Identification of Quantitative Trait Loci and Development of Intermediate Breeding Parent for Rice Sheath Blight Resistance
Article information
Abstract
The soil-borne pathogen Rhizoctonia solani is one of the most devastating necrotrophic pathogens worldwide, responsible for causing rice sheath blight (RSB). This pathogen has a broad host range, affecting economically important monocots and dicots such as rice, wheat, potato, soybean, sugar beet, and cucumber. Despite extensive screening of rice germplasm, genes that confer full resistance to RSB have rarely been identified, leading to slow progress in breeding resistant varieties. To identify RSB-resistant rice cultivars in Korea, variations in quantitatively inherited resistance have been observed. We conducted a study to visually assess the RSB resistance phenotypes of 250 cultivated varieties under natural disease conditions in the field over several years. Notable candidates included P1401, which showed resistance, while Junam was susceptible. To identify the quantitative trait loci (QTLs) associated with resistance, we developed an F2 mapping population by crossing P1401 and Junam, followed by bulked segregant analysis. These QTLs were mapped to specific locations on seven of the 12 rice chromosomes. This mapping population and the resulting datasets provide valuable resources for advancing genomic research in rice, particularly for marker-assisted breeding strategies for enhancing resistance to R. solani and other important agronomic traits.
Rice (Oryza sativa L.) is an important staple food for more than half of the world’s population (Guimaraes, 2009). However, rice production is continually threatened by biotic stresses caused by various pathogens. Among the pathogens that negatively affect rice production R. solani causes rice sheath blight and is one of the most destructive fungal pathogens. This pathogen produces sclerotia, which serve as dormant structures, allowing it to survive in the soil and on dead plant debris for long time. This pathogen has a broad host range, including sugar beet, potato, squash, and grasses. Sheath Blight is known to cause significant rice quality deterioration and yield losses ranging from 20% to 50% (Gangopadhyay and Chakrabarti, 1982; Lee, 1983; Savary et al., 2000; Yu et al., 2019).
Sheath Blight primarily affects the leaf sheaths and leaves of rice plants near the water surface, forming irregular, light brown lesions (Lee, 1983). In recent decades, the primary causes of sheath blight outbreaks have been the use of high doses of nitrogen fertilizers and high planting densities (Li et al., 2021). Under increased temperatures and humid conditions during the summer, the disease symptoms spread rapidly (Senapati et al., 2022; Yellareddygari et al., 2014). Traditional management strategies for sheath blight include the use of chemical fungicides, water management, optimal plant spacing and density, balanced nitrogen fertilization, field sanitation by burning or plowing crop residues into the soil, and crop rotation. However, these methods have several limitations, such as environmental impact, high costs, and the emergence of fungicide-resistant pathogens (Datta and Varukonda, 2017; Singh et al., 2019). Consequently, breeding resistant rice varieties has emerged as a sustainable and effective approach to managing this disease (Senapati et al., 2022).
Analyzing genomic regions linked to disease resistance has proven highly useful for identifying genetic variations that influence resistance traits in crops (McCough and Doerge, 1995; Yano and Sasaki, 1997). These regions, known as quantitative trait loci (QTLs), serve as critical markers for breeders aiming to develop rice varieties with enhanced resistance. The first QTL associated with sheath blight resistance was identified in 1995 (Li et al., 1995). Since then, over 100 resistance-related regions have been mapped across various rice chromosomes, leading to extensive research aimed at pinpointing the genes contributing to sheath blight resistance (Channamallikarjuna et al., 2010; Zuo et al., 2013, 2014). However, only a few definitive resistance loci have been confirmed, with most findings suggesting the presence of minor regions (Zuo et al., 2013, 2014). Therefore, investigating loci associated with sheath blight traits in previously unstudied diverse populations is one of the best methods for identifying additional resistance loci.
