Genome-wide association mapping for identification of sheath blight resistance loci from wild rice Oryza rufipogon

Sheath blight (ShB) caused by Rhizoctonia solani is one of the serious constraints that hamper global rice production. It causes severe yield loss of up to 69% under favorable environmental conditions. The application of chemical fungicides remains the primary approach for the management of this disease. But, overuse of chemical fungicides causes potential health and environmental risk. The use of host plant resistance is a very effective, economic, and sustainable strategy to control sheath blight disease in rice. In this report, we have evaluated 405 accessions of Oryza rufipogon for four ShB-related traits i.e. lesion height (LH), plant height (PH), relative lesion length (RLH), and disease score (DS) during the years 2015, 2016, and 2017. A total of 44,109 high-quality SNP markers on 301 accessions were employed to identify significant marker-trait associations using a genome-wide association study (GWAS). The GWAS analysis revealed a total of 22 significant SNPs associated with the ShB-related traits distributed on all the rice chromosomes except 10 and 12. Among them, eleven were associated with RLH, seven with PH, and one each with LH and DS. The SNPs on chromosomes 3 and 9 were associated with multiple traits. Furthermore, seven ShB resistance loci were also found to be co-localized with previously reported ShB resistance genes/QTLs. These findings provide valuable information to identify key SNPs associated with ShB resistance. Significantly associated SNPs could be used for introgression of ShB resistance traits into rice cultivars using marker-assisted selection.


Introduction
Rice sheath blight (ShB) disease, caused by a soilborne, necrotrophic fungus Rhizoctonia solani Kühn (R. solani, teleomorph: Thanatephorus cucumeris), is one of the most destructive diseases of rice (Oryza sativa L.). Worldwide, the yield losses have been estimated up to 69% (Lee and Rush 1983;Rush and Lee 1992;Sivalingam et al. 2006) and in India alone, it causes approximately 20% yield loss (Ghosh et al. 2016;Yuan et al. 2019). The widespread adaptation of high-yielding semi-dwarf cultivars combined with the application of high levels of nitrogenous fertilizers has led to increased severity of this pathogen (Savary et al. 2000). The disease initially occurs in the leaf sheath and spreads upwards, creating lesions in the leaf and leaf sheath (Goad et al. 2020). The disease is highly destructive when it reaches the panicle and reduces the grain quality by infecting the grains.
Application of fungicides and cultural practices remain the primary method to manage this disease (Chin and Bhandhufalck 1990;Damicone et al. 1993). However, the use of chemical fungicides is costly, ineffective, and potentially harmful to the environment, and human health (Groth et al. 1990). Furthermore, overuse of these chemical fungicides has resulted in the resurgence of new resistant pathotypes. Breeding resistant varieties are believed to be one of the most economical, safe, and effective strategies to manage this disease. So far, no germplasm with complete resistance to sheath blight is known in O. sativa and its wild relatives Eizenga et al. 2002;Liu et al. 2006;Zhang et al. 2006). However, significant variation in resistance to sheath blight has been observed among rice cultivars in many reports (Pinson et al. 2005;Jia et al. 2007;Channamallikarjuna et al., 2010;Zuo et al., 2013Zuo et al., , 2014Yadav et al. 2015;Goad et al. 2020). To date, only a few varieties and lines have been reported as promising resistance sources that exhibit stable resistance to sheath blight, such as Tetep (Channamallikarjuna et al. 2010), Teqing (Li et al. 1995;Pinson et al. 2005), and Jasmine 85 (Liu et al. 2009;Zou et al. 2000), etc. Thus, exploring new resistant germplasm has become an international mandate (Xie et al. 1992;Zuo et al. 2009;Jia et al. 2007).
