FER resistance, a mapping population consisting of 158 F₂ individuals along with their F_2:3 families was developed from a cross of the resistant inbred line K10 with the susceptible inbred line ZW18. The two parental lines and the 158 F_2:3 families were grown in the field, inoculated, and scored for disease severity. The estimated broad-sense heritability (H²) of FER resistance reached 0.78, which suggested that genetic factors were the main contributors to the phenotypic variation. Five QTLs for FER resistance were identified in total, accounting for 6.57%–38.24% of the phenotypic variation, and four of these QTLs were consistently detected in more than one replication. The FER resistance QTLs found in this work may serve as promising targets for improving maize FER resistance by transferring them into susceptible inbred lines through marker-assisted selection.

Keywords

ear rot / Fusarium verticillioides / exploration / quantitative trait locus

1 INTRODUCTION

Ear rot is a fungal disease that seriously threatens maize production. It not only directly causes ear rot and yield reduction, but the toxins produced by its pathogens also harm human and animal health and food safety (Wang et al., 2011). In recent years, affected by factors such as global climate change, continuous cropping, straw return to the field, and high-water and high-fertilizer cultivation patterns, the occurrence of maize ear rot in China has intensified year by year: in severe years, the incidence rate can reach 30%–40%, the incidence rate of susceptible varieties can be as high as 50%, annual yield losses generally exceed 10%, and in heavily affected areas can reach more than 30% (Duan et al., 2020). It has become a biotic stress factor that seriously restricts the sustainable development of the maize industry. In particular, against the background of the increasing popularity of fully mechanized maize harvesting, the grain direct-harvest model makes it difficult to remove diseased ears during harvest, making the disease resistance of the varieties themselves increasingly important. Currently, chemical agents and cultivation measures are mainly adopted for the prevention and control of ear rot in maize, but the effects are limited. In comparison, breeding and promoting disease-resistant varieties is the most economical and effective approach.

The pathogens causing maize ear rot are diverse, and Fusarium verticillioides, the causal agent of Fusarium ear rot (FER), is among the most common. When FER occurs, a white mold layer is usually formed on maize kernels. Introgression of resistance genes/quantitative trait loci (QTLs) from donors into elite maize germplasm would be a feasible approach to develop resistant germplasm and commercial hybrids. For FER, researchers have made progress in QTL mapping, associated locus mining, and resistance gene cloning through linkage analysis, genome-wide association study (GWAS), transcriptome sequencing, and other approaches. Pérez-Brito et al. (2001) mapped nine and seven FER resistance QTLs in two F_2:3 populations, respectively, among which 3 QTLs could be detected in both populations. Using 187 recombinant inbred lines, two common FER resistance QTLs were detected under different environments, among which the major QTL explained 13%–22% of the phenotypic variation (Ding et al., 2008). Li et al. (2011) mapped four FER resistance QTLs on chromosomes 3, 4, 5, and 6, each explaining 2.5%–10.2% of the phenotypic variation. Using three F₂ populations and their corresponding F_2:3 families, a total of 20 FER resistance QTLs were mapped, six of which shared overlapping intervals (Wen et al., 2020). Xia et al. (2022) mapped 17 resistance QTLs significantly associated with FER, among which qFER1.03 was fine-mapped to 0.2 Mb and explained 3.88%–15.62% of the phenotypic variation. In addition, some studies have reported FER resistance QTLs (Neupane et al., 2025; Butrón et al., 2019; Coan et al., 2018; Ju et al., 2017; Maschietto et al., 2017).