Rice sheath blight (RSB) is one of the most common rice diseases in Korea, particularly affecting the southern and southwestern regions like Jeollanam-do and Gyeongsangnam-do, where the climate is more favorable for the pathogen each year. The warm and humid rice-growing season (typically from June to August) creates ideal conditions for the disease to proliferate. The disease typically appears every year, and most rice fields in Korea experience varying levels of infection. Severe outbreaks occur especially in years with extended rainy periods or in heavily fertilized, dense rice fields (Hu et al., 2016). Despite its severity in every year, the attempt to find QTLs associated with RSB resistance is rare in Korea.
This study aims to develop intermediate breeding parents related to resistance against sheath blight in rice while identifying resistance QTLs and developing new molecular markers. An intermediate breeding parent exhibits desirable traits but has not yet been developed into a distinct variety and is suitable for direct use in the development of new cultivars. To achieve this, we performed phenotypic evaluation and genomic analysis using populations derived from crosses between resistant and susceptible varieties. Through this approach, we seek to elucidate the genetic basis of sheath blight resistance and provide useful molecular markers for rice breeding programs. The findings from this study are expected to contribute to the development of rice varieties resistant to sheath blight, reducing the dependence on chemical control methods and enhancing sustainable rice production. Furthermore, these results will improve our understanding of the genetic mechanisms of disease resistance, providing insights into the broader fields of plant-pathogen interactions and crop improvement.
Materials and Methods
Plant variety and growth
In this study, the resistance levels of different rice varieties were assessed using the susceptible variety Lemont (Japonica), the resistant variety Jasmine85 (Indica) (Jia et al., 2015), Junam (Japonica) developed by the Southern Crops Division of the National Institute of Crop Science, and P1401 (Indica) collected by the National Agrobiodiversity Center. These varieties were transplanted on May 10 from 2020 to 2024, at the Southern Crops Division in Miryang, Gyeongnam-do, Republic of Korea. Each variety was planted in 10 rows per line, with three plants per row and a planting distance of 30 cm × 12 cm. Fertilization was carried out with a balanced fertilizer (N-P2O5-K2O = 18-9-11), and other management practices were performed, including water management and pest control, followed the standard cultivation methods of the Rural Development Administration.
Pathogen growth and infection
To test resistance to rice sheath blight, the pathogen Rhizoctonia solani AG-1 (KACC no. 40101) was obtained from the National Agrobiodiversity Center. Mycelia or sclerotia were inoculated onto PDA medium (potato dextrose agar; potato dextrose broth 24 g, sucrose 20 g, agar powder 16 g/l) and incubated at 28°C under dark conditions for 7 days. The culture was then subcultured on the same medium to propagate the fungus for 4 days. For inoculation in the field, the pathogen grown on PDA medium was divided into four parts and pressed into the middle of the lower stem of the rice plant. For laboratory inoculation, the middle part of the fourth leaf of rice plants, which were 4 weeks old after transplantation, was used (Zhang et al., 2017). Leaves of each variety were cut to size of 20 and placed on filter paper soaked with distilled water in square Petri dishes (23 cm × 23 cm) (Sayler and Yang, 2007). The R. solani culture, grown for five days on PDA medium, was cut into 0.3 cm2 squares and inoculated in the center of the leaves. The Petri dishes were then sealed with lids and incubated at 25°C in the dark for 1–2 days to observe disease lesion development.