Rice sheath blight resistance loci have been reported to be quantitative in nature and controlled by many quantitative traits loci (QTLs) or polygenes (Li et al. 1995;Jia et al. 2007). More than 110 QTLs for sheath blight have been detected on all the 12 chromosomes of rice using different bi-parental mapping populations including F 2 , backcross, doubled haploid, recombinant inbred lines, etc. (Li et al. 2021;Molla et al. 2020;Zhang et al. 2019). Among them, the majority of the QTLs exhibit minor effects on the trait. Only a few loci such as qShB9-2 (Liu et al. 2009) and qSBR11-1 (Channamallikarjuna et al. 2010) have been reported as major QTLs. In addition, qSB9TQ ) and qSB-11LE (Zuo et al. 2013) have been fine mapped but none of the ShB resistance QTLs have been cloned yet. Despite the rapid progress in the mapping of ShB resistance QTLs, very few QTLs are being exploited in rice breeding programs. Traditional QTL mapping is very time-consuming and requires a large bi-parental population segregating for the trait of interest. Moreover, limited recombination in the bi-parental population reduces the resolution of QTL analysis. In contrast, genome-wide association study (GWAS) allows the identification of genes at higher resolution as GWAS captures ancient recombination by employing diverse and natural populations. In rice, very few reports have utilized the GWAS approach for the identification of sheath blight resistance genes by employing cultivated varieties or lines Jia et al. 2012;Sun et al. 2014;Zhang et al. 2019).
Wild relatives of rice are valuable sources of important agronomic traits as well as genes for abiotic and biotic stress tolerance (Brar et al. 1991). So far, wild species of rice have not been widely utilized for the identification and mapping of ShB resistance QTLs despite their potential to carry unique resistance alleles.
Earlier studies have identified a few wild relatives of rice such as O. minuta acc. IRGC101089 and O. rufipogon acc. IRGC100907 to be potential sources of resistance to ShB (Amante et al. 1990). Few accessions of O. nivara were also shown to be resistant to sheath blight (Eizenga et al. 2009;Prasad and Eizenga 2008). Out of them, two moderately resistant accessions of O. nivara (IRGC100898 and IRGC104705) were used to map sheath blight and blast resistance genes (Eizenga et al. 2013). Recently, Aggarwal et al (2019) reported novel sources of sheath blight resistance in O. nivara. More recently, mapping the population derived from weedy rice was employed to map new genes conferring resistance to sheath blight disease (Goad et al. 2020).
In this study, O. rufipogon diversity panel comprising 405 accessions was evaluated for sheath blight resistance for three years. Among them, 301 accessions were employed for GWAS analysis revealed a total of 22 strong associations for the sheath blight resistance. Candidate genes were predicted underlying significant SNP markers identified 13 genic SNPs. The functional annotation revealed several novel genes involved in various disease resistance such as protease inhibitors, ATPases, cyclins, F-box domaincontaining proteins, patatins. Some of the known genes such as thaumatin, Ser/Thr protein kinase, receptor-like protein kinase, and Lucine-rich repeat receptor kinase have also been identified in this study which showed the reliability of GWAS method. These genes could be used for cloning and functional characterization to elucidate the resistance mechanism to sheath blight disease. Our report catalogues some novel genes against sheath blight disease. SNP markers linked to resistance loci would serve as potential genomic resources for developing sheath blightresistant elite cultivars with broadened genetic bases. Evaluation of sheath blight resistance

Materials and methods
The fungal strain was isolated from the susceptible cultivar PR121 showing sheath blight symptoms. The infected leaf sheaths were sliced into 1 cm pieces and washed in running tap water followed by surface sterilization with 0.1 percent aqueous solution of mercuric chloride for one minute in a Petri plate and washed thrice with autoclaved distilled water to remove remnants of the chemical. The surface-sterilized sheath pieces were plated into the sterilized Petri-plates containing potato dextrose agar medium (PDA) followed by their incubation at 26 ± 2 °C for 5-7 under a BOD incubator. The pathogen was mass multiplied on maize meal-sand (1:3) medium supplemented with 20 g sucrose. The one-week-old culture of R. solani was transferred to a maize meal-sand medium and incubated at 27 ± 2 °C. The inoculum was ready to use for inoculation within 10 days (Lore et al. 2012). Disease evaluation was done by placing the inoculum prepared on maize meal-sand medium at the rate of 5 gm in the central region of plant hills at maximum tillering stage (Lore et al. 2012). Disease assessment was made 21 days after inoculation (DAI) under field conditions. The lesion height (LH) was recorded from the base of the plant to the tip of the topmost lesions on the stem. Plant height (PH) was also measured from the bottom of the plant to the top leaf. Relative lesion height (RLH) was calculated using the following formula given by Sharma et al (1990).