GWAS was also used to mine resistance-associated loci. In 2013, Zila et al. (2013) detected three associated SNP loci through GWAS, which together explained 3%–10% of the phenotypic variation, and the genes corresponding to two SNP loci were functionally related to programmed cell death. In 2018, 12 molecular markers associated with FER resistance were detected, and the gene corresponding to one locus was an NBS-LRR-type receptor (Jong et al., 2018); in 2020, Yao et al. (2020) identified 69 genes potentially associated with FER resistance using GWAS, and transcriptome sequencing analysis further showed that most differentially expressed genes between resistant and susceptible materials were enriched in metabolic pathways such as plant hormone signal transduction, phenylalanine metabolism, and cytochrome P450. Ayesiga et al. (2024) identified seven SNP loci associated with FER resistance, explaining 4%–11% of the phenotypic variation, and identified seven metabolic pathways related to FER resistance. Additionally, there are other reports on the identification of loci associated with FER resistance (Wu et al., 2020; Liu et al., 2021). In terms of resistance gene cloning, ZmAuxRP1 is, to date, the only cloned FER resistance gene, which encodes an auxin-related regulatory protein and exhibits quantitative resistance to Gibberella stalk rot and FER, reflecting the important role of auxin signaling in broad-spectrum disease resistance (Ye et al., 2019).

Although many QTLs and candidate genes have been reported, most genes or loci exhibit minor effects on FER resistance in maize. This indicates that pyramiding of QTLs/genes from different sources would be an effective approach to improve FER resistance. Therefore, more effective QTLs need to be identified to provide resources for molecular breeding of FER resistance in maize. In this study, QTL mapping was conducted using an F₂ population and its corresponding F_2:3 families developed by crossing a highly FER-resistant inbred line with a susceptible inbred line. In addition, a high-resolution genetic linkage map was constructed, which can be used to identify markers for cloning QTLs or to introduce QTLs via marker-assisted selection (MAS), thereby improving maize resistance to FER.

2 RESULTS

2.1 Phenotypic analysis and broad-sense heritability (H²) estimation

To identify QTLs conferring FER resistance in maize, both parents and the F_2:3 families derived from the F₂ population were artificially inoculated with Fusarium verticillioides, and disease severity was then scored. Disease severity was scored in two experimental replications in 2025 and another two in 2026. The mean disease severity score of each F_2:3 family was used as the phenotypic value of the corresponding F₂ individual. The results showed that in the two 2025 replications (2025_Rep1 and 2025_Rep2), the FER severity scores of the resistant parent K10 were 1.00 and 1.73, respectively, whereas those of the susceptible parent ZW18 were 6.25 and 5.75. In the two 2026 replications (2026_Rep1 and 2026_Rep2), the FER disease severity scores were 1.00 and 1.18 for K10, and 6.20 and 7.00 for ZW18. Across the four replications in two years, the resistant parent K10 maintained stable resistance across environments, while the susceptible parent ZW18 showed a consistent susceptible response (Fig. 1). In the F₂ segregating population, FER severity did not show a bimodal distribution (Fig. 2), suggesting that this trait is not controlled by a single major gene but may be jointly regulated by multiple QTLs.

Skewness tests showed that among the four replications (2025_Rep1, 2025_Rep2, 2026_Rep1, and 2026_Rep2), only the FER severity of the F₂ population in 2025_Rep1 was normally distributed, while the other three were not (Fig. 2; Table 1). By contrast, the residuals were all normally distributed (Fig. 3; Table 1). Therefore, the phenotypic data from all four replications could be used for QTL mapping of resistance. Analysis of variance (ANOVA) based on the phenotypic data from each replication of the F₂ population showed that FER resistance differed significantly among genotypes and between years, whereas interaction effects between genotype and environment were not significant (Table 2). Furthermore, the broad-sense heritability (H²) of FER resistance was estimated using multiple replication phenotypes of the F₂ population, yielding a value of 0.78.

2.2 Construction of the genetic linkage map

An F₂ population generated from a cross between the parental lines K10 and ZW18 was used to construct a genetic linkage map. From a total of 11535 raw molecular markers, 4301 SNP markers that were polymorphic between the two parents were selected. After further removing 1249 markers showing segregation distortion, 3052 high-quality SNP markers were retained for map construction. Using the “MAP” module in QTL IciMapping software (version 4.2), a genetic map containing 10 linkage groups was successfully constructed (Fig. 4). The total genetic length of the map was 2589.8 cM (Table 3), and the average genetic distance between adjacent markers was 0.85 cM. The markers were relatively evenly distributed across the linkage groups (Fig. 4), indicating that the constructed map is suitable for subsequent QTL mapping analysis.