Genomic DNA extraction and PCR
To extract genomic DNA, rice leaf sheath samples were cut into 5 cm pieces and then placed in liquid nitrogen. The samples were ground to a fine powder using a pre-cooled mortar and pestle with the addition of liquid nitrogen. The ground leaf sheath samples were treated with 1 ml of 2× CTAB buffer (2% CTAB, 0.1 M Tris, pH 8.0, 1.4 M NaCl, 1% PVP) at 65°C for 30 min. Then, 500 μl of PCI solution (phenol:chloroform:isoamyl alcohol = 25:24:1) was added, and the mixture was centrifuged at 13,000 rpm for 10 min. The upper aqueous phase (500 μl) was transferred to a 1.5 ml Eppendorf tube. To precipitate the DNA, an equal volume of isopropanol was added, and the solution was stored at −80°C for 30 min. The mixture was then centrifuged at 13,000 rpm for 10 min, and the supernatant was removed. The resulting pellet was washed with 70% ethanol and dried. The extracted DNA was dissolved in DEPC-treated water, and its concentration was quantified using a NanoDrop Lite Spectrophotometer (Thermo Scientific, Waltham, MA, USA). PCR reactions were conducted using Solg e-Taq DNA Polymerase (SolGent Co. Ltd., Seoul, Korea). The PCR products were analyzed by electrophoresis on a 3% agarose gel containing a fluorescent dye, run at 200 V for 50 min to determine the genotype. The PCR cycle consisted of an initial denaturation step at 95°C for 2 min, followed by 35 cycles of denaturation at 95°C for 20 s, annealing at 55°C for 1 min, and extension at 72°C for 30 s. A final extension step was carried out at 72°C for 7 min.
Total RNA extraction and reverse transcription polymerase chain reaction
To extract total RNA, rice leaf samples were immediately flash-frozen in liquid nitrogen. The samples were then ground with liquid nitrogen in a pre-chilled mortar and pestle, and 1 ml of TRIzol Reagent (Invitrogen, Carlsbad, CA, USA) was added. After adding 0.2 ml of Chloroform, the mixture was centrifuged at 12,000 rpm for 15 min at 4°C to separate the aqueous phase. To precipitate the RNA, 0.5 ml of isopropanol was added to the aqueous phase, followed by centrifugation at 12,000 rpm for 15 min at 4°C, and removal of the supernatant. The RNA pellet was washed with 70% ethanol and dissolved in DEPC-treated water for reverse transcription polymerase chain reaction (RT-PCR) analysis. For DNase treatment, 3 μg of RNA was treated with DNase I, RNase-free (Thermo Scientific), followed by cDNA synthesis using the SuperScript III First-Strand Synthesis System (Invitrogen). RT-PCR conditions were performed according to the manufacturer’s recommendations. The purity of the cDNA was assessed using a NanoDrop Lite Spectrophotometer (Thermo Scientific).
Real-time PCR analysis
For quantitative reverse transcription polymerase chain reaction (qRT-PCR), GoTaq qPCR Master Mix (Promega, Madison, WI, USA) and the QuantStudio 5 Real-Time PCR System (Applied Biosystems, Foster City, CA, USA) were used. The qRT-PCR conditions included an initial pre-denaturation step at 95°C for 10 min, followed by 40 cycles of denaturation at 95°C for 15 s, annealing at 60°C for 1 min, and extension at 95°C for 15 s. Melting curve analysis was performed from 60°C to 95°C, increasing by 0.15°C every 5 s. OsEF1 was used as the internal control. Relative gene expression was calculated using the ΔΔCt method (Livak and Schmittgen, 2001). Primer sequences used were followed; OsEF1: forward (OsEF1-F), AGTCACATGCTGCCTAAGGTT; reverse (OsEF1-R), TCA-CTGCCAGCTTACGGAGG; OsPR1a: forward (OsPR1a-F), CGTCTTCATCACCTGCAACTACTC; reverse (OsPR1a-R), CATGCATAAACACGTAGCATA-GCA; OsPR10b: forward (OsPR10b-F), GCACCATCCACA-TCATGAAGC; reverse (OsPR10b-R), TTGCC-CACCCTGCTCTTAAC.