Statistical analysis
Data were analysed using the general linear model procedure (PROC GLM) in the SAS system (SAS, Cary, NC). Correlation coefficients for sheath blight and its component traits recorded across different seasons were determined by the correlation procedure (PROC CORR) in SAS. The least significant difference (LSD) values were calculated using PROC GLM. The cluster is reported as an unweighted pair group method using arithmetic means (UPGMA) based on the Mahalanobis distance algorithm using PAST 3.0 software (Hammer et al. 2001).

Genome-wide association mapping
The genome-wide association mapping was performed in a panel of 301 accessions using Genomic Association and Prediction Integrated Tool version 3.0 (GAPIT 3.0) program (Lipka et al. 2012;Wang and Zhang 2019). A total of 44,109 SNPs comprised the working SNP set for the association study. Of the 405 accessions, genotypic data of 301 accessions was procured from Malik et al. (2022). The working set of SNP markers from 301 accessions together with pooled phenotypic data was used for GWAS analysis The fixed and Random Model Circulating Unification (FarmCPU) model was used to determine the associations between each SNP and traits related to ShB resistance (LH, PH, RLH, and DS). Principal components (PC) and FaSTLMM algorithm-based kinship computed from SNP data describing the genetic structure of the panel were iteratively added to the FarmCPU model to avoid false positives.

Prediction of putative candidate gene
The SNP markers associated with ShB-related traits were used to predict putative candidate genes using the MSU Rice Genome Annotation Project database (RGAP) V.7 (https:// rice. plant biolo gy. msu. edu/). LD was calculated using the parameter of r2. LD was calculated using popLDdecay program in unix. The distance where the r2 value dropped to half of the maximum value was designated to be the LD region and that value was calculated to be 10 Kb. Thus, 20 kb region pertaining to 10 kb upstream and 10 Kb downstream of the significant SNP was scanned to find out loci/genes that could be associated with the trait of interest. This was followed by in-silico functional annotation of the loci to establish their relationship with ShB resistance.

Evaluation of wild accessions of Oryza rufipogon and statistical analysis
A total of 405 accessions of O. rufipogon with susceptible check PR114 were screened under natural field conditions. Significant variations were observed for three ShB-related traits during three years of screening. Based on the standard evaluation system of rice, 22 accessions (5.43%) were moderately resistant (MR), 297 accessions (73.33%) were moderately susceptible (MS), and 86 accessions (21.23%) were susceptible ( Fig. 1). The PH ranged from 53 to 200 cm, LH ranged from 18 to 103 cm, relative lesion height (RLH) ranged from 13.15% to 93.18% and DS ranged from 1 to 9. The distance-based hierarchical clustering was performed using the Mahalanobis method based on RLH and DS. All the accessions were clustered into 4 groups (namely G1, G2, G3, and G4). Group G4 comprised accessions that showed the MR reaction with the minimum values of LH, RLH, and DS i.e., 37.52 cm, 27.38%, and 3 respectively. This was followed by the G2 group which showed MS reaction with moderate LH (49.03 cm), RLH (35.86%), and DS (5.01), respectively. Group G1 had the highest values of LH, RLH and DS i.e. 65.10 cm, 70.12%, and 8.23 respectively (Table 1). Further, cluster analysis performed on twenty-two  All the genotypes had a significant (P < 0.001) effect on all the disease variables. The experiment interaction had a significant (P < 0.001) effect on all the disease variables except LH (P > 0.12). The O. rufipogon genotype x experiment interaction had a significant (P < 0.001) effect on all ShB components traits except RLH (P = 0.37) and disease score (P = 0.21) ( Table 2). Of these, 19 accessions namely CR100004, CR100472B, CR100469, CR100465, CR100462A, CR100461B, CR100438, CR100436, CR100309, CR100036, CR100018A, IRGC104404D, IRGC104404A, IRGC103404, IRGC80762, IRGC80660, IRGC80610, IRGC80600, IRGC805629 were found moderately resistant, 3 accessions namely CR100002, CR100006A, IRGC106512 were moderately susceptible during the year 2016 (Table 3). No accession showed a completely resistant reaction. PH ranged from 82 to 189 cm, LH ranged from 19 to 66 cm, RLH ranged from 12.96 to 66%, and DS ranged from 1 to 9 for both years (Table 3).

Disease variables and their correlation with genotypes
The different disease variables measured were significantly (P < 0.05) correlated. The RLH had a positive correlation with LH (0.76) and DS (0.89). The LH also showed a positive correlation of 0.68 with the disease score. There was a negative correlation between PH and DS with a correlation coefficient of − 0.24 (Table 4).