2.3 QTL mapping for FER resistance

QTL mapping for FER resistance in maize was performed using phenotypic data from four replications (2025_Rep1, 2025_Rep2, 2026_Rep1, and 2026_Rep2) and across two years (2025 and 2026), together with the best linear unbiased estimator (BLUE) value calculated from the phenotypic data of four replications in the F₂ population. A total of five FER-resistant QTLs were detected—qRferK2.1, qRferK4.1, qRferK7.1, qRferK7.2, and qRferK7.3—with explained phenotypic variation ranging from 6.57% to 38.24% (Fig. 5, Table 4). All five FER-resistant QTLs derived from the resistant parent K10, four of which could be detected in more than one replication.

qRferK2.1, which was located in bins 2.01–2.02, was mapped using phenotypic data from 2025 and the BLUE value, explaining 13.86%–17.05% of the phenotypic variation (Fig. 5, Table 4). Based on phenotypic data from 2025_Rep2, 2026_Rep1, 2025, 2026, and the BLUE value, qRferK4.1 was identified, which was located in bins 4.06–4.07 and accounted for 10.88%–38.24% of the phenotypic variation (Fig. 5, Table 4). For qRferK7.1 in bins 7.01–7.02, detection was achieved using the 2025_Rep2 phenotype, explaining 6.57% of the phenotypic variation (Fig. 5, Table 4). qRferK7.2, which was detected in bin 7.02 using phenotypic data from 2025 and the BLUE value, accounted for 9.42%–10.79% of the phenotypic variation (Fig. 5, Table 4). qRferK7.3, located in bins 7.02–7.03, was detected from phenotypic data of 2026_Rep1, 2026_Rep2, 2026, and the BLUE value, accounting for 15.84%–31.26% of the phenotypic variation (Fig. 5, Table 4). In addition, the FER resistance conferred by all five mapped QTLs exhibited both additive and dominant effects, although additive effects were predominant (Table 4).

3 DISCUSSION

Obtaining objective and maximally accurate disease phenotypic data is the most critical step in this study, and also the most challenging task. The core technical difficulty lies in the fact that, due to differences in the flowering periods of different materials, if inoculation is performed at the same time, the differences in grain development stages caused by the different flowering periods of each material will lead to differences in their resistance to infection at different developmental stages, thereby introducing considerable experimental error. To circumvent this problem, all plants in this study were open-pollinated, and each material was manually inoculated with a spore suspension of the pathogen on the 12th day after its own silking, so as to ensure that pathogen infection occurred at a stage when grain development was essentially consistent across all materials. Although this procedure prolongs the duration of manual inoculation work and may introduce certain experimental error due to subtle differences in the inoculation environment among materials, such error is far smaller than that caused by inconsistent grain developmental progression. In terms of disease evaluation, this study adopted a scoring method based on estimation of the diseased area, which may produce some bias due to subjective factors. However, because there are substantial resistance differences between the resistant and susceptible parents themselves, and the disease phenotypes in the segregating population also show pronounced differentiation, the error caused by subjective factors does not substantially affect subsequent QTL mapping work. In addition, in the field of disease evaluation, researchers have recently developed more precise automated analysis systems, providing new technical solutions for high-throughput phenotyping of maize ear rot (Yao et al., 2026). Subsequent studies will refer to the latest research findings and attempt to improve the current disease evaluation system of this study.