QTL analysis and marker development
For quantitative trait loci-seq (QTL-seq) analysis, disease severity of rice sheath blight was evaluated in 60 F2 plants from the cross between Junam and P1401. Plants with distinct resistance (10 individuals) and susceptibility (10 individuals) were selected, and DNA was extracted from their leaves using a DNA extraction kit. The extracted DNA was diluted to a concentration of 100 ng/μl, mixed to form bulks, and then subjected to re-sequencing using the Illumina NovaSeq 6000 platform (Phyzen, Seongnam, Korea). This sequencing data was used to perform bulk segregant analysis (BSA) to compare allele patterns and frequencies between the bulks and to conduct QTL-seq analysis based on the genotypic and phenotypic data of disease severity (Michelmore et al., 1991). To identify single nucleotide polymorphism (SNP) variants, the sequencing data were preprocessed using Trimmomatic v0.39 (Bolger et al., 2014). Subsequently, SNP discovery was conducted by mapping the processed data to the reference genome (Oryza_indica.ASM465v1) using BWA v0.7.17 (Li and Durbin, 2009), SAMtools v1.11 (Li et al., 2009), and BCFtools v1.11. The QTL-seq analysis involved constructing a consensus genome by substituting SNPs identified in the parental lines into the reference genome sequence. The frequencies of the detected variants (alleles) between the two bulks of the segregating progeny were calculated using SNP-index as described by (Takagi et al., 2013). The average SNP-index values were calculated using a sliding window approach with a 2 Mb window size and a 100 kb step size. QTL regions associated with sheath blight were defined based on a significance level of 95%. Derived cleaved amplified polymorphic sequences (dCAPS) markers were developed based on the variant sequences of the parental lines using dCAPS Finder 2.0 (Neff et al., 2002).
Haplotype analysis
Three dCAPS markers were developed for genotyping analysis (Table 1). All dCAPS markers used for the QTL analysis were located on chromosome 1. After performing PCR, 7 μl of the PCR product was mixed with 1 μl of 10× buffer and 1 U of restriction enzyme, making a total reaction volume of 10 μl. This mixture was incubated at 37°C for 1 h. Following restriction enzyme treatment, the samples were subjected to electrophoresis on a 4% agarose gel, stained with EtBr, and visualized under a UV transilluminator to determine the genotypes.
Results
P1401 is a resistant variety to sheath blight
Initially, the indica variety P1401 was identified as resistant to natural sheath blight outbreaks in rice paddy fields through visual observation, based on a multi-year survey (data not shown). To validate this observation, we conducted the following series of experiments. The daily temperature and humidity data collected from the Miryang area from May to August in 2023 were analyzed, and it was found that the conditions for the development of sheath blight disease were satisfied in July and August (Fig. 1A). During this period, the growth and disease severity of different rice cultivars, Lemont (susceptible), Junam, P1401, and Jasmine85 (resistant), were analyzed following inoculation with R. solani (Li et al., 1995). Although a trend of rice growth retardation was observed, there were no statistically significant differences in culm length, panicle length, panicle number, or heading date between the control and infected groups across all four cultivars (Table 2). These results suggest that under the artificial infection conditions, the impact of disease inoculation on growth characteristics was limited. The Junam, which is popularly grown in the Yeongnam region of South Korea, exhibited disease symptoms similar to the susceptible cultivar Lemont, while P1401 showed a resistant phenotype similar to Jasmine85 (Fig. 1B). The analysis of lesion ratio showed that Junam had a high susceptibility with a lesion ratio of 73.2%, while P1401 demonstrated the highest resistance with a lesion ratio of 15.3%. After calculating the disease index according to the International Rice Research Institute’s criteria and normalizing the lesion ratio, the resistant cultivar Jasmine85 was set to 1 and the susceptible cultivar Lemont to 9, showing similar results (Fig. 1C). The International Rice Research Institute (IRRI) employs the standard evaluation system (SES) for assessing rice disease, particularly lesion-based diseases such as rice blast. This system utilizes a scoring framework that quantifies disease severity based on various criteria, including lesion size, color, and distribution. The SES operates on a 0–9 scale, where lower scores (0–3) indicate resistance and higher scores (7–9) denote susceptibility (International Rice Research Institute, 2013). The analysis of rice yield changes due to sheath blight disease showed that the resistant cultivar Jasmine85 exhibited a yield reduction of approximately 