Genome-wide association mapping GWAS performed on a set of 44,109 SNPs identified 22 strong associations for the four traits LH, PH, RLH, and DS (Table 5). The SNP density has been presented chromosome-wise in Fig. 3. The issue of false positives was controlled by modelling kinship and 2 PCs as identified by the BIC criterion. P-value based on the minimum Bayes Factor calculated to be 4.21838E-05 was used as a threshold for determining the significance level in the current study. Significant associations were obtained on all the chromosomes except the 10th and 12th as evident from Manhattan plots (Fig. 4). Only two genomic regions on chromosomes 3 and 9 were strongly associated with multiple traits, PH and RLH. Besides these regions, 7 and 11 MTAs were obtained for PH and RLH, respectively, in addition to one strong association obtained each for LH and DS. It has also been observed that 81.8% of the genomic regions significantly associated with RLH were also associated with PH at p < 0.05. Similarly, SNPs significantly related to PH were also associated with DS and LH. The genomic regions strongly associated with LH showed association with DS and vice-versa at p-value < 0.05, indicating these two traits to be correlated. The association trends seen at the genomic level were also observed at the phenotypic level as evident from significant positive values of the Pearson correlation coefficient observed between the genotypically-associated traits as evident in Table 4. The present study revealed only a single genomic region on chromosome 1 to be strongly associated with LH which altered the trait over its mean value by 18%. However, for RLH, 11 genomic regions on chromosomes 2, 3, 4, 5, 6, 7, 8, and 11 showed significant association with effects ranging from 3.12% to 8.66% over the mean value of the trait. Most of these regions also showed an association with LH and PH at a p-value < 0.05. Of the 9 SNPs significantly associated with PH, two SNPs with the highest effect on chromosomes 3 and 6 altered the trait by a value of 9% over the mean value. The MTA that altered the trait RLH maximally was found on chromosome 2. A single genomic region on chromosome 6 was found to be strongly associated with DS and altered the trait over its mean value by 15.84%.

Candidate genes prediction
Candidate genes were predicted by searching the physical position of significant SNP markers in a 20-Kb window in the rice genome annotation project database. A list of predicted candidate genes is given in Table 5. Of the 22 significant MTAs, 13 were genic SNPs. The functional annotation revealed their roles as protease inhibitors, ATPases, cyclins, F-box domain-containing proteins, patatins. Apart from these, functional annotation of 36 more loci in the LD region was also done. Moreover, 6 strongly associated SNPs were found to be in the LD region/ overlapped previously reported 5 genes/QTLs associated with ShB resistance, providing analytical proof of our study.

Variations in O. rufipogon for ShB resistance
Developing sheath blight-resistant rice cultivars is likely the most effective, economic, and environmentfriendly approach for managing sheath blight disease compared to the overall usage of fungicides (Molla et al. 2020). Several germplasm resources showing sheath blight resistance have been reported (Aggarwal et al. 2019;Bashyal et al. 2017;Eizenga et al. 2002;Pavani et al. 2020;Prasad and Eizenga 2008;Srinivasachary et al. 2013). But being a quantitative trait, sheath blight resistance is controlled by multiple minor genes, thus, highly influenced by environmental conditions which limit the accuracy of phenotypic data. However, most of these studies have been conducted in cultivated germplasm. A very few reports have been published that have shown the evaluation of wild species against sheath blight (Aggarwal et al. 2019;Bashyal et al. 2017;Ram et al. 2008). So far, wild species of rice have not been extensively exploited in the breeding program for developing sheath blightresistant cultivars. In the present study, 405 accessions of O. rufipogon showed a variable reaction to sheath blight indicating the existence of genetic variability among them. Based on disease reaction over three years of screening, 19 accessions showing moderately resistant reactions have been identified.