In resistance QTL mapping studies, the existence of significant resistance differences between resistant and susceptible parents is a prerequisite for conducting mapping work. In this study, the resistant parent K10 exhibited stable high resistance to FER (mean disease severity score of 1.23), whereas the susceptible parent ZW18 was stably susceptible (mean disease severity score of 6.30). Therefore, the use of these two inbred lines to construct a segregating population for mapping QTLs conferring FER resistance in maize is reasonable and relatively ideal in terms of applicability. In previous studies, joint mapping using three populations identified resistance QTLs in bins 1.03, 1.04–1.07, 2.04–2.08, 4.05–4.08, 6.03–6.06, and 7.01–7.02 across different resistance sources, suggesting that these overlapping regions may represent potential resistance “hotspots” (Wen et al., 2020). In the present study, qRferK4.1 and qRferK7.1 both reside within the aforementioned “hotspot” regions. Specifically, the mapping interval of qRferK4.1 completely overlaps with those of the previously mapped qRfer3, qRfer8, and qRfer18, and the mapping interval of qRferK7.1 completely overlaps with that of the previously mapped qRfer10 and partially overlaps with that of qRfer4. These results further reinforce the reliability of the six “hotspot” regions previously identified.

Previous reports have indicated that FER resistance is inherited as a quantitative trait, with both additive and dominant effects (Zhou et al., 2021). In this study, the broad-sense heritability of FER resistance was estimated to be as high as 0.78, indicating that FER resistance is inherited as a quantitative trait. Furthermore, all five resistance QTLs detected in this study exhibited both additive and dominant effects, but were predominantly governed by additive effects. In addition, the sum of the dominant effects of all mapped QTLs was negative, indicating that individuals carrying heterozygous genotypes within the target QTL intervals are more likely to exhibit FER resistance.

In recent years, the incidence and severity of maize ear rot have been increasing year by year. Meanwhile, with the acceleration of full-process mechanization in maize production, the grain direct-harvesting model cannot effectively remove diseased ears during the harvesting stage, so that the intrinsic resistance of varieties has become the primary line of defense against ear rot damage. However, since it was first reported in 1987 that the predominant pathogen of maize ear rot in China was Fusarium verticillioides, numerous subsequent identifications of maize ear rot pathogens have consistently shown that Fusarium verticillioides remains the dominant pathogen of maize ear rot in China (Pan et al., 1987). This fact indicates that, over a long period, the varieties deployed in maize production in China have consistently lacked resistance to Fusarium verticillioides. Therefore, there is an urgent need to clone resistance genes and to breed and promote resistant varieties. Over the past two decades, researchers have mapped a number of FER resistance QTLs on different maize chromosomes through linkage analysis and genome-wide association studies. However, most of these QTLs have only small effects, and cloning genes conferring FER resistance has progressed slowly because of complex genetic mechanisms. To date, resistance QTLs or genes that can be stably inherited and directly applied in breeding have rarely been reported, and the regulatory network of resistance genes remains unclear. Therefore, continuing to mine more stable resistance QTLs and achieving the transition from “mapping QTLs” to “cloning major genes and dissecting resistance mechanisms” represent the core tasks and current developmental trends. This study provides a preliminary approach for such genetic analyses.

4 MATERIALS AND METHODS

4.1 Plant materials

A mapping population of 158 F₂ individuals and their corresponding F_2:3 families was derived from a cross between the FER-resistant inbred line K10 and the susceptible inbred line ZW18 to map FER resistance QTLs.

4.2 Pathogen culture and artificial inoculation

The Fusarium verticillioides strain was cultured on potato dextrose agar (PDA) plates at 25 °C in the dark for approximately 7 days. After the mycelia had fully covered the plates, the cultures were stored at 4 °C for later use. Prior to inoculation, each medium plate with pathogen was cut into 1 cm² pieces and transferred into 1000 mL of 5% mung bean broth liquid medium, followed by incubation at 25 °C on a rotary shaker at 150 r/min for 3 days. After incubation, the mycelial mats were removed by filtration, and the conidial suspension was collected and stored at 4 °C. One day before inoculation, the conidial concentration was adjusted to 5 × 10⁶ conidia·mL⁻¹ with sterile distilled water, and the surfactant Tween-80 was added to a final concentration of 2 µL·mL⁻¹. At 12 days after silking, 2 mL of conidial suspension was injected into the middle of each maize ear with a syringe.