14%. In contrast, the Junam cultivar had a 15% yield reduction in the disease-treated group compared to the untreated group. Meanwhile, the susceptible cultivar Lemont exhibited a 40% decrease in yield (Fig. 2A). P1401, which exhibits long culm and panicle lengths typical of indica cultivars, could not be assessed for yield due to damage caused by summer typhoons resulting in plant collapse (Fig. 2B). Therefore, we examined the expression levels of pathogen-related genes (PR genes), specifically OsPR1a and OsPR10b (Datta et al., 1999). Both PR1a and PR10b genes showed more active transcript expression in P1401 than in Junam starting 12 h after disease inoculation (Fig. 2C and D). This indicates that P1401 responds more rapidly to R. solani than Junam by activating the plant’s defense system, thereby exhibiting greater resistance. These findings suggest that selecting resistant cultivars plays a crucial role in reducing yield loss due to sheath blight. In particular, P1401 quickly activates its immune system to resist R. solani, we conclude P1401 is a suitable cultivar for producing an intermediate breeding parent and exploring sheath blight resistance QTL through crossbreeding with Junam, a representative susceptible cultivar grown in the southern regions of Korea.

The P1401 variety exhibits resistance to rice sheath blight. (A) Average monthly temperature and relative humidity during the rice growing period from May to August in 2023. (B) The disease phenotypes of Lemont, Junam, P1401, and Jasmine85 were evaluated. Representative plants were photographed three post-pathogen infection. (C) Analysis of the incidence rate of rice sheath blight disease by variety.

P1401 shows resistance to rice sheath blight, with strongly increased expression of the pathogen-related (PR) gene compared to Junam. (A) Comparison of untreated and treated rice yield in the three varieties. Student’s t-test was applied on the data analysis. * indicates a significant difference (P < 0.05). (B) Phenotype in the field following infection for four varieties. (C) OsPR1a expression is significantly elevated in P1401 compared to Junam from 12 hours post-inoculation (hpi), and continues to increase gradually until 48 hpi. (D) The expression of OsPR1a is slowly elevated in P1401 compared to Junam from 12 hpi, and reaches its peak at 48 hpi. The data were normalized to Oryzae sativa L. elongation factor 1 (OsEF1) mRNA levels, which served as an internal control. The plants were incubated with R. solani and analyzed for mRNA levels in the 5th leaves. The values and error bars represent the means and standard deviations of two biological replicates. Student’s t-test was applied on the data analysis. * indicates a significant difference (P < 0.05).
Development of an F2 population for QTL mapping
To develop intermediate breeding lines for sheath blight resistance and to explore QTLs, we performed a cross between Junam and P1401. To analyze the genetic traits for resistance to R. solani, we tested RSB resistance in the F1 population. Due to the variability in field conditions making it difficult to obtain consistent and reproducible results, we carried out laboratory validation. Starting 24 h after inoculation, the disease spread from the inoculation site, forming brown lesions approximately 1 cm in size. While there were slight variations among individuals, most exhibited disease symptoms similar to the susceptible control Junam (Fig. 3A). This suggests that the resistance gene for R. solani is a recessive trait. To confirm the inheritance pattern of the disease symptoms, we assessed the disease resistance in the F2 generation derived from the F1 progeny. We examined lesion development and the expression of PR genes following pathogen inoculation. Disease severity was evaluated more than 150 F2 individuals. Unexpectedly, nearly 50% segregation in resistance was observed (data not shown), suggesting that the resistance is likely horizontally controlled by multiple genes, rather than vertical resistance typically mediated by single R genes in response to R. solani. This observation similar with the inheritance patterns reported for damping-off disease in watermelon in response to R. solani (da Cunha et al., 2022). After analyzing the phenotypes and OsPR10b expression, we collected samples that separated extreme resistant and susceptible traits. R1 to R5 were categorized as resistant bulk samples, S1 to S3 were classified as susceptible bulk samples (Fig. 3B and C). Interestingly, we frequently observed individuals with OsPR10b expression levels that were higher than those of the resistant parent P1401 such as R5 (Fig. 3C). This observation implies that the presence of additional genes, which may inhibit or regulate the resistance gene responsible for sheath blight resistance in P1401 or Junam, could have been lost or gain during the gene recombination process in the cross.