Wild rice O. rufipogon is a progenitor of present day rice cultivars. It carries several novel genetic variations for various agronomically important traits including resistance to pathogens, insect pests, and abiotic stresses. Several wild accessions of O. rufipogon have been identified as potential donors for diseases and insect pest resistance genes/QTL such as bacterial blight (Wang et al. 2015a, b;Xing et al. 2021), Blast (Huang et al. 2008), brown planthopper (Huang et al. 2013;Kaur et al. 2022;Li et al. 2019a, b;Wang et al. 2015a, b). Neelam et al. (2017) utilized wild accessions of O. rufipogon for assessment of the phosphorus uptake efficiency. Wild species O. rufipogon also acts as a rich pool for yield and yield component traits (Reddy et al. 2005). It has been exploited for the identification of yield-enhancing traits such as grain size (Septiningsih et al. 2003;Xie et al. 2006), grain number (Fu et al. 2010;Xiao et al. 1998;Xie et al. 2008), spikelet per panicle (Gaikwad et al. 2014;Luo et al. 2013) and grain weight (Bhatia et al. 2018). In this study, we found that large diversity (14 accessions out of 22 accessions) for sheath blight resistance was reflected that were collected from pan India (CR series) and 8 accessions (IRGC series) were from other countries, suggesting that India and the Asian continent could be a possible hot spot zone for finding sheath blight resistance genes. In our previous report, we found that several accessions of O. nivara were found as promising donors against sheath blight (Aggarwal et al. ShB resistance loci GWAS is frequently used to dissect complex traits governed by multiple QTLs at the molecular level. In contrast to the few recombinations observed in a structured biparental population, GWAS takes advantage of numerous historical recombination in a large, diverse panel, leading to a higher mapping resolution. Moreover, with the availability of high-density SNP markers, the causative QTNs underlying the trait of interest are easily captured. Zhu et al 2015 identified six QTLs for cold tolerance at the booting stage by employing the technique of GWAS. By utilizing a high-density SNP marker set along with NILs, the researchers fine mapped the stably expressed QTL, qCT-3-2 to a 192.9 Kb region. Moreover, GWAS coupled with DNA sequence alignments and haplotype analysis helps breeders identify haplotypes associated with the desirable phenotypic value of the trait under study, as has been documented in Malik et al (2022) and Hussain et al. (2022) for productivity-related traits and grain length, respectively. Genetic basis of ionomic variations was revealed through GWAS in rice (Yang et al. 2018). GWAS was employed for identifying candidate gene underlying grain quality traits rice (Verma et al. 2022). Thus, GWAS coupled with post-analysis not only helps dissect the genetic region at a short interval but also leads to the identification of causal SNPs associated with functional variation of the trait ultimately leading to quick utilization of genes/QTLs in breeding programs. Moreover, with the availability of high-density SNP markers, the causative QTNs underlying the trait of interest are easily captured (Malik et al 2022). The present GWAS study identified fifteen genomic locations to be associated with sheath blight resistance in O. rufipogon accessions. These genomic regions were distributed across all the rice chromosomes except 10 and 12. Besides these regions, 7 significant MTAs on chromosomes 2, 3, 4, 5, 6, and 7 were obtained for PH. In the present study, the utilization of dense marker coverage allowed the identification of highly significant SNP markers associated with the ShB resistance at a higher resolution that could accelerate the marker-assisted breeding of ShB resistance rice cultivars. The majority of the loci identified in this study are co-localized with the previously mapped ShB resistance QTLS which authenticates our finding. In the present report, out of 22 genomic regions associated with the ShB traits, seven regions overlapped with previously reported genes/QTLs for ShB namely, qSB2 (Pinson et al. 2005), qSBR2 (Kunihiro et al. 2002), qSB-5 (Han et al. 2003, qSB-7 , qshb7.2 (Yadav et al. 2015), qshb8.1 is at 5.1 Mb (Yadav et al. 2015) and qSBR11 (Kunihiro et al. 2002). This information indicates the reliability of GWAS methods for the detection of significant SNPs.