4.3 FER disease evaluation and phenotypic analysis

The parental inbred lines and the 158 F_2:3 families were planted for field inoculation and disease evaluation in the spring of 2025 and 2026 at the Gongzhuling Experimental Station, Jilin Province. The experiment was arranged in a randomized block design with two replications (blocks) per year and four replications in total across the two years. Each block contained the 158 F_2:3 families and the two parental inbred lines. Each entry was planted in a single row per replication, with a row length of 5 m, row spacing of 0.6 m, and plant spacing of 0.25 m (21 plants per row). The survival rate of all plots was above 90%, and all surviving plants were artificially inoculated and scored for disease severity. At 30 days after inoculation, the severity of FER was scored using a 5-grade scale (1, 3, 5, 7, and 9), corresponding to the following infected area percentages: grade 1 (0%–2%), grade 3 (3%–10%), grade 5 (11%–25%), grade 7 (26%–50%), and grade 9 (51%–100%). The field management was identical in both years. The temperature from mid-July to early August in 2026 was higher than that in 2025, while the other climatic conditions were basically the same.

After FER disease evaluation, phenotypic data were analyzed by ANOVA using R software, based on the model Y = μ + α_G + β_Y + (αβ)_GY + ε_GYR, where α_G represented the effect of Gth line, β_Y was the effect of the Yth year, (αβ)_GY was the effect of the genotype by year interaction, and ε_GYR was the residual effect. Variance components were estimated using the lme4 package in R. Broad-sense heritability was calculated from the estimated variance components as H² = σ²_G/(σ²_G + σ²_GY/R + σ²_e/YR) (Knapp et al., 1985), where σ²_G was the genetic variance, σ²_GY was an interaction variance of genotype by year, σ²_e was the residual error, R was the number of replications per year, and Y was the number of years. Based on the decomposition of variance components, the mean square (MS) for G can be decomposed as σ²_e + Rσ²_GY + RYσ²_G, the MS for Y as σ²_e + Rσ²_GY + RGσ²_Y, the MS for G:Y as σ²_e + Rσ²_GY, and the residual MS as σ²_e. From these, σ²_G, σ²_GY, and σ²_e can be estimated separately, allowing the broad-sense heritability to be calculated.

4.4 Genotyping and construction of linkage maps

Young leaves of the parental inbred lines and the F₂ population were collected at the 5–6 leaf stage for genomic DNA extraction. Genotyping of each individual was performed using the GenoBaits Maize10K chip based on the Genotyping by Target Sequencing (GBTS) platform. Then SNP markers showing polymorphism between the two parents were selected from the raw genotype data, and markers with significant segregation distortion (P < 0.05) were removed. A linkage map was constructed using the “MAP” module of QTL IciMapping software (version 4.2) (Meng et al., 2015). Linkage groups were defined with a LOD threshold, and recombination frequencies were converted into genetic distances (cM) using the Kosambi mapping function.

4.5 QTL detection for resistance to FER

The mean phenotypic values of each F_2:3 family were used as the phenotypic values of the corresponding F₂ individual. Combined with the genetic linkage data and phenotypic data from the F₂ population, QTL mapping for resistance to FER in maize was performed using the composite interval mapping (CIM) method implemented in QTL Cartographer software (version 2.5) (Zeng, 1994; Basten et al., 1997), based on phenotypic data of four replications (2025_Rep1, 2025_Rep2, 2026_Rep1, 2026_Rep2) and two years (average value of two corresponding replications, represented accordingly as 2025 and 2026), as well as the BLUE value calculated from the phenotypic data of four replications. In the CIM analysis, Model 6 (Standard Model) was selected, with a walk speed of 1.0 cM and a window size of 10.0 cM, so that markers within 10 cM on either side of the tested position were excluded as cofactors to avoid masking the signal of the target interval. A maximum of five background markers were used as cofactors, which were selected by backward regression from the genome-wide markers and dynamically chosen within the Model 6 framework according to the specified marker number and window size to control background genetic effects. The LOD significance threshold was determined automatically by the software through a permutation test (1000 permutations, significance level 0.05).

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