The disease phenotypes of F1 and F2 inoculated with Rhizoctonia solani, along with the results of the gene expression analysis. (A) The resistance to rice sheath blight is due to a recessive allele. F1 plants were inoculated with R. solani, and pictures were taken at 2 days post-inoculation (dpi). Scale bars = 1 cm. These experiments were repeated 2 times, with similar results. (B) Phenotype of Junam, P1401 and F2 plants infected with R. solani and pictures were taken at 2 dpi. (C) The expression of OsPR10b was analyzed in rice leaves 48 hours after disease treatment. Infected rice leaves in (B) were used for this analysis. Rice ubiquitin was used as an internal control. Error bar represent standard deviation based on data from two biological replicates.
Selection of sheath blight resistance variants in rice
For QTL analysis, F2 individuals from the Junam × P1401 cross were phenotypically analyzed. We selected 10 individuals with extreme resistant phenotypes and 10 with extreme susceptible phenotypes for QTL-seq (Fig. 3B and C). Given that the resistance trait is believed to be derived from P1401, an indica cultivar, we used the indica reference genome and compared variations between the resistant P1401 and the susceptible Junam to identify QTLs in the progeny.
Using a sliding window approach with a window size of 2 Mb and a step size of 100 kb, we searched for significant QTL regions but did not find any meaningful intervals. This lack of significant results is attributed to the small size of the F2 population, which resulted in insufficient numbers of individuals showing distinct disease symptoms, limiting the segregation analysis of the extremes.
However, significant differences were observed in individual SNP variants. When comparing to the P1401 reference genome, we identified 540 statistically significant variants (P < 0.05) across 12 chromosomes, including 306 SNPs and 234 insertions and deletions (InDels). Conversely, when comparing the R/S-bulk to the Junam reference genome, we found a total of 455 statistically significant variants (P < 0.05) across 12 chromosomes, consisting of 260 SNPs and 195 InDels (Supplementary Table 1). QTL-seq analysis involves comparing the frequency of variants between bulks using one of the parental genotypes as a standard, and the detected variants can vary depending on the consensus genome used. To ensure reliable identification, we compared results from two analyses and identified a total of 348 common variants (233 SNPs and 115 InDels).
To increase the confidence in these variants, we applied additional selection criteria by examining polymorphisms among samples. We first selected distinct variants between the R-bulk and S-bulk based on genotype information mapped to the reference genome. Similarly, we identified different variants between the parental lines. Next, to focus on high-confidence variants associated with phenotypic differences, we selected variants showing differential genotypes between P1401 (resistant) and the S-bulk, or between Junam (susceptible) and the R-bulk.
Ultimately, to distinguish high-confidence variants derived from the susceptible P1401, we selected only those variants in the S-bulk that were homozygous. This resulted in 11 selected variants (1 SNP and 10 InDels) distributed across 7 chromosomes (Fig. 4).

A genetic map constructed using dCAPS markers with the F2 plants derived from the cross between Junam and P1401. Chromosome numbers are displayed at the top of each chromosome, and their positions are shown on the right side. Black lines indicated common variants. Red lines indicated selected variants.