Apart from the correlation of the level of infection with the resistance response of germplasm, the biggest complication is its correlation with plant growth, particularly plant height (PH) and heading date (HD) (Channamallikarjuna et al. 2010;Zou et al. 2000). The standard evaluation system for disease scoring is based on a 0-9 scale which measures lesion length above the waterline . According to this system, greater PH is correlated with higher resistance. Many reports showed that plant height is frequently correlated with ShB resistance (Channamallikarjuna et al. 2010;Eizenga et al. 2013;Kunihiro et al. 2002;Li et al. 1995;Wen et al. 2015;Zou et al. 2000), and loci for plant height and ShB resistance often co-localized at the same region (Channamallikarjuna et al. 2010;Li et al. 1995;Wen et al. 2015;Zou et al. 2000). Similarly, our study also determined seven regions significantly associated with RLH to be also associated with PH. Moreover, two SNPs on chromosomes 3 and 9 showed strong associations with both PH and RLH. functional annotation of candidate genomic regions helped establish the roles of genic and LD-region loci in conferring ShB resistance. Functional annotation of loci in the LD region of significant SNPs followed by a detailed literature survey helped establish the role of genomic regions in providing ShB resistance. The role of aspartyl tRNA synthetases in conferring disease resistance has been demonstrated by Luna et al. (2014). Similarly, the role of ATPases linked to S6_4,029,679 in conferring defense response has been functionally analyzed in response to blast fungus Magnaporthe oryzae by Liu et al. (2020). Likewise, the role of Dynamin-related proteins in conferring immunity against bacterial and fungal pathogens has been reported by Tang et al. (2006);Smith et al. (2006);Smith et al. (2014) and Chaparro-Garcia et al. (2015). Durian et al. (2016) have highlighted the role of protein phosphatases in defense response against biotic stresses. The role of cyclins in the detection and adaptation to stress by altering their level and altering the cell cycle in plants has been documented in several studies (Qi and Zhang 2020). Likewise, Liu et al. (2018) established the defense role of pectin methylesterase-inhibiting proteins in response to a fungal infection caused by Verticillium dahliae in cotton where the enzyme interacts with Pectin Methylesterases to regulate the expression of specific fungal polygalacturonase which enhanced resistance. Similarly, the role of adaptor protein complexes in conferring resistance to bacterial blight and rice blast has been determined by Qiao et al (2010). Tameling et al. (2007) have determined Ran GTPase-Activating proteins to physically associate with NB-LRR resistance proteins to confer resistance to Potato virus X in potato. Apart from this, SNP S4_25,149,326 on chromosome 4 was found to be closely linked to LOC_ Os04g42480 which encodes a receptor-like protein kinase. Earlier, fine-mapping of sheath blight resistance locus qSB11-LE revealed a receptor-like protein kinase in the QTL region on chromosome 11 (Zuo et al. 2013). Other than chromosome 4, we have also determined the receptor-like protein kinase genes on chromosome 2 affecting relative lesion height closely linked to the SNPs S2_7,170,057 and S2_25,483,277. Previously, the sheath blight resistance QTL, qSB-2, was identified on chromosome 2 which overlapped these two SNPs (Pinson et al. 2005). Recently, comparative transcriptome analysis showed up-regulation of receptor-like protein kinase with other pathogenesis-related genes when challenged with R. solani (Yuan et al. 2018). For successful pathogenesis, host-pathogen interaction is very crucial for producing disease symptoms under a favorable environment. Several small molecular weight proteins act as receptors to mediate the interaction of pathogen effectors on the cell surface. The lipid transfers proteins (LTP) belong to a large family of pathogenesisrelated 14 (PR-14) proteins (Finkina et al. 2016). The functional characterization of LTP genes showed that upregulation of these proteins confers the resistance response to the major fungal and bacterial pathogens in plants (Ge et al. 2003;Kim et al. 2006;Maldonado et al. 2002). The inducible expression of several LTP protease inhibitors was also seen during pollen development in the male-sterile plants (Khlaimongkhon et al. 2021;Zhang et al. 2010). In the present report, identification of LTPL51, LTPL52, and LTPL75 linked to S3_15,307,919 and S7_3,920,076 on chromosomes 3 and 7 suggested the possible role of lipid-mediated long-distance signalling of a systemic immune response upon R. solani infection. Several studies suggested that LTP genes are co-expressed with other transcription factors. To date, numerous transcription factors have been reported that are critical for elicitation of resistance response upon pathogen infection. Among them, WRKY30 and WRKY80 are known to enhance the sheath blight resistance by up-regulation of the jasmonic acid biosynthesis pathway (Peng et al. 2012(Peng et al. , 2016. Another locus in the LD region of S3_15,307,919 encoded F-box proteins which have been reported by Li et al (2019a, b) to provide resistance to banded leaf and sheath blight in maize, making this genomic region a strong candidate for conferring SHB resistance. Both the genomic regions encoding LTP proteins showed association with RLH. LOC_Os06g47600 in LD region of significant SNP on chromosome 6, encoded thaumatin-like proteins which belong to an important class of pathogenesisrelated protein family (PR-5). The antifungal role of thaumatin-like proteins is well documented in rice against sheath blight disease by overexpression studies (Datta et al. 1999;Kalpana et al. 2006;Velazhahan et al. 1998;Xue et al. 2016). Thaumatin-like proteins associated with the S6_28,836,919 in the GWAS analysis could be a novel gene resource that could be deployed in the breeding elite ShB resistant cultivars. Another significant association obtained on chromosome 6, S6_9975427, harbored in locus encoding Page 15 of 20 144 Vol.: (0123456789) UDP-glycosyltransferases whose over-expression has been reported to confer root tolerance to the mycotoxin and spike resistance to the fungus Fusarium graminearum by Pasquet et al. (2016).