Development of molecular markers for sheath blight resistance in rice
To develop resistance molecular markers derived from P1401, we designed 10 markers targeting either InDel or SNP locations, including 3 dCAPS markers (Table 1). Genotyping was performed on F2 progeny from the Junam × P1401 cross. Of these, only 3 dCAPS markers successfully differentiated the genotypes of the F2 progeny (Fig. 5A). The locations of these markers are Chr01: 30828812, Chr01: 30985448, and Chr01: 30906560. Interestingly, these positions are associated with the major QTL for sheath blight resistance, qSB1-2HJX74 on Chromosome 1, as reported in previous studies (Channamallikarjuna et al., 2010; Goad et al., 2020; Zhu et al., 2014). This suggests that some of genes responsible for sheath blight resistance are located in the lower part of chromosome 1. Interestingly, the susceptible phenotypes, S43-2, S43-3, and S43-14, all exhibited the genotype of the susceptible cultivar Junam for the three markers, while the resistant phenotypes, R110-6, R110-7, R110-12, and R110-13, showed homozygous genotypes for P1401 across all three markers (Fig. 5B). Among the 36 F3 individuals analyzed with dCAPS_1, 23 exhibited the P1401 genotype. For dCAPS_2 and dCAPS_3, 20 and 15 individuals, respectively, exhibited the P1401 genotype (data not shown). Given the absence of accurate gDNA sequence information for other regions, further detailed analysis is required after verifying the DNA sequence information of these regions. Additional confirmation is needed in advanced generations or in lines that have been stabilized through backcrossing or other methods to ensure reliable identification of these resistance traits.

The confirmation of association between phenotype and genotypes of selected variatns. (A) Haplotype analysis of each variant to distinguish between indica and japonica types. Each gDNA extracted from Junam (Japonica) or P1401 (Indica) was used PCR template. (B) The genotype of F3 were confirmed with three dCAPS marker 1 cleaved by Hpy188I (chr01_30828812), dCAPS marker 2 (chr01_30906560) and 3 (chr01_30985448) cleaved by HinfI, respectively. J; Junam, P; P1401. Red asterisk: confirmed variant. (C) Phenotype of Junam, P1401, and F3 plants infected with Rhizoctonia solani and picture were taken at 2 days post-inoculation. Scale bar = 1 cm.
Discussion
RSB is a severe fungal disease caused by R. solani, significantly decreasing rice production. This study aimed to produce an intermediate breeding parent and to identify rice varieties with resistance to RSB among 250 rice varieties and breeding lines developed or collected by the Southern Crops Division of the National Institute of Crop Science. As a result, the resistant variety P1401 and the susceptible variety Junam were selected. BSA was conducted on the F2 generation derived from a cross between P1401 and Junam to identify QTL associated with resistance. This analysis revealed resistance QTLs located on seven of the 12 chromosomes in rice. The data associated with these QTLs are expected to contribute to the development of RSB-resistant rice varieties and to the enhancement of disease resistance and other important agricultural traits through the development of genetic markers.
Before the widespread cultivation of semi-dwarf rice varieties through commercial breeding, sheath blight disease was not a major concern. However, with the adoption of semi-dwarf varieties, this disease has become one of the significant threats to rice quality and yield. Sheath blight is a serious issue affecting rice cultivation worldwide, making the development of resistant cultivars a crucial objective. Various studies have observed different levels of susceptibility, and numerous efforts are ongoing to identify and develop resistant varieties. These efforts include genetic screening, breeding programs, and the use of molecular markers to enhance resistance and mitigate the impact of this disease (Jia et al., 2007; Marchetti and Bollich, 1991). In particular, wild rice is believed to harbor genes that confer resistance to sheath blight. This potential for resistance in wild rice makes it a valuable resource for identifying and utilizing resistance genes in breeding programs aimed at developing resistant cultivars (Eizenga et al., 2002). Unfortunately, major R genes conferring resistance to sheath blight in rice have been rarely reported, highlighting a significant shortage of resistance resources. The limited availability of well-characterized resistance genes presents challenges in developing resistant cultivars and emphasizes the need for continued research to identify and utilize these critical genetic resources (Brooks, 2007). Research on genes related to resistance against sheath blight in rice is quite limited. However, various rice varieties have shown genes that confer multiple levels of resistance. Some of these resistance genes are associated with molecular markers, such as OsRLCK5, which encodes receptor-like cytoplasmic kinases. These markers can be used to facilitate the development of resistant cultivars, highlighting the potential for further exploration and utilization to improve resistance in rice breeding programs (Jia et al., 2009; Wang et al., 2021). A study involving 6,000 rice varieties from 40 countries showed partial resistance to sheath blight; however, no major resistance genes were identified (Hashiba, 1984). It has been reported that culm length and heading date are closely related to resistance against sheath blight. Often, QTLs associated with sheath blight resistance overlap with QTLs related to heading date. This may lead to reduced differences in resistance when the transplanting dates are the same (Lee, 1983). This suggests that the genetic factors influencing disease resistance may also be linked to traits such as heading date, which could be important in the development of resistant rice cultivars (Pinson et al., 2005; Sharma et al., 2009).
Globally, various rice varieties exhibit different levels of susceptibility or resistance to sheath blight. While there have been efforts to develop commercial resistant cultivars through breeding programs and the use of engineered resistance genes, these efforts have not yet been fully effective (Zuo et al., 2009). This may be due to the fact that multiple genes or QTLs control resistance to sheath blight (Pinson et al., 2005). Research is needed to map populations derived from highly resistant cultivars to identify new QTLs. These new QTLs can be combined with known resistance QTLs to develop rice varieties with enhanced resistance to sheath blight (Zarbafi and Ham, 2019). To date, over 200 QTLs associated with sheath blight resistance have been identified using various populations, including double haploid lines, recombinant inbred lines, near-isogenic lines, and backcross populations (Zarbafi and Ham, 2019). This suggests that, unlike other diseases, resistance to sheath blight in rice involves a very complex process. In this study, potential resistance QTLs were isolated from P1401 (Indica) and developed into molecular markers, but no functionally characterized resistance genes were identified. Therefore, additional research is needed to explore genes around these markers to identify more significant resistance genes.
Vertical resistance, mediated by single major resistance genes, has been shown to provide effective protection against specific pathogen races. This type of resistance is characterized by its high efficacy in preventing infection and limiting disease progression. For example, genes such as R-genes in rice provide robust defense against specific strains of pathogens like Magnaporthe oryzae (Jones and Dangl, 2006). However, vertical resistance can be relatively short-lived due to the rapid evolution of pathogen populations, which may lead to the emergence of new races capable of overcoming the resistance (McDonald and Linde, 2002). In contrast, horizontal resistance involves multiple genes contributing to a broad-spectrum, moderate level of resistance. The resistance observed against R. solani might belong to this category (da Cunha et al., 2022). This form of resistance does not target specific pathogen races but instead enhances the plant’s general ability to withstand a range of pathogen strains. Although it may not provide complete immunity, horizontal resistance reduces the severity of disease and is less likely to be overcome by pathogen evolution, making it a valuable component in integrated disease management strategies (Keane, 2012). Building the durability of horizontal resistance in rice will be particularly advantageous in maintaining crop health against R. solani over extended periods. Further studies on QTLs identified in this study would be valuable for discovering novel RSB resistance genes and establishing horizontal resistance against R. solani.
Notes
Conflicts of Interest
No potential conflict of interest relevant to this article was reported.
Acknowledgments
This paper represents part of the results supported by the Rural Development Administration research project (Project Title: Development of Resistance Breeding Materials for Improving Rice Cultivation Stability in the Yeongnam Region of South Korea, Project Number: PJ01477402-2024). We thank all those who cooperated in the execution of the research project.
Electronic Supplementary Material
Supplementary materials are available at The Plant Pathology Journal website (http://www.ppjonline.org/).