The function of retinoblastoma-related genes in conferring powdery mildew resistance in grapes has been documented by Wen et al. (2012). Li et al (2008) have demonstrated the role of an OsBIRH1 coding for DEADbox RNA helicase protein in blast resistance in rice and over-expression studies in Arabidopsis confirmed its role in both abiotic and biotic stresses. However, identification of loci regions coding for MYB transcription factors, Sirohydrochlorin ferrochelatase could not establish a relationship with defense response, indicating that regulation of sheath blight resistance in O. rufipogon might have a diverse mechanism that is poorly understood and might require further investigation.

The breeding implication of GWAS for developing ShB resistance cultivars
The development of rice cultivars resistant to ShB is challenging due to the non-availability of promising donors and effective resistance genes, a wide range of host compatibility, and dynamic variability in resistance pathotypes (Molla et al. 2020). So far, no source of competing resistance has been identified in wild and cultivated rice. This report promises identification of a total of 22 accessions of O. rufipogon which showed consistent resistant response across three years of screening to R. solani. These accessions could be used as potential donors for breeding elite ShB-resistant cultivars. Sheath blight resistance in rice is mostly quantitative and controlled by multiple genes. However, due to poor mapping resolution and the use of few SSR markers, most of the loci were mapped to large genomic regions that limit the suitability of QTLs in gene pyramiding through marker-assisted breeding. Fine-mapping of ShB resistance loci allowed the pyramiding of multiple genes in the same genetic background. Numerous studies showed pyramiding of ShB resistance QTLs for developing resistance cultivars Ramalingam et al. 2020;Wang et al. 2012;Zuo et al. 2014Zuo et al. , 2011. Pyramiding of two ShB resistance QTL qSB-7 TQ and qSB-9 TQ from Teqing (TQ) was demonstrated to enhance the resistance to sheath blight disease . Rice lines carrying a single gene separately reduce the disease severity by a 1.0 score in the 0-9 scoring system. While rice lines carrying both genes reduce the disease severity by 1.7. Altering plant architecture plays a pivotal role in disease resistance. Fine mapping of qSB-9 TQ showed its co-localization in the genomic region of tiller angle gene TAC1 TQ ). Pyramiding of tiller angle gene TAC1 TQ and ShB resistance QTL qSB-9 TQ showed the equal effect of resistance as its donor parent Teqing (TQ) with no undesirable effect on phenotypes. It is reasonable that a large tiller angle reduces the compactness of tillers thereby increasing the unfavorable micro-climate for disease development. An equal effect of resistance was also achieved by pyramiding qSBR7-1, qSBR11-1, and qSBR11-2 genes from Tetep without inheritance of undesirable loci from the donor parent (Ramalingam et al. 2020). Presently, the use of dense SNP markers using GWAS allows the identification of the resistance locus at a higher resolution. In this report, a total of 22 significant associations harboring 79 genes in LD regions contributed to moderate resistance in O. rufipogon accessions using GWAS. The significant SNPs could be converted into PCR-based markers or KASP markers for introgression of resistance gene into cultivated rice through MAS.
In conclusion, many accessions of O. rufipogon display significant variations in resistance to sheath blight. Most of them are collected from a specific zone of India i.e. National Rice Research Institute (NRRI), Cuttack. To transfer the resistance genes into cultivars, the promising resistance accessions were crossed with susceptible cultivars. Of which, few of them are now at advanced yield trials for varietal development. GWAS identified significant SNP markers linked to ShB resistance. These markers could be used for cloning and characterization of the resistance genes. Further, Significant SNPs can be converted to PCR-based markers for marker-assisted selection.